Principle:Bigscience workshop Petals Model Evaluation
| Knowledge Sources | |
|---|---|
| Domains | Deep_Learning, Evaluation, NLP |
| Last Updated | 2026-02-09 14:00 GMT |
Overview
The process of running a trained distributed model on validation or test data without gradient computation to measure task performance metrics such as accuracy and F1 score.
Description
Model Evaluation measures the performance of a prompt-tuned distributed model on held-out data. The evaluation loop is structurally similar to training but with key differences:
- No gradients: Wrapped in torch.no_grad() to disable autograd, reducing memory usage and improving speed
- No parameter updates: No optimizer step; the forward pass is purely for prediction
- Metric computation: Predictions are compared against ground truth labels to compute accuracy, F1, or other task-specific metrics
In the Petals context, the forward pass during evaluation still routes through remote servers via RemoteSequential.forward(), but without the autograd function — it uses the simpler non-training forward path that delegates to inference session or direct forwarding.
This is a user-defined pattern, not a specific library API. Users implement their own evaluation loop following standard PyTorch conventions.
Usage
Use this pattern after each training epoch or at the end of training to assess model quality. The evaluation loop should use the validation or test split of the dataset.
Theoretical Basis
Classification evaluation:
# Abstract evaluation pattern
model.eval()
all_preds, all_labels = [], []
with torch.no_grad():
for batch in eval_dataloader:
outputs = model(**batch)
logits = outputs.logits
preds = torch.argmax(logits, dim=-1)
all_preds.extend(preds.tolist())
all_labels.extend(batch["labels"].tolist())
accuracy = sum(p == l for p, l in zip(all_preds, all_labels)) / len(all_labels)