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choose.go
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choose.go
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// Copyright (c) 2022, The Emergent Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.
/*
choose: This project tests the Rubicon framework making cost-benefit based choices
*/
package main
//go:generate core generate -add-types
import (
"fmt"
"log"
"log/slog"
"math"
"os"
"reflect"
"cogentcore.org/core/base/errors"
"cogentcore.org/core/base/mpi"
"cogentcore.org/core/base/num"
"cogentcore.org/core/base/randx"
"cogentcore.org/core/base/timer"
"cogentcore.org/core/core"
"cogentcore.org/core/icons"
"cogentcore.org/core/math32"
"cogentcore.org/core/math32/minmax"
"cogentcore.org/core/plot/plotcore"
"cogentcore.org/core/tensor/stats/split"
"cogentcore.org/core/tensor/stats/stats"
"cogentcore.org/core/tensor/table"
"cogentcore.org/core/tree"
"github.com/emer/axon/v2/axon"
"github.com/emer/axon/v2/examples/choose/armaze"
"github.com/emer/emergent/v2/econfig"
"github.com/emer/emergent/v2/egui"
"github.com/emer/emergent/v2/elog"
"github.com/emer/emergent/v2/emer"
"github.com/emer/emergent/v2/env"
"github.com/emer/emergent/v2/estats"
"github.com/emer/emergent/v2/etime"
"github.com/emer/emergent/v2/looper"
"github.com/emer/emergent/v2/netview"
"github.com/emer/emergent/v2/params"
"github.com/emer/emergent/v2/paths"
)
func main() {
sim := &Sim{}
sim.New()
sim.ConfigAll()
if sim.Config.GUI {
sim.RunGUI()
} else {
sim.RunNoGUI()
}
}
var (
CSAggStats = []string{"PVposEst", "PVnegEst", "PVposVar", "PVnegVar", "GateVMtxGo", "GateVMtxNo", "GateVMtxGoNo", "GateBLAposAcq", "GateBLAposExt", "GateBLAposAcqExt"}
USAggStats = []string{"Rew_R", "DA_R", "RewPred_R", "VtaDA"}
)
// see params.go for network params, config.go for Config
// Sim encapsulates the entire simulation model, and we define all the
// functionality as methods on this struct. This structure keeps all relevant
// state information organized and available without having to pass everything around
// as arguments to methods, and provides the core GUI interface (note the view tags
// for the fields which provide hints to how things should be displayed).
type Sim struct {
// simulation configuration parameters -- set by .toml config file and / or args
Config Config `new-window:"+"`
// the network -- click to view / edit parameters for layers, paths, etc
Net *axon.Network `new-window:"+" display:"no-inline"`
// if true, stop running at end of a sequence (for NetView Di data parallel index)
StopOnSeq bool
// if true, stop running when an error programmed into the code occurs
StopOnErr bool
// network parameter management
Params emer.NetParams `display:"add-fields"`
// contains looper control loops for running sim
Loops *looper.Manager `new-window:"+" display:"no-inline"`
// contains computed statistic values
Stats estats.Stats `new-window:"+"`
// Contains all the logs and information about the logs.'
Logs elog.Logs `new-window:"+"`
// Environments
Envs env.Envs `new-window:"+" display:"no-inline"`
// axon timing parameters and state
Context axon.Context `new-window:"+"`
// netview update parameters
ViewUpdate netview.ViewUpdate `display:"add-fields"`
// manages all the gui elements
GUI egui.GUI `display:"-"`
// gui for viewing env
EnvGUI *armaze.GUI `display:"-"`
// a list of random seeds to use for each run
RandSeeds randx.Seeds `display:"-"`
// testing data, from -test arg
TestData map[string]float32 `display:"-"`
}
// New creates new blank elements and initializes defaults
func (ss *Sim) New() {
ss.Net = axon.NewNetwork("Choose")
_, err := econfig.Config(&ss.Config, "config.toml")
if err != nil {
slog.Error(err.Error())
}
ss.Params.Config(ParamSets, ss.Config.Params.Sheet, ss.Config.Params.Tag, ss.Net)
ss.Stats.Init()
ss.RandSeeds.Init(100) // max 100 runs
ss.InitRandSeed(0)
ss.Context.Defaults()
ss.Context.ThetaCycles = int32(ss.Config.Run.NCycles)
}
////////////////////////////////////////////////////////////////////////////////
// Configs
// ConfigAll configures all the elements using the standard functions
func (ss *Sim) ConfigAll() {
ss.ConfigEnv()
ss.ConfigNet(ss.Net)
ss.ConfigLogs()
ss.ConfigLoops()
if ss.Config.Params.SaveAll {
ss.Config.Params.SaveAll = false
ss.Net.SaveParamsSnapshot(&ss.Params.Params, &ss.Config, ss.Config.Params.Good)
os.Exit(0)
}
}
func (ss *Sim) ConfigEnv() {
// Can be called multiple times -- don't re-create
newEnv := (len(ss.Envs) == 0)
if ss.Config.Env.Config != "" {
fmt.Println("Env Config:", ss.Config.Env.Config)
}
for di := 0; di < ss.Config.Run.NData; di++ {
var trn *armaze.Env
if newEnv {
trn = &armaze.Env{}
} else {
trn = ss.Envs.ByModeDi(etime.Train, di).(*armaze.Env)
}
// note: names must be standard here!
trn.Name = env.ModeDi(etime.Train, di)
trn.Defaults()
trn.RandSeed = 73
if !ss.Config.Env.SameSeed {
trn.RandSeed += int64(di) * 73
}
trn.Config.NDrives = ss.Config.Env.NDrives
if ss.Config.Env.Config != "" {
args := os.Args
os.Args = args[:1]
_, err := econfig.Config(&trn.Config, ss.Config.Env.Config)
if err != nil {
slog.Error(err.Error())
}
}
trn.ConfigEnv(di)
trn.Validate()
trn.Init(0)
// note: names must be in place when adding
ss.Envs.Add(trn)
if di == 0 {
ss.ConfigRubicon(trn)
}
}
}
func (ss *Sim) ConfigRubicon(trn *armaze.Env) {
rp := &ss.Net.Rubicon
rp.SetNUSs(&ss.Context, trn.Config.NDrives, 1)
rp.Defaults()
rp.USs.PVposGain = 2 // higher = more pos reward (saturating logistic func)
rp.USs.PVnegGain = 1 // global scaling of RP neg level -- was 1
rp.LHb.VSPatchGain = 4
rp.LHb.VSPatchNonRewThr = 0.15
rp.USs.USnegGains[0] = 2 // big salient input!
rp.Drive.DriveMin = 0.5 // 0.5 -- should be
rp.Urgency.U50 = 10
if ss.Config.Params.Rubicon != nil {
params.ApplyMap(rp, ss.Config.Params.Rubicon, ss.Config.Debug)
}
}
func (ss *Sim) ConfigNet(net *axon.Network) {
ctx := &ss.Context
ev := ss.Envs.ByModeDi(etime.Train, 0).(*armaze.Env)
net.SetMaxData(ctx, ss.Config.Run.NData)
net.SetRandSeed(ss.RandSeeds[0]) // init new separate random seed, using run = 0
nuBgY := 5
nuBgX := 5
nuCtxY := 6
nuCtxX := 6
nAct := int(armaze.ActionsN)
popY := 4
popX := 4
space := float32(2)
pone2one := paths.NewPoolOneToOne()
one2one := paths.NewOneToOne()
full := paths.NewFull()
mtxRandPath := paths.NewPoolUniformRand()
mtxRandPath.PCon = 0.75
_ = mtxRandPath
_ = pone2one
pathClass := "PFCPath"
ny := ev.Config.Params.NYReps
narm := ev.Config.NArms
vSgpi, vSmtxGo, vSmtxNo, urgency, pvPos, blaPosAcq, blaPosExt, blaNegAcq, blaNegExt, blaNov, ofcPosUS, ofcPosUSCT, ofcPosUSPT, ofcPosUSPTp, ilPos, ilPosCT, ilPosPT, ilPosPTp, ofcNegUS, ofcNegUSCT, ofcNegUSPT, ofcNegUSPTp, ilNeg, ilNegCT, ilNegPT, ilNegPTp, accCost, plUtil, sc := net.AddRubicon(ctx, ny, popY, popX, nuBgY, nuBgX, nuCtxY, nuCtxX, space)
_, _ = plUtil, urgency
_, _ = ofcNegUSCT, ofcNegUSPTp
_, _ = vSmtxGo, vSmtxNo
plUtilPTp := net.LayerByName("PLutilPTp")
cs, csP := net.AddInputPulv2D("CS", ny, narm, space)
dist, distP := net.AddInputPulv2D("Dist", ny, ev.MaxLength+1, space)
///////////////////////////////////////////
// M1, VL, ALM
act := net.AddLayer2D("Act", axon.InputLayer, ny, nAct) // Action: what is actually done
vl := net.AddPulvLayer2D("VL", ny, nAct) // VL predicts brainstem Action
vl.SetBuildConfig("DriveLayName", act.Name)
m1, m1CT := net.AddSuperCT2D("M1", "PFCPath", nuCtxY, nuCtxX, space, one2one)
m1P := net.AddPulvForSuper(m1, space)
alm, almCT, almPT, almPTp, almMD := net.AddPFC2D("ALM", "MD", nuCtxY, nuCtxX, true, true, space)
_ = almPT
net.ConnectLayers(vSgpi, almMD, full, axon.InhibPath)
// net.ConnectToMatrix(alm, vSmtxGo, full) // todo: explore
// net.ConnectToMatrix(alm, vSmtxNo, full)
net.ConnectToPFCBidir(m1, m1P, alm, almCT, almPT, almPTp, full, "M1ALM") // alm predicts m1
// vl is a predictive thalamus but we don't have direct access to its source
net.ConnectToPulv(m1, m1CT, vl, full, full, pathClass)
net.ConnectToPFC(nil, vl, alm, almCT, almPT, almPTp, full, "VLALM") // alm predicts m1
// sensory inputs guiding action
// note: alm gets effort, dist via predictive coding below
net.ConnectLayers(dist, m1, full, axon.ForwardPath).AddClass("ToM1")
net.ConnectLayers(ofcNegUS, m1, full, axon.ForwardPath).AddClass("ToM1")
// shortcut: not needed
// net.ConnectLayers(dist, vl, full, axon.ForwardPath).AddClass("ToVL")
// these pathways are *essential* -- must get current state here
net.ConnectLayers(m1, vl, full, axon.ForwardPath).AddClass("ToVL")
net.ConnectLayers(alm, vl, full, axon.ForwardPath).AddClass("ToVL")
net.ConnectLayers(m1, accCost, full, axon.ForwardPath).AddClass("MToACC")
net.ConnectLayers(alm, accCost, full, axon.ForwardPath).AddClass("MToACC")
// key point: cs does not project directly to alm -- no simple S -> R mappings!?
///////////////////////////////////////////
// CS -> BLA, OFC
net.ConnectToSC1to1(cs, sc)
net.ConnectCSToBLApos(cs, blaPosAcq, blaNov)
net.ConnectToBLAExt(cs, blaPosExt, full)
net.ConnectToBLAAcq(cs, blaNegAcq, full)
net.ConnectToBLAExt(cs, blaNegExt, full)
// for some reason this really makes things worse:
// net.ConnectToVSMatrix(cs, vSmtxGo, full)
// net.ConnectToVSMatrix(cs, vSmtxNo, full)
// OFCus predicts cs
net.ConnectToPFCBack(cs, csP, ofcPosUS, ofcPosUSCT, ofcPosUSPT, ofcPosUSPTp, full, "CSToPFC")
net.ConnectToPFCBack(cs, csP, ofcNegUS, ofcNegUSCT, ofcPosUSPT, ofcNegUSPTp, full, "CSToPFC")
///////////////////////////////////////////
// OFC, ACC, ALM predicts dist
// todo: a more dynamic US rep is needed to drive predictions in OFC
// using distance and effort here in the meantime
net.ConnectToPFCBack(dist, distP, ofcPosUS, ofcPosUSCT, ofcPosUSPT, ofcPosUSPTp, full, "DistToPFC")
net.ConnectToPFCBack(dist, distP, ilPos, ilPosCT, ilPosPT, ilPosPTp, full, "PosToPFC")
net.ConnectToPFC(dist, distP, ofcNegUS, ofcNegUSCT, ofcNegUSPT, ofcNegUSPTp, full, "DistToPFC")
net.ConnectToPFC(dist, distP, ilNeg, ilNegCT, ilNegPT, ilNegPTp, full, "DistToPFC")
// alm predicts all effort, cost, sensory state vars
net.ConnectToPFC(dist, distP, alm, almCT, almPT, almPTp, full, "DistToPFC")
///////////////////////////////////////////
// ALM, M1 <-> OFC, ACC
// action needs to know if maintaining a goal or not
// using plUtil as main summary "driver" input to action system
// PTp provides good notmaint signal for action.
net.ConnectLayers(plUtilPTp, alm, full, axon.ForwardPath).AddClass("ToALM")
net.ConnectLayers(plUtilPTp, m1, full, axon.ForwardPath).AddClass("ToM1")
// note: in Obelisk this helps with the Consume action
// but here in this example it produces some instability
// at later time points -- todo: investigate later.
// net.ConnectLayers(notMaint, vl, full, axon.ForwardPath).AddClass("ToVL")
////////////////////////////////////////////////
// position
cs.PlaceRightOf(pvPos, space*2)
dist.PlaceRightOf(cs, space)
m1.PlaceRightOf(dist, space)
alm.PlaceRightOf(m1, space)
vl.PlaceBehind(m1P, space)
act.PlaceBehind(vl, space)
net.Build(ctx)
net.Defaults()
net.SetNThreads(ss.Config.Run.NThreads)
ss.ApplyParams()
ss.Net.InitWeights(ctx)
}
func (ss *Sim) ApplyParams() {
net := ss.Net
ss.Params.SetAll() // first hard-coded defaults
// params that vary as number of CSs
ev := ss.Envs.ByModeDi(etime.Train, 0).(*armaze.Env)
nCSTot := ev.Config.NArms
cs := net.LayerByName("CS")
cs.Params.Inhib.ActAvg.Nominal = 0.32 / float32(nCSTot)
csp := net.LayerByName("CSP")
csp.Params.Inhib.ActAvg.Nominal = 0.32 / float32(nCSTot)
bla := net.LayerByName("BLAposAcqD1")
pji, _ := bla.RecvPathBySendName("BLANovelCS")
pj := pji.(*axon.Path)
// this is very sensitive param to get right
// too little and the hamster does not try CSs at the beginning,
// too high and it gets stuck trying the same location over and over
pj.Params.PathScale.Abs = float32(math32.Min(2.3+(float32(nCSTot)/10.0), 3.0))
// then apply config-set params.
if ss.Config.Params.Network != nil {
ss.Params.SetNetworkMap(ss.Net, ss.Config.Params.Network)
}
}
////////////////////////////////////////////////////////////////////////////////
// Init, utils
// Init restarts the run, and initializes everything, including network weights
// and resets the epoch log table
func (ss *Sim) Init() {
if ss.Config.GUI {
ss.Stats.SetString("RunName", ss.Params.RunName(0)) // in case user interactively changes tag
}
ss.Loops.ResetCounters()
ss.Logs.ResetLog(etime.Debug, etime.Trial)
ss.InitRandSeed(0)
ss.ConfigEnv() // re-config env just in case a different set of patterns was
// selected or patterns have been modified etc
ss.GUI.StopNow = false
ss.ApplyParams()
ss.Net.GPU.SyncParamsToGPU()
ss.NewRun()
ss.ViewUpdate.RecordSyns()
ss.ViewUpdate.Update()
}
// InitRandSeed initializes the random seed based on current training run number
func (ss *Sim) InitRandSeed(run int) {
ss.RandSeeds.Set(run)
ss.RandSeeds.Set(run, &ss.Net.Rand)
}
// ConfigLoops configures the control loops: Training, Testing
func (ss *Sim) ConfigLoops() {
man := looper.NewManager()
// ev := ss.Envs.ByModeDi(etime.Train, 0).(*armaze.Env)
ncyc := ss.Config.Run.NCycles
nplus := ss.Config.Run.NPlusCycles
// note: sequence stepping does not work in NData > 1 mode -- just going back to raw trials
trls := int(math32.IntMultipleGE(float32(ss.Config.Run.NTrials), float32(ss.Config.Run.NData)))
man.AddStack(etime.Train).
AddTime(etime.Run, ss.Config.Run.NRuns).
AddTime(etime.Epoch, ss.Config.Run.NEpochs).
AddTimeIncr(etime.Trial, trls, ss.Config.Run.NData).
AddTime(etime.Cycle, ncyc)
axon.LooperStdPhases(man, &ss.Context, ss.Net, ncyc-nplus, ncyc-1) // plus phase timing
axon.LooperSimCycleAndLearn(man, ss.Net, &ss.Context, &ss.ViewUpdate) // std algo code
for m := range man.Stacks {
stack := man.Stacks[m]
stack.Loops[etime.Trial].OnStart.Add("ApplyInputs", func() {
ss.ApplyInputs()
})
}
// note: phase is shared between all stacks!
plusPhase := man.Stacks[etime.Train].Loops[etime.Cycle].EventByName("PlusPhase")
plusPhase.OnEvent.InsertBefore("PlusPhase:Start", "TakeAction", func() {
// note: critical to have this happen *after* MinusPhase:End and *before* PlusPhase:Start
// because minus phase end has gated info, and plus phase start applies action input
ss.TakeAction(ss.Net)
})
man.GetLoop(etime.Train, etime.Run).OnStart.Add("NewRun", ss.NewRun)
/////////////////////////////////////////////
// Logging
// man.GetLoop(etime.Train, etime.Epoch).OnEnd.Add("PCAStats", func() {
// trnEpc := man.Stacks[etime.Train].Loops[etime.Epoch].Counter.Cur
// if (ss.Config.Run.PCAInterval > 0) && (trnEpc%ss.Config.Run.PCAInterval == 0) {
// axon.PCAStats(ss.Net, &ss.Logs, &ss.Stats)
// ss.Logs.ResetLog(etime.Analyze, etime.Trial)
// }
// })
man.AddOnEndToAll("Log", ss.Log)
axon.LooperResetLogBelow(man, &ss.Logs)
if ss.Config.GUI {
man.GetLoop(etime.Train, etime.Trial).OnStart.Add("ResetDebugTrial", func() {
di := uint32(ss.ViewUpdate.View.Di)
hadRew := axon.GlbV(&ss.Context, di, axon.GvHadRew) > 0
if hadRew {
ss.Logs.ResetLog(etime.Debug, etime.Trial)
}
})
}
man.GetLoop(etime.Train, etime.Trial).OnEnd.Add("LogAnalyze", func() {
trnEpc := man.Stacks[etime.Train].Loops[etime.Epoch].Counter.Cur
if (ss.Config.Run.PCAInterval > 0) && (trnEpc%ss.Config.Run.PCAInterval == 0) {
ss.Log(etime.Analyze, etime.Trial)
}
})
if ss.Config.Log.Testing {
man.GetLoop(etime.Train, etime.Trial).OnEnd.Add("RecordTestData", func() {
ss.RecordTestData()
})
}
// Save weights to file, to look at later
man.GetLoop(etime.Train, etime.Run).OnEnd.Add("SaveWeights", func() {
ctrString := ss.Stats.PrintValues([]string{"Run", "Epoch"}, []string{"%03d", "%05d"}, "_")
axon.SaveWeightsIfConfigSet(ss.Net, ss.Config.Log.SaveWeights, ctrString, ss.Stats.String("RunName"))
})
man.GetLoop(etime.Train, etime.Epoch).OnEnd.Add("PctCortex", func() {
trnEpc := ss.Loops.Stacks[etime.Train].Loops[etime.Epoch].Counter.Cur
ss.Config.Env.CurPctCortex(trnEpc)
})
////////////////////////////////////////////
// GUI
if !ss.Config.GUI {
if ss.Config.Log.NetData {
man.GetLoop(etime.Test, etime.Trial).Main.Add("NetDataRecord", func() {
ss.GUI.NetDataRecord(ss.ViewUpdate.Text)
})
}
} else {
axon.LooperUpdateNetView(man, &ss.ViewUpdate, ss.Net, ss.NetViewCounters)
axon.LooperUpdatePlots(man, &ss.GUI)
man.GetLoop(etime.Train, etime.Trial).OnEnd.Add("UpdateWorldGui", func() {
ss.UpdateEnvGUI(etime.Train)
})
}
if ss.Config.Debug {
mpi.Println(man.DocString())
}
ss.Loops = man
}
// TakeAction takes action for this step, using either decoded cortical
// or reflexive subcortical action from env.
// Called at end of minus phase. However, it can still gate sometimes
// after this point, so that is dealt with at end of plus phase.
func (ss *Sim) TakeAction(net *axon.Network) {
ctx := &ss.Context
rp := &ss.Net.Rubicon
mtxLy := ss.Net.LayerByName("VMtxGo")
vlly := ss.Net.LayerByName("VL")
threshold := float32(0.1)
for di := 0; di < int(ctx.NData); di++ {
diu := uint32(di)
ev := ss.Envs.ByModeDi(ctx.Mode, di).(*armaze.Env)
justGated := mtxLy.AnyGated(diu) // not updated until plus phase: rp.VSMatrix.JustGated.IsTrue()
hasGated := axon.GlbV(ctx, diu, axon.GvVSMatrixHasGated) > 0
ev.InstinctAct(justGated, hasGated)
csGated := (justGated && !rp.HasPosUS(ctx, diu))
deciding := !csGated && !hasGated && (axon.GlbV(ctx, diu, axon.GvACh) > threshold && mtxLy.Pool(0, diu).AvgMax.SpkMax.Cycle.Max > threshold) // give it time
wasDeciding := num.ToBool(ss.Stats.Float32Di("Deciding", di))
if wasDeciding {
deciding = false // can't keep deciding!
}
ss.Stats.SetFloat32Di("Deciding", di, num.FromBool[float32](deciding))
trSt := armaze.TrSearching
if hasGated {
trSt = armaze.TrApproaching
}
if csGated || deciding {
act := "CSGated"
trSt = armaze.TrJustEngaged
if !csGated {
act = "Deciding"
trSt = armaze.TrDeciding
}
ss.Stats.SetStringDi("Debug", di, act)
ev.Action("None", nil)
ss.ApplyAction(di)
ss.Stats.SetStringDi("ActAction", di, "None")
ss.Stats.SetStringDi("Instinct", di, "None")
ss.Stats.SetStringDi("NetAction", di, act)
ss.Stats.SetFloatDi("ActMatch", di, 1) // whatever it is, it is ok
vlly.Pool(0, uint32(di)).Inhib.Clamped.SetBool(false) // not clamped this trial
} else {
ss.Stats.SetStringDi("Debug", di, "acting")
netAct := ss.DecodeAct(ev, di)
genAct := ev.InstinctAct(justGated, hasGated)
ss.Stats.SetStringDi("NetAction", di, netAct.String())
ss.Stats.SetStringDi("Instinct", di, genAct.String())
if netAct == genAct {
ss.Stats.SetFloatDi("ActMatch", di, 1)
} else {
ss.Stats.SetFloatDi("ActMatch", di, 0)
}
actAct := genAct
if ss.Stats.FloatDi("CortexDriving", di) > 0 {
actAct = netAct
}
ss.Stats.SetStringDi("ActAction", di, actAct.String())
ev.Action(actAct.String(), nil)
ss.ApplyAction(di)
switch {
case rp.HasPosUS(ctx, diu):
trSt = armaze.TrRewarded
case actAct == armaze.Consume:
trSt = armaze.TrConsuming
}
}
if axon.GlobalScalars[axon.GvGiveUp, diu] > 0 {
trSt = armaze.TrGiveUp
}
ss.Stats.SetIntDi("TraceStateInt", di, int(trSt))
ss.Stats.SetStringDi("TraceState", di, trSt.String())
}
ss.Net.ApplyExts(ctx)
ss.Net.GPU.SyncPoolsToGPU()
}
// DecodeAct decodes the VL ActM state to find closest action pattern
func (ss *Sim) DecodeAct(ev *armaze.Env, di int) armaze.Actions {
vt := ss.Stats.SetLayerTensor(ss.Net, "VL", "CaSpkP", di) // was "Act"
return ev.DecodeAct(vt)
}
func (ss *Sim) ApplyAction(di int) {
ctx := &ss.Context
net := ss.Net
ev := ss.Envs.ByModeDi(ss.Context.Mode, di).(*armaze.Env)
ap := ev.State("Action")
ly := net.LayerByName("Act")
ly.ApplyExt(ctx, uint32(di), ap)
}
// ApplyInputs applies input patterns from given environment.
// It is good practice to have this be a separate method with appropriate
// args so that it can be used for various different contexts
// (training, testing, etc).
func (ss *Sim) ApplyInputs() {
ctx := &ss.Context
ss.Stats.SetString("Debug", "") // start clear
net := ss.Net
lays := []string{"Dist", "CS"}
ss.Net.InitExt(ctx)
for di := uint32(0); di < ctx.NData; di++ {
ev := ss.Envs.ByModeDi(ctx.Mode, int(di)).(*armaze.Env)
giveUp := axon.GlobalScalars[axon.GvGiveUp, di] > 0
if giveUp {
ev.JustConsumed = true // triggers a new start -- we just consumed the giving up feeling :)
}
ev.Step()
if ev.Tick == 0 {
ss.Stats.SetFloat32Di("CortexDriving", int(di), num.FromBool[float32](randx.BoolP32(ss.Config.Env.PctCortex)))
}
for _, lnm := range lays {
ly := net.LayerByName(lnm)
itsr := ev.State(lnm)
ly.ApplyExt(ctx, di, itsr)
}
ss.ApplyRubicon(ctx, ev, di)
}
ss.Net.ApplyExts(ctx)
}
// ApplyRubicon applies current Rubicon values to Context.Rubicon,
// from given trial data.
func (ss *Sim) ApplyRubicon(ctx *axon.Context, ev *armaze.Env, di uint32) {
rp := &ss.Net.Rubicon
rp.NewState(ctx, di, &ss.Net.Rand) // first before anything else is updated
rp.SetGoalMaintFromLayer(ctx, di, ss.Net, "PLutilPT", 0.2)
rp.DecodePVEsts(ctx, di, ss.Net)
rp.SetGoalDistEst(ctx, di, float32(ev.Dist))
rp.EffortUrgencyUpdate(ctx, di, ev.Effort)
if ev.USConsumed >= 0 {
rp.SetUS(ctx, di, axon.Positive, ev.USConsumed, ev.USValue)
}
rp.SetDrives(ctx, di, 0.5, ev.Drives...)
rp.Step(ctx, di, &ss.Net.Rand)
}
// NewRun intializes a new run of the model, using the TrainEnv.Run counter
// for the new run value
func (ss *Sim) NewRun() {
ctx := &ss.Context
ss.InitRandSeed(ss.Loops.GetLoop(etime.Train, etime.Run).Counter.Cur)
for di := 0; di < int(ctx.NData); di++ {
ss.Envs.ByModeDi(etime.Train, di).Init(0)
}
ctx.Reset()
ctx.Mode = etime.Train
ss.Config.Env.PctCortex = 0
ss.Net.InitWeights(ctx)
ss.InitStats()
ss.StatCounters(0)
ss.Logs.ResetLog(etime.Train, etime.Epoch)
if ss.Config.OpenWeights != "" {
ss.Net.OpenWeightsJSON(core.Filename(ss.Config.OpenWeights))
log.Println("Opened weights:", ss.Config.OpenWeights)
}
}
////////////////////////////////////////////////////////////////////////////////////////////
// Stats
// InitStats initializes all the statistics.
// called at start of new run
func (ss *Sim) InitStats() {
ss.Stats.SetInt("Di", 0)
ss.Stats.SetFloat("PctCortex", 0)
ss.Stats.SetFloat("Pos", 0)
ss.Stats.SetFloat("Arm", 0)
ss.Stats.SetFloat("Dist", 0)
ss.Stats.SetFloat("Drive", 0)
ss.Stats.SetFloat("CS", 0)
ss.Stats.SetFloat("US", 0)
ss.Stats.SetString("NetAction", "")
ss.Stats.SetString("Instinct", "")
ss.Stats.SetString("ActAction", "")
ss.Stats.SetString("TraceState", "")
ss.Stats.SetInt("TraceStateInt", 0)
ss.Stats.SetFloat("ActMatch", 0)
ss.Stats.SetFloat("AllGood", 0)
ss.Stats.SetFloat("Should", 0)
ss.Stats.SetFloat("GateUS", 0)
ss.Stats.SetFloat("GateCS", 0)
ss.Stats.SetFloat("Deciding", 0)
ss.Stats.SetFloat("GatedEarly", 0)
ss.Stats.SetFloat("MaintEarly", 0)
ss.Stats.SetFloat("MaintIncon", 0)
ss.Stats.SetFloat("GatedAgain", 0)
ss.Stats.SetFloat("BadCSGate", 0)
ss.Stats.SetFloat("AChShould", 0)
ss.Stats.SetFloat("AChShouldnt", 0)
ss.Stats.SetFloat("DA", 0)
ss.Stats.SetFloat("DA_NR", 0)
ss.Stats.SetFloat("RewPred_NR", 0)
ss.Stats.SetFloat("DA_GiveUp", 0)
ss.Stats.SetFloat("SC", 0)
lays := ss.Net.LayersByType(axon.PTMaintLayer)
for _, lnm := range lays {
ss.Stats.SetFloat("Maint"+lnm, 0)
ss.Stats.SetFloat("MaintFail"+lnm, 0)
ss.Stats.SetFloat("PreAct"+lnm, 0)
}
ss.Stats.SetString("Debug", "") // special debug notes per trial
}
// StatCounters saves current counters to Stats, so they are available for logging etc
func (ss *Sim) StatCounters(di int) {
ctx := &ss.Context
mode := ctx.Mode
ss.ActionStatsDi(di)
ev := ss.Envs.ByModeDi(mode, di).(*armaze.Env)
ss.Loops.Stacks[mode].CountersToStats(&ss.Stats)
// always use training epoch..
trnEpc := ss.Loops.Stacks[etime.Train].Loops[etime.Epoch].Counter.Cur
ss.Stats.SetInt("Epoch", trnEpc)
trl := ss.Stats.Int("Trial")
ss.Stats.SetInt("Trial", trl+di)
ss.Stats.SetInt("Di", di)
ss.Stats.SetInt("Cycle", int(ctx.Cycle))
ss.Stats.SetFloat32("PctCortex", ss.Config.Env.PctCortex)
ss.Stats.SetFloat32("Arm", float32(ev.Arm))
ss.Stats.SetFloat32("Pos", float32(ev.Pos))
ss.Stats.SetFloat32("Dist", float32(ev.Dist))
ss.Stats.SetFloat32("Drive", float32(ev.MaxDrive()))
ss.Stats.SetFloat32("CS", float32(ev.CurCS()))
ss.Stats.SetFloat32("US", float32(ev.USConsumed))
ss.Stats.SetString("TrialName", "trl") // todo: could have dist, US etc
}
func (ss *Sim) NetViewCounters(tm etime.Times) {
if ss.ViewUpdate.View == nil {
return
}
di := ss.ViewUpdate.View.Di
if tm == etime.Trial {
ss.TrialStats(di) // get trial stats for current di
}
ss.StatCounters(di)
ss.ViewUpdate.Text = ss.Stats.Print([]string{"Run", "Epoch", "Trial", "Di", "Cycle", "NetAction", "Instinct", "ActAction", "ActMatch", "JustGated", "Should"})
}
// TrialStats computes the trial-level statistics.
// Aggregation is done directly from log data.
func (ss *Sim) TrialStats(di int) {
ss.GatedStats(di)
ss.MaintStats(di)
diu := uint32(di)
ctx := &ss.Context
rp := &ss.Net.Rubicon
rp.DecodePVEsts(ctx, diu, ss.Net) // get this for current trial!
hasRew := axon.GlobalScalars[axon.GvHasRew, diu] > 0
if hasRew { // exclude data for logging -- will be re-computed at start of next trial
// this allows the BadStats to only record estimates, not actuals
nan := math32.NaN()
axon.GlobalScalars[axon.GvPVposEst, diu] = nan
axon.GlobalScalars[axon.GvPVposVar, diu] = nan
axon.GlobalScalars[axon.GvPVnegEst, diu] = nan
axon.GlobalScalars[axon.GvPVnegVar, diu] = nan
}
ss.Stats.SetFloat32("SC", ss.Net.LayerByName("SC").Pool(0, 0).AvgMax.CaSpkD.Cycle.Max)
var allGood float64
agN := 0
if fv := ss.Stats.Float("GateUS"); !math.IsNaN(fv) {
allGood += fv
agN++
}
if fv := ss.Stats.Float("GateCS"); !math.IsNaN(fv) {
allGood += fv
agN++
}
if fv := ss.Stats.Float("ActMatch"); !math.IsNaN(fv) {
allGood += fv
agN++
}
if fv := ss.Stats.Float("GatedEarly"); !math.IsNaN(fv) {
allGood += 1 - fv
agN++
}
if fv := ss.Stats.Float("GatedAgain"); !math.IsNaN(fv) {
allGood += 1 - fv
agN++
}
if fv := ss.Stats.Float("BadCSGate"); !math.IsNaN(fv) {
allGood += 1 - fv
agN++
}
if agN > 0 {
allGood /= float64(agN)
}
ss.Stats.SetFloat("AllGood", allGood)
}
// ActionStatsDi copies the action info from given data parallel index
// into the global action stats
func (ss *Sim) ActionStatsDi(di int) {
if _, has := ss.Stats.Strings[estats.DiName("NetAction", di)]; !has {
return
}
ss.Stats.SetString("NetAction", ss.Stats.StringDi("NetAction", di))
ss.Stats.SetString("Instinct", ss.Stats.StringDi("Instinct", di))
ss.Stats.SetFloat("ActMatch", ss.Stats.FloatDi("ActMatch", di))
ss.Stats.SetString("ActAction", ss.Stats.StringDi("ActAction", di))
ss.Stats.SetString("TraceState", ss.Stats.StringDi("TraceState", di))
ss.Stats.SetInt("TraceStateInt", ss.Stats.IntDi("TraceStateInt", di))
}
// MaxPoolSpkMax returns the maximum across pools of the SpkMax.Plus.Avg stat
func (ss *Sim) MaxPoolSpkMax(ly *axon.Layer, diu uint32) float32 {
np := ly.NPools
mx := float32(0)
for pi := uint32(1); pi < np; pi++ {
v := ly.Pool(pi, diu).AvgMax.SpkMax.Plus.Avg
if v > mx {
mx = v
}
}
return mx
}
// GatedStats updates the gated states
func (ss *Sim) GatedStats(di int) {
ctx := &ss.Context
net := ss.Net
rp := &net.Rubicon
diu := uint32(di)
ev := ss.Envs.ByModeDi(ctx.Mode, di).(*armaze.Env)
justGated := axon.GlobalScalars[axon.GvVSMatrixJustGated, diu] > 0
justGatedF := num.FromBool[float32](justGated)
hasGated := axon.GlobalScalars[axon.GvVSMatrixHasGated, diu] > 0
nan := math32.NaN()
ss.Stats.SetString("Debug", ss.Stats.StringDi("Debug", di))
ss.ActionStatsDi(di)
ss.Stats.SetFloat32("JustGated", justGatedF)
ss.Stats.SetFloat32("Should", num.FromBool[float32](ev.ShouldGate))
ss.Stats.SetFloat32("HasGated", num.FromBool[float32](hasGated))
ss.Stats.SetFloat32("GateUS", nan)
ss.Stats.SetFloat32("GateCS", nan)
ss.Stats.SetFloat32("GatedEarly", nan)
ss.Stats.SetFloat32("MaintEarly", nan)
ss.Stats.SetFloat32("GatedAgain", nan)
ss.Stats.SetFloat32("AChShould", nan)
ss.Stats.SetFloat32("AChShouldnt", nan)
ss.Stats.SetFloat32("BadCSGate", nan)
ss.Stats.SetFloat32("BadUSGate", nan)
ss.Stats.SetFloat32("GateVMtxGo", nan)
ss.Stats.SetFloat32("GateVMtxNo", nan)
ss.Stats.SetFloat32("GateVMtxGoNo", nan)
ss.Stats.SetFloat32("GateBLAposAcq", nan)
ss.Stats.SetFloat32("GateBLAposExt", nan)
ss.Stats.SetFloat32("GateBLAposAcqExt", nan)
hasPos := rp.HasPosUS(ctx, diu)
armIsBest := ev.ArmIsBest(ev.Arm)
armIsBad := num.FromBool[float32](!armIsBest)
if justGated {
if hasPos {
ss.Stats.SetFloat32("BadUSGate", armIsBad)
} else {
ss.Stats.SetFloat32("BadCSGate", armIsBad)
vsgo := net.LayerByName("VMtxGo")
vsno := net.LayerByName("VMtxNo")
goact := ss.MaxPoolSpkMax(vsgo, diu)
noact := ss.MaxPoolSpkMax(vsno, diu)
ss.Stats.SetFloat32("GateVMtxGo", goact)
ss.Stats.SetFloat32("GateVMtxNo", noact)
ss.Stats.SetFloat32("GateVMtxGoNo", goact-noact)
bla := net.LayerByName("BLAposAcqD1")
ble := net.LayerByName("BLAposExtD2")
blaact := ss.MaxPoolSpkMax(bla, diu)
bleact := ss.MaxPoolSpkMax(ble, diu)
ss.Stats.SetFloat32("GateBLAposAcq", blaact)
ss.Stats.SetFloat32("GateBLAposExt", bleact)
ss.Stats.SetFloat32("GateBLAposAcqExt", blaact-bleact)
blanov := net.LayerByName("BLANovelCS")
blanovact := blanov.Pool(0, diu).AvgMax.SpkMax.Plus.Avg
ss.Stats.SetFloat32("GateBLANovelCS", blanovact)
}
}
if ev.ShouldGate {
if hasPos {
ss.Stats.SetFloat32("GateUS", justGatedF)
} else {
ss.Stats.SetFloat32("GateCS", justGatedF)
}
} else {
if hasGated {
ss.Stats.SetFloat32("GatedAgain", justGatedF)
} else { // !should gate means early..
ss.Stats.SetFloat32("GatedEarly", justGatedF)
}
}
// We get get ACh when new CS or Rew
ach := axon.GlobalScalars[axon.GvACh, diu]
if hasPos || ev.LastCS != ev.CurCS() {
ss.Stats.SetFloat32("AChShould", ach)
} else {
ss.Stats.SetFloat32("AChShouldnt", ach)
}
}
// MaintStats updates the PFC maint stats
func (ss *Sim) MaintStats(di int) {
ctx := &ss.Context
diu := uint32(di)
nan := math32.NaN()
ev := ss.Envs.ByModeDi(ctx.Mode, di).(*armaze.Env)
// should be maintaining while going forward
isFwd := ev.LastAct == armaze.Forward
isCons := ev.LastAct == armaze.Consume
actThr := float32(0.05) // 0.1 too high
net := ss.Net
hasGated := axon.GlobalScalars[axon.GvVSMatrixHasGated, diu] > 0
goalMaint := axon.GlobalScalars[axon.GvGoalMaint, diu]
ss.Stats.SetFloat32("GoalMaint", goalMaint)
hasGoalMaint := goalMaint > actThr
lays := net.LayersByType(axon.PTMaintLayer)
otherMaint := false
for _, lnm := range lays {
mnm := "Maint" + lnm
fnm := "MaintFail" + lnm
pnm := "PreAct" + lnm
ptly := net.LayerByName(lnm)
var mact float32
if ptly.Is4D() {
for pi := uint32(1); pi < ptly.NPools; pi++ {
avg := ptly.Pool(pi, diu).AvgMax.Act.Plus.Avg
if avg > mact {
mact = avg
}
}
} else {
mact = ptly.Pool(0, diu).AvgMax.Act.Plus.Avg
}
overThr := mact > actThr
if overThr {
otherMaint = true
}
ss.Stats.SetFloat32(pnm, math32.NaN())
ss.Stats.SetFloat32(mnm, math32.NaN())
ss.Stats.SetFloat32(fnm, math32.NaN())
if isFwd {
ss.Stats.SetFloat32(mnm, mact)
ss.Stats.SetFloat32(fnm, num.FromBool[float32](!overThr))
} else if !isCons {
ss.Stats.SetFloat32(pnm, num.FromBool[float32](overThr))
}
}
ss.Stats.SetFloat32("MaintIncon", num.FromBool[float32](otherMaint != hasGoalMaint))
if hasGoalMaint && !hasGated {
ss.Stats.SetFloat32("MaintEarly", 1)
} else if !hasGated {
ss.Stats.SetFloat32("MaintEarly", 0)
} else {
ss.Stats.SetFloat32("MaintEarly", nan)
}
}
//////////////////////////////////////////////////////////////////////////////
// Logging
func (ss *Sim) ConfigLogs() {
ss.Stats.SetString("RunName", ss.Params.RunName(0)) // used for naming logs, stats, etc
ss.Logs.AddCounterItems(etime.Run, etime.Epoch, etime.Trial, etime.Cycle)
ss.Logs.AddStatIntNoAggItem(etime.AllModes, etime.Trial, "Di")
ss.Logs.AddStatStringItem(etime.AllModes, etime.AllTimes, "RunName")