[Quant] [Bugfix] Fix quantization config matching with hf_to_vllm_mapper (#20046)

tests/quantization/test_register_quantization_config.py

@@ -53,6 +53,7 @@ class CustomQuantConfig(QuantizationConfig):
 
     def __init__(self, num_bits: int = 8) -> None:
         """Initialize the quantization config."""
+        super().__init__()
         self.num_bits = num_bits
 
     def get_name(self) -> QuantizationMethods:

vllm/lora/models.py

@@ -805,7 +805,7 @@ def create_lora_manager(
         lora_manager_cls: type[LoRAModelManager] = LoRAModelManager,
         **kwargs) -> LoRAModelManager:
     """Create a LoRA adapter for a given model."""
-    if not hasattr(model, "packed_modules_mapping"):
+    if not isinstance(model, SupportsLoRA):
         raise ValueError(f"Model {type(model)} is not supported for LoRA.")
     lora_manager = lora_manager_cls(
         model=model,

vllm/lora/worker_manager.py

@@ -111,10 +111,7 @@ class WorkerLoRAManager(AbstractWorkerManager):
             # For some models like Qwen2VL, we need to use hf_to_vllm_mapper
             # to ensure correct loading of lora weights.
             model = self._adapter_manager.model
-            hf_to_vllm_mapper = None
+            hf_to_vllm_mapper = getattr(model, "hf_to_vllm_mapper", None)
-            if (hasattr(model, "hf_to_vllm_mapper")
-                    and model.hf_to_vllm_mapper is not None):
-                hf_to_vllm_mapper = model.hf_to_vllm_mapper
 
             lora = self._lora_model_cls.from_local_checkpoint(
                 lora_path,

vllm/model_executor/layers/quantization/base_config.py

@@ -10,6 +10,7 @@ from torch import nn
 
 if TYPE_CHECKING:
     from vllm.model_executor.layers.quantization import QuantizationMethods
+    from vllm.model_executor.models.utils import WeightsMapper
 else:
     QuantizationMethods = str
 
@@ -149,3 +150,15 @@ class QuantizationConfig(ABC):
 
     def get_cache_scale(self, name: str) -> Optional[str]:
         return None
+
+    def apply_vllm_mapper(  # noqa: B027
+            self, hf_to_vllm_mapper: "WeightsMapper"):
+        """
+        Interface for models to update module names referenced in
+        quantization configs in order to reflect the vllm model structure
+
+        :param hf_to_vllm_mapper: maps from hf model structure (the assumed
+            structure of the qconfig) to vllm model structure
+        """
+        # TODO (@kylesayrs): add implementations for all subclasses
+        pass

vllm/model_executor/layers/quantization/bitblas.py

@@ -63,6 +63,7 @@ class BitBLASConfig(QuantizationConfig):
             # (since we have only one group per output channel)
             desc_act = False
 
+        super().__init__()
         self.weight_bits = weight_bits
         self.group_size = group_size
         self.desc_act = desc_act

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from contextlib import suppress
-from typing import Any, Literal, Optional, cast
+from typing import TYPE_CHECKING, Any, Literal, Optional, cast
 
 import torch
 from compressed_tensors.config import (CompressionFormat,
@@ -37,6 +37,9 @@ from vllm.model_executor.layers.quantization.utils.nvfp4_emulation_utils import
     cutlass_fp4_supported)
 from vllm.platforms import current_platform
 
+if TYPE_CHECKING:
+    from vllm.model_executor.models.utils import WeightsMapper
+
 logger = init_logger(__name__)
 
 __all__ = ["CompressedTensorsLinearMethod"]
@@ -80,6 +83,18 @@ class CompressedTensorsConfig(QuantizationConfig):
     def get_name(self) -> QuantizationMethods:
         return "compressed-tensors"
 
+    def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
+        self.target_scheme_map = hf_to_vllm_mapper.apply_dict(
+            self.target_scheme_map)
+        self.ignore = hf_to_vllm_mapper.apply_list(self.ignore)
+        self.sparsity_scheme_map = hf_to_vllm_mapper.apply_dict(
+            self.sparsity_scheme_map)
+        self.sparsity_ignore_list = hf_to_vllm_mapper.apply_list(
+            self.sparsity_ignore_list)
+        if self.kv_cache_scheme is not None:
+            self.kv_cache_scheme = hf_to_vllm_mapper.apply_dict(
+                self.kv_cache_scheme)
+
     def get_quant_method(
         self,
         layer: torch.nn.Module,

vllm/model_executor/layers/quantization/fp8.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import functools
-from typing import Any, Callable, Optional, Union
+from typing import TYPE_CHECKING, Any, Callable, Optional, Union
 
 import torch
 import torch.nn.functional as F
@@ -39,6 +39,9 @@ from vllm.platforms import current_platform
 from vllm.scalar_type import scalar_types
 from vllm.utils import has_deep_gemm
 
+if TYPE_CHECKING:
+    from vllm.model_executor.models.utils import WeightsMapper
+
 ACTIVATION_SCHEMES = ["static", "dynamic"]
 
 logger = init_logger(__name__)
@@ -100,6 +103,11 @@ class Fp8Config(QuantizationConfig):
     def get_config_filenames(cls) -> list[str]:
         return []
 
+    def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
+        if self.ignored_layers is not None:
+            self.ignored_layers = hf_to_vllm_mapper.apply_list(
+                self.ignored_layers)
+
     @classmethod
     def from_config(cls, config: dict[str, Any]) -> "Fp8Config":
         quant_method = cls.get_from_keys(config, ["quant_method"])

vllm/model_executor/layers/quantization/gptq_bitblas.py

@@ -81,6 +81,7 @@ class GPTQBitBLASConfig(QuantizationConfig):
             # (since we have only one group per output channel)
             desc_act = False
 
+        super().__init__()
         self.weight_bits = weight_bits
         self.group_size = group_size
         self.desc_act = desc_act

vllm/model_executor/layers/quantization/marlin.py

@@ -32,6 +32,8 @@ class MarlinConfig(QuantizationConfig):
         group_size: int,
         lm_head_quantized: bool,
     ) -> None:
+        super().__init__()
+
         # Group size for the quantization.
         self.group_size = group_size
         self.lm_head_quantized = lm_head_quantized

vllm/model_executor/layers/quantization/modelopt.py

@@ -181,6 +181,7 @@ class ModelOptNvFp4Config(QuantizationConfig):
         exclude_modules: list[str],
         group_size: int = 16,
     ) -> None:
+        super().__init__()
         self.is_checkpoint_nvfp4_serialized = is_checkpoint_nvfp4_serialized
         if is_checkpoint_nvfp4_serialized:
             logger.warning(

vllm/model_executor/layers/quantization/torchao.py

@@ -55,6 +55,7 @@ class TorchAOConfig(QuantizationConfig):
             os.environ["VLLM_DISABLE_COMPILE_CACHE"] = "1"
             logger.info("Using TorchAO: Setting VLLM_DISABLE_COMPILE_CACHE=1")
         """
+        super().__init__()
         self.torchao_config = torchao_config
         self.skip_modules = skip_modules or []
 

vllm/model_executor/model_loader/utils.py

@@ -24,6 +24,7 @@ from vllm.model_executor.models import ModelRegistry
 from vllm.model_executor.models.adapters import (as_classification_model,
                                                  as_embedding_model,
                                                  as_reward_model)
+from vllm.model_executor.models.interfaces import SupportsQuant
 from vllm.utils import is_pin_memory_available
 
 logger = init_logger(__name__)
@@ -294,13 +295,16 @@ def configure_quant_config(quant_config: QuantizationConfig,
 
     Note that model attributes are passed by reference to quant_config,
     enabling them to be updated by model_class.__new__ (ex. chatglm, qwen)
+
+    Once the `SupportsQuant` mixin has been added to all models, this
+    function can be removed
     """
-    packed_mapping = getattr(model_class, "packed_modules_mapping", None)
+    if not issubclass(model_class, SupportsQuant):
-    if packed_mapping is not None:
+        hf_to_vllm_mapper = getattr(model_class, "hf_to_vllm_mapper", None)
-        # pass packed_modules_mapping by reference to quant_config
+        packed_mapping = getattr(model_class, "packed_modules_mapping", None)
-        quant_config.packed_modules_mapping = packed_mapping
+
-    else:
+        # pass mappings by reference to quant_config
-        logger.warning(
+        if hf_to_vllm_mapper is not None:
-            "The model class %s has not defined `packed_modules_mapping`, "
+            quant_config.apply_vllm_mapper(hf_to_vllm_mapper)
-            "this may lead to incorrect mapping of quantized or ignored "
+        if packed_mapping is not None:
-            "modules", model_class.__name__)
+            quant_config.packed_modules_mapping = packed_mapping

vllm/model_executor/models/interfaces.py

@@ -18,6 +18,7 @@ from .interfaces_base import is_pooling_model
 
 if TYPE_CHECKING:
     from vllm.attention import AttentionMetadata
+    from vllm.model_executor.models.utils import WeightsMapper
     from vllm.sequence import IntermediateTensors
 
 logger = init_logger(__name__)
@@ -566,20 +567,36 @@ def has_step_pooler(model: Union[type[object], object]) -> bool:
 class SupportsQuant:
     """The interface required for all models that support quantization."""
 
-    packed_modules_mapping: ClassVar[dict[str, list[str]]] = {}
+    hf_to_vllm_mapper: ClassVar[Optional["WeightsMapper"]] = None
+    packed_modules_mapping: ClassVar[Optional[dict[str, list[str]]]] = None
     quant_config: Optional[QuantizationConfig] = None
 
     def __new__(cls, *args, **kwargs) -> Self:
         instance = super().__new__(cls)
+
+        # find config passed in arguments
         quant_config = cls._find_quant_config(*args, **kwargs)
         if quant_config is not None:
+
+            # attach config to model for general use
             instance.quant_config = quant_config
-            instance.quant_config.packed_modules_mapping.update(
+
-                cls.packed_modules_mapping)
+            # apply model mappings to config for proper config-model matching
+            # NOTE: `TransformersForCausalLM` is not supported due to how this
+            # class defines `hf_to_vllm_mapper` as a post-init `@property`.
+            # After this is fixed, get `instance.hf_to_vllm_mapper` directly
+            if getattr(instance, "hf_to_vllm_mapper", None) is not None:
+                instance.quant_config.apply_vllm_mapper(
+                    instance.hf_to_vllm_mapper)
+            if getattr(instance, "packed_modules_mapping", None) is not None:
+                instance.quant_config.packed_modules_mapping.update(
+                    instance.packed_modules_mapping)
+
         return instance
 
     @staticmethod
     def _find_quant_config(*args, **kwargs) -> Optional[QuantizationConfig]:
+        """Find quant config passed through model constructor args"""
         from vllm.config import VllmConfig  # avoid circular import
 
         args_values = list(args) + list(kwargs.values())

vllm/model_executor/models/qwen2_5_vl.py

@@ -61,7 +61,7 @@ from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.config import uses_mrope
 
 from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
-                         SupportsMultiModal, SupportsPP)
+                         SupportsMultiModal, SupportsPP, SupportsQuant)
 from .qwen2_vl import Qwen2VLDummyInputsBuilder as Qwen2_5_VLDummyInputsBuilder
 from .qwen2_vl import (Qwen2VLMultiModalProcessor, Qwen2VLProcessingInfo,
                        apply_rotary_pos_emb_vision)
@@ -821,7 +821,8 @@ class Qwen2_5_VLMultiModalProcessor(Qwen2VLMultiModalProcessor):
     info=Qwen2_5_VLProcessingInfo,
     dummy_inputs=Qwen2_5_VLDummyInputsBuilder)
 class Qwen2_5_VLForConditionalGeneration(nn.Module, SupportsMultiModal,
-                                         SupportsLoRA, SupportsPP):
+                                         SupportsLoRA, SupportsPP,
+                                         SupportsQuant):
 
     # To ensure correct weight loading and mapping.
     hf_to_vllm_mapper = WeightsMapper(
@@ -837,7 +838,6 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module, SupportsMultiModal,
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config: Qwen2_5_VLConfig = vllm_config.model_config.hf_config
-        quant_config = vllm_config.quant_config
         multimodal_config = vllm_config.model_config.multimodal_config
 
         self.config = config
@@ -846,7 +846,7 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module, SupportsMultiModal,
         self.visual = Qwen2_5_VisionTransformer(
             config.vision_config,
             norm_eps=getattr(config, "rms_norm_eps", 1e-6),
-            quant_config=self._maybe_ignore_quant_config(quant_config),
+            quant_config=self._maybe_ignore_quant_config(self.quant_config),
             prefix=maybe_prefix(prefix, "visual"),
         )
 
@@ -859,12 +859,12 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module, SupportsMultiModal,
         self.make_empty_intermediate_tensors = (
             self.language_model.make_empty_intermediate_tensors)
 
-    def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig):
+    def _maybe_ignore_quant_config(self, config: Optional[QuantizationConfig]):
         # GPTQ configs do not have a list of ignored modules, however AutoGPTQ
         # seems to avoid vision encoder sections for some models.
-        if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)):
+        if isinstance(config, (GPTQConfig, GPTQMarlinConfig)):
             return None
-        return quant_config
+        return config
 
     def _validate_and_reshape_mm_tensor(self, mm_input: object,
                                         name: str) -> torch.Tensor:

vllm/model_executor/models/transformers.py

@@ -467,6 +467,7 @@ class TransformersForCausalLM(nn.Module, SupportsQuant, SupportsLoRA,
     # FIXME(Isotr0py): Don't use any weights mapper for Transformers backend,
     # this makes thing complicated. We need to remove this mapper after refactor
     # `TransformersModel` in the future.
+    # NOTE: `SupportsQuant` can be updated after property decorator is removed
     @property
     def hf_to_vllm_mapper(self):
         prefix_mapper = {

vllm/model_executor/models/utils.py

@@ -4,7 +4,7 @@
 import itertools
 from collections.abc import Iterable, Mapping
 from dataclasses import dataclass, field
-from typing import Callable, Literal, Optional, Protocol, Union, overload
+from typing import Any, Callable, Literal, Optional, Protocol, Union, overload
 
 import torch
 import torch.nn as nn
@@ -64,6 +64,19 @@ class WeightsMapper:
         return ((out_name, data) for name, data in weights
                 if (out_name := self._map_name(name)) is not None)
 
+    def apply_list(self, values: list[str]) -> list[str]:
+        return [
+            out_name for name in values
+            if (out_name := self._map_name(name)) is not None
+        ]
+
+    def apply_dict(self, values: dict[str, Any]) -> dict[str, Any]:
+        return {
+            out_name: value
+            for name, value in values.items()
+            if (out_name := self._map_name(name)) is not None
+        }
+
 
 class AutoWeightsLoader:
     """

vllm/model_executor/utils.py

@@ -58,7 +58,8 @@ def _make_synced_weight_loader(original_weight_loader):
 
 
 def get_packed_modules_mapping(model: torch.nn.Module) -> dict[str, list[str]]:
-    parent_map = copy.deepcopy(getattr(model, "packed_modules_mapping", {}))
+    parent_map = getattr(model, "packed_modules_mapping", None)
+    parent_map = copy.deepcopy(parent_map) if parent_map is not None else {}
 
     # don't infer mapping if the model has defined it explicitly.
     if parent_map:
@@ -66,7 +67,9 @@ def get_packed_modules_mapping(model: torch.nn.Module) -> dict[str, list[str]]:
 
     # We only check main components instead of whole model submodules
     for child in model.children():
-        child_map = getattr(child, "packed_modules_mapping", {})
+        child_map = getattr(child, "packed_modules_mapping", None)
+        child_map = copy.deepcopy(child_map) if child_map is not None else {}
+
         if any((k in parent_map and parent_map[k] != v)
                for k, v in child_map.items()):
             raise ValueError(