Merge f42de2426e into 8db003a19d

* Support of converting local models added to convert-hf-to-gguf-update.py

* Description fixed

* shutil added to imports

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_reset_context(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);
@@ -941,11 +942,37 @@ struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_p
 
 #ifdef LLAMA_USE_CURL
 
+#define CURL_MAX_RETRY 3
+#define CURL_RETRY_DELAY_SECONDS 2
+
+
 static bool starts_with(const std::string & str, const std::string & prefix) {
     // While we wait for C++20's std::string::starts_with...
     return str.rfind(prefix, 0) == 0;
 }
 
+static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_attempts, int retry_delay_seconds) {
+    int remaining_attempts = max_attempts;
+
+    while (remaining_attempts > 0) {
+        fprintf(stderr, "%s: Trying to download from %s (attempt %d of %d)...\n", __func__ , url.c_str(), max_attempts - remaining_attempts + 1, max_attempts);
+
+        CURLcode res = curl_easy_perform(curl);
+        if (res == CURLE_OK) {
+            return true;
+        }
+
+        int exponential_backoff_delay = std::pow(retry_delay_seconds, max_attempts - remaining_attempts) * 1000;
+        fprintf(stderr, "%s: curl_easy_perform() failed: %s, retrying after %d milliseconds...\n", __func__, curl_easy_strerror(res), exponential_backoff_delay);
+
+        remaining_attempts--;
+        std::this_thread::sleep_for(std::chrono::milliseconds(exponential_backoff_delay));
+    }
+
+    fprintf(stderr, "%s: curl_easy_perform() failed after %d attempts\n", __func__, max_attempts);
+    return false;
+}
+
 static bool llama_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
 
     // Initialize libcurl
@@ -1049,9 +1076,8 @@ static bool llama_download_file(const std::string & url, const std::string & pat
         curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
         curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
 
-        CURLcode res = curl_easy_perform(curl.get());
+        bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS);
-        if (res != CURLE_OK) {
+        if (!was_perform_successful) {
-            fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
             return false;
         }
 
@@ -1126,11 +1152,10 @@ static bool llama_download_file(const std::string & url, const std::string & pat
         };
 
         // start the download
-        fprintf(stderr, "%s: downloading from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
+        fprintf(stderr, "%s: trying to download model from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
             llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
-        auto res = curl_easy_perform(curl.get());
+        bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS);
-        if (res != CURLE_OK) {
+        if (!was_perform_successful) {
-            fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
             return false;
         }
 

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_print_sampler(gsmpl->chain);
     }
     if (ctx) {
-        llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
+        llama_perf_print_context(ctx);
     }
 }
 

convert_hf_to_gguf_update.py

@@ -31,6 +31,7 @@ import re
 import requests
 import sys
 import json
+import shutil
 
 from hashlib import sha256
 from enum import IntEnum, auto
@@ -125,6 +126,21 @@ def download_model(model):
     if tokt == TOKENIZER_TYPE.UGM:
         files.append("spiece.model")
 
+    if os.path.isdir(repo):
+        # If repo is a path on the file system, copy the directory
+        for file in files:
+            src_path = os.path.join(repo, file)
+            dst_path = f"models/tokenizers/{name}/{file}"
+            if os.path.isfile(dst_path):
+                logger.info(f"{name}: File {dst_path} already exists - skipping")
+                continue
+            if os.path.isfile(src_path):
+                shutil.copy2(src_path, dst_path)
+                logger.info(f"{name}: Copied {src_path} to {dst_path}")
+            else:
+                logger.warning(f"{name}: Source file {src_path} does not exist")
+    else:
+        # If repo is a URL, download the files
         for file in files:
             save_path = f"models/tokenizers/{name}/{file}"
             if os.path.isfile(save_path):

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_print_context(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_sampler(smpl)
-llama_perf_print(UnsafeRawPointer(smpl),    LLAMA_PERF_TYPE_SAMPLER_CHAIN)
+llama_perf_print_context(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_sampler(smpl);
-    llama_perf_print(ctx,  LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_print_context(ctx);
 
     fprintf(stderr, "\n");
 

examples/embedding/embedding.cpp

@@ -306,7 +306,7 @@ int main(int argc, char ** argv) {
     }
 
     LOG_TEE("\n");
-    llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_print_context(ctx);
 
     // clean up
     llama_batch_free(batch);

examples/eval-callback/eval-callback.cpp

@@ -182,7 +182,7 @@ int main(int argc, char ** argv) {
     }
 
     LOG_TEE("\n");
-    llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_print_context(ctx);
 
     llama_free(ctx);
     llama_free_model(model);

examples/imatrix/imatrix.cpp

@@ -637,7 +637,7 @@ int main(int argc, char ** argv) {
     g_collector.save_imatrix();
 
     LOG_TEE("\n");
-    llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_print_context(ctx);
 
     llama_free(ctx);
     llama_free_model(model);

examples/llama-bench/llama-bench.cpp

@@ -1630,7 +1630,7 @@ int main(int argc, char ** argv) {
             fflush(p_err->fout);
         }
 
-        llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
+        llama_perf_print_context(ctx);
 
         llama_free(ctx);
 

examples/llava/llava-cli.cpp

@@ -308,7 +308,7 @@ int main(int argc, char ** argv) {
         // process the prompt
         process_prompt(ctx_llava, image_embed, &params, params.prompt);
 
-        llama_perf_print(ctx_llava->ctx_llama, LLAMA_PERF_TYPE_CONTEXT);
+        llama_perf_print_context(ctx_llava->ctx_llama);
         llava_image_embed_free(image_embed);
         ctx_llava->model = NULL;
         llava_free(ctx_llava);
@@ -325,7 +325,7 @@ int main(int argc, char ** argv) {
             // process the prompt
             process_prompt(ctx_llava, image_embed, &params, params.prompt);
 
-            llama_perf_print(ctx_llava->ctx_llama, LLAMA_PERF_TYPE_CONTEXT);
+            llama_perf_print_context(ctx_llava->ctx_llama);
             llava_image_embed_free(image_embed);
             ctx_llava->model = NULL;
             llava_free(ctx_llava);

examples/llava/minicpmv-cli.cpp

@@ -18,8 +18,8 @@ struct llava_context {
 };
 
 static void show_additional_info(int /*argc*/, char ** argv) {
-    LOG_TEE("\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
+    LOG_TEE("\nexample usage:\n\n%s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
-    LOG_TEE("  note: a lower temperature value like 0.1 is recommended for better quality.\n");
+    LOG_TEE("\nnote: a lower temperature value like 0.1 is recommended for better quality.\n");
 }
 
 static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
@@ -255,7 +255,7 @@ int main(int argc, char ** argv) {
 
     gpt_params params;
 
-    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, show_additional_info)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, show_additional_info)) {
         return 1;
     }
 
@@ -319,7 +319,7 @@ int main(int argc, char ** argv) {
             }
         }
         printf("\n");
-        llama_perf_print(ctx_llava->ctx_llama, LLAMA_PERF_TYPE_CONTEXT);
+        llama_perf_print_context(ctx_llava->ctx_llama);
 
         ctx_llava->model = NULL;
         llava_free(ctx_llava);

examples/lookup/lookup.cpp

@@ -240,8 +240,7 @@ int main(int argc, char ** argv){
     LOG_TEE("accept       = %.3f%%\n", 100.0f * n_accept / n_drafted);
 
     LOG_TEE("\ntarget:\n\n");
-    llama_perf_print(smpl, LLAMA_PERF_TYPE_SAMPLER_CHAIN);
+    gpt_perf_print(ctx, smpl);
-    llama_perf_print(ctx,  LLAMA_PERF_TYPE_CONTEXT);
 
     gpt_sampler_free(smpl);
 

examples/parallel/parallel.cpp

@@ -415,7 +415,7 @@ int main(int argc, char ** argv) {
     LOG_TEE("\n");
 
     // TODO: print sampling/grammar timings for all clients
-    llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_print_context(ctx);
 
     llama_batch_free(batch);
 

examples/passkey/passkey.cpp

@@ -256,7 +256,7 @@ 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(ctx, LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_print_context(ctx);
 
     fprintf(stderr, "\n");
 

examples/perplexity/perplexity.cpp

@@ -2047,7 +2047,7 @@ int main(int argc, char ** argv) {
     }
 
     LOG_TEE("\n");
-    llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_print_context(ctx);
     write_logfile(ctx, params, model, results);
 
     llama_free(ctx);

examples/retrieval/retrieval.cpp

@@ -292,7 +292,7 @@ int main(int argc, char ** argv) {
     }
 
     LOG_TEE("\n");
-    llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_print_context(ctx);
 
     // clean up
     llama_batch_free(query_batch);

examples/simple/simple.cpp

@@ -154,8 +154,8 @@ 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_sampler(smpl);
-    llama_perf_print(ctx,  LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_print_context(ctx);
 
     fprintf(stderr, "\n");
 

examples/speculative/speculative.cpp

@@ -616,7 +616,7 @@ int main(int argc, char ** argv) {
 
     LOG_TEE("\ndraft:\n\n");
     // TODO: print sampling/grammar timings for all drafts
-    llama_perf_print(ctx_dft, LLAMA_PERF_TYPE_CONTEXT);
+    llama_perf_print_context(ctx_dft);
 
     LOG_TEE("\ntarget:\n\n");
     gpt_perf_print(ctx_tgt, smpl);

ggml/src/ggml-cuda/mmq.cu

@@ -26,7 +26,11 @@ void ggml_cuda_op_mul_mat_q(
     // nrows_dst == nrows of the matrix that the kernel writes into
     const int64_t nrows_dst = id == ctx.device ? ne0 : row_diff;
 
-    const mmq_args args = {src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stride00, src1_padded_row_size, src1_ncols, ne11, nrows_dst};
+    // The stream-k decomposition is only faster for recent NVIDIA GPUs.
+    // Also its fixup needs to allocate a temporary buffer in the memory pool.
+    // There are multiple parallel CUDA streams for src1_ncols != ne11 which would introduce a race condition for this buffer.
+    const bool use_stream_k = compute_capability >= CC_VOLTA && compute_capability < CC_OFFSET_AMD && src1_ncols == ne11;
+    const mmq_args args = {src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stride00, src1_padded_row_size, src1_ncols, ne11, nrows_dst, use_stream_k};
 
     switch (src0->type) {
         case GGML_TYPE_Q4_0:

ggml/src/ggml-cuda/mmq.cuh

@@ -2742,6 +2742,7 @@ struct mmq_args {
     int64_t ne00; int64_t ne01; int64_t stride01;
     int64_t ne10; int64_t ne11; int64_t stride11;
     int64_t ne0;
+    bool use_stream_k;
 };
 
 template<ggml_type type>
@@ -2777,8 +2778,7 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a
     const int ntx = (args.ne11 + mmq_x - 1) / mmq_x;
     const dim3 block_nums_xy_tiling(nty, ntx, 1);
 
-    const bool use_stream_k = cc >= CC_VOLTA && cc < CC_OFFSET_AMD;
+    if (!args.use_stream_k) {
-    if (!use_stream_k) {
         if (args.ne01 % mmq_y == 0) {
             constexpr bool need_check = false;
             mul_mat_q<type, mmq_x, MMQ_NWARPS, need_check><<<block_nums_xy_tiling, block_dims, shmem, stream>>>

include/llama.h

@@ -343,7 +343,7 @@ extern "C" {
         bool embeddings;  // if true, extract embeddings (together with logits)
         bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
         bool flash_attn;  // whether to use flash attention [EXPERIMENTAL]
-      //bool no_perf;     // whether to measure performance timings, TODO: implement
+        bool no_perf;     // whether to measure performance timings
 
         // Abort callback
         // if it returns true, execution of llama_decode() will be aborted
@@ -1173,13 +1173,30 @@ extern "C" {
     // NOTE: Used by llama.cpp examples, avoid using in third-party apps. Instead, do your own performance measurements.
     //
 
-    enum llama_perf_type {
+    struct llama_perf_data_context {
-        LLAMA_PERF_TYPE_CONTEXT       = 0,
+        double t_start_ms;
-        LLAMA_PERF_TYPE_SAMPLER_CHAIN = 1,
+        double t_load_ms;
+        double t_p_eval_ms;
+        double t_eval_ms;
+
+        int32_t n_p_eval;
+        int32_t n_eval;
     };
 
-    LLAMA_API void llama_perf_print(const void * ctx, enum llama_perf_type type);
+    struct llama_perf_data_sampler {
-    LLAMA_API void llama_perf_reset(      void * ctx, enum llama_perf_type type);
+        double t_sample_ms;
+
+        int32_t n_sample;
+    };
+
+    LLAMA_API struct llama_perf_data_context llama_perf_context(const struct llama_context * ctx);
+    LLAMA_API struct llama_perf_data_sampler llama_perf_sampler(const struct llama_sampler * chain);
+
+    LLAMA_API void llama_perf_print_context(const struct llama_context * ctx);
+    LLAMA_API void llama_perf_print_sampler(const struct llama_sampler * chain);
+
+    LLAMA_API void llama_perf_reset_context(struct llama_context * ctx);
+    LLAMA_API void llama_perf_reset_sampler(struct llama_sampler * chain);
 
     LLAMA_API void llama_perf_dump_yaml(FILE * stream, const struct llama_context * ctx);
 

src/llama.cpp

@@ -2482,6 +2482,7 @@ struct llama_cparams {
     bool causal_attn;
     bool offload_kqv;
     bool flash_attn;
+    bool no_perf;
 
     enum llama_pooling_type pooling_type;
 
@@ -6657,8 +6658,6 @@ static bool llm_load_tensors(
         bool use_mlock,
         llama_progress_callback progress_callback,
         void * progress_callback_user_data) {
-    model.t_start_us = ggml_time_us();
-
     auto & hparams = model.hparams;
 
     model.split_mode   = split_mode;
@@ -8589,14 +8588,13 @@ static bool llm_load_tensors(
         }
     }
 
-    // loading time will be recalculate after the first eval, so
-    // we take page faults deferred by mmap() into consideration
-    model.t_load_us = ggml_time_us() - model.t_start_us;
     return true;
 }
 
 // Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
 static int llama_model_load(const std::string & fname, llama_model & model, llama_model_params & params) {
+    model.t_start_us = ggml_time_us();
+
     try {
         llama_model_loader ml(fname, params.use_mmap, params.check_tensors, params.kv_overrides);
 
@@ -8658,6 +8656,10 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
         return -1;
     }
 
+    // loading time will be recalculate after the first eval, so
+    // we take page faults deferred by mmap() into consideration
+    model.t_load_us = ggml_time_us() - model.t_start_us;
+
     return 0;
 }
 
@@ -17941,6 +17943,7 @@ struct llama_context_params llama_context_default_params() {
         /*.embeddings                  =*/ false,
         /*.offload_kqv                 =*/ true,
         /*.flash_attn                  =*/ false,
+        /*.no_perf                     =*/ true,
         /*.abort_callback              =*/ nullptr,
         /*.abort_callback_data         =*/ nullptr,
     };
@@ -18151,6 +18154,7 @@ struct llama_context * llama_new_context_with_model(
     cparams.embeddings       = params.embeddings;
     cparams.offload_kqv      = params.offload_kqv;
     cparams.flash_attn       = params.flash_attn;
+    cparams.no_perf          = params.no_perf;
     cparams.pooling_type     = params.pooling_type;
 
     cparams.n_ctx            = params.n_ctx           == 0    ? hparams.n_ctx_train           : params.n_ctx;
@@ -20069,10 +20073,14 @@ void llama_synchronize(struct llama_context * ctx) {
 
     // add the evaluation to the stats
     if (ctx->n_queued_tokens == 1) {
+        if (!ctx->cparams.no_perf) {
             ctx->t_eval_us += ggml_time_us() - ctx->t_compute_start_us;
+        }
         ctx->n_eval++;
     } else if (ctx->n_queued_tokens > 1) {
+        if (!ctx->cparams.no_perf) {
             ctx->t_p_eval_us += ggml_time_us() - ctx->t_compute_start_us;
+        }
         ctx->n_p_eval += ctx->n_queued_tokens;
     }
 
@@ -20679,65 +20687,68 @@ const char * llama_print_system_info(void) {
     return s.c_str();
 }
 
-void llama_perf_print(const void * ctx, enum llama_perf_type type) {
+struct llama_perf_data_context llama_perf_context(const struct llama_context * ctx) {
-    switch (type) {
+    struct llama_perf_data_context data = {};
-        case LLAMA_PERF_TYPE_CONTEXT:
-            {
-                const auto * p = (const struct llama_context *) ctx;
 
-                const double t_start_ms   = 1e-3 * p->t_start_us;
+    if (ctx == nullptr) {
-                const double t_end_ms     = 1.00 * ggml_time_ms();
+        return data;
-                const double t_load_ms    = 1e-3 * p->t_load_us;
+    }
-                const double t_p_eval_ms  = 1e-3 * p->t_p_eval_us;
-                const double t_eval_ms    = 1e-3 * p->t_eval_us;
 
-                const int32_t n_p_eval  = std::max(0, p->n_p_eval);
+    data.t_start_ms  = 1e-3 * ctx->t_start_us;
-                const int32_t n_eval    = std::max(1, p->n_eval);
+    data.t_load_ms   = 1e-3 * ctx->t_load_us;
+    data.t_p_eval_ms = 1e-3 * ctx->t_p_eval_us;
+    data.t_eval_ms   = 1e-3 * ctx->t_eval_us;
+    data.n_p_eval    = std::max(1, ctx->n_p_eval);
+    data.n_eval      = std::max(1, ctx->n_eval);
 
-                LLAMA_LOG_INFO("%s:        load time = %10.2f ms\n", __func__, t_load_ms);
+    return data;
+}
+
+struct llama_perf_data_sampler llama_perf_sampler(const struct llama_sampler * chain) {
+    struct llama_perf_data_sampler data = {};
+
+    if (chain == nullptr) {
+        return data;
+    }
+
+    const auto * p = (const struct llama_sampler_chain *) chain->ctx;
+
+    data.t_sample_ms = 1e-3 * p->t_sample_us;
+    data.n_sample    = std::max(0, p->n_sample);
+
+    return data;
+}
+
+void llama_perf_print_context(const struct llama_context * ctx) {
+    const auto data = llama_perf_context(ctx);
+
+    const double t_end_ms = 1e-3 * ggml_time_us();
+
+    LLAMA_LOG_INFO("%s:        load time = %10.2f ms\n", __func__, data.t_load_ms);
     LLAMA_LOG_INFO("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
-                        __func__, t_p_eval_ms, n_p_eval, t_p_eval_ms / n_p_eval, 1e3 / t_p_eval_ms * n_p_eval);
+            __func__, data.t_p_eval_ms, data.n_p_eval, data.t_p_eval_ms / data.n_p_eval, 1e3 / data.t_p_eval_ms * data.n_p_eval);
     LLAMA_LOG_INFO("%s:        eval time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
-                        __func__, t_eval_ms, n_eval, t_eval_ms / n_eval, 1e3 / t_eval_ms * n_eval);
+            __func__, data.t_eval_ms, data.n_eval, data.t_eval_ms / data.n_eval, 1e3 / data.t_eval_ms * data.n_eval);
-                LLAMA_LOG_INFO("%s:       total time = %10.2f ms / %5d tokens\n", __func__, (t_end_ms - t_start_ms), (n_p_eval + n_eval));
+    LLAMA_LOG_INFO("%s:       total time = %10.2f ms / %5d tokens\n", __func__, (t_end_ms - data.t_start_ms), (data.n_p_eval + data.n_eval));
-            } break;
+}
-        case LLAMA_PERF_TYPE_SAMPLER_CHAIN:
-            {
-                const auto * smpl = (const struct llama_sampler *) ctx;
-                const auto * p = (const struct llama_sampler_chain *) smpl->ctx;
 
-                const double t_sampler_ms = 1e-3 * p->t_sample_us;
+void llama_perf_print_sampler(const struct llama_sampler * chain) {
-
+    const auto data = llama_perf_sampler(chain);
-                const int32_t n_sampler = std::max(0, p->n_sample);
 
     LLAMA_LOG_INFO("%s:    sampling time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
-                        __func__, t_sampler_ms, n_sampler, t_sampler_ms / n_sampler, 1e3 / t_sampler_ms * n_sampler);
+            __func__, data.t_sample_ms, data.n_sample, data.t_sample_ms / data.n_sample, 1e3 / data.t_sample_ms * data.n_sample);
-            } break;
-        default:
-            GGML_ABORT("invalid perf type");
-    }
 }
 
-void llama_perf_reset(void * ctx, enum llama_perf_type type) {
+void llama_perf_reset_context(struct llama_context * ctx) {
-    switch (type) {
+    ctx->t_start_us  = ggml_time_us();
-        case LLAMA_PERF_TYPE_CONTEXT:
+    ctx->t_eval_us   = ctx->n_eval = 0;
-            {
+    ctx->t_p_eval_us = ctx->n_p_eval = 0;
-                auto * p = (struct llama_context *) ctx;
+}
 
-                p->t_start_us  = ggml_time_us();
+void llama_perf_reset_sampler(struct llama_sampler * chain) {
-                p->t_eval_us   = p->n_eval = 0;
+    auto * p = (struct llama_sampler_chain *) chain->ctx;
-                p->t_p_eval_us = p->n_p_eval = 0;
-            } break;
-        case LLAMA_PERF_TYPE_SAMPLER_CHAIN:
-            {
-                auto * smpl = (struct llama_sampler *) ctx;
-                auto * p = (struct llama_sampler_chain *) smpl->ctx;
 
     p->t_sample_us = p->n_sample = 0;
-            } break;
-        default:
-            GGML_ABORT("invalid perf type");
-    }
 }
 
 void llama_perf_dump_yaml(FILE * stream, const llama_context * ctx) {