Renames mask to live_targets for cross_entropy_loss and binary_cross_entropy_loss. #329
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markblee
approved these changes
Feb 18, 2024
…_cross_entropy_loss`.
…_cross_entropy_loss`.
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markblee
approved these changes
Feb 18, 2024
apivovarov
reviewed
Feb 10, 2025
| predicted_labels = jnp.argmax(logits, axis=-1) | ||
| accuracy = (jnp.equal(predicted_labels, target_labels) * mask).sum() / num_unmasked | ||
| accuracy = (jnp.equal(predicted_labels, target_labels) * live_targets).sum() / denominator |
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Hi Ruoming, should we use this accuracy inside CrossEntropyLossMetrics::forward() instead of recalculating it in CrossEntropyLossMetrics::forward() ?
see: https://github.com/apple/axlearn/blob/main/axlearn/common/causal_lm.py#L108C1-L117C10
we can use loss_dict["accuracy"] instead of recalculating accuracy in causal_lm.py::CrossEntropyLossMetrics
@ruomingp @markblee
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Motivation: mask is an ambiguous term and should be avoided. https://twitter.com/ericjang11/status/1739411438148739579