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.

tpu_mtj_backend.py

@@ -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)
         # 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
-        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
         # log probabilities
         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)
         # 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
-        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
         # log probabilities
         entropy_deviation = jnp.abs(neg_entropy - log_probs)

warpers.py

@@ -123,7 +123,7 @@ class TypicalLogitsWarper(LogitsWarper):
 
         # 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
-        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
         # log probabilities