add requiredMem method to llmodel impls

most of these can just shortcut out of the model loading logic llama is a bit worse to deal with because we submodule it so I have to at least parse the hparams, and then I just use the size on disk as an estimate for the mem size (which seems reasonable since we mmap() the llama files anyway)

gpt4all-backend/gptj.cpp

@@ -158,8 +158,11 @@ static bool kv_cache_init(
 }
 
 // load the model's weights from a stream
-bool gptj_model_load(const std::string &fname, std::istream &fin, gptj_model & model, gpt_vocab & vocab) {
+bool gptj_model_load(const std::string &fname, std::istream &fin, gptj_model & model, gpt_vocab & vocab, size_t * mem_req = nullptr) {
     printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
+    if(mem_req != nullptr) {
+        *mem_req = 0;
+    }
 
     // verify magic
     {
@@ -276,6 +279,19 @@ bool gptj_model_load(const std::string &fname, std::istream &fin, gptj_model & m
         printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
     }
 
+    if (mem_req != nullptr) {
+        *mem_req += ctx_size;
+        const int n_embd  = model.hparams.n_embd;
+        const int n_layer = model.hparams.n_layer;
+
+        const int64_t n_mem      = (int64_t)n_layer*model.hparams.n_ctx;
+        const int64_t n_elements = n_embd*n_mem;
+
+        *mem_req += (2u*n_elements*ggml_type_size(wtype) + 2_MiB);
+        return false;
+    }
+
+
     // create the ggml context
     {
         struct ggml_init_params params = {
@@ -837,6 +853,15 @@ GPTJ::GPTJ()
     d_ptr->modelLoaded = false;
 }
 
+size_t GPTJ::requiredMem(const std::string &modelPath) {
+    gptj_model dummy_model;
+    gpt_vocab dummy_vocab;
+    size_t mem_req;
+    auto fin = std::ifstream(modelPath, std::ios::binary);
+    gptj_model_load(modelPath, fin, dummy_model, dummy_vocab, &mem_req);
+    return mem_req;
+}
+
 bool GPTJ::loadModel(const std::string &modelPath) {
     std::mt19937 rng(time(NULL));
     d_ptr->rng = rng;