Packages

package root

Definition Classes: root

package lamp

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 nn

Provides building blocks for neural networks

Notable types:

nn.GenericModule is an abstraction on parametric functions
nn.Optimizer is an abstraction of gradient based optimizers
nn.LossFunction is an abstraction of loss functions, see the companion object for the implemented losses
nn.SupervisedModel combines a module with a loss

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:

nn.Linear implements W X + b with parameters W and b and input X
nn.BatchNorm, nn.LayerNorm implement batch and layer normalization
nn.MLP is a factory of a multilayer perceptron architecture

Definition Classes: lamp

package bert

Definition Classes: nn

package graph

Definition Classes: nn

package languagemodel

Definition Classes: nn

DependentHyperparameter

Fun

GRU

MLP

OptimizerHyperparameter

PTag

PerturbedLossCalculation

RNN

SimpleLossCalculation

TransformerDecoderBlock

TransformerEmbedding

TransformerEncoder

TransformerEncoderBlock

PositionalEmbedding

object PositionalEmbedding

Linear Supertypes

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def simpleSequence(sequenceLength: Int, dimension: Int, maxDistance: Int, device: Device, precision: FloatingPointPrecision)(implicit scope: Scope): STen
Linearly decomposed sequence distance encoding
Linearly decomposed sequence distance encoding
dimension
output dimension
returns
the first dimension left singular vectors of the row normalized p where p(i,j) = min(maxDist,abs(i-j))
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def vaswani[S](sequenceLength: Int, dimension: Int, device: Device, precision: FloatingPointPrecision)(implicit arg0: Sc[S]): STen
The trigonometric position encoding from https://arxiv.org/abs/1706.03762
The trigonometric position encoding from https://arxiv.org/abs/1706.03762
dimension
output dimension of the encoding
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Packages

Overview

How to get data into lamp

How to define a custom neural network layer

How to compose neural network layers

How to train models

PositionalEmbedding

object PositionalEmbedding

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Packages

Overview

How to get data into lamp

How to define a custom neural network layer

How to compose neural network layers

How to train models

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object PositionalEmbedding

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