Add memoized cache to llama_grammar_reject_candidates_for_stack (#1615)

* Add memoized cache to llama_grammar_reject_candidates_for_stack

* make size cutoff more aggressive and move to outer branch

* update comment

* add cache reset whenever grammar is reloaded

* remove explicit reference types for compiler transportability

gpttype_adapter.cpp

@@ -1773,6 +1773,7 @@ static void load_grammar(const std::string & gammarstr)
 {
     if(grammar!=nullptr) //on demand free when next grammar is loaded
     {
+        llama_grammar_reset_memos();
         llama_grammar_free_impl(grammar);
         grammar = nullptr;
     }

src/llama-grammar.cpp

@@ -8,10 +8,34 @@
 #include <algorithm>
 #include <stdexcept>
 
+#include <iostream>
+#include <string_view>
+#include <unordered_set>
+
 //
 // helpers
 //
 
+using bytes = std::pair<const char*, size_t>;
+using hash_entry_size = std::pair<size_t, size_t>;
+
+template <>
+struct std::hash<bytes>
+{
+    std::size_t operator()(const bytes& x) const noexcept
+    {
+        return std::hash<std::string_view>{}({x.first, x.second});
+    }
+};
+
+using candidates_memos = std::unordered_map<size_t, llama_grammar_candidates>;
+using stack_memos = std::unordered_map<size_t, candidates_memos>;
+static stack_memos memo_cache;
+
+static void llama_grammar_reset_memos() {
+    memo_cache.clear();
+}
+
 // NOTE: assumes valid utf8 (but checks for overrun)
 static std::pair<uint32_t, const char *> decode_utf8(const char * src) {
     static const int lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
@@ -864,6 +888,38 @@ llama_grammar_candidates llama_grammar_reject_candidates_for_stack(
         }
         return rejects;
     }
+    
+    auto stack_hash_start = reinterpret_cast<const char *>(stack.data());
+    auto stack_hash_size  = sizeof(stack[0]) * stack.size();
+    auto stack_hash       = std::hash<bytes>{}({ stack_hash_start, stack_hash_size });
+
+    llama_grammar_candidates * cache_target = nullptr;
+
+    // Tests show that >75% of candidate lists are under 1280 and 50% are under 640b.
+    // Most 'problem' loops are under 24b. However, candidate lists can be over 72k,
+    // so we need to limit our checks.
+
+    // We'll only attempt to memoize candidate lists under 80b
+    // Doing an over-aggressive size cutoff first befor any other processing 'saves' easy cases
+    // extra processing but still rescues 'hard' cases from slow down or hangs.
+    // This leads to a speed up of both easy and hard cases.
+    const size_t hash_cutoff          = 80;
+    auto         candidates_hash_size = sizeof(candidates[0]) * candidates.size();
+    if (candidates_hash_size < hash_cutoff) {
+        // Only check stash hash first - these are usually ~24b, and almost always under 64b
+        if (auto cache_hit = memo_cache.find(stack_hash); cache_hit != memo_cache.end()) {
+            auto & candidates_memos      = cache_hit->second;
+            auto   candidates_hash_start = reinterpret_cast<const char *>(candidates.data());
+            auto   candidates_hash       = std::hash<bytes>{}({ candidates_hash_start, candidates_hash_size });
+            if (auto cache_hit2 = candidates_memos.find(candidates_hash); cache_hit2 != candidates_memos.end()) {
+                return cache_hit2->second;
+            } else {
+                cache_target = &(candidates_memos[candidates_hash]);
+            }
+        } else {
+            memo_cache[stack_hash];
+        }
+    }
 
     const llama_grammar_element * stack_pos = stack.back();
 
@@ -900,6 +956,9 @@ llama_grammar_candidates llama_grammar_reject_candidates_for_stack(
         rejects.push_back({ tok.index, tok.code_points - 1, tok.partial_utf8 });
     }
 
+    if (cache_target) {
+        *cache_target = rejects;
+    }
     return rejects;
 }