That’s what we were doing (pretty much) with our first example (the one you suggested the modified set_attr & del_attr).
Or perhaps you mean to implement my own custom optimizer and inside of it let it have/be a nn and then use the higher library so that gradients flow all the way (as in the example that you made work but for more than 1 step)?
I will go try that right now…
Oh, that’s an interesting idea. I wonder if the gradients will flow to the beginning as I required as in the example you made work. The fill in for .grad has to not be in place because I want gradients to flow through this step…
I tried using higher but something isn’t working…I’ll paste my code just in case you have time to take a look:
import torch
import torch.nn as nn
from torch.optim.optimizer import Optimizer
import higher
from higher.optim import DifferentiableOptimizer
from higher.optim import DifferentiableSGD
import torchvision
import torchvision.transforms as transforms
from torchviz import make_dot
import copy
import itertools
from collections import OrderedDict
#mini class to add a flatten layer to the ordered dictionary
class Flatten(nn.Module):
def forward(self, input):
'''
Note that input.size(0) is usually the batch size.
So what it does is that given any input with input.size(0) # of batches,
will flatten to be 1 * nb_elements.
'''
batch_size = input.size(0)
out = input.view(batch_size,-1)
return out # (batch_size, *size)
def get_cifar10():
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=2)
return trainloader, testloader
class MySGD(Optimizer):
def __init__(self, params, eta, prev_lr):
defaults = {'eta':eta, 'prev_lr':prev_lr}
super().__init__(params, defaults)
class TrainableSGD(DifferentiableOptimizer):
def _update(self, grouped_grads, **kwargs):
prev_lr = self.param_groups[0]['prev_lr']
eta = self.param_groups[0]['eta']
# start differentiable & trainable update
zipped = zip(self.param_groups, grouped_grads)
lr = 0.1*eta(prev_lr).view(1)
for group_idx, (group, grads) in enumerate(zipped):
for p_idx, (p, g) in enumerate(zip(group['params'], grads)):
if g is None:
continue
#group['params'][p_idx] = _add(p, -group['lr'], g)
p_new = p + lr*g
group['params'][p_idx] = p_new
# fake returns
self.param_groups[0]['prev_lr'] = lr
higher.register_optim(MySGD, TrainableSGD)
def main():
# get dataloaders
trainloader, testloader = get_cifar10()
criterion = nn.CrossEntropyLoss()
child_model = nn.Sequential(OrderedDict([
('conv1', nn.Conv2d(in_channels=3,out_channels=2,kernel_size=5)),
('relu1', nn.ReLU()),
('Flatten', Flatten()),
('fc', nn.Linear(in_features=28*28*2,out_features=10) )
]))
hidden = torch.randn(size=(1,1),requires_grad=True)
print(f'-> hidden = {hidden}')
eta = nn.Sequential(OrderedDict([
('fc', nn.Linear(1,1)),
('sigmoid', nn.Sigmoid())
]))
inner_opt = MySGD(child_model.parameters(), eta=eta, prev_lr=hidden)
meta_params = itertools.chain(child_model.parameters(),eta.parameters())
#meta_params = itertools.chain(eta.parameters(),[hidden])
meta_opt = torch.optim.Adam(meta_params, lr=1e-3)
# do meta-training/outer training minimize outerloop: min_{theta} sum_t L^val( theta^{T} - eta* Grad L^train(theta^{T}) )
print()
nb_outer_steps = 1 # note, in this case it's the same as number of meta-train steps (but it's could not be the same depending how you loop through the val set)
for outer_i, (outer_inputs, outer_targets) in enumerate(testloader, 0):
meta_opt.zero_grad()
if outer_i >= nb_outer_steps:
break
# do inner-training/MAML; minimize innerloop: theta^{T} - eta * Grad L^train(theta^{T}) ~ argmin L^train(theta)
nb_inner_steps = 3
#with higher.innerloop_ctx(child_model, inner_opt, copy_initial_weights=False) as (fmodel, diffopt):
with higher.innerloop_ctx(child_model, inner_opt) as (fmodel, diffopt):
for inner_i, (inner_inputs, inner_targets) in enumerate(trainloader, 0):
if inner_i >= nb_inner_steps:
break
logits = fmodel(inner_inputs)
inner_loss = criterion(logits, inner_targets)
print(f'--> inner_i = {inner_i}')
print(f'inner_loss^<{inner_i}>: {inner_loss}')
print(f'lr^<{inner_i-1}> = {diffopt.param_groups[0]["prev_lr"]}')
diffopt.step(inner_loss) # changes params P[t+1] using P[t] and loss[t] in a differentiable manner
print(f'lr^<{inner_i}> = {diffopt.param_groups[0]["prev_lr"]}')
print()
# compute the meta-loss L^val( theta^{T} - eta* Grad L^train(theta^{T}) )
outer_outputs = fmodel(outer_inputs)
meta_loss = criterion(outer_outputs, outer_targets) # L^val
make_dot(meta_loss).render('meta_loss',format='png')
meta_loss.backward()
#grad_of_grads = torch.autograd.grad(outputs=meta_loss, inputs=eta.parameters()) # dmeta_loss/dw0
print(f'----> outer_i = {outer_i}')
print(f'-> outer_loss/meta_loss^<{outer_i}>: {meta_loss}')
print(f'child_model.fc.weight.grad = {child_model.fc.weight.grad}')
print(f'hidden.grad = {hidden.grad}')
print(f'eta.fc.weight = {eta.fc.weight.grad}')
meta_opt.step() # meta-optimizer step: more or less theta^<t> := theta^<t> - meta_eta * Grad L^val( theta^{T} - eta* Grad L^train(theta^{T}) )
if __name__ == "__main__":
main()
print('---> Done\a')
notice the None’s:
Files already downloaded and verifiedFiles already downloaded and verified
-> hidden = tensor([[0.8459]], requires_grad=True)
--> inner_i = 0
inner_loss^<0>: 2.2696359157562256
lr^<-1> = tensor([[0.8459]], requires_grad=True)
lr^<0> = tensor([0.0567], grad_fn=<MulBackward0>)
--> inner_i = 1
inner_loss^<1>: 2.0114920139312744
lr^<0> = tensor([0.0567], grad_fn=<MulBackward0>)
lr^<1> = tensor([0.0720], grad_fn=<MulBackward0>)
--> inner_i = 2
inner_loss^<2>: 2.3866422176361084
lr^<1> = tensor([0.0720], grad_fn=<MulBackward0>)
lr^<2> = tensor([0.0717], grad_fn=<MulBackward0>)
----> outer_i = 0
-> outer_loss/meta_loss^<0>: 4.021303176879883
child_model.fc.weight.grad = None
hidden.grad = None
eta.fc.weight = None
---> Done
related:
The git issue if for the same thing? Does it answer your problem?
yes it’s the same thing as I posted here.
My problem is NOT solved. I’m working on it.
@albanD my thing is nearly working but the only issue is that pytorch thinks I’m doing a backward pass twice on the same graph. I am not sure why it thinks that because I delete the output node as you said (here: How to free graph manually?).
Do you know why it might not be working?
Code:
import torch
import torch.nn as nn
from torch.optim.optimizer import Optimizer, required
import higher
from higher.optim import DifferentiableOptimizer
from higher.optim import DifferentiableSGD
import torchvision
import torchvision.transforms as transforms
from torchviz import make_dot
import copy
import itertools
import sys
from collections import OrderedDict
from pdb import set_trace as st
#mini class to add a flatten layer to the ordered dictionary
class Flatten(nn.Module):
def forward(self, input):
'''
Note that input.size(0) is usually the batch size.
So what it does is that given any input with input.size(0) # of batches,
will flatten to be 1 * nb_elements.
'''
batch_size = input.size(0)
out = input.view(batch_size,-1)
return out # (batch_size, *size)
def get_cifar10():
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=2)
return trainloader, testloader
def load_new_params(optimizer, params):
optimizer.param_groups = []
param_groups = list(params)
if len(param_groups) == 0:
raise ValueError("optimizer got an empty parameter list")
if not isinstance(param_groups[0], dict):
param_groups = [{'params': param_groups}]
for param_group in param_groups:
optimizer.add_param_group(param_group)
def reload_param_groups(opt, params):
if isinstance(params, torch.Tensor):
raise TypeError("params argument given to the optimizer should be "
"an iterable of Tensors or dicts, but got " +
torch.typename(params))
# replace params
params = list(params)
if isinstance(params[0], dict):
raise ValueError(f'The hacked higher version does not support proper pytorch grouped params yet.')
opt.param_groups[0]['params'] = params
# opt.param_groups = []
# param_groups = list(params)
# if len(param_groups) == 0:
# raise ValueError("optimizer got an empty parameter list")
# if not isinstance(param_groups[0], dict):
# param_groups = [{'params': param_groups}]
# for param_group in param_groups:
# opt.add_param_group(param_group)
class MySGD(Optimizer):
def __init__(self, params, trainable_opt_params, trainable_opt_state):
defaults = {'trainable_opt_params':trainable_opt_params, 'trainable_opt_state':trainable_opt_state}
super().__init__(params, defaults)
class TrainableSGD(DifferentiableOptimizer):
def _update(self, grouped_grads, **kwargs):
prev_lr = self.param_groups[0]['trainable_opt_state']['prev_lr']
eta = self.param_groups[0]['trainable_opt_params']['eta']
# start differentiable & trainable update
zipped = zip(self.param_groups, grouped_grads)
lr = 0.01*eta(prev_lr).view(1)
for group_idx, (group, grads) in enumerate(zipped):
for p_idx, (p, g) in enumerate(zip(group['params'], grads)):
if g is None:
continue
p_new = p - lr*g
group['params'][p_idx] = p_new
# fake returns
self.param_groups[0]['trainable_opt_state']['prev_lr'] = lr
# update model
# new_params = self.param_groups[0]['params']
# new_params = self._track_higher_grads_for_new_params(new_params, self._track_higher_grads)
# self._fmodel.update_params(new_params)
higher.register_optim(MySGD, TrainableSGD)
def main():
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
# get dataloaders
trainloader, testloader = get_cifar10()
criterion = nn.CrossEntropyLoss()
# get trainable opt params
hidden = torch.randn(size=(1,1),requires_grad=True)
print(f'-> hidden = {hidden}')
eta = nn.Sequential(OrderedDict([
('fc', nn.Linear(1,1,bias=False)),
('sigmoid', nn.Sigmoid())
]))
lr = 0.01
meta_params = []
meta_params.append( {'params': hidden, 'lr':lr} )
meta_params.append( {'params': eta.parameters(), 'lr':lr} )
# get meta optimizer
#meta_opt = torch.optim.SGD(meta_params)
meta_opt = torch.optim.Adam(meta_params)
#
trainable_opt_params = {'eta':eta, 'hidden':hidden}
trainable_opt_state = {'prev_lr':hidden}
#inner_opt = MySGD(eta.parameters(), trainable_opt_params=trainable_opt_params, trainable_opt_state=trainable_opt_state)
# diffopt = higher.optim.get_diff_optim(
# inner_opt,
# eta.parameters(), # for this hack it can be anything
# fmodel=None, # None
# device=device,
# override=None, # None default
# track_higher_grads=True # True default
# )
# do meta-training/ outerloop argmin L^val(theta)
nb_outer_steps = 2 # note, in this case it's the same as number of meta-train steps (but it's could not be the same depending how you loop through the val set)
for outer_i, (outer_inputs, outer_targets) in enumerate(testloader, 0):
meta_opt.zero_grad()
if outer_i >= nb_outer_steps:
break
# sample child_model
child_model = nn.Sequential(OrderedDict([
('conv1', nn.Conv2d(in_channels=3,out_channels=2,kernel_size=5,bias=False)),
('relu1', nn.ReLU()),
('Flatten', Flatten()),
('fc', nn.Linear(in_features=28*28*2,out_features=10,bias=False) )
]))
# do inner-training: ~ argmin L^train(psi)
nb_inner_steps = 3
print('==== Inner Loop ====')
fmodel = higher.patch.monkeypatch(
child_model,
device,
copy_initial_weights=True # True default
)
inner_opt = MySGD(child_model.parameters(), trainable_opt_params=trainable_opt_params, trainable_opt_state=trainable_opt_state)
diffopt = higher.optim.get_diff_optim(
inner_opt,
child_model.parameters(), # for this hack it can be anything
fmodel=fmodel, # None
device=device,
override=None, # None default
track_higher_grads=True # True default
)
for inner_i, (inner_inputs, inner_targets) in enumerate(trainloader, 0):
if inner_i >= nb_inner_steps:
break
print(f'-> outer_i = {outer_i}')
print(f'-> inner_i = {inner_i}')
print(f'hidden^<{inner_i}> = {hidden}')
logits = fmodel(inner_inputs)
inner_loss = criterion(logits, inner_targets)
print(f'lr^<{inner_i-1}> = {diffopt.param_groups[0]["trainable_opt_state"]["prev_lr"]}')
#child_model_params = [{'params':child_model.parameters()}]
child_model_params = child_model.parameters()
reload_param_groups(diffopt, child_model_params)
diffopt._fmodel = fmodel
diffopt.step(inner_loss)
print(f'lr^<{inner_i}> = {diffopt.param_groups[0]["trainable_opt_state"]["prev_lr"]}')
print(f'hidden^<{inner_i}> = {hidden}')
# compute the meta-loss L^val( theta^{T} - eta* Grad L^train(theta^{T}) )
outer_outputs = fmodel(outer_inputs)
meta_loss = criterion(outer_outputs, outer_targets) # L^val
#grad_of_grads = torch.autograd.grad(outputs=meta_loss, inputs=eta.parameters()) # dmeta_loss/dw0
print('\n---- Outer loop print statements ----')
print(f'----> outer_i = {outer_i}')
print(f'-> outer_loss/meta_loss^<{outer_i}>: {meta_loss}')
#print(f'child_model.fc.weight.grad = {child_model.fc.weight.grad}')
meta_loss.backward()
print(f'hidden.grad = {hidden.grad}')
assert hidden.grad is not None
print(f'eta.fc.weight.grad = {eta.fc.weight.grad}')
print(f'> hidden^<{outer_i-1}> = {hidden}') # before update
print(f'> eta.fc.weight^<{outer_i-1}> = {eta.fc.weight.T}')
meta_opt.step() # meta-optimizer step: more or less theta^<t> := theta^<t> - meta_eta * Grad L^val( theta^{T} - eta* Grad L^train(theta^{T}) )
print(f'>> hidden^<{outer_i}> = {meta_opt.param_groups[0]["params"][0]}') # after update
print(f'>> eta.fc.weight^<{outer_i-1}> = {eta.fc.weight.T}')
del meta_loss
meta_opt.zero_grad()
print()
if __name__ == "__main__":
main()
print('---> Done\a')
Most likely because you re-use some pre-computed states from one iteration to the next?
Not sure why you have to manually unset/set the fmodel and the different elements from higher…
So the issue is that this line of code of higher
self.param_groups = _copy.deepcopy(other.param_groups)
breaks the trainable step size I am trying to build.
I tried uncommenting it before but my code was still breaking.
With a lot of exploration it seems that only when I re-instantiate/rebuild the inner optimizer + differentiable optimizer before every inner loop then the code works (I think…)
import torch
import torch.nn as nn
from torch.optim.optimizer import Optimizer, required
import higher
from higher.optim import DifferentiableOptimizer
from higher.optim import DifferentiableSGD
import torchvision
import torchvision.transforms as transforms
from torchviz import make_dot
import copy
import itertools
import sys
from collections import OrderedDict
from pdb import set_trace as st
#mini class to add a flatten layer to the ordered dictionary
class Flatten(nn.Module):
def forward(self, input):
'''
Note that input.size(0) is usually the batch size.
So what it does is that given any input with input.size(0) # of batches,
will flatten to be 1 * nb_elements.
'''
batch_size = input.size(0)
out = input.view(batch_size,-1)
return out # (batch_size, *size)
def get_cifar10():
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=2)
return trainloader, testloader
class MySGD(Optimizer):
def __init__(self, params, trainable_opt_params, trainable_opt_state):
defaults = {'trainable_opt_params':trainable_opt_params, 'trainable_opt_state':trainable_opt_state}
super().__init__(params, defaults)
class TrainableSGD(DifferentiableOptimizer):
def _update(self, grouped_grads, **kwargs):
prev_lr = self.param_groups[0]['trainable_opt_state']['prev_lr']
eta = self.param_groups[0]['trainable_opt_params']['eta']
# start differentiable & trainable update
zipped = zip(self.param_groups, grouped_grads)
lr = 0.01*eta(prev_lr).view(1)
for group_idx, (group, grads) in enumerate(zipped):
for p_idx, (p, g) in enumerate(zip(group['params'], grads)):
if g is None:
continue
p_new = p - lr*g
group['params'][p_idx] = p_new
# fake returns
self.param_groups[0]['trainable_opt_state']['prev_lr'] = lr
higher.register_optim(MySGD, TrainableSGD)
def main():
# get dataloaders
trainloader, testloader = get_cifar10()
criterion = nn.CrossEntropyLoss()
hidden = torch.randn(size=(1,1),requires_grad=True)
print(f'-> hidden = {hidden}')
eta = nn.Sequential(OrderedDict([
('fc', nn.Linear(1,1,bias=False)),
('sigmoid', nn.Sigmoid())
]))
lr = 0.01
meta_params = []
meta_params.append( {'params': hidden, 'lr':lr} )
meta_params.append( {'params': eta.parameters(), 'lr':lr} )
#meta_opt = torch.optim.SGD(meta_params)
meta_opt = torch.optim.Adam(meta_params)
# do meta-training/outer training minimize outerloop: min_{theta} sum_t L^val( theta^{T} - eta* Grad L^train(theta^{T}) )
nb_outer_steps = 5 # note, in this case it's the same as number of meta-train steps (but it's could not be the same depending how you loop through the val set)
for outer_i, (outer_inputs, outer_targets) in enumerate(testloader, 0):
meta_opt.zero_grad()
if outer_i >= nb_outer_steps:
break
#
child_model = nn.Sequential(OrderedDict([
('conv1', nn.Conv2d(in_channels=3,out_channels=2,kernel_size=5,bias=False)),
('relu1', nn.ReLU()),
('Flatten', Flatten()),
('fc', nn.Linear(in_features=28*28*2,out_features=10,bias=False) )
]))
# do inner-training: ~ argmin L^train(theta)
nb_inner_steps = 3
trainable_opt_params = {'eta':eta, 'hidden':hidden}
trainable_opt_state = {'prev_lr':hidden}
child_model_params = [{'params':child_model.parameters()}]
inner_opt = MySGD(child_model_params, trainable_opt_params=trainable_opt_params, trainable_opt_state=trainable_opt_state)
print('==== Inner Loop ====')
with higher.innerloop_ctx(child_model, inner_opt, copy_initial_weights=False) as (fmodel, diffopt):
for inner_i, (inner_inputs, inner_targets) in enumerate(trainloader, 0):
if inner_i >= nb_inner_steps:
break
print(f'-> outer_i = {outer_i}')
print(f'-> inner_i = {inner_i}')
print(f'hidden^<{inner_i}> = {hidden}')
logits = fmodel(inner_inputs)
inner_loss = criterion(logits, inner_targets)
print(f'lr^<{inner_i-1}> = {diffopt.param_groups[0]["trainable_opt_state"]["prev_lr"]}')
diffopt.step(inner_loss)
print(f'lr^<{inner_i}> = {diffopt.param_groups[0]["trainable_opt_state"]["prev_lr"]}')
print(f'hidden^<{inner_i}> = {hidden}')
# compute the meta-loss L^val( theta^{T} - eta* Grad L^train(theta^{T}) )
outer_outputs = fmodel(outer_inputs)
meta_loss = criterion(outer_outputs, outer_targets) # L^val
meta_loss.backward()
#grad_of_grads = torch.autograd.grad(outputs=meta_loss, inputs=eta.parameters()) # dmeta_loss/dw0
print('\n---- Outer loop print statements ----')
print(f'----> outer_i = {outer_i}')
print(f'-> outer_loss/meta_loss^<{outer_i}>: {meta_loss}')
#print(f'child_model.fc.weight.grad = {child_model.fc.weight.grad}')
print(f'hidden.grad = {hidden.grad}')
assert hidden.grad is not None
assert eta.fc.weight is not None
print(f'eta.fc.weight.grad = {eta.fc.weight.grad}')
print(f'> hidden^<{outer_i-1}> = {hidden}') # before update
print(f'> eta.fc.weight^<{outer_i-1}> = {eta.fc.weight.T}')
meta_opt.step() # meta-optimizer step: more or less theta^<t> := theta^<t> - meta_eta * Grad L^val( theta^{T} - eta* Grad L^train(theta^{T}) )
print(f'>> hidden^<{outer_i}> = {meta_opt.param_groups[0]["params"][0]}') # after update
print(f'>> eta.fc.weight^<{outer_i-1}> = {eta.fc.weight.T}')
print()
if __name__ == "__main__":
main()
print('---> Done\a')
this works now:
import torch
import torch.nn as nn
from torch.optim.optimizer import Optimizer, required
import higher
from higher.optim import DifferentiableOptimizer
from higher.optim import DifferentiableSGD
import torchvision
import torchvision.transforms as transforms
from torchviz import make_dot
import copy
import itertools
import sys
from collections import OrderedDict
from pdb import set_trace as st
#mini class to add a flatten layer to the ordered dictionary
class Flatten(nn.Module):
def forward(self, input):
'''
Note that input.size(0) is usually the batch size.
So what it does is that given any input with input.size(0) # of batches,
will flatten to be 1 * nb_elements.
'''
batch_size = input.size(0)
out = input.view(batch_size,-1)
return out # (batch_size, *size)
def get_cifar10():
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=2)
return trainloader, testloader
class MySGD(Optimizer):
def __init__(self, params, trainable_opt_params, trainable_opt_state):
defaults = {'trainable_opt_params':trainable_opt_params, 'trainable_opt_state':trainable_opt_state}
super().__init__(params, defaults)
class TrainableSGD(DifferentiableOptimizer):
def _update(self, grouped_grads, **kwargs):
prev_lr = self.param_groups[0]['trainable_opt_state']['prev_lr']
eta = self.param_groups[0]['trainable_opt_params']['eta']
# start differentiable & trainable update
zipped = zip(self.param_groups, grouped_grads)
lr = 0.01*eta(prev_lr).view(1)
for group_idx, (group, grads) in enumerate(zipped):
for p_idx, (p, g) in enumerate(zip(group['params'], grads)):
if g is None:
continue
p_new = p - lr*g
group['params'][p_idx] = p_new
# fake returns
self.param_groups[0]['trainable_opt_state']['prev_lr'] = lr
higher.register_optim(MySGD, TrainableSGD)
def main():
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
print(f'device = {device}')
# get dataloaders
trainloader, testloader = get_cifar10()
criterion = nn.CrossEntropyLoss()
# get inner opt
hidden = torch.randn(size=(1,1), device=device, requires_grad=True)
print(f'-> hidden = {hidden}')
eta = nn.Sequential(OrderedDict([
('fc', nn.Linear(1,1, bias=False)),
('sigmoid', nn.Sigmoid())
])).to(device)
trainable_opt_params = {'eta':eta, 'hidden':hidden}
trainable_opt_state = {'prev_lr':hidden}
# get outer opt
lr = 0.05
meta_params = []
meta_params.append( {'params': hidden, 'lr':lr} )
meta_params.append( {'params': eta.parameters(), 'lr':lr} )
#meta_opt = torch.optim.SGD(meta_params)
meta_opt = torch.optim.Adam(meta_params)
# do meta-training/outer training minimize outerloop: min_{theta} sum_t L^val( theta^{T} - eta* Grad L^train(theta^{T}) )
nb_outer_steps = 10 # note, in this case it's the same as number of meta-train steps (but it's could not be the same depending how you loop through the val set)
for outer_i, (outer_inputs, outer_targets) in enumerate(testloader, 0):
outer_inputs, outer_targets = outer_inputs.to(device), outer_targets.to(device)
meta_opt.zero_grad()
if outer_i >= nb_outer_steps:
break
#
child_model = nn.Sequential(OrderedDict([
('conv1', nn.Conv2d(in_channels=3,out_channels=2,kernel_size=5,bias=False)),
('relu1', nn.ReLU()),
('Flatten', Flatten()),
('fc', nn.Linear(in_features=28*28*2,out_features=10,bias=False) )
])).to(device)
# do inner-training: ~ argmin L^train(theta)
nb_inner_steps = 2
trainable_opt_params = {'eta':eta, 'hidden':hidden}
trainable_opt_state = {'prev_lr':hidden}
print(trainable_opt_state)
child_model_params = [{'params':child_model.parameters()}]
inner_opt = MySGD(child_model_params, trainable_opt_params=trainable_opt_params, trainable_opt_state=trainable_opt_state)
print('==== Inner Loop ====')
with higher.innerloop_ctx(child_model, inner_opt, copy_initial_weights=True) as (fmodel, diffopt):
for inner_i, (inner_inputs, inner_targets) in enumerate(trainloader, 0):
inner_inputs, inner_targets = inner_inputs.to(device), inner_targets.to(device)
if inner_i >= nb_inner_steps:
break
print(f'-> outer_i = {outer_i}')
print(f'-> inner_i = {inner_i}')
print(f'hidden^<{inner_i}> = {hidden}')
logits = fmodel(inner_inputs)
inner_loss = criterion(logits, inner_targets)
print(f'lr^<{inner_i-1}> = {diffopt.param_groups[0]["trainable_opt_state"]["prev_lr"]}')
diffopt.step(inner_loss)
print(f'lr^<{inner_i}> = {diffopt.param_groups[0]["trainable_opt_state"]["prev_lr"]}')
print(f'hidden^<{inner_i}> = {hidden}')
# compute the meta-loss L^val( theta^{T} - eta* Grad L^train(theta^{T}) )
outer_outputs = fmodel(outer_inputs)
meta_loss = criterion(outer_outputs, outer_targets) # L^val
meta_loss.backward()
#grad_of_grads = torch.autograd.grad(outputs=meta_loss, inputs=eta.parameters()) # dmeta_loss/dw0
print('\n---- Outer loop print statements ----')
print(f'----> outer_i = {outer_i}')
print(f'-> outer_loss/meta_loss^<{outer_i}>: {meta_loss}')
#print(f'child_model.fc.weight.grad = {child_model.fc.weight.grad}')
print(f'hidden.grad = {hidden.grad}')
assert hidden.grad is not None
assert eta.fc.weight is not None
print(f'eta.fc.weight.grad = {eta.fc.weight.grad}')
print(f'> hidden^<{outer_i-1}> = {hidden}') # before update
print(f'> eta.fc.weight^<{outer_i-1}> = {eta.fc.weight.T}')
meta_opt.step() # meta-optimizer step: more or less theta^<t> := theta^<t> - meta_eta * Grad L^val( theta^{T} - eta* Grad L^train(theta^{T}) )
print(f'>> hidden^<{outer_i}> = {meta_opt.param_groups[0]["params"][0]}') # after update
print(f'>> eta.fc.weight^<{outer_i-1}> = {eta.fc.weight.T}')
print()
if __name__ == "__main__":
main()
print('---> Done\a')
I got lucky. It breaks if you uncomment:
trainable_opt_state = {'prev_lr':hidden}
inside here:
# do inner-training: ~ argmin L^train(theta)
nb_inner_steps = 2
trainable_opt_params = {'eta':eta, 'hidden':hidden}
trainable_opt_state = {'prev_lr':hidden}
print(trainable_opt_state)
child_model_params = [{'params':child_model.parameters()}]
inner_opt = MySGD(child_model_params, trainable_opt_params=trainable_opt_params, trainable_opt_state=trainable_opt_state)
print('==== Inner Loop ====')
I believe is because the next time I compute meta_loss it has a reference to the previous computation graph because prev_lr belongs to the previous iteration or the previous outerloop.