jax-metrics 0.2.3

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JAX Metrics

A Metrics library for the JAX ecosystem

Main Features

• Standard metrics that can be used in any JAX project.
• Pytree abstractions that can natively integrate with all JAX APIs and pytree-supporting frameworks (flax.struct, equinox, treex, etc).
• Distributed-friendly APIs that make it super easy to synchronize metrics across devices.
• Automatic accumulation over epochs.

JAX Metrics is implemented on top of Treeo.

What is included?

• The Keras-like Loss and Metric abstractions.
• A metrics module containing popular metrics.
• The losses and regularizers modules containing popular losses.
• The Metrics and Losses combinators.

Installation

Install using pip:

pip install jax_metrics

Status

Metrics on this library are usually tested against their Keras or Torchmetrics counterparts for numerical equivalence. This code base comes from Treex and Elegy so it's already in use.

Getting Started

Metric

The Metric API consists of 3 basic methods:

• reset: Used to both initialize and reset a metric.
• update: Takes in new data and updates the metric state.
• compute: Returns the current value of the metric.

Simple usage looks like this:

import jax_metrics as jm

metric = jm.metrics.Accuracy()

# Update the metric with a batch of predictions and labels
metric = metric.update(target=y, preds=logits)

# Get the current value of the metric
acc = metric.compute() # 0.95

# alternatively, produce a logs dict
logs = metric.compute_logs() # {'accuracy': 0.95}

# Reset the metric
metric = metric.reset()

Note that update enforces the use of keyword arguments. Also the Metric.name property is used as the key in the returned dict, by default this is the name of the class in lowercase but can be overridden in the constructor via the name argument.

Tipical Training Setup

Because Metrics are pytrees they can be used with jit, pmap, etc. On a more realistic scenario you will proably want to use them inside some of your JAX functions in a setup similar to this:

import jax_metrics as jm

metric = jm.metrics.Accuracy()

def loss_fn(params, metric, x, y):
    ...
    metric = metric.update(target=y, preds=logits)
    ...

    return loss, metric

@jax.jit
def train_step(params, metric, x, y):
    grads, metric = jax.grad(loss_fn, has_aux=True)(
        params, metric, x, y
    )
    ...
    return params, metric

@jax.jit
def reset_step(metric: jm.Metric) -> jm.Metric:
    return metric.reset()

Since the loss function usually has access to the predictions and labels, its usually where you would call metric.update, and the new metric state can be returned as an auxiliary output.

Distributed Training

JAX Metrics has a distributed friendly API via the batch_updates and reduce methods. A simple example of a loss function inside a data parallel setup could look like this:

def loss_fn(params, metric, x, y):
    ...
    # compuate batch update
    batch_updates = metric.batch_updates(target=y, preds=logits)
    # gather over all devices and reduce
    batch_updates = jax.lax.all_gather(batch_updates, "device").reduce()
    # update metric
    metric = metric.merge(batch_updates)
    ...

The batch_updates method behaves similar to update but returns a new metric state with only information about that batch, jax.lax.all_gather "gathers" the metric state over all devices plus adds a new axis to the metric state, and reduce reduces the metric state over all devices (first axis). Finally, merge combines the accumulated metric state over the previous batches with the batch updates.

Loss

The Loss API just consists of a __call__ method. Simple usage looks like this:

import jax_metrics as jm

crossentropy = jm.losses.Crossentropy()

# get reduced loss value
loss = crossentropy(target=y, preds=logits) # 0.23

Note that losses are not pytrees so they should be marked as static. Similar to Keras, all losses have a reduction strategy that can be specified in the constructor and (usually) makes sure that the output is a scalar.

Why have losses in a metrics library?

There are a few reasons for having losses in a metrics library:

1. Most code from this library was originally written for and will still be consumed by Elegy. Since Elegy needs support for calculating cumulative losses, as you will see later, a Metric abstraction called Losses was created for this.
2. A couple of API design decisions are shared between the Loss and Metric APIs. This includes:
   • __call__ and update both accept any number keyword only arguments. This is used to facilitate composition (see Combinators section).
   • Both classes have the index_into and rename_arguments methods that allow them to modify how arguments are consumed.
   • Argument names are standardized to be consistent when ever possible, e.g. both metrics.Accuracy and losses.Crossentropy use the target and preds arguments.

Combinators

Combinators enable you to group together multiple metrics while also being instances of Metric and thus maintaining the same API.

Metrics

The Metrics combinator lets you combine multiple metrics into a single Metric object.

metrics = jm.Metrics([
    jm.metrics.Accuracy(),
    jm.metrics.F1(), # not yet implemented 😅, coming soon?
])

# same API
metrics = metrics.update(target=y, preds=logits)
# compute now returns a dict
metrics.compute() # {'accuracy': 0.95, 'f1': 0.87}
# same as compute_logs in the case
metrics.compute_logs() # {'accuracy': 0.95, 'f1': 0.87}
# Reset the metrics
metrics = metrics.reset()

As you can see the Metrics.update method accepts and forwards all the arguments required by the individual metrics. In this example they use the same arguments, but in practice they may consume different subsets of the arguments. Also, if names are repeated then unique names are generated for each metric by appending a number to the metric name.

If a dictionary is used instead of a list, the keys are used instead of the name property of the metrics to determine the key in the returned dict.

metrics = jm.Metrics({
    "acc": jm.metrics.Accuracy(),
    "f_one": jm.metrics.F1(), # not yet implemented 😅, coming soon?
})

# same API
metrics = metrics.update(target=y, preds=logits)
# compute new returns a dict
metrics.compute() # {'acc': 0.95, 'f_one': 0.87}
# same as compute_logs in the case
metrics.compute_logs() # {'acc': 0.95, 'f_one': 0.87}
# Reset the metrics
metrics = metrics.reset()

You can use nested structures of dicts and lists to group metrics, the keys of the dicts are used to determine group names. Group names and metrics names are concatenated using "/" e.g. "{group_name}/{metric_name}".

Losses

Losses is a Metric combinator that behaves very similarly to Metrics but contains Loss instances. Losses calculates the cumulative mean value of each loss over the batches.

losses = jm.Losses([
    jm.losses.Crossentropy(),
    jm.regularizers.L2(1e-4),
])

# same API
losses = losses.update(target=y, preds=logits, parameters=params)
# compute new returns a dict
losses.compute() # {'crossentropy': 0.23, 'l2': 0.005}
# same as compute_logs in the case
losses.compute_logs() # {'crossentropy': 0.23, 'l2': 0.005}
# you can also compute the total loss
loss = losses.total_loss() # 0.235
# Reset the losses
losses = losses.reset()

As with Metrics, the update method accepts and forwards all the arguments required by the individual losses. In this example target and preds are used by the Crossentropy, while parameters is used by the L2. The total_loss method returns the sum of all values returned by compute.

If a dictionary is used instead of a list, the keys are used instead of the name property of the losses to determine the key in the returned dict.

losses = jm.Losses({
    "xent": jm.losses.Crossentropy(),
    "l2": jm.regularizers.L2(1e-4),
})

# same API
losses = losses.update(target=y, preds=logits, parameters=params)
# compute new returns a dict
losses.compute() # {'xent': 0.23, 'l2': 0.005}
# same as compute_logs in the case
losses.compute_logs() # {'xent': 0.23, 'l2': 0.005}
# you can also compute the total loss
loss = losses.total_loss() # 0.235
# Reset the losses
losses = losses.reset()

If you want to use Losses to calculate the loss of a model, you should use batch_updates followed by total_loss to get the correct batch loss. For example, a loss function could be written as:

def loss_fn(..., losses):
    ...
    batch_updates = losses.batch_updates(target=y, preds=logits, parameters=params)
    loss = batch_updates.total_loss()
    losses = losses.merge(batch_updates)
    ...
    return loss, losses

For convenience, the previous pattern can be simplified to a single line using the loss_and_update method:

def loss_fn(...):
    ...
    loss, lossses = losses.loss_and_update(target=y, preds=logits, parameters=params)
    ...
    return loss, losses