Evaluation Metrics#
We provide Cerebras-compatible metrics that can be used during evaluation to measure how well the model has been trained.
These metrics can be found in the metrics.Metric module.
For example:
model = Model()
compiled_model = cstorch.compile(model, backend)
accuracy = cstorch.metrics.AccuracyMetric("accuracy")
@cstorch.trace
def eval_step(batch):
inputs, targets = batch
outputs = compiled_model(inputs)
accuracy(
labels=targets.clone(),
predictions=outputs.argmax(-1).int(),
)
...
for batch in executor:
...
# Log accumulated eval metric
print(f"Accuracy: {float(accuracy)}")
Writing Custom Metrics#
To define a Cerebras compliant metrics, create a subclass of
cerebras_pytorch.metrics.Metric.
For example,
class CustomMetric(cstorch.metrics.Metric):
def __init__(self, name: str):
super().__init__(name)
...
...
def reset(self):
pass
def update(self, *args, **kwargs):
...
def compute(self):
...
As can be seen in the above example, the base
Metric class expects one argument.
Namely, the metric name.
In addition, there are three abstract methods that must be overridden:
-
This method resets (or defines if its the first time its called) the metrics’ internal state.
States can be registered via calls to
register_state -
This method is used to update the metric’s registered states.
Note that to remain Cerebras compliant, no tensor may be evaluated/inspected here. The update call is intended to be fully traced.
-
This method is used to compute the final accumulated metric value using the state that was updated in
update