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CHORE: Add tests for Leaky MetaOperator

Merged CHORE: Add tests for Leaky MetaOperator
jeromeh/aidge_backend_cpu:leaky-operator-tests into dev
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Merged Jerome Hue requested to merge jeromeh/aidge_backend_cpu:leaky-operator-tests into dev
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unit_tests/operator/Test_MetaOperator.cpp
+ 573
− 192
@@ -9,70 +9,79 @@
*
********************************************************************************/
#include <catch2/catch_test_macros.hpp>
#include <cmath>
#include <cstdlib>
#include <memory>
#include <random>
#include <catch2/catch_test_macros.hpp>
#include "aidge/utils/TensorUtils.hpp"
#include "aidge/backend/cpu/operator/ConvImpl.hpp"
#include "aidge/backend/cpu/operator/PadImpl.hpp"
#include "aidge/data/Tensor.hpp"
#include "aidge/filler/Filler.hpp"
#include "aidge/operator/Conv.hpp"
#include "aidge/operator/FC.hpp"
#include "aidge/operator/Identity.hpp"
#include "aidge/operator/MetaOperator.hpp"
#include "aidge/operator/MetaOperatorDefs.hpp"
#include "aidge/operator/Pad.hpp"
#include "aidge/operator/Pop.hpp"
#include "aidge/scheduler/SequentialScheduler.hpp"
#include "aidge/operator/Stack.hpp"
#include "aidge/scheduler/ParallelScheduler.hpp"
#include "aidge/scheduler/SequentialScheduler.hpp"
#include "aidge/utils/TensorUtils.hpp"
using namespace Aidge;
TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
SECTION("PaddedConv(forward)") {
std::shared_ptr<Tensor> myWeights = std::make_shared<Tensor>(
Array4D<double, 4, 3, 3, 3>{{{{{6.20986394e-01, 1.19775136e-03, 7.22876095e-02},
{1.16492919e-01, 8.21634093e-02, 1.17413265e-01},
{2.23743494e-01, 3.99495413e-01, 5.55552411e-01}},
{{6.64970077e-01, 9.62199940e-01, 4.87531967e-01},
{6.12586558e-01, 8.09918671e-02, 8.40649383e-01},
{4.15264406e-01, 8.28247138e-01, 1.52301135e-01}},
{{1.76992844e-02, 7.78697112e-01, 8.14531592e-01},
{1.36960611e-01, 4.64806728e-01, 4.85150000e-01},
{4.34776520e-01, 9.51740977e-01, 9.05793799e-01}}},
{{{1.71925246e-02, 1.91082720e-01, 3.67982644e-01},
{1.56806559e-01, 6.22280998e-01, 3.15827594e-01},
{6.04359038e-01, 2.83095947e-01, 6.11168892e-01}},
{{2.76942832e-01, 1.89768419e-01, 8.07988176e-01},
{1.67925807e-01, 2.68356150e-01, 6.28875602e-01},
{1.69093357e-04, 9.64788636e-01, 7.29254981e-01}},
{{6.34030122e-01, 1.32087038e-01, 3.33857107e-01},
{7.63047502e-01, 5.12539506e-02, 9.77400493e-01},
{8.06151288e-01, 2.60237147e-01, 3.93729313e-01}}},
{{{5.84605240e-01, 4.74648725e-01, 8.54111741e-01},
{7.10897067e-02, 5.02579011e-01, 3.35236224e-01},
{9.08637408e-01, 8.02903830e-01, 2.83929907e-01}},
{{3.68206999e-01, 9.18579021e-02, 7.33168098e-01},
{1.59875539e-01, 9.13163381e-01, 3.59806060e-01},
{1.41295882e-01, 7.00312185e-01, 5.63728289e-01}},
{{9.39513546e-01, 1.91704891e-01, 1.11454944e-01},
{5.46298282e-01, 2.89698587e-01, 2.62612651e-01},
{1.18554992e-01, 4.32147376e-02, 7.53016994e-01}}},
{{{9.53179175e-01, 2.05041054e-02, 1.11318451e-01},
{8.67878485e-01, 2.93263422e-01, 8.03912714e-01},
{8.93620255e-01, 1.37831128e-01, 3.83640583e-01}},
{{3.96020188e-01, 6.24959320e-01, 1.90709175e-01},
{5.80538620e-01, 6.63031275e-01, 2.07247191e-01},
{5.65672171e-01, 5.57014317e-01, 9.26909496e-01}},
{{3.43901418e-01, 4.47741636e-01, 6.59249367e-01},
{7.34639028e-01, 2.84957200e-02, 9.70225217e-01},
{1.33578790e-02, 6.12054702e-01, 9.36685235e-02}}}}});
std::shared_ptr<Tensor> myBias = std::make_shared<Tensor>(
Array1D<double, 4>{{0.16884905, 0.27994487, 0.57227465, 0.06435205}});
std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(Array4D<double, 2, 3, 5, 5>{
SECTION("PaddedConv(forward)") {
std::shared_ptr<Tensor> myWeights =
std::make_shared<Tensor>(Array4D<double, 4, 3, 3, 3>{
{{{{6.20986394e-01, 1.19775136e-03, 7.22876095e-02},
{1.16492919e-01, 8.21634093e-02, 1.17413265e-01},
{2.23743494e-01, 3.99495413e-01, 5.55552411e-01}},
{{6.64970077e-01, 9.62199940e-01, 4.87531967e-01},
{6.12586558e-01, 8.09918671e-02, 8.40649383e-01},
{4.15264406e-01, 8.28247138e-01, 1.52301135e-01}},
{{1.76992844e-02, 7.78697112e-01, 8.14531592e-01},
{1.36960611e-01, 4.64806728e-01, 4.85150000e-01},
{4.34776520e-01, 9.51740977e-01, 9.05793799e-01}}},
{{{1.71925246e-02, 1.91082720e-01, 3.67982644e-01},
{1.56806559e-01, 6.22280998e-01, 3.15827594e-01},
{6.04359038e-01, 2.83095947e-01, 6.11168892e-01}},
{{2.76942832e-01, 1.89768419e-01, 8.07988176e-01},
{1.67925807e-01, 2.68356150e-01, 6.28875602e-01},
{1.69093357e-04, 9.64788636e-01, 7.29254981e-01}},
{{6.34030122e-01, 1.32087038e-01, 3.33857107e-01},
{7.63047502e-01, 5.12539506e-02, 9.77400493e-01},
{8.06151288e-01, 2.60237147e-01, 3.93729313e-01}}},
{{{5.84605240e-01, 4.74648725e-01, 8.54111741e-01},
{7.10897067e-02, 5.02579011e-01, 3.35236224e-01},
{9.08637408e-01, 8.02903830e-01, 2.83929907e-01}},
{{3.68206999e-01, 9.18579021e-02, 7.33168098e-01},
{1.59875539e-01, 9.13163381e-01, 3.59806060e-01},
{1.41295882e-01, 7.00312185e-01, 5.63728289e-01}},
{{9.39513546e-01, 1.91704891e-01, 1.11454944e-01},
{5.46298282e-01, 2.89698587e-01, 2.62612651e-01},
{1.18554992e-01, 4.32147376e-02, 7.53016994e-01}}},
{{{9.53179175e-01, 2.05041054e-02, 1.11318451e-01},
{8.67878485e-01, 2.93263422e-01, 8.03912714e-01},
{8.93620255e-01, 1.37831128e-01, 3.83640583e-01}},
{{3.96020188e-01, 6.24959320e-01, 1.90709175e-01},
{5.80538620e-01, 6.63031275e-01, 2.07247191e-01},
{5.65672171e-01, 5.57014317e-01, 9.26909496e-01}},
{{3.43901418e-01, 4.47741636e-01, 6.59249367e-01},
{7.34639028e-01, 2.84957200e-02, 9.70225217e-01},
{1.33578790e-02, 6.12054702e-01, 9.36685235e-02}}}}});
std::shared_ptr<Tensor> myBias =
std::make_shared<Tensor>(Array1D<double, 4>{
{0.16884905, 0.27994487, 0.57227465, 0.06435205}});
std::shared_ptr<Tensor> myInput = std::make_shared<
Tensor>(Array4D<double, 2, 3, 5, 5>{
// NCHW
{{{{0.43224481, 0.9047832, 0.18402257, 0.06162838, 0.52490127},
{0.27773404, 0.55402353, 0.9485062, 0.31197083, 0.80328607},
@@ -108,93 +117,107 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
{0.95873236, 0.6742374, 0.55679676, 0.6323497, 0.34072958},
{0.49694061, 0.79173045, 0.19738225, 0.14755281, 0.80818177},
{0.02332061, 0.74270703, 0.59415632, 0.08195934, 0.46295434},
{0.71426058, 0.85032931, 0.90750818, 0.28768431, 0.4401146}}}}});
std::shared_ptr<Tensor> myOutput = std::make_shared<Tensor>(
Array4D<double, 2, 4, 5, 5>{{{{{3.40294218, 3.74021220, 4.02050114, 4.07054710, 2.46286273},
{4.61770582, 6.70517588, 6.50356627, 6.29688787, 3.53332567},
{5.47480106, 5.92094421, 6.64605665, 7.95090199, 4.28721523},
{4.01485729, 6.06748962, 7.52447891, 7.37980652, 5.28401136},
{2.83065438, 3.62033439, 3.56222963, 5.56103945, 3.23335814}},
{{3.30230498, 4.92814112, 4.34710836, 3.96262765, 2.97987890},
{4.49693012, 6.68929291, 5.53603029, 5.68874264, 4.28756475},
{4.20528078, 6.82776880, 6.70569849, 7.12809610, 4.40845442},
{4.31169367, 6.73352146, 6.30962515, 7.45826864, 4.99164438},
{2.18136287, 4.28968000, 4.20080042, 4.89814138, 2.87394023}},
{{3.54787683, 4.35851812, 4.63881302, 4.23359537, 3.16992092},
{5.25099468, 7.54282856, 6.69849157, 5.64309788, 4.56919575},
{4.71914101, 7.52830601, 6.71450949, 7.81113863, 5.84658146},
{4.97893143, 7.39293909, 6.89905310, 8.14430809, 5.62998581},
{2.79735112, 4.80967140, 5.57630205, 5.38828325, 4.57078695}},
{{3.03048635, 5.04540300, 4.21824932, 4.87323284, 2.35113740},
{4.45167351, 6.47721338, 7.40922976, 6.70445728, 3.60700107},
{3.77927423, 6.82826376, 7.41777134, 7.57402420, 5.13131523},
{4.08747244, 7.07994175, 7.57206821, 8.51897335, 5.26987123},
{2.34426999, 4.60127831, 4.86486769, 6.01579571, 3.97803569}}},
{{{3.84700942, 4.25972605, 3.05269003, 3.78043652, 2.08771229},
{6.00459957, 6.05633259, 4.45951605, 4.54089880, 4.03066444},
{5.41579390, 7.29543972, 6.18680000, 5.58812714, 3.45964241},
{6.04531050, 7.70924091, 5.52207708, 5.02131319, 4.09403706},
{3.18092418, 4.45422697, 4.04294252, 3.86577177, 2.18776536}},
{{4.02600670, 4.27603531, 3.81011319, 4.03631020, 2.57254648},
{5.33471155, 5.72588634, 5.12079763, 5.11733150, 3.76836705},
{5.62947607, 5.92492962, 6.24170446, 6.44130468, 3.44276404},
{5.38414621, 6.02679539, 5.88985586, 5.90263271, 3.15044069},
{3.31261086, 4.44371319, 3.47660780, 4.15411520, 1.48961508}},
{{3.95879412, 4.17324543, 3.70114422, 3.27447152, 3.09713888},
{5.78258181, 6.57920837, 4.99913597, 6.20961237, 4.98552179},
{5.84685421, 7.19971228, 6.66386652, 6.68013430, 4.90963316},
{5.24417877, 7.06430531, 6.58512402, 6.02492285, 4.48986387},
{3.64294529, 5.00678444, 5.04760027, 4.72895622, 2.67990756}},
{{3.48610687, 4.12853813, 4.07563591, 3.51327014, 2.44217038},
{4.80529881, 7.33211374, 5.14774036, 4.77281189, 4.44612408},
{5.11703110, 7.55168772, 7.14374542, 6.43696356, 4.10621357},
{5.41270018, 6.85949135, 6.73503923, 5.74601364, 4.46150303},
{3.16612267, 4.38248920, 5.23248482, 4.21292210, 2.86031270}}}}});
std::shared_ptr<Node> myConv = Conv<2>(3, 4, {3, 3}, "myconv");
auto convOp = std::static_pointer_cast<OperatorTensor>(myConv->getOperator());
std::shared_ptr<Node> myPad =
{0.71426058,
0.85032931,
0.90750818,
0.28768431,
0.4401146}}}}});
std::shared_ptr<Tensor> myOutput = std::make_shared<
Tensor>(Array4D<double, 2, 4, 5, 5>{
{{{{3.40294218, 3.74021220, 4.02050114, 4.07054710, 2.46286273},
{4.61770582, 6.70517588, 6.50356627, 6.29688787, 3.53332567},
{5.47480106, 5.92094421, 6.64605665, 7.95090199, 4.28721523},
{4.01485729, 6.06748962, 7.52447891, 7.37980652, 5.28401136},
{2.83065438, 3.62033439, 3.56222963, 5.56103945, 3.23335814}},
{{3.30230498, 4.92814112, 4.34710836, 3.96262765, 2.97987890},
{4.49693012, 6.68929291, 5.53603029, 5.68874264, 4.28756475},
{4.20528078, 6.82776880, 6.70569849, 7.12809610, 4.40845442},
{4.31169367, 6.73352146, 6.30962515, 7.45826864, 4.99164438},
{2.18136287, 4.28968000, 4.20080042, 4.89814138, 2.87394023}},
{{3.54787683, 4.35851812, 4.63881302, 4.23359537, 3.16992092},
{5.25099468, 7.54282856, 6.69849157, 5.64309788, 4.56919575},
{4.71914101, 7.52830601, 6.71450949, 7.81113863, 5.84658146},
{4.97893143, 7.39293909, 6.89905310, 8.14430809, 5.62998581},
{2.79735112, 4.80967140, 5.57630205, 5.38828325, 4.57078695}},
{{3.03048635, 5.04540300, 4.21824932, 4.87323284, 2.35113740},
{4.45167351, 6.47721338, 7.40922976, 6.70445728, 3.60700107},
{3.77927423, 6.82826376, 7.41777134, 7.57402420, 5.13131523},
{4.08747244, 7.07994175, 7.57206821, 8.51897335, 5.26987123},
{2.34426999, 4.60127831, 4.86486769, 6.01579571, 3.97803569}}},
{{{3.84700942, 4.25972605, 3.05269003, 3.78043652, 2.08771229},
{6.00459957, 6.05633259, 4.45951605, 4.54089880, 4.03066444},
{5.41579390, 7.29543972, 6.18680000, 5.58812714, 3.45964241},
{6.04531050, 7.70924091, 5.52207708, 5.02131319, 4.09403706},
{3.18092418, 4.45422697, 4.04294252, 3.86577177, 2.18776536}},
{{4.02600670, 4.27603531, 3.81011319, 4.03631020, 2.57254648},
{5.33471155, 5.72588634, 5.12079763, 5.11733150, 3.76836705},
{5.62947607, 5.92492962, 6.24170446, 6.44130468, 3.44276404},
{5.38414621, 6.02679539, 5.88985586, 5.90263271, 3.15044069},
{3.31261086, 4.44371319, 3.47660780, 4.15411520, 1.48961508}},
{{3.95879412, 4.17324543, 3.70114422, 3.27447152, 3.09713888},
{5.78258181, 6.57920837, 4.99913597, 6.20961237, 4.98552179},
{5.84685421, 7.19971228, 6.66386652, 6.68013430, 4.90963316},
{5.24417877, 7.06430531, 6.58512402, 6.02492285, 4.48986387},
{3.64294529, 5.00678444, 5.04760027, 4.72895622, 2.67990756}},
{{3.48610687, 4.12853813, 4.07563591, 3.51327014, 2.44217038},
{4.80529881, 7.33211374, 5.14774036, 4.77281189, 4.44612408},
{5.11703110, 7.55168772, 7.14374542, 6.43696356, 4.10621357},
{5.41270018, 6.85949135, 6.73503923, 5.74601364, 4.46150303},
{3.16612267,
4.38248920,
5.23248482,
4.21292210,
2.86031270}}}}});
std::shared_ptr<Node> myConv = Conv<2>(3, 4, {3, 3}, "myconv");
auto convOp =
std::static_pointer_cast<OperatorTensor>(myConv->getOperator());
std::shared_ptr<Node> myPad =
Pad<2>({1, 1, 1, 1}, "myPad", PadBorderType::Constant, 0.0);
auto padOp = std::static_pointer_cast<OperatorTensor>(myPad->getOperator());
convOp->setInput(1, myWeights);
convOp->setInput(2, myBias);
myPad->addChild(myConv, 0, 0);
padOp->setInput(0, myInput);
padOp->setDataType(DataType::Float64);
padOp->setBackend("cpu");
convOp->setDataType(DataType::Float64);
convOp->setBackend("cpu");
myPad->forward();
myConv->forward();
convOp -> getOutput(0) -> print();
double* computedOutput = static_cast<double*>(convOp->getOutput(0)->getImpl()->rawPtr());
double* expectedOutput = static_cast<double*>(myOutput->getImpl()->rawPtr());
for (std::size_t i = 0; i < myOutput->size(); ++i) {
REQUIRE(std::abs(computedOutput[i] - expectedOutput[i]) < 1e-5);
}
auto padOp =
std::static_pointer_cast<OperatorTensor>(myPad->getOperator());
convOp->setInput(1, myWeights);
convOp->setInput(2, myBias);
myPad->addChild(myConv, 0, 0);
padOp->setInput(0, myInput);
padOp->setDataType(DataType::Float64);
padOp->setBackend("cpu");
convOp->setDataType(DataType::Float64);
convOp->setBackend("cpu");
myPad->forward();
myConv->forward();
convOp->getOutput(0)->print();
double *computedOutput =
static_cast<double *>(convOp->getOutput(0)->getImpl()->rawPtr());
double *expectedOutput =
static_cast<double *>(myOutput->getImpl()->rawPtr());
for (std::size_t i = 0; i < myOutput->size(); ++i) {
REQUIRE(std::abs(computedOutput[i] - expectedOutput[i]) < 1e-5);
}
std::shared_ptr<Node> myPaddedConv =
std::shared_ptr<Node> myPaddedConv =
PaddedConv(3, 4, {3, 3}, "myPaddedConv", {1, 1}, {1, 1, 1, 1});
}
}
SECTION("LSTM(forward)") {
auto pop = Pop();
auto myLSTM = LSTM(32, 64, 0, true, "ltsm");
auto op = std::dynamic_pointer_cast<MetaOperator_Op>(myLSTM->getOperator());
auto op =
std::dynamic_pointer_cast<MetaOperator_Op>(myLSTM->getOperator());
auto microGraph = op->getMicroGraph();
microGraph->save("lstm", false, true);
@@ -209,14 +232,14 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
}
REQUIRE(myLSTM->nbOutputs() == 2);
std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(
Array2D<float, 16, 32>{});
std::shared_ptr<Tensor> myInit = std::make_shared<Tensor>(
Array2D<float, 32, 64>{});
std::shared_ptr<Tensor> myInitW = std::make_shared<Tensor>(
Array2D<float, 64, 32>{});
std::shared_ptr<Tensor> myInitR = std::make_shared<Tensor>(
Array2D<float, 64, 64>{});
std::shared_ptr<Tensor> myInput =
std::make_shared<Tensor>(Array2D<float, 16, 32>{});
std::shared_ptr<Tensor> myInit =
std::make_shared<Tensor>(Array2D<float, 32, 64>{});
std::shared_ptr<Tensor> myInitW =
std::make_shared<Tensor>(Array2D<float, 64, 32>{});
std::shared_ptr<Tensor> myInitR =
std::make_shared<Tensor>(Array2D<float, 64, 64>{});
pop->addChild(myLSTM, 0, 0);
pop->getOperator()->associateInput(0, myInput);
@@ -246,7 +269,9 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
microGraph->save("lstm_dims", true, true);
REQUIRE(op->dimsForwarded());
auto microGraphScheduler = std::dynamic_pointer_cast<MetaOperator_Op>(op)->getMicroGraphScheduler();
auto microGraphScheduler =
std::dynamic_pointer_cast<MetaOperator_Op>(op)
->getMicroGraphScheduler();
microGraphScheduler->saveSchedulingDiagram("lstm_scheduling");
REQUIRE(op->getNbConsumedData(0).data == 512);
@@ -257,11 +282,14 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
REQUIRE(microGraphScheduler->getStaticScheduling(1).size() == 24);
REQUIRE(microGraphScheduler->getStaticScheduling(15).size() == 24);
}
SECTION("LSTM(forward_values)") {
auto myLSTM = LSTM(2, 3, 0, true, "ltsm");
auto op = std::static_pointer_cast<OperatorTensor>(myLSTM->getOperator());
auto op =
std::static_pointer_cast<OperatorTensor>(myLSTM->getOperator());
auto microGraph = std::dynamic_pointer_cast<MetaOperator_Op>(op)->getMicroGraph();
auto microGraph =
std::dynamic_pointer_cast<MetaOperator_Op>(op)->getMicroGraph();
microGraph->save("lstm", false, false);
REQUIRE(myLSTM->nbInputs() == 3 + 8 + 8);
@@ -276,12 +304,14 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(
Array2D<float, 3, 2>{{{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}}});
std::shared_ptr<Tensor> myInit = std::make_shared<Tensor>(
Array2D<float, 3, 3>{{{0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}}});
std::shared_ptr<Tensor> myInit =
std::make_shared<Tensor>(Array2D<float, 3, 3>{
{{0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}}});
std::shared_ptr<Tensor> myInitW = std::make_shared<Tensor>(
Array2D<float, 3, 2>{{{0.1, 0.1}, {0.1, 0.1}, {0.1, 0.1}}});
std::shared_ptr<Tensor> myInitR = std::make_shared<Tensor>(
Array2D<float, 3, 3>{{{0.1, 0.1, 0.1}, {0.1, 0.1, 0.1}, {0.1, 0.1, 0.1}}});
std::shared_ptr<Tensor> myInitR =
std::make_shared<Tensor>(Array2D<float, 3, 3>{
{{0.1, 0.1, 0.1}, {0.1, 0.1, 0.1}, {0.1, 0.1, 0.1}}});
op->associateInput(0, myInput);
op->associateInput(17, myInit);
@@ -308,12 +338,13 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
microGraph->save("lstm_values_dims", false, true);
std::shared_ptr<Tensor> myHiddenState = std::make_shared<Tensor>(
Array2D<float, 3, 3>{{{0.0952412, 0.0952412, 0.0952412},
{0.25606447, 0.25606447, 0.25606447},
{0.40323776, 0.40323776, 0.40323776}}});
Array2D<float, 3, 3>{{{0.0952412, 0.0952412, 0.0952412},
{0.25606447, 0.25606447, 0.25606447},
{0.40323776, 0.40323776, 0.40323776}}});
auto microGraphScheduler = std::dynamic_pointer_cast<MetaOperator_Op>(op)->getMicroGraphScheduler();
auto microGraphScheduler =
std::dynamic_pointer_cast<MetaOperator_Op>(op)
->getMicroGraphScheduler();
microGraphScheduler->saveSchedulingDiagram("lstm_values_scheduling");
op->getOutput(0)->print();
@@ -321,11 +352,13 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
REQUIRE(approxEq<float>(*(op->getOutput(0)), *myHiddenState));
}
SECTION("LSTM(forward_values_seq)") {
auto pop = Pop();
auto myLSTM = LSTM(2, 3, 2, true, "ltsm");
auto myGraph = Sequential({pop, myLSTM});
auto op = std::static_pointer_cast<OperatorTensor>(myLSTM->getOperator());
auto op =
std::static_pointer_cast<OperatorTensor>(myLSTM->getOperator());
REQUIRE(myLSTM->nbInputs() == 3 + 8 + 8);
REQUIRE(myLSTM->inputCategory(0) == InputCategory::Data);
@@ -338,13 +371,16 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
REQUIRE(myLSTM->nbOutputs() == 2);
std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(
Array3D<float, 2, 3, 2>{{{{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}}, {{2.0, 3.0}, {4.0, 5.0}, {6.0, 7.0}}}});
std::shared_ptr<Tensor> myInit = std::make_shared<Tensor>(
Array2D<float, 3, 3>{{{0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}}});
Array3D<float, 2, 3, 2>{{{{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}},
{{2.0, 3.0}, {4.0, 5.0}, {6.0, 7.0}}}});
std::shared_ptr<Tensor> myInit =
std::make_shared<Tensor>(Array2D<float, 3, 3>{
{{0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}}});
std::shared_ptr<Tensor> myInitW = std::make_shared<Tensor>(
Array2D<float, 3, 2>{{{0.1, 0.1}, {0.1, 0.1}, {0.1, 0.1}}});
std::shared_ptr<Tensor> myInitR = std::make_shared<Tensor>(
Array2D<float, 3, 3>{{{0.1, 0.1, 0.1}, {0.1, 0.1, 0.1}, {0.1, 0.1, 0.1}}});
std::shared_ptr<Tensor> myInitR =
std::make_shared<Tensor>(Array2D<float, 3, 3>{
{{0.1, 0.1, 0.1}, {0.1, 0.1, 0.1}, {0.1, 0.1, 0.1}}});
pop->getOperator()->associateInput(0, myInput);
op->associateInput(17, myInit);
@@ -371,9 +407,9 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
scheduler.saveSchedulingDiagram("lstm_seq_schedule");
std::shared_ptr<Tensor> myHiddenState = std::make_shared<Tensor>(
Array2D<float, 3, 3>{{{0.24439372, 0.24439372, 0.24439372},
{0.49801484, 0.49801484, 0.49801484},
{0.67162132, 0.67162132, 0.67162132}}});
Array2D<float, 3, 3>{{{0.24439372, 0.24439372, 0.24439372},
{0.49801484, 0.49801484, 0.49801484},
{0.67162132, 0.67162132, 0.67162132}}});
myGraph->save("lstm_seq_mygraph", true, true);
@@ -382,10 +418,12 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
REQUIRE(approxEq<float>(*(op->getOutput(0)), *myHiddenState));
}
SECTION("LSTM(forward_values_seq_flatten)(sequential)") {
auto pop = Pop();
auto myLSTM = LSTM(2, 3, 2, true, "ltsm");
auto op = std::static_pointer_cast<MetaOperator_Op>(myLSTM->getOperator());
auto op =
std::static_pointer_cast<MetaOperator_Op>(myLSTM->getOperator());
// Here we test LSTM as it is was flatten in the graph.
// We just borrow its micro-graph into our larger myGraph graph.
@@ -405,13 +443,16 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
REQUIRE(myLSTM->nbOutputs() == 2);
std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(
Array3D<float, 2, 3, 2>{{{{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}}, {{2.0, 3.0}, {4.0, 5.0}, {6.0, 7.0}}}});
std::shared_ptr<Tensor> myInit = std::make_shared<Tensor>(
Array2D<float, 3, 3>{{{0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}}});
Array3D<float, 2, 3, 2>{{{{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}},
{{2.0, 3.0}, {4.0, 5.0}, {6.0, 7.0}}}});
std::shared_ptr<Tensor> myInit =
std::make_shared<Tensor>(Array2D<float, 3, 3>{
{{0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}}});
std::shared_ptr<Tensor> myInitW = std::make_shared<Tensor>(
Array2D<float, 3, 2>{{{0.1, 0.1}, {0.1, 0.1}, {0.1, 0.1}}});
std::shared_ptr<Tensor> myInitR = std::make_shared<Tensor>(
Array2D<float, 3, 3>{{{0.1, 0.1, 0.1}, {0.1, 0.1, 0.1}, {0.1, 0.1, 0.1}}});
std::shared_ptr<Tensor> myInitR =
std::make_shared<Tensor>(Array2D<float, 3, 3>{
{{0.1, 0.1, 0.1}, {0.1, 0.1, 0.1}, {0.1, 0.1, 0.1}}});
pop->getOperator()->associateInput(0, myInput);
op->associateInput(17, myInit);
@@ -419,16 +460,32 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
// Weights X
auto prodX = Producer(myInitW);
prodX->addChild(op->getMicroGraph()->getOrderedInputs()[1].first, 0, 1);
prodX->addChild(op->getMicroGraph()->getOrderedInputs()[2].first, 0, 1);
prodX->addChild(op->getMicroGraph()->getOrderedInputs()[3].first, 0, 1);
prodX->addChild(op->getMicroGraph()->getOrderedInputs()[4].first, 0, 1);
prodX->addChild(op->getMicroGraph()->getOrderedInputs()[1].first,
0,
1);
prodX->addChild(op->getMicroGraph()->getOrderedInputs()[2].first,
0,
1);
prodX->addChild(op->getMicroGraph()->getOrderedInputs()[3].first,
0,
1);
prodX->addChild(op->getMicroGraph()->getOrderedInputs()[4].first,
0,
1);
// Weights H
auto prodH = Producer(myInitR);
prodH->addChild(op->getMicroGraph()->getOrderedInputs()[5].first, 0, 1);
prodH->addChild(op->getMicroGraph()->getOrderedInputs()[6].first, 0, 1);
prodH->addChild(op->getMicroGraph()->getOrderedInputs()[7].first, 0, 1);
prodH->addChild(op->getMicroGraph()->getOrderedInputs()[8].first, 0, 1);
prodH->addChild(op->getMicroGraph()->getOrderedInputs()[5].first,
0,
1);
prodH->addChild(op->getMicroGraph()->getOrderedInputs()[6].first,
0,
1);
prodH->addChild(op->getMicroGraph()->getOrderedInputs()[7].first,
0,
1);
prodH->addChild(op->getMicroGraph()->getOrderedInputs()[8].first,
0,
1);
myGraph->add({prodX, prodH});
myGraph->setDataType(DataType::Float32);
@@ -436,9 +493,9 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
myGraph->save("lstm_seq_flatten", true, true);
std::shared_ptr<Tensor> myHiddenState = std::make_shared<Tensor>(
Array2D<float, 3, 3>{{{0.24439372, 0.24439372, 0.24439372},
{0.49801484, 0.49801484, 0.49801484},
{0.67162132, 0.67162132, 0.67162132}}});
Array2D<float, 3, 3>{{{0.24439372, 0.24439372, 0.24439372},
{0.49801484, 0.49801484, 0.49801484},
{0.67162132, 0.67162132, 0.67162132}}});
auto scheduler = SequentialScheduler(myGraph);
scheduler.generateScheduling();
@@ -454,7 +511,8 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
SECTION("LSTM(forward_values_seq_flatten)(parallel)") {
auto pop = Pop();
auto myLSTM = LSTM(2, 3, 2, true, "ltsm");
auto op = std::static_pointer_cast<MetaOperator_Op>(myLSTM->getOperator());
auto op =
std::static_pointer_cast<MetaOperator_Op>(myLSTM->getOperator());
// Here we test LSTM as it is was flatten in the graph.
// We just borrow its micro-graph into our larger myGraph graph.
@@ -474,13 +532,16 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
REQUIRE(myLSTM->nbOutputs() == 2);
std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(
Array3D<float, 2, 3, 2>{{{{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}}, {{2.0, 3.0}, {4.0, 5.0}, {6.0, 7.0}}}});
std::shared_ptr<Tensor> myInit = std::make_shared<Tensor>(
Array2D<float, 3, 3>{{{0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}}});
Array3D<float, 2, 3, 2>{{{{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}},
{{2.0, 3.0}, {4.0, 5.0}, {6.0, 7.0}}}});
std::shared_ptr<Tensor> myInit =
std::make_shared<Tensor>(Array2D<float, 3, 3>{
{{0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}}});
std::shared_ptr<Tensor> myInitW = std::make_shared<Tensor>(
Array2D<float, 3, 2>{{{0.1, 0.1}, {0.1, 0.1}, {0.1, 0.1}}});
std::shared_ptr<Tensor> myInitR = std::make_shared<Tensor>(
Array2D<float, 3, 3>{{{0.1, 0.1, 0.1}, {0.1, 0.1, 0.1}, {0.1, 0.1, 0.1}}});
std::shared_ptr<Tensor> myInitR =
std::make_shared<Tensor>(Array2D<float, 3, 3>{
{{0.1, 0.1, 0.1}, {0.1, 0.1, 0.1}, {0.1, 0.1, 0.1}}});
pop->getOperator()->associateInput(0, myInput);
op->associateInput(17, myInit);
@@ -488,16 +549,32 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
// Weights X
auto prodX = Producer(myInitW);
prodX->addChild(op->getMicroGraph()->getOrderedInputs()[1].first, 0, 1);
prodX->addChild(op->getMicroGraph()->getOrderedInputs()[2].first, 0, 1);
prodX->addChild(op->getMicroGraph()->getOrderedInputs()[3].first, 0, 1);
prodX->addChild(op->getMicroGraph()->getOrderedInputs()[4].first, 0, 1);
prodX->addChild(op->getMicroGraph()->getOrderedInputs()[1].first,
0,
1);
prodX->addChild(op->getMicroGraph()->getOrderedInputs()[2].first,
0,
1);
prodX->addChild(op->getMicroGraph()->getOrderedInputs()[3].first,
0,
1);
prodX->addChild(op->getMicroGraph()->getOrderedInputs()[4].first,
0,
1);
// Weights H
auto prodH = Producer(myInitR);
prodH->addChild(op->getMicroGraph()->getOrderedInputs()[5].first, 0, 1);
prodH->addChild(op->getMicroGraph()->getOrderedInputs()[6].first, 0, 1);
prodH->addChild(op->getMicroGraph()->getOrderedInputs()[7].first, 0, 1);
prodH->addChild(op->getMicroGraph()->getOrderedInputs()[8].first, 0, 1);
prodH->addChild(op->getMicroGraph()->getOrderedInputs()[5].first,
0,
1);
prodH->addChild(op->getMicroGraph()->getOrderedInputs()[6].first,
0,
1);
prodH->addChild(op->getMicroGraph()->getOrderedInputs()[7].first,
0,
1);
prodH->addChild(op->getMicroGraph()->getOrderedInputs()[8].first,
0,
1);
myGraph->add({prodX, prodH});
myGraph->setDataType(DataType::Float32);
@@ -505,9 +582,9 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
myGraph->save("lstm_seq_flatten", true, true);
std::shared_ptr<Tensor> myHiddenState = std::make_shared<Tensor>(
Array2D<float, 3, 3>{{{0.24439372, 0.24439372, 0.24439372},
{0.49801484, 0.49801484, 0.49801484},
{0.67162132, 0.67162132, 0.67162132}}});
Array2D<float, 3, 3>{{{0.24439372, 0.24439372, 0.24439372},
{0.49801484, 0.49801484, 0.49801484},
{0.67162132, 0.67162132, 0.67162132}}});
auto scheduler = ParallelScheduler(myGraph);
scheduler.generateScheduling();
@@ -519,4 +596,308 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
REQUIRE(approxEq<float>(*(op->getOutput(0)), *myHiddenState));
}
}
\ No newline at end of file
SECTION("Leaky(forward)(fixed)") {
constexpr auto inChannels = 10;
constexpr auto outChannels = 5;
constexpr auto beta = 0.95;
constexpr auto threshold = 1.0;
constexpr auto nbTimeSteps = 2;
auto myWeights =
std::make_shared<Tensor>(Array2D<float, outChannels, inChannels>{{
{0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0},
{1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1},
{0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 0.1, 0.2, 0.3, 0.4},
{0.4, 0.3, 0.2, 0.1, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5},
{0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0},
}});
auto myWeights2 =
std::make_shared<Tensor>(Array2D<float, inChannels, outChannels>{{
{0.1, 0.2, 0.3, 0.4, 0.5},
{0.6, 0.7, 0.8, 0.9, 1.0},
{1.0, 0.9, 0.8, 0.7, 0.6},
{0.5, 0.4, 0.3, 0.2, 0.1},
{0.5, 0.6, 0.7, 0.8, 0.9},
{1.0, 0.1, 0.2, 0.3, 0.4},
{0.4, 0.3, 0.2, 0.1, 0.0},
{0.1, 0.2, 0.3, 0.4, 0.5},
{0.9, 0.8, 0.7, 0.6, 0.5},
{0.4, 0.3, 0.2, 0.1, 0.0},
}});
auto myInput = std::make_shared<Tensor>(Array2D<float, 2, 10>{{
{0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0},
{1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1},
}});
// py/snn Torch computed result, output of fc1 at time step 1
auto expectedOutputlif1ts1 =
std::make_shared<Tensor>(Array2D<float, 2, 5>{{
{3.850, 2.2000, 2.6500, 1.5000, 1.6500},
{2.200, 3.8500, 3.4000, 1.2500, 3.3000},
}});
auto expectedOutputfc2ts1 =
std::make_shared<Tensor>(Array2D<float, 2, 10>{{
{1.5000,
4.0000,
4.0000,
1.5000,
3.5000,
2.0000,
1.0000,
1.5000,
3.5000,
1.0000},
{1.5000,
4.0000,
4.0000,
1.5000,
3.5000,
2.0000,
1.0000,
1.5000,
3.5000,
1.0000},
}});
auto expectedOutputlif1ts2 =
std::make_shared<Tensor>(Array2D<float, 2, 5>{{
{6.5075, 3.2900, 4.1675, 1.9250, 2.2175},
{3.2900, 6.5075, 5.6300, 1.4375, 5.4350},
}});
// NOTE: Same output as before, because for all channels, we have a
// potential higher than threshold. Thus the lif neuron fires at every
// timestep for every channel.
auto expectedOutputfc2ts2 =
std::make_shared<Tensor>(Array2D<float, 2, 10>{{
{1.5000,
4.0000,
4.0000,
1.5000,
3.5000,
2.0000,
1.0000,
1.5000,
3.5000,
1.0000},
{1.5000,
4.0000,
4.0000,
1.5000,
3.5000,
2.0000,
1.0000,
1.5000,
3.5000,
1.0000},
}});
auto init = std::make_shared<Tensor>(Array2D<float, 2, 5>{});
uniformFiller<float>(init, 0.0, 0.0);
auto fc1 = FC(inChannels, outChannels, true, "myfc");
auto fc2 = FC(outChannels, inChannels, true, "fc2");
// NOTE: Account for init step by adding 1 to the max timestep
// parameter.
auto lif1 = Leaky(nbTimeSteps + 1, beta, threshold, "leaky");
// associateInput() does not work
fc1->input(1).first->getOperator()->setOutput(0, myWeights);
fc2->input(1).first->getOperator()->setOutput(0, myWeights2);
auto fc1Op =
std::static_pointer_cast<OperatorTensor>(fc1->getOperator());
auto lif1Op =
std::static_pointer_cast<MetaOperator_Op>(lif1->getOperator());
auto fc2Op =
std::static_pointer_cast<OperatorTensor>(fc2->getOperator());
fc1Op->associateInput(0, myInput);
lif1Op->associateInput(1, init);
lif1Op->associateInput(2, init);
fc1->addChild(lif1, 0, 0);
lif1->addChild(fc2, 1, 0);
auto g = std::make_shared<GraphView>();
g->add({fc1, lif1, fc2});
g->compile("cpu", DataType::Float32);
auto scheduler = SequentialScheduler(g);
// Forward 1 (simulate timestep 0)
scheduler.forward(true);
REQUIRE(approxEq<float>(*(lif1Op->getOutput(0)),
*(expectedOutputlif1ts1)));
REQUIRE(
approxEq<float>(*(fc2Op->getOutput(0)), *(expectedOutputfc2ts1)));
// Forward 1 (simulate timestep 1)
scheduler.forward(true);
REQUIRE(approxEq<float>(*(lif1Op->getOutput(0)),
*(expectedOutputlif1ts2)));
REQUIRE(
approxEq<float>(*(fc2Op->getOutput(0)), *(expectedOutputfc2ts2)));
}
SECTION("Leaky(forward)") {
std::random_device rd;
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(4));
std::uniform_int_distribution<std::size_t> nbDimsDist(std::size_t(3),
std::size_t(3));
std::uniform_int_distribution<int> boolDist(0, 1);
std::uniform_real_distribution<float> betaDist(0,1);
const std::size_t nbDims = nbDimsDist(gen);
Log::info("Nbdims : {}", nbDims);
std::vector<std::size_t> dims;
for (std::size_t i = 0; i < nbDims; ++i) {
dims.push_back(dimSizeDist(gen));
}
Log::info("timesteps : {}", dims[0]);
Log::info("dimensions : ");
for (auto dim : dims) {
Log::info("{}", dim);
}
const auto nbTimeSteps = dims[0];
const auto beta = betaDist(gen);
auto myLeaky = Leaky(nbTimeSteps, beta, 1.0, "leaky");
auto op =
std::static_pointer_cast<MetaOperator_Op>(myLeaky->getOperator());
// auto stack = Stack(2);
auto mem_rec = Stack(nbTimeSteps, "mem_rec");
auto spk_rec = Stack(nbTimeSteps, "spk_rec");
auto pop = Pop("popinput");
// Here we test LSTM as it is was flatten in the graph.
// We just borrow its micro-graph into our larger myGraph graph.
auto myGraph = std::make_shared<GraphView>();
pop->addChild(op->getMicroGraph()->getOrderedInputs()[0].first, 0, 0);
// 0 for mem 1 for stack
op->getMicroGraph()->getOrderedOutputs()[1].first->addChild(mem_rec,
0,
0);
op->getMicroGraph()->getOrderedOutputs()[0].first->addChild(spk_rec,
0,
0);
for (auto node : op->getMicroGraph()->getOrderedOutputs()) {
Log::info("name of output {}", node.first->name());
}
myGraph->add(pop);
myGraph->add(op->getMicroGraph());
myGraph->add(mem_rec);
myGraph->add(spk_rec);
myGraph->save("mg", true, true);
// 3 outputs
REQUIRE(myLeaky->nbInputs() == 3);
REQUIRE(myLeaky->inputCategory(0) == InputCategory::Data);
// Two spikes connected to nothing, + the Add node real output
REQUIRE(myLeaky->nbOutputs() == 4);
std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(
Array3D<float, 2, 3, 2>{{{{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}},
{{2.0, 3.0}, {4.0, 5.0}, {6.0, 7.0}}}});
// std::shared_ptr<Tensor> expectedOutput = std::make_shared<Tensor>(
// Array3D<float, 2, 3, 2>{{{{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}},
// {{2.0, 3.0}, {4.0, 5.0},
// {6.0, 7.0}}}});
// Generate input
std::shared_ptr<Tensor> T0 = std::make_shared<Tensor>();
T0->setDataType(DataType::Float32);
T0->setBackend("cpu");
std::shared_ptr<Tensor> expectedOutput = std::make_shared<Tensor>();
expectedOutput->setDataType(DataType::Float32);
expectedOutput->setBackend("cpu");
const auto nb_elements =
std::accumulate(dims.cbegin(),
dims.cend(),
std::size_t(1),
std::multiplies<std::size_t>());
float *input = new float[nb_elements];
float *result = new float[nb_elements];
for (std::size_t i = 0; i < nb_elements; ++i) {
input[i] = valueDist(gen);
}
T0->resize(dims);
T0->getImpl()->setRawPtr(input, nb_elements);
T0->print();
// Elements popped at each time step
auto nbElementsPerTimeStep = nb_elements / dims[0];
// Init
for (int i = 0; i < nbElementsPerTimeStep; ++i) {
result[i] = input[i];
}
// Reccurence
for (int i = 1; i < dims[0]; ++i) {
auto offset = nbElementsPerTimeStep * i;
auto prev = nbElementsPerTimeStep * (i - 1);
for (int j = 0; j < nbElementsPerTimeStep; ++j) {
auto reset = (result[prev + j] > 1.0 ? 1 : 0);
result[offset + j] =
result[prev + j] * beta + input[offset + j] - reset;
}
}
expectedOutput->resize(dims);
expectedOutput->getImpl()->setRawPtr(result, nb_elements);
Log::info("Expected ouptut : ");
expectedOutput->print();
std::shared_ptr<Tensor> myInit =
std::make_shared<Tensor>(Array2D<float, 3, 3>{
{{0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}, {0.0, 0.0, 0.0}}});
auto initMemdims =
std::vector<std::size_t>(dims.begin() + 1, dims.end());
Log::info("dimensions : ");
for (auto dim : initMemdims) {
Log::info("{}", dim);
}
std::shared_ptr<Tensor> myInitW = std::make_shared<Tensor>(
Array2D<float, 3, 2>{{{0.0, 0.0}, {0.0, 0.0}, {0.0, 0.0}}});
std::shared_ptr<Tensor> myInitR =
std::make_shared<Tensor>(initMemdims);
myInitR->setDataType(DataType::Float32);
myInitR->setBackend("cpu");
uniformFiller<float>(myInitR, 0, 0);
pop->getOperator()->associateInput(0, T0);
op->associateInput(1, myInitR);
op->associateInput(2, myInitR);
myGraph->compile("cpu", DataType::Float32);
auto scheduler = SequentialScheduler(myGraph);
REQUIRE_NOTHROW(scheduler.generateScheduling());
REQUIRE_NOTHROW(scheduler.forward(true));
auto memOp =
std::static_pointer_cast<OperatorTensor>(spk_rec->getOperator());
REQUIRE(approxEq<float>(*(memOp->getOutput(0)), *(expectedOutput)));
}
}

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