added tests and fixed nsigma impl

Author: VJHack

src/llama-sampling.cpp

@@ -1655,36 +1655,32 @@ struct llama_sampler_top_n_sigma {
 static const char * llama_sampler_top_n_sigma_name(const struct llama_sampler * /*smpl*/) {
     return "top-n-sigma";
 }
+#include <iostream>
 
 static void llama_sampler_top_n_sigma_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
     const auto * ctx = (llama_sampler_top_n_sigma *) smpl->ctx;
-    // 1. Find max logit: M
-    // 2. Find standard deviation of logits: sig
-    // 3. Create a mask where m[i] = 1 if ith logit  >= M - n (sig), else m[i] = 0
-    // 4. Apply mask: ith logit itself if m[i]==1, else ith logit = -inf
-    // 5. p = softmax(l)
 
     // find max logit and calculate mean
-    int32_t max = cur_p->data[0].logit;
-    int32_t logits_sum = 0;
+    float max = cur_p->data[0].logit;
+    float logits_sum = 0;
     for (size_t i = 0; i < cur_p->size; ++i) {
         if(cur_p->data[i].logit > max){
             max = cur_p->data[i].logit;
         }
         logits_sum += cur_p->data[i].logit;
     }
-    int32_t mean = logits_sum/cur_p->size;
+    float mean = (float)logits_sum/cur_p->size;
 
     // calculate standard deviation
-    int32_t acc = 0;
+    float acc = 0;
     for(size_t i = 0; i < cur_p->size; ++i){
-        acc += (cur_p->data[i].logit - mean) * (cur_p->data[i].logit - mean);
+        acc += pow(cur_p->data[i].logit - mean, 2);
     }
-    int32_t std = sqrt(acc/cur_p->size);
-
+    float std = sqrt((float)acc/cur_p->size);
+    
     //apply mask
     for(size_t i = 0; i < cur_p->size; ++i){
-        if(cur_p->data[i].logit < max - (ctx->n * std)) {
+        if(cur_p->data[i].logit < max - ((float)ctx->n * std)) {
             cur_p->data[i].logit = -INFINITY;
         }
     }

tests/test-sampling.cpp

@@ -182,6 +182,17 @@ static void test_dry(
     tester.check();
 }
 
+static void test_top_n_sigma(const std::vector<float> & probs, const std::vector<float> & probs_expected, int n) {
+    sampler_tester tester(probs, probs_expected);
+
+    DUMP(&tester.cur_p);
+    tester.apply(llama_sampler_init_top_n_sigma(n));
+    tester.apply(llama_sampler_init_dist (0));
+    DUMP(&tester.cur_p);
+
+    tester.check();
+}
+
 static void test_sampler_queue(const size_t n_vocab, const std::string & samplers_sequence, const int top_k, const float top_p, const float min_p
 ) {
     sampler_tester tester(n_vocab);
@@ -349,6 +360,14 @@ int main(void) {
     test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 1}, {0.241818f, 0.241818f, 0.241818f, 0.241818f, 0.032727f}, 2.0f, 1.1f, 2, 5, {});
     test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 3, 4, 0, 1}, {0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, 1.0f, 1.1f, 4, 7, {});
 
+    test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.571429f, 0.428571f, 0.0f, 0.0f}, 1);
+    test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f, 0.0f, 0.0f, 0.0f}, 0);
+    test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 3);
+
+    // test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.44444f, 0.33333f, 0.22222f}, 3);
+    // test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 4);
+    // test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 0);
+
     test_sampler_queue(10000, "k", 10000, 1.0f, 1.0f);
     test_sampler_queue(10000, "k",     1, 1.0f, 1.0f);
     test_sampler_queue(10000, "p", 10000, 1.0f, 1.0f);