merged chnages

Author: VJHack

Makefile

@@ -1440,6 +1440,7 @@ llama-server: \
 	examples/server/system-prompts.js.hpp \
 	examples/server/prompt-formats.js.hpp \
 	examples/server/json-schema-to-grammar.mjs.hpp \
+	examples/server/loading.html.hpp \
 	common/json.hpp \
 	common/stb_image.h \
 	$(OBJ_ALL)

README.md

@@ -173,6 +173,7 @@ Unless otherwise noted these projects are open-source with permissive licensing:
 - [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from HuggingFace Hub and convert them to GGML
 - [crashr/gppm](https://github.com/crashr/gppm) – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption
 - [gpustack/gguf-parser](https://github.com/gpustack/gguf-parser-go/tree/main/cmd/gguf-parser) - review/check the GGUF file and estimate the memory usage
+- [Styled Lines](https://marketplace.unity.com/packages/tools/generative-ai/styled-lines-llama-cpp-model-292902) (proprietary licensed, async wrapper of inference part for game development in Unity3d with prebuild Mobile and Web platform wrappers and a model example)
 
 **Infrastructure:**
 

common/arg.cpp

@@ -720,6 +720,14 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
             params.prompt = value;
         }
     ));
+    add_opt(llama_arg(
+        {"--no-perf"},
+        format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"),
+        [](gpt_params & params) {
+            params.no_perf = true;
+            params.sparams.no_perf = true;
+        }
+    ).set_env("LLAMA_ARG_NO_PERF"));
     add_opt(llama_arg(
         {"-f", "--file"}, "FNAME",
         "a file containing the prompt (default: none)",

common/common.cpp

@@ -820,7 +820,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
         }
         llama_kv_cache_clear(lctx);
         llama_synchronize(lctx);
-        llama_perf_reset(lctx, LLAMA_PERF_TYPE_CONTEXT);
+        llama_perf_context_reset(lctx);
     }
 
     iparams.model   = model;
@@ -916,6 +916,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
     cparams.cb_eval_user_data = params.cb_eval_user_data;
     cparams.offload_kqv       = !params.no_kv_offload;
     cparams.flash_attn        = params.flash_attn;
+    cparams.no_perf           = params.no_perf;
 
     cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
     cparams.type_v = kv_cache_type_from_str(params.cache_type_v);

common/common.h

@@ -124,6 +124,7 @@ struct gpt_sampler_params {
     float   mirostat_eta      = 0.10f; // learning rate
     bool    penalize_nl       = false; // consider newlines as a repeatable token
     bool    ignore_eos        = false;
+    bool    no_perf           = false; // disable performance metrics
 
     std::vector<enum gpt_sampler_type> samplers = {
         GPT_SAMPLER_TYPE_TOP_K,
@@ -246,6 +247,7 @@ struct gpt_params {
     bool simple_io         = false; // improves compatibility with subprocesses and limited consoles
     bool cont_batching     = true;  // insert new sequences for decoding on-the-fly
     bool flash_attn        = false; // flash attention
+    bool no_perf           = false; // disable performance metrics
 
     bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
     bool logits_all        = false; // return logits for all tokens in the batch

common/sampling.cpp

@@ -142,7 +142,7 @@ std::string gpt_sampler_params::print() const {
 struct gpt_sampler * gpt_sampler_init(const struct llama_model * model, const struct gpt_sampler_params & params) {
     llama_sampler_chain_params lparams = llama_sampler_chain_default_params();
 
-    lparams.no_perf = false; // TODO: control via params
+    lparams.no_perf = params.no_perf;
 
     auto * result = new gpt_sampler {
         /* .params = */ params,
@@ -257,10 +257,10 @@ void gpt_perf_print(const struct llama_context * ctx, const struct gpt_sampler *
     // TODO: measure grammar performance
 
     if (gsmpl) {
-        llama_perf_print(gsmpl->chain, LLAMA_PERF_TYPE_SAMPLER_CHAIN);
+        llama_perf_sampler_print(gsmpl->chain);
     }
     if (ctx) {
-        llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
+        llama_perf_context_print(ctx);
     }
 }
 

convert_hf_to_gguf.py

@@ -1487,7 +1487,7 @@ def prepare_tensors(self):
                 raise ValueError(f"Unprocessed norms: {norms}")
 
 
-@Model.register("LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM")
+@Model.register("LLaMAForCausalLM", "LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM")
 class LlamaModel(Model):
     model_arch = gguf.MODEL_ARCH.LLAMA
 

examples/batched-bench/batched-bench.cpp

@@ -187,7 +187,7 @@ int main(int argc, char ** argv) {
     }
 
     LOG_TEE("\n");
-    llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_context_print(ctx);
 
     llama_batch_free(batch);
 

examples/batched.swift/Sources/main.swift

@@ -200,8 +200,8 @@ let t_main_end = ggml_time_us()
 
 print("decoded \(n_decode) tokens in \(String(format: "%.2f", Double(t_main_end - t_main_start) / 1_000_000.0)) s, speed: \(String(format: "%.2f", Double(n_decode) / (Double(t_main_end - t_main_start) / 1_000_000.0))) t/s\n\n")
 
-llama_perf_print(UnsafeRawPointer(context), LLAMA_PERF_TYPE_CONTEXT)
-llama_perf_print(UnsafeRawPointer(smpl),    LLAMA_PERF_TYPE_SAMPLER_CHAIN)
+llama_perf_sampler_print(smpl)
+llama_perf_context_print(context)
 
 private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
     let utf8Count = text.utf8.count

examples/batched/batched.cpp

@@ -229,8 +229,8 @@ int main(int argc, char ** argv) {
             __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
 
     LOG_TEE("\n");
-    llama_perf_print(smpl, LLAMA_PERF_TYPE_SAMPLER_CHAIN);
-    llama_perf_print(ctx,  LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_sampler_print(smpl);
+    llama_perf_context_print(ctx);
 
     fprintf(stderr, "\n");