From d4d09c91b42e17b0628db6af9fb0b056c04c4235 Mon Sep 17 00:00:00 2001
From: Jerome Hue <jerome.hue@cea.fr>
Date: Fri, 7 Mar 2025 14:20:11 +0100
Subject: [PATCH] chore: Clean and improve the Leaky MetaOperator test
---
unit_tests/operator/Test_MetaOperator.cpp | 166 +++++++---------------
1 file changed, 55 insertions(+), 111 deletions(-)
diff --git a/unit_tests/operator/Test_MetaOperator.cpp b/unit_tests/operator/Test_MetaOperator.cpp
index de720f5b..f781e5e2 100644
--- a/unit_tests/operator/Test_MetaOperator.cpp
+++ b/unit_tests/operator/Test_MetaOperator.cpp
@@ -750,155 +750,99 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
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_real_distribution<float> valueDist(0.1f,1.1f);
+ std::uniform_int_distribution<std::size_t> dimSizeDist(2,4);
+ std::uniform_int_distribution<std::size_t> nbDimsDist(3,3); // fixed to 3.
std::uniform_int_distribution<int> boolDist(0, 1);
std::uniform_real_distribution<float> betaDist(0,1);
+ std::uniform_real_distribution<float> thresholDist(0.1,3);
- 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);
+ const auto threshold = thresholDist(gen);
+ const auto nbDims = nbDimsDist(gen);
+ std::vector<std::size_t> dims(nbDims);
+ std::generate(dims.begin(), dims.end(), [&]() { return dimSizeDist(gen); });
+ const auto nbTimeSteps = dims[0];
- auto myLeaky = Leaky(nbTimeSteps, beta, 1.0, LeakyReset::Subtraction, "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");
+ auto myLeaky = Leaky(nbTimeSteps, beta, threshold, LeakyReset::Subtraction, "leaky");
+ auto op = std::static_pointer_cast<MetaOperator_Op>(myLeaky->getOperator());
+ auto memoryRecord = Stack(nbTimeSteps, "mem_rec");
+ auto spikeRecord = Stack(nbTimeSteps, "spk_rec");
+ auto pop = Pop("input");
- // 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>();
+ auto leakyOutputs = op->getMicroGraph()->getOrderedOutputs();
+ auto leakyInputs = op->getMicroGraph()->getOrderedInputs();
+ pop->addChild(leakyInputs[0].first, 0, 0);
+ leakyOutputs[1].first->addChild(memoryRecord,0,0);
+ leakyOutputs[0].first->addChild(spikeRecord,0,0);
- 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);
+ auto myGraph = std::make_shared<GraphView>();
myGraph->add(op->getMicroGraph());
- myGraph->add(mem_rec);
- myGraph->add(spk_rec);
- myGraph->save("mg", true, true);
+ myGraph->add({pop, memoryRecord, spikeRecord});
- // 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];
+ const auto nbElementsPerTimeStep = nb_elements / dims[0];
- 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];
+ // Compute the expected result using ad-hoc implementation
// 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;
+ auto *input = new float[nb_elements];
+ std::generate_n(input, nb_elements, [&]() { return valueDist(gen); });
+ auto *result = new float[nb_elements];
+ std::copy(input, input + nbElementsPerTimeStep, result);
+
+ // Recurrence calculation for each timestep
+ for (int timestep = 1; timestep < nbTimeSteps; ++timestep) {
+ const auto currentOffset = nbElementsPerTimeStep * timestep;
+ const auto previousOffset = nbElementsPerTimeStep * (timestep - 1);
+
+ for (int element = 0; element < nbElementsPerTimeStep; ++element) {
+ const auto previousValue = result[previousOffset + element];
+ const auto resetValue = (previousValue > threshold) ? threshold : 0;
+
+ result[currentOffset + element] =
+ previousValue * beta + input[currentOffset + element] - resetValue;
}
}
+ auto expectedOutput = std::make_shared<Tensor>(DataType::Float32);
+ expectedOutput->setBackend("cpu");
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}}});
+ // Compute the real result using our operator implemenation
+ auto inputTensor = std::make_shared<Tensor>(DataType::Float32);
+ inputTensor->setBackend("cpu");
+ inputTensor->resize(dims);
+ inputTensor->getImpl()->setRawPtr(input, nb_elements);
- std::shared_ptr<Tensor> myInitR =
- std::make_shared<Tensor>(initMemdims);
- myInitR->setDataType(DataType::Float32);
- myInitR->setBackend("cpu");
- uniformFiller<float>(myInitR, 0, 0);
+ auto memoryInit = std::make_shared<Tensor>(DataType::Float32);
+ memoryInit->setBackend("cpu");
+ memoryInit->resize(std::vector<std::size_t>(dims.begin() + 1, dims.end()));
+ memoryInit->zeros();
- pop->getOperator()->associateInput(0, T0);
- op->associateInput(1, myInitR);
- op->associateInput(2, myInitR);
+ pop->getOperator()->associateInput(0, inputTensor);
+ op->associateInput(1, memoryInit);
+ op->associateInput(2, memoryInit);
myGraph->compile("cpu", DataType::Float32);
-
auto scheduler = SequentialScheduler(myGraph);
REQUIRE_NOTHROW(scheduler.generateScheduling());
REQUIRE_NOTHROW(scheduler.forward(true));
+ // Compare expected output with actual output
auto memOp =
- std::static_pointer_cast<OperatorTensor>(spk_rec->getOperator());
+ std::static_pointer_cast<OperatorTensor>(spikeRecord->getOperator());
REQUIRE(approxEq<float>(*(memOp->getOutput(0)), *(expectedOutput)));
}
}
--
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