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moduleprivate.go
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moduleprivate.go
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package gocunets
import (
"errors"
"fmt"
"github.com/dereklstinson/gocunets/devices/gpu/nvidia"
"github.com/dereklstinson/gocunets/trainer"
gocudnn "github.com/dereklstinson/gocudnn"
// "github.com/dereklstinson/cutil"
)
type module struct {
id int64
c *Concat
b *Builder
layers []*Layer
activ *Layer
x, dx, y, dy *Tensor
batchsize int
deconvolutional bool
}
type initialization struct {
dims []int32
strides []int32
batch int32
nearuons []int32
falpha, fbeta, balpha, bbeta float64
}
//CreateSimpleModule will create a simple module with each of the convolution layers being in parallel.
//The parallel convolution will have the same hw, but the channels for each can be changed.
//The number of convolutions depends on the length of the channel array.
//Each convolution will have pad = ((dim-1)/2) *dilation. This will make the output for each convolution equal in the spacial dims.
//The strides are stuck at 2.
//
//This considers a stride of 2 spacial dims (hw) need to be odd.
//This will preform a deconvolution with the formula for the output tensor:
//
//N= batch;
//
//C = [neurons[0]+ ... +neurons[i]];
//
//H,W and more (spacial dims) = input
//
func createModule(id int64, bldr *Builder,
batch, inputchannels int32, outputchannels []int32,
spacialdims []int32,
paddingoffset int32,
falpha, fbeta float64,
strides, deconvolution bool) (m *module, err error) {
m = new(module)
m.b = bldr
m.id = id
m.batchsize = int(batch)
m.deconvolutional = deconvolution
m.layers = make([]*Layer, len(outputchannels))
var stride = int32(1)
if strides {
stride = 2
}
//divback := float64(len(m.layers))
if deconvolution {
for i := range m.layers {
filterdims, pads, strides, dilations, err := deconvolutionparameterdims(inputchannels,
outputchannels[i],
stride,
spacialdims,
paddingoffset,
bldr.Frmt,
i)
// fmt.Println("filterdims,pads,strides,dilations", filterdims, pads, strides, dilations)
if err != nil {
return nil, err
}
w, dw, b, db, err := bldr.CreateDeconvolutionWeights(filterdims)
if err != nil {
return nil, err
}
m.layers[i], err = bldr.ReverseConvolutionLayer(int64(i), 1, w, dw, b, db, pads, strides, dilations)
if err != nil {
return nil, err
}
m.layers[i].SetForwardScalars(1, 0)
m.layers[i].SetOtherScalars(1, 0)
m.layers[i].SetBackwardScalars(1, 1)
}
} else {
for i := range m.layers {
//fmt.Println("outputchannels, stride spacialdims, paddingoffset, fmt, index", outputchannels[i], stride, spacialdims, paddingoffset, bldr.Frmt, i)
filterdims, pads, strides, dilations, err := convolutionparameterdims(inputchannels,
outputchannels[i],
stride,
spacialdims,
paddingoffset,
bldr.Frmt,
i)
if err != nil {
return nil, err
}
w, dw, b, db, err := bldr.CreateConvolutionWeights(filterdims)
if err != nil {
return nil, err
}
m.layers[i], err = bldr.ConvolutionLayer(int64(i), 1, w, dw, b, db, pads, strides, dilations)
if err != nil {
return nil, err
}
m.layers[i].SetForwardScalars(1, 0)
m.layers[i].SetOtherScalars(1, 0)
m.layers[i].SetBackwardScalars(1, 1)
}
}
m.c, err = CreateConcat(bldr.h)
if err != nil {
return nil, err
}
m.c.c.SetForwardAlpha(1)
m.c.c.SetForwardBeta(0)
m.c.c.SetBackwardAlpha(1)
m.c.c.SetBackwardBeta(0)
m.activ, err = bldr.Activation(int64(len(m.layers)))
if err != nil {
return nil, err
}
m.activ.activation.SetForwardScalars(falpha, fbeta)
m.activ.activation.SetBackwardScalars(1, 0)
return m, nil
}
func convolutionparameterdims(inputchannels, outputchannel, stride int32,
spacialdims []int32, paddingoffset int32, frmt TensorFormat, index int) (fdims, pads, strides, dils []int32, err error) {
fdims = make([]int32, len(spacialdims)+2)
fdims[0] = outputchannel
pads = make([]int32, len(spacialdims))
strides = make([]int32, len(spacialdims))
dils = make([]int32, len(spacialdims))
flg := frmt
switch frmt {
case flg.NCHW():
fdims[1] = inputchannels //output channel size for deconv is the neuron channels
for i := 0; i < len(spacialdims); i++ {
dim := spacialdims[i]
dilation, pad, err := recommendedpaddilation(dim, (int32)(index), stride, paddingoffset)
if err != nil {
return nil, nil, nil, nil, err
}
fdims[i+2] = dim
dils[i] = dilation
pads[i] = pad
strides[i] = stride
}
case flg.NHWC():
for i := 0; i < len(spacialdims); i++ {
fdims[i+1] = spacialdims[i]
dim := spacialdims[i]
dilation, pad, err := recommendedpaddilation(dim, (int32)(index), stride, paddingoffset)
if err != nil {
return nil, nil, nil, nil, err
}
fdims[i+1] = dim
dils[i] = dilation
pads[i] = pad
strides[i] = stride
}
fdims[len(fdims)-1] = inputchannels
default:
return nil, nil, nil, nil, errors.New("Unsupported Format")
}
return fdims, pads, strides, dils, nil
}
func deconvolutionparameterdims(inputchannels,
outputchannel,
stride int32,
spacialdims []int32,
paddingoffset int32,
frmt TensorFormat,
index int) (fdims, pads, strides, dils []int32, err error) {
fdims = make([]int32, len(spacialdims)+2)
//convolution filter of NCHW (OuputChannels,Inputchannels h,w) could actually be thought of
//reverse convolution would be (InputChannels,OutputChannels, h,w)
fdims[0] = inputchannels
pads = make([]int32, len(spacialdims))
strides = make([]int32, len(spacialdims))
dils = make([]int32, len(spacialdims))
flg := frmt
switch frmt {
case flg.NCHW():
fdims[1] = outputchannel //output channel size for deconv is the neuron channels
for i := 0; i < len(spacialdims); i++ {
dim := spacialdims[i]
dilation, pad, err := recommendedpaddilation(dim, (int32)(index), stride, paddingoffset)
if err != nil {
return nil, nil, nil, nil, err
}
fdims[i+2] = dim
dils[i] = dilation
pads[i] = pad
strides[i] = stride
}
case flg.NHWC():
for i := 0; i < len(spacialdims); i++ {
fdims[i+1] = spacialdims[i]
dim := spacialdims[i]
dilation, pad, err := recommendedpaddilation(dim, (int32)(index), stride, paddingoffset)
if err != nil {
return nil, nil, nil, nil, err
}
fdims[i+2] = dim
dils[i] = dilation
pads[i] = pad
strides[i] = stride
}
fdims[len(fdims)-1] = outputchannel
default:
return nil, nil, nil, nil, errors.New("Unsupported Format")
}
return fdims, pads, strides, dils, nil
}
//ID is the id
func (m *module) ID() int64 {
return m.id
}
//Forward does the forward operation
func (m *module) Forward() (err error) {
for i := range m.layers {
err = m.layers[i].forwardprop()
if err != nil {
if moduleforwarddebugging {
fmt.Println("error on forward index:", i)
fmt.Println(m.layers[i])
}
return err
}
}
err = m.c.Forward()
if err != nil {
return err
}
err = m.activ.forwardprop()
if err != nil {
return err
}
return nil
}
//Backward does the backward propagation
func (m *module) Backward() (err error) {
if m.dx != nil {
err = m.dx.SetValues(m.b.h.Handler, 0)
if err != nil {
return err
}
}
err = m.activ.backpropdata()
if err != nil {
return err
}
if moduleactivationdebugging {
m.activ.dx.TogglePrintValueForStringer()
fmt.Println("ActivationDX", m.activ.dx)
m.activ.dx.TogglePrintValueForStringer()
}
err = m.c.Backward()
if err != nil {
return err
}
if moduleconcatdebugging {
// m.concatdy.TogglePrintValueForStringer()
// fmt.Println("ConcatDy", m.concatdy)
// m.concatdy.TogglePrintValueForStringer()
for _, src := range m.c.deltasrcs {
src.TogglePrintValueForStringer()
fmt.Println("Concat Src: ", src)
src.TogglePrintValueForStringer()
}
}
for i := range m.layers {
err = m.layers[i].backpropfilterdata()
if err != nil {
return err
}
}
if err != nil {
return err
}
return nil
}
//Update updates the weights of the hidden convolution layer
func (m *module) Update(epoch int) (err error) {
for _, l := range m.layers {
err = l.updateWeights(epoch)
if err != nil {
return err
}
}
return nil
}
//FindOutputDims returns the output dims of the module
func (m *module) FindOutputDims() (dims []int32, err error) {
if m.x == nil {
return nil, errors.New(" (m *SimpleModule) FindOutputDims() : input tensor is nil")
}
preconcatdims := make([][]int32, len(m.layers))
for i := range m.layers {
preconcatdims[i], err = m.layers[i].GetOutputDims(m.x)
if err != nil {
return nil, err
}
if moduleconcatdebugging {
fmt.Println("Preconcatdims", preconcatdims[i])
}
}
dims, err = m.c.c.GetOutputDimsfromInputDims(preconcatdims, m.x.Tensor.Format())
return dims, err
}
//InitHiddenLayers will init the hidden layers. If
func (m *module) InitHiddenLayers(rate, decay1, decay2 float32) (err error) {
if m.x == nil || m.dy == nil || m.y == nil {
return errors.New("(m *module) InitHiddenLayers(): inputtensor x is nil || dy is nil || y is nil")
}
for i := range m.layers {
outputdims, err := m.layers[i].GetOutputDims(m.x)
if err != nil {
return err
}
m.layers[i].x, m.layers[i].dx = m.x, m.dx
m.layers[i].y, err = m.b.CreateTensor(outputdims)
if err != nil {
return err
}
m.layers[i].dy, err = m.b.CreateTensor(outputdims)
if err != nil {
return err
}
if m.layers[i].cnn != nil {
err = m.layers[i].cnn.MakeRandom(m.layers[i].h.Handler, m.layers[i].x.Dims())
m.b.h.Sync()
} else if m.layers[i].cnntranspose != nil {
err = m.layers[i].cnntranspose.MakeRandom(m.layers[i].h.Handler, m.layers[i].x.Dims())
m.b.h.Sync()
}
m.b.h.Sync()
if err != nil {
return err
}
w, bias, err := trainer.SetupAdamWandB(m.b.h.XHandle(), decay1, decay2, int32(m.batchsize))
if err != nil {
return errors.New("(m *module) InitHiddenLayers(b *Builder, decay1,decay2 float32, batch int32)" + err.Error())
}
w.SetRates(rate, 0)
bias.SetRates(rate, 0)
err = m.layers[i].LoadTrainer(m.b.h.Handler, m.batchsize, w, bias)
if err != nil {
return errors.New("(m *module) InitHiddenLayers(b *Builder, decay1,decay2 float32, batch int32)" + err.Error())
}
}
srcs := make([]*Tensor, len(m.layers))
deltasrcs := make([]*Tensor, len(m.layers))
for i := range m.layers {
srcs[i] = m.layers[i].y
deltasrcs[i] = m.layers[i].dy
}
outputdims, err := m.c.FindOutputDims(srcs)
if err != nil {
return err
}
concaty, err := m.b.CreateTensor(outputdims)
if err != nil {
return err
}
concatdy, err := m.b.CreateTensor(outputdims)
if err != nil {
return err
}
m.c.SetInputSrcs(srcs)
m.c.SetInputDeltaSrcs(deltasrcs)
m.c.SetDest(concaty)
m.c.SetDeltaDest(concatdy)
m.activ.dy, m.activ.dx = m.dy, concatdy
m.activ.y, m.activ.x = m.y, concaty
return nil
}
var performancedebugging bool
//PerformanceDebugging - if function called it raises flag to print performance for inner layers
func PerformanceDebugging() {
performancedebugging = true
}
//InitWorkspace inits the hidden workspace
func (m *module) InitWorkspace() (err error) {
noerror := gocudnn.Status(0)
var flag bool
for _, l := range m.layers {
if l.cnn != nil {
fwds, err := l.cnn.GetFwdAlgoPerfList(l.h.Handler, l.x.Tensor, l.y.Tensor, nil)
for _, fwd := range fwds {
if noerror == fwd.Status {
if performancedebugging {
fmt.Println(fwd)
}
l.cnn.SetFwdAlgoPerformance(fwd)
if fwd.Memory > 0 {
l.workspacefwd, err = nvidia.MallocGlobal(l.h.Handler, fwd.Memory)
if err != nil {
return err
}
}
flag = true
break
}
}
if !flag {
return errors.New("cnnInitForwardPerformanceFail")
}
bwds, err := l.cnn.GetBwdDataAlgoPerfList(l.h.Handler, l.x.Tensor, l.y.Tensor, nil)
for _, bwd := range bwds {
if noerror == bwd.Status {
if performancedebugging {
fmt.Println(bwd)
}
l.cnn.SetBwdDataAlgoPerformance(bwd)
if bwd.Memory > 0 {
l.workspacebwd, err = nvidia.MallocGlobal(l.h.Handler, bwd.Memory)
if err != nil {
return err
}
}
flag = true
break
}
}
if !flag {
return errors.New("cnnInitBackwardDataPerformanceFail")
}
bwfs, err := l.cnn.GetBwdFiltAlgoPerfList(l.h.Handler, l.x.Tensor, l.y.Tensor, nil)
for _, bwf := range bwfs {
if noerror == bwf.Status {
if performancedebugging {
fmt.Println(bwf)
}
l.cnn.SetBwdFiltAlgoPerformance(bwf)
if bwf.Memory > 0 {
l.workspacebwf, err = nvidia.MallocGlobal(l.h.Handler, bwf.Memory)
if err != nil {
return err
}
}
flag = true
break
}
}
if !flag {
return errors.New("cnnInitBackwardFilterPerformanceFail")
}
}
if l.cnntranspose != nil {
fwds, err := l.cnntranspose.GetFwdAlgoPerfList(l.h.Handler, l.x.Tensor, l.y.Tensor, nil)
for _, fwd := range fwds {
if noerror == fwd.Status {
if performancedebugging {
fmt.Println(fwd)
}
l.cnntranspose.SetFwdAlgoPerformance(fwd)
if fwd.Memory > 0 {
l.workspacefwd, err = nvidia.MallocGlobal(l.h.Handler, fwd.Memory)
if err != nil {
return err
}
}
flag = true
break
}
}
if !flag {
return errors.New("cnntransposeInitForwardPerformanceFail")
}
bwds, err := l.cnntranspose.GetBwdDataAlgoPerfList(l.h.Handler, l.x.Tensor, l.y.Tensor, nil)
for _, bwd := range bwds {
if noerror == bwd.Status {
if performancedebugging {
fmt.Println(bwd)
}
l.cnntranspose.SetBwdDataAlgoPerformance(bwd)
if bwd.Memory > 0 {
l.workspacebwd, err = nvidia.MallocGlobal(l.h.Handler, bwd.Memory)
if err != nil {
return err
}
}
flag = true
break
}
}
if !flag {
return errors.New("cnntransposeInitBackwardDataPerformanceFail")
}
bwfs, err := l.cnntranspose.GetBwdFiltAlgoPerfList(l.h.Handler, l.x.Tensor, l.y.Tensor, nil)
for _, bwf := range bwfs {
if noerror == bwf.Status {
if performancedebugging {
fmt.Println(bwf)
}
l.cnntranspose.SetBwdFiltAlgoPerformance(bwf)
if bwf.Memory > 0 {
l.workspacebwf, err = nvidia.MallocGlobal(l.h.Handler, bwf.Memory)
if err != nil {
return err
}
}
flag = true
break
}
}
if !flag {
return errors.New("cnntransposeInitBackwardFilterPerformanceFail")
}
}
}
return nil
}
//Inference does the inference forward operation
func (m *module) Inference() (err error) {
return m.Forward()
}
//GetTensorX returns set x tensor
func (m *module) GetTensorX() (x *Tensor) { return m.x }
//GetTensorDX returns set dx tensor
func (m *module) GetTensorDX() (dx *Tensor) { return m.dx }
//GetTensorY returns set y tensor
func (m *module) GetTensorY() (y *Tensor) { return m.y }
//GetTensorDY returns set dy tensor
func (m *module) GetTensorDY() (dy *Tensor) { return m.dy }
//SetTensorX sets x tensor
func (m *module) SetTensorX(x *Tensor) {
m.x = x
if m.layers[0] != nil {
m.layers[0].x = x
}
}
//SetTensorDX sets dx tensor
func (m *module) SetTensorDX(dx *Tensor) {
m.dx = dx
if m.layers[0] != nil {
m.layers[0].dx = dx
}
}
//SetTensorY sets y tensor
func (m *module) SetTensorY(y *Tensor) {
m.y = y
if m.activ != nil {
m.activ.y = y
}
}
//SetTensorDY sets dy tensor
func (m *module) SetTensorDY(dy *Tensor) {
m.dy = dy
if m.activ != nil {
m.activ.dy = dy
}
}