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Data and Configurations

1. Dataloaders

When using Qadence, you can supply classical data to a quantum machine learning algorithm by using a standard PyTorch DataLoader instance. Qadence also provides the DictDataLoader convenience class which allows to build dictionaries of DataLoaders instances and easily iterate over them.

import torch
from torch.utils.data import DataLoader, TensorDataset
from qadence.ml_tools import DictDataLoader, to_dataloader


def dataloader(data_size: int = 25, batch_size: int = 5, infinite: bool = False) -> DataLoader:
    x = torch.linspace(0, 1, data_size).reshape(-1, 1)
    y = torch.sin(x)
    return to_dataloader(x, y, batch_size=batch_size, infinite=infinite)


def dictdataloader(data_size: int = 25, batch_size: int = 5) -> DictDataLoader:
    dls = {}
    for k in ["y1", "y2"]:
        x = torch.rand(data_size, 1)
        y = torch.sin(x)
        dls[k] = to_dataloader(x, y, batch_size=batch_size, infinite=True)
    return DictDataLoader(dls)


# iterate over standard DataLoader
for (x,y) in dataloader(data_size=6, batch_size=2):
    print(f"Standard {x = }")

# construct an infinite dataset which will keep sampling indefinitely
n_epochs = 5
dl = iter(dataloader(data_size=6, batch_size=2, infinite=True))
for _ in range(n_epochs):
    (x, y) = next(dl)
    print(f"Infinite {x = }")

# iterate over DictDataLoader
ddl = dictdataloader()
data = next(iter(ddl))
print(f"{data = }")

Standard x = tensor([[0.0000],
        [0.2000]])
Standard x = tensor([[0.4000],
        [0.6000]])
Standard x = tensor([[0.8000],
        [1.0000]])
Infinite x = tensor([[1.0000],
        [0.2000]])
Infinite x = tensor([[0.6000],
        [0.4000]])
Infinite x = tensor([[0.8000],
        [0.0000]])
Infinite x = tensor([[1.0000],
        [0.2000]])
Infinite x = tensor([[0.6000],
        [0.4000]])
data = {'y1': [tensor([[0.2423],
        [0.2795],
        [0.4343],
        [0.3862],
        [0.2573]]), tensor([[0.2399],
        [0.2759],
        [0.4208],
        [0.3767],
        [0.2544]])], 'y2': [tensor([[0.3375],
        [0.1561],
        [0.7423],
        [0.7705],
        [0.4121]]), tensor([[0.3311],
        [0.1555],
        [0.6760],
        [0.6965],
        [0.4005]])]}

Note: In case of infinite=True, the dataloader iterator will provide a random sample from the dataset.

2. Training Configuration

The TrainConfig class provides a comprehensive configuration setup for training quantam machine learning models in Qadence. This configuration includes settings for batch size, logging, check-pointing, validation, and additional custom callbacks that control the training process's granularity and flexibility.

The TrainConfig tells Trainer what batch_size should be used, how many epochs to train, in which intervals to print/log metrics and how often to store intermediate checkpoints. It is also possible to provide custom callback functions by instantiating a Callback with a function callback.

For example of how to use the TrainConfig with Trainer, please see Examples in Trainer

2.1 Explanation of TrainConfig Attributes

Attribute Type Default Description
max_iter int 10000 Total number of training epochs.
batch_size int 1 Batch size for training.
print_every int 0 Frequency of console output. Set to 0 to disable.
write_every int 0 Frequency of logging metrics. Set to 0 to disable.
plot_every int 0 Frequency of plotting metrics. Set to 0 to disable.
checkpoint_every int 0 Frequency of saving checkpoints. Set to 0 to disable.
val_every int 0 Frequency of validation checks. Set to 0 to disable.
val_epsilon float 1e-5 Threshold for validation improvement.
validation_criterion Callable None Function for validating metric improvement.
trainstop_criterion Callable None Function to stop training early.
callbacks list[Callback] [] List of custom callbacks.
root_folder Path "./qml_logs" Root directory for saving logs and checkpoints.
log_folder Path "./qml_logs" Logging directory for saving logs and checkpoints.
log_model bool False Enables model logging.
verbose bool True Enables detailed logging.
tracking_tool ExperimentTrackingTool TENSORBOARD Tool for tracking training metrics.
plotting_functions tuple () Functions for plotting metrics.
hyperparams dict {} Dictionary of hyperparameters
nprocs int 1 Number of processes to use when spawning subprocesses; for multi-GPU setups, set this to the total number of GPUs.
compute_setup str "cpu" Specifies the compute device: "auto", "gpu", or "cpu".
backend str "gloo" Backend for distributed training communication (e.g., "gloo", "nccl", or "mpi").
log_setup str "cpu" Device setup for logging; use "cpu" to avoid GPU conflicts
dtype dtype or None None Data type for computations (e.g., torch.float32)
all_reduce_metrics bool False If True, aggregates metrics (e.g., loss) across processes 
from qadence.ml_tools import OptimizeResult, TrainConfig
from qadence.ml_tools.callbacks import Callback

batch_size = 5
n_epochs = 100

print_parameters = lambda opt_res: print(opt_res.model.parameters())
condition_print = lambda opt_res: opt_res.loss < 1.0e-03
modify_extra_opt_res = {"n_epochs": n_epochs}
custom_callback = Callback(on="train_end", callback = print_parameters, callback_condition=condition_print, modify_optimize_result=modify_extra_opt_res, called_every=10,)

config = TrainConfig(
    root_folder="some_path/",
    max_iter=n_epochs,
    checkpoint_every=100,
    write_every=100,
    batch_size=batch_size,
    callbacks = [custom_callback]
)

2.2 Key Configuration Options in TrainConfig

Iterations and Batch Size

  • max_iter (int): Specifies the total number of training iterations (epochs). For an InfiniteTensorDataset, each epoch contains one batch; for a TensorDataset, it contains len(dataloader) batches.
  • batch_size (int): Defines the number of samples processed in each training iteration.

Example: 

config = TrainConfig(max_iter=2000, batch_size=32)

Training Parameters

  • print_every (int): Controls how often loss and metrics are printed to the console.
  • write_every (int): Determines how frequently metrics are written to the tracking tool, such as TensorBoard or MLflow.
  • checkpoint_every (int): Sets the frequency for saving model checkpoints.

Note: Set 0 to diable.

Example: 

config = TrainConfig(print_every=100, write_every=50, checkpoint_every=50)

The user can provide either the root_folder or the log_folder for saving checkpoints and logging. When neither are provided, the default root_folder "./qml_logs" is used.

  • root_folder (Path): The root directory for saving checkpoints and logs. All training logs will be saved inside a subfolder in this root directory. (The path to these subfolders can be accessed using config._subfolders, and the current logging folder is config.log_folder)
  • create_subfolder_per_run (bool): Creates a unique subfolder for each training run within the specified folder.
  • tracking_tool (ExperimentTrackingTool): Specifies the tracking tool to log metrics, e.g., TensorBoard or MLflow.
  • log_model (bool): Enables logging of a serialized version of the model, which is useful for model versioning. Thi happens at the end of training.

Note - The user can also provide log_folder argument - which will only be used when create_subfolder_per_run = False. - log_folder (Path): The log folder used for saving checkpoints and logs.

Example: 

config = TrainConfig(root_folder="path/to/checkpoints", tracking_tool=ExperimentTrackingTool.MLFLOW, checkpoint_best_only=True)

Validation Parameters

  • checkpoint_best_only (bool): If set to True, saves checkpoints only when there is an improvement in the validation metric.
  • val_every (int): Frequency of validation checks. Setting this to 0 disables validation.
  • val_epsilon (float): A small threshold used to compare the current validation loss with previous best losses.
  • validation_criterion (Callable): A custom function to assess if the validation metric meets a specified condition.

Example: 

config = TrainConfig(val_every=200, checkpoint_best_only = True, validation_criterion=lambda current, best: current < best - 0.001)

If it is desired to only the save the "best" checkpoint, the following must be ensured:

(a) `checkpoint_best_only = True` is used while creating the configuration through `TrainConfig`,
(b) `val_every` is set to a valid integer value (for example, `val_every = 10`) which controls the no. of iterations after which the validation data should be used to evaluate the model during training, which can also be set through `TrainConfig`,
(c) a validation criterion is provided through the `validation_criterion`, set through `TrainConfig` to quantify the definition of "best", and
(d) the validation dataloader passed to `Trainer` is of type `DataLoader`. In this case, it is expected that a validation dataloader is also provided along with the train dataloader since the validation data will be used to decide the "best" checkpoint.

The criterion used to decide the "best" checkpoint can be customized by validation_criterion, which should be a function that can take val_loss, best_loss, and val_epsilon arguments and return a boolean value (True or False) indicating whether some validation metric is satisfied or not. An example of a simple validation_criterion is: 

def validation_criterion(val_loss: float, best_val_loss: float, val_epsilon: float) -> bool:
    return val_loss < (best_val_loss - val_epsilon)

Custom Callbacks

TrainConfig supports custom callbacks that can be triggered at specific stages of training. The callbacks attribute accepts a list of callback instances, which allow for custom behaviors like early stopping or additional logging. See Callbacks for more details.

  • callbacks (list[Callback]): List of custom callbacks to execute during training.

Example: 

from qadence.ml_tools.callbacks import Callback

def callback_fn(trainer, config, writer):
    if trainer.opt_res.loss < 0.001:
        print("Custom Callback: Loss threshold reached!")

custom_callback = Callback(on = "train_epoch_end", called_every = 10, callback_function = callback_fn )

config = TrainConfig(callbacks=[custom_callback])

Hyperparameters and Plotting

  • hyperparams (dict): A dictionary of hyperparameters (e.g., learning rate, regularization) to be tracked by the tracking tool.
  • plot_every (int): Determines how frequently plots are saved to the tracking tool, such as TensorBoard or MLflow.
  • plotting_functions (tuple[LoggablePlotFunction, ...]): Functions for in-training plotting of metrics or model state.

Note: Please ensure that plotting_functions are provided when plot_every > 0

Example: 

config = TrainConfig(
    plot_every=10,
    hyperparams={"learning_rate": 0.001, "batch_size": 32},
    plotting_functions=(plot_loss_function,)
)

Advanced Distributed Training

  • nprocs (int): Specifies the number of processes to be used. For multi-GPU training, this should match the total number of GPUs available. When nprocs is greater than 1, Trainer spawns additional subprocesses for training. This is useful for parallel or distributed training setups.

  • compute_setup (str): Determines the compute device configuration: 1."auto" (automatically selects GPU if available), 2. "gpu" - (forces GPU usage and errors if no GPU is detected), and 3. "cpu" (Forces the use of the CPU).

  • backend (str): Specifies the communication backend for distributed training. Common options are "gloo" (default), "nccl" (optimized for GPUs), or "mpi", depending on your setup. It should be one of the backends supported by torch.distributed. For further details, please look at torch backends

Notes: - Logging Specific Callbacks: Logging is available only through the main process, i.e. process 0. Model logging, plotting, logging metrics will only be performed for a single process, even if multiple processes are run. - Training with specific callbacks: Callbacks specific to training, e.g., EarlyStopping, LRSchedulerStepDecay, etc will be called from each process. - PrintMetrics (set through the print_every argument in TrainCongig) is available from all processes.

Example: For CPU MultiProcessing 

config = TrainConfig(
    compute_setup="cpu",
    nprocs=5,
    backend="gloo"
)

Example: For GPU multiprocessing training 

config = TrainConfig(
    compute_setup="gpu",
    nprocs=2, # World-size/Total number of GPUs
    backend="nccl"
)

Precision Options

  • dtype (dtype or None): Sets the numerical precision (data type) for computations. For instance, you can use torch.float32 or torch.float16 depending on your performance and precision needs. Both model parameters, and dataset will be of the provided precision.

    • If not specified or None, the default torch precision (usually torch.float32) is used.
    • If provided dtype is complex dtype, appropriate precision for the data and model parameters will be used as follows:
    Data Type (dtype) Data Precision Model Precision Model Parameters Precision (Real Part & Imaginary Part )
    torch.float16 16-bit 16-bit N/A
    torch.float32 32-bit 32-bit N/A
    torch.float64 64-bit 64-bit N/A
    torch.complex32 16-bit 32-bit 16-bit
    torch.complex64 32-bit 64-bit 32-bit
    torch.complex128 64-bit 128-bit 64-bit

    Complex Dtypes: Complex data types are useful for Quantum Neural Networks - such as QNN provided by qadence. The industry standard is to use torch.complex128, however, the user can also specify a lower precision (torch.complex64 or torch.complex32) for faster training.

Furthermore, the user can also utilize the following options:

  • log_setup (str): Configures the device used for logging. Using "cpu" ensures logging runs on the CPU (which may avoid conflicts with GPU operations), while "auto" aligns logging with the compute device.

  • all_reduce_metrics (bool): When enabled, aggregates metrics (such as loss or accuracy) across all training processes to provide a unified summary, though it may introduce additional synchronization overhead.

3. Experiment tracking with mlflow

Qadence allows to track runs and log hyperparameters, models and plots with tensorboard and mlflow. In the following, we demonstrate the integration with mlflow.

mlflow configuration

We have control over our tracking configuration by setting environment variables. First, let's look at the tracking URI. For the purpose of this demo we will be working with a local database, in a similar fashion as described here, 

export MLFLOW_TRACKING_URI=sqlite:///mlruns.db

Qadence can also read the following two environment variables to define the mlflow experiment name and run name 

export MLFLOW_EXPERIMENT=test_experiment
export MLFLOW_RUN_NAME=run_0

If no tracking URI is provided, mlflow stores run information and artifacts in the local ./mlflow directory and if no names are defined, the experiment and run will be named with random UUIDs.