Explicitly explain quant method override ordering and ensure all overrides are ordered (#17256)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

vllm/config.py

@@ -28,6 +28,7 @@ import vllm.envs as envs
 from vllm.compilation.inductor_pass import CallableInductorPass, InductorPass
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization import (QUANTIZATION_METHODS,
+                                                     QuantizationMethods,
                                                      get_quantization_config)
 from vllm.model_executor.models import ModelRegistry
 from vllm.platforms import CpuArchEnum, current_platform
@@ -767,12 +768,43 @@ class ModelConfig:
                                                 "compressed-tensors")
             quant_cfg["quant_method"] = quant_method
 
+            # Quantization methods which are overrides (i.e. they have a
+            # `override_quantization_method` method) must be checked in order
+            # of preference (this is particularly important for GPTQ).
+            overrides = [
+                "marlin",
+                "bitblas",
+                "gptq_marlin_24",
+                "gptq_marlin",
+                "gptq_bitblas",
+                "awq_marlin",
+                "ipex",
+                "moe_wna16",
+            ]
+            quantization_methods = [
+                q for q in supported_quantization if q not in overrides
+            ]
+            # Any custom overrides will be in quantization_methods so we place
+            # them at the start of the list so custom overrides have preference
+            # over the built in ones.
+            quantization_methods = quantization_methods + overrides
+
             # Detect which checkpoint is it
-            for name in QUANTIZATION_METHODS:
+            for name in quantization_methods:
                 method = get_quantization_config(name)
                 quantization_override = method.override_quantization_method(
                     quant_cfg, self.quantization)
-                if quantization_override:
+                if quantization_override is not None:
+                    # Raise error if the override is not custom (custom would
+                    # be in QUANTIZATION_METHODS but not QuantizationMethods)
+                    # and hasn't been added to the overrides list.
+                    if (name in get_args(QuantizationMethods)
+                            and name not in overrides):
+                        raise ValueError(
+                            f"Quantization method {name} is an override but "
+                            "is has not been added to the `overrides` list "
+                            "above. This is necessary to ensure that the "
+                            "overrides are checked in order of preference.")
                     quant_method = quantization_override
                     self.quantization = quantization_override
                     break

vllm/model_executor/layers/quantization/__init__.py

@@ -1,11 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 
-from typing import Dict, List, Type
+from typing import Literal, Type, get_args
 
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 
-QUANTIZATION_METHODS: List[str] = [
+QuantizationMethods = Literal[
     "aqlm",
     "awq",
     "deepspeedfp",
@@ -15,8 +15,6 @@ QUANTIZATION_METHODS: List[str] = [
     "fbgemm_fp8",
     "modelopt",
     "nvfp4",
-    # The order of gptq methods is important for config.py iteration over
-    # override_quantization_method(..)
     "marlin",
     "bitblas",
     "gguf",
@@ -36,6 +34,7 @@ QUANTIZATION_METHODS: List[str] = [
     "moe_wna16",
     "torchao",
 ]
+QUANTIZATION_METHODS: list[str] = list(get_args(QuantizationMethods))
 
 # The customized quantization methods which will be added to this dict.
 _CUSTOMIZED_METHOD_TO_QUANT_CONFIG = {}
@@ -111,7 +110,7 @@ def get_quantization_config(quantization: str) -> Type[QuantizationConfig]:
     from .torchao import TorchAOConfig
     from .tpu_int8 import Int8TpuConfig
 
-    method_to_config: Dict[str, Type[QuantizationConfig]] = {
+    method_to_config: dict[str, Type[QuantizationConfig]] = {
         "aqlm": AQLMConfig,
         "awq": AWQConfig,
         "deepspeedfp": DeepSpeedFPConfig,
@@ -120,8 +119,6 @@ def get_quantization_config(quantization: str) -> Type[QuantizationConfig]:
         "fbgemm_fp8": FBGEMMFp8Config,
         "modelopt": ModelOptFp8Config,
         "nvfp4": ModelOptNvFp4Config,
-        # The order of gptq methods is important for config.py iteration over
-        # override_quantization_method(..)
         "marlin": MarlinConfig,
         "bitblas": BitBLASConfig,
         "gguf": GGUFConfig,
@@ -150,6 +147,7 @@ def get_quantization_config(quantization: str) -> Type[QuantizationConfig]:
 
 __all__ = [
     "QuantizationConfig",
+    "QuantizationMethods",
     "get_quantization_config",
     "QUANTIZATION_METHODS",
 ]