[FIX] SWA and SE with non cyclic schedulers #395

ravinkohli · 2022-02-28T14:32:29Z

This PR fixes a bug in swa and se when they are used with noncyclic schedulers. Also enables learned entity embeddings for reg_cocktails.

codecov · 2022-02-28T14:57:03Z

Codecov Report

Merging #395 (989f3ac) into reg_cocktails (484ead4) will increase coverage by 0.56%.
The diff coverage is 79.31%.

❗ Current head 989f3ac differs from pull request most recent head 0cc6b46. Consider uploading reports for the commit 0cc6b46 to get more accurate results

@@                Coverage Diff                @@
##           reg_cocktails     #395      +/-   ##
=================================================
+ Coverage          82.89%   83.46%   +0.56%     
=================================================
  Files                172      172              
  Lines              10336    10369      +33     
  Branches            1815     1827      +12     
=================================================
+ Hits                8568     8654      +86     
+ Misses              1244     1184      -60     
- Partials             524      531       +7

Impacted Files	Coverage Δ
...ine/components/setup/network_embedding/__init__.py	`65.95% <63.63%> (-1.17%)`	⬇️
autoPyTorch/pipeline/tabular_regression.py	`82.56% <75.00%> (+11.28%)`	⬆️
autoPyTorch/pipeline/tabular_classification.py	`82.17% <87.50%> (+9.44%)`	⬆️
...peline/components/training/trainer/base_trainer.py	`95.58% <95.00%> (-0.07%)`	⬇️
...eline/components/setup/optimizer/base_optimizer.py	`80.95% <0.00%> (-19.05%)`	⬇️
...abular_preprocessing/base_tabular_preprocessing.py	`66.66% <0.00%> (-13.34%)`	⬇️
...ne/components/setup/lr_scheduler/base_scheduler.py	`93.10% <0.00%> (-6.90%)`	⬇️
...up/network_initializer/base_network_initializer.py	`93.54% <0.00%> (-6.46%)`	⬇️
...nts/setup/early_preprocessor/EarlyPreprocessing.py	`85.29% <0.00%> (-5.89%)`	⬇️
.../pipeline/components/setup/network/base_network.py	`89.87% <0.00%> (-5.07%)`	⬇️
... and 14 more

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nabenabe0928

Hi Thanks for the PR.
I checked except base_trainer.

nabenabe0928 · 2022-03-01T15:46:54Z

autoPyTorch/pipeline/tabular_classification.py

+                    exclude=exclude if bool(exclude) else None,
+                    include=include if bool(include) else None)


Do we need to use if statements here?

yes because our get_available_components logic checks if include or exclude are None or not. I think though we dont need to use get_available_components as I only need the string names. I'll fix this.

nabenabe0928 · 2022-03-01T15:47:47Z

autoPyTorch/pipeline/tabular_classification.py

+        # Disable CyclicLR until todo is completed.
+        if 'lr_scheduler' in self.named_steps.keys() and 'trainer' in self.named_steps.keys():
+            trainers = cs.get_hyperparameter('trainer:__choice__').choices
+            for trainer in trainers:
+                available_schedulers = self.named_steps['lr_scheduler'].get_available_components(
+                    dataset_properties=dataset_properties,
+                    exclude=exclude if bool(exclude) else None,
+                    include=include if bool(include) else None)
+                # TODO: update cyclic lr to use n_restarts and adjust according to batch size
+                cyclic_lr_name = 'CyclicLR'
+                if cyclic_lr_name in available_schedulers:
+                    # disable snapshot ensembles and stochastic weight averaging
+                    cs.add_forbidden_clause(ForbiddenAndConjunction(
+                        ForbiddenEqualsClause(cs.get_hyperparameter(
+                            f'trainer:{trainer}:use_snapshot_ensemble'), True),
+                        ForbiddenEqualsClause(cs.get_hyperparameter('lr_scheduler:__choice__'), cyclic_lr_name)
+                    ))
+                    cs.add_forbidden_clause(ForbiddenAndConjunction(
+                        ForbiddenEqualsClause(cs.get_hyperparameter(
+                            f'trainer:{trainer}:use_stochastic_weight_averaging'), True),
+                        ForbiddenEqualsClause(cs.get_hyperparameter('lr_scheduler:__choice__'), cyclic_lr_name)
+                    ))


Can we make a function for it because we do the same thing for both tabular_{classification, regression}?
And this method is long.

autoPyTorch/pipeline/components/setup/network_embedding/__init__.py

nabenabe0928 · 2022-03-01T16:09:09Z

autoPyTorch/pipeline/components/setup/network_embedding/__init__.py

+        updates = self._get_search_space_updates()
+        if '__choice__' in updates.keys():
+            choice_hyperparameter = updates['__choice__']
+            if not set(choice_hyperparameter.value_range).issubset(available_embedding):
+                raise ValueError("Expected given update for {} to have "
+                                 "choices in {} got {}".format(self.__class__.__name__,
+                                                               available_embedding,
+                                                               choice_hyperparameter.value_range))
+            if len(categorical_columns) == 0:
+                assert len(choice_hyperparameter.value_range) == 1
+                if 'NoEmbedding' not in choice_hyperparameter.value_range:
+                    raise ValueError("Provided {} in choices, however, the dataset "
+                                     "is incompatible with it".format(choice_hyperparameter.value_range))
+            embedding = CSH.CategoricalHyperparameter('__choice__',
+                                                      choice_hyperparameter.value_range,
+                                                      default_value=choice_hyperparameter.default_value)
+        else:
+
+            if len(categorical_columns) == 0:
+                default = 'NoEmbedding'
+                if include is not None and default not in include:
+                    raise ValueError("Provided {} in include, however, the dataset "
+                                     "is incompatible with it".format(include))
+                embedding = CSH.CategoricalHyperparameter('__choice__',
+                                                          ['NoEmbedding'],
+                                                          default_value=default)
+            else:
+                embedding = CSH.CategoricalHyperparameter('__choice__',
+                                                          list(available_embedding.keys()),
+                                                          default_value=default)


Can we create a method for this?

def _get_embedding_config(self, updates, avail_embeddings, default: str = "NoEmbedding"): if '__choice__' in updates.keys(): choices, default = updates['__choice__'].value_range, updates['__choice__'].default_value if not set(choices).issubset(avail_embeddings): raise ValueError( f"The choices for {self.__class__.__name__} must be in {avail_embeddings}, but got {choices}" ) if len(categorical_columns) == 0: assert len(choices) == 1 if 'NoEmbedding' not in choices: raise ValueError(f"The choices must include `NoEmbedding`, but got {choices}") embedding = CSH.CategoricalHyperparameter('__choice__', choices, default_value=default) elif len(categorical_columns) == 0: default = 'NoEmbedding' if include is not None and default not in include: raise ValueError(f"default `{default}` must be in `include`: {include}") embedding = CSH.CategoricalHyperparameter('__choice__', ['NoEmbedding'], default_value=default) else: choices = list(available_embedding.keys()) embedding = CSH.CategoricalHyperparameter('__choice__', choices, default_value=default) return embedding

let me do that after we are done with the experiments. There is an issue raised for this. #377 .

autoPyTorch/pipeline/components/training/trainer/base_trainer.py

nabenabe0928 · 2022-03-02T14:23:28Z

autoPyTorch/pipeline/components/training/trainer/base_trainer.py

-                    model_copy = deepcopy(self.swa_model) if self.use_stochastic_weight_averaging \
-                        else deepcopy(self.model)


Is it ok to add the condition is_last_epoch in the new implementation?

what do you mean?

He caught a bug, when there was a wrong implementation initially for se and swa when both activated and it was fixed, it was not fixed for the case of a non-cyclic scheduler. Hence now that you fixed it, it is different from the previous implementation since you had no if last epoch check to add the swa model only in the end, but you add 3 copies of it throughout.

Basically, now it is ok and correct.

Co-authored-by: nabenabe0928 <[email protected]>

ArlindKadra

I had one small question and except that everything looks good, especially refactoring to remove the code duplication.

ArlindKadra · 2022-03-07T20:45:16Z

autoPyTorch/pipeline/base_pipeline.py

+                            ))
+                            break
+                        except ValueError:
+                            # change the default and try again


Why would we get an error here and why do we need to change the default?
It is not the case that we might have a default value that is not in the list of choices right?

we will get a value error in case LearnedEntityEmbedding was the default value for the __choice__ hyperparameter

* Enable learned embeddings, fix bug with non cyclic schedulers * add forbidden condition cyclic lr * refactor base_pipeline forbidden conditions * Apply suggestions from code review Co-authored-by: nabenabe0928 <[email protected]> Co-authored-by: nabenabe0928 <[email protected]>

ravinkohli added 2 commits February 28, 2022 15:22

Enable learned embeddings, fix bug with non cyclic schedulers

8bf6280

add forbidden condition cyclic lr

989f3ac

ravinkohli changed the base branch from master to reg_cocktails February 28, 2022 14:32

ravinkohli changed the title ~~Fix swa se~~ [FIX] SWA and SE with non cyclic schedulers Feb 28, 2022

ravinkohli requested a review from ArlindKadra February 28, 2022 19:09

nabenabe0928 requested changes Mar 1, 2022

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nabenabe0928 reviewed Mar 2, 2022

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ravinkohli and others added 2 commits March 2, 2022 17:10

refactor base_pipeline forbidden conditions

cda66c8

Apply suggestions from code review

0cc6b46

Co-authored-by: nabenabe0928 <[email protected]>

ArlindKadra approved these changes Mar 7, 2022

View reviewed changes

ravinkohli merged commit ce89f92 into reg_cocktails Mar 9, 2022

ravinkohli deleted the fix_swa_se branch June 15, 2022 13:15

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[FIX] SWA and SE with non cyclic schedulers #395

[FIX] SWA and SE with non cyclic schedulers #395

ravinkohli commented Feb 28, 2022

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ravinkohli Mar 8, 2022

		exclude=exclude if bool(exclude) else None,
		include=include if bool(include) else None)

		model_copy = deepcopy(self.swa_model) if self.use_stochastic_weight_averaging \
		else deepcopy(self.model)

[FIX] SWA and SE with non cyclic schedulers #395

[FIX] SWA and SE with non cyclic schedulers #395

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