# default_exp optimizer

#export
from fastai2.torch_basics import *

#hide
from nbdev.showdoc import *

#export
class _BaseOptimizer():
    "Common functionality between `Optimizer` and `OptimWrapper`"
    def all_params(self, n=slice(None), with_grad=False):
        res = L((p,pg,self.state[p],hyper) for pg,hyper in zip(self.param_lists[n],self.hypers[n]) for p in pg)
        return L(o for o in res if o[0].grad is not None) if with_grad else res

    def _set_require_grad(self, rg, p,pg,state,h): p.requires_grad_(rg or state.get('force_train', False))
    def freeze_to(self, n):
        self.frozen_idx = n if n >= 0 else len(self.param_lists) + n
        if self.frozen_idx >= len(self.param_lists):
            warn(f"Freezing {self.frozen_idx} groups; model has {len(self.param_lists)}; whole model is frozen.")
        for o in self.all_params(slice(n, None)): self._set_require_grad(True,  *o)
        for o in self.all_params(slice(None, n)): self._set_require_grad(False, *o)

    def freeze(self):
        assert(len(self.param_lists)>1)
        self.freeze_to(-1)

    def set_freeze(self, n, rg, ignore_force_train=False):
        for p in self.param_lists[n]: p.requires_grad_(rg or (state.get('force_train', False) and not ignore_force_train))

    def unfreeze(self): self.freeze_to(0)
    def set_hypers(self, **kwargs): L(kwargs.items()).starmap(self.set_hyper)
    def _set_hyper(self, k, v):
        for v_,h in zip(v, self.hypers): h[k] = v_

    def set_hyper(self, k, v):
        if isinstance(v, slice):
            if v.start: v = even_mults(v.start, v.stop, len(self.param_lists))
            else: v = [v.stop/10]*(len(self.param_lists)-1) + [v.stop]
        v = L(v, use_list=None)
        if len(v)==1: v = v*len(self.param_lists)
        assert len(v) == len(self.hypers), f"Trying to set {len(v)} values for {k} but there are {len(self.param_lists)} parameter groups."
        self._set_hyper(k, v)

    @property
    def param_groups(self): return [{**{'params': pg}, **hp} for pg,hp in zip(self.param_lists, self.hypers)]
    @param_groups.setter
    def param_groups(self, v):
        for pg,v_ in zip(self.param_lists,v): pg = v_['params']
        for hyper,v_ in zip(self.hypers,v):
            for k,t in v_.items():
                if k != 'params': hyper[k] = t

add_docs(_BaseOptimizer, 
         all_params="List of param_groups, parameters, and hypers",
         freeze_to="Freeze parameter groups up to `n`",
         freeze="Freeze up to last parameter group",
         set_freeze="Set `rg` for parameter group `n` only",
         unfreeze="Unfreeze the entire model",
         set_hypers="`set_hyper` for all `kwargs`",
         set_hyper="Set the value(s) in `v` for hyper-parameter `k`")

#export
def _update(state, new=None):
    if new is None: return state
    if isinstance(new, dict): state.update(new)
    return state

# export
@log_args(but='params,cbs,defaults')
class Optimizer(_BaseOptimizer):
    "Base optimizer class for the fastai library, updating `params` with `cbs`"
    _keep_on_clear = ['force_train', 'do_wd']
    def __init__(self, params, cbs, train_bn=True, **defaults):
        params = L(params)
        self.cbs,self.state,self.train_bn = L(cbs),defaultdict(dict),train_bn
        defaults = merge(*self.cbs.attrgot('defaults'), defaults)
        self.param_lists = L(L(p) for p in params) if isinstance(params[0], (L,list)) else L([params])
        self.hypers = L({} for _ in range_of(self.param_lists))
        self.set_hypers(**defaults)
        self.frozen_idx = 0

    def zero_grad(self):
        for p,*_ in self.all_params(with_grad=True):
            p.grad.detach_()
            p.grad.zero_()

    def step(self):
        for p,pg,state,hyper in self.all_params(with_grad=True):
            for cb in self.cbs: state = _update(state, cb(p, **{**state, **hyper}))
            self.state[p] = state

    def clear_state(self):
        for p,pg,state,hyper in self.all_params():
            self.state[p] = {k: state[k] for k in self._keep_on_clear if k in state}

    def state_dict(self):
        state = [self.state[p] for p,*_ in self.all_params()]
        return {'state': state, 'hypers': self.hypers}

    def load_state_dict(self, sd):
        assert len(sd["hypers"]) == len(self.param_lists)
        assert len(sd["state"])  == sum([len(pg) for pg in self.param_lists])
        self.hypers = sd['hypers']
        self.state = {p: s for p,s in zip(self.all_params().itemgot(0), sd['state'])}

add_docs(Optimizer, 
         zero_grad="Standard PyTorch API: Zero all the grad attributes of the parameters",
         step="Standard PyTorch API: Update the stats and execute the steppers in on all parameters that have a grad",
         state_dict="Return the state of the optimizer in a dictionary",
         load_state_dict="Load the content of `sd`",
         clear_state="Reset the state of the optimizer")

opt = Optimizer([1,2,3], noop)
test_eq(opt.param_lists, [[1,2,3]])
opt = Optimizer(range(3), noop)
test_eq(opt.param_lists, [[0,1,2]])
opt = Optimizer([[1,2],[3]], noop)
test_eq(opt.param_lists, [[1,2],[3]])
opt = Optimizer(([o,o+1] for o in range(0,4,2)), noop)
test_eq(opt.param_lists, [[0,1],[2,3]])

def tst_arg(p, lr=0, **kwargs): return p
tst_arg.defaults = dict(lr=1e-2)

def tst_arg2(p, lr2=0, **kwargs): return p
tst_arg2.defaults = dict(lr2=1e-3)

def tst_arg3(p, mom=0, **kwargs): return p
tst_arg3.defaults = dict(mom=0.9)

def tst_arg4(p, **kwargs): return p

opt = Optimizer([1,2,3], [tst_arg,tst_arg2, tst_arg3])
test_eq(opt.hypers, [{'lr2': 1e-3, 'mom': 0.9, 'lr': 1e-2}])
opt = Optimizer([1,2,3], tst_arg, lr=0.1)
test_eq(opt.hypers, [{'lr': 0.1}])
opt = Optimizer([[1,2],[3]], tst_arg)
test_eq(opt.hypers, [{'lr': 1e-2}, {'lr': 1e-2}])
opt = Optimizer([[1,2],[3]], tst_arg, lr=0.1)
test_eq(opt.hypers, [{'lr': 0.1}, {'lr': 0.1}])

opt = Optimizer([[1,2],[3]], tst_arg, lr=[0.1,0.2])
test_eq(opt.hypers, [{'lr': 0.1}, {'lr': 0.2}])
opt = Optimizer([[1,2],[3],[4]], tst_arg, lr=slice(1e-2))
test_eq(opt.hypers, [{'lr': 1e-3}, {'lr': 1e-3}, {'lr': 1e-2}])
opt = Optimizer([[1,2],[3],[4]], tst_arg, lr=slice(1e-4,1e-2))
test_eq(opt.hypers, [{'lr': 1e-4}, {'lr': 1e-3}, {'lr': 1e-2}])
test_eq(opt.param_groups, [{'params': [1,2], 'lr': 1e-4}, {'params': [3], 'lr': 1e-3}, {'params': [4], 'lr': 1e-2}])
test_fail(lambda: Optimizer([[1,2],[3],[4]], tst_arg, lr=np.array([0.1,0.2])))

#export
def sgd_step(p, lr, **kwargs):
    p.data.add_(p.grad.data, alpha=-lr)

def tst_param(val, grad=None):
    "Create a tensor with `val` and a gradient of `grad` for testing"
    res = tensor([val]).float()
    res.grad = tensor([val/10 if grad is None else grad]).float()
    return res

p = tst_param(1., 0.1)
sgd_step(p, 1.)
test_eq(p, tensor([0.9]))
test_eq(p.grad, tensor([0.1]))

#export
def weight_decay(p, lr, wd, do_wd=True, **kwargs):
    "Weight decay as decaying `p` with `lr*wd`"
    if do_wd and wd!=0: p.data.mul_(1 - lr*wd)

weight_decay.defaults = dict(wd=0.)

p = tst_param(1., 0.1)
weight_decay(p, 1., 0.1)
test_eq(p, tensor([0.9]))
test_eq(p.grad, tensor([0.1]))

#export
def l2_reg(p, lr, wd, do_wd=True, **kwargs):
    "L2 regularization as adding `wd*p` to `p.grad`"
    if do_wd and wd!=0: p.grad.data.add_(p.data, alpha=wd)

l2_reg.defaults = dict(wd=0.)

p = tst_param(1., 0.1)
l2_reg(p, 1., 0.1)
test_eq(p, tensor([1.]))
test_eq(p.grad, tensor([0.2]))

show_doc(Optimizer.step)

#test basic step
r = L.range(4)
def tst_params(): return r.map(tst_param)

params = tst_params()
opt = Optimizer(params, sgd_step, lr=0.1)
opt.step()
test_close([p.item() for p in params], r.map(mul(0.99)))

#test two steps
params = tst_params()
opt = Optimizer(params, [weight_decay, sgd_step], lr=0.1, wd=0.1)
opt.step()
test_close([p.item() for p in params], r.map(mul(0.98)))

#test None gradients are ignored
params = tst_params()
opt = Optimizer(params, sgd_step, lr=0.1)
params[-1].grad = None
opt.step()
test_close([p.item() for p in params], [0., 0.99, 1.98, 3.])

#test discriminative lrs
params = tst_params()
opt = Optimizer([params[:2], params[2:]], sgd_step, lr=0.1)
opt.hypers[0]['lr'] = 0.01
opt.step()
test_close([p.item() for p in params], [0., 0.999, 1.98, 2.97])

show_doc(Optimizer.zero_grad)

params = tst_params()
opt = Optimizer(params, [weight_decay, sgd_step], lr=0.1, wd=0.1)
opt.zero_grad()
[test_eq(p.grad, tensor([0.])) for p in params];

def tst_stat(p, **kwargs): 
    s = kwargs.get('sum', torch.zeros_like(p)) + p.data
    return {'sum': s}
tst_stat.defaults = {'mom': 0.9}

#Test Optimizer init
opt = Optimizer([1,2,3], tst_stat)
test_eq(opt.hypers, [{'mom': 0.9}])
opt = Optimizer([1,2,3], tst_stat, mom=0.99)
test_eq(opt.hypers, [{'mom': 0.99}])

#Test stat
x = torch.randn(4,5)
state = tst_stat(x)
assert 'sum' in state
test_eq(x, state['sum'])
state = tst_stat(x, **state)
test_eq(state['sum'], 2*x)

# export
def average_grad(p, mom, dampening=False, grad_avg=None, **kwargs):
    "Keeps track of the avg grads of `p` in `state` with `mom`."
    if grad_avg is None: grad_avg = torch.zeros_like(p.grad.data)
    damp = 1-mom if dampening else 1.
    grad_avg.mul_(mom).add_(p.grad.data, alpha=damp)
    return {'grad_avg': grad_avg}

average_grad.defaults = dict(mom=0.9)

p = tst_param([1,2,3], [4,5,6])
state = {}
state = average_grad(p, mom=0.9, **state)
test_eq(state['grad_avg'], p.grad)
state = average_grad(p, mom=0.9, **state)
test_eq(state['grad_avg'], p.grad * 1.9)

#Test dampening
state = {}
state = average_grad(p,  mom=0.9, dampening=True, **state)
test_eq(state['grad_avg'], 0.1*p.grad)
state = average_grad(p, mom=0.9, dampening=True, **state)
test_close(state['grad_avg'], (0.1*0.9+0.1)*p.grad)

# export
def average_sqr_grad(p, sqr_mom, dampening=True, sqr_avg=None, **kwargs):
    if sqr_avg is None: sqr_avg = torch.zeros_like(p.grad.data)
    damp = 1-sqr_mom if dampening else 1.
    sqr_avg.mul_(sqr_mom).addcmul_(p.grad.data, p.grad.data, value=damp)
    return {'sqr_avg': sqr_avg}

average_sqr_grad.defaults = dict(sqr_mom=0.99)

p = tst_param([1,2,3], [4,5,6])
state = {}
state = average_sqr_grad(p, sqr_mom=0.99, dampening=False, **state)
test_eq(state['sqr_avg'], p.grad.pow(2))
state = average_sqr_grad(p, sqr_mom=0.99, dampening=False, **state)
test_eq(state['sqr_avg'], p.grad.pow(2) * 1.99)

#Test dampening
state = {}
state = average_sqr_grad(p, sqr_mom=0.99, **state)
test_close(state['sqr_avg'], 0.01*p.grad.pow(2))
state = average_sqr_grad(p, sqr_mom=0.99, **state)
test_close(state['sqr_avg'], (0.01*0.99+0.01)*p.grad.pow(2))

show_doc(Optimizer.freeze, name="Optimizer.freeze")

show_doc(Optimizer.freeze_to, name="Optimizer.freeze_to")

show_doc(Optimizer.unfreeze, name="Optimizer.unfreeze")

#Freezing the first layer
params = [tst_params(), tst_params(), tst_params()]
opt = Optimizer(params, sgd_step, lr=0.1)
opt.freeze_to(1)
req_grad = Self.requires_grad()
test_eq(L(params[0]).map(req_grad), [False]*4)
for i in {1,2}: test_eq(L(params[i]).map(req_grad), [True]*4)
    
#Unfreezing
opt.unfreeze()
for i in range(2): test_eq(L(params[i]).map(req_grad), [True]*4)

#TODO: test warning
# opt.freeze_to(3)

params = [tst_params(), tst_params(), tst_params()]
opt = Optimizer(params, sgd_step, lr=0.1)
for p in L(params[1])[[1,3]]: opt.state[p] = {'force_train': True}
opt.freeze()
test_eq(L(params[0]).map(req_grad), [False]*4)
test_eq(L(params[1]).map(req_grad), [False, True, False, True])
test_eq(L(params[2]).map(req_grad), [True]*4)

show_doc(Optimizer.state_dict)

show_doc(Optimizer.load_state_dict)

p = tst_param([1,2,3], [4,5,6])
opt = Optimizer(p, average_grad)
opt.step()
test_eq(opt.state[p]['grad_avg'], tensor([[4., 5., 6.]]))

sd = opt.state_dict()
p1 = tst_param([10,20,30], [40,50,60])
opt = Optimizer(p1, average_grad, mom=0.99)
test_eq(opt.hypers[0]['mom'], 0.99)
test_eq(opt.state, {})

opt.load_state_dict(sd)
test_eq(opt.hypers[0]['mom'], 0.9)
test_eq(opt.state[p1]['grad_avg'], tensor([[4., 5., 6.]]))

show_doc(Optimizer.clear_state)

p = tst_param([1,2,3], [4,5,6])
opt = Optimizer(p, average_grad)
opt.state[p] = {'force_train': True}
opt.step()
test_eq(opt.state[p]['grad_avg'], tensor([[4., 5., 6.]]))

opt.clear_state()
test_eq(opt.state[p], {'force_train': True})

#export
def momentum_step(p, lr, grad_avg, **kwargs):
    "Step for SGD with momentum with `lr`"
    p.data.add_(grad_avg, alpha=-lr)

#export
@log_args(to_return=True, but_as=Optimizer.__init__)
def SGD(params, lr, mom=0., wd=0., decouple_wd=True):
    "A `Optimizer` for SGD with `lr` and `mom` and `params`"
    cbs = [weight_decay] if decouple_wd else [l2_reg]
    if mom != 0: cbs.append(average_grad)
    cbs.append(sgd_step if mom==0 else momentum_step)
    return Optimizer(params, cbs, lr=lr, mom=mom, wd=wd)

#Vanilla SGD
params = tst_params()
opt = SGD(params, lr=0.1)
opt.step()
test_close([p.item() for p in params], [i*0.99 for i in range(4)])
opt.step()
[p.item() for p in params]
test_close([p.item() for p in params], [i*0.98 for i in range(4)])

#SGD with momentum
params = tst_params()
opt = SGD(params, lr=0.1, mom=0.9)
assert isinstance(opt, Optimizer)
opt.step()
test_close([p.item() for p in params], [i*0.99 for i in range(4)])
opt.step()
[p.item() for p in params]
test_close([p.item() for p in params], [i*(1 - 0.1 * (0.1 + 0.1*1.9)) for i in range(4)])
for i,p in enumerate(params): test_close(opt.state[p]['grad_avg'].item(), i*0.19)

params = tst_params()
#Weight decay
opt = SGD(params, lr=0.1, mom=0.9, wd=0.1)
opt.step()
test_close([p.item() for p in params], [i*0.98 for i in range(4)])
#L2 reg
opt = SGD(params, lr=0.1, mom=0.9, wd=0.1, decouple_wd=False)
opt.step()
#TODO: fix cause this formula was wrong
#test_close([p.item() for p in params], [i*0.97 for i in range(4)])

#export
def rms_prop_step(p, lr, sqr_avg, eps, grad_avg=None, **kwargs):
    "Step for SGD with momentum with `lr`"
    denom = sqr_avg.sqrt().add_(eps)
    p.data.addcdiv_((grad_avg if grad_avg is not None else p.grad), denom, value=-lr)

rms_prop_step.defaults = dict(eps=1e-8)

#export
@log_args(to_return=True, but_as=Optimizer.__init__)
def RMSProp(params, lr, sqr_mom=0.99, mom=0., wd=0., decouple_wd=True):
    "A `Optimizer` for RMSProp with `lr`, `sqr_mom`, `mom` and `params`"
    cbs = [weight_decay] if decouple_wd else [l2_reg]
    cbs += ([average_sqr_grad] if mom==0. else [average_grad, average_sqr_grad])
    cbs.append(rms_prop_step)
    return Optimizer(params, cbs, lr=lr, mom=mom, sqr_mom=sqr_mom, wd=wd)

#Without momentum
import math
params = tst_param([1,2,3], [0.1,0.2,0.3])
opt = RMSProp(params, lr=0.1)
opt.step()
test_close(params[0], tensor([0.,1.,2.]))
opt.step()
step = - 0.1 * 0.1 / (math.sqrt((0.01*0.99+0.01) * 0.1**2) + 1e-8)
test_close(params[0], tensor([step, 1+step, 2+step]))

#With momentum
params = tst_param([1,2,3], [0.1,0.2,0.3])
opt = RMSProp(params, lr=0.1, mom=0.9)
opt.step()
test_close(params[0], tensor([0.,1.,2.]))
opt.step()
step = - 0.1 * (0.1 + 0.9*0.1) / (math.sqrt((0.01*0.99+0.01) * 0.1**2) + 1e-8)
test_close(params[0], tensor([step, 1+step, 2+step]))

#export
def step_stat(p, step=0, **kwargs):
    "Register the number of steps done in `state` for `p`"
    step += 1
    return {'step' : step}

p = tst_param(1,0.1)
state = {}
state = step_stat(p, **state)
test_eq(state['step'], 1)
for _ in range(5): state = step_stat(p, **state)
test_eq(state['step'], 6)

#export
def debias(mom, damp, step): return damp * (1 - mom**step) / (1-mom)

#export
def adam_step(p, lr, mom, step, sqr_mom, grad_avg, sqr_avg, eps, **kwargs):
    "Step for Adam with `lr` on `p`"
    debias1 = debias(mom,     1-mom,     step)
    debias2 = debias(sqr_mom, 1-sqr_mom, step)
    p.data.addcdiv_(grad_avg, (sqr_avg/debias2).sqrt() + eps, value = -lr / debias1)
    return p

adam_step._defaults = dict(eps=1e-5)

#export
@log_args(to_return=True, but_as=Optimizer.__init__)
def Adam(params, lr, mom=0.9, sqr_mom=0.99, eps=1e-5, wd=0.01, decouple_wd=True):
    "A `Optimizer` for Adam with `lr`, `mom`, `sqr_mom`, `eps` and `params`"
    cbs = [weight_decay] if decouple_wd else [l2_reg]
    cbs += [partial(average_grad, dampening=True), average_sqr_grad, step_stat, adam_step]
    return Optimizer(params, cbs, lr=lr, mom=mom, sqr_mom=sqr_mom, eps=eps, wd=wd)

params = tst_param([1,2,3], [0.1,0.2,0.3])
opt = Adam(params, lr=0.1, wd=0)
opt.step()
step = -0.1 * 0.1 / (math.sqrt(0.1**2) + 1e-8)
test_close(params[0], tensor([1+step, 2+step, 3+step]))
opt.step()
test_close(params[0], tensor([1+2*step, 2+2*step, 3+2*step]), eps=1e-3)

#export
def radam_step(p, lr, mom, step, sqr_mom, grad_avg, sqr_avg, eps, beta, **kwargs):
    "Step for RAdam with `lr` on `p`"
    debias1 = debias(mom,     1-mom,     step)
    debias2 = debias(sqr_mom, 1-sqr_mom, step)
    r_inf = 2/(1-sqr_mom) - 1
    r = r_inf - 2*step*sqr_mom**step/(1-sqr_mom**step)
    if r > 5:
        v = math.sqrt(((r-4) * (r-2) * r_inf)/((r_inf-4)*(r_inf-2)*r))
        denom = (sqr_avg/debias2).sqrt()
        if eps: denom += eps
        if beta: denom = F.softplus(denom, beta)
        p.data.addcdiv_(grad_avg, denom, value = -lr*v / debias1)
    else: p.data.add_(grad_avg, alpha=-lr / debias1)
    return p

radam_step._defaults = dict(eps=1e-5)

#export
@log_args(to_return=True, but_as=Optimizer.__init__)
def RAdam(params, lr, mom=0.9, sqr_mom=0.99, eps=1e-5, wd=0., beta=0., decouple_wd=True):
    "A `Optimizer` for Adam with `lr`, `mom`, `sqr_mom`, `eps` and `params`"
    cbs = [weight_decay] if decouple_wd else [l2_reg]
    cbs += [partial(average_grad, dampening=True), average_sqr_grad, step_stat, radam_step]
    return Optimizer(params, cbs, lr=lr, mom=mom, sqr_mom=sqr_mom, eps=eps, wd=wd, beta=beta)

beta = 0.99
r_inf = 2/(1-beta) - 1
rs = np.array([r_inf - 2*s*beta**s/(1-beta**s) for s in range(5,500)])
v = np.sqrt(((rs-4) * (rs-2) * r_inf)/((r_inf-4)*(r_inf-2)*rs))
plt.plot(v);

params = tst_param([1,2,3], [0.1,0.2,0.3])
opt = RAdam(params, lr=0.1)
#The r factor is lower than 5 during the first 5 steps so updates use the aveage of gradients (all the same)
r_inf = 2/(1-0.99) - 1
for i in range(5): 
    r = r_inf - 2*(i+1)*0.99**(i+1)/(1-0.99**(i+1))
    assert r <= 5
    opt.step()
p = tensor([0.95, 1.9, 2.85])
test_close(params[0], p)

#The r factor is greater than 5 for the sixth step so we update with RAdam
r = r_inf - 2*6*0.99**6/(1-0.99**6)
assert r > 5
opt.step()
v = math.sqrt(((r-4) * (r-2) * r_inf)/((r_inf-4)*(r_inf-2)*r))
step = -0.1*0.1*v/(math.sqrt(0.1**2) + 1e-8)
test_close(params[0], p+step)

#export
def qhadam_step(p, lr, mom, sqr_mom, sqr_avg, nu_1, nu_2, step, grad_avg, eps, **kwargs):
    debias1 = debias(mom,     1-mom,     step)
    debias2 = debias(sqr_mom, 1-sqr_mom, step)
    p.data.addcdiv_(((1-nu_1) * p.grad.data) + (nu_1 * (grad_avg / debias1)),
                    (((1 - nu_2) * (p.grad.data)**2) + (nu_2 * (sqr_avg / debias2))).sqrt() + eps,
                    value = -lr)
    return p

qhadam_step._defaults = dict(eps=1e-8)

#export
@log_args(to_return=True, but_as=Optimizer.__init__)
def QHAdam(params, lr, mom=0.999, sqr_mom=0.999, nu_1=0.7, nu_2 = 1.0, eps=1e-8, wd=0., decouple_wd=True):
    "An `Optimizer` for Adam with `lr`, `mom`, `sqr_mom`, `nus`, eps` and `params`"
    cbs = [weight_decay] if decouple_wd else [l2_reg]
    cbs += [partial(average_grad, dampening=True), partial(average_sqr_grad, dampening=True), step_stat, qhadam_step]
    return Optimizer(params, cbs, lr=lr, nu_1=nu_1, nu_2=nu_2 ,
                     mom=mom, sqr_mom=sqr_mom, eps=eps, wd=wd)

params = tst_param([1,2,3], [0.1,0.2,0.3])
opt = QHAdam(params, lr=0.1)
opt.step()
step = -0.1 * (((1-0.7) * 0.1) + (0.7 * 0.1)) / (
     math.sqrt(((1-1.0) * 0.1**2) + (1.0 * 0.1**2)) + 1e-8) 
test_close(params[0], tensor([1+step, 2+step, 3+step]))
opt.step()
test_close(params[0], tensor([1+2*step, 2+2*step, 3+2*step]), eps=1e-3)

#export
def larc_layer_lr(p, lr, trust_coeff, wd, eps, clip=True, **kwargs):
    "Computes the local lr before weight decay is applied"
    p_norm,g_norm = torch.norm(p.data),torch.norm(p.grad.data)
    local_lr = lr*trust_coeff * (p_norm) / (g_norm + p_norm * wd + eps)
    return {'local_lr': min(lr, local_lr) if clip else local_lr}

larc_layer_lr.defaults = dict(trust_coeff=0.02, wd=0., eps=1e-8)

#export
def larc_step(p, local_lr, grad_avg=None, **kwargs):
    "Step for LARC `local_lr` on `p`"
    p.data.add_(p.grad.data if grad_avg is None else grad_avg, alpha = -local_lr)

#export
@log_args(to_return=True, but_as=Optimizer.__init__)
def Larc(params, lr, mom=0.9, clip=True, trust_coeff=0.02, eps=1e-8, wd=0., decouple_wd=True):
    "A `Optimizer` for Adam with `lr`, `mom`, `sqr_mom`, `eps` and `params`"
    cbs = [weight_decay] if decouple_wd else [l2_reg]
    if mom!=0.: cbs.append(average_grad)
    cbs += [partial(larc_layer_lr, clip=clip), larc_step]
    return Optimizer(params, cbs, lr=lr, mom=mom, trust_coeff=trust_coeff, eps=eps, wd=wd)

params = [tst_param([1,2,3], [0.1,0.2,0.3]), tst_param([1,2,3], [0.01,0.02,0.03])]
opt = Larc(params, lr=0.1)
opt.step()
#First param local lr is 0.02 < lr so it's not clipped
test_close(opt.state[params[0]]['local_lr'], 0.02)
#Second param local lr is 0.2 > lr so it's clipped
test_eq(opt.state[params[1]]['local_lr'], 0.1)
test_close(params[0], tensor([0.998,1.996,2.994]))
test_close(params[1], tensor([0.999,1.998,2.997]))

params = [tst_param([1,2,3], [0.1,0.2,0.3]), tst_param([1,2,3], [0.01,0.02,0.03])]
opt = Larc(params, lr=0.1, clip=False)
opt.step()
#No clipping
test_close(opt.state[params[0]]['local_lr'], 0.02)
test_close(opt.state[params[1]]['local_lr'], 0.2)
test_close(params[0], tensor([0.998,1.996,2.994]))
test_close(params[1], tensor([0.998,1.996,2.994]))

#export
def lamb_step(p, lr, mom, step, sqr_mom, grad_avg, sqr_avg, eps, **kwargs):
    "Step for LAMB with `lr` on `p`"
    debias1 = debias(mom,     1-mom,     step)
    debias2 = debias(sqr_mom, 1-sqr_mom, step)
    r1 = p.data.pow(2).mean().sqrt()
    step = (grad_avg/debias1) / ((sqr_avg/debias2).sqrt()+eps)
    r2 = step.pow(2).mean().sqrt()
    q = 1 if r1 == 0 or r2 == 0 else min(r1/r2,10)
    p.data.add_(step, alpha = -lr * q)

lamb_step._defaults = dict(eps=1e-6, wd=0.)

#export
@log_args(to_return=True, but_as=Optimizer.__init__)
def Lamb(params, lr, mom=0.9, sqr_mom=0.99, eps=1e-5, wd=0., decouple_wd=True):
    "A `Optimizer` for Adam with `lr`, `mom`, `sqr_mom`, `eps` and `params`"
    cbs = [weight_decay] if decouple_wd else [l2_reg]
    cbs += [partial(average_grad, dampening=True), average_sqr_grad, step_stat, lamb_step]
    return Optimizer(params, cbs, lr=lr, mom=mom, sqr_mom=sqr_mom, eps=eps, wd=wd)

params = tst_param([1,2,3], [0.1,0.2,0.3])
opt = Lamb(params, lr=0.1)
opt.step()
test_close(params[0], tensor([0.7840,1.7840,2.7840]), eps=1e-3)

#export
@log_args(but='opt')
class Lookahead(Optimizer, GetAttr):
    "Wrap `opt` in a lookahead optimizer"
    _default='opt'
    def __init__(self, opt, k=6, alpha=0.5):
        store_attr(self, 'opt,k,alpha')
        self._init_state()

    def step(self):
        if self.slow_weights is None: self._copy_weights()
        self.opt.step()
        self.count += 1
        if self.count%self.k != 0: return
        for slow_pg,fast_pg in zip(self.slow_weights,self.param_lists):
            for slow_p,fast_p in zip(slow_pg,fast_pg):
                slow_p.data.add_(fast_p.data-slow_p.data, alpha=self.alpha)
                fast_p.data.copy_(slow_p.data)

    def clear_state(self):
        self.opt.clear_state()
        self._init_state()

    def state_dict(self):
        state = self.opt.state_dict()
        state.update({'count': self.count, 'slow_weights': self.slow_weights})
        return state

    def load_state_dict(self, sd):
        self.count = sd.pop('count')
        self.slow_weights = sd.pop('slow_weights')
        self.opt.load_state_dict(sd)

    def _init_state(self): self.count,self.slow_weights = 0,None
    def _copy_weights(self): self.slow_weights = L(L(p.clone().detach() for p in pg) for pg in self.param_lists)

    @property
    def param_lists(self): return self.opt.param_lists
    @param_lists.setter
    def param_lists(self, v): self.opt.param_lists = v

params = tst_param([1,2,3], [0.1,0.2,0.3])
p,g = params[0].data.clone(),tensor([0.1,0.2,0.3])
opt = Lookahead(SGD(params, lr=0.1))
for k in range(5): opt.step()
#first 5 steps are normal SGD steps
test_close(params[0], p - 0.5*g)
#Since k=6, sixth step is a moving average of the 6 SGD steps with the intial weight
opt.step()
test_close(params[0], p * 0.5 + (p-0.6*g) * 0.5)

#export
@delegates(RAdam)
def ranger(p, lr, mom=0.95, wd=0.01, eps=1e-6, **kwargs):
    "Convenience method for `Lookahead` with `RAdam`"
    return Lookahead(RAdam(p, lr=lr, mom=mom, wd=wd, eps=eps, **kwargs))

#export
def detuplify_pg(d):
    res = {}
    for k,v in d.items():
        if k == 'params': continue
        if is_listy(v): res.update(**{f'{k}__{i}': v_ for i,v_ in enumerate(v)})
        else: res[k] = v
    return res

tst = {'lr': 1e-2, 'mom': 0.9, 'params':[0,1,2]}
test_eq(detuplify_pg(tst), {'lr': 1e-2, 'mom': 0.9})
tst = {'lr': 1e-2, 'betas': (0.9,0.999), 'params':[0,1,2]}
test_eq(detuplify_pg(tst), {'lr': 1e-2, 'betas__0': 0.9, 'betas__1': 0.999})

#export
def set_item_pg(pg, k, v):
    if '__' not in k: pg[k] = v
    else:
        name,idx = k.split('__')
        pg[name] = tuple(v if i==int(idx) else pg[name][i] for i in range_of(pg[name]))
    return pg

tst = {'lr': 1e-2, 'mom': 0.9, 'params':[0,1,2]}
test_eq(set_item_pg(tst, 'lr', 1e-3), {'lr': 1e-3, 'mom': 0.9, 'params':[0,1,2]})
tst = {'lr': 1e-2, 'betas': (0.9,0.999), 'params':[0,1,2]}
test_eq(set_item_pg(tst, 'betas__0', 0.95), {'lr': 1e-2, 'betas': (0.95,0.999), 'params':[0,1,2]})

#export
pytorch_hp_map = {'momentum': 'mom', 'weight_decay': 'wd', 'alpha': 'sqr_mom', 'betas__0': 'mom', 'betas__1': 'sqr_mom'}

#export
class OptimWrapper(_BaseOptimizer, GetAttr):
    _xtra=['zero_grad', 'step', 'state_dict', 'load_state_dict']
    _default='opt'
    def __init__(self, opt, hp_map=None):
        self.opt = opt
        if hp_map is None: hp_map = pytorch_hp_map
        self.fwd_map = {k: hp_map[k] if k in hp_map else k for k in detuplify_pg(opt.param_groups[0]).keys()}
        self.bwd_map = {v:k for k,v in self.fwd_map.items()}
        self.state = defaultdict(dict, {})
        self.frozen_idx = 0

    @property
    def hypers(self):
        return [{self.fwd_map[k]:v for k,v in detuplify_pg(pg).items() if k != 'params'} for pg in self.opt.param_groups]

    def _set_hyper(self, k, v):
        for pg,v_ in zip(self.opt.param_groups,v): pg = set_item_pg(pg, self.bwd_map[k], v_)

    def clear_state(self): self.opt.state = defaultdict(dict, {})

    @property
    def param_lists(self): return [pg['params'] for pg in self.opt.param_groups]
    @param_lists.setter
    def param_lists(self, v):
        for pg,v_ in zip(self.opt.param_groups,v): pg['params'] = v_

sgd = SGD([tensor([1,2,3])], lr=1e-3, mom=0.9, wd=1e-2)
tst_sgd = OptimWrapper(torch.optim.SGD([tensor([1,2,3])], lr=1e-3, momentum=0.9, weight_decay=1e-2))
#Access to param_groups
test_eq(tst_sgd.param_lists, sgd.param_lists)
#Set param_groups
tst_sgd.param_lists = [[tensor([4,5,6])]]
test_eq(tst_sgd.opt.param_groups[0]['params'], [tensor(4,5,6)])
#Access to hypers
test_eq(tst_sgd.hypers, [{**sgd.hypers[0], 'dampening': 0., 'nesterov': False}])
#Set hypers
tst_sgd.set_hyper('mom', 0.95)
test_eq(tst_sgd.opt.param_groups[0]['momentum'], 0.95)

tst_sgd = OptimWrapper(torch.optim.SGD([{'params': [tensor([1,2,3])], 'lr': 1e-3}, 
                                        {'params': [tensor([4,5,6])], 'lr': 1e-2}], momentum=0.9, weight_decay=1e-2))
sgd = SGD([[tensor([1,2,3])], [tensor([4,5,6])]], lr=[1e-3, 1e-2], mom=0.9, wd=1e-2)
#Access to param_groups
test_eq(tst_sgd.param_lists, sgd.param_lists)
#Set param_groups
tst_sgd.param_lists = [[tensor([4,5,6])], [tensor([1,2,3])]]
test_eq(tst_sgd.opt.param_groups[0]['params'], [tensor(4,5,6)])
test_eq(tst_sgd.opt.param_groups[1]['params'], [tensor(1,2,3)])
#Access to hypers
test_eq(tst_sgd.hypers, [{**sgd.hypers[i], 'dampening': 0., 'nesterov': False} for i in range(2)])
#Set hypers
tst_sgd.set_hyper('mom', 0.95)
test_eq([pg['momentum'] for pg in tst_sgd.opt.param_groups], [0.95,0.95])
tst_sgd.set_hyper('lr', [1e-4,1e-3])
test_eq([pg['lr'] for pg in tst_sgd.opt.param_groups], [1e-4,1e-3])

#hide
#check it works with tuply hp names like in Adam
tst_adam = OptimWrapper(torch.optim.Adam([tensor([1,2,3])], lr=1e-2, betas=(0.9, 0.99)))
test_eq(tst_adam.hypers, [{'lr': 0.01, 'mom': 0.9, 'sqr_mom': 0.99, 'eps': 1e-08, 'wd': 0, 'amsgrad': False}])
tst_adam.set_hyper('mom', 0.95)
test_eq(tst_adam.opt.param_groups[0]['betas'], (0.95, 0.99))
tst_adam.set_hyper('sqr_mom', 0.9)
test_eq(tst_adam.opt.param_groups[0]['betas'], (0.95, 0.9))

def _mock_train(m, x, y, opt):
    m.train()
    for i in range(0, 100, 25):
        z = m(x[i:i+25])
        loss = F.mse_loss(z, y[i:i+25])
        loss.backward()
        opt.step()
        opt.zero_grad()

m = nn.Linear(4,5)
x = torch.randn(100, 3, 4)
y = torch.randn(100, 3, 5)
try:
    torch.save(m.state_dict(), 'tmp.pth')
    wgt,bias = m.weight.data.clone(),m.bias.data.clone()

    m.load_state_dict(torch.load('tmp.pth'))
    opt1 = OptimWrapper(torch.optim.AdamW(m.parameters(), betas=(0.9, 0.99), eps=1e-5, weight_decay=1e-2))
    _mock_train(m, x.clone(), y.clone(), opt1)
    wgt1,bias1 = m.weight.data.clone(),m.bias.data.clone()

    m.load_state_dict(torch.load('tmp.pth'))
    opt2 = Adam(m.parameters(), 1e-3, wd=1e-2)
    _mock_train(m, x.clone(), y.clone(), opt2)
    wgt2,bias2 = m.weight.data.clone(),m.bias.data.clone()
    
    test_close(wgt1,wgt2,eps=1e-3)
    test_close(bias1,bias2,eps=1e-3)
finally: os.remove('tmp.pth')

m = nn.Linear(4,5)
x = torch.randn(100, 3, 4)
y = torch.randn(100, 3, 5)
try:
    torch.save(m.state_dict(), 'tmp.pth')
    wgt,bias = m.weight.data.clone(),m.bias.data.clone()

    m.load_state_dict(torch.load('tmp.pth'))
    opt1 = OptimWrapper(torch.optim.Adam(m.parameters(), betas=(0.9, 0.99), eps=1e-5, weight_decay=1e-2))
    _mock_train(m, x.clone(), y.clone(), opt1)
    wgt1,bias1 = m.weight.data.clone(),m.bias.data.clone()

    m.load_state_dict(torch.load('tmp.pth'))
    opt2 = Adam(m.parameters(), 1e-3, wd=1e-2, decouple_wd=False)
    _mock_train(m, x.clone(), y.clone(), opt2)
    wgt2,bias2 = m.weight.data.clone(),m.bias.data.clone()
    
    test_close(wgt1,wgt2,eps=1e-3)
    test_close(bias1,bias2,eps=1e-3)
finally: os.remove('tmp.pth')

#hide
from nbdev.export import *
notebook2script()