Autograd

Usage

use Autograd;

or

import Autograd;

proc checkRank(te: shared(TensorEssence(?eltType)), param rank: int) : bool

proc getRank(te: shared(TensorEssence(?eltType))) : int

proc forceRank(te: shared(TensorEssence(?eltType)), param rank: int) : shared(BaseTensorResource(eltType, rank))

class TensorEssence : serializable

type eltType = real

proc runtimeRank : int

iter children() : borrowed(TensorEssence(eltType))

proc treeHeight() : int

class ForgetfulTensor : TensorEssence

param rank : int

class BaseTensorResource : TensorEssence, serializable

param rank : int

var dataResource : shared(Remote(ndarray(rank, eltType)))

var gradResource : shared(Remote(ndarray(rank, eltType))?)

proc init(type eltType, param rank: int)

proc init(in dataResource: shared(Remote(ndarray(?rank, ?eltType))), in gradResource: shared(Remote(ndarray(rank, eltType))?) = nil)

proc init(data: ndarray(?rank, ?eltType), device: locale = Remote.defaultDevice)

proc to(dest: locale)

proc device : locale

proc array ref : ndarray(rank, eltType)

proc grad ref : ndarray(rank, eltType)

proc forward()

proc backward(grad: remote(ndarray(rank, eltType)), param alreadyPopulated = false)

proc backward()  where rank == 1

proc detach(copy: bool = true, keepGrad: bool = false) : owned(TensorResource(eltType, rank, baseValue))

override proc runtimeRank : int

class TensorResource : BaseTensorResource(?), serializable

type operationType

var operationCtx : operationType

proc init(type eltType, param rank: int, operationCtx: ?operationType)

proc init(in dataResource: shared(Remote(ndarray(?rank, ?eltType))), in gradResource: shared(Remote(ndarray(rank, eltType))?) = nil, operationCtx: ?operationType)

proc init(in dataResource: shared(Remote(ndarray(?rank, ?eltType))))

proc init(bt: borrowed(BaseTensorResource(?eltType, ?rank)), operationCtx: ?operationType)

override proc detach(copy: bool = true, keepGrad: bool = false) : owned(TensorResource(eltType, rank, baseValue))

override proc forward()

override iter children() : borrowed(TensorEssence(eltType))

record baseValue : serializable

proc forward()

proc children

record reluOp : serializable

var input : shared(BaseTensorResource(?))

proc children

proc forward()

proc _relu(x)

record expOp : serializable

var input : shared(BaseTensorResource(?))

proc children

proc forward()

record addOp : serializable

param rank : int

type eltType

var lhs : shared(BaseTensorResource(eltType, rank))

var rhs : shared(BaseTensorResource(eltType, rank))

proc children

proc forward() : ndarray(rank, eltType)

proc backward(grad: ndarray(rank, eltType)) : (ndarray(rank, eltType), ndarray(rank, eltType))

record subOp : serializable

var lhs : shared(BaseTensorResource(?))

var rhs : shared(BaseTensorResource(?))

proc children

proc forward()

record divOp : serializable

var lhs : shared(BaseTensorResource(?))

var rhs : shared(BaseTensorResource(?))

proc children

proc forward()

record multOp : serializable

param rank : int

type eltType

var lhs : shared(BaseTensorResource(eltType, rank))

var rhs : shared(BaseTensorResource(eltType, rank))

proc children

proc forward()

proc backward(grad: ndarray(rank, eltType)) : (ndarray(rank, eltType), ndarray(rank, eltType))

record reshapeOp : serializable

param oldRank : int

param newRank : int

type eltType

var shape : newRank*int

var input : shared(BaseTensorResource(eltType, oldRank))

proc children

proc forward() : ndarray(newRank, eltType)

proc backward(grad: ndarray(newRank, eltType)) : (ndarray(oldRank, eltType),)

record permuteOp : serializable

param rank : int

type eltType = real

var permutation

var input : shared(BaseTensorResource(eltType, rank))

proc children

proc forward()

proc backward(grad: ndarray(rank, eltType)) : (ndarray(rank, eltType),)

record expandOp : serializable

param rank : int

type eltType = real

var expandedShape : rank*int

var input : shared(BaseTensorResource(eltType, rank))

proc children

proc forward()

proc backward(grad: ndarray(rank, eltType)) : (ndarray(rank, eltType),)

record padOp : serializable

param rank : int

type eltType = real

var arg : rank*(2*(int))

var value : eltType

var input : shared(BaseTensorResource(eltType, rank))

proc children

proc forward() : ndarray(rank, eltType)

proc backward(grad: ndarray(rank, eltType)) : (ndarray(rank, eltType),)

record shrinkOp : serializable

param rank : int

type eltType = real

var arg : rank*(2*(int))

var input : shared(BaseTensorResource(eltType, rank))

proc children

proc forward() : ndarray(rank, eltType)

proc backward(grad: ndarray(rank, eltType)) : (ndarray(rank, eltType),)

record sliceOp : serializable

param rank : int

type eltType = real

var dom : domain(rank, int)

var input : shared(BaseTensorResource(eltType, rank))

proc children

proc forward() : ndarray(rank, eltType)

proc backward(grad: ndarray(rank, eltType)) : (ndarray(rank, eltType),)

record layerSliceOp : serializable

param rank : int

type eltType = real

var base : shared(BaseTensorResource(eltType, rank))

var mask : shared(BaseTensorResource(eltType, rank))

var maskDomain : domain(rank, int)

proc children

proc forward() : ndarray(rank, eltType)

proc backward(grad: ndarray(rank, eltType)) : (ndarray(rank, eltType), ndarray(rank, eltType))

record sumOp : serializable

param rank : int

type eltType = real

param sumRank : int

var axes : sumRank*int

var input : shared(BaseTensorResource(eltType, rank))

proc children

proc outRank param : int

proc forward()

proc backward(grad: ndarray(outRank, eltType)) : (ndarray(rank, eltType),)

record maxOp : serializable

param rank : int

type eltType = real

param maxRank : int

var axes : maxRank*int

var input : shared(BaseTensorResource(eltType, rank))

proc children

proc forward()

proc backward(grad) : (ndarray(rank, eltType),)

record conv2DOp : serializable

type eltType = real

var features : shared(BaseTensorResource(eltType, 3))

var kernel : shared(BaseTensorResource(eltType, 4))

var stride : int

proc children

proc forward() : ndarray(3, eltType)

proc backward(grad: ndarray(3, eltType)) : (ndarray(3, eltType), ndarray(4, eltType))