forked from kscience/kmath
Add TensorFlow prototype
This commit is contained in:
parent
41fc6b4dd9
commit
7bb66f6a00
@ -18,6 +18,7 @@
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- Integration between `MST` and Symja `IExpr`
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- Complex power
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- Separate methods for UInt, Int and Number powers. NaN safety.
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- Tensorflow prototype
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### Changed
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- Exponential operations merged with hyperbolic functions
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29
README.md
29
README.md
@ -50,35 +50,6 @@ module definitions below. The module stability could have the following levels:
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with [binary-compatibility-validator](https://github.com/Kotlin/binary-compatibility-validator) tool.
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* **STABLE**. The API stabilized. Breaking changes are allowed only in major releases.
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<!--Current feature list is [here](/docs/features.md)-->
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<!--* **Array-like structures** Full support of many-dimensional array-like structures -->
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<!--including mixed arithmetic operations and function operations over arrays and numbers (with the added benefit of static type checking).-->
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<!--* **Histograms** Fast multi-dimensional histograms.-->
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<!--* **Streaming** Streaming operations on mathematical objects and objects buffers.-->
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<!--* **Type-safe dimensions** Type-safe dimensions for matrix operations.-->
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<!--* **Commons-math wrapper** It is planned to gradually wrap most parts of -->
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<!--[Apache commons-math](http://commons.apache.org/proper/commons-math/) library in Kotlin code and maybe rewrite some -->
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<!--parts to better suit the Kotlin programming paradigm, however there is no established roadmap for that. Feel free to -->
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<!--submit a feature request if you want something to be implemented first.-->
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<!-- -->
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<!--## Planned features-->
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<!--* **Messaging** A mathematical notation to support multi-language and multi-node communication for mathematical tasks.-->
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<!--* **Array statistics** -->
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<!--* **Integration** Univariate and multivariate integration framework.-->
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<!--* **Probability and distributions**-->
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<!--* **Fitting** Non-linear curve fitting facilities-->
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## Modules
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<hr/>
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29
docs/templates/README-TEMPLATE.md
vendored
29
docs/templates/README-TEMPLATE.md
vendored
@ -50,35 +50,6 @@ module definitions below. The module stability could have the following levels:
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with [binary-compatibility-validator](https://github.com/Kotlin/binary-compatibility-validator) tool.
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* **STABLE**. The API stabilized. Breaking changes are allowed only in major releases.
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<!--Current feature list is [here](/docs/features.md)-->
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<!--* **Array-like structures** Full support of many-dimensional array-like structures -->
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<!--including mixed arithmetic operations and function operations over arrays and numbers (with the added benefit of static type checking).-->
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<!--* **Histograms** Fast multi-dimensional histograms.-->
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<!--* **Streaming** Streaming operations on mathematical objects and objects buffers.-->
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<!--* **Type-safe dimensions** Type-safe dimensions for matrix operations.-->
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<!--* **Commons-math wrapper** It is planned to gradually wrap most parts of -->
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<!--[Apache commons-math](http://commons.apache.org/proper/commons-math/) library in Kotlin code and maybe rewrite some -->
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<!--parts to better suit the Kotlin programming paradigm, however there is no established roadmap for that. Feel free to -->
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<!--submit a feature request if you want something to be implemented first.-->
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<!-- -->
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<!--## Planned features-->
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<!--* **Messaging** A mathematical notation to support multi-language and multi-node communication for mathematical tasks.-->
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<!--* **Array statistics** -->
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<!--* **Integration** Univariate and multivariate integration framework.-->
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<!--* **Probability and distributions**-->
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<!--* **Fitting** Non-linear curve fitting facilities-->
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## Modules
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$modules
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@ -12,4 +12,4 @@ org.gradle.configureondemand=true
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org.gradle.parallel=true
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org.gradle.jvmargs=-XX:MaxMetaspaceSize=1G
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toolsVersion=0.10.9-kotlin-1.6.10
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toolsVersion=0.11.1-kotlin-1.6.10
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@ -6,8 +6,8 @@ description = "Google tensorflow connector"
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dependencies {
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api(project(":kmath-tensors"))
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api("org.tensorflow:tensorflow-core-api:0.3.3")
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testImplementation("org.tensorflow:tensorflow-core-platform:0.3.3")
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api("org.tensorflow:tensorflow-core-api:0.4.0")
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testImplementation("org.tensorflow:tensorflow-core-platform:0.4.0")
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}
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readme {
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@ -11,6 +11,7 @@ import space.kscience.kmath.nd.DefaultStrides
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import space.kscience.kmath.nd.Shape
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import space.kscience.kmath.nd.StructureND
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import space.kscience.kmath.operations.DoubleField
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import space.kscience.kmath.operations.PowerOperations
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public class DoubleTensorFlowOutput(
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graph: Graph,
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@ -23,7 +24,7 @@ public class DoubleTensorFlowOutput(
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public class DoubleTensorFlowAlgebra internal constructor(
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graph: Graph,
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) : TensorFlowAlgebra<Double, TFloat64, DoubleField>(graph) {
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) : TensorFlowAlgebra<Double, TFloat64, DoubleField>(graph), PowerOperations<StructureND<Double>> {
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override val elementAlgebra: DoubleField get() = DoubleField
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@ -57,9 +58,22 @@ public class DoubleTensorFlowAlgebra internal constructor(
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override fun const(value: Double): Constant<TFloat64> = ops.constant(value)
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override fun divide(
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left: StructureND<Double>,
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right: StructureND<Double>,
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): TensorFlowOutput<Double, TFloat64> = left.operate(right) { l, r ->
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ops.math.div(l, r)
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}
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override fun power(arg: StructureND<Double>, pow: Number): TensorFlowOutput<Double, TFloat64> =
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arg.operate { ops.math.pow(it, const(pow.toDouble())) }
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}
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/**
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* Compute a tensor with TensorFlow in a single run.
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*
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* The resulting tensor is available outside of scope
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*/
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public fun DoubleField.produceWithTF(
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block: DoubleTensorFlowAlgebra.() -> StructureND<Double>,
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): StructureND<Double> = Graph().use { graph ->
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@ -67,6 +81,11 @@ public fun DoubleField.produceWithTF(
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scope.export(scope.block())
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}
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/**
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* Compute several outputs with TensorFlow in a single run.
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*
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* The resulting tensors are available outside of scope
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*/
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public fun DoubleField.produceMapWithTF(
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block: DoubleTensorFlowAlgebra.() -> Map<Symbol, StructureND<Double>>,
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): Map<Symbol, StructureND<Double>> = Graph().use { graph ->
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@ -12,6 +12,7 @@ import org.tensorflow.op.core.Max
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import org.tensorflow.op.core.Min
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import org.tensorflow.op.core.Sum
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import org.tensorflow.types.TInt32
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import org.tensorflow.types.family.TNumber
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import org.tensorflow.types.family.TType
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import space.kscience.kmath.misc.PerformancePitfall
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import space.kscience.kmath.misc.UnstableKMathAPI
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@ -29,6 +30,9 @@ internal val <T> NdArray<T>.scalar: T get() = getObject()
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public sealed interface TensorFlowTensor<T> : Tensor<T>
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/**
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* Static (eager) in-memory TensorFlow tensor
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*/
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@JvmInline
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public value class TensorFlowArray<T>(public val tensor: NdArray<T>) : Tensor<T> {
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override val shape: Shape get() = tensor.shape().asArray().toIntArray()
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@ -42,6 +46,11 @@ public value class TensorFlowArray<T>(public val tensor: NdArray<T>) : Tensor<T>
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}
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}
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/**
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* Lazy graph-based TensorFlow tensor. The tensor is actualized on call.
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*
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* If the tensor is used for intermediate operations, actualizing it could impact performance.
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*/
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public abstract class TensorFlowOutput<T, TT : TType>(
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protected val graph: Graph,
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output: Output<TT>,
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@ -72,11 +81,11 @@ public abstract class TensorFlowOutput<T, TT : TType>(
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}
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public abstract class TensorFlowAlgebra<T, TT : TType, A : Ring<T>> internal constructor(
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public abstract class TensorFlowAlgebra<T, TT : TNumber, A : Ring<T>> internal constructor(
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protected val graph: Graph,
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) : TensorAlgebra<T, A> {
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protected val ops: Ops by lazy { Ops.create(graph) }
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public val ops: Ops by lazy { Ops.create(graph) }
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protected abstract fun StructureND<T>.asTensorFlow(): TensorFlowOutput<T, TT>
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@ -87,7 +96,10 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A : Ring<T>> internal con
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override fun StructureND<T>.valueOrNull(): T? = if (shape contentEquals intArrayOf(1))
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get(Shape(0)) else null
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private inline fun StructureND<T>.biOp(
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/**
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* Perform binary lazy operation on tensor. Both arguments are implicitly converted
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*/
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public fun StructureND<T>.operate(
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other: StructureND<T>,
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operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>,
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): TensorFlowOutput<T, TT> {
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@ -96,7 +108,7 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A : Ring<T>> internal con
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return operation(left, right).asOutput().wrap()
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}
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private inline fun T.biOp(
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public fun T.operate(
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other: StructureND<T>,
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operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>,
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): TensorFlowOutput<T, TT> {
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@ -105,7 +117,7 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A : Ring<T>> internal con
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return operation(left, right).asOutput().wrap()
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}
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private inline fun StructureND<T>.biOp(
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public fun StructureND<T>.operate(
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value: T,
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operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>,
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): TensorFlowOutput<T, TT> {
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@ -114,7 +126,7 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A : Ring<T>> internal con
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return operation(left, right).asOutput().wrap()
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}
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private inline fun Tensor<T>.inPlaceOp(
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public fun Tensor<T>.operateInPlace(
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other: StructureND<T>,
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operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>,
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): Unit {
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@ -124,7 +136,7 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A : Ring<T>> internal con
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origin.output = operation(left, right).asOutput()
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}
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private inline fun Tensor<T>.inPlaceOp(
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public fun Tensor<T>.operateInPlace(
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value: T,
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operation: (left: Operand<TT>, right: Operand<TT>) -> Operand<TT>,
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): Unit {
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@ -134,58 +146,58 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A : Ring<T>> internal con
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origin.output = operation(left, right).asOutput()
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}
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private inline fun StructureND<T>.unOp(operation: (Operand<TT>) -> Operand<TT>): TensorFlowOutput<T, TT> =
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public fun StructureND<T>.operate(operation: (Operand<TT>) -> Operand<TT>): TensorFlowOutput<T, TT> =
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operation(asTensorFlow().output).asOutput().wrap()
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override fun T.plus(arg: StructureND<T>): TensorFlowOutput<T, TT> = biOp(arg, ops.math::add)
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override fun T.plus(arg: StructureND<T>): TensorFlowOutput<T, TT> = operate(arg, ops.math::add)
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override fun StructureND<T>.plus(arg: T): TensorFlowOutput<T, TT> = biOp(arg, ops.math::add)
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override fun StructureND<T>.plus(arg: T): TensorFlowOutput<T, TT> = operate(arg, ops.math::add)
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override fun StructureND<T>.plus(arg: StructureND<T>): TensorFlowOutput<T, TT> = biOp(arg, ops.math::add)
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override fun StructureND<T>.plus(arg: StructureND<T>): TensorFlowOutput<T, TT> = operate(arg, ops.math::add)
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override fun Tensor<T>.plusAssign(value: T): Unit = inPlaceOp(value, ops.math::add)
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override fun Tensor<T>.plusAssign(value: T): Unit = operateInPlace(value, ops.math::add)
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override fun Tensor<T>.plusAssign(arg: StructureND<T>): Unit = inPlaceOp(arg, ops.math::add)
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override fun Tensor<T>.plusAssign(arg: StructureND<T>): Unit = operateInPlace(arg, ops.math::add)
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override fun StructureND<T>.minus(arg: T): TensorFlowOutput<T, TT> = biOp(arg, ops.math::sub)
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override fun StructureND<T>.minus(arg: T): TensorFlowOutput<T, TT> = operate(arg, ops.math::sub)
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override fun StructureND<T>.minus(arg: StructureND<T>): TensorFlowOutput<T, TT> = biOp(arg, ops.math::sub)
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override fun StructureND<T>.minus(arg: StructureND<T>): TensorFlowOutput<T, TT> = operate(arg, ops.math::sub)
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override fun T.minus(arg: StructureND<T>): Tensor<T> = biOp(arg, ops.math::sub)
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override fun T.minus(arg: StructureND<T>): Tensor<T> = operate(arg, ops.math::sub)
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override fun Tensor<T>.minusAssign(value: T): Unit = inPlaceOp(value, ops.math::sub)
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override fun Tensor<T>.minusAssign(value: T): Unit = operateInPlace(value, ops.math::sub)
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override fun Tensor<T>.minusAssign(arg: StructureND<T>): Unit = inPlaceOp(arg, ops.math::sub)
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override fun Tensor<T>.minusAssign(arg: StructureND<T>): Unit = operateInPlace(arg, ops.math::sub)
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override fun T.times(arg: StructureND<T>): TensorFlowOutput<T, TT> = biOp(arg, ops.math::mul)
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override fun T.times(arg: StructureND<T>): TensorFlowOutput<T, TT> = operate(arg, ops.math::mul)
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override fun StructureND<T>.times(arg: T): TensorFlowOutput<T, TT> = biOp(arg, ops.math::mul)
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override fun StructureND<T>.times(arg: T): TensorFlowOutput<T, TT> = operate(arg, ops.math::mul)
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override fun StructureND<T>.times(arg: StructureND<T>): TensorFlowOutput<T, TT> = biOp(arg, ops.math::mul)
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override fun StructureND<T>.times(arg: StructureND<T>): TensorFlowOutput<T, TT> = operate(arg, ops.math::mul)
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override fun Tensor<T>.timesAssign(value: T): Unit = inPlaceOp(value, ops.math::mul)
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override fun Tensor<T>.timesAssign(value: T): Unit = operateInPlace(value, ops.math::mul)
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override fun Tensor<T>.timesAssign(arg: StructureND<T>): Unit = inPlaceOp(arg, ops.math::mul)
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override fun Tensor<T>.timesAssign(arg: StructureND<T>): Unit = operateInPlace(arg, ops.math::mul)
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override fun StructureND<T>.unaryMinus(): TensorFlowOutput<T, TT> = unOp(ops.math::neg)
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override fun StructureND<T>.unaryMinus(): TensorFlowOutput<T, TT> = operate(ops.math::neg)
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override fun Tensor<T>.get(i: Int): Tensor<T> = unOp {
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override fun Tensor<T>.get(i: Int): Tensor<T> = operate {
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TODO("Not yet implemented")
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}
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override fun Tensor<T>.transpose(i: Int, j: Int): Tensor<T> = unOp {
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override fun Tensor<T>.transpose(i: Int, j: Int): Tensor<T> = operate {
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ops.linalg.transpose(it, ops.constant(intArrayOf(i, j)))
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}
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override fun Tensor<T>.view(shape: IntArray): Tensor<T> = unOp {
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override fun Tensor<T>.view(shape: IntArray): Tensor<T> = operate {
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ops.reshape(it, ops.constant(shape))
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}
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override fun Tensor<T>.viewAs(other: StructureND<T>): Tensor<T> = biOp(other) { l, r ->
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override fun Tensor<T>.viewAs(other: StructureND<T>): Tensor<T> = operate(other) { l, r ->
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ops.reshape(l, ops.shape(r))
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}
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override fun StructureND<T>.dot(other: StructureND<T>): TensorFlowOutput<T, TT> = biOp(other) { l, r ->
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override fun StructureND<T>.dot(other: StructureND<T>): TensorFlowOutput<T, TT> = operate(other) { l, r ->
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ops.linalg.matMul(
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if (l.asTensor().shape().numDimensions() == 1) ops.expandDims(l, ops.constant(0)) else l,
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if (r.asTensor().shape().numDimensions() == 1) ops.expandDims(r, ops.constant(-1)) else r)
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@ -196,31 +208,31 @@ public abstract class TensorFlowAlgebra<T, TT : TType, A : Ring<T>> internal con
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offset: Int,
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dim1: Int,
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dim2: Int,
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): TensorFlowOutput<T, TT> = diagonalEntries.unOp {
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TODO()
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): TensorFlowOutput<T, TT> = diagonalEntries.operate {
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TODO("Not yet implemented")
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}
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override fun StructureND<T>.sum(): T = unOp {
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override fun StructureND<T>.sum(): T = operate {
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ops.sum(it, ops.constant(intArrayOf()))
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}.value()
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override fun StructureND<T>.sum(dim: Int, keepDim: Boolean): TensorFlowOutput<T, TT> = unOp {
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override fun StructureND<T>.sum(dim: Int, keepDim: Boolean): TensorFlowOutput<T, TT> = operate {
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ops.sum(it, ops.constant(dim), Sum.keepDims(keepDim))
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}
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override fun StructureND<T>.min(): T = unOp {
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override fun StructureND<T>.min(): T = operate {
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ops.min(it, ops.constant(intArrayOf()))
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}.value()
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override fun StructureND<T>.min(dim: Int, keepDim: Boolean): Tensor<T> = unOp {
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override fun StructureND<T>.min(dim: Int, keepDim: Boolean): Tensor<T> = operate {
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ops.min(it, ops.constant(dim), Min.keepDims(keepDim))
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}
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override fun StructureND<T>.max(): T = unOp {
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override fun StructureND<T>.max(): T = operate {
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ops.max(it, ops.constant(intArrayOf()))
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}.value()
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override fun StructureND<T>.max(dim: Int, keepDim: Boolean): Tensor<T> = unOp {
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override fun StructureND<T>.max(dim: Int, keepDim: Boolean): Tensor<T> = operate {
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ops.max(it, ops.constant(dim), Max.keepDims(keepDim))
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}
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@ -0,0 +1,23 @@
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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.tensorflow
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import org.tensorflow.types.family.TNumber
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import space.kscience.kmath.nd.StructureND
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import space.kscience.kmath.operations.Ring
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import space.kscience.kmath.operations.TrigonometricOperations
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//
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// TODO add other operations
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public fun <T, TT : TNumber, A> TensorFlowAlgebra<T, TT, A>.sin(
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arg: StructureND<T>,
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): TensorFlowOutput<T, TT> where A : TrigonometricOperations<T>, A : Ring<T> = arg.operate { ops.math.sin(it) }
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public fun <T, TT : TNumber, A> TensorFlowAlgebra<T, TT, A>.cos(
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arg: StructureND<T>,
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): TensorFlowOutput<T, TT> where A : TrigonometricOperations<T>, A : Ring<T> = arg.operate { ops.math.cos(it) }
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@ -1,9 +1,10 @@
|
||||
package space.kscience.kmath.tensorflow
|
||||
|
||||
import org.junit.jupiter.api.Test
|
||||
import space.kscience.kmath.nd.StructureND
|
||||
import space.kscience.kmath.nd.get
|
||||
import space.kscience.kmath.nd.structureND
|
||||
import space.kscience.kmath.operations.DoubleField
|
||||
import kotlin.test.assertEquals
|
||||
|
||||
class DoubleTensorFlowOps {
|
||||
@Test
|
||||
@ -13,7 +14,20 @@ class DoubleTensorFlowOps {
|
||||
|
||||
initial + (initial * 2.0)
|
||||
}
|
||||
println(StructureND.toString(res))
|
||||
//println(StructureND.toString(res))
|
||||
assertEquals(3.0, res[0, 0])
|
||||
}
|
||||
|
||||
@Test
|
||||
fun extensionOps(){
|
||||
val res = DoubleField.produceWithTF {
|
||||
val i = structureND(2, 2) { 0.5 }
|
||||
|
||||
sin(i).pow(2) + cos(i).pow(2)
|
||||
}
|
||||
|
||||
assertEquals(1.0, res[0,0],0.01)
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user