Fix fed-yogi executor model download

[EricDinging]

@fanlai0990

Why are these changes needed?

There is a bug when executor pulls the model from the aggregator. In original implementation, the model adapter will execute the optimizer step at the executor, which should theoretically be executed only at the aggregator end, leading to poor performance for fed-yogi.
What I did:

1. Turn off optimizer step at the executor
2. Edit gradient_policy (optimizer) naming in several config files, from yogi to fed-yogi. If use yogi, the real optimizer would be fed-avg as if statement for fed-yogi in optimizer is not entered.
3. Change initial hyperparameter weight of fed-yogi according to the fed-yogi paper. Original setup might cause the model to drift a little bit before starting to converge

            self.v_t = [torch.full_like(g, self.tau) for g in gradients]
            self.m_t = [torch.full_like(g, 0.0) for g in gradients]

Related issue number

#243

Checks

• I've included any doc changes needed for https://fedscale.readthedocs.io/en/latest/
• I've made sure the following tests are passing.
• Testing Configurations
  • Dry Run (20 training rounds & 1 evaluation round)
  • Cifar 10 (20 training rounds & 1 evaluation round)
  • Femnist (20 training rounds & 1 evaluation round)

FEMNIST fed-yogi optimizer run result

[fanlai0990]

I wonder whether we can reverse this as it leads to failures in unit tests.

[EricDinging]

Oh I see. Actually there seems to be a bug for q-fedavg. I am working on a new PR for this, and will address this issue.
Have you ever encoutered this error before, in executor?

RuntimeError: Error(s) in loading state_dict for ResNet:
	size mismatch for layer1.0.conv1.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
	size mismatch for layer1.0.bn1.bias: copying a param with shape torch.Size([]) from checkpoint, the shape in current model is torch.Size([64]).

[fanlai0990]

Ah. Actually not.

[EricDinging]

ic will spend some time working on that

[EricDinging]

I wonder whether we can add self.client_training_results to the MockAggregator, as this attribute is necessary for q-fedavg to keep track of individual client training result.

The reason we have this test error is that I put the q-fedavg optimizer logic into model_wrapper for the sake of clean code (I think the original code is doing this optimizer step outside model_wrapper if I remember correctly), and feed in model_wrapper with client_training_results (which MockAggregator does not have).

I forgot to change the API for tensorflow_model_wrapper. I will update it accordingly. But let me know whether you think it is a good idea.

BTW the error above has been fixed.

[fanlai0990]

Sounds good. No worries to update it. Just make sure there is consistency across all function calls and unit tests. Thanks.

[AmberLJC]

May I know the results for cat femnist_logging |grep "FL Testing" while specify - gradient_policy: fed-yogi ?

[EricDinging]

@AmberLJC Here is the result

    - yogi_eta: 0.01
    - yogi_tau: 0.001
    - yogi_beta: 0.01
    - yogi_beta2: 0.99

[AmberLJC]

Thank you!