From fcf5c6f82eec64edbc03f1cc691ddc2df5d53448 Mon Sep 17 00:00:00 2001
From: Parth <utkarsh@192.168.1.11>
Date: Tue, 16 Dec 2025 16:01:03 +0530
Subject: [PATCH] [SYSTEMDS-3933] Generalize OOC matrix-vector binary to
 support streamed vector input

---
 .../ooc/MatrixVectorBinaryOOCInstruction.java | 53 ++++++++++---------
 1 file changed, 29 insertions(+), 24 deletions(-)

diff --git a/src/main/java/org/apache/sysds/runtime/instructions/ooc/MatrixVectorBinaryOOCInstruction.java b/src/main/java/org/apache/sysds/runtime/instructions/ooc/MatrixVectorBinaryOOCInstruction.java
index 38586428e1e..731ad8fa810 100644
--- a/src/main/java/org/apache/sysds/runtime/instructions/ooc/MatrixVectorBinaryOOCInstruction.java
+++ b/src/main/java/org/apache/sysds/runtime/instructions/ooc/MatrixVectorBinaryOOCInstruction.java
@@ -22,7 +22,6 @@
 import java.util.HashMap;
 
 import org.apache.sysds.common.Opcodes;
-import org.apache.sysds.conf.ConfigurationManager;
 import org.apache.sysds.runtime.DMLRuntimeException;
 import org.apache.sysds.runtime.controlprogram.caching.MatrixObject;
 import org.apache.sysds.runtime.controlprogram.context.ExecutionContext;
@@ -51,7 +50,7 @@ public static MatrixVectorBinaryOOCInstruction parseInstruction(String str) {
 		InstructionUtils.checkNumFields(parts, 4);
 		String opcode = parts[0];
 		CPOperand in1 = new CPOperand(parts[1]); // the larget matrix (streamed)
-		CPOperand in2 = new CPOperand(parts[2]); // the small vector (in-memory)
+		CPOperand in2 = new CPOperand(parts[2]); // vector operand (may be OOC)
 		CPOperand out = new CPOperand(parts[3]);
 
 		AggregateOperator agg = new AggregateOperator(0, Plus.getPlusFnObject());
@@ -62,40 +61,46 @@ public static MatrixVectorBinaryOOCInstruction parseInstruction(String str) {
 
 	@Override
 	public void processInstruction( ExecutionContext ec ) {
-		// 1. Identify the inputs
-		MatrixObject min = ec.getMatrixObject(input1); // big matrix
-		MatrixBlock vin = ec.getMatrixObject(input2)
-			.acquireReadAndRelease(); // in-memory vector
-
-		// 2. Pre-partition the in-memory vector into a hashmap
-		HashMap<Long, MatrixBlock> partitionedVector = new HashMap<>();
-		int blksize = vin.getDataCharacteristics().getBlocksize();
-		if (blksize < 0)
-			blksize = ConfigurationManager.getBlocksize();
-		for (int i=0; i<vin.getNumRows(); i+=blksize) {
-			long key = (long) (i/blksize) + 1; // the key starts at 1
-			int end_row = Math.min(i + blksize, vin.getNumRows());
-			MatrixBlock vectorSlice = vin.slice(i, end_row - 1);
-			partitionedVector.put(key, vectorSlice);
-		}
+		// Fetch both inputs without assuming which one fits in memory
+		MatrixObject min = ec.getMatrixObject(input1); 
+		MatrixObject vin = ec.getMatrixObject(input2);
 
 		// number of colBlocks for early block output
 		long emitThreshold = min.getDataCharacteristics().getNumColBlocks();
 		OOCMatrixBlockTracker aggTracker = new OOCMatrixBlockTracker(emitThreshold);
 
-		OOCStream<IndexedMatrixValue> qIn = min.getStreamHandle();
+		OOCStream<IndexedMatrixValue> qIn1 = min.getStreamHandle();
+		OOCStream<IndexedMatrixValue> qIn2 = vin.getStreamHandle(); // Stream handles for matrix and vector (both may be OOC)
 		OOCStream<IndexedMatrixValue> qOut = createWritableStream();
 		BinaryOperator plus = InstructionUtils.parseBinaryOperator(Opcodes.PLUS.toString());
 		ec.getMatrixObject(output).setStreamHandle(qOut);
 
 		submitOOCTask(() -> {
-				IndexedMatrixValue tmp = null;
+
 				try {
-					while((tmp = qIn.dequeue()) != LocalTaskQueue.NO_MORE_TASKS) {
+					// Cache vector blocks indexed by their block row id
+					// This removes the assumption that the vector is fully in-memory
+					HashMap<Long, MatrixBlock> vectorCache = new HashMap<>();
+
+					// Consume the entire vector stream and cache it block-wise
+					IndexedMatrixValue vecVal;
+					while ((vecVal = qIn2.dequeue()) != LocalTaskQueue.NO_MORE_TASKS) {
+						vectorCache.put(
+							vecVal.getIndexes().getRowIndex(),
+							(MatrixBlock) vecVal.getValue());
+					}
+					
+					// Stream through matrix blocks and match them with vector blocks
+					IndexedMatrixValue tmp = null;
+					while((tmp = qIn1.dequeue()) != LocalTaskQueue.NO_MORE_TASKS) {
 						MatrixBlock matrixBlock = (MatrixBlock) tmp.getValue();
 						long rowIndex = tmp.getIndexes().getRowIndex();
 						long colIndex = tmp.getIndexes().getColumnIndex();
-						MatrixBlock vectorSlice = partitionedVector.get(colIndex);
+						MatrixBlock vectorSlice = vectorCache.get(colIndex);
+
+						// Fail fast if the corresponding vector block is missing
+						if (vectorSlice == null)
+							throw new DMLRuntimeException("Missing vector block for column block " + colIndex);
 
 						// Now, call the operation with the correct, specific operator.
 						MatrixBlock partialResult = matrixBlock.aggregateBinaryOperations(
@@ -103,7 +108,7 @@ public void processInstruction( ExecutionContext ec ) {
 
 						// for single column block, no aggregation neeeded
 						if(emitThreshold == 1) {
-							qOut.enqueue(new IndexedMatrixValue(tmp.getIndexes(), partialResult));
+							qOut.enqueue(new IndexedMatrixValue(new MatrixIndexes(rowIndex, 1), partialResult));
 						}
 						else {
 							// aggregation
@@ -129,6 +134,6 @@ public void processInstruction( ExecutionContext ec ) {
 				finally {
 					qOut.closeInput();
 				}
-		}, qIn, qOut);
+		}, qIn1, qIn2, qOut);
 	}
 }