Use more C++ style memory management

native/jni/org_futo_inputmethod_latin_xlm_LanguageModel.cpp

@@ -161,7 +161,7 @@ bool isExactMatch(const std::string &a, const std::string &b){
 
 
 struct LanguageModelState {
-    LanguageModel *model;
+    std::unique_ptr<LanguageModel> model;
 
     struct {
         int SPACE;
@@ -186,7 +186,8 @@ struct LanguageModelState {
     } specialTokens;
 
     bool Initialize(const std::string &paths){
-        model = LlamaAdapter::createLanguageModel(paths);
+        model = std::unique_ptr<LanguageModel>(LlamaAdapter::createLanguageModel(paths));
+
         if(!model) {
             AKLOGE("GGMLDict: Could not load model");
             return false;
@@ -246,7 +247,7 @@ struct LanguageModelState {
             }
         }
 
-        size_t n_vocab = llama_n_vocab(llama_get_model( ((LlamaAdapter *) model->adapter)->context ));
+        size_t n_vocab = llama_n_vocab(model->model());
         for(size_t i=0; i < n_vocab; i++) {
             const char *text = model->adapter->getToken(i);
             if(isFirstCharLowercase(text)) {
@@ -325,9 +326,9 @@ struct LanguageModelState {
 
     DecodeResult DecodePromptAndMixes(const token_sequence &prompt, const std::vector<TokenMix> &mixes) {
         TIME_START(PromptDecode)
-        llama_context *ctx = ((LlamaAdapter *) model->adapter)->context;
+        llama_context *ctx = model->context();
-        llama_batch batch = ((LlamaAdapter *) model->adapter)->batch;
+        llama_batch batch = model->adapter->batch;
-        LlamaAdapter *llamaAdapter = ((LlamaAdapter *)model->adapter);
+        LlamaAdapter *llamaAdapter = model->adapter.get();
 
         size_t n_embd = llama_n_embd(llama_get_model(ctx));
         size_t n_vocab = llama_n_vocab(llama_get_model(ctx));
@@ -396,7 +397,7 @@ struct LanguageModelState {
                     if (t.weight < EPS) continue;
                     if (t.token < 0 || t.token >= (int)n_vocab) continue;
 
-                    float *src = ((LlamaAdapter *) model->adapter)->embeddings.data() +
+                    float *src = llamaAdapter->embeddings.data() +
                                  (t.token * n_embd);
                     float weight = t.weight;
 
@@ -547,8 +548,8 @@ struct LanguageModelState {
     }
 
     std::vector<std::pair<float, token_sequence>> Sample(DecodeResult decodeResult, int n_results, WordCapitalizeMode capitals, const std::vector<banned_sequence> &banned_sequences) {
-        llama_context *ctx = ((LlamaAdapter *) model->adapter)->context;
+        llama_context *ctx = model->context();
-        llama_batch batch = ((LlamaAdapter *) model->adapter)->batch;
+        llama_batch batch = model->adapter->batch;
 
         size_t n_vocab = llama_n_vocab(llama_get_model(ctx));
 
@@ -878,7 +879,6 @@ namespace latinime {
         AKLOGI("LanguageModel_close called!");
         LanguageModelState *state = reinterpret_cast<LanguageModelState *>(statePtr);
         if(state == nullptr) return;
-        state->model->free();
         delete state;
     }
 
@@ -928,7 +928,7 @@ namespace latinime {
 
 
         // TODO: Transform here
-        llama_context *ctx = ((LlamaAdapter *) state->model->adapter)->context;
+        llama_context *ctx = state->model->context();
         size_t n_vocab = llama_n_vocab(llama_get_model(ctx));
 
         token_sequence next_context = state->model->tokenize(trim(contextString) + " ");

native/jni/src/ggml/LanguageModel.cpp

@@ -8,6 +8,10 @@
 
 LanguageModel::LanguageModel(LlamaAdapter *adapter): adapter(adapter) { }
 
+LlamaAdapter::~LlamaAdapter() {
+    llama_free_model(model);
+    llama_free(context);
+}
 
 int LlamaAdapter::getVocabSize() const {
     // assert(modelVocabSize >= sentencepieceVocabSize)

native/jni/src/ggml/LanguageModel.h

@@ -52,8 +52,12 @@ public:
     inline bool hasFeature(const std::string &feature) const {
         return metadata.HasFeature(feature);
     }
+
+    ~LlamaAdapter();
+
 private:
     LlamaAdapter();
+
     sentencepiece::SentencePieceProcessor spm;
 };
 
@@ -137,23 +141,21 @@ public:
         return pendingEvaluationSequence.size() > 0;
     }
 
-    AK_FORCE_INLINE void free() {
+    AK_FORCE_INLINE llama_context *context() {
-        llama_free(adapter->context);
+        return adapter->context;
-        llama_free_model(adapter->model);
-        delete adapter;
-        adapter = nullptr;
-        delete this;
     }
 
-    LlamaAdapter *adapter;
+    AK_FORCE_INLINE llama_model *model() {
+        return adapter->model;
+    }
+
+    std::unique_ptr<LlamaAdapter> adapter;
     transformer_context transformerContext;
 private:
     token_sequence pendingContext;
     token_sequence pendingEvaluationSequence;
     int pendingNPast = 0;
 
-
-
     std::vector<float> outLogits;
     std::vector<float> tmpOutLogits;