Revert "[Misc] Enable weights loading tracking for quantized models" (#35309)

vllm/model_executor/model_loader/default_loader.py

@@ -14,7 +14,6 @@ from transformers.utils import SAFE_WEIGHTS_INDEX_NAME
 from vllm.config import ModelConfig
 from vllm.config.load import LoadConfig
 from vllm.logger import init_logger
-from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
 from vllm.model_executor.layers.quantization.torchao import torchao_version_at_least
 from vllm.model_executor.model_loader.base_loader import BaseModelLoader
 from vllm.model_executor.model_loader.weight_utils import (
@@ -287,6 +286,7 @@ class DefaultModelLoader(BaseModelLoader):
             ):
                 self.load_config.safetensors_load_strategy = "torchao"
 
+        weights_to_load = {name for name, _ in model.named_parameters()}
         loaded_weights = model.load_weights(self.get_all_weights(model_config, model))
 
         self.counter_after_loading_weights = time.perf_counter()
@@ -295,20 +295,9 @@ class DefaultModelLoader(BaseModelLoader):
             self.counter_after_loading_weights - self.counter_before_loading_weights,
             scope="local",
         )
-        self.track_weights_loading(model, loaded_weights)
-
-    def track_weights_loading(
-        self, model: nn.Module, loaded_weights: set[str] | None
-    ) -> None:
-        weights_to_load = {name for name, _ in model.named_parameters()}
-        if loaded_weights is not None:
-            for name, module in model.named_modules():
-                quant_method = getattr(module, "quant_method", None)
-                # ignore kv_cache scale, which can be missing in checkpoints
-                if isinstance(quant_method, BaseKVCacheMethod):
-                    for param_name, _ in module.named_parameters():
-                        full_name = f"{name}.{param_name}" if name else param_name
-                        loaded_weights.add(full_name)
+        # We only enable strict check for non-quantized models
+        # that have loaded weights tracking currently.
+        if model_config.quantization is None and loaded_weights is not None:
             weights_not_loaded = weights_to_load - loaded_weights
             if weights_not_loaded:
                 raise ValueError(