@FIR-736 - lama.cpp: Disable all logs except token generation log

common/log.h

@@ -90,11 +90,20 @@ void common_log_set_timestamps(struct common_log * log,       bool   timestamps)
 #define LOG(...)             LOG_TMPL(GGML_LOG_LEVEL_NONE, 0,         __VA_ARGS__)
 #define LOGV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_NONE, verbosity, __VA_ARGS__)
 
+#if ENABLE_LOG
 #define LOG_INF(...) LOG_TMPL(GGML_LOG_LEVEL_INFO,  0,                 __VA_ARGS__)
 #define LOG_WRN(...) LOG_TMPL(GGML_LOG_LEVEL_WARN,  0,                 __VA_ARGS__)
 #define LOG_ERR(...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, 0,                 __VA_ARGS__)
 #define LOG_DBG(...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, LOG_DEFAULT_DEBUG, __VA_ARGS__)
 #define LOG_CNT(...) LOG_TMPL(GGML_LOG_LEVEL_CONT,  0,                 __VA_ARGS__)
+#else
+#define LOG_INF(...)
+#define LOG_WRN(...)
+#define LOG_ERR(...)
+#define LOG_DBG(...)
+#define LOG_CNT(...)
+#endif
+#define LOG_TSAVORITE(...) LOG_TMPL(GGML_LOG_LEVEL_TSAVORITE,  0,                 __VA_ARGS__)
 
 #define LOG_INFV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_INFO,  verbosity, __VA_ARGS__)
 #define LOG_WRNV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_WARN,  verbosity, __VA_ARGS__)

ggml/include/ggml.h

@@ -554,6 +554,7 @@ extern "C" {
         GGML_LOG_LEVEL_WARN  = 3,
         GGML_LOG_LEVEL_ERROR = 4,
         GGML_LOG_LEVEL_CONT  = 5, // continue previous log
+        GGML_LOG_LEVEL_TSAVORITE  = 42,
     };
 
     // this tensor...

ggml/src/ggml-impl.h

@@ -85,6 +85,7 @@ GGML_API void ggml_log_callback_default(enum ggml_log_level level, const char *
 #define GGML_LOG_ERROR(...) ggml_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
 #define GGML_LOG_DEBUG(...) ggml_log_internal(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__)
 #define GGML_LOG_CONT(...)  ggml_log_internal(GGML_LOG_LEVEL_CONT , __VA_ARGS__)
+#define GGML_LOG_TSAVORITE(...)  ggml_log_internal(GGML_LOG_LEVEL_TSAVORITE , __VA_ARGS__)
 
 #define GGML_DEBUG 0
 

ggml/src/ggml-tsavorite/ggml-tsavorite.cpp

@@ -504,7 +504,6 @@ static void *ggml_tsavorite_host_malloc(size_t n) {
   GGML_TSAVORITE_LOG_INFO("Start %s\n", __func__);
 
   GGML_TSAVORITE_LOG_INFO("\n Allocating memory from tsi_alloc with size  %ld \n", n);
-  printf("\n ANoop Allocating memory from tsi_alloc with size  %ld \n", n);
   data = tsi_alloc(n);
   GGML_TSAVORITE_LOG_CONT("\n Allocating memory from tsi_alloc with size  %ld starting memory %p\n",
                           n, data);
@@ -1800,7 +1799,6 @@ static bool ggml_backend_tsavorite_device_supports_buft(ggml_backend_dev_t dev,
 // ggml_backend_sched_backend_id_from_cur  -> ggml_backend_offload_op ->
 static bool ggml_backend_tsavorite_device_offload_op(ggml_backend_dev_t dev,
                                                      const struct ggml_tensor *op) {
-  // printf("\n ANoop Calling %s \n ", __func__);
   if (op->type != GGML_TYPE_F32)
     return false;
   switch (op->op) {
@@ -1894,8 +1892,9 @@ static struct ggml_backend_reg_i ggml_backend_tsavorite_reg_i = {
     /* .get_proc_address = */ NULL,
 };
 
+
 ggml_backend_reg_t ggml_backend_tsavorite_reg(void) {
-  ggml_tsavorite_log_type_val = GGML_TSAVORITE_LOG_ERROR;
+  ggml_tsavorite_log_type_val = GGML_TSAVORITE_LOG_NONE;
   ggml_tsavorite_kernel_mode_flag = GGML_TSAVORITE_KERNEL_MODE_MLIR;
   GGML_TSAVORITE_LOG_INFO("Start %s\n", __func__);
   g_ggml_backend_tsavorite_reg.iface = ggml_backend_tsavorite_reg_i;

ggml/src/ggml.c

@@ -249,6 +249,7 @@ static void ggml_log_internal_v(enum ggml_log_level level, const char * format,
 void ggml_log_internal(enum ggml_log_level level, const char * format, ...) {
     va_list args;
     va_start(args, format);
+    if (level == GGML_LOG_LEVEL_TSAVORITE)
     ggml_log_internal_v(level, format, args);
     va_end(args);
 }

src/llama-context.cpp

@@ -2615,13 +2615,19 @@ void llama_perf_context_print(const 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__, 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__, 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 - data.t_start_ms), (data.n_p_eval + data.n_eval));
+
+    LLAMA_LOG_TSAVORITE("%s:        load time = %10.2f ms\n", __func__, data.t_load_ms);
+    LLAMA_LOG_TSAVORITE("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
+            __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_TSAVORITE("%s:        eval time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
+            __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_TSAVORITE("%s:       total time = %10.2f ms / %5d tokens\n", __func__, (t_end_ms - data.t_start_ms), (data.n_p_eval + data.n_eval));
 }
 
 void llama_perf_context_reset(llama_context * ctx) {

src/llama-impl.h

@@ -29,6 +29,7 @@ void llama_log_callback_default(ggml_log_level level, const char * text, void *
 #define LLAMA_LOG_ERROR(...) llama_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
 #define LLAMA_LOG_DEBUG(...) llama_log_internal(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__)
 #define LLAMA_LOG_CONT(...)  llama_log_internal(GGML_LOG_LEVEL_CONT , __VA_ARGS__)
+#define LLAMA_LOG_TSAVORITE(...) llama_log_internal(GGML_LOG_LEVEL_TSAVORITE, __VA_ARGS__)
 
 //
 // helpers

src/llama-sampling.cpp

@@ -2562,6 +2562,8 @@ void llama_perf_sampler_print(const struct llama_sampler * chain) {
 
     LLAMA_LOG_INFO("%s:    sampling time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
             __func__, data.t_sample_ms, data.n_sample, data.t_sample_ms / data.n_sample, 1e3 / data.t_sample_ms * data.n_sample);
+    LLAMA_LOG_TSAVORITE("\n\n%s:    sampling time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)",
+            __func__, data.t_sample_ms, data.n_sample, data.t_sample_ms / data.n_sample, 1e3 / data.t_sample_ms * data.n_sample);
 }
 
 void llama_perf_sampler_reset(struct llama_sampler * chain) {

tools/main/main.cpp

@@ -41,6 +41,12 @@ static std::vector<llama_token> * g_output_tokens;
 static bool is_interacting  = false;
 static bool need_insert_eot = false;
 
+static void my_logger(ggml_log_level level, const char *text, void *user_data) {
+    if (level == GGML_LOG_LEVEL_TSAVORITE) {
+        fprintf(stderr, "%s", text);  // only show warnings or errors
+    }
+}
+
 static void print_usage(int argc, char ** argv) {
     (void) argc;
 
@@ -120,6 +126,7 @@ int main(int argc, char ** argv) {
         LOG_WRN("%s: warning: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale);
     }
 
+    llama_log_set(my_logger, nullptr);
     LOG_INF("%s: llama backend init\n", __func__);
 
     llama_backend_init();