72 lines
2.2 KiB
C++
72 lines
2.2 KiB
C++
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#include <iostream>
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#include <memory>
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#include <vector>
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#include "kompute/Kompute.hpp"
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int
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main()
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{
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#if KOMPUTE_ENABLE_SPDLOG
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spdlog::set_level(
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static_cast<spdlog::level::level_enum>(SPDLOG_ACTIVE_LEVEL));
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#endif
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uint32_t ITERATIONS = 100;
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float learningRate = 0.1;
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kp::Manager mgr;
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auto xI = mgr.tensor({ 0, 1, 1, 1, 1 });
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auto xJ = mgr.tensor({ 0, 0, 0, 1, 1 });
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auto y = mgr.tensor({ 0, 0, 0, 1, 1 });
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auto wIn = mgr.tensor({ 0.001, 0.001 });
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auto wOutI = mgr.tensor({ 0, 0, 0, 0, 0 });
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auto wOutJ = mgr.tensor({ 0, 0, 0, 0, 0 });
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auto bIn = mgr.tensor({ 0 });
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auto bOut = mgr.tensor({ 0, 0, 0, 0, 0 });
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auto 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(
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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::
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shaders_glsl_logisticregression_comp_spv_len));
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std::shared_ptr<kp::Algorithm> algo = mgr.algorithm(
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params, spirv, kp::Workgroup({ 5 }), std::vector<float>({ 5.0 }));
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mgr.sequence()->eval<kp::OpTensorSyncDevice>(params);
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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>(algo)
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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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std::cout << "RESULTS" << std::endl;
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std::cout << "w1: " << wIn->data()[0] << std::endl;
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std::cout << "w2: " << wIn->data()[1] << std::endl;
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std::cout << "b: " << bIn->data()[0] << std::endl;
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}
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