[Quant] Add SupportsQuant to phi3 and clip

vllm/model_executor/layers/quantization/aqlm.py

@@ -169,6 +169,7 @@ def __init__(
         num_codebooks: int,
         out_group_size: int,
     ) -> None:
+        super().__init__()
         self.in_group_size = in_group_size
         self.nbits_per_codebook = nbits_per_codebook
         self.num_codebooks = num_codebooks

vllm/model_executor/layers/quantization/awq.py

@@ -26,6 +26,7 @@ def __init__(
         zero_point: bool,
         modules_to_not_convert: Optional[List[str]] = None,
     ) -> None:
+        super().__init__()
         self.weight_bits = weight_bits
         self.group_size = group_size
         self.zero_point = zero_point

vllm/model_executor/layers/quantization/awq_marlin.py

@@ -47,6 +47,7 @@ def __init__(self, weight_bits: int, group_size: int, zero_point: bool,
                  lm_head_quantized: bool,
                  modules_to_not_convert: Optional[List[str]],
                  full_config: Dict[str, Any]) -> None:
+        super().__init__()
         self.pack_factor = 32 // weight_bits  # packed into int32
         self.group_size = group_size
         self.zero_point = zero_point

vllm/model_executor/layers/quantization/base_config.py

@@ -2,7 +2,7 @@
 
 import inspect
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Mapping, Optional, Type
+from typing import Any, Dict, List, Optional, Type
 
 import torch
 from torch import nn
@@ -59,7 +59,11 @@ def method_has_implemented_embedding(
 
 class QuantizationConfig(ABC):
     """Base class for quantization configs."""
-    packed_modules_mapping: Mapping[str, List[str]] = dict()
+
+    def __init__(self):
+        super().__init__()
+        # mapping is updated by models as they initialize
+        self.packed_modules_mapping: Dict[str, List[str]] = dict()
 
     @abstractmethod
     def get_name(self) -> str:

vllm/model_executor/layers/quantization/bitsandbytes.py

@@ -30,7 +30,7 @@ def __init__(
         llm_int8_skip_modules: Optional[List[str]] = None,
         llm_int8_threshold: float = 6.0,
     ) -> None:
-
+        super().__init__()
         self.load_in_8bit = load_in_8bit
         self.load_in_4bit = load_in_4bit
         self.bnb_4bit_compute_dtype = bnb_4bit_compute_dtype

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

@@ -51,7 +51,7 @@ def __init__(
         kv_cache_scheme: Optional[Dict[str, Any]] = None,
         config: Optional[Dict[str, Any]] = None,
     ):
-
+        super().__init__()
         self.ignore = ignore
         self.quant_format = quant_format
         # Map from [target -> scheme]

vllm/model_executor/layers/quantization/deepspeedfp.py

@@ -25,6 +25,7 @@ def __init__(
         weight_bits: int = 8,
         group_size: int = 512,
     ) -> None:
+        super().__init__()
         self.weight_bits = weight_bits
         self.group_size = group_size
         self.valid_types = [torch.bfloat16, torch.float16]

vllm/model_executor/layers/quantization/experts_int8.py

@@ -17,7 +17,7 @@ class ExpertsInt8Config(QuantizationConfig):
     """Config class for Int8 experts quantization."""
 
     def __init__(self) -> None:
-        pass
+        super().__init__()
 
     @classmethod
     def get_name(cls) -> str:

vllm/model_executor/layers/quantization/fbgemm_fp8.py

@@ -29,6 +29,7 @@ class FBGEMMFp8Config(QuantizationConfig):
     """Config class for FBGEMM Fp8."""
 
     def __init__(self, ignore_list: List[str], input_scale_ub: float):
+        super().__init__()
         self.ignore_list = ignore_list if ignore_list else []
         self.input_scale_ub = input_scale_ub
 

vllm/model_executor/layers/quantization/fp8.py

@@ -47,6 +47,7 @@ def __init__(
         ignored_layers: Optional[List[str]] = None,
         weight_block_size: Optional[List[int]] = None,
     ) -> None:
+        super().__init__()
         self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
         if is_checkpoint_fp8_serialized:
             logger.warning("Detected fp8 checkpoint. Please note that the "

vllm/model_executor/layers/quantization/gguf.py

@@ -20,7 +20,7 @@ class GGUFConfig(QuantizationConfig):
     """Config class for GGUF."""
 
     def __init__(self, ) -> None:
-        pass
+        super().__init__()
 
     def __repr__(self) -> str:
         return ("GGUFConfig()")

vllm/model_executor/layers/quantization/gptq.py

@@ -58,6 +58,7 @@ def __init__(
         #  r"+:.*\.(?:1[6-9]|20|21)\..*": {"bits": 8, "group_size": 64,},
         #  r"-:.*\.moe\..*": {}, # negative match (skip) all `moe` layers
         # }
+        super().__init__()
         self.dynamic = dynamic
 
         self.weight_bits = weight_bits

vllm/model_executor/layers/quantization/gptq_marlin.py

@@ -46,6 +46,7 @@ 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:
+        super().__init__()
         if desc_act and group_size == -1:
             # In this case, act_order == True is the same as act_order == False
             # (since we have only one group per output channel)

vllm/model_executor/layers/quantization/gptq_marlin_24.py

@@ -38,6 +38,7 @@ def __init__(
         weight_bits: int,
         group_size: int,
     ) -> None:
+        super().__init__()
         quant_type = {
             4: scalar_types.uint4b8,
             8: scalar_types.uint8b128,

vllm/model_executor/layers/quantization/hqq_marlin.py

@@ -33,6 +33,7 @@ def __init__(
         group_size: int,
         skip_modules: Optional[List[str]] = None,
     ) -> None:
+        super().__init__()
         assert group_size == 64, ("The only supported HQQ group size is "
                                   "currently 64.")
         assert weight_bits == 4, ("The only supported HQQ quantization "

vllm/model_executor/layers/quantization/ipex_quant.py

@@ -35,6 +35,7 @@ def __init__(
         desc_act: Optional[bool] = None,
         lm_head_quantized: Optional[bool] = None,
     ) -> None:
+        super().__init__()
         self.method = method
         self.weight_bits = weight_bits
         self.group_size = group_size

vllm/model_executor/layers/quantization/modelopt.py

@@ -28,6 +28,7 @@ def __init__(
         self,
         is_checkpoint_fp8_serialized: bool = False,
     ) -> None:
+        super().__init__()
         self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
         if is_checkpoint_fp8_serialized:
             logger.warning("Detected ModelOpt fp8 checkpoint. Please note that"

vllm/model_executor/layers/quantization/moe_wna16.py

@@ -24,6 +24,7 @@ def __init__(self, linear_quant_method: str, weight_bits: int,
                  group_size: int, has_zp: bool, lm_head_quantized: bool,
                  modules_to_not_convert: Optional[List[str]],
                  full_config: Dict[str, Any]) -> None:
+        super().__init__()
         self.weight_bits = weight_bits
         self.group_size = group_size
         self.has_zp = has_zp

vllm/model_executor/layers/quantization/neuron_quant.py

@@ -20,6 +20,7 @@ def __init__(
         dequant_dtype: str = "f16",
         quantize_method: str = "vector_dynamic",
     ) -> None:
+        super().__init__()
         self.quant_dtype = os.getenv("NEURON_QUANT_DTYPE", "s8")
         if self.quant_dtype not in SUPPORTED_QUANT_DTYPE_LIST:
             raise ValueError(

vllm/model_executor/layers/quantization/qqq.py

@@ -39,6 +39,7 @@ def __init__(
         group_size: int,
         is_sym: bool = True,
     ) -> None:
+        super().__init__()
         self.weight_bits = weight_bits
         self.group_size = group_size
         self.is_sym = is_sym

vllm/model_executor/layers/quantization/quark/quark.py

@@ -30,6 +30,7 @@ def __init__(self,
                  kv_cache_group: Optional[List[str]] = None,
                  kv_cache_config: Optional[Dict[str, Any]] = None,
                  pack_method: str = "reorder"):
+        super().__init__()
         if kv_cache_group is None:
             kv_cache_group = []
         self.quant_config = quant_config

vllm/model_executor/layers/quantization/tpu_int8.py

@@ -21,6 +21,7 @@ def __init__(
         self,
         activation_scheme: str = "none",
     ) -> None:
+        super().__init__()
         if activation_scheme not in ACTIVATION_SCHEMES:
             raise ValueError(
                 f"Unsupported activation scheme {activation_scheme}")

vllm/model_executor/models/clip.py

@@ -15,6 +15,7 @@
                                                RowParallelLinear)
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.interfaces import SupportsQuant
 
 from .vision import VisionEncoderInfo, resolve_visual_encoder_outputs
 
@@ -335,10 +336,10 @@ def forward(
         return encoder_outputs
 
 
-class CLIPVisionModel(nn.Module):
-
+class CLIPVisionModel(nn.Module, SupportsQuant):
     config_class = CLIPVisionConfig
     main_input_name = "pixel_values"
+    packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
 
     def __init__(
         self,

vllm/model_executor/models/interfaces.py

@@ -7,6 +7,8 @@
 from typing_extensions import TypeIs, TypeVar
 
 from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 from vllm.utils import supports_kw
 
 from .interfaces_base import is_pooling_model
@@ -443,6 +445,36 @@ def supports_cross_encoding(
     return is_pooling_model(model) and _supports_cross_encoding(model)
 
 
+class SupportsQuant:
+    """The interface required for all models that support quantization."""
+
+    packed_modules_mapping: ClassVar[Dict[str, List[str]]] = {}
+    quant_config: Optional[QuantizationConfig] = None
+
+    def __new__(cls, *args, **kwargs) -> "SupportsQuant":
+        instance = super().__new__(cls)
+        quant_config = cls._find_quant_config(*args, **kwargs)
+        if quant_config is not None:
+            instance.quant_config = quant_config
+            instance.quant_config.packed_modules_mapping.update(
+                cls.packed_modules_mapping)
+        return instance
+
+    @staticmethod
+    def _find_quant_config(*args, **kwargs) -> Optional[QuantizationConfig]:
+        from vllm.config import VllmConfig  # avoid circular import
+
+        args_values = list(args) + list(kwargs.values())
+        for arg in args_values:
+            if isinstance(arg, VllmConfig):
+                return arg.quant_config
+
+            if isinstance(arg, QuantizationConfig):
+                return arg
+
+        return None
+
+
 @runtime_checkable
 class SupportsTranscription(Protocol):
     """The interface required for all models that support transcription."""

vllm/model_executor/models/phi3v.py

@@ -50,7 +50,7 @@
 from vllm.utils import is_list_of
 
 from .clip import CLIPVisionModel
-from .interfaces import SupportsMultiModal, SupportsPP
+from .interfaces import SupportsMultiModal, SupportsPP, SupportsQuant
 from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
                     init_vllm_registered_model, maybe_prefix,
                     merge_multimodal_embeddings)
@@ -498,7 +498,8 @@ def _apply_prompt_replacements(
 @MULTIMODAL_REGISTRY.register_processor(Phi3VMultiModalProcessor,
                                         info=Phi3VProcessingInfo,
                                         dummy_inputs=Phi3VDummyInputsBuilder)
-class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
+class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP,
+                       SupportsQuant):
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_prefix={
             "model.vision_embed_tokens.wte": "embed_tokens",
@@ -510,7 +511,6 @@ class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
-        quant_config = vllm_config.quant_config
         multimodal_config = vllm_config.model_config.multimodal_config
         self.config = config
         self.multimodal_config = multimodal_config
@@ -520,14 +520,14 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
             config.vocab_size,
             config.hidden_size,
             org_num_embeddings=config.vocab_size,
-            quant_config=quant_config,
+            quant_config=self.quant_config,
             prefix=maybe_prefix(prefix, "model.embed_tokens"),
         )
 
         # TODO: Optionally initializes this for supporting input embeddings.
         self.vision_embed_tokens = Phi3HDImageEmbedding(
             config,
-            quant_config,
+            self.quant_config,
             prefix=maybe_prefix(prefix, "model.vision_embed_tokens"))
 
         self.language_model = init_vllm_registered_model(