ipex-llm/docs/readthedocs/source/doc/Nano/QuickStart/hpo.md

AutoML Overview

Nano provides built-in AutoML support through hyperparameter optimization.

By simply changing imports, you are able to search the model architecture (e.g. by specifying search spaces in layer/activation/function arguments when defining the model), or the training procedure (e.g. by specifying search spaces in learning_rate or batch_size). You can simply use model.search (tensorflow)/Trainer.search(pytorch) to launch trials, and model.search_summary(tensorflow)/Trainer.search_summary(pytorch) to review the search results.

Under the hood, the objects (layers, activations, model, etc.) are implicitly turned into searchable objects at creation, which allows search spaces to be specified in their init arguments. Nano HPO collects those search spaces and pass them to the underlying HPO engine (i.e. Optuna) which generates hyperparameter suggestions accordingly. The instantiation and execution of the corresponding objects are delayed until the hyperparameter values are available in each trial.

Install

If you have not installed BigDL-Nano yet, follow the Nano Install Guide to install it according to your system and framework (i.e. tensorflow or pytorch).

Next, install a few dependencies required for Nano HPO using below commands.

pip install ConfigSpace
pip install optuna

Search Spaces

Search spaces are value range specifications that the search engine use for sampling hyperparameters. The available search spaces in Nano HPO is defined in bigdl.nano.automl.hpo.space. Refer to Search Space API doc for more details.

Tensorflow HPO

Enable/Disable HPO for tensorflow

For tensorflow training, you should call hpo_config.enable_hpo_tf before using Nano HPO.

hpo_config.enable_hpo_tf will dynamically add searchable layers, activations, functions, optimizers, etc into the bigdl.nano.tf module. When importing layers, you need to change the imports from tf.keras.layers to bigdl.nano.tf.keras.layers, so that you can specify search spaces in their init arguments. Note that even if you don't need to search the model architecture, you still need to change the imports.

import bigdl.nano.automl as nano_automl
nano_automl.hpo_config.enable_hpo_tf()

To disable HPO, use hpo_config.disable_hpo_tf. This will remove the searchable objects from bigdl.nano.tf module.

import bigdl.nano.automl as nano_automl
nano_automl.hpo_config.disable_hpo_tf()

Search the learning rate

To search the learning rate, specify search space in learning_rate argument in the optimizer in model.compile. Remember to import the optimizer from bigdl.nano.tf.optimizers instead of tf.keras.optimizers.

import bigdl.nano.automl.hpo.space as space
from bigdl.nano.tf.optimizers import RMSprop
model.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=RMSprop(learning_rate=space.Real(0.0001, 0.01, log=True)),
    metrics=["accuracy"],
)

Search the batch size

To search the batch size, specify search space in batch_size argument in model.fit.

import bigdl.nano.automl.hpo.space as space
model.search(n_trials=2, target_metric='accuracy', direction="maximize",
    x=x_train, y=y_train,validation_data=(x_valid, y_valid),
    batch_size=space.Categorical(128,64))

Search the Model Architecture

To search different versions of model, you can specify search spaces when defining the model using either sequential API, functional API or by subclassing tf.keras.Model.

using Sequential API

You can specify search spaces in layer arguments. Note that search spaces can only be specified in key-word argument (which means Dense(space.xxx) should be changes to Dense(units=space.xxx)).

from bigdl.nano.tf.keras.layers import Dense, Conv2D, Flatten
from bigdl.nano.automl.tf.keras import Sequential
model = Sequential()
model.add(Conv2D(
    filters=space.Categorical(32, 64),
    kernel_size=space.Categorical(3, 5),
    strides=space.Categorical(1, 2),
    activation=space.Categorical("relu", "linear"),
    input_shape=input_shape))
model.add(Flatten())
model.add(Dense(10, activation="softmax"))

using Functional API

You can specify search spaces in layer arguments. Note that if a layer is used more than once in the model, we strongly suggest you to specify a prefix for each of the search space in the layer to distinguish them, or they will share the same search space (the last space will override all previous definition), as shown in the below example.

import bigdl.nano.automl.hpo.space as space
from bigdl.nano.tf.keras import Input
from bigdl.nano.tf.keras.layers import Dense, Dropout
from bigdl.nano.automl.tf.keras import Model

inputs = Input(shape=(784,))
x = Dense(units=space.Categorical(8,16,prefix='dense_1'), activation="linear")(inputs)
x = Dense(units=space.Categorical(32,64,prefix='dense_2'), activation="tanh")(x)
x = Dropout(rate=space.Real(0.1,0.5, prefix='dropout'))(x)
outputs = Dense(units=10)(x)
model = Model(inputs=inputs, outputs=outputs, name="mnist_model")

by subclassing tf.keras.Model

For models defined by subclassing tf.keras.Model, use the decorator @hpo.tfmodel to turn the model into a searchable object. Then you will able to specify either search spaces or normal values in the model init arguments.

import bigdl.nano.automl.hpo.space as space
import bigdl.nano.automl.hpo as hpo
@hpo.tfmodel()
class MyModel(tf.keras.Model):
    def __init__(self, filters, kernel_size, strides, num_classes=10):
        super().__init__()
        self.conv1 = tf.keras.layers.Conv2D(filters=filters,
                            kernel_size=kernel_size,
                            strides=strides,
                            activation="relu")
        self.max1  = tf.keras.layers.MaxPooling2D(3)
        self.bn1   = tf.keras.layers.BatchNormalization()

        self.gap   = tf.keras.layers.GlobalAveragePooling2D()
        self.dense = tf.keras.layers.Dense(num_classes)

    def call(self, inputs, training=False):
        x = self.conv1(inputs)
        x = self.max1(x)
        x = self.bn1(x)
        x = self.gap(x)
        return self.dense(x)

model = MyModel(
    filters=hpo.space.Categorical(32, 64),
    kernel_size=hpo.space.Categorical(3, 5),
    strides=hpo.space.Categorical(1, 2)
)

Launch Hyperparameter Search and Review the Results

To launch hyperparameter search, call model.search after compile, as shown below. model.search runs the n_trials number of trials (meaning n_trials set of hyperparameter combinations are searched), and optimizes the target_metric in the specified direction. Besides search arguments, you also need to specify fit arguments in model.search which will be used in the fitting process in each trial. Refer to (API docs)[] for details.

Use model.search_summary to retrieve the search results, which you can use to review in data frames, pick the best trial, or do visualizations. Examples of search results analysis and visualization can be found here.

Finally, model.fit will automatically fit the model using the best set of hyper parameters found in the search. You can also use a set of hyperparameters from a trial other than the best one. Refer to API docs for details.

model = ... # define the model
model.compile(...)
model.search(n_trials=100, target_metric='accuracy', direction="maximize",
    x=x_train, y=y_train, batch_size=32, epochs=20, validation_split=0.2)
    study = model.search_summary()
model.fit(...)

---

PyTorch HPO

Nano-HPO now only support hyperparameter search for pytorch-lightning modules.

Search the Model Architecture

To search the model architecture, use the decorator @hpo.plmodel() to turn the model into a searchable object. Put the arguments that needs to be searched in the init arguments and use the arguments to construct the model. The arguments can be either space or non-space values, as shown below.

import bigdl.nano.automl.hpo.space as space
import bigdl.nano.automl.hpo as hpo

@hpo.plmodel()
class MyModel(pl.LightningModule):
    """Customized Model."""
    def __init__(self,out_dim1,out_dim2,dropout_1,dropout_2):
        super().__init__()
        layers = []
        input_dim = 32
        for out_dim, dropout in [(out_dim1, dropout_1),(out_dim2,dropout_2)]:
            layers.append(torch.nn.Linear(input_dim, out_dim))
            layers.append(torch.nn.Tanh())
            layers.append(torch.nn.Dropout(dropout))
            input_dim = out_dim
        layers.append(torch.nn.Linear(input_dim, 2))
        self.layers: torch.nn.Module = torch.nn.Sequential(*layers)
        self.save_hyperparameters()
    def forward(self, x):
        return self.layers(x)

model = MyModel(
    out_dim1=space.Categorical(16,32),
    out_dim2=space.Categorical(16,32),
    dropout_1=space.Categorical(0.1, 0.2, 0.3, 0.4, 0.5),
    dropout_2 = 0.5)

Search the learning rate

learning_rate can be specified in the init arguments of your model. You can use learning_rate to construct the optimizer in configure_optimizers(), as shown below.

import bigdl.nano.automl.hpo.space as space
import bigdl.nano.automl.hpo as hpo
@hpo.plmodel()
class MyModel(pl.LightningModule):
    def __init__(self, ..., learning_rate=0.1):
        ...
        self.save_hyperparameters()
    def configure_optimizers(self):
        # set learning rate in the optimizer
        self.optimizer = torch.optim.Adam(self.layers.parameters(),
                                          lr=self.hparams.learning_rate)
        return [self.optimizer], []
model = MyModel(..., learning_rate=space.Real(0.001,0.01,log=True))

Search the batch size

batch_size can be specified in the init arguments of your model. You can use the batch_size to construct the train DataLoader in train_dataloader(), as shown below.

import bigdl.nano.automl.hpo.space as space
import bigdl.nano.automl.hpo as hpo
@hpo.plmodel()
class MyModel(pl.LightningModule):
    def __init__(self, ..., batch_size=16):
        ...
        self.save_hyperparameters()
    def train_dataloader(self):
        # set the batch size in train dataloader
        return DataLoader(RandomDataset(32, 64),
                          batch_size=self.hparams.batch_size)
model = MyModel(..., batch_size = space.Categorical(32,64))

Launch Hyperparameter Search and Review the Results

To launch hyperparameter search, call Trainer.search after model is defined. Remember to set use_hpo=True in when initializing the Trainer.

Trainer.search takes the decorated model as input. Similar to tensorflow, trainer.search runs the n_trials number of trials (meaning n_trials set of hyperparameter combinations are searched), and optimizes the target_metric in the specified direction. There's an extra argument max_epochs which is only used for the fitting process in search trials (do not affect the Trainer.fit). Trainer.search returns a model configured with the best set of hyper parameters.

Use model.search_summary to retrieve the search results, which you can use to review in data frames, pick the best trial, or do visualizations. Examples of search results analysis and visualization can be found here.

Finally you can use Trainer.fit() to fit the best model. You may get models constructed with hyperparameters of a specific trial. Refer to Trainer.search API doc for more details.

from bigdl.nano.pytorch import Trainer
model = MyModel(...)
trainer = Trainer(...,use_hpo=True)
best_model = trainer.search(
    model,
    target_metric='val_loss',
    direction='minimize',
    n_trials=100,
    max_epochs=20,
)
study = trainer.search_summary()
trainer.fit(best_model)

Visualization