From 1cd0d0715b5b55c092cf0229c2c5ce593a240f53 Mon Sep 17 00:00:00 2001
From: Jerome Hue <jerome.hue@cea.fr>
Date: Fri, 6 Sep 2024 17:03:46 +0200
Subject: [PATCH] Add backward pass CPU implementation for Mul operator
---
.../aidge/backend/cpu/operator/MulImpl.hpp | 24 +-
.../cpu/operator/MulImpl_backward_kernels.hpp | 92 ++++
src/operator/MulImpl.cpp | 31 ++
unit_tests/operator/Test_MulImpl.cpp | 412 +++++++++++++++++-
4 files changed, 534 insertions(+), 25 deletions(-)
create mode 100644 include/aidge/backend/cpu/operator/MulImpl_backward_kernels.hpp
diff --git a/include/aidge/backend/cpu/operator/MulImpl.hpp b/include/aidge/backend/cpu/operator/MulImpl.hpp
index 2d42194c..008edf17 100644
--- a/include/aidge/backend/cpu/operator/MulImpl.hpp
+++ b/include/aidge/backend/cpu/operator/MulImpl.hpp
@@ -25,11 +25,25 @@ namespace Aidge {
// compute kernel registry for forward and backward
class MulImplForward_cpu
- : public Registrable<MulImplForward_cpu, std::tuple<DataType, DataType, DataType>, void(const std::vector<std::size_t>&, const std::vector<std::size_t>&, const std::vector<std::size_t>&, const void*, const void*,void*)> {
-};
+ : public Registrable<MulImplForward_cpu, std::tuple<DataType, DataType, DataType>, void(const std::vector<std::size_t>&,
+ const std::vector<std::size_t>&,
+ const std::vector<std::size_t>&,
+ const void*,
+ const void*,
+ void*)> {};
+
class MulImplBackward_cpu
- : public Registrable<MulImplBackward_cpu, std::tuple<DataType, DataType, DataType>, void(const std::vector<std::size_t>&, const std::vector<std::size_t>&, const std::vector<std::size_t>&, const void*, const void*, void*)> {
-};
+ : public Registrable<MulImplBackward_cpu, std::tuple<DataType, DataType, DataType>, 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 void*,
+ const void*,
+ const void*,
+ void*,
+ void*)> {};
+
class MulImpl_cpu : public OperatorImpl {
public:
@@ -40,7 +54,9 @@ public:
}
Elts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
+
void forward() override;
+ void backward() override;
};
namespace {
diff --git a/include/aidge/backend/cpu/operator/MulImpl_backward_kernels.hpp b/include/aidge/backend/cpu/operator/MulImpl_backward_kernels.hpp
new file mode 100644
index 00000000..db4cf81f
--- /dev/null
+++ b/include/aidge/backend/cpu/operator/MulImpl_backward_kernels.hpp
@@ -0,0 +1,92 @@
+#ifndef AIDGE_CPU_OPERATOR_MULIMPL_BACKWARD_KERNEL_H_
+#define AIDGE_CPU_OPERATOR_MULIMPL_BACKWARD_KERNEL_H_
+
+
+#include "aidge/utils/Registrar.hpp"
+
+#include <cstdint> // std::int32_t, std::int64_t
+#include <algorithm>
+
+#include "aidge/backend/cpu/data/Broadcasting.hpp"
+#include "aidge/backend/cpu/operator/MulImpl.hpp"
+
+
+namespace Aidge {
+
+template <class I1, class I2, class O>
+void MulImpl_cpu_backward_kernel(const std::size_t input0Length,
+ const std::size_t input1Length,
+ const std::size_t grad0Length,
+ const std::vector<std::size_t> input0Dims,
+ const std::vector<std::size_t> input1Dims,
+ const void* input0_,
+ const void* input1_,
+ const void* grad_output_,
+ void* gradientInput0,
+ void* gradientInput1)
+{
+ const auto* input0 = static_cast<const I1*>(input0_);
+ const auto* input1 = static_cast<const I1*>(input1_);
+ const auto* grad_output = static_cast<const O*>(grad_output_);
+ auto* grad_input_0 = static_cast<I1*>(gradientInput0);
+ auto* grad_input_1 = static_cast<I2*>(gradientInput1);
+
+
+ if(input0Dims.size() >= input1Dims.size())
+ {
+ AIDGE_ASSERT(input0Length == grad0Length, "Incorrect dimensions between Mul input and output tensors");
+
+ for(auto i = 0U; i < input0Length; ++i)
+ {
+ const auto indices = getMultiDimIndices(input1Dims, i);
+ const auto flattenedIndex = getFlattenedIndex(input1Dims, indices);
+
+ grad_input_0[i] = input1[flattenedIndex] * grad_output[i];
+ }
+
+ for(std::size_t i = 0 ; i < grad0Length; ++i)
+ {
+ const auto indices = getMultiDimIndices(input1Dims, i);
+ const auto flattenedIndex = getFlattenedIndex(input1Dims, indices);
+
+ grad_input_1[flattenedIndex] += input0[i] * grad_output[i];
+ }
+
+ } else {
+ AIDGE_ASSERT(input1Length == grad0Length, "Incorrect dimensions between Mul input and output tensors");
+
+ for(auto i = 0U; i < input1Length; ++i)
+ {
+ const auto indices = getMultiDimIndices(input0Dims, i);
+ const auto flattenedIndex = getFlattenedIndex(input0Dims, indices);
+
+ grad_input_1[i] = input0[flattenedIndex] * grad_output[i];
+ }
+
+ for(std::size_t i = 0 ; i < grad0Length; ++i)
+ {
+ const auto indices = getMultiDimIndices(input0Dims, i);
+ const auto flattenedIndex = getFlattenedIndex(input0Dims, indices);
+
+ grad_input_0[flattenedIndex] += input1[i] * grad_output[i];
+ }
+ }
+}
+
+
+namespace {
+static Registrar<MulImplBackward_cpu> registrarMulImplBackward_cpu_Float32(
+ {DataType::Float32, DataType::Float32, DataType::Float32},
+ Aidge::MulImpl_cpu_backward_kernel<float, float, float>);
+static Registrar<MulImplBackward_cpu> registrarMulImplBackward_cpu_Float64(
+ {DataType::Float64, DataType::Float64, DataType::Float64},
+ Aidge::MulImpl_cpu_backward_kernel<double, double, double>);
+static Registrar<MulImplBackward_cpu> registrarMulImplBackward_cpu_Int32(
+ {DataType::Int32, DataType::Int32, DataType::Int32},
+ Aidge::MulImpl_cpu_backward_kernel<std::int32_t, std::int32_t, std::int32_t>);
+static Registrar<MulImplBackward_cpu> registrarMulImplBackward_cpu_Int64(
+ {DataType::Int64, DataType::Int64, DataType::Int64},
+ Aidge::MulImpl_cpu_backward_kernel<std::int64_t, std::int64_t, std::int64_t>);
+} // namespace
+} // namespace Aidge
+#endif
diff --git a/src/operator/MulImpl.cpp b/src/operator/MulImpl.cpp
index d7feb9b7..e5fd911c 100644
--- a/src/operator/MulImpl.cpp
+++ b/src/operator/MulImpl.cpp
@@ -22,6 +22,7 @@
#include "aidge/backend/cpu/operator/MulImpl.hpp"
#include "aidge/backend/cpu/operator/MulImpl_forward_kernels.hpp"
+#include "aidge/backend/cpu/operator/MulImpl_backward_kernels.hpp"
Aidge::Elts_t Aidge::MulImpl_cpu::getNbRequiredProtected(const Aidge::IOIndex_t /*inputIdx*/) const {
// this implementation can be in-place
@@ -40,6 +41,7 @@ void Aidge::MulImpl_cpu::forward() {
const std::vector<std::size_t> inputDims1 = getBroadcastedDims(std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dims(),
std::static_pointer_cast<Tensor>(mOp.getRawInput(1))->dims());
+
// Call kernel
kernelFunc(inputDims0,
inputDims1,
@@ -48,3 +50,32 @@ void Aidge::MulImpl_cpu::forward() {
getCPUPtr(mOp.getRawInput(1)),
getCPUPtr(mOp.getRawOutput(0)));
}
+
+void Aidge::MulImpl_cpu::backward() {
+
+ const Mul_Op& op_ = dynamic_cast<const Mul_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();
+
+ // Find kernel function
+ auto kernelFunc = Registrar<MulImplBackward_cpu>::create({
+ out0grad->dataType(),
+ in0grad->dataType(),
+ in1grad->dataType()});
+
+ kernelFunc(/* input0Length */ in0grad->size(),
+ /* input1Length */ in1grad->size(),
+ /* grad0Length */ out0grad->size(),
+ /* input0Dims */ in0->dims(),
+ /* input1Dims */ in1->dims(),
+ getCPUPtr(in0),
+ getCPUPtr(in1),
+ getCPUPtr(out0grad),
+ getCPUPtr(in0grad),
+ getCPUPtr(in1grad));
+}
+
diff --git a/unit_tests/operator/Test_MulImpl.cpp b/unit_tests/operator/Test_MulImpl.cpp
index 9d592d31..3378861d 100644
--- a/unit_tests/operator/Test_MulImpl.cpp
+++ b/unit_tests/operator/Test_MulImpl.cpp
@@ -24,6 +24,337 @@
namespace Aidge {
+ TEST_CASE("[CPU/Operator] Mul Backward", "[Mul][CPU][Backward]")
+ {
+ std::shared_ptr<Node> myMul = Mul();
+ auto op = std::static_pointer_cast<OperatorTensor>(myMul->getOperator());
+ op->setDataType(DataType::Float32);
+ op->setBackend("cpu");
+
+ SECTION("Case 1: 2D and 1D tensors") {
+ const auto T0 = std::make_shared<Tensor>(Array2D<float,2,3>(
+ {
+ {
+ {1,2,3},{4,5,6}
+ }
+ }
+ ));
+
+ const auto T1 = std::make_shared<Tensor>(Array1D<float,3>(
+ {0.1,0.2,0.3}
+ ));
+
+ T0->setDataType(DataType::Float32);
+ T0->setBackend("cpu");
+ T1->setDataType(DataType::Float32);
+ T1->setBackend("cpu");
+
+ 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->associateInput(0,T0);
+ op->associateInput(1,T1);
+ op->forwardDims();
+
+ myMul->forward();
+ myMul->backward();
+
+ auto T0Grad = std::make_shared<Tensor>(Array2D<float, 2,3>({{{0.1,0.2,0.3},{0.1, 0.2, 0.3}}}));
+ auto T1Grad = std::make_shared<Tensor>(Array1D<float, 3>({5,7,9}));
+
+ REQUIRE(approxEq<float>(*(op->getInput(0)->grad()), *T0Grad));
+ REQUIRE(approxEq<float>(*(op->getInput(1)->grad()), *T1Grad));
+ }
+
+ 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 auto expectedGrad0 = std::make_shared<Tensor>(Array3D<float,2,2,3>(
+ {
+ {
+ {
+ {0.3, 0.2, 0.1},
+ {0.3, 0.2, 0.1}
+ },
+ {
+ {0.3, 0.2, 0.1},
+ {0.3, 0.2, 0.1}
+ }
+ }
+ }
+ ));
+
+ const auto expectedGrad1 = std::make_shared<Tensor>(Array1D<float,3>(
+ {22.0, 26.0, 30.0}
+ ));
+
+ for(auto T: {T0, T1, newGrad, expectedGrad0, expectedGrad1})
+ {
+ T->setBackend("cpu") ;
+ T->setDataType(DataType::Float32);
+ }
+
+ op->associateInput(0, T0);
+ op->associateInput(1, T1);
+ op->getOutput(0)->setGrad(newGrad);
+ op->forwardDims();
+
+ myMul->backward();
+
+ REQUIRE(approxEq<float>(*(op->getInput(0)->grad()), *expectedGrad0));
+ REQUIRE(approxEq<float>(*(op->getInput(1)->grad()), *expectedGrad1));
+ }
+
+ SECTION("Case 3: 4D and 2D tensors") {
+ const auto T0 = std::make_shared<Tensor>(Array4D<float,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<float, 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<float,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 auto expectedGrad0 = std::make_shared<Tensor>(Array4D<float,2,2,3,3>(
+ {
+ {
+ {
+ {
+ {0.5, 0.3, 0.1},
+ {0.4, 0.2, 0.6},
+ {0.7, 0.8, 0.9}
+ },
+ {
+ {0.5, 0.3, 0.1},
+ {0.4, 0.2, 0.6},
+ {0.7, 0.8, 0.9}
+ }
+ },
+ {
+ {
+ {0.5, 0.3, 0.1},
+ {0.4, 0.2, 0.6},
+ {0.7, 0.8, 0.9}
+ },
+ {
+ {0.5, 0.3, 0.1},
+ {0.4, 0.2, 0.6},
+ {0.7, 0.8, 0.9}
+ }
+ }
+ }
+ }
+ ));
+
+ const auto expectedGrad1 = std::make_shared<Tensor>(Array2D<float,3, 3>(
+ {
+ {
+ {58.0, 62.0, 66.0},
+ {70.0, 74.0, 78.0},
+ {82.0, 86.0, 90.0}
+ }
+ }
+ ));
+
+ for(const auto T: {T0, T1, newGrad, expectedGrad0, expectedGrad1})
+ {
+ T->setBackend("cpu") ;
+ T->setDataType(DataType::Float32);
+ }
+
+ op->associateInput(0, T0);
+ op->associateInput(1, T1);
+ op->getOutput(0)->setGrad(newGrad);
+ op->forwardDims();
+
+ myMul->backward();
+
+ REQUIRE(approxEq<float>(*(op->getInput(0)->grad()), *expectedGrad0));
+ REQUIRE(approxEq<float>(*(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<float, 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<float, 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 auto expectedGrad0 = std::make_shared<Tensor>(Array3D<float,2,3,4>(
+ {
+ {
+ {
+ {0.1, 0.2, 0.3, 0.4},
+ {0.5, 0.6, 0.7, 0.8},
+ {0.9, 1.0, 1.1, 1.2}
+ },
+ {
+ {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 expectedGrad1 = std::make_shared<Tensor>(Array2D<float,3, 4>(
+ {
+ {
+ {14.0, 16.0, 18.0, 20.0},
+ {22.0, 24.0, 26.0, 28.0},
+ {30.0, 32.0, 34.0, 36.0}
+ }
+ }
+ ));
+
+ for(const auto T: {T0, T1, newGrad, expectedGrad0, expectedGrad1})
+ {
+ T->setBackend("cpu") ;
+ T->setDataType(DataType::Float32);
+ }
+
+ op->associateInput(0, T0);
+ op->associateInput(1, T1);
+ op->getOutput(0)->setGrad(newGrad);
+ op->forwardDims();
+
+ myMul->backward();
+
+ REQUIRE(approxEq<float>(*(op->getInput(0)->grad()), *expectedGrad0));
+ REQUIRE(approxEq<float>(*(op->getInput(1)->grad()), *expectedGrad1));
+ }
+ }
+
TEST_CASE("[cpu/operator] Mul", "[Mul][CPU]") {
constexpr std::uint16_t NBTRIALS = 10;
// Create a random number generator
@@ -31,7 +362,7 @@ TEST_CASE("[cpu/operator] Mul", "[Mul][CPU]") {
std::mt19937 gen(rd());
std::uniform_real_distribution<float> valueDist(0.1f, 1.1f); // Random float distribution between 0 and 1
std::uniform_int_distribution<std::size_t> dimSizeDist(std::size_t(2), std::size_t(10));
- std::uniform_int_distribution<std::size_t> nbDimsDist(std::size_t(1), std::size_t(5));
+ std::uniform_int_distribution<std::size_t> nbDimsDist(std::size_t(1), std::size_t(3));
std::uniform_int_distribution<int> boolDist(0,1);
// Create MatMul Operator
@@ -60,6 +391,7 @@ TEST_CASE("[cpu/operator] Mul", "[Mul][CPU]") {
std::chrono::time_point<std::chrono::system_clock> end;
std::chrono::duration<double, std::micro> duration{};
+
SECTION("MulImpl_cpu::forward()") {
SECTION("Scalar / Scalar") {
@@ -68,16 +400,20 @@ TEST_CASE("[cpu/operator] Mul", "[Mul][CPU]") {
}
SECTION("+1-D Tensor / +1-D Tensor - same dimensions") {
+
std::size_t number_of_operation = 0;
for (std::uint16_t trial = 0; trial < NBTRIALS; ++trial) {
+
// generate 2 random Tensors
- const std::size_t nbDims = nbDimsDist(gen);
- std::vector<std::size_t> dims;
+ const auto nbDims = nbDimsDist(gen);
+ auto dims = std::vector<std::size_t>{};
+
for (std::size_t i = 0; i < nbDims; ++i) {
dims.push_back(dimSizeDist(gen));
}
- const std::size_t nb_elements = std::accumulate(dims.cbegin(), dims.cend(), std::size_t(1), std::multiplies<std::size_t>());
+
+ const auto nb_elements = std::accumulate(dims.cbegin(), dims.cend(), std::size_t(1), std::multiplies<std::size_t>());
number_of_operation += nb_elements;
// without broadcasting
@@ -114,67 +450,101 @@ TEST_CASE("[cpu/operator] Mul", "[Mul][CPU]") {
delete[] array0;
delete[] array1;
delete[] result;
-
- // with broadcasting
}
std::cout << "number of elements over time spent: " << (number_of_operation / duration.count())<< std::endl;
std::cout << "total time: " << duration.count() << "μs" << std::endl;
}
+
SECTION("+1-D Tensor / +1-D Tensor - broadcasting") {
std::size_t number_of_operation = 0;
for (std::uint16_t trial = 0; trial < NBTRIALS; ++trial) {
+
// generate 2 random Tensors
// handle dimensions, replace some dimensions with '1' to get broadcasting
+
constexpr std::size_t nbDims = 4;
- std::vector<std::size_t> dims;
- for (std::size_t i = 0; i < nbDims; ++i) {
- dims.push_back(dimSizeDist(gen));
+ std::vector<std::size_t> dimensions;
+
+ for (std::size_t i = 0; i < nbDims; ++i)
+ {
+ dimensions.push_back(dimSizeDist(gen));
}
- std::vector<std::size_t> dims0 = dims;
- std::vector<std::size_t> dims1 = dims;
- std::vector<std::size_t> dimsOut = dims;
- for (std::size_t i = 0; i < nbDims; ++i) {
- if (boolDist(gen)) {
+
+ auto dims0 = dimensions;
+ auto dims1 = dimensions;
+ auto dimsOut = dimensions;
+
+ for (std::size_t i = 0; i < nbDims; ++i)
+ {
+ if (boolDist(gen))
+ {
dims0[i] = 1;
}
- if (boolDist(gen)) {
+
+ if (boolDist(gen))
+ {
dims1[i] = 1;
}
+
dimsOut[i] = (dims0[i] == 1) ? dims1[i] : dims0[i];
}
+ for(auto dim : dims0)
+ {
+ Log::info("Dimension of input 0 : {}", dim);
+ }
+
+ for(auto dim : dims1)
+ {
+ Log::info("Dimension of input 1 : {}", dim);
+ }
+
// create arrays and fill them with random values
float* array0 = new float[dims0[0]*dims0[1]*dims0[2]*dims0[3]];
float* array1 = new float[dims1[0]*dims1[1]*dims1[2]*dims1[3]];
float* result = new float[dimsOut[0]*dimsOut[1]*dimsOut[2]*dimsOut[3]];
- for (std::size_t i = 0; i < dims0[0]*dims0[1]*dims0[2]*dims0[3]; ++i) {
+
+ for (std::size_t i = 0; i < dims0[0]*dims0[1]*dims0[2]*dims0[3]; ++i)
+ {
array0[i] = valueDist(gen);
}
- for (std::size_t i = 0; i < dims1[0]*dims1[1]*dims1[2]*dims1[3]; ++i) {
+
+ for (std::size_t i = 0; i < dims1[0]*dims1[1]*dims1[2]*dims1[3]; ++i)
+ {
array1[i] = valueDist(gen);
}
// compute true result
const std::size_t strides0[nbDims] = {dims0[1]*dims0[2]*dims0[3], dims0[2]*dims0[3], dims0[3], 1};
const std::size_t strides1[nbDims] = {dims1[1]*dims1[2]*dims1[3], dims1[2]*dims1[3], dims1[3], 1};
- for (std::size_t a = 0; a < dimsOut[0]; ++a) {
- for (std::size_t b = 0; b < dimsOut[1]; ++b) {
+
+ for (std::size_t a = 0; a < dimsOut[0]; ++a)
+ {
+ for (std::size_t b = 0; b < dimsOut[1]; ++b)
+ {
const std::size_t idx0_0 = strides0[0] * ((dims0[0] > 1) ? a : 0)
+ strides0[1] * ((dims0[1] > 1) ? b : 0);
+
const std::size_t idx1_0 = strides1[0] * ((dims1[0] > 1) ? a : 0)
+ strides1[1] * ((dims1[1] > 1) ? b : 0);
- for (std::size_t c = 0; c < dimsOut[2]; ++c) {
+
+ for (std::size_t c = 0; c < dimsOut[2]; ++c)
+ {
const std::size_t idx_out = dimsOut[3] * (c + dimsOut[2] * (b + dimsOut[1] * a));
- for (std::size_t d = 0; d < dimsOut[3]; ++d) {
+
+ for (std::size_t d = 0; d < dimsOut[3]; ++d)
+ {
std::size_t idx0 = idx0_0
+ strides0[2] * ((dims0[2] > 1) ? c : 0)
+ ((dims0[3] > 1) ? d : 0);
+
std::size_t idx1 = idx1_0
+ strides1[2] * ((dims1[2] > 1) ? c : 0)
+ ((dims1[3] > 1) ? d : 0);
+
result[idx_out + d] = array0[idx0] * array1[idx1];
// std::cout << "(" << idx0 << ", " << idx1 << ") -> " << array0[idx0] << " * " << array1[idx1] << " -> " << idx_out + d << std::endl;
}
--
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