Fix nans with higher epsilon #19
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codekansas
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Aug 8, 2024
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| entropy_loss = jnp.mean(0.5 * (jnp.log(2 * jnp.pi * (std_b + 1e-8) ** 2) + 1)) | ||
| entropy_loss = jnp.mean(0.5 * (jnp.log(2 * jnp.pi * (std_b + 1e-4) ** 2) + 1)) |
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oh yea - you should use 1e-5 for eps when training fp16 models bc the minimum positive value is
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damn knowing that would've saved me a lot of time lol
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Nans resulted from logprobability being miscalculated from near-zero std