YaRN : store rope scaling type as int32_t in memory (#5285)

* YaRN : store rope scaling type as int32_t in memory

* llama : store mapped names as const char *

diff --git a/common/common.h b/common/common.h
index 24a99d7281089e284a52c4d135da42ec60e4e688..62de25d6a287c6955c64c7a2d1ffd7ca08509376 100644 (file)
--- a/common/common.h
+++ b/common/common.h
@@ -75,8 +75,7 @@ struct gpt_params {
     float   yarn_beta_fast        = 32.0f; // YaRN low correction dim
     float   yarn_beta_slow        = 1.0f;  // YaRN high correction dim
     int32_t yarn_orig_ctx         = 0;     // YaRN original context length
-    int8_t  rope_scaling_type     = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment
-                                                                    //       pinging @cebtenzzre
+    int32_t rope_scaling_type     = LLAMA_ROPE_SCALING_UNSPECIFIED;
 
     // // sampling parameters
     struct llama_sampling_params sparams;

diff --git a/llama.cpp b/llama.cpp
index 6bf7f9efb666def771e987b5fb2bba3fadd175ce..4787a92fea030c82a2f3e48c0b8dfa8e5e929820 100644 (file)
--- a/llama.cpp
+++ b/llama.cpp
@@ -208,7 +208,7 @@ enum llm_arch {
     LLM_ARCH_UNKNOWN,
 };
 
-static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
+static std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_LLAMA,           "llama"     },
     { LLM_ARCH_FALCON,          "falcon"    },
     { LLM_ARCH_GPT2,            "gpt2"      },
@@ -285,7 +285,7 @@ enum llm_kv {
     LLM_KV_TOKENIZER_RWKV,
 };
 
-static std::map<llm_kv, std::string> LLM_KV_NAMES = {
+static std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_GENERAL_ARCHITECTURE,          "general.architecture"                  },
     { LLM_KV_GENERAL_QUANTIZATION_VERSION,  "general.quantization_version"          },
     { LLM_KV_GENERAL_ALIGNMENT,             "general.alignment"                     },
@@ -346,7 +346,7 @@ struct LLM_KV {
     llm_arch arch;
 
     std::string operator()(llm_kv kv) const {
-        return ::format(LLM_KV_NAMES[kv].c_str(), LLM_ARCH_NAMES[arch].c_str());
+        return ::format(LLM_KV_NAMES[kv], LLM_ARCH_NAMES[arch]);
     }
 };
 
@@ -747,13 +747,13 @@ struct LLM_TN {
 // gguf helpers
 //
 
-static std::map<int8_t, std::string> LLAMA_ROPE_SCALING_TYPES = {
+static std::map<int32_t, const char *> LLAMA_ROPE_SCALING_TYPES = {
     { LLAMA_ROPE_SCALING_NONE,   "none"   },
     { LLAMA_ROPE_SCALING_LINEAR, "linear" },
     { LLAMA_ROPE_SCALING_YARN,   "yarn"   },
 };
 
-static int8_t llama_rope_scaling_type_from_string(const std::string & name) {
+static int32_t llama_rope_scaling_type_from_string(const std::string & name) {
     for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) {
         if (kv.second == name) {
             return kv.first;
@@ -1415,6 +1415,7 @@ static const size_t GiB = 1024*MiB;
 
 struct llama_hparams {
     bool     vocab_only;
+    bool     rope_finetuned;
     uint32_t n_vocab;
     uint32_t n_ctx_train; // context size the model was trained on
     uint32_t n_embd;
@@ -1434,8 +1435,7 @@ struct llama_hparams {
     float    rope_freq_base_train;
     float    rope_freq_scale_train;
     uint32_t n_yarn_orig_ctx;
-    int8_t   rope_scaling_type_train : 3;
-    bool     rope_finetuned : 1;
+    int32_t  rope_scaling_type_train;
 
     float f_clamp_kqv;
     float f_max_alibi_bias;
@@ -2701,7 +2701,7 @@ struct llama_model_loader {
 // load LLaMA models
 //
 
-static std::string llama_model_arch_name(llm_arch arch) {
+static const char * llama_model_arch_name(llm_arch arch) {
     auto it = LLM_ARCH_NAMES.find(arch);
     if (it == LLM_ARCH_NAMES.end()) {
         return "unknown";
@@ -3310,11 +3310,11 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     const auto & hparams = model.hparams;
     const auto & vocab   = model.vocab;
 
-    const auto rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);
+    const char * rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);
 
     // hparams
     LLAMA_LOG_INFO("%s: format           = %s\n",     __func__, llama_file_version_name(ml.fver));
-    LLAMA_LOG_INFO("%s: arch             = %s\n",     __func__, LLM_ARCH_NAMES.at(model.arch).c_str());
+    LLAMA_LOG_INFO("%s: arch             = %s\n",     __func__, LLM_ARCH_NAMES.at(model.arch));
     LLAMA_LOG_INFO("%s: vocab type       = %s\n",     __func__, llama_model_vocab_type_name(vocab.type));
     LLAMA_LOG_INFO("%s: n_vocab          = %u\n",     __func__, hparams.n_vocab);
     LLAMA_LOG_INFO("%s: n_merges         = %u\n",     __func__, (int) vocab.bpe_ranks.size());
@@ -3336,7 +3336,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     LLAMA_LOG_INFO("%s: n_ff             = %u\n",     __func__, hparams.n_ff);
     LLAMA_LOG_INFO("%s: n_expert         = %u\n",     __func__, hparams.n_expert);
     LLAMA_LOG_INFO("%s: n_expert_used    = %u\n",     __func__, hparams.n_expert_used);
-    LLAMA_LOG_INFO("%s: rope scaling     = %s\n",     __func__, rope_scaling_type.c_str());
+    LLAMA_LOG_INFO("%s: rope scaling     = %s\n",     __func__, rope_scaling_type);
     LLAMA_LOG_INFO("%s: freq_base_train  = %.1f\n",   __func__, hparams.rope_freq_base_train);
     LLAMA_LOG_INFO("%s: freq_scale_train = %g\n",     __func__, hparams.rope_freq_scale_train);
     LLAMA_LOG_INFO("%s: n_yarn_orig_ctx  = %u\n",     __func__, hparams.n_yarn_orig_ctx);
@@ -10735,7 +10735,7 @@ int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int3
 
 int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
     return snprintf(buf, buf_size, "%s %s %s",
-            llama_model_arch_name(model->arch).c_str(),
+            llama_model_arch_name(model->arch),
             llama_model_type_name(model->type),
             llama_model_ftype_name(model->ftype).c_str());
 }

diff --git a/llama.h b/llama.h
index 9a60e9bfbb6d37c6d59e11eb72788014787ce12e..cec4158bc8e803840de2a242dd212c9f2e3b9d80 100644 (file)
--- a/llama.h
+++ b/llama.h
@@ -213,7 +213,7 @@ extern "C" {
         uint32_t n_batch;           // prompt processing maximum batch size
         uint32_t n_threads;         // number of threads to use for generation
         uint32_t n_threads_batch;   // number of threads to use for batch processing
-        int8_t   rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
+        int32_t  rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
 
         // ref: https://github.com/ggerganov/llama.cpp/pull/2054
         float    rope_freq_base;   // RoPE base frequency, 0 = from model