model-verification-unit-tests

Model Verification with Unit Tests

Verifying individual components of a training pipeline, such as a loss function, ensures mathematical correctness before full-scale training.

Running Tests

Use the unittest framework or pytest. For a script like unit_test_1.py:

python unit_test/unit_test_1.py

Data Persistence

To allow external verification of results, save computed tensors to a .npz file:

import numpy as np

# In the test code:
np.savez(
    "/path/to/loss.npz",
    losses=losses.detach().cpu().numpy(),
)

Fixed Tensor Inputs

When verifying a loss function, use fixed tensors to ensure deterministic output:

# Loading pre-computed tensors
policy_chosen_logps = torch.load("path/to/tensor.pt")