Base optimizer tracking

[bhack]

Description

Brief Description of the PR:
Inner optimizer is not tracked for checkpoint

Fixes # (issue)
#2094

Type of change

• [x ] Bug fix
• New Tutorial
• Updated or additional documentation
• Additional Testing
• New Activation and the changes conform to the activation contribution guidelines
• New Callback and the changes conform to the callback contribution guidelines
• New Image addition and the changes conform to the image op contribution guidelines
• New Layer and the changes conform to the layer contribution guidelines
• New Loss and the changes conform to the loss contribution guidelines
• New Metric and the changes conform to the metric contribution guidelines
• New Optimizer and the changes conform to the optimizer contribution guidelines
• New RNN Cell and the changes conform to the rnn contribution guidelines
• New Seq2seq addition and the changes conform to the seq2seq contribution guidelines
• New Text addition and the changes conform to the text op contribution guidelines

Checklist:

• [X ] I've properly formatted my code according to the guidelines
  • [X ] By running Black + Flake8
  • [ X] By running pre-commit hooks
• This PR addresses an already submitted issue for TensorFlow Addons
• I have made corresponding changes to the documentation
• I have added tests that prove my fix is effective or that my feature works
• This PR contains modifications to C++ custom-ops

How Has This Been Tested?

Tested in #2102

If you're adding a bugfix or new feature please describe the tests that you ran to verify your changes:
It still need to pass save and load_model case.

[bot-of-gabrieldemarmiesse]

@CyberZHG

You are owner of some files modified in this pull request.
Would you kindly review the changes whenever you have the time to?
Thank you very much.

[bhack]

/cc @reedwm Can you give us a feedback here. What we need to correctly support Keras save and load_model?
Need we to introduce something like your _DelegatingTrackableMixin in https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/mixed_precision/experimental/loss_scale_optimizer.py#L58?

I see that in that case in Tensorflow your are just testing checkpoint but not save and load_model.

See user proposed tests in #2102

[reedwm]

The purpose of _DelegatingTrackableMixin is so the checkpoint format of the LossScaleOptimizer is the same as the checkpoint format of the inner optimizer. This allows you to, e.g., save a checkpoint of float32 model with an Adam optimizer, then load the checkpoint into the equivalent mixed precision model with a LossScaleOptimizer wrapping an Adam optimizer.

I'm not very familiar with the Lookahead optimizer, but it seems this use case doesn't apply: I don't think people would want to train partially with Lookahead and partially without, and this would be hard anyway since Lookahead has an extra set of "slow weights". If I'm correct, the current approach in this PR is fine.

Note we test a SavedModel with LossScaleOptimizer here.

[bhack]

@reedwm Thank you for the feedback. I am going to check your SaveModel test that I missed.

[bhack]

@reedwm I've tried to expand your test more in the style of the user proposed one and it is passing on Tensorflow: tensorflow/tensorflow#42749

In our Lookahead case at #2102 instead is passing only with Checkpoint, with save_weight/load_weight cases or model.save and model.load_model but in h5.
So I think that we are still missing something for the model.save in tf format.

Any hint?

[reedwm]

Hmm I'm not sure. What is the error?

Just a guess, but maybe the issue is because you return additional weights from the weights property:

addons/tensorflow_addons/optimizers/lookahead.py

Lines 135 to 137 in ef8b0c9

	@property
	defweights(self):
	returnself._weights+self._optimizer.weights

AFAIK, no other optimizer does this. Maybe it's causing SavedModel to be confused.

Also last time I checked, SavedModel does not save slot variables, but checkpoints do. Make sure your SavedModel test is not relying on slot variables being restored.

Also note LossScaleOptimizer used to use an approach similar to this PR. tensorflow/tensorflow@f9e99f4 changed it, but before that commit SavedModel still worked, so this approach should be possible.

/CC @k-w-w any ideas what the issue could be?

[bhack]

As you can see we have slots in

addons/tensorflow_addons/optimizers/lookahead.py

Line 84 in ef8b0c9

def_create_slots(self, var_list):

[bhack]

I still see this @allenlavoie commit in the current master:
tensorflow/tensorflow@147e677

Is it the motivation of this claim?
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/engine/training.py#L1993-L1994

[allenlavoie]

I don't think that's related. IIRC it was because Checkpoint(model=tf.keras.Model()).save is not compatible with Model().save_weights (since Model is the root of the save in the second case).

What's the error/issue if you try to save with SavedModel?

[bhack]

The optimizer or the base/inner optimizer (or both) don't seems to be in the correct state after load_model in tf format.
Instead It seems ok in h5, checkpoint or save_weights and load_weights.

[allenlavoie]

Hrm. Does tf.saved_model.save / tf.saved_model.load have the same issue? There's a bit of extra logic in Model.save/load_model that could be causing issues here.

[bhack]

With tf.saved_model.save is the same.

[bhack]

@allenlavoie What is exactly the RestoredOptimizer object?

[bhack]

Cause I don't understand this case https://github.com/tensorflow/tensorflow/blame/master/tensorflow/python/keras/saving/saving_utils.py#L179

[allenlavoie]

RestoredOptimizer is just a container for holding the restored slot variables. If nothing owned the tf.Variable objects they would just go out of scope immediately and be deleted. It doesn't implement any of the other Optimizer methods.

Maybe this line is the issue: https://github.com/tensorflow/tensorflow/blob/be2870d0db035d12a207039922d76ae908b1101c/tensorflow/python/keras/optimizer_v2/optimizer_v2.py#L1391

When we tell SavedModel to load something back as a Python object, it takes a Python function to set attributes with. It's calling _set_hyper and passing the inner optimizer, and maybe that's not doing anything reasonable. You could try using a more complicated setter that e.g. typechecks for OptimizerV2s and uses setattr+_track_trackable or something.

[bhack]

I don't find so much entries of ResotredOptimizer object in the code. Where is it created?

What is the logic of:

 if isinstance(model.optimizer, optimizer_v2.RestoredOptimizer):
raise NotImplementedError(
'As of now, Optimizers loaded from SavedModel cannot be saved. '
'If you\'re calling `model.save` or `tf.keras.models.save_model`,'
' please set the `include_optimizer` option to `False`. For '
'`tf.saved_model.save`, delete the optimizer from the model.')

[bhack]

/cc @omalleyt12 What is the use case of this condition that you have introduced?

[bhack]

Ok nevermind I've seen the logic of RestoredOptimizer.

[bhack]

I've just extracted a very small code snippet removing Lookahead wrapping.
If you see it is working with the plain optimizer="SGD" but not with optimizer="adam" so I suppose it is something related to slots:

import os
import numpy as np
import tensorflow as tf
import tempfile
from tensorflow_addons.optimizers import Lookahead
def _init_model(optimizer, init_w):
model = tf.keras.models.Sequential()
dense = tf.keras.layers.Dense(input_shape=(3,), units=1)
model.add(dense)
dense.set_weights([init_w, np.zeros(1,)])
model.compile(optimizer, loss="mse")
return model
optimizer="adam"
np.random.seed(0)
x = np.random.standard_normal((10000, 3))
w = np.random.standard_normal((3, 1))
y = np.dot(x, w) + np.random.standard_normal((10000, 1)) * 1e-4
init_w = np.random.standard_normal((3, 1))
model = _init_model(optimizer, init_w)
model.fit(x, y, epochs=2, shuffle=False)
with tempfile.TemporaryDirectory() as ckpt_dir:
new_model = _init_model(optimizer, init_w)
new_model.fit(x, y, epochs=1, shuffle=False)
new_model.save(ckpt_dir)
new_model = _init_model(optimizer, init_w)
new_model = tf.keras.models.load_model(
ckpt_dir
)
new_model.fit(x, y, epochs=1, shuffle=False)
assert np.allclose(model.predict(x), new_model.predict(x))

[bhack]

@reedwm With Adam Is also failing the tensorflow test on tour test if the change is correct: tensorflow/tensorflow#42749.

[reedwm]

I commented in tensorflow/tensorflow#42749 (comment). Since slot variables are not restored and Adam has slot variables, the sample code you gave is expected to fail.

[bhack]

Ok I commented also there.

[bhack]

I think that this could be merged as is.
Instead we are drafting a general warning on Tensorflow at tensorflow/tensorflow#42846 for the SavedModel case.

[WindQAQ]

Thanks 😃