Android: Add Tensor.ones() and Tensor.zeros() factory method to create tensors initialized with ones and zeros resp. (#15125)

Author: shubham0204

extension/android/executorch_android/src/main/java/org/pytorch/executorch/Tensor.java

@@ -341,6 +341,82 @@ public static Tensor fromBlob(DoubleBuffer data, long[] shape) {
     return new Tensor_float64(data, shape);
   }
 
+  /**
+   *
+   * Creates a new Tensor instance with given data-type and all elements initialized to one.
+   *
+   * @param shape Tensor shape
+   * @param dtype Tensor data-type
+   */
+  public static Tensor ones(long[] shape, DType dtype) {
+    checkArgument(shape != null, ERROR_MSG_SHAPE_NOT_NULL);
+    checkShape(shape);
+    int numElements = (int) numel(shape);
+    switch (dtype) {
+      case UINT8:
+        byte[] uInt8Data = new byte[numElements];
+        Arrays.fill(uInt8Data, (byte) 1);
+        return Tensor.fromBlobUnsigned(uInt8Data, shape);
+      case INT8:
+        byte[] int8Data = new byte[numElements];
+        Arrays.fill(int8Data, (byte) 1);
+        return Tensor.fromBlob(int8Data, shape);
+      case INT32:
+        int[] int32Data = new int[numElements];
+        Arrays.fill(int32Data, 1);
+        return Tensor.fromBlob(int32Data, shape);
+      case FLOAT:
+        float[] float32Data = new float[numElements];
+        Arrays.fill(float32Data, 1.0f);
+        return Tensor.fromBlob(float32Data, shape);
+      case INT64:
+        long[] int64Data = new long[numElements];
+        Arrays.fill(int64Data, 1L);
+        return Tensor.fromBlob(int64Data, shape);
+      case DOUBLE:
+        double[] float64Data = new double[numElements];
+        Arrays.fill(float64Data, 1.0);
+        return Tensor.fromBlob(float64Data, shape);
+      default:
+        throw new IllegalArgumentException(String.format("Tensor.ones() cannot be used with DType %s", dtype));
+    }
+  }
+
+  /**
+   *
+   * Creates a new Tensor instance with given data-type and all elements initialized to zero.
+   *
+   * @param shape Tensor shape
+   * @param dtype Tensor data-type
+   */
+  public static Tensor zeros(long[] shape, DType dtype) {
+    checkArgument(shape != null, ERROR_MSG_SHAPE_NOT_NULL);
+    checkShape(shape);
+    int numElements = (int) numel(shape);
+    switch (dtype) {
+      case UINT8:
+        byte[] uInt8Data = new byte[numElements];
+        return Tensor.fromBlobUnsigned(uInt8Data, shape);
+      case INT8:
+        byte[] int8Data = new byte[numElements];
+        return Tensor.fromBlob(int8Data, shape);
+      case INT32:
+        int[] int32Data = new int[numElements];
+        return Tensor.fromBlob(int32Data, shape);
+      case FLOAT:
+        float[] float32Data = new float[numElements];
+        return Tensor.fromBlob(float32Data, shape);
+      case INT64:
+        long[] int64Data = new long[numElements];
+        return Tensor.fromBlob(int64Data, shape);
+      case DOUBLE:
+        double[] float64Data = new double[numElements];
+        return Tensor.fromBlob(float64Data, shape);
+      default:
+        throw new IllegalArgumentException(String.format("Tensor.zeros() cannot be used with DType %s", dtype));
+    }
+  }
+
   @DoNotStrip private HybridData mHybridData;
 
   private Tensor(long[] shape) {

extension/android/executorch_android/src/test/java/org/pytorch/executorch/TensorTest.kt

@@ -274,4 +274,32 @@ class TensorTest {
       assertEquals(shape[i], deserShape[i])
     }
   }
+
+  @Test
+  fun testOnes_DTypeIsFloat() {
+    val shape = longArrayOf(2, 2)
+    val tensor = Tensor.ones(shape, DType.FLOAT)
+    val data = tensor.dataAsFloatArray
+    assertEquals(DType.FLOAT, tensor.dtype())
+    for (i in shape.indices) {
+      assertEquals(shape[i], tensor.shape[i])
+    }
+    for (i in data.indices) {
+      assertEquals(data[i], 1.0f, 1e-5.toFloat())
+    }
+  }
+
+  @Test
+  fun testZeros_DTypeIsFloat() {
+    val shape = longArrayOf(2, 2)
+    val tensor = Tensor.zeros(shape, DType.FLOAT)
+    val data = tensor.dataAsFloatArray
+    assertEquals(DType.FLOAT, tensor.dtype())
+    for (i in shape.indices) {
+      assertEquals(shape[i], tensor.shape[i])
+    }
+    for (i in data.indices) {
+      assertEquals(data[i], 0.0f, 1e-5.toFloat())
+    }
+  }
 }