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building forwared()

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ton
2023-07-14 13:59:23 +07:00
parent fc676d1ccd
commit 2e34679f73
5 changed files with 191 additions and 135 deletions
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12
src/IronpenGPU.jl
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@@ -10,8 +10,14 @@ files and each file can only depend on the file included before it.
include("type.jl")
using .type # bring model into this module namespace (this module is a parent module)
include("snnUtils.jl")
using .snnUtils
include("snnUtil.jl")
using .snnUtil
include("forward.jl")
using .forward
include("learn.jl")
using .learn
include("interface.jl")
using .interface
@@ -19,7 +25,7 @@ using .interface
#------------------------------------------------------------------------------------------------100
"""
""" version 0.0.1
Todo:
[*1] knowledgeFn in GPU format
[] use partial error update for computeNeuron

60
src/forward.jl
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@@ -2,10 +2,68 @@ module forward
# export
# using
using GeneralUtils
using ..type, ..snnUtil
#------------------------------------------------------------------------------------------------100
# kfn forward
function (kfn::kfn_1)(input::AbstractArray)
kfn.timeStep .+= 1
#TODO time step forward
if kfn.learningStage == [1]
# reset learning params
end
println(">>> input ", size(input))
println(">>> z_i_t1 ", size(kfn.z_i_t1))
# pass input_data into input neuron.
GeneralUtils.cartesianAssign!(kfn.z_i_t1, input)
println(">>> z_i_t1 ", size(kfn.z_i_t1))
println(">>> lif_recSignal ", size(kfn.lif_recSignal))
println(">>> lif_w ", size(kfn.lif_w))
println(">>> lif_refractoryActive ", size(kfn.lif_refractoryActive))
println(">>> lif_alpha ", size(kfn.lif_alpha))
println(">>> lif_vt0 ", size(kfn.lif_vt0))
println(">>> lif_vt0 sum ", sum(kfn.lif_vt0))
# check active/inactive neurons
refractoryStatus!(kfn.lif_refractoryCounter, kfn.lif_refractoryActive, kfn.lif_refractoryInactive)
refractoryStatus!(kfn.alif_refractoryCounter, kfn.alif_refractoryActive, kfn.alif_refractoryInactive)
# LIF forward active neurons
kfn.lif_recSignal .= GeneralUtils.sumAlongDim3(
GeneralUtils.matMul_3Dto4D_batchwise(kfn.z_i_t1, kfn.lif_refractoryActive .* kfn.lif_w))
kfn.lif_vt1 = (kfn.lif_alpha .* kfn.lif_vt0) .+ kfn.lif_recSignal
# for (i, v) in enumerate(kfn.lif_vt1)
# if v <
# LIF forward inactive neurons
# GeneralUtils.batchMatEleMul(kfn.z_i_t1, kfn.alif_w, resultStorage=kfn.alif_recSignal)
error("debug end kfn forward")
end

78
src/snnUtil.jl Normal file
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@@ -0,0 +1,78 @@
module snnUtil
export refractoryStatus!
# using
#------------------------------------------------------------------------------------------------100
function refractoryStatus!(refractoryCounter, refractoryActive, refractoryInactive)
d1, d2, d3, d4 = size(refractoryCounter)
for j in 1:d4
for i in 1:d3
if refractoryCounter[1, 1, i, j] > 0 # inactive
view(refractoryActive, 1, 1, i, j) .= 0
view(refractoryInactive, 1, 1, i, j) .= 1
else
view(refractoryActive, 1, 1, i, j) .= 1
view(refractoryInactive, 1, 1, i, j) .= 0
end
end
end
end
end # module

73
src/snnUtils.jl
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@@ -1,73 +0,0 @@
module snnUtils
# export
# using
#------------------------------------------------------------------------------------------------100
end # module

103
src/type.jl
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@@ -20,16 +20,35 @@ Base.@kwdef mutable struct kfn_1 <: knowledgeFn
params::Dict = Dict() # store params of knowledgeFn itself for later use
timeStep::AbstractArray = [0]
refractory::Union{AbstractArray, Nothing} = nothing
learningStage::AbstractArray = [0] # 0 inference, 1 start, 2 during, 3 end learning
z_i_t1::Union{AbstractArray, Nothing} = nothing # 2D activation matrix
z_i_t0::Union{AbstractArray, Nothing} = nothing
# LIF
lif_w::Union{AbstractArray, Nothing} = nothing
lif_recSignal::Union{AbstractArray, Nothing} = nothing
lif_vt0::Union{AbstractArray, Nothing} = nothing
lif_vt1::Union{AbstractArray, Nothing} = nothing
lif_vth::Union{AbstractArray, Nothing} = nothing
lif_zt0::Union{AbstractArray, Nothing} = nothing
lif_zt1::Union{AbstractArray, Nothing} = nothing
lif_refractoryCounter::Union{AbstractArray, Nothing} = nothing
lif_refractoryActive::Union{AbstractArray, Nothing} = nothing
lif_refractoryInactive::Union{AbstractArray, Nothing} = nothing
lif_alpha::Union{AbstractArray, Nothing} = nothing
lif_delta::AbstractFloat = 1.0
lif_tau_m::AbstractFloat = 20.0
# ALIF
alif_w::Union{AbstractArray, Nothing} = nothing
alif_recSignal::Union{AbstractArray, Nothing} = nothing
alif_zt0::Union{AbstractArray, Nothing} = nothing
alif_zt1::Union{AbstractArray, Nothing} = nothing
alif_refractoryCounter::Union{AbstractArray, Nothing} = nothing
alif_refractoryActive::Union{AbstractArray, Nothing} = nothing
alif_refractoryInactive::Union{AbstractArray, Nothing} = nothing
end
# outer constructor
@@ -43,20 +62,25 @@ function kfn_1(params::Dict)
col += kfn.params[:computeNeuron][:lif][:numbers][2]
col += kfn.params[:computeNeuron][:alif][:numbers][2]
kfn.z_i_t1 = zeros(row, col, batch)
kfn.z_i_t0 = zeros(row, col, batch)
# activation matrix
kfn.z_i_t0 = zeros(row, col, batch)
kfn.z_i_t1 = zeros(row, col, batch)
# LIF
z = kfn.params[:computeNeuron][:lif][:numbers][1] * kfn.params[:computeNeuron][:lif][:numbers][2]
kfn.lif_w = zeros(row, col, z) # matrix z-axis represent each neurons
kfn.lif_recSignal = zeros(row, col, z, batch)
kfn.lif_recSignal = zeros(1, 1, z, batch)
kfn.lif_vt0 = zeros(1, 1, z, batch)
kfn.lif_vt1 = zeros(1, 1, z, batch)
kfn.lif_vth = ones(1, 1, z, batch)
kfn.lif_zt0 = zeros(1, 1, z, batch)
kfn.lif_zt1 = zeros(1, 1, z, batch)
kfn.lif_refractoryCounter = zeros(1, 1, z, batch)
kfn.lif_refractoryActive = zeros(1, 1, z, batch)
kfn.lif_refractoryInactive = zeros(1, 1, z, batch)
kfn.lif_alpha = ones(1, 1, z, batch) .* (exp(-kfn.lif_delta / kfn.lif_tau_m))
# ALIF
z = kfn.params[:computeNeuron][:alif][:numbers][1] * kfn.params[:computeNeuron][:alif][:numbers][2]
kfn.alif_w = zeros(row, col, z)
kfn.alif_recSignal = zeros(row, col, z, batch)
# lif subscription
# subscription
row, col, _ = size(kfn.lif_w) # row*col is synaptic subscribe weight for each neuron in z-axis
synapticConnectionPercent = kfn.params[:computeNeuron][:lif][:params][:synapticConnectionPercent]
synapticConnection = Int(floor(row*col * synapticConnectionPercent/100))
@@ -67,7 +91,17 @@ function kfn_1(params::Dict)
end
end
# alif subscription
# ALIF
z = kfn.params[:computeNeuron][:alif][:numbers][1] * kfn.params[:computeNeuron][:alif][:numbers][2]
kfn.alif_w = zeros(row, col, z)
kfn.alif_recSignal = zeros(1, 1, z, batch)
kfn.alif_zt0 = zeros(1, 1, z, batch)
kfn.alif_zt1 = zeros(1, 1, z, batch)
kfn.alif_refractoryCounter = zeros(1, 1, z, batch)
kfn.alif_refractoryActive = zeros(1, 1, z, batch)
kfn.alif_refractoryInactive = zeros(1, 1, z, batch)
# subscription
row, col, _ = size(kfn.alif_w) # row*col is synaptic subscribe weight for each neuron in z-axis
synapticConnectionPercent = kfn.params[:computeNeuron][:alif][:params][:synapticConnectionPercent]
synapticConnection = Int(floor(row*col * synapticConnectionPercent/100))
@@ -92,53 +126,6 @@ function kfn_1(params::Dict)
return kfn
end
# kfn forward
function (kfn::kfn_1)(input::AbstractArray)
kfn.timeStep .+= 1
# time step forward
# row, col = size(input) # if input is a 2D matrix
println(">>> 1 ", size(input))
println(">>> 2 ", size(kfn.z_i_t1))
# multiply input with kfn.z_i_t1 may be using cartesian coordinates
GeneralUtils.cartesianAssign!(kfn.z_i_t1, input)
println(">>> 3 ", sum(kfn.z_i_t1))
println(">>> 4 ", size(kfn.lif_recSignal))
println(">>> 5 ", size(kfn.lif_w))
kfn.lif_recSignal .= GeneralUtils.batchMatEleMul(kfn.z_i_t1, kfn.lif_w)
kfn.alif_recSignal .= GeneralUtils.batchMatEleMul(kfn.z_i_t1, kfn.alif_w)
error("debug end kfn forward")
end