复制模型对象，跳过加载模型

scripts/evaluation/world_model_interaction.py

@@ -1026,9 +1026,26 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
     csv_path = os.path.join(args.prompt_dir, f"{args.dataset}.csv")
     df = pd.read_csv(csv_path)
 
-    # Load config
+    # Load config (always needed for data setup)
-    with profiler.profile_section("model_loading/config"):
     config = OmegaConf.load(args.config)
+
+    prepared_path = args.ckpt_path + ".prepared.pt"
+    if os.path.exists(prepared_path):
+        # ---- Fast path: load the fully-prepared model ----
+        print(f">>> Loading prepared model from {prepared_path} ...")
+        with profiler.profile_section("model_loading/prepared"):
+            model = torch.load(prepared_path,
+                               map_location=f"cuda:{gpu_no}",
+                               weights_only=False,
+                               mmap=True)
+        model.eval()
+        diffusion_autocast_dtype = (torch.bfloat16
+                                    if args.diffusion_dtype == "bf16"
+                                    else None)
+        print(f">>> Prepared model loaded.")
+    else:
+        # ---- Normal path: construct + checkpoint + casting ----
+        with profiler.profile_section("model_loading/config"):
             config['model']['params']['wma_config']['params'][
                 'use_checkpoint'] = False
             model = instantiate_from_config(config.model)
@@ -1043,14 +1060,6 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
         model = model.cuda(gpu_no)  # move residual buffers not in state_dict
         print(f'>>> Load pre-trained model ...')
 
-    # Build unnomalizer
-    logging.info("***** Configing Data *****")
-    with profiler.profile_section("data_loading"):
-        data = instantiate_from_config(config.data)
-        data.setup()
-    print(">>> Dataset is successfully loaded ...")
-    device = get_device_from_parameters(model)
-
         diffusion_autocast_dtype = None
         if args.diffusion_dtype == "bf16":
             maybe_cast_module(
@@ -1175,6 +1184,20 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
                 print(">>> export_only set; skipping inference.")
                 return
 
+        # Save prepared model for fast loading next time
+        if prepared_path:
+            print(f">>> Saving prepared model to {prepared_path} ...")
+            torch.save(model, prepared_path)
+            print(f">>> Prepared model saved ({os.path.getsize(prepared_path) / 1024**3:.1f} GB).")
+
+    # Build normalizer (always needed, independent of model loading path)
+    logging.info("***** Configing Data *****")
+    with profiler.profile_section("data_loading"):
+        data = instantiate_from_config(config.data)
+        data.setup()
+    print(">>> Dataset is successfully loaded ...")
+    device = get_device_from_parameters(model)
+
     profiler.record_memory("after_model_load")
 
     # Run over data