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optimizer.go
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optimizer.go
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package nn
// Optimizers to be used for gradient-descent based training.
import (
"fmt"
"log"
"math"
"github.com/nullbull/gotch/ts"
)
// Optimizer is a struct object to run gradient descent.
type Optimizer struct {
varstore *VarStore
opt *ts.COptimizer
// variablesInOptimizer uint8
variablesInOptimizer map[string]struct{}
config interface{}
stepCount int
}
// OptimizerConfig defines Optimizer configurations. These configs can be used to build optimizer.
type OptimizerConfig interface {
buildCOpt(lr float64) (*ts.COptimizer, error)
// Build builds an optimizer with the specified learning rate handling variables stored in `vs`.
//
// NOTE: Build is a 'default' method. It can be called by wrapping
// 'DefaultBuild' function
// E.g. AdamOptimizerConfig struct have a method to fullfil `Build` method of
// OptimizerConfig by wrapping `DefaultBuild` like
// (config AdamOptimizerConfig) Build(vs VarStore, lr float64) (retVal Optimizer, err error){
// return defaultBuild(config, vs, lr)
// }
Build(vs *VarStore, lr float64) (*Optimizer, error)
}
// defaultBuild is `default` Build method for OptimizerConfig interface
func defaultBuild(config OptimizerConfig, vs *VarStore, lr float64) (*Optimizer, error) {
opt, err := config.buildCOpt(lr)
if err != nil {
return nil, err
}
names := make(map[string]struct{})
for name, v := range vs.vars {
if v.Trainable {
if err = opt.AddParameter(v.Tensor, v.Group); err != nil {
err = fmt.Errorf("Optimizer defaultBuild - AddParameter failed: %w\n", err)
return nil, err
}
}
names[name] = struct{}{}
}
return &Optimizer{
varstore: vs,
opt: opt,
variablesInOptimizer: names,
config: config,
stepCount: 0,
}, nil
}
// SGD Optimizer:
//===============
// SGDConfig holds parameters for building the SGD (Stochastic Gradient Descent) optimizer.
type SGDConfig struct {
Momentum float64
Dampening float64
Wd float64
Nesterov bool
}
// DefaultSGDConfig creates SGDConfig with default values.
func DefaultSGDConfig() *SGDConfig {
return &SGDConfig{
Momentum: 0.0,
Dampening: 0.0,
Wd: 0.0,
Nesterov: false,
}
}
// NewSGD creates the configuration for a SGD optimizer with specified values
func NewSGDConfig(momentum, dampening, wd float64, nesterov bool) *SGDConfig {
return &SGDConfig{
Momentum: momentum,
Dampening: dampening,
Wd: wd,
Nesterov: nesterov,
}
}
// Implement OptimizerConfig interface for SGDConfig
func (c *SGDConfig) buildCOpt(lr float64) (*ts.COptimizer, error) {
return ts.Sgd(lr, c.Momentum, c.Dampening, c.Wd, c.Nesterov)
}
func (c *SGDConfig) Build(vs *VarStore, lr float64) (*Optimizer, error) {
return defaultBuild(c, vs, lr)
}
// Adam optimizer:
// ===============
type AdamConfig struct {
Beta1 float64
Beta2 float64
Wd float64
}
// DefaultAdamConfig creates AdamConfig with default values
func DefaultAdamConfig() *AdamConfig {
return &AdamConfig{
Beta1: 0.9,
Beta2: 0.999,
Wd: 0.0,
}
}
// NewAdamConfig creates AdamConfig with specified values
func NewAdamConfig(beta1, beta2, wd float64) *AdamConfig {
return &AdamConfig{
Beta1: beta1,
Beta2: beta2,
Wd: wd,
}
}
// Implement OptimizerConfig interface for AdamConfig
func (c *AdamConfig) buildCOpt(lr float64) (*ts.COptimizer, error) {
return ts.Adam(lr, c.Beta1, c.Beta2, c.Wd)
}
func (c *AdamConfig) Build(vs *VarStore, lr float64) (*Optimizer, error) {
return defaultBuild(c, vs, lr)
}
// AdamW optimizer:
// ===============
type AdamWConfig struct {
Beta1 float64
Beta2 float64
Wd float64
}
// DefaultAdamWConfig creates AdamWConfig with default values
func DefaultAdamWConfig() *AdamWConfig {
return &AdamWConfig{
Beta1: 0.9,
Beta2: 0.999,
Wd: 0.01,
}
}
// NewAdamWConfig creates AdamWConfig with specified values
func NewAdamWConfig(beta1, beta2, wd float64) *AdamWConfig {
return &AdamWConfig{
Beta1: beta1,
Beta2: beta2,
Wd: wd,
}
}
// Implement OptimizerConfig interface for AdamWConfig
func (c *AdamWConfig) buildCOpt(lr float64) (*ts.COptimizer, error) {
return ts.AdamW(lr, c.Beta1, c.Beta2, c.Wd)
}
// Build builds AdamW optimizer
func (c *AdamWConfig) Build(vs *VarStore, lr float64) (*Optimizer, error) {
return defaultBuild(c, vs, lr)
}
// RMSProp optimizer:
// ===============
type RMSPropConfig struct {
Alpha float64
Eps float64
Wd float64
Momentum float64
Centered bool
}
// DefaultAdamConfig creates AdamConfig with default values
func DefaultRMSPropConfig() *RMSPropConfig {
return &RMSPropConfig{
Alpha: 0.99,
Eps: 1e-8,
Wd: 0.0,
Momentum: 0.0,
Centered: false,
}
}
// NewRMSPropConfig creates RMSPropConfig with specified values
func NewRMSPropConfig(alpha, eps, wd, momentum float64, centered bool) *RMSPropConfig {
return &RMSPropConfig{
Alpha: alpha,
Eps: eps,
Wd: wd,
Momentum: momentum,
Centered: centered,
}
}
// Implement OptimizerConfig interface for RMSPropConfig
func (c *RMSPropConfig) buildCOpt(lr float64) (*ts.COptimizer, error) {
return ts.RmsProp(lr, c.Alpha, c.Eps, c.Wd, c.Momentum, c.Centered)
}
func (c *RMSPropConfig) Build(vs *VarStore, lr float64) (*Optimizer, error) {
return defaultBuild(c, vs, lr)
}
// Optimizer methods:
// ==================
func (opt *Optimizer) addMissingVariables() {
type param struct {
tensor *ts.Tensor
group uint
}
trainables := make(map[string]param)
for name, v := range opt.varstore.vars {
if v.Trainable {
trainables[name] = param{tensor: v.Tensor, group: v.Group}
}
}
missingVariables := len(trainables) - len(opt.variablesInOptimizer)
if missingVariables > 0 {
log.Println("INFO: Optimizer.addMissingVariables()...")
for name, x := range trainables {
if _, ok := opt.variablesInOptimizer[name]; !ok {
opt.opt.AddParameter(x.tensor, x.group)
opt.variablesInOptimizer[name] = struct{}{}
}
}
}
}
// ZeroGrad zeroes the gradient for the tensors tracked by this optimizer.
func (opt *Optimizer) ZeroGrad() error {
if err := opt.opt.ZeroGrad(); err != nil {
err = fmt.Errorf("Optimizer.ZeroGrad() failed: %w\n", err)
return err
}
return nil
}
// MustZeroGrad zeroes the gradient for the tensors tracked by this optimizer.
func (opt *Optimizer) MustZeroGrad() {
err := opt.ZeroGrad()
if err != nil {
log.Fatal(err)
}
}
// Clips gradient value at some specified maximum value.
func (opt *Optimizer) ClipGradValue(max float64) {
opt.varstore.Lock()
defer opt.varstore.Unlock()
for _, v := range opt.varstore.vars {
if v.Trainable {
// v.Tensor.MustGrad().Clamp_(ts.FloatScalar(-max), ts.FloatScalar(max))
gradTs := v.Tensor.MustGrad(false)
gradTs.Clamp_(ts.FloatScalar(-max), ts.FloatScalar(max))
}
}
}
// Step performs an optimization step, updating the tracked tensors based on their gradients.
func (opt *Optimizer) Step() error {
err := opt.opt.Step()
if err != nil {
err = fmt.Errorf("Optimizer.Step() failed: %w\n", err)
return err
}
opt.stepCount += 1
return nil
}
// MustStep performs an optimization step, updating the tracked tensors based on their gradients.
func (opt *Optimizer) MustStep() {
err := opt.Step()
if err != nil {
log.Fatal(err)
}
}
// ResetStepCount set step count to zero.
func (opt *Optimizer) ResetStepCount() {
opt.stepCount = 0
}
// StepCount get current step count.
func (opt *Optimizer) StepCount() int {
return opt.stepCount
}
// BackwardStep applies a backward step pass, update the gradients, and performs an optimization step.
func (opt *Optimizer) BackwardStep(loss *ts.Tensor) error {
err := opt.opt.ZeroGrad()
if err != nil {
err = fmt.Errorf("Optimizer.BackwardStep() failed: %w\n", err)
return err
}
loss.MustBackward()
err = opt.opt.Step()
if err != nil {
err = fmt.Errorf("Optimizer.BackwardStep() failed: %w\n", err)
return err
}
return nil
}
// MustBackwardStep applies a backward step pass, update the gradients, and performs an optimization step.
func (opt *Optimizer) MustBackwardStep(loss *ts.Tensor) {
err := opt.BackwardStep(loss)
if err != nil {
log.Fatal(err)
}
}
// BackwardStepClip applies a backward step pass, update the gradients, and performs an optimization step.
//
// The gradients are clipped based on `max` before being applied.
func (opt *Optimizer) BackwardStepClip(loss *ts.Tensor, max float64) error {
err := opt.opt.ZeroGrad()
if err != nil {
err = fmt.Errorf("Optimizer.BackwardStepClip() failed: %w\n", err)
return err
}
loss.MustBackward()
opt.ClipGradValue(max)
err = opt.opt.Step()
if err != nil {
err = fmt.Errorf("Optimizer.BackwardStepClip() failed: %w\n", err)
return err
}
return nil
}
// MustBackwardStepClip applies a backward step pass, update the gradients, and performs an optimization step.
//
// The gradients are clipped based on `max` before being applied.
func (opt *Optimizer) MustBackwardStepClip(loss *ts.Tensor, max float64) {
err := opt.BackwardStepClip(loss, max)
if err != nil {
log.Fatal(err)
}
}
type ClipOpts struct {
NormType float64
ErrorIfNonFinite bool
}
type ClipOpt func(*ClipOpts)
func defaultClipOpts() *ClipOpts {
return &ClipOpts{
NormType: 2.0,
ErrorIfNonFinite: false, // will switch to "true" in the future.
}
}
func WithNormType(v float64) ClipOpt {
return func(o *ClipOpts) {
o.NormType = v
}
}
func WithErrorIfNonFinite(v bool) ClipOpt {
return func(o *ClipOpts) {
o.ErrorIfNonFinite = v
}
}
// / Clips gradient L2 norm over all trainable parameters.
//
// The norm is computed over all gradients together, as if they were
// concatenated into a single vector.
//
// / Args:
// - max: max norm of the gradient
// - o.NormType. Type of the used p-norm, can be "inf" for infinity norm. Default= 2.0
// - o.ErrorIfNonFinite bool. If true, throw error if total norm of the gradients from paramters is "nan", "inf" or "-inf". Default=false
// Returns: total norm of the parameters (viewed as a single vector)
// ref. https://github.com/pytorch/pytorch/blob/cb4aeff7d8e4c70bb638cf159878c5204d0cc2da/torch/nn/utils/clip_grad.py#L59
func (opt *Optimizer) ClipGradNorm(max float64, opts ...ClipOpt) error {
o := defaultClipOpts()
for _, option := range opts {
option(o)
}
opt.varstore.Lock()
defer opt.varstore.Unlock()
parameters := opt.varstore.TrainableVariables()
if len(parameters) == 0 {
// return ts.MustOfSlice([]float64{0.0}), nil
return nil
}
var (
norms []*ts.Tensor
totalNorm *ts.Tensor
)
device := opt.varstore.device
// FIXME. What about mixed-precision?
dtype := parameters[0].DType()
if o.NormType == math.Inf(1) {
for _, v := range opt.varstore.vars {
n := v.Tensor.MustGrad(false).MustDetach(true).MustAbs(true).MustMax(true).MustTo(device, true)
norms = append(norms, n)
}
// total_norm = norms[0] if len(norms) == 1 else torch.max(torch.stack(norms))
totalNorm = ts.MustStack(norms, 0).MustMax(true)
} else {
for _, v := range opt.varstore.vars {
// x := v.Tensor.MustGrad(false).MustNorm(true)
// NOTE. tensor.Norm() is going to be deprecated. So use linalg_norm
// Ref. https://pytorch.org/docs/stable/generated/torch.linalg.norm.html#torch.linalg.norm
x := v.Tensor.MustGrad(false).MustDetach(true).MustLinalgNorm(ts.FloatScalar(o.NormType), nil, false, dtype, true)
norms = append(norms, x)
}
}
// totalNorm = ts.MustStack(norms, 0).MustNorm(true).MustAddScalar(ts.FloatScalar(1e-6), true)
// total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type)
totalNorm = ts.MustStack(norms, 0).MustLinalgNorm(ts.FloatScalar(o.NormType), nil, false, dtype, true)
for _, x := range norms {
x.MustDrop()
}
totalNormVal := totalNorm.Float64Values(true)[0]
// if error_if_nonfinite and torch.logical_or(total_norm.isnan(), total_norm.isinf()):
if o.ErrorIfNonFinite && (math.IsNaN(totalNormVal) || math.IsInf(totalNormVal, 1)) {
err := fmt.Errorf("The total norm of order (%v) for gradients from 'parameters' is non-finite, so it cannot be clipped. To disable this error and scale the gradients by the non-finite norm anyway, set option.ErrorIfNonFinite= false", o.NormType)
return err
}
// clip_coef = max_norm / (total_norm + 1e-6)
// clipCoefTs := ts.TensorFrom([]float64{max}).MustDiv(totalNorm, true)
clipCoef := max / (totalNormVal + 1e-6)
// NOTE: multiplying by the clamped coef is redundant when the coef is clamped to 1, but doing so
// avoids a `if clip_coef < 1:` conditional which can require a CPU <=> device synchronization
// when the gradients do not reside in CPU memory.
// clip_coef_clamped = torch.clamp(clip_coef, max=1.0)
if clipCoef > 1.0 {
clipCoef = 1.0
}
for _, v := range opt.varstore.vars {
if v.Trainable {
// p.grad.detach().mul_(clip_coef_clamped.to(p.grad.device))
// v.Tensor.MustGrad(false).MustDetach(true).MustMulScalar_(ts.FloatScalar(clipCoef))
v.Tensor.MustGrad(false).MustMulScalar_(ts.FloatScalar(clipCoef))
}
}
return nil
}
// BackwardStepClipNorm applies a backward step pass, update the gradients, and performs an optimization step.
//
// The gradients L2 norm is clipped based on `max`.
func (opt *Optimizer) BackwardStepClipNorm(loss *ts.Tensor, max float64, opts ...ClipOpt) error {
err := opt.opt.ZeroGrad()
if err != nil {
err := fmt.Errorf("Optimizer.BackwardStepClipNorm() failed: %w\n", err)
return err
}
err = loss.Backward()
if err != nil {
err := fmt.Errorf("Optimizer.BackwardStepClipNorm() failed: %w\n", err)
return err
}
err = opt.ClipGradNorm(max, opts...)
if err != nil {
err := fmt.Errorf("Optimizer.BackwardStepClipNorm() failed: %w\n", err)
return err
}
err = opt.Step()
if err != nil {
err := fmt.Errorf("Optimizer.BackwardStepClipNorm() failed: %w\n", err)
return err
}
return nil
}
// MustBackwardStepClipNorm applies a backward step pass, update the gradients, and performs an optimization step.
//
// The gradients L2 norm is clipped based on `max`.
func (opt *Optimizer) MustBackwardStepClipNorm(loss *ts.Tensor, max float64, opts ...ClipOpt) {
err := opt.BackwardStepClipNorm(loss, max, opts...)
if err != nil {
log.Fatal(err)
}
}
// SetLR sets the optimizer learning rate.
//
// NOTE. it sets a SINGLE value of learning rate for all parameter groups.
// Most of the time, there's one parameter group.
func (opt *Optimizer) SetLR(lr float64) {
err := opt.opt.SetLearningRate(lr)
if err != nil {
log.Fatalf("Optimizer - SetLR method call error: %v\n", err)
}
}
func (opt *Optimizer) GetLRs() []float64 {
lrs, err := opt.opt.GetLearningRates()
if err != nil {
log.Fatalf("Optimizer - GetLRs method call error: %v\n", err)
}
return lrs
}
// SetLRs sets learning rates for ALL parameter groups respectively.
func (opt *Optimizer) SetLRs(lrs []float64) {
err := opt.opt.SetLearningRates(lrs)
if err != nil {
log.Fatalf("Optimizer - SetLRs method call error: %v\n", err)
}
}
// SetMomentum sets the optimizer momentum.
func (opt *Optimizer) SetMomentum(m float64) {
err := opt.opt.SetMomentum(m)
if err != nil {
log.Fatalf("Optimizer - SetMomentum method call error: %v\n", err)
}
}
func (opt *Optimizer) ParamGroupNum() int {
ngroup, err := opt.opt.ParamGroupNum()
if err != nil {
log.Fatalf("Optimizer - ParamGroupNum method call error: %v\n", err)
}
return int(ngroup)
}
func (opt *Optimizer) AddParamGroup(tensors []*ts.Tensor) {
err := opt.opt.AddParamGroup(tensors)
if err != nil {
log.Fatalf("Optimizer - ParamGroupNum method call error: %v\n", err)
}
}