Migrate pca daal to DPC++ (oap-project#223)

* 1. add JNI wrapper 2. modify compiler config 3. add unit test Signed-off-by: minmingzhu <minming.zhu@intel.com> * add ci test Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * java code style Signed-off-by: minmingzhu <minming.zhu@intel.com> * cpp code style Signed-off-by: minmingzhu <minming.zhu@intel.com> * Update ci-local-test.sh * Update ci-local-test.sh * Update install-build-deps-ubuntu.sh * Update install-build-deps-ubuntu.sh * Update ci-local-test.sh * Update ci-local-test.sh * Update ci-local-test.sh * Update install-build-deps-ubuntu.sh * Update install-build-deps-ubuntu.sh * Update install-build-deps-ubuntu.sh * Update install-build-deps-ubuntu.sh * Update install-build-deps-ubuntu.sh * Update prepare-build-deps.sh * add convert array, vector to homogentable Signed-off-by: minmingzhu <minming.zhu@intel.com> * 1. rollback ci config 2. remove CI test becaue homogentable only support GPU Signed-off-by: minmingzhu <minming.zhu@intel.com> * 1. update prepare-build-deps.sh Signed-off-by: minmingzhu <minming.zhu@intel.com> * remove ci GPU test Signed-off-by: minmingzhu <minming.zhu@intel.com> * 1. remove GPU CI test Signed-off-by: minmingzhu <minming.zhu@intel.com> * update code style Signed-off-by: minmingzhu <minming.zhu@intel.com> * update code style Signed-off-by: minmingzhu <minming.zhu@intel.com> * fix comments Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Makefile Signed-off-by: minmingzhu <minming.zhu@intel.com> * fix comments Signed-off-by: minmingzhu <minming.zhu@intel.com> * 1. fix ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * update header Signed-off-by: minmingzhu <minming.zhu@intel.com> * update header Signed-off-by: minmingzhu <minming.zhu@intel.com> * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * Update ci-cluster-test.sh * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * update ci conf Signed-off-by: minmingzhu <minming.zhu@intel.com> * update oneapi to 2022.1.2 Signed-off-by: minmingzhu <minming.zhu@intel.com> * update ci conf Signed-off-by: minmingzhu <minming.zhu@intel.com> * fix comment Signed-off-by: minmingzhu <minming.zhu@intel.com> * Update build-jni.sh * update Makefile Signed-off-by: minmingzhu <minming.zhu@intel.com> * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * Update test.sh * Update test.sh * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * Update test.sh * Update test.sh * update test.sh Signed-off-by: minmingzhu <minming.zhu@intel.com> * update test.sh Signed-off-by: minmingzhu <minming.zhu@intel.com> * update test.sh Signed-off-by: minmingzhu <minming.zhu@intel.com> * fix link libze_loader.so.1 Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * update ci-local-dpc-test.sh Signed-off-by: minmingzhu <minming.zhu@intel.com> * update oneapi to 2022.1.2 Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * Update ci-local-dpc-test.sh * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Makefile Signed-off-by: minmingzhu <minming.zhu@intel.com> * Update ci-local-dpc-test.sh * Update prepare-build-deps-gpu.sh * Update prepare-build-deps-gpu.sh * Update prepare-build-deps.sh * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * Update ci-local-dpc-test.sh * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * Update install-build-level-zero-deps-ubuntun.sh * Update install-build-level-zero-deps-ubuntun.sh * Update install-build-level-zero-deps-ubuntun.sh * Update ci-local-dpc-test.sh * Update GPU.cpp * Update GPU.cpp * Update ci-build-test.sh * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * update HomogenTableImpl.cpp Signed-off-by: minmingzhu <minming.zhu@intel.com> * Update ci-local-dpc-test.sh * Update ci-local-dpc-test.sh * Update ci-local-dpc-test.sh * update HomogenTable API Signed-off-by: minmingzhu <minming.zhu@intel.com> * update install-build-level-zero-deps-ubuntu.sh Signed-off-by: minmingzhu <minming.zhu@intel.com> * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * update code style Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * Update ci-local-dpc-test.sh * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * Update ci-local-dpc-test.sh * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * update ci Signed-off-by: minmingzhu <minming.zhu@intel.com> * Update run.sh * Update build-jni.sh * 1. Merge branch 'make_homogen_table' into convert_homogentable 2. update ci 3. add a new parameters for HomogenTable class Signed-off-by: minmingzhu <minming.zhu@intel.com> * Update pom.xml * Update test.sh * Update Makefile * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * Update ConvertHomogenTableSuite.scala * Update install-level-zero-ubuntu.sh * Update install-level-zero-ubuntu.sh * Update build.sh * Update test.sh * Update test.sh * Update HomogenTableImpl.cpp * Update install-build-deps-ubuntu.sh * Update HomogenTableImpl.cpp * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * update code style Signed-off-by: minmingzhu <minming.zhu@intel.com> * Update install-build-deps-ubuntu.sh * fix comments Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * Update list-compute-devices.cpp * Convert homogentable to array/vector/matrix (#4) * 1. convert homogenTable to array/vector/matrix 2. add unit test Signed-off-by: minmingzhu <minming.zhu@intel.com> * Merge branch 'make_homogen_table' into convert_homogentable Signed-off-by: minmingzhu <minming.zhu@intel.com> * update code style Signed-off-by: minmingzhu <minming.zhu@intel.com> * fix comments Signed-off-by: minmingzhu <minming.zhu@intel.com> * Convert RDD[Vector]/RDD[LabeledPoint] to HomogenTable (#8) * 1. convert rdd to HomogenTable * 2. add unit test Signed-off-by: minmingzhu <minming.zhu@intel.com> * add empty homogentable Signed-off-by: minmingzhu <minming.zhu@intel.com> * Mirgate kmeans daal to DPC++ (#7) Signed-off-by: minmingzhu <minming.zhu@intel.com> * 1. Mirgate kmeans daal to DPC++ * 2. add unit test * 1. move local host to distributed host 2. add cluster test 3. code style Signed-off-by: minmingzhu <minming.zhu@intel.com> * update code style Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * Update ci-cluster-test.sh * Update ci-cluster-test.sh * Update build.sh * Update build.sh * Update OneDAL.cpp * Update OneDAL.cpp * Update OneDAL.cpp * Update com_intel_oap_mllib_OneDAL__.h * update code style Signed-off-by: minmingzhu <minming.zhu@intel.com> * Update NaiveBayesResult.java * 1. update code style 2. When using the Host/CPU device throws a fail Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * rollback code style Signed-off-by: minmingzhu <minming.zhu@intel.com> * rollback code style Signed-off-by: minmingzhu <minming.zhu@intel.com> * rollback checkstyle.xml Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * rollback checkstyle.xml Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * update OneDAL.scala Signed-off-by: minmingzhu <minming.zhu@intel.com> * revert ALS.scala * revert checkstyle.xml * :Revert "revert checkstyle.xml" This reverts commit 4f74bac. * disable cluster test for gpu * disable cluster test for gpu * nit * migrate cpa to DPC++ Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * Update OutputHelpers.hpp * update com_intel_oap_mllib_feature_PCADALImpl.h Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * update PCADALImpl.scala Signed-off-by: minmingzhu <minming.zhu@intel.com> * 1. update function name and variable name Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * update Signed-off-by: minmingzhu <minming.zhu@intel.com> * update stayHomogenTablePtrToVector to saveHomogenTablePtrToVector Signed-off-by: minmingzhu <minming.zhu@intel.com> * change compute_device to ComputeDevice Signed-off-by: minmingzhu <minming.zhu@intel.com> Co-authored-by: minmingz <minming.zhu@inte.com> Co-authored-by: Wu, Xiaochang <xiaochang.wu@intel.com>
minmingzhu · May 9, 2023 · 10818de · 10818de
1 parent 7ea290e
commit 10818de
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Showing 4 changed files with 119 additions and 51 deletions.
diff --git a/mllib-dal/src/main/native/KMeansOneAPIImpl.cpp b/mllib-dal/src/main/native/KMeansOneAPIImpl.cpp
@@ -35,46 +35,32 @@ using namespace std;
 using namespace oneapi::dal;
 const int ccl_root = 0;
 
-typedef std::shared_ptr<homogen_table> homogenPtr;
-
-std::mutex kmtx;
-std::vector<homogenPtr> cVector;
-
-static void saveShareHomogenPtrVector(const homogenPtr &ptr) {
-    kmtx.lock();
-    cVector.push_back(ptr);
-    kmtx.unlock();
-}
-
 static jlong doKMeansOneAPICompute(JNIEnv *env, jint rankId, jlong pNumTabData,
-                                   jlong pNumTabCenters, jint cluster_num,
-                                   jdouble tolerance, jint iteration_num,
-                                   jint executor_num, const ccl::string &ipPort,
-                                   jint cComputeDevice, jobject resultObj) {
-    std::cout << "oneDAL (native): OneAPI compute start , rankid %ld " << rankId
+                                   jlong pNumTabCenters, jint clusterNum,
+                                   jdouble tolerance, jint iterationNum,
+                                   jint executorNum, const ccl::string &ipPort,
+                                   jint computeDeviceOrdinal,
+                                   jobject resultObj) {
+    std::cout << "oneDAL (native): GPU/CPU compute start , rankid = " << rankId
+              << "; device = " << computeDeviceOrdinal << "(0:HOST;1:GPU;2:CPU)"
               << std::endl;
     const bool isRoot = (rankId == ccl_root);
-    compute_device device = getComputeDevice(cComputeDevice);
+    ComputeDevice device = getComputeDeviceByOrdinal(computeDeviceOrdinal);
     homogen_table htable =
         *reinterpret_cast<const homogen_table *>(pNumTabData);
     homogen_table centroids =
         *reinterpret_cast<const homogen_table *>(pNumTabCenters);
     const auto kmeans_desc = kmeans::descriptor<>()
-                                 .set_cluster_count(cluster_num)
-                                 .set_max_iteration_count(iteration_num)
+                                 .set_cluster_count(clusterNum)
+                                 .set_max_iteration_count(iterationNum)
                                  .set_accuracy_threshold(tolerance);
     kmeans::train_input local_input{htable, centroids};
     auto queue = getQueue(device);
     auto comm = preview::spmd::make_communicator<preview::spmd::backend::ccl>(
-        queue, executor_num, rankId, ipPort);
+        queue, executorNum, rankId, ipPort);
     kmeans::train_result result_train =
         preview::train(comm, kmeans_desc, local_input);
     if (isRoot) {
-        std::cout << "iteration_num: " << iteration_num << std::endl;
-        std::cout << "Iteration count: " << result_train.get_iteration_count()
-                  << std::endl;
-        std::cout << "Objective function value: "
-                  << result_train.get_objective_function_value() << std::endl;
         // Get the class of the input object
         jclass clazz = env->GetObjectClass(resultObj);
         // Get Field references
@@ -88,9 +74,9 @@ static jlong doKMeansOneAPICompute(JNIEnv *env, jint rankId, jlong pNumTabData,
         env->SetDoubleField(resultObj, totalCostField,
                             result_train.get_objective_function_value());
 
-        homogenPtr centroidsPtr = std::make_shared<homogen_table>(
+        HomogenTablePtr centroidsPtr = std::make_shared<homogen_table>(
             result_train.get_model().get_centroids());
-        saveShareHomogenPtrVector(centroidsPtr);
+        saveHomogenTablePtrToVector(centroidsPtr);
         return (jlong)centroidsPtr.get();
     } else {
         return (jlong)0;
@@ -105,18 +91,16 @@ static jlong doKMeansOneAPICompute(JNIEnv *env, jint rankId, jlong pNumTabData,
 JNIEXPORT jlong JNICALL
 Java_com_intel_oap_mllib_clustering_KMeansDALImpl_cKMeansOneapiComputeWithInitCenters(
     JNIEnv *env, jobject obj, jlong pNumTabData, jlong pNumTabCenters,
-    jint cluster_num, jdouble tolerance, jint iteration_num, jint executor_num,
-    jint cComputeDevice, jint rankId, jstring ip_port, jobject resultObj) {
-    std::cout << "oneDAL (native): use GPU DPC++ kernels with " << std::endl;
-    const char *ipport = env->GetStringUTFChars(ip_port, 0);
-    std::string ipPort = std::string(ipport);
+    jint clusterNum, jdouble tolerance, jint iterationNum, jint executorNum,
+    jint computeDeviceOrdinal, jint rankId, jstring ipPort, jobject resultObj) {
+    std::cout << "oneDAL (native): use GPU DPC++ kernels " << std::endl;
+    const char *ipPortPtr = env->GetStringUTFChars(ipPort, 0);
+    std::string ipPortStr = std::string(ipPortPtr);
     jlong ret = 0L;
-    printf("oneDAL (native):  KMeansOneapiComputeWithInitCenters %d \n",
-           cComputeDevice);
     ret = doKMeansOneAPICompute(
-        env, rankId, pNumTabData, pNumTabCenters, cluster_num, tolerance,
-        iteration_num, executor_num, ipPort, cComputeDevice, resultObj);
-    env->ReleaseStringUTFChars(ip_port, ipport);
+        env, rankId, pNumTabData, pNumTabCenters, clusterNum, tolerance,
+        iterationNum, executorNum, ipPortStr, computeDeviceOrdinal, resultObj);
+    env->ReleaseStringUTFChars(ipPort, ipPortPtr);
     return ret;
 }
 #endif
diff --git a/mllib-dal/src/main/native/PCAOneAPIImpl.cpp b/mllib-dal/src/main/native/PCAOneAPIImpl.cpp
@@ -0,0 +1,90 @@
+/*******************************************************************************
+ * Copyright 2020 Intel Corporation
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *******************************************************************************/
+
+#include <chrono>
+#include <iostream>
+
+#ifdef CPU_GPU_PROFILE
+#include "GPU.h"
+#ifndef ONEDAL_DATA_PARALLEL
+#define ONEDAL_DATA_PARALLEL
+#endif
+#include "Communicator.hpp"
+#include "OutputHelpers.hpp"
+#include "com_intel_oap_mllib_feature_PCADALImpl.h"
+#include "oneapi/dal/algo/pca.hpp"
+#include "oneapi/dal/table/homogen.hpp"
+#include "service.h"
+
+using namespace std;
+using namespace oneapi::dal;
+const int ccl_root = 0;
+
+static void doPCAOneAPICompute(JNIEnv *env, jint rankId, jlong pNumTabData,
+                               jint executorNum, const ccl::string &ipPort,
+                               jint computeDeviceOrdinal, jobject resultObj) {
+    const bool isRoot = (rankId == ccl_root);
+    ComputeDevice device = getComputeDeviceByOrdinal(computeDeviceOrdinal);
+    std::cout << "oneDAL (native): GPU/CPU compute start , rankid = " << rankId
+              << "; device = " << computeDeviceOrdinal << "(0:HOST;1:GPU;2:CPU)"
+              << std::endl;
+    homogen_table htable =
+        *reinterpret_cast<const homogen_table *>(pNumTabData);
+
+    const auto pca_desc = pca::descriptor{};
+    auto queue = getQueue(device);
+    auto comm = preview::spmd::make_communicator<preview::spmd::backend::ccl>(
+        queue, executorNum, rankId, ipPort);
+    pca::train_input local_input{htable};
+    const auto result_train = preview::train(comm, pca_desc, local_input);
+    if (isRoot) {
+        // Return all eigenvalues & eigenvectors
+        // Get the class of the input object
+        jclass clazz = env->GetObjectClass(resultObj);
+        // Get Field references
+        jfieldID pcNumericTableField =
+            env->GetFieldID(clazz, "pcNumericTable", "J");
+        jfieldID explainedVarianceNumericTableField =
+            env->GetFieldID(clazz, "explainedVarianceNumericTable", "J");
+
+        HomogenTablePtr eigenvectors =
+            std::make_shared<homogen_table>(result_train.get_eigenvectors());
+        saveHomogenTablePtrToVector(eigenvectors);
+
+        HomogenTablePtr eigenvalues =
+            std::make_shared<homogen_table>(result_train.get_eigenvalues());
+        saveHomogenTablePtrToVector(eigenvalues);
+
+        env->SetLongField(resultObj, pcNumericTableField,
+                          (jlong)eigenvectors.get());
+        env->SetLongField(resultObj, explainedVarianceNumericTableField,
+                          (jlong)eigenvalues.get());
+    }
+}
+
+JNIEXPORT jlong JNICALL
+Java_com_intel_oap_mllib_feature_PCADALImpl_cPCATrainDAL(
+    JNIEnv *env, jobject obj, jlong pNumTabData, jint executorNum,
+    jint computeDeviceOrdinal, jint rankId, jstring ipPort, jobject resultObj) {
+    std::cout << "oneDAL (native): use GPU DPC++ kernels " << std::endl;
+    const char *ipPortPtr = env->GetStringUTFChars(ipPort, 0);
+    std::string ipPortStr = std::string(ipPortPtr);
+    doPCAOneAPICompute(env, rankId, pNumTabData, executorNum, ipPortStr,
+                       computeDeviceOrdinal, resultObj);
+    env->ReleaseStringUTFChars(ipPort, ipPortPtr);
+    return 0;
+}
+#endif
diff --git a/mllib-dal/src/main/scala/com/intel/oap/mllib/clustering/KMeansDALImpl.scala b/mllib-dal/src/main/scala/com/intel/oap/mllib/clustering/KMeansDALImpl.scala
@@ -17,11 +17,7 @@
 package com.intel.oap.mllib.clustering
 
 import com.intel.oap.mllib.Utils.getOneCCLIPPort
-<<<<<<< HEAD
 import com.intel.oap.mllib.{OneCCL, OneDAL, Utils}
-=======
-import com.intel.oap.mllib.{OneCCL, OneDAL}
->>>>>>> Migrate KMeans  daal to DPC++ (#209)
 import com.intel.oneapi.dal.table.Common
 import org.apache.spark.TaskContext
 import org.apache.spark.internal.Logging

diff --git a/mllib-dal/src/main/scala/com/intel/oap/mllib/feature/PCADALImpl.scala b/mllib-dal/src/main/scala/com/intel/oap/mllib/feature/PCADALImpl.scala
@@ -164,15 +164,15 @@ class PCADALImpl(val k: Int,
     val numCols = table.getNumberOfColumns.toInt
     require(k <= numRows, "k should be less or equal to row number")
 
-    val arrayDouble = getDoubleBufferDataFromDAL(table, numRows, numCols)
+    val arrayDouble = getDoubleBufferDataFromDAL(table, numRows, device)
 
     // Column-major, transpose of top K rows of NumericTable
     new DenseMatrix(numCols, k, arrayDouble.slice(0, numCols * k), false)
   }
 
-  private def getExplainedVarianceFromDAL(table_1xn: NumericTable, k: Int): DenseVector = {
-    val dataNumCols = table_1xn.getNumberOfColumns.toInt
-    val arrayDouble = getDoubleBufferDataFromDAL(table_1xn, 1, dataNumCols)
+  private[mllib] def getExplainedVarianceFromDAL(table_1xn: HomogenTable, k: Int,
+                                          device: Common.ComputeDevice): DenseVector = {
+    val arrayDouble = getDoubleBufferDataFromDAL(table_1xn, 1, device)
     val sum = arrayDouble.sum
     val topK = Arrays.copyOfRange(arrayDouble, 0, k)
     for (i <- 0 until k)
@@ -182,15 +182,13 @@ class PCADALImpl(val k: Int,
 
   // table.asInstanceOf[HomogenNumericTable].getDoubleArray() would error on GPU,
   // so use table.getBlockOfRows instead of it.
-  private def getDoubleBufferDataFromDAL(table: NumericTable,
+  private[mllib] def getDoubleBufferDataFromDAL(table: HomogenTable,
                                          numRows: Int,
-                                         numCols: Int): Array[Double] = {
-    var dataDouble: DoubleBuffer = null
+                                         device: Common.ComputeDevice): Array[Double] = {
 
     // returned DoubleBuffer is ByteByffer, need to copy as double array
-    dataDouble = table.getBlockOfRows(0, numRows, dataDouble)
-    val arrayDouble: Array[Double] = new Array[Double](numRows * numCols)
-    dataDouble.get(arrayDouble)
+    val accessor = new RowAccessor(table.getcObejct(), device)
+    val arrayDouble: Array[Double] = accessor.pullDouble(0, numRows)
 
     arrayDouble
   }