testing copying
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@ -36,14 +36,14 @@ protected constructor(protected val scope: NoaScope) :
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override fun Tensor<T>.value(): T = tensor.item()
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public abstract fun randDiscrete(low: Long, high: Long, shape: IntArray, device: Device): TensorType
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public abstract fun randDiscrete(low: Long, high: Long, shape: IntArray, device: Device = Device.CPU): TensorType
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@PerformancePitfall
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public abstract fun Tensor<T>.copyToArray(): PrimitiveArray
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public abstract fun copyFromArray(array: PrimitiveArray, shape: IntArray, device: Device): TensorType
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public abstract fun copyFromArray(array: PrimitiveArray, shape: IntArray, device: Device = Device.CPU): TensorType
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public abstract fun full(value: T, shape: IntArray, device: Device): TensorType
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public abstract fun full(value: T, shape: IntArray, device: Device = Device.CPU): TensorType
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override operator fun Tensor<T>.times(other: Tensor<T>): TensorType {
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return wrap(JNoa.timesTensor(tensor.tensorHandle, other.tensor.tensorHandle))
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@ -136,10 +136,10 @@ protected constructor(protected val scope: NoaScope) :
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public fun Tensor<T>.copy(): TensorType =
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wrap(JNoa.copyTensor(tensor.tensorHandle))
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public fun Tensor<T>.copyToDevice(device: Device): TensorType =
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public fun Tensor<T>.copyToDevice(device: Device = Device.CPU): TensorType =
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wrap(JNoa.copyToDevice(tensor.tensorHandle, device.toInt()))
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public abstract fun loadJitModule(path: String, device: Device): NoaJitModule
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public abstract fun loadJitModule(path: String, device: Device = Device.CPU): NoaJitModule
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public fun NoaJitModule.forward(parameters: Tensor<T>): TensorType =
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wrap(JNoa.forwardPass(jitModuleHandle, parameters.tensor.tensorHandle))
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@ -193,9 +193,9 @@ protected constructor(scope: NoaScope) :
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override fun Tensor<T>.variance(dim: Int, keepDim: Boolean): TensorType =
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wrap(JNoa.varDimTensor(tensor.tensorHandle, dim, keepDim))
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public abstract fun randNormal(shape: IntArray, device: Device): TensorType
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public abstract fun randNormal(shape: IntArray, device: Device = Device.CPU): TensorType
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public abstract fun randUniform(shape: IntArray, device: Device): TensorType
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public abstract fun randUniform(shape: IntArray, device: Device = Device.CPU): TensorType
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public fun Tensor<T>.randUniform(): TensorType =
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wrap(JNoa.randLike(tensor.tensorHandle))
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@ -301,7 +301,7 @@ protected constructor(scope: NoaScope) :
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return Pair(wrap(S), wrap(V))
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}
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public fun TensorType.autoGradient(variable: TensorType, retainGraph: Boolean): TensorType =
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public fun TensorType.autoGradient(variable: TensorType, retainGraph: Boolean = false): TensorType =
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wrap(JNoa.autoGradTensor(tensorHandle, variable.tensorHandle, retainGraph))
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public fun TensorType.autoHessian(variable: TensorType): TensorType =
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@ -0,0 +1,40 @@
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/*
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* Copyright 2018-2021 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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package space.kscience.kmath.noa
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import kotlin.test.Test
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import kotlin.test.assertEquals
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import kotlin.test.assertTrue
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internal fun NoaFloat.testingCopying(device: Device = Device.CPU): Unit {
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val array = (1..24).map { 10f * it * it }.toFloatArray()
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val shape = intArrayOf(2, 3, 4)
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val tensor = copyFromArray(array, shape = shape, device = device)
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val copyOfTensor = tensor.copy()
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tensor[intArrayOf(1, 2, 3)] = 0.1f
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assertTrue(copyOfTensor.copyToArray() contentEquals array)
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assertEquals(0.1f, tensor[intArrayOf(1, 2, 3)])
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if(device != Device.CPU){
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val normalCpu = randNormal(intArrayOf(2, 3))
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val normalGpu = normalCpu.copyToDevice(device)
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assertTrue(normalCpu.copyToArray() contentEquals normalGpu.copyToArray())
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val uniformGpu = randUniform(intArrayOf(3,2),device)
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val uniformCpu = uniformGpu.copyToDevice(Device.CPU)
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assertTrue(uniformGpu.copyToArray() contentEquals uniformCpu.copyToArray())
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}
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}
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class TestTensor {
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@Test
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fun testCopying(): Unit = NoaFloat {
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withCuda { device ->
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testingCopying(device)
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}
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}!!
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}
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@ -59,6 +59,4 @@ class TestUtils {
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}
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}!!
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}
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