Packages

  • package root
    Definition Classes
    root
  • package lamp

    Lamp provides utilities to build state of the art machine learning applications

    Lamp provides utilities to build state of the art machine learning applications

    Overview

    Notable types and packages:

    • lamp.STen is a memory managed wrapper around aten.ATen, an off the heap, native n-dimensionl array backed by libtorch.
    • lamp.autograd implements reverse mode automatic differentiation.
    • lamp.nn contains neural network building blocks, see e.g. lamp.nn.Linear.
    • lamp.data.IOLoops implements a training loop and other data related abstractions.
    • lamp.knn implements k-nearest neighbor search on the CPU and GPU
    • lamp.umap.Umap implements the UMAP dimension reduction algorithm
    • lamp.onnx implements serialization of computation graphs into ONNX format
    • lamp.io contains CSV and NPY readers
    How to get data into lamp

    Use one of the file readers in lamp.io or one of the factories in lamp.STen$.

    How to define a custom neural network layer

    See the documentation on lamp.nn.GenericModule

    How to compose neural network layers

    See the documentation on lamp.nn

    How to train models

    See the training loops in lamp.data.IOLoops

    Definition Classes
    root
  • package autograd

    Implements reverse mode automatic differentiaton

    Implements reverse mode automatic differentiaton

    The main types in this package are lamp.autograd.Variable and lamp.autograd.Op. The computational graph built by this package consists of vertices representing values (as lamp.autograd.Variable) and vertices representing operations (as lamp.autograd.Op).

    Variables contain the value of a Rn => Rm function. Variables may also contain the partial derivative of their argument with respect to a single scalar. A Variable whose value is a scalar (m=1) can trigger the computation of partial derivatives of all the intermediate upstream Variables. Computing partial derivatives with respect to non-scalar variables is not supported.

    A constant Variable may be created with the const or param factory method in this package. const may be used for constants which do not need their partial derivatives to be computed. param on the other hand create Variables which will fill in their partial derivatives. Further variables may be created by the methods in this class, eventually expressing more complex Rn => Rm functions.

    Example
    lamp.Scope.root{ implicit scope =>
      // x is constant (depends on no other variables) and won't compute a partial derivative
      val x = lamp.autograd.const(STen.eye(3, STenOptions.d))
      // y is constant but will compute a partial derivative
      val y = lamp.autograd.param(STen.ones(List(3,3), STenOptions.d))
    
      // z is a Variable with x and y dependencies
      val z = x+y
    
      // w is a Variable with z as a direct and x, y as transient dependencies
      val w = z.sum
      // w is a scalar (number of elements is 1), thus we can call backprop() on it.
      // calling backprop will fill out the partial derivatives of the upstream variables
      w.backprop()
    
      // partialDerivative is empty since we created `x` with `const`
      assert(x.partialDerivative.isEmpty)
    
      // `y`'s partial derivatie is defined and is computed
      // it holds `y`'s partial derivative with respect to `w`, the scalar which we called backprop() on
      assert(y.partialDerivative.isDefined)
    
    }

    This package may be used to compute the derivative of any function, provided the function can be composed out of the provided methods. A particular use case is gradient based optimization.

    Definition Classes
    lamp
    See also

    https://arxiv.org/pdf/1811.05031.pdf for a review of the algorithm

    lamp.autograd.Op for how to implement a new operation

  • package data
    Definition Classes
    lamp
  • package distributed
    Definition Classes
    lamp
  • package extratrees
    Definition Classes
    lamp
  • package knn
    Definition Classes
    lamp
  • package nn

    Provides building blocks for neural networks

    Provides building blocks for neural networks

    Notable types:

    Optimizers:

    Modules facilitating composing other modules:

    • nn.Sequential composes a homogenous list of modules (analogous to List)
    • nn.sequence composes a heterogeneous list of modules (analogous to tuples)
    • nn.EitherModule composes two modules in a scala.Either

    Examples of neural network building blocks, layers etc:

    Definition Classes
    lamp
  • package onnx
    Definition Classes
    lamp
  • package saddle
    Definition Classes
    lamp
  • package umap
    Definition Classes
    lamp
  • package util
    Definition Classes
    lamp
  • BufferPair
  • CPU
  • CudaDevice
  • Device
  • DoublePrecision
  • EmptyMovable
  • FloatingPointPrecision
  • HalfPrecision
  • MPS
  • Movable
  • NcclUniqueId
  • STen
  • STenOptions
  • Scope
  • SinglePrecision
  • TensorHelpers

final class Scope extends AnyRef

Faciliates memory management of off-heap data structures.

Tracks allocations of aten.Tensor and aten.TensorOption instances.

aten.Tensor and aten.TensorOption instances are not freed up by the garbage collector. Lamp implements zoned memory management around these object. The managed counterpart of aten.Tensor is lamp.STen, while for aten.TensorOption it is lamp.STenOptions.

One can only create a lamp.STen instance with a lamp.Scope in implicit scope.

Create new scopes with lamp.Scope.root, lamp.Scope.apply or lamp.Scope.root.

Examples

// Scope.root returns Unit
Scope.root { implicit scope =>
    val sum = Scope { implicit scope =>
    // Intermediate values allocated in this block (`ident` and `ones`) are freed when
    // this block returns
    // The return value (`ident + ones`) of this block is moved to the outer scope
    val ident = STen.eye(3, STenOptions.d)
    val ones = STen.ones(List(3, 3), STenOptions.d)
    ident + ones
    }
    assert(sum.toMat == mat.ones(3, 3) + mat.ident(3))
    // `sum` is freed once this block exits
}
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Type Members

  1. type ResourceType = Either[Tensor, TensorOptions]

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. def apply(resource: TensorOptions): TensorOptions

    Adds a resource to the managed resources, then returns it unchanged.

    Adds a resource to the managed resources, then returns it unchanged.

    The resources will be released when this Scope goes out of scope or otherwise releases.

  5. def apply(resource: Tensor): Tensor

    Adds a resource to the managed resources, then returns it unchanged.

    Adds a resource to the managed resources, then returns it unchanged.

    The resources will be released when this Scope goes out of scope or otherwise releases.

  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. def clone(): AnyRef
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    protected[lang]
    Definition Classes
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    @throws(classOf[java.lang.CloneNotSupportedException]) @native() @IntrinsicCandidate()
  8. final def eq(arg0: AnyRef): Boolean
    Definition Classes
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  9. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  10. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
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    @native() @IntrinsicCandidate()
  11. def hashCode(): Int
    Definition Classes
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    Annotations
    @native() @IntrinsicCandidate()
  12. final def isInstanceOf[T0]: Boolean
    Definition Classes
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  13. final def ne(arg0: AnyRef): Boolean
    Definition Classes
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  14. final def notify(): Unit
    Definition Classes
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    @native() @IntrinsicCandidate()
  15. final def notifyAll(): Unit
    Definition Classes
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    @native() @IntrinsicCandidate()
  16. def register(resource: TensorOptions): Unit

    Adds a resource to the managed resources.

    Adds a resource to the managed resources.

    The resources will be released when this Scope goes out of scope or otherwise releases.

  17. def register(resource: Tensor): Unit

    Adds a resource to the managed resources.

    Adds a resource to the managed resources.

    The resources will be released when this Scope goes out of scope or otherwise releases.

  18. def release(): Unit

    Immediately release the resources managed by this Scope

  19. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  20. def toString(): String
    Definition Classes
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  21. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
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    @throws(classOf[java.lang.InterruptedException])
  22. final def wait(arg0: Long): Unit
    Definition Classes
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    @throws(classOf[java.lang.InterruptedException]) @native()
  23. final def wait(): Unit
    Definition Classes
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    @throws(classOf[java.lang.InterruptedException])

Deprecated Value Members

  1. def finalize(): Unit
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    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

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