[Quantization] Improve BitsAndBytesModelLoader

vllm/model_executor/model_loader/bitsandbytes_loader.py

@@ -20,8 +20,6 @@
                               get_tensor_model_parallel_world_size)
 # yapf: enable
 from vllm.logger import init_logger
-# yapf conflicts with isort for this block
-# yapf: disable
 from vllm.model_executor.layers.linear import (LinearBase,
                                                MergedColumnParallelLinear,
                                                QKVParallelLinear,
@@ -39,6 +37,8 @@
                                        set_weight_attrs)
 from vllm.platforms import current_platform
 
+# yapf conflicts with isort for this block
+
 logger = init_logger(__name__)
 
 
@@ -54,11 +54,17 @@ def __init__(self, load_config: LoadConfig):
         self.unsharded_weights_modules: list[str] = []
         # Save the module names that are sharded by column.
         self.column_sharded_weights_modules: list[str] = []
+        # Modules whose weights might have fused on disk
+        # we need their output_sizes to make shard in flight correctly with TP
+        self.maybe_fused_weights_modules: dict[str, list[int]] = {}
         # Store all module names (from transformers) that support
         # BNB quantization.
         self.target_modules: list[str] = []
         # mapping weight names from transformers to vllm.
         self.weight_mapper: Callable = lambda name: name
+        self.pre_quant: bool = False
+        self.load_8bit: bool = False
+        self.is_pool_model: bool = False
 
     def _get_weight_files(
         self,
@@ -134,13 +140,14 @@ def _prepare_weights(self, model_name_or_path: str,
         return hf_weights_files, use_safetensors
 
     def _hf_weight_iter(self, hf_weights_files, use_safetensors: bool):
-        def _maybe_pool_model(module_name:str):
+
+        def _maybe_pool_model(module_name: str):
             # For pool model, we need to add the prefix `model.`
             # for the weight name if possible.
             if self.is_pool_model and self.target_modules[0]. \
                 startswith("model.") and not module_name.startswith(
                     "model."):
-                return "model."+module_name
+                return "model." + module_name
 
             return module_name
 
@@ -159,17 +166,14 @@ def _maybe_pool_model(module_name:str):
             # mapping weight names from transformers to vllm while preserving
             # original names.
             mapped_name = self.weight_mapper(org_name)
-            mapped_name=_maybe_pool_model(mapped_name)
-
+            mapped_name = _maybe_pool_model(mapped_name)
 
             yield org_name, mapped_name, param
 
     def _get_quantized_weights_iterator(
         self,
         model_name_or_path: str,
         revision: Optional[str],
-        pre_quant: bool,
-        load_8bit: bool,
     ) -> tuple[Generator[tuple[str, torch.Tensor], None, None], dict[str,
                                                                      Any]]:
         """Get an iterator to the model weights with bitsandbytes quantization,
@@ -192,8 +196,8 @@ def _get_quantized_weights_iterator(
 
         quant_state_dict: dict[str, Any] = {}
 
-        if pre_quant:
-            if load_8bit:
+        if self.pre_quant:
+            if self.load_8bit:
                 return self._quantized_8bit_generator(
                     hf_weights_files, use_safetensors,
                     quant_state_dict), quant_state_dict
@@ -390,10 +394,13 @@ def _unquantized_generator(self, hf_weights_files, use_safetensors,
             yield org_weight_name, processed_weight
 
     def _get_bnb_target_modules(self, model: nn.Module) -> None:
-
+        """
+        Identify and collect all modules that support BitsAndBytes 
+        quantization.
+        """
         for name, module in model.named_modules():
-            if (isinstance(module, LinearBase) and
-                    hasattr(module.quant_method, "quant_config")):
+            if (isinstance(module, LinearBase)
+                    and hasattr(module.quant_method, "quant_config")):
                 if modules_info := self.modules_mapping.get_sub_modules(name):
                     # Map vllm's names to transformers's names.
                     rep_name, sub_modules = modules_info
@@ -409,29 +416,11 @@ def _get_bnb_target_modules(self, model: nn.Module) -> None:
                 ), "vllm currently does not support BNB quantization for"
         f" {type(model).__name__}"
 
-    def load_weights(self, model: nn.Module, model_config: ModelConfig) -> None:
-        if not hasattr(model, "load_weights"):
-            raise AttributeError(
-                "The required method 'load_weights' is not defined in class"
-                f" {type(model).__name__}.")
-
-        if not hasattr(model, "packed_modules_mapping"):
-            raise AttributeError(
-                f"Model {type(model).__name__} does not support BitsAndBytes "
-                "quantization yet. No 'packed_modules_mapping' found.")
-        self.is_pool_model=is_pooling_model(model)
-
-        self.modules_mapping = ParamMapping(get_packed_modules_mapping(model))
-
-        # For some models like Molmo, we need to use hf_to_vllm_mapper
-        # to ensure correct loading of weights.
-        if hf_to_vllm_mapper := getattr(model, "hf_to_vllm_mapper", None):
-            self.weight_mapper = lambda name: hf_to_vllm_mapper._map_name(name)
-
-        # Modules whose weights might have fused on disk
-        # we need their output_sizes to make shard in flight correctly with TP
-        self.maybe_fused_weights_modules: dict[str, list[int]] = {}
-        self._get_bnb_target_modules(model)
+    def _classify_module_sharding(self, model: nn.Module):
+        """
+        Categorize modules based on their weight sharding requirements 
+        for tensor parallelism.
+        """
         for name, module in model.named_modules():
             # Some modules like `ReplicatedLinear` should not have their weights
             # sharded. The reason for implementing it this way is to avoid new
@@ -449,40 +438,71 @@ def load_weights(self, model: nn.Module, model_config: ModelConfig) -> None:
             elif isinstance(module, (RowParallelLinear, )):
                 self.column_sharded_weights_modules.append(name)
 
-        self.model_type = type(model).__name__
+    def _verify_model_compatibility(self, model: nn.Module,
+                                    model_config: ModelConfig) -> None:
+        """
+        Verify that the model is compatible with BitsAndBytes quantization.
+        """
+        if not hasattr(model, "load_weights"):
+            raise AttributeError(
+                "The required method 'load_weights' is not defined in class"
+                f" {type(model).__name__}.")
 
-        logger.info("Loading weights with BitsAndBytes quantization. "
-                    "May take a while ...")
+        if not hasattr(model, "packed_modules_mapping"):
+            raise AttributeError(
+                f"Model {type(model).__name__} does not support BitsAndBytes "
+                "quantization yet. No 'packed_modules_mapping' found.")
 
         quant_config = getattr(model_config.hf_config, "quantization_config",
                                None)
-
-        pre_quant = False
         if quant_config is not None:
             quant_method = quant_config.get("quant_method")
             if quant_method == "bitsandbytes":
-                pre_quant = True
+                self.pre_quant = True
             else:
                 raise ValueError(
                     f"BitsAndBytes loader does not support {quant_method} "
                     "quantization")
 
         # The quant_states in pre_quantized models cannot work with a split
         # weight tensor. So TP does not work with pre_quantized bnb models.
-        if pre_quant and get_tensor_model_parallel_world_size() > 1:
+        if self.pre_quant and get_tensor_model_parallel_world_size() > 1:
             raise ValueError(
                 "Prequant BitsAndBytes models with tensor parallelism is not "
                 "supported. Please try with pipeline parallelism.")
+        if self.pre_quant:
+            self.load_8bit = quant_config.get("load_in_8bit", False)
+
+    def _initialize_loader_state(self, model: nn.Module,
+                                 model_config: ModelConfig) -> None:
+        """
+        Initialize the loader's internal state based on the model and 
+        configuration.
+        """
+        self.is_pool_model = is_pooling_model(model)
+        self.modules_mapping = ParamMapping(get_packed_modules_mapping(model))
 
-        load_8bit = False
-        if pre_quant:
-            load_8bit = quant_config.get("load_in_8bit", False)
+        # For some models like Molmo, we need to use hf_to_vllm_mapper
+        # to ensure correct loading of weights.
+        if hf_to_vllm_mapper := getattr(model, "hf_to_vllm_mapper", None):
+            self.weight_mapper = lambda name: hf_to_vllm_mapper._map_name(name)
 
-        qweight_iterator, quant_state_dict = (
-            self._get_quantized_weights_iterator(model_config.model,
-                                                 model_config.revision,
-                                                 pre_quant, load_8bit))
+        self._get_bnb_target_modules(model)
+        self._classify_module_sharding(model)
 
+    def load_weights(self, model: nn.Module,
+                     model_config: ModelConfig) -> None:
+
+        self._verify_model_compatibility(model, model_config)
+        self._initialize_loader_state(model, model_config)
+
+        logger.info("Loading weights with BitsAndBytes quantization. "
+                    "May take a while ...")
+        qweight_iterator, quant_state_dict = (
+            self._get_quantized_weights_iterator(
+                model_config.model,
+                model_config.revision,
+            ))
         weights_to_load = {name for name, _ in model.named_parameters()}
         loaded_weights = model.load_weights(qweight_iterator)
         # Some models may have weights loading tracker unimplemented.
@@ -562,10 +582,11 @@ def load_weights(self, model: nn.Module, model_config: ModelConfig) -> None:
                 offsets = torch.tensor(offsets).cpu()
                 set_weight_attrs(param, {"bnb_shard_offsets": offsets})
 
-                if load_8bit:
+                if self.load_8bit:
                     set_weight_attrs(
                         param, {"matmul_state": [None] * len(quant_states)})
         torch.cuda.empty_cache()
+
     def download_model(self, model_config: ModelConfig) -> None:
         self._prepare_weights(model_config.model, model_config.revision)