perplexity : clean-up

ggml-ci

examples/perplexity/perplexity.cpp

@@ -591,23 +591,27 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
     const int n_ctx   = llama_n_ctx(ctx);
     const int n_batch = params.n_batch;
 
+    GGML_ASSERT(params.n_batch >= n_ctx && "HellaSwag currently requires n_batch >= n_ctx");
+
     const int max_tasks_per_batch = 32;
     const int max_seq = 4*max_tasks_per_batch;
 
-    llama_batch batch = llama_batch_init(n_batch, 0, max_seq);
-
-    std::vector<std::vector<int>> ending_tokens(4);
+    llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
 
     std::vector<float> tok_logits(n_vocab);
 
     for (size_t i0 = 0; i0 < hs_task_count; i0++) {
         int n_cur = 0;
+
         size_t i1 = i0;
-        size_t i_batch = 0;
+        size_t i_batch = 0; // this tells us where in `llama_batch` we are currently
 
         llama_batch_clear(batch);
 
         // batch as much tasks as possible into the available context
+        // each task has 4 unique seuqnce ids
+        // the common prefix is shared among the 4 sequences to save tokens
+        // we extract logits only from the last common token and all ending tokens of each sequence
         while (n_cur + (int) hs_data[i1].required_tokens <= n_ctx) {
             auto & hs_cur = hs_data[i1];
 
@@ -649,6 +653,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
             return;
         }
 
+        // evaluate the computed tasks
         for (size_t i = i0; i < i1; ++i) {
             auto & hs_cur = hs_data[i];