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advanced slicing

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This commit is contained in:
Roland Grinis 2021-07-31 21:23:15 +01:00
parent c6acedf9e0
commit 3d1a3e3b69
5 changed files with 118 additions and 15 deletions
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2
kmath-noa/README.md
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@ -77,7 +77,7 @@ import torch
tensor_reg = list(torch.jit.load('tensorReg.pt').parameters())[0]
```
The most efficient way pass data between the `JVM` and the native backend
The most efficient way passing data between the `JVM` and the native backend
is to rely on primitive arrays:
```kotlin
val array = (1..8).map { 100f * it }.toFloatArray()

14
kmath-noa/src/main/java/space/kscience/kmath/noa/JNoa.java
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@ -354,4 +354,18 @@ class JNoa {
public static native void getBlobInt(long tensorHandle, int[] data);
public static native void setTensor(long tensorHandle, int i, long tensorValue);
public static native long getSliceTensor(long tensorHandle, int dim, int start, int end);
public static native void setSliceTensor(long tensorHandle, int dim, int start, int end, long tensorValue);
public static native void setSliceBlobDouble(long tensorHandle, int dim, int start, int end, double[] data);
public static native void setSliceBlobFloat(long tensorHandle, int dim, int start, int end, float[] data);
public static native void setSliceBlobLong(long tensorHandle, int dim, int start, int end, long[] data);
public static native void setSliceBlobInt(long tensorHandle, int dim, int start, int end, int[] data);
}

24
kmath-noa/src/main/kotlin/space/kscience/kmath/noa/algebras.kt
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@ -87,6 +87,19 @@ protected constructor(protected val scope: NoaScope) :
override operator fun Tensor<T>.get(i: Int): TensorType =
wrap(JNoa.getIndex(tensor.tensorHandle, i))
public operator fun TensorType.set(i: Int, value: Tensor<T>): Unit =
JNoa.setTensor(tensorHandle, i, value.tensor.tensorHandle)
public abstract operator fun TensorType.set(i: Int, array: PrimitiveArray): Unit
public operator fun Tensor<T>.get(dim: Int, start: Int, end: Int): TensorType =
wrap(JNoa.getSliceTensor(tensor.tensorHandle, dim, start, end))
public operator fun TensorType.set(dim: Int, start: Int, end: Int, value: Tensor<T>): Unit =
JNoa.setSliceTensor(tensorHandle, dim, start, end, value.tensor.tensorHandle)
public abstract operator fun TensorType.set(dim: Int, start: Int, end: Int, array: PrimitiveArray): Unit
override fun diagonalEmbedding(
diagonalEntries: Tensor<T>, offset: Int, dim1: Int, dim2: Int
): TensorType =
@ -166,8 +179,6 @@ protected constructor(protected val scope: NoaScope) :
public abstract fun TensorType.assignFromArray(array: PrimitiveArray): Unit
public abstract operator fun TensorType.set(i: Int, array: PrimitiveArray): Unit
}
public sealed class NoaPartialDivisionAlgebra<T, PrimitiveArray, TensorType : NoaTensor<T>>
@ -418,6 +429,8 @@ protected constructor(scope: NoaScope) :
override fun NoaDoubleTensor.set(i: Int, array: DoubleArray): Unit =
JNoa.setBlobDouble(tensorHandle, i, array)
override fun NoaDoubleTensor.set(dim: Int, start: Int, end: Int, array: DoubleArray): Unit =
JNoa.setSliceBlobDouble(tensorHandle, dim, start, end, array)
}
public sealed class NoaFloatAlgebra
@ -508,6 +521,9 @@ protected constructor(scope: NoaScope) :
override fun NoaFloatTensor.set(i: Int, array: FloatArray): Unit =
JNoa.setBlobFloat(tensorHandle, i, array)
override fun NoaFloatTensor.set(dim: Int, start: Int, end: Int, array: FloatArray): Unit =
JNoa.setSliceBlobFloat(tensorHandle, dim, start, end, array)
}
public sealed class NoaLongAlgebra
@ -583,6 +599,8 @@ protected constructor(scope: NoaScope) :
override fun NoaLongTensor.set(i: Int, array: LongArray): Unit =
JNoa.setBlobLong(tensorHandle, i, array)
override fun NoaLongTensor.set(dim: Int, start: Int, end: Int, array: LongArray): Unit =
JNoa.setSliceBlobLong(tensorHandle, dim, start, end, array)
}
public sealed class NoaIntAlgebra
@ -658,4 +676,6 @@ protected constructor(scope: NoaScope) :
override fun NoaIntTensor.set(i: Int, array: IntArray): Unit =
JNoa.setBlobInt(tensorHandle, i, array)
override fun NoaIntTensor.set(dim: Int, start: Int, end: Int, array: IntArray): Unit =
JNoa.setSliceBlobInt(tensorHandle, dim, start, end, array)
}

56
kmath-noa/src/main/resources/space_kscience_kmath_noa_JNoa.h
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@ -1343,6 +1343,62 @@ JNIEXPORT void JNICALL Java_space_kscience_kmath_noa_JNoa_getBlobLong
JNIEXPORT void JNICALL Java_space_kscience_kmath_noa_JNoa_getBlobInt
(JNIEnv *, jclass, jlong, jintArray);
/*
* Class: space_kscience_kmath_noa_JNoa
* Method: setTensor
* Signature: (JIJ)V
*/
JNIEXPORT void JNICALL Java_space_kscience_kmath_noa_JNoa_setTensor
(JNIEnv *, jclass, jlong, jint, jlong);
/*
* Class: space_kscience_kmath_noa_JNoa
* Method: getSliceTensor
* Signature: (JIII)J
*/
JNIEXPORT jlong JNICALL Java_space_kscience_kmath_noa_JNoa_getSliceTensor
(JNIEnv *, jclass, jlong, jint, jint, jint);
/*
* Class: space_kscience_kmath_noa_JNoa
* Method: setSliceTensor
* Signature: (JIIIJ)V
*/
JNIEXPORT void JNICALL Java_space_kscience_kmath_noa_JNoa_setSliceTensor
(JNIEnv *, jclass, jlong, jint, jint, jint, jlong);
/*
* Class: space_kscience_kmath_noa_JNoa
* Method: setSliceBlobDouble
* Signature: (JIII[D)V
*/
JNIEXPORT void JNICALL Java_space_kscience_kmath_noa_JNoa_setSliceBlobDouble
(JNIEnv *, jclass, jlong, jint, jint, jint, jdoubleArray);
/*
* Class: space_kscience_kmath_noa_JNoa
* Method: setSliceBlobFloat
* Signature: (JIII[F)V
*/
JNIEXPORT void JNICALL Java_space_kscience_kmath_noa_JNoa_setSliceBlobFloat
(JNIEnv *, jclass, jlong, jint, jint, jint, jfloatArray);
/*
* Class: space_kscience_kmath_noa_JNoa
* Method: setSliceBlobLong
* Signature: (JIII[J)V
*/
JNIEXPORT void JNICALL Java_space_kscience_kmath_noa_JNoa_setSliceBlobLong
(JNIEnv *, jclass, jlong, jint, jint, jint, jlongArray);
/*
* Class: space_kscience_kmath_noa_JNoa
* Method: setSliceBlobInt
* Signature: (JIII[I)V
*/
JNIEXPORT void JNICALL Java_space_kscience_kmath_noa_JNoa_setSliceBlobInt
(JNIEnv *, jclass, jlong, jint, jint, jint, jintArray);
#ifdef __cplusplus
}
#endif

31
kmath-noa/src/test/kotlin/space/kscience/kmath/noa/TestTensor.kt
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@ -45,18 +45,31 @@ internal fun NoaFloat.testingSerialisation(tensorPath: String, device: Device =
assertTrue(tensor.copyToArray() contentEquals loadedTensor.copyToArray())
}
internal fun NoaFloat.testingArrayTransfer(device: Device = Device.CPU) {
internal fun NoaFloat.testingBatchedGetterSetter(device: Device = Device.CPU) {
val array = (1..8).map { 100f * it }.toFloatArray()
val tensor = full(0.0f, intArrayOf(2, 2, 2), device)
tensor.assignFromArray(array)
assertTrue(tensor.copyToArray() contentEquals array)
val updateArray = floatArrayOf(15f, 20f)
val resArray = NoaFloat {
val tensor = copyFromArray(array, intArrayOf(2, 2, 2), device)
val updateTensor = full(5.0f, intArrayOf(4), device)
updateTensor[0, 1, 3] = updateArray
NoaFloat {
tensor[0][1] = updateArray
tensor[1] = updateTensor.view(intArrayOf(2, 2))
updateTensor[0, 2, 4] = updateTensor[0, 0, 2]
}!!
assertEquals(tensor[intArrayOf(0, 1, 0)], 15f)
tensor.copyToArray()
}!!
assertEquals(resArray[3], 20f)
assertTrue(
tensor.copyToArray() contentEquals
floatArrayOf(100f, 200f, 15f, 20f, 5f, 15f, 20f, 5f)
)
assertTrue(
updateTensor.copyToArray() contentEquals
floatArrayOf(5f, 15f, 5f, 15f)
)
}
class TestTensor {
@ -110,9 +123,9 @@ class TestTensor {
}!!
@Test
fun testArrayTransfer() = NoaFloat {
fun testBatchedGetterSetter() = NoaFloat {
withCuda { device ->
testingArrayTransfer(device)
testingBatchedGetterSetter(device)
}
}!!