update code style and unit test

oap-project · Aug 19, 2024 · 4fe2416 · 4fe2416
1 parent b07fe36
commit 4fe2416
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Showing 11 changed files with 35 additions and 34 deletions.
diff --git a/mllib-dal/src/main/native/CorrelationImpl.cpp b/mllib-dal/src/main/native/CorrelationImpl.cpp
@@ -197,9 +197,9 @@ static void doCorrelationOneAPICompute(
 
 JNIEXPORT jlong JNICALL
 Java_com_intel_oap_mllib_stat_CorrelationDALImpl_cCorrelationTrainDAL(
-    JNIEnv *env, jobject obj, jint rank, jlong pNumTabData, jlong numRows, jlong numCols,
-    jint executorNum, jint executorCores, jint computeDeviceOrdinal,
-    jintArray gpuIdxArray, jobject resultObj) {
+    JNIEnv *env, jobject obj, jint rank, jlong pNumTabData, jlong numRows,
+    jlong numCols, jint executorNum, jint executorCores,
+    jint computeDeviceOrdinal, jintArray gpuIdxArray, jobject resultObj) {
     logger::println(logger::INFO,
                     "oneDAL (native): use DPC++ kernels; device %s",
                     ComputeDeviceString[computeDeviceOrdinal].c_str());

diff --git a/mllib-dal/src/main/native/DecisionForestClassifierImpl.cpp b/mllib-dal/src/main/native/DecisionForestClassifierImpl.cpp
@@ -300,10 +300,10 @@ static jobject doRFClassifierOneAPICompute(
  */
 JNIEXPORT jobject JNICALL
 Java_com_intel_oap_mllib_classification_RandomForestClassifierDALImpl_cRFClassifierTrainDAL(
-    JNIEnv *env, jobject obj, jint rank, jlong pNumTabFeature, jlong featureRows,
-    jlong featureCols, jlong pNumTabLabel, jlong labelCols, jint executorNum,
-    jint computeDeviceOrdinal, jint classCount, jint treeCount,
-    jint numFeaturesPerNode, jint minObservationsLeafNode,
+    JNIEnv *env, jobject obj, jint rank, jlong pNumTabFeature,
+    jlong featureRows, jlong featureCols, jlong pNumTabLabel, jlong labelCols,
+    jint executorNum, jint computeDeviceOrdinal, jint classCount,
+    jint treeCount, jint numFeaturesPerNode, jint minObservationsLeafNode,
     jint minObservationsSplitNode, jdouble minWeightFractionLeafNode,
     jdouble minImpurityDecreaseSplitNode, jint maxTreeDepth, jlong seed,
     jint maxBins, jboolean bootstrap, jintArray gpuIdxArray,

diff --git a/mllib-dal/src/main/native/DecisionForestRegressorImpl.cpp b/mllib-dal/src/main/native/DecisionForestRegressorImpl.cpp
@@ -292,11 +292,12 @@ static jobject doRFRegressorOneAPICompute(
 
 JNIEXPORT jobject JNICALL
 Java_com_intel_oap_mllib_regression_RandomForestRegressorDALImpl_cRFRegressorTrainDAL(
-    JNIEnv *env, jobject obj, jint rank, jlong pNumTabFeature, jlong featureRows,
-    jlong featureCols, jlong pNumTabLabel, jlong labelCols, jint executorNum,
-    jint computeDeviceOrdinal, jint treeCount, jint numFeaturesPerNode,
-    jint minObservationsLeafNode, jint maxTreeDepth, jlong seed, jint maxbins,
-    jboolean bootstrap, jintArray gpuIdxArray, jobject resultObj) {
+    JNIEnv *env, jobject obj, jint rank, jlong pNumTabFeature,
+    jlong featureRows, jlong featureCols, jlong pNumTabLabel, jlong labelCols,
+    jint executorNum, jint computeDeviceOrdinal, jint treeCount,
+    jint numFeaturesPerNode, jint minObservationsLeafNode, jint maxTreeDepth,
+    jlong seed, jint maxbins, jboolean bootstrap, jintArray gpuIdxArray,
+    jobject resultObj) {
     logger::println(logger::INFO,
                     "OneDAL (native): use DPC++ kernels; device %s",
                     ComputeDeviceString[computeDeviceOrdinal].c_str());

diff --git a/mllib-dal/src/main/native/KMeansImpl.cpp b/mllib-dal/src/main/native/KMeansImpl.cpp
@@ -305,10 +305,10 @@ static jlong doKMeansOneAPICompute(
  */
 JNIEXPORT jlong JNICALL
 Java_com_intel_oap_mllib_clustering_KMeansDALImpl_cKMeansOneapiComputeWithInitCenters(
-    JNIEnv *env, jobject obj, jint rank, jlong pNumTabData, jlong numRows, jlong numCols,
-    jlong pNumTabCenters, jint clusterNum, jdouble tolerance, jint iterationNum,
-    jint executorNum, jint executorCores, jint computeDeviceOrdinal,
-    jintArray gpuIdxArray, jobject resultObj) {
+    JNIEnv *env, jobject obj, jint rank, jlong pNumTabData, jlong numRows,
+    jlong numCols, jlong pNumTabCenters, jint clusterNum, jdouble tolerance,
+    jint iterationNum, jint executorNum, jint executorCores,
+    jint computeDeviceOrdinal, jintArray gpuIdxArray, jobject resultObj) {
     logger::println(logger::INFO,
                     "OneDAL (native): use DPC++ kernels; device %s",
                     ComputeDeviceString[computeDeviceOrdinal].c_str());

diff --git a/mllib-dal/src/main/native/LinearRegressionImpl.cpp b/mllib-dal/src/main/native/LinearRegressionImpl.cpp
@@ -214,8 +214,7 @@ ridge_regression_compute(size_t rankId, ccl::communicator &comm,
 }
 
 #ifdef CPU_GPU_PROFILE
-static jlong doLROneAPICompute(JNIEnv *env, size_t rankId,
-                               sycl::queue &queue,
+static jlong doLROneAPICompute(JNIEnv *env, size_t rankId, sycl::queue &queue,
                                jlong pNumTabFeature, jlong featureRows,
                                jlong featureCols, jlong pNumTabLabel,
                                jlong labelCols, jboolean jfitIntercept,
@@ -289,9 +288,9 @@ Java_com_intel_oap_mllib_regression_LinearRegressionDALImpl_cLinearRegressionTra
         jint *gpuIndices = env->GetIntArrayElements(gpuIdxArray, 0);
         auto queue = getAssignedGPU(device, gpuIndices);
 
-        resultptr = doLROneAPICompute(
-            env, rank, queue, feature, featureRows, featureCols,
-            label, labelCols, fitIntercept, executorNum, resultObj);
+        resultptr = doLROneAPICompute(env, rank, queue, feature, featureRows,
+                                      featureCols, label, labelCols,
+                                      fitIntercept, executorNum, resultObj);
         env->ReleaseIntArrayElements(gpuIdxArray, gpuIndices, 0);
 #endif
     } else {
@@ -320,7 +319,7 @@ Java_com_intel_oap_mllib_regression_LinearRegressionDALImpl_cLinearRegressionTra
 
         NumericTablePtr *coeffvectors = new NumericTablePtr(resultTable);
         resultptr = (jlong)coeffvectors;
-         if (rankId == ccl_root) {
+        if (rankId == ccl_root) {
             // Get the class of the result object
             jclass clazz = env->GetObjectClass(resultObj);
             // Get Field references

diff --git a/mllib-dal/src/main/native/PCAImpl.cpp b/mllib-dal/src/main/native/PCAImpl.cpp
@@ -250,9 +250,9 @@ static void doPCAOneAPICompute(
 
 JNIEXPORT jlong JNICALL
 Java_com_intel_oap_mllib_feature_PCADALImpl_cPCATrainDAL(
-    JNIEnv *env, jobject obj, jint rank, jlong pNumTabData, jlong numRows, jlong numCols,
-    jint executorNum, jint executorCores, jint computeDeviceOrdinal,
-    jintArray gpuIdxArray, jobject resultObj) {
+    JNIEnv *env, jobject obj, jint rank, jlong pNumTabData, jlong numRows,
+    jlong numCols, jint executorNum, jint executorCores,
+    jint computeDeviceOrdinal, jintArray gpuIdxArray, jobject resultObj) {
     logger::println(logger::INFO,
                     "oneDAL (native): use DPC++ kernels; device %s",
                     ComputeDeviceString[computeDeviceOrdinal].c_str());

diff --git a/mllib-dal/src/main/native/SummarizerImpl.cpp b/mllib-dal/src/main/native/SummarizerImpl.cpp
@@ -268,9 +268,9 @@ static void doSummarizerOneAPICompute(
 
 JNIEXPORT jlong JNICALL
 Java_com_intel_oap_mllib_stat_SummarizerDALImpl_cSummarizerTrainDAL(
-    JNIEnv *env, jobject obj, jint rank, jlong pNumTabData, jlong numRows, jlong numCols,
-    jint executorNum, jint executorCores, jint computeDeviceOrdinal,
-    jintArray gpuIdxArray, jobject resultObj) {
+    JNIEnv *env, jobject obj, jint rank, jlong pNumTabData, jlong numRows,
+    jlong numCols, jint executorNum, jint executorCores,
+    jint computeDeviceOrdinal, jintArray gpuIdxArray, jobject resultObj) {
     logger::println(logger::INFO,
                     "oneDAL (native): use DPC++ kernels; device %s",
                     ComputeDeviceString[computeDeviceOrdinal].c_str());
@@ -315,7 +315,8 @@ Java_com_intel_oap_mllib_stat_SummarizerDALImpl_cSummarizerTrainDAL(
     }
 #endif
     default: {
-        deviceError("Summarizer", ComputeDeviceString[computeDeviceOrdinal].c_str());
+        deviceError("Summarizer",
+                    ComputeDeviceString[computeDeviceOrdinal].c_str());
     }
     }
     return 0;

diff --git a/mllib-dal/src/test/scala/com/intel/oap/mllib/CorrelationHomogenTableSuite.scala b/mllib-dal/src/test/scala/com/intel/oap/mllib/CorrelationHomogenTableSuite.scala
@@ -45,7 +45,7 @@ class CorrelationHomogenTableSuite extends FunctionsSuite with Logging {
         val correlationDAL = new CorrelationDALImpl(1, 1)
         val gpuIndices = Array(0)
         val result = new CorrelationResult()
-        correlationDAL.cCorrelationTrainDAL(dataTable.getcObejct(), sourceData.length, sourceData(0).length, 1, 1, Common.ComputeDevice.HOST.ordinal(), gpuIndices, result);
+        correlationDAL.cCorrelationTrainDAL(0, dataTable.getcObejct(), sourceData.length, sourceData(0).length, 1, 1, Common.ComputeDevice.HOST.ordinal(), gpuIndices, result);
         val correlationMatrix = TestCommon.getMatrixFromTable(OneDAL.makeHomogenTable(
             result.getCorrelationNumericTable), TestCommon.getComputeDevice)
 

diff --git a/mllib-dal/src/test/scala/com/intel/oap/mllib/KmeansHomogenTableSuite.scala b/mllib-dal/src/test/scala/com/intel/oap/mllib/KmeansHomogenTableSuite.scala
@@ -49,7 +49,7 @@ class KmeansHomogenTableSuite extends FunctionsSuite with Logging {
         OneCCL.init(1, 1, "127.0.0.1_3000")
         val gpuIndices = Array(0)
         val result = new KMeansResult();
-        val centroids = kmeansDAL.cKMeansOneapiComputeWithInitCenters(dataTable.getcObejct(), sourceData.length, sourceData(0).length, centroidsTable.getcObejct(),10, 0.001,
+        val centroids = kmeansDAL.cKMeansOneapiComputeWithInitCenters(0, dataTable.getcObejct(), sourceData.length, sourceData(0).length, centroidsTable.getcObejct(),10, 0.001,
             5, 1, 1, TestCommon.getComputeDevice.ordinal(), gpuIndices, result);
         val resultVectors = OneDAL.homogenTableToVectors(OneDAL.makeHomogenTable(centroids), TestCommon.getComputeDevice);
         assertArrayEquals(TestCommon.convertArray(expectCentroids), TestCommon.convertArray(resultVectors), 0.000001)

diff --git a/mllib-dal/src/test/scala/com/intel/oap/mllib/PCAHomogenTableSuite.scala b/mllib-dal/src/test/scala/com/intel/oap/mllib/PCAHomogenTableSuite.scala
@@ -47,7 +47,7 @@ class PCAHomogenTableSuite extends FunctionsSuite with Logging {
         val pcaDAL = new PCADALImpl(5, 1, 1)
         val gpuIndices = Array(0)
         val result = new PCAResult()
-        pcaDAL.cPCATrainDAL(dataTable.getcObejct(), sourceData.length, sourceData(0).length, 1,  1, TestCommon.getComputeDevice.ordinal(), gpuIndices, result);
+        pcaDAL.cPCATrainDAL(0, dataTable.getcObejct(), sourceData.length, sourceData(0).length, 1,  1, TestCommon.getComputeDevice.ordinal(), gpuIndices, result);
         val pcNumericTable = OneDAL.makeHomogenTable(result.getPcNumericTable)
         val explainedVarianceNumericTable = OneDAL.makeHomogenTable(
             result.getExplainedVarianceNumericTable)
@@ -79,7 +79,7 @@ class PCAHomogenTableSuite extends FunctionsSuite with Logging {
         val pcaDAL = new PCADALImpl(5, 1, 1)
         val gpuIndices = Array(0)
         val result = new PCAResult()
-        pcaDAL.cPCATrainDAL(dataTable.getcObejct(), sourceData.length, sourceData(0).length, 1, 1, TestCommon.getComputeDevice.ordinal(), gpuIndices, result);
+        pcaDAL.cPCATrainDAL(0, dataTable.getcObejct(), sourceData.length, sourceData(0).length, 1, 1, TestCommon.getComputeDevice.ordinal(), gpuIndices, result);
         val pcNumericTable = OneDAL.makeHomogenTable(result.getPcNumericTable)
         val explainedVarianceNumericTable = OneDAL.makeHomogenTable(
             result.getExplainedVarianceNumericTable)

diff --git a/mllib-dal/src/test/scala/com/intel/oap/mllib/SummarizerHomogenTableSuite.scala b/mllib-dal/src/test/scala/com/intel/oap/mllib/SummarizerHomogenTableSuite.scala
@@ -35,7 +35,7 @@ class SummarizerHomogenTableSuite extends FunctionsSuite with Logging{
         val summarizerDAL = new SummarizerDALImpl(1, 1)
         val gpuIndices = Array(0)
         val result = new SummarizerResult()
-        summarizerDAL.cSummarizerTrainDAL(dataTable.getcObejct(), sourceData.length, sourceData(0).length, 1, 1, Common.ComputeDevice.HOST.ordinal(), gpuIndices, result)
+        summarizerDAL.cSummarizerTrainDAL(0, dataTable.getcObejct(), sourceData.length, sourceData(0).length, 1, 1, Common.ComputeDevice.HOST.ordinal(), gpuIndices, result)
         val meanTable = OneDAL.homogenTable1xNToVector(OneDAL.makeHomogenTable(result.getMeanNumericTable), Common.ComputeDevice.HOST)
         val varianceTable = OneDAL.homogenTable1xNToVector(OneDAL.makeHomogenTable(result.getVarianceNumericTable), Common.ComputeDevice.HOST)
         val minimumTable = OneDAL.homogenTable1xNToVector(OneDAL.makeHomogenTable(result.getMinimumNumericTable), Common.ComputeDevice.HOST)