backend: fix buffer overrun in repeat penalty code

Caught with AddressSanitizer running a basic prompt test against llmodel
standalone. This fix allows ASan builds to complete a simple prompt
without illegal accesses but there are still notably several leaks.

gpt4all-backend/gptj.cpp

@@ -993,9 +993,10 @@ void GPTJ::prompt(const std::string &prompt,
         gpt_vocab::id id = 0;
         {
             const int64_t t_start_sample_us = ggml_time_us();
+            const size_t n_prev_toks = std::min((size_t) promptCtx.repeat_last_n, promptCtx.tokens.size());
             id = gpt_sample_top_k_top_p(d_ptr->vocab, n_vocab,
-                promptCtx.tokens.data() + promptCtx.n_ctx - promptCtx.n_ctx,
-                promptCtx.n_ctx,
+                promptCtx.tokens.data() + promptCtx.tokens.size() - n_prev_toks,
+                n_prev_toks,
                 promptCtx.logits,
                 promptCtx.top_k, promptCtx.top_p, promptCtx.temp,
                 promptCtx.repeat_penalty,

gpt4all-backend/llamamodel.cpp

@@ -180,9 +180,10 @@ void LLamaModel::prompt(const std::string &prompt,
     int32_t totalPredictions = 0;
     for (int i = 0; i < promptCtx.n_predict; i++) {
         // sample next token
+        const size_t n_prev_toks = std::min((size_t) promptCtx.repeat_last_n, promptCtx.tokens.size());
         llama_token id = llama_sample_top_p_top_k(d_ptr->ctx,
-            promptCtx.tokens.data() + promptCtx.n_ctx - promptCtx.repeat_last_n,
-            promptCtx.repeat_last_n, promptCtx.top_k, promptCtx.top_p, promptCtx.temp,
+            promptCtx.tokens.data() + promptCtx.tokens.size() - n_prev_toks,
+            n_prev_toks, promptCtx.top_k, promptCtx.top_p, promptCtx.temp,
             promptCtx.repeat_penalty);
 
         // Check if the context has run out...

gpt4all-backend/mpt.cpp

@@ -918,9 +918,10 @@ void MPT::prompt(const std::string &prompt,
         int id = 0;
         {
             const int64_t t_start_sample_us = ggml_time_us();
+            const size_t n_prev_toks = std::min((size_t) promptCtx.repeat_last_n, promptCtx.tokens.size());
             id = gpt_sample_top_k_top_p(d_ptr->vocab, n_vocab,
-                promptCtx.tokens.data() + promptCtx.n_ctx - promptCtx.n_ctx,
-                promptCtx.n_ctx,
+                promptCtx.tokens.data() + promptCtx.tokens.size() - n_prev_toks,
+                n_prev_toks,
                 promptCtx.logits,
                 promptCtx.top_k, promptCtx.top_p, promptCtx.temp,
                 promptCtx.repeat_penalty,