remove old debug log, style nit

src/llama-sampling.cpp

@@ -2389,10 +2389,12 @@ static const char * llama_sampler_power_law_name(const struct llama_sampler * /*
 // Finally, the computed target is clamped to [min_target, max_target] to
 // prevent extreme values that could destabilize sampling.
 //
-static float llama_sampler_power_law_compute_target(const llama_sampler_power_law * ctx,
+static float llama_sampler_power_law_compute_target(
+    const llama_sampler_power_law * ctx,
                             float   min_target,
                             float   max_target,
                             float   tail_decay) {
+
     float  computed_target = ctx->target;
     size_t sz              = ctx->window.size();
 
@@ -2416,6 +2418,10 @@ static float llama_sampler_power_law_compute_target(const llama_sampler_power_la
             weight *= tail_decay;
         }
 
+        // Shift weights to account for new value taking position 0
+        // All existing values age by 1, so multiply their weights by decay
+        float shifted_weighted_sum = weighted_sum * tail_decay;
+
         // Compute total weight after new value is inserted
         // When full: sz elements remain (oldest evicted, new added)
         // When not full: sz + 1 elements (new added, nothing evicted)
@@ -2428,10 +2434,6 @@ static float llama_sampler_power_law_compute_target(const llama_sampler_power_la
             total_weight = (1.0f - std::pow(tail_decay, (float) final_element_count)) / (1.0f - tail_decay);
         }
 
-        // Shift weights to account for new value taking position 0
-        // All existing values age by 1, so multiply their weights by decay
-        float shifted_weighted_sum = weighted_sum * tail_decay;
-
         // Solve for the new value that achieves target weighted average
         float next_value = (ctx->target * total_weight) - shifted_weighted_sum;
 
@@ -2446,7 +2448,6 @@ static void llama_sampler_power_law_apply(struct llama_sampler * smpl, llama_tok
     auto * ctx = (llama_sampler_power_law *) smpl->ctx;
 
     if (ctx->target < 0.0f) {
-        fprintf(stderr, "Target below zero, sampling from distribution\n");
         // no-op: just sample from the distribution as-is
         llama_sampler_softmax_impl(cur_p, false);
         const int idx   = llama_sample_dist(cur_p, ctx->rng);
@@ -2462,9 +2463,9 @@ static void llama_sampler_power_law_apply(struct llama_sampler * smpl, llama_tok
     // target computation parameters
     const float min_target = 0.0f;
     const float max_target = 1.0f;
-    const float tail_decay = 0.50f;  // Exponential decay factor for history weighting
-                                     // Lower = faster response, higher = more stability
-                                     // Effective window ≈ 1/(1-decay) ≈ 20 tokens
+    const float tail_decay = 0.50f;  // exponential decay factor for history weighting
+                                     // lower = faster response, higher = more stability
+                                     // effective window ≈ 1/(1-decay) ≈ 20 tokens
 
     // compute probabilities to get the "original" values
     llama_sampler_softmax_impl(cur_p, false);
@@ -2479,7 +2480,10 @@ static void llama_sampler_power_law_apply(struct llama_sampler * smpl, llama_tok
     // calculate adaptive target
     float computed_target = llama_sampler_power_law_compute_target(ctx, min_target, max_target, tail_decay);
 
+    //
     // power law transform
+    //
+
     for (size_t i = 0; i < cur_p->size; ++i) {
         float p                   = cur_p->data[i].p;
         float normalized_distance = std::abs(p - computed_target) / distribution_width;