forked from kscience/kmath
Moving benchmarks implementations to common
This commit is contained in:
Roland Grinis
parent
274d1a3105
commit
d599d1132b
@ -0,0 +1,24 @@
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@file:Suppress("NOTHING_TO_INLINE")
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package kscience.kmath.torch
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import kotlin.time.measureTime
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internal inline fun <T, PrimitiveArrayType, TorchTensorType : TorchTensorOverField<T>,
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TorchTensorAlgebraType : TorchTensorPartialDivisionAlgebra<T, PrimitiveArrayType, TorchTensorType>>
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TorchTensorAlgebraType.benchmarkMatMul(
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scale: Int,
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numWarmUp: Int,
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numIter: Int,
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fieldName: String,
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device: Device = Device.CPU
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): Unit {
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println("Benchmarking $scale x $scale $fieldName matrices on $device: ")
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setSeed(SEED)
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val lhs = randNormal(shape = intArrayOf(scale, scale), device = device)
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val rhs = randNormal(shape = intArrayOf(scale, scale), device = device)
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repeat(numWarmUp) { lhs dotAssign rhs }
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val measuredTime = measureTime { repeat(numIter) { lhs dotAssign rhs } }
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println(" ${measuredTime / numIter} p.o. with $numIter iterations")
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}
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@ -0,0 +1,132 @@
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@file:Suppress("NOTHING_TO_INLINE")
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package kscience.kmath.torch
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import kotlin.time.measureTime
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internal inline fun <TorchTensorType : TorchTensorOverField<Float>,
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TorchTensorAlgebraType : TorchTensorPartialDivisionAlgebra<Float, FloatArray, TorchTensorType>>
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TorchTensorAlgebraType.benchmarkRand(
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samples: Int,
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numWarmUp: Int,
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numIter: Int,
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device: Device,
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distName: String,
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initBock: TorchTensorAlgebraType.(IntArray, Device) -> TorchTensorType,
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runBlock: TorchTensorAlgebraType.(TorchTensorType) -> Unit
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): Unit{
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println("Benchmarking generation of $samples $distName samples on $device: ")
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setSeed(SEED)
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val shape = intArrayOf(samples)
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val tensor = this.initBock(shape,device)
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repeat(numWarmUp) { this.runBlock(tensor) }
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val measuredTime = measureTime { repeat(numIter) { this.runBlock(tensor) } }
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println(" ${measuredTime / numIter} p.o. with $numIter iterations")
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}
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internal inline fun <TorchTensorType : TorchTensorOverField<Float>,
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TorchTensorAlgebraType : TorchTensorPartialDivisionAlgebra<Float, FloatArray, TorchTensorType>>
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TorchTensorAlgebraType.benchmarkRandNormal(
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samples: Int,
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numWarmUp: Int,
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numIter: Int,
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device: Device = Device.CPU): Unit{
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benchmarkRand(
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samples,
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numWarmUp,
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numIter,
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device,
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"Normal",
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{sh, dc -> randNormal(shape = sh, device = dc)},
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{ten -> ten.randNormalAssign() }
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)
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}
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internal inline fun <TorchTensorType : TorchTensorOverField<Float>,
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TorchTensorAlgebraType : TorchTensorPartialDivisionAlgebra<Float, FloatArray, TorchTensorType>>
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TorchTensorAlgebraType.benchmarkRandUniform(
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samples: Int,
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numWarmUp: Int,
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numIter: Int,
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device: Device = Device.CPU): Unit{
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benchmarkRand(
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samples,
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numWarmUp,
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numIter,
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device,
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"Uniform",
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{sh, dc -> randUniform(shape = sh, device = dc)},
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{ten -> ten.randUniformAssign() }
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)
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}
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internal inline fun <TorchTensorType : TorchTensorOverField<Float>,
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TorchTensorAlgebraType : TorchTensorPartialDivisionAlgebra<Float, FloatArray, TorchTensorType>>
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TorchTensorAlgebraType.benchmarkRandIntegral(
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samples: Int,
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numWarmUp: Int,
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numIter: Int,
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device: Device = Device.CPU): Unit{
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benchmarkRand(
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samples,
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numWarmUp,
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numIter,
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device,
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"integer [0,100]",
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{sh, dc -> randIntegral(0f, 100f, shape = sh, device = dc)},
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{ten -> ten.randIntegralAssign(0f, 100f) }
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)
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}
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internal inline fun <TorchTensorType : TorchTensorOverField<Float>,
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TorchTensorAlgebraType : TorchTensorPartialDivisionAlgebra<Float, FloatArray, TorchTensorType>>
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TorchTensorAlgebraType.benchmarkingRand1(): Unit {
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benchmarkRandNormal(10, 10, 100000)
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benchmarkRandUniform(10, 10, 100000)
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benchmarkRandIntegral(10, 10, 100000)
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if(cudaAvailable()) {
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benchmarkRandNormal(10, 10, 100000, device = Device.CUDA(0))
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benchmarkRandUniform(10, 10, 100000, device = Device.CUDA(0))
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benchmarkRandIntegral(10, 10, 100000, device = Device.CUDA(0))
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}
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}
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internal inline fun <TorchTensorType : TorchTensorOverField<Float>,
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TorchTensorAlgebraType : TorchTensorPartialDivisionAlgebra<Float, FloatArray, TorchTensorType>>
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TorchTensorAlgebraType.benchmarkingRand3(): Unit {
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benchmarkRandNormal(1000, 10, 10000)
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benchmarkRandUniform(1000, 10, 10000)
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benchmarkRandIntegral(1000, 10, 10000)
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if(cudaAvailable()) {
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benchmarkRandNormal(1000, 10, 100000, device = Device.CUDA(0))
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benchmarkRandUniform(1000, 10, 100000, device = Device.CUDA(0))
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benchmarkRandIntegral(1000, 10, 100000, device = Device.CUDA(0))
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}
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}
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internal inline fun <TorchTensorType : TorchTensorOverField<Float>,
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TorchTensorAlgebraType : TorchTensorPartialDivisionAlgebra<Float, FloatArray, TorchTensorType>>
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TorchTensorAlgebraType.benchmarkingRand5(): Unit {
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benchmarkRandNormal(100000, 5, 100)
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benchmarkRandUniform(100000, 5, 100)
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benchmarkRandIntegral(100000, 5, 100)
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if(cudaAvailable()){
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benchmarkRandNormal(100000, 10, 100000, device = Device.CUDA(0))
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benchmarkRandUniform(100000, 10, 100000, device = Device.CUDA(0))
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benchmarkRandIntegral(100000, 10, 100000, device = Device.CUDA(0))
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}
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}
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internal inline fun <TorchTensorType : TorchTensorOverField<Float>,
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TorchTensorAlgebraType : TorchTensorPartialDivisionAlgebra<Float, FloatArray, TorchTensorType>>
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TorchTensorAlgebraType.benchmarkingRand7(): Unit {
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benchmarkRandNormal(10000000, 3, 20)
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benchmarkRandUniform(10000000, 3, 20)
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benchmarkRandIntegral(10000000, 3, 20)
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if(cudaAvailable()){
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benchmarkRandNormal(10000000, 10, 10000, device = Device.CUDA(0))
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benchmarkRandUniform(10000000, 10, 10000, device = Device.CUDA(0))
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benchmarkRandIntegral(10000000, 10, 10000, device = Device.CUDA(0))
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}
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}
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@ -1,20 +0,0 @@
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package kscience.kmath.torch
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import kotlin.test.Test
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class BenchmarkMatMultGPU {
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@Test
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fun benchmarkMatMultFloat20() =
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benchmarkingMatMultFloat(20, 10, 100000,
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device = Device.CUDA(0))
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@Test
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fun benchmarkMatMultFloat200() =
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benchmarkingMatMultFloat(200, 10, 10000,
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device = Device.CUDA(0))
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@Test
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fun benchmarkMatMultFloat2000() =
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benchmarkingMatMultFloat(2000, 10, 1000,
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device = Device.CUDA(0))
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}
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package kscience.kmath.torch
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import kotlin.test.Test
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class BenchmarkRandomGeneratorsGPU {
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@Test
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fun benchmarkRandNormal1() =
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benchmarkingRandNormal(10, 10, 100000,
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device = Device.CUDA(0))
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@Test
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fun benchmarkRandUniform1() =
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benchmarkingRandUniform(10, 10, 100000,
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device = Device.CUDA(0))
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@Test
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fun benchmarkRandIntegral1() =
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benchmarkingRandIntegral(10, 10, 100000,
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device = Device.CUDA(0))
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@Test
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fun benchmarkRandNormal3() =
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benchmarkingRandNormal(1000, 10, 100000,
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device = Device.CUDA(0))
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@Test
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fun benchmarkRandUniform3() =
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benchmarkingRandUniform(1000, 10, 100000,
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device = Device.CUDA(0))
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@Test
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fun benchmarkRandIntegral3() =
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benchmarkingRandIntegral(1000, 10, 100000,
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device = Device.CUDA(0))
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@Test
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fun benchmarkRandNormal5() =
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benchmarkingRandNormal(100000, 10, 100000,
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device = Device.CUDA(0))
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@Test
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fun benchmarkRandUniform5() =
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benchmarkingRandUniform(100000, 10, 100000,
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device = Device.CUDA(0))
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@Test
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fun benchmarkRandIntegral5() =
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benchmarkingRandIntegral(100000, 10, 100000,
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device = Device.CUDA(0))
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@Test
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fun benchmarkRandNormal7() =
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benchmarkingRandNormal(10000000, 10, 10000,
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device = Device.CUDA(0))
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@Test
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fun benchmarkRandUniform7() =
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benchmarkingRandUniform(10000000, 10, 10000,
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device = Device.CUDA(0))
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@Test
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fun benchmarkRandIntegral7() =
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benchmarkingRandIntegral(10000000, 10, 10000,
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device = Device.CUDA(0))
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}
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@ -0,0 +1,27 @@
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package kscience.kmath.torch
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import kotlin.test.Test
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internal class BenchmarkMatMul {
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@Test
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fun benchmarkMatMulDouble() = TorchTensorRealAlgebra {
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benchmarkMatMul(20, 10, 100000, "Real")
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benchmarkMatMul(200, 10, 10000, "Real")
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benchmarkMatMul(2000, 3, 20, "Real")
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}
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@Test
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fun benchmarkMatMulFloat() = TorchTensorFloatAlgebra {
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benchmarkMatMul(20, 10, 100000, "Float")
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benchmarkMatMul(200, 10, 10000, "Float")
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benchmarkMatMul(2000, 3, 20, "Float")
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if (cudaAvailable()) {
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benchmarkMatMul(20, 10, 100000, "Float", Device.CUDA(0))
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benchmarkMatMul(200, 10, 10000, "Float", Device.CUDA(0))
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benchmarkMatMul(2000, 10, 1000, "Float", Device.CUDA(0))
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}
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}
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}
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@ -1,66 +0,0 @@
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package kscience.kmath.torch
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import kotlin.test.Test
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import kotlin.time.measureTime
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internal fun benchmarkingMatMultDouble(
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scale: Int,
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numWarmUp: Int,
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numIter: Int,
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device: Device = Device.CPU
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): Unit {
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TorchTensorRealAlgebra {
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println("Benchmarking $scale x $scale matrices over Double's on $device: ")
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setSeed(SEED)
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val lhs = randNormal(shape = intArrayOf(scale, scale), device = device)
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val rhs = randNormal(shape = intArrayOf(scale, scale), device = device)
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repeat(numWarmUp) { lhs dotAssign rhs }
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val measuredTime = measureTime { repeat(numIter) { lhs dotAssign rhs } }
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println(" ${measuredTime / numIter} p.o. with $numIter iterations")
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}
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}
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internal fun benchmarkingMatMultFloat(
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scale: Int,
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numWarmUp: Int,
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numIter: Int,
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device: Device = Device.CPU
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): Unit {
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TorchTensorFloatAlgebra {
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println("Benchmarking $scale x $scale matrices over Float's on $device: ")
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setSeed(SEED)
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val lhs = randNormal(shape = intArrayOf(scale, scale), device = device)
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val rhs = randNormal(shape = intArrayOf(scale, scale), device = device)
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repeat(numWarmUp) { lhs dotAssign rhs }
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val measuredTime = measureTime { repeat(numIter) { lhs dotAssign rhs } }
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println(" ${measuredTime / numIter} p.o. with $numIter iterations")
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}
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}
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internal class BenchmarkMatMult {
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@Test
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fun benchmarkMatMultDouble20() =
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benchmarkingMatMultDouble(20, 10, 100000)
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@Test
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fun benchmarkMatMultFloat20() =
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benchmarkingMatMultFloat(20, 10, 100000)
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@Test
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fun benchmarkMatMultDouble200() =
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benchmarkingMatMultDouble(200, 10, 10000)
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@Test
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fun benchmarkMatMultFloat200() =
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benchmarkingMatMultFloat(200, 10, 10000)
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@Test
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fun benchmarkMatMultDouble2000() =
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benchmarkingMatMultDouble(2000, 3, 20)
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@Test
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fun benchmarkMatMultFloat2000() =
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benchmarkingMatMultFloat(2000, 3, 20)
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}
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@ -1,107 +1,28 @@
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package kscience.kmath.torch
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package kscience.kmath.torch
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import kotlin.test.Test
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import kotlin.test.Test
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import kotlin.time.measureTime
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internal fun benchmarkingRandNormal(
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samples: Int,
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numWarmUp: Int,
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numIter: Int,
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device: Device = Device.CPU): Unit
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{
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TorchTensorFloatAlgebra{
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println("Benchmarking generation of $samples Normal samples on $device: ")
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setSeed(SEED)
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val shape = intArrayOf(samples)
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val tensor = randNormal(shape = shape, device = device)
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repeat(numWarmUp) { tensor.randNormalAssign() }
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val measuredTime = measureTime { repeat(numIter) { tensor.randNormalAssign() } }
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println(" ${measuredTime / numIter} p.o. with $numIter iterations")
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}
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}
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internal fun benchmarkingRandUniform(
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samples: Int,
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numWarmUp: Int,
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numIter: Int,
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device: Device = Device.CPU): Unit
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{
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TorchTensorFloatAlgebra{
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println("Benchmarking generation of $samples Uniform samples on $device: ")
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setSeed(SEED)
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val shape = intArrayOf(samples)
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val tensor = randUniform(shape = shape, device = device)
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repeat(numWarmUp) { tensor.randUniformAssign() }
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val measuredTime = measureTime { repeat(numIter) { tensor.randUniformAssign() } }
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println(" ${measuredTime / numIter} p.o. with $numIter iterations")
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}
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}
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internal fun benchmarkingRandIntegral(
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samples: Int,
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numWarmUp: Int,
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numIter: Int,
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device: Device = Device.CPU): Unit
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{
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TorchTensorIntAlgebra {
|
|
||||||
println("Benchmarking generation of $samples integer [0,100] samples on $device: ")
|
|
||||||
setSeed(SEED)
|
|
||||||
val shape = intArrayOf(samples)
|
|
||||||
val tensor = randIntegral(0,100, shape = shape, device = device)
|
|
||||||
repeat(numWarmUp) { tensor.randIntegralAssign(0,100) }
|
|
||||||
val measuredTime = measureTime { repeat(numIter) { tensor.randIntegralAssign(0,100) } }
|
|
||||||
println(" ${measuredTime / numIter} p.o. with $numIter iterations")
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
internal class BenchmarkRandomGenerators {
|
internal class BenchmarkRandomGenerators {
|
||||||
|
|
||||||
@Test
|
@Test
|
||||||
fun benchmarkRandNormal1() =
|
fun benchmarkRand1() = TorchTensorFloatAlgebra{
|
||||||
benchmarkingRandNormal(10, 10, 100000)
|
benchmarkingRand1()
|
||||||
|
}
|
||||||
|
|
||||||
@Test
|
@Test
|
||||||
fun benchmarkRandUniform1() =
|
fun benchmarkRand3() = TorchTensorFloatAlgebra{
|
||||||
benchmarkingRandUniform(10, 10, 100000)
|
benchmarkingRand3()
|
||||||
|
}
|
||||||
|
|
||||||
@Test
|
@Test
|
||||||
fun benchmarkRandIntegral1() =
|
fun benchmarkRand5() = TorchTensorFloatAlgebra{
|
||||||
benchmarkingRandIntegral(10, 10, 100000)
|
benchmarkingRand5()
|
||||||
|
}
|
||||||
|
|
||||||
@Test
|
@Test
|
||||||
fun benchmarkRandNormal3() =
|
fun benchmarkRand7() = TorchTensorFloatAlgebra{
|
||||||
benchmarkingRandNormal(1000, 10, 10000)
|
benchmarkingRand7()
|
||||||
|
}
|
||||||
@Test
|
|
||||||
fun benchmarkRandUniform3() =
|
|
||||||
benchmarkingRandUniform(1000, 10, 10000)
|
|
||||||
|
|
||||||
@Test
|
|
||||||
fun benchmarkRandIntegral3() =
|
|
||||||
benchmarkingRandIntegral(1000, 10, 10000)
|
|
||||||
|
|
||||||
@Test
|
|
||||||
fun benchmarkRandNormal5() =
|
|
||||||
benchmarkingRandNormal(100000, 5, 100)
|
|
||||||
|
|
||||||
@Test
|
|
||||||
fun benchmarkRandUniform5() =
|
|
||||||
benchmarkingRandUniform(100000, 5, 100)
|
|
||||||
|
|
||||||
@Test
|
|
||||||
fun benchmarkRandIntegral5() =
|
|
||||||
benchmarkingRandIntegral(100000, 5, 100)
|
|
||||||
|
|
||||||
@Test
|
|
||||||
fun benchmarkRandNormal7() =
|
|
||||||
benchmarkingRandNormal(10000000, 3, 20)
|
|
||||||
|
|
||||||
@Test
|
|
||||||
fun benchmarkRandUniform7() =
|
|
||||||
benchmarkingRandUniform(10000000, 3, 20)
|
|
||||||
|
|
||||||
@Test
|
|
||||||
fun benchmarkRandIntegral7() =
|
|
||||||
benchmarkingRandIntegral(10000000, 3, 20)
|
|
||||||
|
|
||||||
}
|
}
|
||||||
Loading…
Reference in New Issue
Block a user