Implement SupportsQuant

Signed-off-by: Kyle Sayers <kylesayrs@gmail.com>

Author: kylesayrs

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

@@ -46,6 +46,7 @@ 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 "