[chore]: improve time tracing of model loading process (#15426)

Co-authored-by: Michael Shin <mmshin@nvidia.com>
Co-authored-by: ishandhanani <82981111+ishandhanani@users.noreply.github.com>

python/sglang/srt/model_executor/model_runner.py

@@ -700,6 +700,7 @@ class ModelRunner(ModelRunnerKVCacheMixin):
                 )
 
     def init_torch_distributed(self):
+        tic = time.perf_counter()
         logger.info("Init torch distributed begin.")
 
         try:
@@ -807,11 +808,13 @@ class ModelRunner(ModelRunnerKVCacheMixin):
                     logger.warning(msg)
 
         logger.info(
-            f"Init torch distributed ends. mem usage={(before_avail_memory - local_gpu_memory):.2f} GB"
+            f"Init torch distributed ends. elapsed={time.perf_counter() - tic:.2f} s, "
+            f"mem usage={(before_avail_memory - local_gpu_memory):.2f} GB"
         )
         return min_per_gpu_memory
 
     def load_model(self):
+        tic_total = time.perf_counter()
         before_avail_memory = get_available_gpu_memory(self.device, self.gpu_id)
         logger.info(
             f"Load weight begin. avail mem={get_available_gpu_memory(self.device, self.gpu_id):.2f} GB"
@@ -957,6 +960,7 @@ class ModelRunner(ModelRunnerKVCacheMixin):
         self.weight_load_mem_usage = before_avail_memory - after_avail_memory
         logger.info(
             f"Load weight end. "
+            f"elapsed={time.perf_counter() - tic_total:.2f} s, "
             f"type={type(self.model).__name__}, "
             f"dtype={self.dtype}, "
             f"avail mem={after_avail_memory:.2f} GB, "
@@ -1604,6 +1608,8 @@ class ModelRunner(ModelRunnerKVCacheMixin):
 
     def init_attention_backend(self):
         """Init attention kernel backend."""
+        tic = time.perf_counter()
+        logger.info("Init attention backend begin.")
         if self.server_args.enable_pdmux:
             self.attn_backend = self._get_attention_backend(init_new_workspace=True)
             self.decode_attn_backend_group = []
@@ -1614,6 +1620,9 @@ class ModelRunner(ModelRunnerKVCacheMixin):
             self.attn_backend = TboAttnBackend.init_new(self._get_attention_backend)
         else:
             self.attn_backend = self._get_attention_backend()
+        logger.info(
+            f"Init attention backend end. elapsed={time.perf_counter() - tic:.2f} s"
+        )
 
     def _get_attention_backend(self, init_new_workspace: bool = False):
         """Init attention kernel backend."""