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Commit 2b33c68d authored by Olivier BICHLER's avatar Olivier BICHLER
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Merge branch 'div-backward' into 'dev' 

feat: Implement backward function for Div operator

See merge request !134
parents 742b763f ff3a3ed7
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3 merge requests!166Update 0.5.0 -> 0.6.0,!136Add selection mechanism in graph,!134feat: Implement backward function for Div operator
Pipeline #65510 passed
Stage: static_analysis
    Stage: build
      Stage: test
        Stage: coverage
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          include/aidge/backend/cpu/operator/DivImpl.hpp
          + 12
          − 1
          View file @ 2b33c68d
          ......@@ -24,7 +24,18 @@
          namespace Aidge {
          // Operator implementation entry point for the backend
          using DivImpl_cpu = OperatorImpl_cpu<Div_Op,
          void(const std::size_t, const std::size_t, const std::size_t, const void*, const void*,void*)>;
          void(const std::size_t, const std::size_t, const std::size_t, const void*, const void*,void*),
          void(const std::size_t,
          const std::size_t,
          const std::size_t,
          const std::vector<std::size_t>,
          const std::vector<std::size_t>,
          const std::vector<std::size_t>,
          const void*,
          const void*,
          const void*,
          void*,
          void*)>;
          // Implementation entry point registration to Operator
          REGISTRAR(Div_Op, "cpu", Aidge::DivImpl_cpu::create);
          ......
          include/aidge/backend/cpu/operator/DivImpl_kernels.hpp
          + 58
          − 3
          View file @ 2b33c68d
          ......@@ -17,6 +17,7 @@
          #include <cstdint> // std::int32_t, std::int64_t
          #include <functional> // std::multiplies
          #include "aidge/backend/cpu/operator/MulImpl_kernels.hpp"
          #include "aidge/utils/Registrar.hpp"
          #include "aidge/backend/cpu/data/Broadcasting.hpp"
          ......@@ -69,16 +70,70 @@ constexpr void DivImpl_cpu_forward_kernel(const std::size_t input1size_,
          }
          }
          template <class I1, class I2, class O>
          void DivImpl_cpu_backward_kernel(const std::size_t input0Length,
          const std::size_t input1Length,
          const std::size_t gradOutputLength,
          const std::vector<std::size_t>& dims0,
          const std::vector<std::size_t>& dims1,
          const std::vector<std::size_t>& outputDims,
          const void* input0_,
          const void* input1_,
          const void* grad_output_,
          void* gradientInput0_,
          void* gradientInput1_)
          {
          const I1* input0 = static_cast<const I1*>(input0_); // a
          const I2* input1 = static_cast<const I2*>(input1_); // b
          const O* grad_output = static_cast<const O*>(grad_output_);
          auto* grad_input_0 = static_cast<I1*>(gradientInput0_); // gradient w.r.t. a
          auto* grad_input_1 = static_cast<I2*>(gradientInput1_); // gradient w.r.t. b
          std::fill_n(grad_input_0, input0Length, static_cast<I1>(0));
          std::fill_n(grad_input_1, input1Length, static_cast<I2>(0));
          // Broadcast dims0 and dims1 to match the shape of outputDims
          auto broadcastedDims0 = getBroadcastedDims(outputDims, dims0);
          auto broadcastedDims1 = getBroadcastedDims(outputDims, dims1);
          for (std::size_t i = 0; i < gradOutputLength; ++i) {
          auto idxOutputGrad = getMultiDimIndices(outputDims, i);
          std::vector<std::size_t> idxInput0(broadcastedDims0.size());
          std::vector<std::size_t> idxInput1(broadcastedDims1.size());
          // Map output indices to input indices, considering broadcasting
          for (std::size_t dimension = 0; dimension < broadcastedDims0.size(); ++dimension) {
          idxInput0[dimension] = (broadcastedDims0[dimension] == 1) ? 0 : idxOutputGrad[dimension];
          }
          for (std::size_t dimension = 0; dimension < broadcastedDims1.size(); ++dimension) {
          idxInput1[dimension] = (broadcastedDims1[dimension] == 1) ? 0 : idxOutputGrad[dimension];
          }
          auto idx0 = getFlattenedIndex(broadcastedDims0, idxInput0);
          auto idx1 = getFlattenedIndex(broadcastedDims1, idxInput1);
          // grad_a = grad_output * (1/b)
          grad_input_0[idx0] += static_cast<I1>(grad_output[i] / input1[idx1]);
          // grad_b = grad_output * (-a/b²)
          grad_input_1[idx1] += static_cast<I2>(grad_output[i] * (-input0[idx0] / (input1[idx1] * input1[idx1])));
          }
          }
          // Kernels registration to implementation entry point
          REGISTRAR(DivImpl_cpu,
          {DataType::Float32},
          {ProdConso::inPlaceModel, Aidge::DivImpl_cpu_forward_kernel<float, float, float>, nullptr});
          {ProdConso::inPlaceModel, Aidge::DivImpl_cpu_forward_kernel<float, float, float>, Aidge::DivImpl_cpu_backward_kernel<float, float, float>});
          REGISTRAR(DivImpl_cpu,
          {DataType::Float64},
          {ProdConso::inPlaceModel, Aidge::DivImpl_cpu_forward_kernel<double, double, double>, nullptr});
          {ProdConso::inPlaceModel, Aidge::DivImpl_cpu_forward_kernel<double, double, double>, Aidge::DivImpl_cpu_backward_kernel<double, double, double>});
          REGISTRAR(DivImpl_cpu,
          {DataType::Int32},
          {ProdConso::inPlaceModel, Aidge::DivImpl_cpu_forward_kernel<std::int32_t, std::int32_t, std::int32_t>, nullptr});
          {ProdConso::inPlaceModel, Aidge::DivImpl_cpu_forward_kernel<std::int32_t, std::int32_t, std::int32_t>,
          Aidge::DivImpl_cpu_backward_kernel<std::int32_t, std::int32_t, std::int32_t>});
          } // namespace Aidge
          #endif /* AIDGE_CPU_OPERATOR_DIVIMPL_KERNELS_H_ */
          src/operator/DivImpl.cpp
          + 22
          − 1
          View file @ 2b33c68d
          ......@@ -152,5 +152,26 @@ void Aidge::DivImpl_cpu::forward() {
          template <>
          void Aidge::DivImpl_cpu::backward() {
          AIDGE_THROW_OR_ABORT(std::runtime_error, "Backward not yet implemented for Div_Op on backend cpu");
          const Div_Op& op_ = dynamic_cast<const Div_Op&>(mOp);
          auto in0 = op_.getInput(0);
          auto in1 = op_.getInput(1);
          auto in0grad = op_.getInput(0)->grad();
          auto in1grad = op_.getInput(1)->grad();
          auto out0grad = op_.getOutput(0)->grad();
          const auto impl = Registrar<DivImpl_cpu>::create(getBestMatch(getRequiredSpec()));
          impl.backward(in0grad->size(),
          in1grad->size(),
          out0grad->size(),
          in0->dims(),
          in1->dims(),
          out0grad->dims(),
          getCPUPtr(in0),
          getCPUPtr(in1),
          getCPUPtr(out0grad),
          getCPUPtr(in0grad),
          getCPUPtr(in1grad));
          }
          unit_tests/operator/Test_DivImpl.cpp
          + 271
          − 0
          View file @ 2b33c68d
          ......@@ -322,4 +322,275 @@ TEST_CASE("[cpu/operator] Div", "[Div][CPU]") {
          }
          }
          }
          TEST_CASE("[CPU/Operator] Div(Backward)", "[Div][CPU][Backward]") {
          std::shared_ptr<Div_Op> op = std::make_shared<Div_Op>();
          op->setDataType(DataType::Float32);
          op->setBackend("cpu");
          // NOTE: The first four tests use fixed values, the last one uses random values but static dimensions.
          SECTION("Case 1: 1D and 2D Tensors") {
          const auto T0 = std::make_shared<Tensor>(
          Array2D<cpptype_t<DataType::Float32>, 2, 3>({{{1, 2, 3}, {4, 5, 6}}}));
          const auto T1 =
          std::make_shared<Tensor>(Array1D<cpptype_t<DataType::Float32>, 3>({0.1, 0.2, 0.3}));
          op->associateInput(0, T0);
          op->associateInput(1, T1);
          op->getOutput(0)->setGrad(std::make_shared<Tensor>(
          Array2D<float, 2, 3>({{{1.0, 1.0, 1.0}, {1.0, 1.0, 1.0}}})));
          op->forwardDims();
          op->backward();
          const Tensor expectedGrad0 =
          Array2D<cpptype_t<DataType::Float32>, 2, 3>({{{10, 5, 3.3333}, {10, 5, 3.3333}}});
          const Tensor expectedGrad1 = Array1D<cpptype_t<DataType::Float32>, 3>({-500, -175, -100});
          REQUIRE(approxEq<cpptype_t<DataType::Float32>>(*(op->getInput(0)->grad()), expectedGrad0));
          REQUIRE(approxEq<cpptype_t<DataType::Float32>>(*(op->getInput(1)->grad()), expectedGrad1));
          }
          SECTION("Case 2: 3D and 1D tensors") {
          const auto T0 = std::make_shared<Tensor>(Array3D<float, 2, 2, 3>(
          {{{{1.0, 2.0, 3.0}, {4.0, 5.0, 6.0}},
          {{7.0, 8.0, 9.0}, {10.0, 11.0, 12.0}}}}));
          const auto T1 =
          std::make_shared<Tensor>(Array1D<float, 3>({0.3, 0.2, 0.1}));
          const auto newGrad = std::make_shared<Tensor>(Array3D<float, 2, 2, 3>(
          {{{{1, 1, 1}, {1, 1, 1}}, {{1, 1, 1}, {1, 1, 1}}}}));
          const Tensor expectedGrad0 =
          Array3D<float, 2, 2, 3>({{{{3.3333, 5.0, 10}, {3.3333, 5.0, 10}},
          {{3.3333, 5.0, 10}, {3.3333, 5.0, 10}}}});
          const Tensor expectedGrad1 = Array1D<cpptype_t<DataType::Float32>, 3>({-244.4444, -650.0, -3000.0});
          op->associateInput(0, T0);
          op->associateInput(1, T1);
          op->getOutput(0)->setGrad(newGrad);
          op->forwardDims();
          op->backward();
          REQUIRE(approxEq<cpptype_t<DataType::Float32>>(*(op->getInput(0)->grad()), expectedGrad0));
          REQUIRE(approxEq<cpptype_t<DataType::Float32>>(*(op->getInput(1)->grad()), expectedGrad1));
          }
          SECTION("Case 3: 4D and 2D tensors") {
          const auto T0 = std::make_shared<Tensor>(Array4D<cpptype_t<DataType::Float32>, 2, 2, 3, 3>(
          {{{{{1.0, 2.0, 3.0}, {4.0, 5.0, 6.0}, {7.0, 8.0, 9.0}},
          {{10.0, 11.0, 12.0}, {13.0, 14.0, 15.0}, {16.0, 17.0, 18.0}}},
          {{{19.0, 20.0, 21.0}, {22.0, 23.0, 24.0}, {25.0, 26.0, 27.0}},
          {{28.0, 29.0, 30.0},
          {31.0, 32.0, 33.0},
          {34.0, 35.0, 36.0}}}}}));
          const auto T1 = std::make_shared<Tensor>(Array2D<cpptype_t<DataType::Float32>, 3, 3>(
          {{{0.5, 0.3, 0.1}, {0.4, 0.2, 0.6}, {0.7, 0.8, 0.9}}}));
          const auto newGrad =
          std::make_shared<Tensor>(Array4D<cpptype_t<DataType::Float32>, 2, 2, 3, 3>(
          {{{{{1.0, 1.0, 1.0}, {1.0, 1.0, 1.0}, {1.0, 1.0, 1.0}},
          {{1.0, 1.0, 1.0}, {1.0, 1.0, 1.0}, {1.0, 1.0, 1.0}}},
          {{{1.0, 1.0, 1.0}, {1.0, 1.0, 1.0}, {1.0, 1.0, 1.0}},
          {{1.0, 1.0, 1.0}, {1.0, 1.0, 1.0}, {1.0, 1.0, 1.0}}}}}));
          const Tensor expectedGrad0 =
          Array4D<cpptype_t<DataType::Float32>, 2, 2, 3, 3>(
          {{{{{2, 3.3333, 10}, {2.5, 5.0, 1.66667}, {1.42857, 1.2500, 1.11111}},
          {{2, 3.3333, 10}, {2.5, 5.0, 1.66667}, {1.42857, 1.2500, 1.11111}}},
          {{{2, 3.3333, 10}, {2.5, 5.0, 1.66667}, {1.42857, 1.2500, 1.11111}},
          {{2, 3.3333, 10}, {2.5, 5.0, 1.66667}, {1.42857, 1.2500, 1.11111}}}}});
          const Tensor expectedGrad1 =
          Array2D<cpptype_t<DataType::Float32>, 3, 3>({{{-232.0, -688.888, -6600.0},
          {-437.5, -1850.0, -216.66667},
          {-167.3469, -134.3750, -111.111}}});
          op->associateInput(0, T0);
          op->associateInput(1, T1);
          op->getOutput(0)->setGrad(newGrad);
          op->forwardDims();
          op->backward();
          REQUIRE(approxEq<cpptype_t<DataType::Float32>>(*(op->getInput(0)->grad()), expectedGrad0));
          REQUIRE(approxEq<cpptype_t<DataType::Float32>>(*(op->getInput(1)->grad()), expectedGrad1));
          }
          SECTION("Case 4: 3D and 2D tensors") {
          const auto T0 = std::make_shared<Tensor>(
          Array3D<float, 2, 3, 4>({{{
          {1.0, 2.0, 3.0, 4.0},
          {5.0, 6.0, 7.0, 8.0},
          {9.0, 10.0, 11.0, 12.0},
          },
          {
          {13.0, 14.0, 15.0, 16.0},
          {17.0, 18.0, 19.0, 20.0},
          {21.0, 22.0, 23.0, 24.0},
          }}}));
          const auto T1 = std::make_shared<Tensor>(
          Array2D<cpptype_t<DataType::Float32>, 3, 4>({{{0.1, 0.2, 0.3, 0.4},
          {0.5, 0.6, 0.7, 0.8},
          {0.9, 1.0, 1.1, 1.2}}}));
          const auto newGrad = std::make_shared<Tensor>(
          Array3D<cpptype_t<DataType::Float32>, 2, 3, 4>({{{
          {1.0, 1.0, 1.0, 1.0},
          {1.0, 1.0, 1.0, 1.0},
          {1.0, 1.0, 1.0, 1.0},
          },
          {
          {1.0, 1.0, 1.0, 1.0},
          {1.0, 1.0, 1.0, 1.0},
          {1.0, 1.0, 1.0, 1.0},
          }}}));
          const Tensor expectedGrad0 =
          Array3D<cpptype_t<DataType::Float32>, 2, 3, 4>({{{
          {10, 5, 3.33333, 2.5},
          {2, 1.66667, 1.42857, 1.2500},
          {1.11111, 1.0, 0.90909, 0.83333}},
          {{10, 5, 3.33333, 2.5},
          {2, 1.66667, 1.42857, 1.2500},
          {1.11111, 1.0, 0.90909, 0.83333}}}});
          const Tensor expectedGrad1 =
          Array2D<cpptype_t<DataType::Float32>, 3, 4>({{
          {-1400.0, -400.0, -200.0, -125.0},
          {-88.0, -66.66667, -53.0612, -43.750},
          {-37.0370, -32.0, -28.0992, -25.00}}});
          op->associateInput(0, T0);
          op->associateInput(1, T1);
          op->getOutput(0)->setGrad(newGrad);
          op->forwardDims();
          op->backward();
          REQUIRE(approxEq<cpptype_t<DataType::Float32>>(*(op->getInput(0)->grad()), expectedGrad0));
          REQUIRE(approxEq<cpptype_t<DataType::Float32>>(*(op->getInput(1)->grad()), expectedGrad1));
          }
          SECTION("Case 5: Tensors with random values") {
          // Use random values
          const std::vector<std::size_t> dims0 = {5, 2, 1, 7}; // First tensor
          const std::vector<std::size_t> dims1 = {2, 6, 7}; // Second tensor
          const std::vector<std::size_t> outputDims = {5, 2, 6, 7};
          std::random_device rd;
          std::mt19937 gen(rd());
          std::uniform_real_distribution<float> dist(0.1f, 1.0f);
          auto T0 = std::make_shared<Tensor>(dims0);
          T0->setDataType(DataType::Float32);
          T0->setBackend("cpu");
          float* input0Data = static_cast<float*>(T0->getImpl()->rawPtr());
          // Fill with random values
          for (std::size_t i = 0; i < T0->size(); ++i) {
          input0Data[i] = dist(gen);
          }
          auto T1 = std::make_shared<Tensor>(dims1);
          T1->setDataType(DataType::Float32);
          T1->setBackend("cpu");
          float* input1Data = static_cast<float*>(T1->getImpl()->rawPtr());
          // Fill with random values
          for (std::size_t i = 0; i < T1->size(); ++i) {
          input1Data[i] = dist(gen);
          }
          op->associateInput(0, T0);
          op->associateInput(1, T1);
          op->forwardDims();
          op->forward();
          Tensor expectedOutput{outputDims};
          expectedOutput.setBackend("cpu");
          float* expectedOutputData = static_cast<float*>(expectedOutput.getImpl()->rawPtr());
          for (std::size_t n = 0; n < 5; ++n) {
          for (std::size_t c = 0; c < 2; ++c) {
          for (std::size_t h = 0; h < 6; ++h) {
          for (std::size_t w = 0; w < 7; ++w) {
          std::size_t outIdx = w + 7 * (h + 6 * (c + 2 * n));
          std::size_t in0Idx =
          w + 7 * (0 + 1 * (c + 2 * n)); // middle dim is 1
          std::size_t in1Idx =
          w + 7 * (h + 6 * c); // no n dimension
          expectedOutputData[outIdx] = input0Data[in0Idx] / input1Data[in1Idx];
          }
          }
          }
          }
          auto outputTensor = op->getOutput(0);
          REQUIRE(approxEq<float>(*outputTensor, expectedOutput));
          // Backward pass
          std::vector<float> gradOutputData(expectedOutput.size());
          for (auto &val : gradOutputData) {
          val = dist(gen);
          }
          op->getOutput(0)->setGrad(std::make_shared<Tensor>());
          op->getOutput(0)->grad()->resize(outputDims);
          op->getOutput(0)->grad()->getImpl()->setRawPtr(gradOutputData.data(),
          expectedOutput.size());
          // Compute reference gradients
          std::vector<float> expectedGrad0(T0->size(), 0.0f);
          std::vector<float> expectedGrad1(T1->size(), 0.0f);
          for (std::size_t n = 0; n < 5; ++n) {
          for (std::size_t c = 0; c < 2; ++c) {
          for (std::size_t h = 0; h < 6; ++h) {
          for (std::size_t w = 0; w < 7; ++w) {
          std::size_t outIdx = w + 7 * (h + 6 * (c + 2 * n));
          std::size_t in0Idx = w + 7 * (0 + 1 * (c + 2 * n));
          std::size_t in1Idx = w + 7 * (h + 6 * c);
          expectedGrad0[in0Idx] +=
          gradOutputData[outIdx] * (1.0f / input1Data[in1Idx]);
          expectedGrad1[in1Idx] +=
          gradOutputData[outIdx] * (-input0Data[in0Idx] / (input1Data[in1Idx] * input1Data[in1Idx]));
          }
          }
          }
          }
          // Perform backward pass
          op->backward();
          auto expectedGrad0Tensor = std::make_shared<Tensor>();
          expectedGrad0Tensor->resize(T0->dims());
          expectedGrad0Tensor->setBackend("cpu");
          expectedGrad0Tensor->setDataType(DataType::Float32);
          expectedGrad0Tensor->getImpl()->setRawPtr(expectedGrad0.data(),
          expectedGrad0.size());
          auto expectedGrad1Tensor = std::make_shared<Tensor>(T1->dims());
          expectedGrad1Tensor->setBackend("cpu");
          expectedGrad1Tensor->setDataType(DataType::Float32);
          expectedGrad1Tensor->getImpl()->setRawPtr(expectedGrad1.data(),
          expectedGrad1.size());
          // Verify backward pass
          REQUIRE(approxEq<float>(*T0->grad(), *expectedGrad0Tensor));
          REQUIRE(approxEq<float>(*T1->grad(), *expectedGrad1Tensor));
          }
          }
          } // namespace Aidge
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