thek0tyara/llama-cpp-turboquant
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Added .clang-format file and formatted everything

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Signed-off-by: Fabian Sauter <sauter.fabian@mailbox.org>
This commit is contained in:
Fabian Sauter 2022-05-02 15:11:40 +02:00
parent f731f2e55c
commit 24cd307042
47 changed files with 5157 additions and 4354 deletions
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43
examples/android/android-simple/app/src/main/cpp/KomputeModelML.cpp Executable file → Normal file
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@ -1,15 +1,15 @@
#include "KomputeModelML.hpp"
KomputeModelML::KomputeModelML() {
KomputeModelML::KomputeModelML() {}
}
KomputeModelML::~KomputeModelML() {}
KomputeModelML::~KomputeModelML() {
}
void KomputeModelML::train(std::vector<float> yData, std::vector<float> xIData, std::vector<float> xJData) {
void
KomputeModelML::train(std::vector<float> yData,
std::vector<float> xIData,
std::vector<float> xJData)
{
std::vector<float> zerosData;
@ -42,17 +42,19 @@ void KomputeModelML::train(std::vector<float> yData, std::vector<float> xIData,
bIn, bOut, lOut };
std::vector<uint32_t> spirv = std::vector<uint32_t>(
(uint32_t*)kp::shader_data::shaders_glsl_logisticregression_comp_spv,
(uint32_t*)(kp::shader_data::shaders_glsl_logisticregression_comp_spv +
kp::shader_data::shaders_glsl_logisticregression_comp_spv_len));
(uint32_t*)kp::shader_data::shaders_glsl_logisticregression_comp_spv,
(uint32_t*)(kp::shader_data::
shaders_glsl_logisticregression_comp_spv +
kp::shader_data::
shaders_glsl_logisticregression_comp_spv_len));
std::shared_ptr<kp::Algorithm> algorithm = mgr.algorithm(
params, spirv, kp::Workgroup({ 5 }), std::vector<float>({ 5.0 }));
params, spirv, kp::Workgroup({ 5 }), std::vector<float>({ 5.0 }));
mgr.sequence()->eval<kp::OpTensorSyncDevice>(params);
std::shared_ptr<kp::Sequence> sq = mgr.sequence()
std::shared_ptr<kp::Sequence> sq =
mgr.sequence()
->record<kp::OpTensorSyncDevice>({ wIn, bIn })
->record<kp::OpAlgoDispatch>(algorithm)
->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
@ -79,7 +81,9 @@ void KomputeModelML::train(std::vector<float> yData, std::vector<float> xIData,
}
}
std::vector<float> KomputeModelML::predict(std::vector<float> xI, std::vector<float> xJ) {
std::vector<float>
KomputeModelML::predict(std::vector<float> xI, std::vector<float> xJ)
{
KP_LOG_INFO("Running prediction inference");
@ -93,9 +97,8 @@ std::vector<float> KomputeModelML::predict(std::vector<float> xI, std::vector<fl
for (size_t i = 0; i < xI.size(); i++) {
float xIVal = xI[i];
float xJVal = xJ[i];
float result = (xIVal * this->mWeights[0]
+ xJVal * this->mWeights[1]
+ this->mBias[0]);
float result = (xIVal * this->mWeights[0] + xJVal * this->mWeights[1] +
this->mBias[0]);
// Instead of using sigmoid we'll just return full numbers
float var = result > 0 ? 1 : 0;
@ -107,13 +110,15 @@ std::vector<float> KomputeModelML::predict(std::vector<float> xI, std::vector<fl
return retVector;
}
std::vector<float> KomputeModelML::get_params() {
std::vector<float>
KomputeModelML::get_params()
{
KP_LOG_INFO("Displaying results");
std::vector<float> retVector;
if(this->mWeights.size() + this->mBias.size() == 0) {
if (this->mWeights.size() + this->mBias.size() == 0) {
return retVector;
}