149 lines
5.4 KiB
C++
149 lines
5.4 KiB
C++
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#include "gtest/gtest.h"
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#include "kompute/Kompute.hpp"
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#include "kompute_test/shaders/shadertest_logistic_regression.hpp"
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TEST(TestLogisticRegression, 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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{
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kp::Manager mgr;
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std::shared_ptr<kp::Tensor> xI = mgr.tensor({ 0, 1, 1, 1, 1 });
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std::shared_ptr<kp::Tensor> xJ = mgr.tensor({ 0, 0, 0, 1, 1 });
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std::shared_ptr<kp::Tensor> y = mgr.tensor({ 0, 0, 0, 1, 1 });
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std::shared_ptr<kp::Tensor> wIn = mgr.tensor({ 0.001, 0.001 });
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std::shared_ptr<kp::Tensor> wOutI = mgr.tensor({ 0, 0, 0, 0, 0 });
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std::shared_ptr<kp::Tensor> wOutJ = mgr.tensor({ 0, 0, 0, 0, 0 });
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std::shared_ptr<kp::Tensor> bIn = mgr.tensor({ 0 });
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std::shared_ptr<kp::Tensor> bOut = mgr.tensor({ 0, 0, 0, 0, 0 });
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std::shared_ptr<kp::Tensor> lOut = mgr.tensor({ 0, 0, 0, 0, 0 });
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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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std::vector<uint32_t> spirv = std::vector<uint32_t>(
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(uint32_t*)kp::shader_data::shaders_glsl_logisticregression_comp_spv,
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(uint32_t*)(kp::shader_data::shaders_glsl_logisticregression_comp_spv +
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kp::shader_data::shaders_glsl_logisticregression_comp_spv_len));
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std::shared_ptr<kp::Algorithm> algorithm =
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mgr.algorithm(params, spirv, kp::Workgroup(), kp::Constants({5.0}));
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std::shared_ptr<kp::Sequence> sq =
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mgr.sequence()
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->record<kp::OpTensorSyncDevice>({ wIn, bIn })
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->record<kp::OpAlgoDispatch>(algorithm)
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->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
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// Iterate across all expected iterations
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for (size_t i = 0; i < ITERATIONS; i++) {
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sq->eval();
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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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}
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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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// * b < 0
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// TODO: Add EXPECT_DOUBLE_EQ instead
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EXPECT_LT(wIn->data()[0], 0.01);
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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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KP_LOG_WARN("Result wIn i: {}, wIn j: {}, bIn: {}",
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wIn->data()[0],
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wIn->data()[1],
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bIn->data()[0]);
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}
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}
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TEST(TestLogisticRegression, 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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{
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kp::Manager mgr;
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std::shared_ptr<kp::Tensor> xI = mgr.tensor({ 0, 1, 1, 1, 1 });
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std::shared_ptr<kp::Tensor> xJ = mgr.tensor({ 0, 0, 0, 1, 1 });
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std::shared_ptr<kp::Tensor> y = mgr.tensor({ 0, 0, 0, 1, 1 });
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std::shared_ptr<kp::Tensor> wIn = mgr.tensor(
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{ 0.001, 0.001 }, kp::Tensor::TensorTypes::eHost);
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std::shared_ptr<kp::Tensor> wOutI = mgr.tensor({ 0, 0, 0, 0, 0 });
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std::shared_ptr<kp::Tensor> wOutJ = mgr.tensor({ 0, 0, 0, 0, 0 });
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std::shared_ptr<kp::Tensor> bIn = mgr.tensor(
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{ 0 },
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kp::Tensor::TensorTypes::eHost);
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std::shared_ptr<kp::Tensor> bOut = mgr.tensor({ 0, 0, 0, 0, 0 });
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std::shared_ptr<kp::Tensor> lOut = mgr.tensor({ 0, 0, 0, 0, 0 });
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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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std::vector<uint32_t> spirv = std::vector<uint32_t>(
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(uint32_t*)kp::shader_data::shaders_glsl_logisticregression_comp_spv,
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(uint32_t*)(kp::shader_data::shaders_glsl_logisticregression_comp_spv +
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kp::shader_data::shaders_glsl_logisticregression_comp_spv_len));
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std::shared_ptr<kp::Algorithm> algorithm =
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mgr.algorithm(params, spirv, kp::Workgroup(), kp::Constants({5.0}));
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std::shared_ptr<kp::Sequence> sq =
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mgr.sequence()
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->record<kp::OpTensorSyncDevice>({ wIn, bIn })
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->record<kp::OpAlgoDispatch>(algorithm)
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->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
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// Iterate across all expected iterations
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for (size_t i = 0; i < ITERATIONS; i++) {
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sq->eval();
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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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}
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wIn->mapDataIntoHostMemory();
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bIn->mapDataIntoHostMemory();
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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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// * b < 0
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// TODO: Add EXPECT_DOUBLE_EQ instead
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EXPECT_LT(wIn->data()[0], 0.01);
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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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KP_LOG_WARN("Result wIn i: {}, wIn j: {}, bIn: {}",
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wIn->data()[0],
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wIn->data()[1],
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bIn->data()[0]);
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}
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}
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