[Quantization] Add field to skip unquantized modules for GPTQ config

vllm/config/__init__.py

@@ -270,6 +270,7 @@ def _get_quantization_config(
                     f"{model_config.dtype} is not supported for quantization "
                     f"method {model_config.quantization}. Supported dtypes: "
                     f"{supported_dtypes}")
+            quant_config.maybe_update_config(model_config.model)
             return quant_config
         return None
 

vllm/model_executor/layers/quantization/base_config.py

@@ -162,3 +162,9 @@ def apply_vllm_mapper(  # noqa: B027
         """
         # TODO (@kylesayrs): add implementations for all subclasses
         pass
+
+    def maybe_update_config(self, model_name: str):  # noqa: B027
+        """
+        Interface to update values after config initialization.
+        """
+        pass

vllm/model_executor/layers/quantization/gptq.py

@@ -7,6 +7,7 @@
 from typing import Any, Optional, Union
 
 import torch
+from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
 from torch.nn.parameter import Parameter
 
 from vllm import _custom_ops as ops
@@ -22,6 +23,8 @@
                                            PackedColumnParameter,
                                            PackedvLLMParameter,
                                            RowvLLMParameter)
+from vllm.transformers_utils.config import get_safetensors_params_metadata
+from vllm.utils import is_list_of
 
 
 class GPTQConfig(QuantizationConfig):
@@ -38,6 +41,7 @@ def __init__(
         lm_head_quantized: bool,
         dynamic: dict[str, dict[str, Union[int, bool]]],
         autoround_version: str = "",
+        modules_in_block_to_quantize: Optional[list[str]] = None,
     ) -> None:
         # GPTQModel use `dynamic` config property to allow per module
         # quantization config so each module can be individually optimized.
@@ -75,15 +79,20 @@ def __init__(
                 "Currently, only 2/3/4/8-bit weight quantization is "
                 f"supported for GPTQ, but got {self.weight_bits} bits.")
 
+        self.modules_in_block_to_quantize = modules_in_block_to_quantize or []
+
         # used to identify GPTQ model quantized by autoround
         self.autoround_version = autoround_version
 
     def __repr__(self) -> str:
-        return (f"GPTQConfig(weight_bits={self.weight_bits}, "
-                f"group_size={self.group_size}, "
-                f"desc_act={self.desc_act}), "
-                f"lm_head_quantized={self.lm_head_quantized}), "
-                f"dynamic={self.dynamic}")
+        return (
+            f"GPTQConfig(weight_bits={self.weight_bits}, "
+            f"group_size={self.group_size}, "
+            f"desc_act={self.desc_act}), "
+            f"lm_head_quantized={self.lm_head_quantized}, "
+            f"dynamic={self.dynamic}, "
+            f"modules_in_block_to_quantize={self.modules_in_block_to_quantize})"
+        )
 
     @classmethod
     def get_name(cls) -> QuantizationMethods:
@@ -114,8 +123,10 @@ def from_config(cls, config: dict[str, Any]) -> "GPTQConfig":
                                                  default=False)
         autoround_version = cls.get_from_keys_or(config, ["autoround_version"],
                                                  default="")
+        modules_in_block_to_quantize = cls.get_from_keys_or(
+            config, ["modules_in_block_to_quantize"], default=None)
         return cls(weight_bits, group_size, desc_act, lm_head_quantized,
-                   dynamic, autoround_version)
+                   dynamic, autoround_version, modules_in_block_to_quantize)
 
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
@@ -136,6 +147,35 @@ def get_quant_method(
 
         return get_linear_quant_method(self, layer, prefix, GPTQLinearMethod)
 
+    def apply_vllm_mapper(self, hf_to_vllm_mapper):
+        if self.modules_in_block_to_quantize is not None:
+            self.modules_in_block_to_quantize = hf_to_vllm_mapper.apply_list(
+                self.modules_in_block_to_quantize)
+
+    def maybe_update_config(self,
+                            model_name: str,
+                            revision: Optional[str] = None):
+        if self.modules_in_block_to_quantize:
+            if is_list_of(self.modules_in_block_to_quantize, list):
+                # original modules_in_block_to_quantize: list[list[str]]
+                # flatten original modules_in_block_to_quantize
+                self.modules_in_block_to_quantize = [
+                    item for sublist in self.modules_in_block_to_quantize
+                    for item in sublist
+                ]
+            return
+
+        unquant_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+        metadata = get_safetensors_params_metadata(model_name,
+                                                   revision=revision)
+        quant_layers: set[str] = {
+            param_name.rsplit(".", 1)[0]
+            for param_name, info in metadata.items()
+            if (dtype := info.get('dtype', None))
+            and _SAFETENSORS_TO_TORCH_DTYPE[dtype] not in unquant_dtypes
+        }
+        self.modules_in_block_to_quantize = list(quant_layers)
+
 
 class ExllamaState(Enum):
 

vllm/model_executor/layers/quantization/gptq_marlin.py

@@ -5,6 +5,7 @@
 from typing import Any, Callable, Optional, Union
 
 import torch
+from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
 
 import vllm.model_executor.layers.fused_moe  # noqa
 from vllm import _custom_ops as ops
@@ -35,6 +36,8 @@
                                            RowvLLMParameter)
 from vllm.platforms import current_platform
 from vllm.scalar_type import scalar_types
+from vllm.transformers_utils.config import get_safetensors_params_metadata
+from vllm.utils import is_list_of
 
 logger = init_logger(__name__)
 
@@ -71,10 +74,16 @@ class GPTQMarlinConfig(QuantizationConfig):
         (8, True): scalar_types.uint8b128,
     }
 
-    def __init__(self, weight_bits: int, group_size: int, desc_act: bool,
-                 is_sym: bool, lm_head_quantized: bool,
-                 dynamic: dict[str, dict[str, Union[int, bool]]],
-                 full_config: dict[str, Any]) -> None:
+    def __init__(
+            self,
+            weight_bits: int,
+            group_size: int,
+            desc_act: bool,
+            is_sym: bool,
+            lm_head_quantized: bool,
+            dynamic: dict[str, dict[str, Union[int, bool]]],
+            full_config: dict[str, Any],
+            modules_in_block_to_quantize: Optional[list[str]] = None) -> None:
         super().__init__()
         if desc_act and group_size == -1:
             # In this case, act_order == True is the same as act_order == False
@@ -121,15 +130,19 @@ def __init__(self, weight_bits: int, group_size: int, desc_act: bool,
 
         self.quant_type = self.TYPE_MAP[(weight_bits, is_sym)]
 
+        self.modules_in_block_to_quantize = modules_in_block_to_quantize or []
         # used to identify GPTQ model quantized by autoround
         self.autoround_version = full_config.get("autoround_version", "")
 
     def __repr__(self) -> str:
-        return (f"GPTQMarlinConfig(quant_type={self.quant_type}, "
-                f"group_size={self.group_size}, "
-                f"desc_act={self.desc_act}, "
-                f"lm_head_quantized={self.lm_head_quantized}), "
-                f"dynamic={self.dynamic}")
+        return (
+            f"GPTQMarlinConfig(quant_type={self.quant_type}, "
+            f"group_size={self.group_size}, "
+            f"desc_act={self.desc_act}, "
+            f"lm_head_quantized={self.lm_head_quantized}, "
+            f"dynamic={self.dynamic}, "
+            f"modules_in_block_to_quantize={self.modules_in_block_to_quantize})"
+        )
 
     @classmethod
     def get_name(cls) -> QuantizationMethods:
@@ -158,8 +171,11 @@ def from_config(cls, config: dict[str, Any]) -> "GPTQMarlinConfig":
         is_sym = cls.get_from_keys(config, ["sym"])
         lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"],
                                                  default=False)
+        modules_in_block_to_quantize = cls.get_from_keys_or(
+            config, ["modules_in_block_to_quantize"], default=None)
         return cls(weight_bits, group_size, desc_act, is_sym,
-                   lm_head_quantized, dynamic, config)
+                   lm_head_quantized, dynamic, config,
+                   modules_in_block_to_quantize)
 
     @classmethod
     def override_quantization_method(
@@ -223,6 +239,35 @@ def is_gptq_marlin_compatible(cls, quant_config: dict[str, Any]):
         return check_marlin_supported(quant_type=cls.TYPE_MAP[(num_bits, sym)],
                                       group_size=group_size)
 
+    def apply_vllm_mapper(self, hf_to_vllm_mapper):
+        if self.modules_in_block_to_quantize is not None:
+            self.modules_in_block_to_quantize = hf_to_vllm_mapper.apply_list(
+                self.modules_in_block_to_quantize)
+
+    def maybe_update_config(self,
+                            model_name: str,
+                            revision: Optional[str] = None):
+        if self.modules_in_block_to_quantize:
+            if is_list_of(self.modules_in_block_to_quantize, list):
+                # original modules_in_block_to_quantize: list[list[str]]
+                # flatten original modules_in_block_to_quantize
+                self.modules_in_block_to_quantize = [
+                    item for sublist in self.modules_in_block_to_quantize
+                    for item in sublist
+                ]
+            return
+
+        unquant_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+        metadata = get_safetensors_params_metadata(model_name,
+                                                   revision=revision)
+        quant_layers: set[str] = {
+            param_name.rsplit(".", 1)[0]
+            for param_name, info in metadata.items()
+            if (dtype := info.get('dtype', None))
+            and _SAFETENSORS_TO_TORCH_DTYPE[dtype] not in unquant_dtypes
+        }
+        self.modules_in_block_to_quantize = list(quant_layers)
+
 
 class GPTQMarlinLinearMethod(LinearMethodBase):
     """Linear method for GPTQ Marlin.

vllm/model_executor/layers/quantization/utils/gptq_utils.py

@@ -1,7 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Mapping
 from copy import deepcopy
 from fractions import Fraction
+from types import MappingProxyType
 from typing import Optional, Union
 
 import regex as re
@@ -70,6 +72,49 @@ def get_dynamic_override(
     return default_value
 
 
+def is_layer_gptq_quantized(
+    prefix: str,
+    quantized_layers: list[str],
+    fused_mapping: Mapping[str, list[str]] = MappingProxyType({})
+) -> bool:
+    # prefix: model.layers.0.self_attn.q_proj
+    # proj_name: q_proj
+
+    # GPTQ's `modules_in_block_to_quantize`:
+    # Substr: ["self_attn.k_proj", "self_attn.v_proj", "self_attn.q_proj"]
+    # Full prefix ["model.layers.0.self_attn.q_proj"]
+
+    proj_name = prefix.split(".")[-1]
+
+    # Fused layers like gate_up_proj or qkv_proj will not be fused
+    # in the safetensors checkpoint. So, we convert the name
+    # from the fused version to unfused + check to make sure that
+    # each shard of the fused layer has the same scheme.
+    if proj_name in fused_mapping:
+        shard_prefixes = [
+            prefix.replace(proj_name, shard_proj_name)
+            for shard_proj_name in fused_mapping[proj_name]
+        ]
+
+        is_quantized = None
+        for shard_prefix in shard_prefixes:
+            is_shard_quantized = any(layer in shard_prefix
+                                     for layer in quantized_layers)
+
+            if is_quantized is None:
+                is_quantized = is_shard_quantized
+            elif is_shard_quantized != is_quantized:
+                raise ValueError(
+                    f"Detected some but not all shards of {prefix} "
+                    "are quantized. All shards of fused layers "
+                    "to have the same precision.")
+    else:
+        is_quantized = any(layer in prefix for layer in quantized_layers)
+
+    assert is_quantized is not None
+    return is_quantized
+
+
 def get_linear_quant_method(
     config: QuantizationConfig,
     layer: torch.nn.Module,
@@ -80,10 +125,15 @@ def get_linear_quant_method(
     parallel_lm_head_quantized = isinstance(
         layer, ParallelLMHead) and cloned_config.lm_head_quantized
     if isinstance(layer, LinearBase) or parallel_lm_head_quantized:
+        is_layer_quantized = is_layer_gptq_quantized(
+            prefix=prefix,
+            quantized_layers=cloned_config.modules_in_block_to_quantize,
+            fused_mapping=cloned_config.packed_modules_mapping)
         # False = skip module, None = no override, else = Positive match
         if get_dynamic_override(  # noqa: E712
                 cloned_config,  # noqa: E712
-                layer_name=prefix) == False:  # noqa: E712
+                layer_name=prefix) == False or (
+                    not is_layer_quantized):  # noqa: E712
             if parallel_lm_head_quantized:
                 return UnquantizedEmbeddingMethod()
             return UnquantizedLinearMethod()

vllm/model_executor/models/keye.py

@@ -25,9 +25,6 @@
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.quantization import QuantizationConfig
-from vllm.model_executor.layers.quantization.gptq import GPTQConfig
-from vllm.model_executor.layers.quantization.gptq_marlin import (
-    GPTQMarlinConfig)
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader, maybe_remap_kv_scale_name)
 from vllm.model_executor.models.module_mapping import MultiModelKeys
@@ -1281,11 +1278,6 @@ def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
 
         raise ValueError("Only image or video modality is supported")
 
-    def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig):
-        if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)):
-            return None
-        return quant_config
-
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config: PretrainedConfig = vllm_config.model_config.hf_config
@@ -1297,14 +1289,14 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
 
         self.visual = KeyeSiglipVisionModel(
             config.vision_config,
-            quant_config=self._maybe_ignore_quant_config(quant_config),
+            quant_config=quant_config,
             prefix=maybe_prefix(prefix, "visual"),
         )
 
         self.mlp_AR = self._build_projector(
             config,
             config.vision_config,
-            quant_config=self._maybe_ignore_quant_config(quant_config),
+            quant_config=quant_config,
             prefix=maybe_prefix(prefix, "mlp_AR"),
         )