trainable optimizer in higher library Pytorch

simple_meta_lstm_meta_learner_parametrized_optimizer.py

# base on the paper "OPTIMIZATION AS A MODEL FOR FEW-SHOT LEARNING": https://openreview.net/pdf?id=rJY0-Kcll

class EmptySimpleMetaLstm(Optimizer):

    def __init__(self, params, trainable_opt_model, trainable_opt_state, *args, **kwargs):
        defaults = {
            'trainable_opt_model':trainable_opt_model, 
            'trainable_opt_state':trainable_opt_state, 
            'args':args, 
            'kwargs':kwargs
        }
        super().__init__(params, defaults)

class SimpleMetaLstm(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_model']['eta']
        # start differentiable & trainable update
        zipped = zip(self.param_groups, grouped_grads)
        for group_idx, (group, grads) in enumerate(zipped):
            for p_idx, (p, g) in enumerate(zip(group['params'], grads)):
                if g is None:
                    continue
                # get gradient as "data"
                g = g.detach() # gradients of gradients are not used (no hessians)
                ## very simplified version of meta-lstm meta-learner
                input_metalstm = torch.stack([p, g, prev_lr.view(1,1)]).view(1,3) # [p, g, prev_lr] note it's missing loss, normalization etc. see original paper
                lr = eta(input_metalstm).view(1)
                fg = 1 - lr # learnable forget rate
                ## update suggested by meta-lstm meta-learner
                p_new = fg*p - lr*g
                group['params'][p_idx] = p_new
        # fake returns
        self.param_groups[0]['trainable_opt_state']['prev_lr'] = lr

higher.register_optim(EmptySimpleMetaLstm, SimpleMetaLstm)

def test_parametrized_inner_optimizer():
    import torch
    import torch.nn as nn
    import torch.optim as optim
    from collections import OrderedDict

    ## training config
    #device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    track_higher_grads = True # if True, during unrolled optimization the graph be retained, and the fast weights will bear grad funcs, so as to permit backpropagation through the optimization process. False during test time for efficiency reasons
    copy_initial_weights = False # if False then we train the base models initial weights (i.e. the base model's initialization)
    episodes = 5
    nb_inner_train_steps = 5
    ## get base model
    base_mdl = nn.Sequential(OrderedDict([
        ('fc', nn.Linear(1,1, bias=False)),
        ('relu', nn.ReLU())
        ]))
    ## parametrization/mdl for the inner optimizer
    opt_mdl = nn.Sequential(OrderedDict([
        ('fc', nn.Linear(3,1, bias=False)), # 3 inputs 1 for parameter, 1 for gradient, 1 for previous lr
        ('sigmoid', nn.Sigmoid())
        ]))
    ## get outer optimizer (not differentiable nor trainable)
    outer_opt = optim.Adam([{'params': base_mdl.parameters()},{'params': opt_mdl.parameters()}], lr=0.01)
    for episode in range(episodes):
        ## get fake support & query data (from a single task and 1 data point)
        spt_x, spt_y, qry_x, qry_y = torch.randn(1), torch.randn(1), torch.randn(1), torch.randn(1)
        ## get differentiable & trainable (parametrized) inner optimizer
        inner_opt = EmptySimpleMetaLstm(base_mdl.parameters(), trainable_opt_model={'eta': opt_mdl}, trainable_opt_state={'prev_lr': 0.9*torch.randn(1)})
        with higher.innerloop_ctx(base_mdl, inner_opt, copy_initial_weights=copy_initial_weights, track_higher_grads=track_higher_grads) as (fmodel, diffopt):
            for i_inner in range(nb_inner_train_steps): # this current version implements full gradient descent on k_shot examples (which is usually small  5)
                fmodel.train()
                # base/child model forward pass
                inner_loss = 0.5*((fmodel(spt_x) - spt_y))**2
                # inner-opt update
                diffopt.step(inner_loss)
            ## Evaluate on query set for current task
            qry_loss = 0.5*((fmodel(qry_x) - qry_y))**2
            qry_loss.backward() # for memory efficient computation
        ## outer update
        print(f'episode = {episode}')
        print(f'base_mdl.grad = {base_mdl.fc.weight.grad}')
        print(f'opt_mdl.grad = {opt_mdl.fc.weight.grad}')
        outer_opt.step()
        outer_opt.zero_grad()
        
if __name__ == '__main__':
    test_parametrized_inner_optimizer()
    print('Done \a')

"""
output when deep copy is uncommented (parametrized optimizer trains properly):

episode = 0
base_mdl.grad = tensor([[-0.0351]])
opt_mdl.grad = tensor([[0.0085, 0.0000, 0.0204]])
episode = 1
base_mdl.grad = tensor([[0.0311]])
opt_mdl.grad = tensor([[-0.0086, -0.0100,  0.0358]])
episode = 2
base_mdl.grad = tensor([[0.]])
opt_mdl.grad = tensor([[0., 0., 0.]])
episode = 3
base_mdl.grad = tensor([[0.0066]])
opt_mdl.grad = tensor([[-0.0016,  0.0000, -0.0032]])
episode = 4
base_mdl.grad = tensor([[-0.0311]])
opt_mdl.grad = tensor([[0.0077, 0.0000, 0.0130]])
Done 

when deep copy is on (paremeters of inner optimizer are not train, sad!):

episode = 0
base_mdl.grad = tensor([[0.]])
opt_mdl.grad = None
episode = 1
base_mdl.grad = tensor([[0.]])
opt_mdl.grad = None
episode = 2
base_mdl.grad = tensor([[0.0069]])
opt_mdl.grad = None
episode = 3
base_mdl.grad = tensor([[0.]])
opt_mdl.grad = None
episode = 4
base_mdl.grad = tensor([[0.]])
opt_mdl.grad = None
Done

The deep copy line in higher I am referencing:

        self.param_groups = _copy.deepcopy(other.param_groups)
        #self.param_groups = other.param_groups

"""

egrefen commented on Mar 5
That line of code (deep copy) is important, as we want to safely branch off the state of the optimizer as used in the outer loop and return to it (or not touch it in the first place, which is what we do with the copy here) at the end of the unrolled inner loop.

Use override, please.

Contributor
egrefen commented on Mar 6
Override is a kwarg for differentiable optims (at creation, or step time,
and you can also use it with the context manager) which allows you to use
arbitrary tensors instead of values held in the optimizer state. For
example, you could override the learning rate with a tensor which requires
grad, which would allow you to unroll your loops, take gradient of the
meta-loss with regard to the learning rate, and update this tensor.

See https://higher.readthedocs.io/en/latest/optim.html for details,
#32 (comment) for
a similar explanation, and https://github.com/denisyarats/densenet_cifar10 for
an example.

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from this issue:

facebookresearch/higher#32