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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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143
examples/android/android-simple/app/src/main/cpp/KomputeJniNative.cpp
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@ -12,17 +12,16 @@
// See the License for the specific language governing permissions and
// limitations under the License.
// Includes the Jni utilities for Android to be able to create the
// relevant bindings for java, including JNIEXPORT, JNICALL , and
// Includes the Jni utilities for Android to be able to create the
// relevant bindings for java, including JNIEXPORT, JNICALL , and
// other "j-variables".
#include <jni.h>
// The ML class exposing the Kompute ML workflow for training and
// The ML class exposing the Kompute ML workflow for training and
// prediction of inference data.
#include "KomputeModelML.hpp"
// Allows us to use the C++ sleep function to wait when loading the
// Allows us to use the C++ sleep function to wait when loading the
// Vulkan library in android
#include <unistd.h>
@ -30,86 +29,92 @@
#define KOMPUTE_VK_INIT_RETRIES 5
#endif
static std::vector<float> jfloatArrayToVector(JNIEnv *env, const jfloatArray & fromArray) {
float *inCArray = env->GetFloatArrayElements(fromArray, NULL);
if (NULL == inCArray) return std::vector<float>();
static std::vector<float>
jfloatArrayToVector(JNIEnv* env, const jfloatArray& fromArray)
{
float* inCArray = env->GetFloatArrayElements(fromArray, NULL);
if (NULL == inCArray)
return std::vector<float>();
int32_t length = env->GetArrayLength(fromArray);
std::vector<float> outVector(inCArray, inCArray + length);
return outVector;
}
static jfloatArray vectorToJFloatArray(JNIEnv *env, const std::vector<float> & fromVector) {
static jfloatArray
vectorToJFloatArray(JNIEnv* env, const std::vector<float>& fromVector)
{
jfloatArray ret = env->NewFloatArray(fromVector.size());
if (NULL == ret) return NULL;
if (NULL == ret)
return NULL;
env->SetFloatArrayRegion(ret, 0, fromVector.size(), fromVector.data());
return ret;
}
extern "C" {
extern "C"
{
JNIEXPORT jboolean JNICALL
Java_com_ethicalml_kompute_KomputeJni_initVulkan(JNIEnv *env, jobject thiz) {
JNIEXPORT jboolean JNICALL
Java_com_ethicalml_kompute_KomputeJni_initVulkan(JNIEnv* env, jobject thiz)
{
KP_LOG_INFO("Initialising vulkan");
KP_LOG_INFO("Initialising vulkan");
uint32_t totalRetries = 0;
uint32_t totalRetries = 0;
while (totalRetries < KOMPUTE_VK_INIT_RETRIES) {
KP_LOG_INFO("VULKAN LOAD TRY NUMBER: %u", totalRetries);
if(InitVulkan()) {
break;
while (totalRetries < KOMPUTE_VK_INIT_RETRIES) {
KP_LOG_INFO("VULKAN LOAD TRY NUMBER: %u", totalRetries);
if (InitVulkan()) {
break;
}
sleep(1);
totalRetries++;
}
sleep(1);
totalRetries++;
return totalRetries < KOMPUTE_VK_INIT_RETRIES;
}
return totalRetries < KOMPUTE_VK_INIT_RETRIES;
}
JNIEXPORT jfloatArray JNICALL
Java_com_ethicalml_kompute_KomputeJni_kompute(
JNIEnv *env,
jobject thiz,
jfloatArray xiJFloatArr,
jfloatArray xjJFloatArr,
jfloatArray yJFloatArr) {
KP_LOG_INFO("Creating manager");
std::vector<float> xiVector = jfloatArrayToVector(env, xiJFloatArr);
std::vector<float> xjVector = jfloatArrayToVector(env, xjJFloatArr);
std::vector<float> yVector = jfloatArrayToVector(env, yJFloatArr);
KomputeModelML kml;
kml.train(yVector, xiVector, xjVector);
std::vector<float> pred = kml.predict(xiVector, xjVector);
return vectorToJFloatArray(env, pred);
}
JNIEXPORT jfloatArray JNICALL
Java_com_ethicalml_kompute_KomputeJni_komputeParams(
JNIEnv *env,
jobject thiz,
jfloatArray xiJFloatArr,
jfloatArray xjJFloatArr,
jfloatArray yJFloatArr) {
KP_LOG_INFO("Creating manager");
std::vector<float> xiVector = jfloatArrayToVector(env, xiJFloatArr);
std::vector<float> xjVector = jfloatArrayToVector(env, xjJFloatArr);
std::vector<float> yVector = jfloatArrayToVector(env, yJFloatArr);
KomputeModelML kml;
kml.train(yVector, xiVector, xjVector);
std::vector<float> params = kml.get_params();
return vectorToJFloatArray(env, params);
}
JNIEXPORT jfloatArray JNICALL
Java_com_ethicalml_kompute_KomputeJni_kompute(JNIEnv* env,
jobject thiz,
jfloatArray xiJFloatArr,
jfloatArray xjJFloatArr,
jfloatArray yJFloatArr)
{
KP_LOG_INFO("Creating manager");
std::vector<float> xiVector = jfloatArrayToVector(env, xiJFloatArr);
std::vector<float> xjVector = jfloatArrayToVector(env, xjJFloatArr);
std::vector<float> yVector = jfloatArrayToVector(env, yJFloatArr);
KomputeModelML kml;
kml.train(yVector, xiVector, xjVector);
std::vector<float> pred = kml.predict(xiVector, xjVector);
return vectorToJFloatArray(env, pred);
}
JNIEXPORT jfloatArray JNICALL
Java_com_ethicalml_kompute_KomputeJni_komputeParams(JNIEnv* env,
jobject thiz,
jfloatArray xiJFloatArr,
jfloatArray xjJFloatArr,
jfloatArray yJFloatArr)
{
KP_LOG_INFO("Creating manager");
std::vector<float> xiVector = jfloatArrayToVector(env, xiJFloatArr);
std::vector<float> xjVector = jfloatArrayToVector(env, xjJFloatArr);
std::vector<float> yVector = jfloatArrayToVector(env, yJFloatArr);
KomputeModelML kml;
kml.train(yVector, xiVector, xjVector);
std::vector<float> params = kml.get_params();
return vectorToJFloatArray(env, params);
}
}

43
examples/android/android-simple/app/src/main/cpp/KomputeModelML.cpp Executable file → Normal file
View file

@ -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;
}

18
examples/android/android-simple/app/src/main/cpp/KomputeModelML.hpp Executable file → Normal file
View file

@ -2,28 +2,30 @@
#ifndef KOMPUTEMODELML_HPP
#define KOMPUTEMODELML_HPP
#include <vector>
#include <string>
#include <memory>
#include <string>
#include <vector>
#include "kompute/Kompute.hpp"
class KomputeModelML {
class KomputeModelML
{
public:
public:
KomputeModelML();
virtual ~KomputeModelML();
void train(std::vector<float> yData, std::vector<float> xIData, std::vector<float> xJData);
void train(std::vector<float> yData,
std::vector<float> xIData,
std::vector<float> xJData);
std::vector<float> predict(std::vector<float> xI, std::vector<float> xJ);
std::vector<float> get_params();
private:
private:
std::vector<float> mWeights;
std::vector<float> mBias;
};
static std::string LR_SHADER = R"(
@ -83,4 +85,4 @@ void main() {
}
)";
#endif //ANDROID_SIMPLE_KOMPUTEMODELML_HPP
#endif // ANDROID_SIMPLE_KOMPUTEMODELML_HPP