Added .clang-format file and formatted everything

.clang-format

@@ -0,0 +1,5 @@
+---
+BasedOnStyle: Mozilla
+IndentWidth: 4
+
+...

Makefile

@@ -198,7 +198,9 @@ win_build_xxd:
 	cd external/bin/ && gcc.exe -o xxd.exe xxd.c -DCYGWIN
 
 format:
-	$(CLANG_FORMAT_BIN) -i -style="{BasedOnStyle: mozilla, IndentWidth: 4}" src/*.cpp src/include/kompute/*.hpp test/*cpp
+	for val in "examples single_include src test" ; do \
+    	find $$val -depth -iname *.h -or -iname *.c -or -iname *.hpp -or -iname *.cpp | grep -v "shaders" | xargs $(CLANG_FORMAT_BIN) -style=file -i; \
+	done
 
 static_scan:
 	cppcheck --project=build/compile_commands.json -iexternal/

examples/android/android-simple/app/src/main/cpp/KomputeJniNative.cpp

@@ -12,104 +12,109 @@
 // 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>
 
 #ifndef KOMPUTE_VK_INIT_RETRIES
 #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;
-}
-
 
-JNIEXPORT jfloatArray JNICALL
-Java_com_ethicalml_kompute_KomputeJni_kompute(
-        JNIEnv *env,
-        jobject thiz,
-        jfloatArray xiJFloatArr,
-        jfloatArray xjJFloatArr,
-        jfloatArray yJFloatArr) {
+        return totalRetries < KOMPUTE_VK_INIT_RETRIES;
+    }
 
-    KP_LOG_INFO("Creating manager");
+    JNIEXPORT jfloatArray JNICALL
+    Java_com_ethicalml_kompute_KomputeJni_kompute(JNIEnv* env,
+                                                  jobject thiz,
+                                                  jfloatArray xiJFloatArr,
+                                                  jfloatArray xjJFloatArr,
+                                                  jfloatArray yJFloatArr)
+    {
 
-    std::vector<float> xiVector = jfloatArrayToVector(env, xiJFloatArr);
-    std::vector<float> xjVector = jfloatArrayToVector(env, xjJFloatArr);
-    std::vector<float> yVector = jfloatArrayToVector(env, yJFloatArr);
+        KP_LOG_INFO("Creating manager");
 
-    KomputeModelML kml;
-    kml.train(yVector, xiVector, xjVector);
+        std::vector<float> xiVector = jfloatArrayToVector(env, xiJFloatArr);
+        std::vector<float> xjVector = jfloatArrayToVector(env, xjJFloatArr);
+        std::vector<float> yVector = jfloatArrayToVector(env, yJFloatArr);
 
-    std::vector<float> pred = kml.predict(xiVector, xjVector);
+        KomputeModelML kml;
+        kml.train(yVector, xiVector, xjVector);
 
-    return vectorToJFloatArray(env, pred);
-}
+        std::vector<float> pred = kml.predict(xiVector, xjVector);
 
-JNIEXPORT jfloatArray JNICALL
-Java_com_ethicalml_kompute_KomputeJni_komputeParams(
-        JNIEnv *env,
-        jobject thiz,
-        jfloatArray xiJFloatArr,
-        jfloatArray xjJFloatArr,
-        jfloatArray yJFloatArr) {
+        return vectorToJFloatArray(env, pred);
+    }
 
-    KP_LOG_INFO("Creating manager");
+    JNIEXPORT jfloatArray JNICALL
+    Java_com_ethicalml_kompute_KomputeJni_komputeParams(JNIEnv* env,
+                                                        jobject thiz,
+                                                        jfloatArray xiJFloatArr,
+                                                        jfloatArray xjJFloatArr,
+                                                        jfloatArray yJFloatArr)
+    {
 
-    std::vector<float> xiVector = jfloatArrayToVector(env, xiJFloatArr);
-    std::vector<float> xjVector = jfloatArrayToVector(env, xjJFloatArr);
-    std::vector<float> yVector = jfloatArrayToVector(env, yJFloatArr);
+        KP_LOG_INFO("Creating manager");
 
-    KomputeModelML kml;
-    kml.train(yVector, xiVector, xjVector);
+        std::vector<float> xiVector = jfloatArrayToVector(env, xiJFloatArr);
+        std::vector<float> xjVector = jfloatArrayToVector(env, xjJFloatArr);
+        std::vector<float> yVector = jfloatArrayToVector(env, yJFloatArr);
 
-    std::vector<float> params = kml.get_params();
+        KomputeModelML kml;
+        kml.train(yVector, xiVector, xjVector);
 
-    return vectorToJFloatArray(env, params);
-}
+        std::vector<float> params = kml.get_params();
 
+        return vectorToJFloatArray(env, params);
+    }
 }

examples/android/android-simple/app/src/main/cpp/KomputeModelML.cpp

@@ -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;
     }
 

examples/android/android-simple/app/src/main/cpp/KomputeModelML.hpp

@@ -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

examples/array_multiplication/src/Main.cpp

@@ -5,23 +5,27 @@
 
 #include "kompute/Kompute.hpp"
 
-static
-std::vector<uint32_t>
-compileSource(
-  const std::string& source)
+static std::vector<uint32_t>
+compileSource(const std::string& source)
 {
     std::ofstream fileOut("tmp_kp_shader.comp");
-	fileOut << source;
-	fileOut.close();
-    if (system(std::string("glslangValidator -V tmp_kp_shader.comp -o tmp_kp_shader.comp.spv").c_str()))
+    fileOut << source;
+    fileOut.close();
+    if (system(
+          std::string(
+            "glslangValidator -V tmp_kp_shader.comp -o tmp_kp_shader.comp.spv")
+            .c_str()))
         throw std::runtime_error("Error running glslangValidator command");
     std::ifstream fileStream("tmp_kp_shader.comp.spv", std::ios::binary);
     std::vector<char> buffer;
-    buffer.insert(buffer.begin(), std::istreambuf_iterator<char>(fileStream), {});
-    return {(uint32_t*)buffer.data(), (uint32_t*)(buffer.data() + buffer.size())};
+    buffer.insert(
+      buffer.begin(), std::istreambuf_iterator<char>(fileStream), {});
+    return { (uint32_t*)buffer.data(),
+             (uint32_t*)(buffer.data() + buffer.size()) };
 }
 
-int main()
+int
+main()
 {
 #if KOMPUTE_ENABLE_SPDLOG
     spdlog::set_level(
@@ -53,21 +57,23 @@ int main()
         }
       )");
 
-    std::vector<std::shared_ptr<kp::Tensor>> params = { tensorInA, tensorInB, tensorOut };
+    std::vector<std::shared_ptr<kp::Tensor>> params = { tensorInA,
+                                                        tensorInB,
+                                                        tensorOut };
 
-    std::shared_ptr<kp::Algorithm> algo = mgr.algorithm(params, compileSource(shader));
+    std::shared_ptr<kp::Algorithm> algo =
+      mgr.algorithm(params, compileSource(shader));
 
     mgr.sequence()
-        ->record<kp::OpTensorSyncDevice>(params)
-        ->record<kp::OpAlgoDispatch>(algo)
-        ->record<kp::OpTensorSyncLocal>(params)
-        ->eval();
+      ->record<kp::OpTensorSyncDevice>(params)
+      ->record<kp::OpAlgoDispatch>(algo)
+      ->record<kp::OpTensorSyncLocal>(params)
+      ->eval();
 
     // prints "Output {  0  4  12  }"
-    std::cout<< "Output: {  ";
+    std::cout << "Output: {  ";
     for (const float& elem : tensorOut->vector()) {
-      std::cout << elem << "  ";
+        std::cout << elem << "  ";
     }
     std::cout << "}" << std::endl;
 }
-