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Android: Add Tensor.ones() and Tensor.zeros() factory methods #15388

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Android: Add Tensor.ones() and Tensor.zeros() factory methods #15388

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76 changes: 76 additions & 0 deletions extension/android/executorch_android/src/main/java/org/pytorch/executorch/Tensor.java
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Original file line number Diff line number Diff line change
Expand Up @@ -392,6 +392,82 @@ 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) {
Expand Down
64 changes: 46 additions & 18 deletions extension/android/executorch_android/src/test/java/org/pytorch/executorch/TensorTest.kt
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Original file line number Diff line number Diff line change
Expand Up @@ -254,24 +254,24 @@ class TensorTest {
assertEquals(tensor.dtype(), DType.FLOAT)

assertThatThrownBy { tensor.dataAsByteArray }
.isInstanceOf(IllegalStateException::class.java)
.hasMessage("Tensor of type Tensor_float32 cannot return data as byte array.")
.isInstanceOf(IllegalStateException::class.java)
.hasMessage("Tensor of type Tensor_float32 cannot return data as byte array.")

assertThatThrownBy { tensor.dataAsUnsignedByteArray }
.isInstanceOf(IllegalStateException::class.java)
.hasMessage("Tensor of type Tensor_float32 cannot return data as unsigned byte array.")
.isInstanceOf(IllegalStateException::class.java)
.hasMessage("Tensor of type Tensor_float32 cannot return data as unsigned byte array.")

assertThatThrownBy { tensor.dataAsIntArray }
.isInstanceOf(IllegalStateException::class.java)
.hasMessage("Tensor of type Tensor_float32 cannot return data as int array.")
.isInstanceOf(IllegalStateException::class.java)
.hasMessage("Tensor of type Tensor_float32 cannot return data as int array.")

assertThatThrownBy { tensor.dataAsDoubleArray }
.isInstanceOf(IllegalStateException::class.java)
.hasMessage("Tensor of type Tensor_float32 cannot return data as double array.")
.isInstanceOf(IllegalStateException::class.java)
.hasMessage("Tensor of type Tensor_float32 cannot return data as double array.")

assertThatThrownBy { tensor.dataAsLongArray }
.isInstanceOf(IllegalStateException::class.java)
.hasMessage("Tensor of type Tensor_float32 cannot return data as long array.")
.isInstanceOf(IllegalStateException::class.java)
.hasMessage("Tensor of type Tensor_float32 cannot return data as long array.")
}

@Test
Expand All @@ -281,20 +281,20 @@ class TensorTest {
val mismatchShape = longArrayOf(1, 2)

assertThatThrownBy { Tensor.fromBlob(null as FloatArray?, mismatchShape) }
.isInstanceOf(IllegalArgumentException::class.java)
.hasMessage("Data array must be not null")
.isInstanceOf(IllegalArgumentException::class.java)
.hasMessage("Data array must be not null")

assertThatThrownBy { Tensor.fromBlob(data, null) }
.isInstanceOf(IllegalArgumentException::class.java)
.hasMessage("Shape must be not null")
.isInstanceOf(IllegalArgumentException::class.java)
.hasMessage("Shape must be not null")

assertThatThrownBy { Tensor.fromBlob(data, shapeWithNegativeValues) }
.isInstanceOf(IllegalArgumentException::class.java)
.hasMessage("Shape elements must be non negative")
.isInstanceOf(IllegalArgumentException::class.java)
.hasMessage("Shape elements must be non negative")

assertThatThrownBy { Tensor.fromBlob(data, mismatchShape) }
.isInstanceOf(IllegalArgumentException::class.java)
.hasMessage("Inconsistent data capacity:4 and shape number elements:2 shape:[1, 2]")
.isInstanceOf(IllegalArgumentException::class.java)
.hasMessage("Inconsistent data capacity:4 and shape number elements:2 shape:[1, 2]")
}

@Test
Expand Down Expand Up @@ -336,4 +336,32 @@ 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())
}
}
}
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