Reformatted
This commit is contained in:
Alejandro Saucedo
parent
c5df89c17b
commit
fdc1d3b91a
23 changed files with 455 additions and 418 deletions
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@ -4,33 +4,35 @@
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#include "fmt/ranges.h"
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#include "kompute/Kompute.hpp"
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TEST(TestLogisticRegressionAlgorithm, TestMainLogisticRegression) {
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TEST(TestLogisticRegressionAlgorithm, TestMainLogisticRegression)
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{
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uint32_t ITERATIONS = 100;
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float learningRate = 0.1;
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std::shared_ptr<kp::Tensor> xI{ new kp::Tensor({ 0, 1, 1, 1, 1 })};
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std::shared_ptr<kp::Tensor> xJ{ new kp::Tensor({ 0, 0, 0, 1, 1 })};
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std::shared_ptr<kp::Tensor> xI{ new kp::Tensor({ 0, 1, 1, 1, 1 }) };
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std::shared_ptr<kp::Tensor> xJ{ new kp::Tensor({ 0, 0, 0, 1, 1 }) };
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std::shared_ptr<kp::Tensor> y{ new kp::Tensor({ 0, 0, 0, 1, 1 })};
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std::shared_ptr<kp::Tensor> y{ new kp::Tensor({ 0, 0, 0, 1, 1 }) };
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std::shared_ptr<kp::Tensor> wIn{ new kp::Tensor({ 0.001, 0.001 })};
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std::shared_ptr<kp::Tensor> wOutI{ new kp::Tensor({ 0, 0, 0, 0, 0 })};
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std::shared_ptr<kp::Tensor> wOutJ{ new kp::Tensor({ 0, 0, 0, 0, 0 })};
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std::shared_ptr<kp::Tensor> wIn{ new kp::Tensor({ 0.001, 0.001 }) };
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std::shared_ptr<kp::Tensor> wOutI{ new kp::Tensor({ 0, 0, 0, 0, 0 }) };
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std::shared_ptr<kp::Tensor> wOutJ{ new kp::Tensor({ 0, 0, 0, 0, 0 }) };
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std::shared_ptr<kp::Tensor> bIn{ new kp::Tensor({ 0 })};
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std::shared_ptr<kp::Tensor> bOut{ new kp::Tensor({ 0, 0, 0, 0, 0 })};
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std::shared_ptr<kp::Tensor> bIn{ new kp::Tensor({ 0 }) };
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std::shared_ptr<kp::Tensor> bOut{ new kp::Tensor({ 0, 0, 0, 0, 0 }) };
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std::shared_ptr<kp::Tensor> lOut{ new kp::Tensor({ 0, 0, 0, 0, 0 })};
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std::shared_ptr<kp::Tensor> lOut{ new kp::Tensor({ 0, 0, 0, 0, 0 }) };
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std::vector<std::shared_ptr<kp::Tensor>> params =
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{xI, xJ, y, wIn, wOutI, wOutJ, bIn, bOut, lOut};
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std::vector<std::shared_ptr<kp::Tensor>> params = { xI, xJ, y,
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wIn, wOutI, wOutJ,
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bIn, bOut, lOut };
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{
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kp::Manager mgr;
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if (std::shared_ptr<kp::Sequence> sq =
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mgr.getOrCreateManagedSequence("createTensors").lock()) {
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if (std::shared_ptr<kp::Sequence> sq =
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mgr.getOrCreateManagedSequence("createTensors").lock()) {
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sq->begin();
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@ -42,13 +44,12 @@ TEST(TestLogisticRegressionAlgorithm, TestMainLogisticRegression) {
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// Record op algo base
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sq->begin();
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sq->record<kp::OpTensorSyncDevice>({wIn, bIn});
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sq->record<kp::OpTensorSyncDevice>({ wIn, bIn });
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sq->record<kp::OpAlgoBase<>>(
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params,
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"test/shaders/glsl/test_logistic_regression.comp");
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params, "test/shaders/glsl/test_logistic_regression.comp");
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sq->record<kp::OpTensorSyncLocal>({wOutI, wOutJ, bOut, lOut});
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sq->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
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sq->end();
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@ -57,7 +58,7 @@ TEST(TestLogisticRegressionAlgorithm, TestMainLogisticRegression) {
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sq->eval();
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for(size_t j = 0; j < bOut->size(); j++) {
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for (size_t j = 0; j < bOut->size(); j++) {
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wIn->data()[0] -= learningRate * wOutI->data()[j];
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wIn->data()[1] -= learningRate * wOutJ->data()[j];
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bIn->data()[0] -= learningRate * bOut->data()[j];
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@ -66,7 +67,6 @@ TEST(TestLogisticRegressionAlgorithm, TestMainLogisticRegression) {
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}
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}
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// Based on the inputs the outputs should be at least:
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// * wi < 0.01
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// * wj > 1.0
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@ -77,11 +77,14 @@ TEST(TestLogisticRegressionAlgorithm, TestMainLogisticRegression) {
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EXPECT_LT(bIn->data()[0], 0.0);
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EXPECT_LT(bIn->data()[0], 0.0);
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SPDLOG_WARN("Result wIn: {}, bIn: {}, loss: {}",
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wIn->data(), bIn->data(), lOut->data());
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SPDLOG_WARN("Result wIn: {}, bIn: {}, loss: {}",
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wIn->data(),
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bIn->data(),
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lOut->data());
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}
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TEST(TestLogisticRegressionAlgorithm, TestMainLogisticRegressionManualCopy) {
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TEST(TestLogisticRegressionAlgorithm, TestMainLogisticRegressionManualCopy)
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{
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uint32_t ITERATIONS = 100;
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float learningRate = 0.1;
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@ -89,30 +92,31 @@ TEST(TestLogisticRegressionAlgorithm, TestMainLogisticRegressionManualCopy) {
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std::vector<float> wInVec = { 0.001, 0.001 };
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std::vector<float> bInVec = { 0 };
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std::shared_ptr<kp::Tensor> xI{ new kp::Tensor({ 0, 1, 1, 1, 1 })};
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std::shared_ptr<kp::Tensor> xJ{ new kp::Tensor({ 0, 0, 0, 1, 1 })};
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std::shared_ptr<kp::Tensor> xI{ new kp::Tensor({ 0, 1, 1, 1, 1 }) };
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std::shared_ptr<kp::Tensor> xJ{ new kp::Tensor({ 0, 0, 0, 1, 1 }) };
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std::shared_ptr<kp::Tensor> y{ new kp::Tensor({ 0, 0, 0, 1, 1 })};
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std::shared_ptr<kp::Tensor> y{ new kp::Tensor({ 0, 0, 0, 1, 1 }) };
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std::shared_ptr<kp::Tensor> wIn{
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new kp::Tensor(wInVec, kp::Tensor::TensorTypes::eStaging)};
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std::shared_ptr<kp::Tensor> wOutI{ new kp::Tensor({ 0, 0, 0, 0, 0 })};
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std::shared_ptr<kp::Tensor> wOutJ{ new kp::Tensor({ 0, 0, 0, 0, 0 })};
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std::shared_ptr<kp::Tensor> wIn{ new kp::Tensor(
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wInVec, kp::Tensor::TensorTypes::eStaging) };
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std::shared_ptr<kp::Tensor> wOutI{ new kp::Tensor({ 0, 0, 0, 0, 0 }) };
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std::shared_ptr<kp::Tensor> wOutJ{ new kp::Tensor({ 0, 0, 0, 0, 0 }) };
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std::shared_ptr<kp::Tensor> bIn{
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new kp::Tensor(bInVec, kp::Tensor::TensorTypes::eStaging)};
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std::shared_ptr<kp::Tensor> bOut{ new kp::Tensor({ 0, 0, 0, 0, 0 })};
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std::shared_ptr<kp::Tensor> bIn{ new kp::Tensor(
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bInVec, kp::Tensor::TensorTypes::eStaging) };
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std::shared_ptr<kp::Tensor> bOut{ new kp::Tensor({ 0, 0, 0, 0, 0 }) };
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std::shared_ptr<kp::Tensor> lOut{ new kp::Tensor({ 0, 0, 0, 0, 0 })};
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std::shared_ptr<kp::Tensor> lOut{ new kp::Tensor({ 0, 0, 0, 0, 0 }) };
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std::vector<std::shared_ptr<kp::Tensor>> params =
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{xI, xJ, y, wIn, wOutI, wOutJ, bIn, bOut, lOut};
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std::vector<std::shared_ptr<kp::Tensor>> params = { xI, xJ, y,
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wIn, wOutI, wOutJ,
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bIn, bOut, lOut };
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{
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kp::Manager mgr;
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if (std::shared_ptr<kp::Sequence> sq =
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mgr.getOrCreateManagedSequence("createTensors").lock()) {
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if (std::shared_ptr<kp::Sequence> sq =
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mgr.getOrCreateManagedSequence("createTensors").lock()) {
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sq->begin();
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@ -125,10 +129,9 @@ TEST(TestLogisticRegressionAlgorithm, TestMainLogisticRegressionManualCopy) {
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sq->begin();
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sq->record<kp::OpAlgoBase<>>(
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params,
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"test/shaders/glsl/test_logistic_regression.comp");
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params, "test/shaders/glsl/test_logistic_regression.comp");
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sq->record<kp::OpTensorSyncLocal>({wOutI, wOutJ, bOut, lOut});
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sq->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
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sq->end();
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@ -137,7 +140,7 @@ TEST(TestLogisticRegressionAlgorithm, TestMainLogisticRegressionManualCopy) {
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sq->eval();
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for(size_t j = 0; j < bOut->size(); j++) {
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for (size_t j = 0; j < bOut->size(); j++) {
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wIn->data()[0] -= learningRate * wOutI->data()[j];
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wIn->data()[1] -= learningRate * wOutJ->data()[j];
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bIn->data()[0] -= learningRate * bOut->data()[j];
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@ -148,7 +151,6 @@ TEST(TestLogisticRegressionAlgorithm, TestMainLogisticRegressionManualCopy) {
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}
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}
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// Based on the inputs the outputs should be at least:
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// * wi < 0.01
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// * wj > 1.0
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@ -158,6 +160,8 @@ TEST(TestLogisticRegressionAlgorithm, TestMainLogisticRegressionManualCopy) {
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EXPECT_GT(wIn->data()[1], 1.0);
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EXPECT_LT(bIn->data()[0], 0.0);
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SPDLOG_WARN("Result wIn: {}, bIn: {}, loss: {}",
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wIn->data(), bIn->data(), lOut->data());
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SPDLOG_WARN("Result wIn: {}, bIn: {}, loss: {}",
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wIn->data(),
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bIn->data(),
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lOut->data());
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}
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