forked from kscience/kmath
Drop duplicate test from DorBenchmark
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@ -15,10 +15,8 @@ import space.kscience.kmath.linear.invoke
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import space.kscience.kmath.linear.linearSpace
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import space.kscience.kmath.linear.linearSpace
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import space.kscience.kmath.multik.multikAlgebra
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import space.kscience.kmath.multik.multikAlgebra
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import space.kscience.kmath.operations.DoubleField
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import space.kscience.kmath.operations.DoubleField
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import space.kscience.kmath.operations.invoke
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import space.kscience.kmath.structures.Buffer
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import space.kscience.kmath.structures.Buffer
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import space.kscience.kmath.tensorflow.produceWithTF
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import space.kscience.kmath.tensorflow.produceWithTF
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
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import space.kscience.kmath.tensors.core.tensorAlgebra
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import space.kscience.kmath.tensors.core.tensorAlgebra
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import kotlin.random.Random
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import kotlin.random.Random
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@ -36,9 +34,6 @@ internal class DotBenchmark {
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random.nextDouble()
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random.nextDouble()
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}
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}
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val tensor1 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12224)
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val tensor2 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12225)
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val cmMatrix1 = CMLinearSpace { matrix1.toCM() }
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val cmMatrix1 = CMLinearSpace { matrix1.toCM() }
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val cmMatrix2 = CMLinearSpace { matrix2.toCM() }
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val cmMatrix2 = CMLinearSpace { matrix2.toCM() }
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@ -48,10 +43,10 @@ internal class DotBenchmark {
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@Benchmark
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@Benchmark
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fun tfDot(blackhole: Blackhole){
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fun tfDot(blackhole: Blackhole) {
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blackhole.consume(
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blackhole.consume(
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DoubleField.produceWithTF {
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DoubleField.produceWithTF {
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tensor1 dot tensor2
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matrix1 dot matrix1
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}
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}
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)
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)
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}
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}
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@ -95,9 +90,4 @@ internal class DotBenchmark {
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fun doubleDot(blackhole: Blackhole) = with(DoubleField.linearSpace) {
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fun doubleDot(blackhole: Blackhole) = with(DoubleField.linearSpace) {
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blackhole.consume(matrix1 dot matrix2)
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blackhole.consume(matrix1 dot matrix2)
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}
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}
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@Benchmark
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fun doubleTensorDot(blackhole: Blackhole) = DoubleTensorAlgebra.invoke {
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blackhole.consume(tensor1 dot tensor2)
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}
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}
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}
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