Examples with Matrix org.apache.sysml.api.mlcontext.Matrix used on opensource projects

Example 16 with Matrix

use of org.apache.sysml.api.mlcontext.Matrix in project incubator-systemml by apache.

the class BinaryOpTests method runSolveTest.

/**
	 * Runs the test for solve (Ax = b) for input with given dimensions and sparsities
	 * A can be overdetermined (rows in A > columns in A)
	 *
	 * @param sparsity sparsity for the block A and b
	 * @param m        rows in A
	 * @param n        columns in A
	 */
protected void runSolveTest(double sparsity, int m, int n) {
    String scriptStr = "x = solve(A, b)";
    System.out.println("In solve, A[" + m + ", " + n + "], b[" + m + ", 1]");
    Matrix A = generateInputMatrix(spark, m, n, sparsity, seed);
    Matrix b = generateInputMatrix(spark, m, 1, sparsity, seed);
    HashMap<String, Object> inputs = new HashMap<>();
    inputs.put("A", A);
    inputs.put("b", b);
    List<Object> outCPU = runOnCPU(spark, scriptStr, inputs, Arrays.asList("x"));
    List<Object> outGPU = runOnGPU(spark, scriptStr, inputs, Arrays.asList("x"));
    assertHeavyHitterPresent("gpu_solve");
    assertEqualObjects(outCPU.get(0), outGPU.get(0));
}

Example 17 with Matrix

use of org.apache.sysml.api.mlcontext.Matrix in project incubator-systemml by apache.

the class MatrixMatrixElementWiseOpTests method runMatrixColumnVectorTest.

/**
	 * Run O = X op Y where X is a matrix, Y is a column vector
	 *
	 * @param scriptStr         the script string
	 * @param matrixInput       name of the matrix input variable in the script string
	 * @param vectorInput       name of the vector input variable in the script string
	 * @param output            name of the output variable in the script string
	 * @param heavyHitterOpcode the string printed for the unary op heavy hitter when executed on gpu
	 */
private void runMatrixColumnVectorTest(String scriptStr, String matrixInput, String vectorInput, String output, String heavyHitterOpcode) {
    int[] rows = new int[] { 64, 130, 1024, 2049 };
    int[] cols = new int[] { 64, 130, 1024, 2049 };
    for (int i = 0; i < rows.length; i++) {
        for (int j = 0; j < cols.length; j++) {
            for (int k = 0; k < sparsities.length; k++) {
                int m = rows[i];
                int n = cols[j];
                double sparsity = sparsities[k];
                Matrix X = generateInputMatrix(spark, m, n, sparsity, seed);
                Matrix Y = generateInputMatrix(spark, m, 1, sparsity, seed);
                HashMap<String, Object> inputs = new HashMap<>();
                inputs.put(matrixInput, X);
                inputs.put(vectorInput, Y);
                System.out.println("Vector[" + m + ", 1] op Matrix[" + m + ", " + n + "], sparsity = " + sparsity);
                List<Object> cpuOut = runOnCPU(spark, scriptStr, inputs, Arrays.asList(output));
                List<Object> gpuOut = runOnGPU(spark, scriptStr, inputs, Arrays.asList(output));
                //assertHeavyHitterPresent(heavyHitterOpcode);
                assertEqualObjects(cpuOut.get(0), gpuOut.get(0));
            }
        }
    }
}

Example 18 with Matrix

use of org.apache.sysml.api.mlcontext.Matrix in project incubator-systemml by apache.

the class GNMFTest method testGNMFWithRDMLAndJava.

@Test
public void testGNMFWithRDMLAndJava() throws IOException, DMLException, ParseException {
    System.out.println("------------ BEGIN " + TEST_NAME + " TEST {" + numRegisteredInputs + ", " + numRegisteredOutputs + "} ------------");
    this.scriptType = ScriptType.DML;
    int m = 2000;
    int n = 1500;
    int k = 50;
    int maxiter = 2;
    double Eps = Math.pow(10, -8);
    getAndLoadTestConfiguration(TEST_NAME);
    List<String> proArgs = new ArrayList<String>();
    proArgs.add(input("v"));
    proArgs.add(input("w"));
    proArgs.add(input("h"));
    proArgs.add(Integer.toString(maxiter));
    proArgs.add(output("w"));
    proArgs.add(output("h"));
    programArgs = proArgs.toArray(new String[proArgs.size()]);
    fullDMLScriptName = getScript();
    rCmd = getRCmd(inputDir(), Integer.toString(maxiter), expectedDir());
    double[][] v = getRandomMatrix(m, n, 1, 5, 0.2, System.currentTimeMillis());
    double[][] w = getRandomMatrix(m, k, 0, 1, 1, System.currentTimeMillis());
    double[][] h = getRandomMatrix(k, n, 0, 1, 1, System.currentTimeMillis());
    writeInputMatrixWithMTD("v", v, true);
    writeInputMatrixWithMTD("w", w, true);
    writeInputMatrixWithMTD("h", h, true);
    for (int i = 0; i < maxiter; i++) {
        double[][] tW = TestUtils.performTranspose(w);
        double[][] tWV = TestUtils.performMatrixMultiplication(tW, v);
        double[][] tWW = TestUtils.performMatrixMultiplication(tW, w);
        double[][] tWWH = TestUtils.performMatrixMultiplication(tWW, h);
        for (int j = 0; j < k; j++) {
            for (int l = 0; l < n; l++) {
                h[j][l] = h[j][l] * (tWV[j][l] / (tWWH[j][l] + Eps));
            }
        }
        double[][] tH = TestUtils.performTranspose(h);
        double[][] vTH = TestUtils.performMatrixMultiplication(v, tH);
        double[][] hTH = TestUtils.performMatrixMultiplication(h, tH);
        double[][] wHTH = TestUtils.performMatrixMultiplication(w, hTH);
        for (int j = 0; j < m; j++) {
            for (int l = 0; l < k; l++) {
                w[j][l] = w[j][l] * (vTH[j][l] / (wHTH[j][l] + Eps));
            }
        }
    }
    boolean oldConfig = DMLScript.USE_LOCAL_SPARK_CONFIG;
    DMLScript.USE_LOCAL_SPARK_CONFIG = true;
    RUNTIME_PLATFORM oldRT = DMLScript.rtplatform;
    try {
        DMLScript.rtplatform = RUNTIME_PLATFORM.HYBRID_SPARK;
        Script script = ScriptFactory.dmlFromFile(fullDMLScriptName);
        // set positional argument values
        for (int argNum = 1; argNum <= proArgs.size(); argNum++) {
            script.in("$" + argNum, proArgs.get(argNum - 1));
        }
        // Read two matrices through RDD and one through HDFS
        if (numRegisteredInputs >= 1) {
            JavaRDD<String> vIn = sc.sc().textFile(input("v"), 2).toJavaRDD();
            MatrixMetadata mm = new MatrixMetadata(MatrixFormat.IJV, m, n);
            script.in("V", vIn, mm);
        }
        if (numRegisteredInputs >= 2) {
            JavaRDD<String> wIn = sc.sc().textFile(input("w"), 2).toJavaRDD();
            MatrixMetadata mm = new MatrixMetadata(MatrixFormat.IJV, m, k);
            script.in("W", wIn, mm);
        }
        if (numRegisteredInputs >= 3) {
            JavaRDD<String> hIn = sc.sc().textFile(input("h"), 2).toJavaRDD();
            MatrixMetadata mm = new MatrixMetadata(MatrixFormat.IJV, k, n);
            script.in("H", hIn, mm);
        }
        // Output one matrix to HDFS and get one as RDD
        if (numRegisteredOutputs >= 1) {
            script.out("H");
        }
        if (numRegisteredOutputs >= 2) {
            script.out("W");
            ml.setConfigProperty("cp.parallel.matrixmult", "false");
        }
        MLResults results = ml.execute(script);
        if (numRegisteredOutputs >= 2) {
            String configStr = ConfigurationManager.getDMLConfig().getConfigInfo();
            if (configStr.contains("cp.parallel.matrixmult: true"))
                Assert.fail("Configuration not updated via setConfig");
        }
        if (numRegisteredOutputs >= 1) {
            RDD<String> hOut = results.getRDDStringIJV("H");
            String fName = output("h");
            try {
                MapReduceTool.deleteFileIfExistOnHDFS(fName);
            } catch (IOException e) {
                throw new DMLRuntimeException("Error: While deleting file on HDFS");
            }
            hOut.saveAsTextFile(fName);
        }
        if (numRegisteredOutputs >= 2) {
            JavaRDD<String> javaRDDStringIJV = results.getJavaRDDStringIJV("W");
            JavaRDD<MatrixEntry> matRDD = javaRDDStringIJV.map(new StringToMatrixEntry());
            Matrix matrix = results.getMatrix("W");
            MatrixCharacteristics mcW = matrix.getMatrixMetadata().asMatrixCharacteristics();
            CoordinateMatrix coordinateMatrix = new CoordinateMatrix(matRDD.rdd(), mcW.getRows(), mcW.getCols());
            JavaPairRDD<MatrixIndexes, MatrixBlock> binaryRDD = RDDConverterUtilsExt.coordinateMatrixToBinaryBlock(sc, coordinateMatrix, mcW, true);
            JavaRDD<String> wOut = RDDConverterUtils.binaryBlockToTextCell(binaryRDD, mcW);
            String fName = output("w");
            try {
                MapReduceTool.deleteFileIfExistOnHDFS(fName);
            } catch (IOException e) {
                throw new DMLRuntimeException("Error: While deleting file on HDFS");
            }
            wOut.saveAsTextFile(fName);
        }
        runRScript(true);
        //compare matrices
        HashMap<CellIndex, Double> hmWDML = readDMLMatrixFromHDFS("w");
        HashMap<CellIndex, Double> hmHDML = readDMLMatrixFromHDFS("h");
        HashMap<CellIndex, Double> hmWR = readRMatrixFromFS("w");
        HashMap<CellIndex, Double> hmHR = readRMatrixFromFS("h");
        TestUtils.compareMatrices(hmWDML, hmWR, 0.000001, "hmWDML", "hmWR");
        TestUtils.compareMatrices(hmHDML, hmHR, 0.000001, "hmHDML", "hmHR");
    } finally {
        DMLScript.rtplatform = oldRT;
        DMLScript.USE_LOCAL_SPARK_CONFIG = oldConfig;
    }
}

Example 19 with Matrix

use of org.apache.sysml.api.mlcontext.Matrix in project incubator-systemml by apache.

the class DataFrameVectorScriptTest method testDataFrameScriptInput.

private void testDataFrameScriptInput(ValueType[] schema, boolean containsID, boolean dense, boolean unknownDims) {
    //TODO fix inconsistency ml context vs jmlc register Xf
    try {
        //generate input data and setup metadata
        int cols = schema.length + colsVector - 1;
        double sparsity = dense ? sparsity1 : sparsity2;
        double[][] A = TestUtils.round(getRandomMatrix(rows1, cols, -10, 1000, sparsity, 2373));
        MatrixBlock mbA = DataConverter.convertToMatrixBlock(A);
        int blksz = ConfigurationManager.getBlocksize();
        MatrixCharacteristics mc1 = new MatrixCharacteristics(rows1, cols, blksz, blksz, mbA.getNonZeros());
        MatrixCharacteristics mc2 = unknownDims ? new MatrixCharacteristics() : new MatrixCharacteristics(mc1);
        //create input data frame
        Dataset<Row> df = createDataFrame(spark, mbA, containsID, schema);
        // Create full frame metadata, and empty frame metadata
        FrameMetadata meta = new FrameMetadata(containsID ? FrameFormat.DF_WITH_INDEX : FrameFormat.DF, mc2.getRows(), mc2.getCols());
        FrameMetadata metaEmpty = new FrameMetadata();
        //run scripts and obtain result
        Script script1 = dml("Xm = as.matrix(Xf);").in("Xf", df, meta).out("Xm");
        Script script2 = dml("Xm = as.matrix(Xf);").in("Xf", df, metaEmpty).out(// empty metadata
        "Xm");
        Matrix Xm1 = ml.execute(script1).getMatrix("Xm");
        Matrix Xm2 = ml.execute(script2).getMatrix("Xm");
        MatrixBlock mbB1 = Xm1.toBinaryBlockMatrix().getMatrixBlock();
        MatrixBlock mbB2 = Xm2.toBinaryBlockMatrix().getMatrixBlock();
        //compare frame blocks
        double[][] B1 = DataConverter.convertToDoubleMatrix(mbB1);
        double[][] B2 = DataConverter.convertToDoubleMatrix(mbB2);
        TestUtils.compareMatrices(A, B1, rows1, cols, eps);
        TestUtils.compareMatrices(A, B2, rows1, cols, eps);
    } catch (Exception ex) {
        ex.printStackTrace();
        throw new RuntimeException(ex);
    }
}

Example 20 with Matrix

use of org.apache.sysml.api.mlcontext.Matrix in project incubator-systemml by apache.

the class MLContextScratchCleanupTest method runMLContextTestMultipleScript.

/**
	 * 
	 * @param platform
	 */
private void runMLContextTestMultipleScript(RUNTIME_PLATFORM platform, boolean wRead) {
    RUNTIME_PLATFORM oldplatform = DMLScript.rtplatform;
    DMLScript.rtplatform = platform;
    //create mlcontext
    SparkSession spark = createSystemMLSparkSession("MLContextScratchCleanupTest", "local");
    MLContext ml = new MLContext(spark);
    ml.setExplain(true);
    String dml1 = baseDirectory + File.separator + "ScratchCleanup1.dml";
    String dml2 = baseDirectory + File.separator + (wRead ? "ScratchCleanup2b.dml" : "ScratchCleanup2.dml");
    try {
        Script script1 = dmlFromFile(dml1).in("$rows", rows).in("$cols", cols).out("X");
        Matrix X = ml.execute(script1).getMatrix("X");
        //clear in-memory/cached data to emulate on-disk storage
        X.toMatrixObject().clearData();
        Script script2 = dmlFromFile(dml2).in("X", X).out("z");
        String z = ml.execute(script2).getString("z");
        System.out.println(z);
    } catch (Exception ex) {
        throw new RuntimeException(ex);
    } finally {
        DMLScript.rtplatform = oldplatform;
        // stop underlying spark context to allow single jvm tests (otherwise the
        // next test that tries to create a SparkContext would fail)
        spark.stop();
        // clear status mlcontext and spark exec context
        ml.close();
    }
}