Updates to LM PR #891
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Updates to LM PR #891
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MLCommons CLA bot All contributors have signed the MLCommons CLA ✍️ ✅ |
priyakasimbeg
changed the title
rka97
reviewed
Oct 9, 2025
| loss = -jnp.sum(targets * jax.nn.log_softmax(logits, axis=-1)) | ||
| return loss | ||
| # TODO(kasimbeg): add weights? | ||
| metrics = self.compute_weighted_cross_entropy(logits, batch['targets'], batch['weights']) |
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For this workload I don't think we need weights for the cross-entropy calculation. Maybe we should explicitly del any weights?
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The weights are used to identify padded elements in the validation split and correctly calculate the number of tokens returned in the eval dict.
| ds, | ||
| ) | ||
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| return iter(it) |
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Should we use itertools.cycle here?
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No I don't think so. The input_pipeline already calls .repeat() on the train split and we don't want cycle on the validation split.
…efficiency into lm_workload_priya
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