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KoboldAI-Client
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Typical sampling needs to use nansum instead of sum 

If `probs` is zero then `log_probs` will be negative infinity, and the
calculation of `neg_entropy` would then give NaN because zero times
infinity is a mathematically indeterminate value.

We need to use nansum so that those NaN values are treated as zeros to
ignore them in the entropy calculation.
This commit is contained in:
Gnome Ann 2022-03-28 00:02:31 -04:00
parent 77ae893f4d
commit 67e28d2b5c
2 changed files with 3 additions and 3 deletions
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4
tpu_mtj_backend.py
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@ -255,7 +255,7 @@ def kobold_sample_dynamic(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, ty
log_probs = np.log(probs) log_probs = np.log(probs)
# Compute the negative of entropy, which is the sum of p*ln(p) for all p # Compute the negative of entropy, which is the sum of p*ln(p) for all p
# in the set of softmax probabilities of the logits # in the set of softmax probabilities of the logits
neg_entropy = (probs * log_probs).sum(axis=-1, keepdims=True) neg_entropy = np.nansum(probs * log_probs, axis=-1, keepdims=True)
# Determine absolute difference between the negative entropy and the # Determine absolute difference between the negative entropy and the
# log probabilities # log probabilities
entropy_deviation = np.abs(neg_entropy - log_probs) entropy_deviation = np.abs(neg_entropy - log_probs)
@ -425,7 +425,7 @@ def kobold_sample_static(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, typ
log_probs = jnp.log(probs) log_probs = jnp.log(probs)
# Compute the negative of entropy, which is the sum of p*ln(p) for all p # Compute the negative of entropy, which is the sum of p*ln(p) for all p
# in the set of softmax probabilities of the logits # in the set of softmax probabilities of the logits
neg_entropy = (probs * log_probs).sum(axis=-1, keepdims=True) neg_entropy = jnp.nansum(probs * log_probs, axis=-1, keepdims=True)
# Determine absolute difference between the negative entropy and the # Determine absolute difference between the negative entropy and the
# log probabilities # log probabilities
entropy_deviation = jnp.abs(neg_entropy - log_probs) entropy_deviation = jnp.abs(neg_entropy - log_probs)

2
warpers.py
View File

@ -123,7 +123,7 @@ class TypicalLogitsWarper(LogitsWarper):
# Compute the negative of entropy, which is the sum of p*ln(p) for all p # Compute the negative of entropy, which is the sum of p*ln(p) for all p
# in the set of softmax probabilities of the logits # in the set of softmax probabilities of the logits
neg_entropy = (probs * log_probs).sum(dim=-1, keepdim=True) neg_entropy = (probs * log_probs).nansum(dim=-1, keepdim=True)
# Determine absolute difference between the negative entropy and the # Determine absolute difference between the negative entropy and the
# log probabilities # log probabilities