Example 11 with Matrix
use of org.apache.sysml.api.mlcontext.Matrix in project incubator-systemml by apache.
the class NeuralNetworkOpTests method testConv2d.
@Test
public void testConv2d() {
String scriptStr = "O = conv2d(image, filter, padding=[padH, padW], stride=[strideH, strideW], input_shape=[N,C,H,W], filter_shape=[K,C,R,S])";
for (long N : Nlst) {
for (long C : Clst) {
for (long H : Hlst) {
for (long W : Wlst) {
for (long K : Klst) {
for (long R : Rlst) {
for (long S : Slst) {
for (long strideH : strideHeightLst) {
for (long strideW : strideWidthLst) {
for (long padH : padHeightLst) {
for (long padW : padWidthLst) {
for (double sparsity : sparsitylst) {
// Make sure ops fit in GPU memory and within constraints of cudnn
long imageSize = N * C * H * W * 8l;
if (// image size
imageSize > MAX_OP_SIZE)
continue;
long filterSize = K * C * R * S * 8l;
if (// filter size
filterSize > MAX_OP_SIZE)
continue;
// filter is smaller than image + padding
if (R > (H + padH) || S > (W + padW))
continue;
int P = (int) ConvolutionUtils.getP(H, R, strideH, padH);
int Q = (int) ConvolutionUtils.getQ(W, S, strideW, padW);
long doutSize = N * K * P * Q * 8l;
if (// dout/output size
doutSize > MAX_OP_SIZE)
continue;
double imageSizeInMB = imageSize / (1024.0 * 1024.0);
double filterSizeInMB = filterSize / (1024.0 * 1024.0);
double doutSizeInMB = doutSize / (1024.0 * 1024.0);
System.out.format("conv2d, image[%d,%d,%d,%d](%.1fMB), filter[%d,%d,%d,%d](%.1f), dout[%d,%d,%d,%d](%.1fMB), stride[%d,%d], padding[%d,%d]", N, C, H, W, imageSizeInMB, N, C, R, S, filterSizeInMB, N, K, P, Q, doutSizeInMB, strideH, strideW, padH, padW);
Matrix image = generateInputMatrix(spark, (int) N, (int) (C * H * W), sparsity, seed);
Matrix filter = generateInputMatrix(spark, (int) K, (int) (C * R * S), sparsity, seed);
HashMap<String, Object> inputs = new HashMap<>();
inputs.put("N", N);
inputs.put("C", C);
inputs.put("H", H);
inputs.put("W", W);
inputs.put("K", K);
inputs.put("R", R);
inputs.put("S", S);
inputs.put("strideH", strideH);
inputs.put("strideW", strideW);
inputs.put("padH", padH);
inputs.put("padW", padW);
inputs.put("image", image);
inputs.put("filter", filter);
List<Object> outCPU = runOnCPU(spark, scriptStr, inputs, Arrays.asList("O"));
List<Object> outGPU = runOnGPU(spark, scriptStr, inputs, Arrays.asList("O"));
assertHeavyHitterPresent("gpu_conv2d");
assertEqualObjects(outCPU.get(0), outGPU.get(0));
clearGPUMemory();
}
}
}
}
}
}
}
}
}
}
}
}
}Example 12 with Matrix
use of org.apache.sysml.api.mlcontext.Matrix in project incubator-systemml by apache.
the class NeuralNetworkOpTests method testMaxPool.
@Test
public void testMaxPool() {
String scriptStr = "O = max_pool(image, padding=[padH, padW], stride=[strideH, strideW], input_shape=[N,C,H,W], pool_size=[R,S])";
for (long N : Nlst) {
for (long C : Clst) {
for (long H : Hlst) {
for (long W : Wlst) {
for (long R : Rlst) {
for (long S : Slst) {
for (long strideH : strideHeightLst) {
for (long strideW : strideWidthLst) {
for (long padH : padHeightLst) {
for (long padW : padWidthLst) {
for (double sparsity : sparsitylst) {
// pool is smaller than image + padding
if (R > (H + padH) || S > (W + padW))
continue;
// Make sure ops fit in GPU memory and within constraints of cudnn
long imageSize = N * C * H * W * 8l;
if (// image size
imageSize > MAX_OP_SIZE)
continue;
long poolSize = R * S * 8l;
if (// filter size
poolSize > MAX_OP_SIZE)
continue;
int P = (int) ConvolutionUtils.getP(H, R, strideH, padH);
int Q = (int) ConvolutionUtils.getQ(W, S, strideW, padW);
long doutSize = N * C * P * Q * 8l;
if (// dout/output size
doutSize > MAX_OP_SIZE)
continue;
double imageSizeInMB = imageSize / (1024.0 * 1024.0);
double poolSizeInMB = poolSize / (1024.0 * 1024.0);
double doutSizeInMB = doutSize / (1024.0 * 1024.0);
System.out.format("max_pool, image[%d,%d,%d,%d](%.1fMB), pool[%d,%d](%.1f), dout[%d,%d,%d,%d](%.1fMB), stride[%d,%d], padding[%d,%d]", N, C, H, W, imageSizeInMB, R, S, poolSizeInMB, N, C, P, Q, doutSizeInMB, strideH, strideW, padH, padW);
Matrix image = generateInputMatrix(spark, (int) N, (int) (C * H * W), sparsity, seed);
HashMap<String, Object> inputs = new HashMap<>();
inputs.put("N", N);
inputs.put("C", C);
inputs.put("H", H);
inputs.put("W", W);
inputs.put("R", R);
inputs.put("S", S);
inputs.put("strideH", strideH);
inputs.put("strideW", strideW);
inputs.put("padH", padH);
inputs.put("padW", padW);
inputs.put("image", image);
List<Object> outCPU = runOnCPU(spark, scriptStr, inputs, Arrays.asList("O"));
List<Object> outGPU = runOnGPU(spark, scriptStr, inputs, Arrays.asList("O"));
assertHeavyHitterPresent("gpu_maxpooling");
assertEqualObjects(outCPU.get(0), outGPU.get(0));
clearGPUMemory();
}
}
}
}
}
}
}
}
}
}
}
}Example 13 with Matrix
use of org.apache.sysml.api.mlcontext.Matrix in project incubator-systemml by apache.
the class NeuralNetworkOpTests method testConv2dBackwardFilter.
@Test
public void testConv2dBackwardFilter() {
String scriptStr = "O = conv2d_backward_filter(image, dout, padding=[padH, padW], stride=[strideH, strideW], input_shape=[N,C,H,W], filter_shape=[K,C,R,S])";
for (long N : Nlst) {
for (long C : Clst) {
for (long H : Hlst) {
for (long W : Wlst) {
for (long K : Klst) {
for (long R : Rlst) {
for (long S : Slst) {
for (long strideH : strideHeightLst) {
for (long strideW : strideWidthLst) {
for (long padH : padHeightLst) {
for (long padW : padWidthLst) {
for (double sparsity : sparsitylst) {
// filter is smaller than image + padding
if (R > (H + padH) || S > (W + padW))
continue;
// Make sure ops fit in GPU memory and within constraints of cudnn
long imageSize = N * C * H * W * 8l;
if (// image size
imageSize > MAX_OP_SIZE)
continue;
long filterSize = K * C * R * S * 8l;
if (// filter size
filterSize > MAX_OP_SIZE)
continue;
int P = (int) ConvolutionUtils.getP(H, R, strideH, padH);
int Q = (int) ConvolutionUtils.getQ(W, S, strideW, padW);
long doutSize = N * K * P * Q * 8l;
if (// dout/output size
doutSize > MAX_OP_SIZE)
continue;
double imageSizeInMB = imageSize / (1024.0 * 1024.0);
double filterSizeInMB = filterSize / (1024.0 * 1024.0);
double doutSizeInMB = doutSize / (1024.0 * 1024.0);
System.out.format("conv2d_backward_filter, image[%d,%d,%d,%d](%.1fMB), filter[%d,%d,%d,%d](%.1f), dout[%d,%d,%d,%d](%.1fMB), stride[%d,%d], padding[%d,%d]", N, C, H, W, imageSizeInMB, N, C, R, S, filterSizeInMB, N, K, P, Q, doutSizeInMB, strideH, strideW, padH, padW);
Matrix image = generateInputMatrix(spark, (int) N, (int) (C * H * W), sparsity, seed);
Matrix dout = generateInputMatrix(spark, (int) N, (int) (K * P * Q), sparsity, seed);
HashMap<String, Object> inputs = new HashMap<>();
inputs.put("N", N);
inputs.put("C", C);
inputs.put("H", H);
inputs.put("W", W);
inputs.put("K", K);
inputs.put("R", R);
inputs.put("S", S);
inputs.put("strideH", strideH);
inputs.put("strideW", strideW);
inputs.put("padH", padH);
inputs.put("padW", padW);
inputs.put("image", image);
inputs.put("dout", dout);
List<Object> outCPU = runOnCPU(spark, scriptStr, inputs, Arrays.asList("O"));
List<Object> outGPU = runOnGPU(spark, scriptStr, inputs, Arrays.asList("O"));
assertHeavyHitterPresent("gpu_conv2d_backward_filter");
assertEqualObjects(outCPU.get(0), outGPU.get(0));
clearGPUMemory();
}
}
}
}
}
}
}
}
}
}
}
}
}Example 14 with Matrix
use of org.apache.sysml.api.mlcontext.Matrix in project incubator-systemml by apache.
the class MatrixMatrixElementWiseOpTests method runMatrixRowVectorTest.
/**
* Run O = X op Y where X is a matrix, Y is a row 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 runMatrixRowVectorTest(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, 1, n, 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 15 with Matrix
use of org.apache.sysml.api.mlcontext.Matrix in project incubator-systemml by apache.
the class MatrixMultiplicationOpTest method assertMatrixMultiplication.
/**
* Assert that matrix multiplication is the same on gpu and cpu
*
* @param scriptStr script string that has matrix multiplication (eg : O = X %*% Y)
* @param rows1 rows in X
* @param cols1 cols in X
* @param rows2 rows in Y
* @param cols2 cols in Y
* @param sparsity1 sparsity for X
* @param sparsity2 sparsity for Y
*/
private void assertMatrixMultiplication(String scriptStr, int rows1, int cols1, int rows2, int cols2, double sparsity1, double sparsity2) {
HashMap<String, Object> inputs = new HashMap<>();
Matrix X = generateInputMatrix(spark, rows1, cols1, sparsity1, seed);
Matrix Y = generateInputMatrix(spark, rows2, cols2, sparsity2, seed);
inputs.put("X", X);
inputs.put("Y", Y);
List<Object> cpuOuts = runOnCPU(spark, scriptStr, inputs, Arrays.asList("O"));
List<Object> gpuOuts = runOnGPU(spark, scriptStr, inputs, Arrays.asList("O"));
//assertHeavyHitterPresent("gpu_ba+*'");
assertEqualObjects(cpuOuts.get(0), gpuOuts.get(0));
}Aggregations
Matrix (org.apache.sysml.api.mlcontext.Matrix)20
HashMap (java.util.HashMap)15
Test (org.junit.Test)8
Script (org.apache.sysml.api.mlcontext.Script)5
DMLScript (org.apache.sysml.api.DMLScript)3
RUNTIME_PLATFORM (org.apache.sysml.api.DMLScript.RUNTIME_PLATFORM)3
MLContext (org.apache.sysml.api.mlcontext.MLContext)3
SparkSession (org.apache.spark.sql.SparkSession)2
DMLRuntimeException (org.apache.sysml.runtime.DMLRuntimeException)2
MatrixCharacteristics (org.apache.sysml.runtime.matrix.MatrixCharacteristics)2
MatrixBlock (org.apache.sysml.runtime.matrix.data.MatrixBlock)2
IOException (java.io.IOException)1
ArrayList (java.util.ArrayList)1
CoordinateMatrix (org.apache.spark.mllib.linalg.distributed.CoordinateMatrix)1
MatrixEntry (org.apache.spark.mllib.linalg.distributed.MatrixEntry)1
Row (org.apache.spark.sql.Row)1
FrameMetadata (org.apache.sysml.api.mlcontext.FrameMetadata)1
MLResults (org.apache.sysml.api.mlcontext.MLResults)1
MatrixMetadata (org.apache.sysml.api.mlcontext.MatrixMetadata)1
MatrixIndexes (org.apache.sysml.runtime.matrix.data.MatrixIndexes)1
