add suggestion bundle support #3041

granthamtaylor · 2025-01-07T22:19:47Z

Why are the changes needed?

I recently created the flytekitplugins.optuna plugin.

This PR adds support to bundle all of the suggestions into any number of dictionaries.

This is for the cases in which one wants to add dozens or more hyperparameters. Adding each of them as a type function input is pretty exhausting. In our case, we can collect all of these suggestions into any number of dictionaries at runtime.

What changes were proposed in this pull request?

I add support for bundled suggestions to flytekitplugins.optuna. Users should provide any kwargs of type dict[str, Suggestion|Any].

How was this patch tested?

I have added an example and unit tests for the new bundling functionality.

Setup process

import math
from typing import Union

import flytekit as fl
from flytekitplugins.optuna import Optimizer, suggest

@fl.task(container_image=image)
async def objective(suggestions: dict[str, Union[int, float]], power: int) -> float:

    # building out a large set of typed inputs is exhausting, so we can just use a dict

    x, y, z = suggestions["x"], suggestions["y"], suggestions["z"]

    return math.log((((x - 5) ** 2) + (y + 4) ** 4 + (3 * z - 3) ** 2)) ** power


@fl.eager(container_image=image)
async def train(concurrency: int, n_trials: int) -> float:
    optimizer = Optimizer(objective, concurrency, n_trials)

    suggestions = {
        "x": suggest.float(low=-10, high=10),
        "y": suggest.integer(low=-10, high=10),
        "z": suggest.category([-5, 0, 3, 6, 9]),
    }

    await optimizer(suggestions=suggestions, power=2)

    return optimizer.study.best_value

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I updated the documentation accordingly.
All new and existing tests passed.
All commits are signed-off.

Summary by Bito

Enhanced flytekit-optuna plugin with bundled hyperparameter suggestions and improved type safety. Implemented dictionary-based parameter passing and callback mechanism for better management. Modified Optimizer class with instance-based suggest method and parameter organization using parent-child relationships. Added proper handling of optimizer's best value and streamlined multiple hyperparameter handling through runtime dictionary collection.

Unit tests added: True

Estimated effort to review (1-5, lower is better): 2

flyte-bot · 2025-01-07T22:20:00Z

Code Review Agent Run #411aad

Actionable Suggestions - 3

plugins/flytekit-optuna/flytekitplugins/optuna/optimizer.py - 2
- Consider revising validation logic for parameters · Line 65-68
- Consider consolidating duplicate suggestion logic · Line 130-145
plugins/flytekit-optuna/tests/test_optimizer.py - 1
- Wrong objective function used in test · Line 47-47

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Files reviewed - 2 · Commit Range: 4298d66..f1d291f
- plugins/flytekit-optuna/flytekitplugins/optuna/optimizer.py
- plugins/flytekit-optuna/tests/test_optimizer.py
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flyte-bot · 2025-01-07T22:23:57Z

Changelist by Bito

This pull request implements the following key changes.

Key Change	Files Impacted
Feature Improvement - Enhanced Optuna Parameter Bundling Support	- `optimizer.py` - Added support for bundled parameter suggestions and callback mechanism - `setup.py` - Added typing-extensions dependency - `test_optimizer.py` - Added tests for bundled suggestions and callback functionality

plugins/flytekit-optuna/flytekitplugins/optuna/optimizer.py

plugins/flytekit-optuna/tests/test_optimizer.py

flyte-bot · 2025-01-07T23:36:18Z

Code Review Agent Run #c016c8

Actionable Suggestions - 3

plugins/flytekit-optuna/flytekitplugins/optuna/optimizer.py - 1
- Consider making suggestions a keyword argument · Line 128-130
plugins/flytekit-optuna/tests/test_optimizer.py - 2
- Consider adding dictionary key validation · Line 37-37
- Consider using TypedDict for suggestions parameter · Line 37-37

Additional Suggestions - 1

plugins/flytekit-optuna/flytekitplugins/optuna/optimizer.py - 1
- Consider using existing value reference · Line 160-163

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plugins/flytekit-optuna/flytekitplugins/optuna/optimizer.py

plugins/flytekit-optuna/tests/test_optimizer.py

codecov · 2025-01-08T00:03:18Z

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 82.79%. Comparing base (f634d53) to head (749ac03).
Report is 5 commits behind head on master.

Additional details and impacted files

@@           Coverage Diff           @@
##           master    #3041   +/-   ##
=======================================
  Coverage   82.79%   82.79%           
=======================================
  Files           3        3           
  Lines         186      186           
=======================================
  Hits          154      154           
  Misses         32       32

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flyte-bot · 2025-01-08T00:37:19Z

Code Review Agent Run #b3b166

Actionable Suggestions - 0

Additional Suggestions - 1

plugins/flytekit-optuna/flytekitplugins/optuna/optimizer.py - 1
- Consider single line method signature · Line 103-105

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flyte-bot · 2025-01-08T02:06:17Z

Code Review Agent Run #af2f75

Actionable Suggestions - 1

plugins/flytekit-optuna/tests/test_optimizer.py - 1
- Consider adding error handling for optimizer · Line 24-29

Additional Suggestions - 1

plugins/flytekit-optuna/flytekitplugins/optuna/optimizer.py - 1
- Consider breaking down list comprehension · Line 115-115

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plugins/flytekit-optuna/tests/test_optimizer.py

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granthamtaylor · 2025-01-08T20:33:24Z

Another example:

import asyncio

import flytekit as fl
import sklearn
from sklearn.model_selection import train_test_split
import xgboost as xgb
import numpy as np
import optuna
import plotly

from flytekitplugins.optuna import Optimizer, suggest

image = fl.ImageSpec(
    builder="union",
    packages=[
        "flytekit==1.15.0b0",
        "optuna>=4.0.0",
        "scikit-learn>=1.6.0",
        "xgboost>=2.1.3",
        "plotly>=5.24.1",
    ],
)


@fl.task(container_image=image)
async def objective(params: dict[str, int | float | str]) -> float:

    (data, target) = sklearn.datasets.load_breast_cancer(return_X_y=True)
    train_x, valid_x, train_y, valid_y = train_test_split(data, target, test_size=0.25)
    dtrain = xgb.DMatrix(train_x, label=train_y)
    dvalid = xgb.DMatrix(valid_x, label=valid_y)

    bst = xgb.train(params, dtrain)
    valid_yhat = bst.predict(dvalid)
    accuracy = sklearn.metrics.accuracy_score(valid_y, np.rint(valid_yhat))

    return accuracy


@fl.eager(container_image=image)
async def train(concurrency: int, n_trials: int):
    
    study = optuna.create_study(direction="maximize")
    
    optimizer = Optimizer(objective, concurrency, n_trials, study)

    params = {
        "lambda": suggest.float(1e-8, 1.0, log=True),
        "alpha": suggest.float(1e-8, 1.0, log=True),
        "subsample": suggest.float(0.2, 1.0),
        "colsample_bytree": suggest.float(0.2, 1.0),
        "max_depth": suggest.integer(3, 9, step=2),
        "min_child_weight": suggest.integer(2, 10),
        "eta": suggest.float(1e-8, 1.0, log=True),
        "gamma": suggest.float(1e-8, 1.0, log=True),
        "grow_policy": suggest.category(["depthwise", "lossguide"]),
        "sample_type": suggest.category(["uniform", "weighted"]),
        "normalize_type": suggest.category(["tree", "forest"]),
        "rate_drop": suggest.float(1e-8, 1.0, log=True),
        "skip_drop": suggest.float(1e-8, 1.0, log=True),
        "objective": "binary:logistic",
        "tree_method": "exact",
        "booster": "dart",
    }

    await optimizer(params=params)

granthamtaylor · 2025-01-09T21:05:21Z

In some extreme cases, it is useful to be able to define suggestions programmatically. However, this cannot happen within an objective task, because we need to tell the Study what each task is doing. Therefore, users should be able to define a callback function in which they may express their programmatic suggestion logic.

This is complicated but I think it is the best solution. It takes advantage of Flyte's static typing and enables caching too.

import asyncio
from typing import Any

import flytekit as fl
import sklearn
from sklearn.model_selection import train_test_split
import xgboost as xgb
import numpy as np
import optuna
import plotly

from optimizer import Optimizer

image = fl.ImageSpec(
    builder="union",
    packages=[
        "flytekit==1.15.0b0",
        "optuna>=4.0.0",
        "scikit-learn>=1.6.0",
        "xgboost>=2.1.3",
        "plotly>=5.24.1",
    ],
)


@fl.task(container_image=image)
async def objective(params: dict[str, int | float | str]) -> float:
    (data, target) = sklearn.datasets.load_breast_cancer(return_X_y=True)
    train_x, valid_x, train_y, valid_y = train_test_split(data, target, test_size=0.25)
    dtrain = xgb.DMatrix(train_x, label=train_y)
    dvalid = xgb.DMatrix(valid_x, label=valid_y)

    bst = xgb.train(params, dtrain)
    valid_yhat = bst.predict(dvalid)
    accuracy = sklearn.metrics.accuracy_score(valid_y, np.rint(valid_yhat))
    
    print(accuracy)

    return accuracy


@fl.eager(container_image=image)
async def train(concurrency: int, n_trials: int):

    study = optuna.create_study(direction="maximize")
    
    def callback(trial: optuna.Trial, inputs: dict[str, Any]) -> dict[str, Any]:

        params = {
            "verbosity": 0,
            "objective": "binary:logistic",
            # use exact for small dataset.
            "tree_method": "exact",
            # defines booster, gblinear for linear functions.
            "booster": trial.suggest_categorical("booster", ["gbtree", "gblinear", "dart"]),
            # L2 regularization weight.
            "lambda": trial.suggest_float("lambda", 1e-8, 1.0, log=True),
            # L1 regularization weight.
            "alpha": trial.suggest_float("alpha", 1e-8, 1.0, log=True),
            # sampling ratio for training data.
            "subsample": trial.suggest_float("subsample", 0.2, 1.0),
            # sampling according to each tree.
            "colsample_bytree": trial.suggest_float("colsample_bytree", 0.2, 1.0),
        }

        if params["booster"] in ["gbtree", "dart"]:
            # maximum depth of the tree, signifies complexity of the tree.
            params["max_depth"] = trial.suggest_int("max_depth", 3, 9, step=2)
            # minimum child weight, larger the term more conservative the tree.
            params["min_child_weight"] = trial.suggest_int("min_child_weight", 2, 10)
            params["eta"] = trial.suggest_float("eta", 1e-8, 1.0, log=True)
            # defines how selective algorithm is.
            params["gamma"] = trial.suggest_float("gamma", 1e-8, 1.0, log=True)
            params["grow_policy"] = trial.suggest_categorical("grow_policy", ["depthwise", "lossguide"])

        if params["booster"] == "dart":
            params["sample_type"] = trial.suggest_categorical("sample_type", ["uniform", "weighted"])
            params["normalize_type"] = trial.suggest_categorical("normalize_type", ["tree", "forest"])
            params["rate_drop"] = trial.suggest_float("rate_drop", 1e-8, 1.0, log=True)
            params["skip_drop"] = trial.suggest_float("skip_drop", 1e-8, 1.0, log=True)

        return dict(params=params)

    optimizer = Optimizer(objective, concurrency, n_trials, study, callback)

    await optimizer()

    show("History", optuna.visualization.plot_optimization_history(optimizer.study))
    show("Timeline", optuna.visualization.plot_timeline(optimizer.study))
    show("Coordinates", optuna.visualization.plot_parallel_coordinate(optimizer.study))
    show("Importance", optuna.visualization.plot_param_importances(optimizer.study))


def show(name: str, fig: plotly.graph_objects.Figure):
    html: str = plotly.io.to_html(
        fig=fig,
        config={
            "scrollZoom": False,
            "displayModeBar": False,
            "doubleClick": "reset",
        },
    )

    fl.Deck(name, html)


if __name__ == "__main__":
    loss = asyncio.run(train(concurrency=1, n_trials=10))
    print(loss)

flyte-bot · 2025-01-09T21:37:55Z

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flyte-bot · 2025-01-10T18:32:17Z

Code Review Agent Run #bf90ad

Actionable Suggestions - 5

plugins/flytekit-optuna/flytekitplugins/optuna/optimizer.py - 4
- Consider extracting trial execution logic · Line 137-140
- Consider extracting trial execution logic · Line 137-140
- Consider keeping objective as required field · Line 59-59
- Use public interface instead of private · Line 83-83
plugins/flytekit-optuna/tests/test_optimizer.py - 1
- Consider passing objective to Optimizer directly · Line 106-106

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- plugins/flytekit-optuna/setup.py
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flyte-bot · 2025-01-10T18:36:47Z

plugins/flytekit-optuna/flytekitplugins/optuna/optimizer.py

+                if self.callback is not None:
+                    promise = self.callback(trial=trial, **inputs)
+                else:
+                    promise = self.objective(**process(trial, inputs))


Consider extracting trial execution logic

Consider extracting the trial execution logic into a separate method to improve code organization and reusability. The conditional logic for handling callback vs direct objective execution could be cleaner in its own method.

Code suggestion

Check the AI-generated fix before applying

Suggested change

if self.callback is not None:

promise = self.callback(trial=trial, **inputs)

else:

promise = self.objective(**process(trial, inputs))

promise = await self._execute_trial(trial, inputs)

async def _execute_trial(self, trial: optuna.Trial, inputs: dict[str, Any]) -> Awaitable[Union[float, tuple[float, ...]]]:

if self.callback is not None:

return await self.callback(trial=trial, **inputs)

return await self.objective(**process(trial, inputs))

Code Review Run #bf90ad

Is this a valid issue, or was it incorrectly flagged by the Agent?

it was incorrectly flagged

flyte-bot · 2025-01-10T18:36:48Z

plugins/flytekit-optuna/tests/test_optimizer.py

+
+        study = optuna.create_study(direction="maximize")
+
+        optimizer = Optimizer(concurrency=concurrency, n_trials=n_trials, study=study, callback=callback)


Consider passing objective to Optimizer directly

Consider passing the objective function directly to the Optimizer constructor. The current implementation relies on the callback to invoke the objective function which may not be the intended pattern.

Code suggestion

Check the AI-generated fix before applying

Suggested change

optimizer = Optimizer(concurrency=concurrency, n_trials=n_trials, study=study, callback=callback)

optimizer = Optimizer(objective=objective, concurrency=concurrency, n_trials=n_trials, study=study, callback=callback)

Code Review Run #bf90ad

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it was incorrectly flagged

flyte-bot · 2025-01-10T18:36:49Z

plugins/flytekit-optuna/flytekitplugins/optuna/optimizer.py

    concurrency: int
    n_trials: int
    study: Optional[optuna.Study] = None
+    objective: Optional[Union[PythonFunctionTask, PythonFunctionWorkflow]] = None


Consider keeping objective as required field

The removal of the required objective field from the class definition may cause issues since it's used in the __post_init__ method. Consider keeping it as required field or updating the initialization logic.

Code suggestion

Check the AI-generated fix before applying

@@ -55,6 +55,7 @@ class Optimizer: concurrency: int n_trials: int + objective: Union[PythonFunctionTask, PythonFunctionWorkflow] study: Optional[optuna.Study] = None callback: Optional[Callable[Concatenate[optuna.Trial, P], Awaitable[Union[float, tuple[float, ...]]]]] = None

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flyte-bot · 2025-01-10T18:36:50Z

plugins/flytekit-optuna/flytekitplugins/optuna/optimizer.py

+            if isinstance(self.objective, PythonFunctionTask):
+                func = self.objective.task_function
+            elif isinstance(self.objective, PythonFunctionWorkflow):
+                func = self.objective._workflow_function


Use public interface instead of private

Consider using public interface instead of accessing private member '_workflow_function'

Code suggestion

Check the AI-generated fix before applying

Suggested change

func = self.objective._workflow_function

func = self.objective.workflow_function

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granthamtaylor added 2 commits January 7, 2025 17:14

add suggestion bundle support

4298d66

run pre-commit

f1d291f

granthamtaylor requested review from wild-endeavor, kumare3, eapolinario, pingsutw, cosmicBboy, samhita-alla, thomasjpfan and Future-Outlier as code owners January 7, 2025 22:19

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granthamtaylor added 3 commits January 7, 2025 17:33

update init

add0bac

simplify bundling setup. Now happens automatically via typing

9995829

remove Suggestion type

f481da6

allow for some suggestions to be defined as kwargs during bundling

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fix unit tests

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remove positional only arg

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replace and with or in tests (again)

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simplify to suggest over any dictionary

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support recursive dictionaries

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add functionality for suggestion callback

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		study = optuna.create_study(direction="maximize")

		optimizer = Optimizer(concurrency=concurrency, n_trials=n_trials, study=study, callback=callback)

	optimizer = Optimizer(concurrency=concurrency, n_trials=n_trials, study=study, callback=callback)
	optimizer = Optimizer(objective=objective, concurrency=concurrency, n_trials=n_trials, study=study, callback=callback)

	func = self.objective._workflow_function
	func = self.objective.workflow_function

add suggestion bundle support #3041

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