class INCConfig(QuantizationConfig):
"""Config class for Intel Neural Compressor (INC).
Repo: https://github.com/intel/neural-compressor
"""
SUPPORTED_BITS = {2, 3, 4, 8}
SUPPORTED_DTYPES = {"int"}
SUPPORTED_FORMATS = {"auto_round:auto_gptq", "auto_round:auto_awq"}
SUPPORTED_BACKENDS = {
"auto",
"gptq",
"gptq:marlin",
"awq",
"awq:marlin",
"marlin",
}
def __init__(
self,
weight_bits: int,
group_size: int,
sym: bool = True,
packing_format: str = "auto_round:auto_gptq",
block_name_to_quantize: str | list[str] | None = None,
extra_config: dict[str, Any] | None = None,
data_type: str = "int",
backend: str = "auto",
) -> None:
super().__init__()
if weight_bits not in self.SUPPORTED_BITS:
raise ValueError(
f"Unsupported weight_bits: {weight_bits}, "
f"currently only support {self.SUPPORTED_BITS}."
)
if data_type not in self.SUPPORTED_DTYPES:
raise ValueError(
f"Unsupported data_type: {data_type},"
f" currently only support {self.SUPPORTED_DTYPES}."
)
if packing_format not in self.SUPPORTED_FORMATS:
raise ValueError(
f"Unsupported packing_format: {packing_format}, "
f"currently only support {self.SUPPORTED_FORMATS}."
)
if backend not in self.SUPPORTED_BACKENDS:
raise ValueError(
f"Unsupported backend: {backend}, "
f"currently only support {self.SUPPORTED_BACKENDS}."
)
self.weight_bits = weight_bits
self.group_size = group_size
self.sym = sym
self.packing_format = packing_format
self.block_name_to_quantize = (
block_name_to_quantize.split(",")
if isinstance(block_name_to_quantize, str)
else block_name_to_quantize
)
self.extra_config = extra_config
self.data_type = data_type
self.backend = backend
self.pack_factor = Fraction(32, weight_bits)
def __repr__(self) -> str:
return (
f"INCConfig(weight_bits={self.weight_bits}, "
f"group_size={self.group_size}, sym={self.sym})"
)
@classmethod
def get_name(cls) -> QuantizationMethods:
return "inc"
@classmethod
def get_supported_act_dtypes(cls) -> list[torch.dtype]:
return [torch.half, torch.bfloat16]
@classmethod
def get_min_capability(cls) -> int:
return 60
@classmethod
def get_config_filenames(cls) -> list[str]:
return ["quantization_config.json"]
@classmethod
def from_config(cls, config: dict[str, Any]) -> "INCConfig":
return cls(
weight_bits=cls.get_from_keys(config, ["bits"]),
group_size=cls.get_from_keys(config, ["group_size"]),
sym=cls.get_from_keys(config, ["sym"]),
packing_format=cls.get_from_keys_or(
config, ["packing_format"], "auto_round:auto_gptq"
),
block_name_to_quantize=cls.get_from_keys_or(
config, ["block_name_to_quantize", "to_quant_block_names"], None
),
extra_config=cls.get_from_keys_or(config, ["extra_config"], None),
data_type=cls.get_from_keys_or(config, ["data_type"], "int"),
backend=cls.get_from_keys_or(config, ["backend", "vllm_backend"], "auto"),
)
def get_layer_config(self, layer, layer_name: str):
def get_config(name: str, quantized: bool = True):
if not self.extra_config:
return (
self.weight_bits if quantized else 16,
self.group_size if quantized else -1,
self.sym if quantized else True,
)
# exact match first
if name in self.extra_config:
cfg = self.extra_config[name]
return (
cfg.get("bits", self.weight_bits if quantized else 16),
cfg.get("group_size", self.group_size if quantized else -1),
cfg.get("sym", self.sym if quantized else True),
)
REGEX_SPECIAL_CHARS = set(r"*+?^$()[]{}|\\")
for pattern, cfg in self.extra_config.items():
if not isinstance(pattern, str) or not any(
c in REGEX_SPECIAL_CHARS for c in pattern
):
continue
try:
if re.search(re.compile(pattern), name) is not None:
return (
cfg.get("bits", self.weight_bits if quantized else 16),
cfg.get("group_size", self.group_size if quantized else -1),
cfg.get("sym", self.sym if quantized else True),
)
except re.error:
# Invalid regex, ignore.
continue
return (
self.weight_bits if quantized else 16,
self.group_size if quantized else -1,
self.sym if quantized else True,
)
# 1. Exact match from config
if self.extra_config and layer_name in self.extra_config:
return get_config(layer_name)
# 2. Determine whether layer should be quantized
quantized = not isinstance(layer, ParallelLMHead)
if self.block_name_to_quantize:
quantized = any(
layer_name.startswith(name) for name in self.block_name_to_quantize
)
# 3. Handle fused MoE
if self.extra_config and "fusedmoe" in layer.__class__.__name__.lower():
moe_configs = [
get_config(name, quantized)
for name in self.extra_config
if name.startswith(layer_name)
]
if moe_configs:
if len(set(moe_configs)) == 1:
return moe_configs[0]
raise ValueError(
f"Fused MoE layer '{layer_name}' requires "
f"consistent quant config for all sub-layers"
)
# 4. Handle fused QKV or other patterns
if self.extra_config:
for fusion_key, sub_keys in self.packed_modules_mapping.items():
if fusion_key in layer_name and layer_name.count(fusion_key) == 1:
sub_names = [
layer_name.replace(fusion_key, sub_key) for sub_key in sub_keys
]
sub_configs = [get_config(name, quantized) for name in sub_names]
if len(set(sub_configs)) == 1:
return sub_configs[0]
raise ValueError(
f"Fused module '{layer_name}' requires "
f"consistent quant config for {sub_names}"
)
# 5. Fallback or try a regular expression match
return get_config(layer_name, quantized)
def check_quantized(self, weight_bits: int) -> bool:
return weight_bits < 16
def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
if self.block_name_to_quantize is not None:
self.block_name_to_quantize = hf_to_vllm_mapper.apply_list(
self.block_name_to_quantize
)
if self.extra_config is not None:
self.extra_config = hf_to_vllm_mapper.apply_dict(self.extra_config)
def apply_awq_quant_layer(self, layer, prefix: str, backend: str = "auto"):
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.quantization.utils.marlin_utils import (
check_marlin_supported,
check_moe_marlin_supports_layer,
)
weight_bits, group_size, sym = self.get_layer_config(layer, prefix)
if not self.check_quantized(weight_bits):
if isinstance(layer, (LinearBase, ParallelLMHead)):
return UnquantizedLinearMethod()
else:
return None
logger.debug(
"[%s] Type: %s, Bits: %s, Group Size: %s, Sym: %s",
prefix,
layer.__class__.__name__,
weight_bits,
group_size,
sym,
)
if backend == "auto" or "marlin" in backend:
AWQ_TYPE_MAP = {
4: scalar_types.uint4,
8: scalar_types.uint8,
}
use_marlin = (weight_bits in AWQ_TYPE_MAP) and check_marlin_supported(
AWQ_TYPE_MAP[weight_bits], group_size, not sym
)
if isinstance(layer, FusedMoE):
use_marlin = use_marlin and check_moe_marlin_supports_layer(
layer, group_size
)
else:
use_marlin = False
if use_marlin:
from vllm.model_executor.layers.quantization.awq_marlin import (
AWQMarlinConfig,
AWQMarlinLinearMethod,
AWQMarlinMoEMethod,
)
quant_args_marlin = AWQMarlinConfig(
weight_bits=weight_bits,
group_size=group_size,
zero_point=not sym,
lm_head_quantized=False,
full_config={},
modules_to_not_convert=[],
)
else:
from vllm.model_executor.layers.quantization.awq import (
AWQConfig,
AWQLinearMethod,
)
quant_args = AWQConfig(
weight_bits=weight_bits,
group_size=group_size,
zero_point=not sym,
)
if isinstance(layer, FusedMoE):
if use_marlin:
return AWQMarlinMoEMethod(quant_args_marlin, layer.moe_config)
from vllm.model_executor.layers.quantization.moe_wna16 import MoeWNA16Config
config = {
"quant_method": "awq",
"bits": weight_bits,
"group_size": group_size,
"zero_point": not sym,
"lm_head": False,
}
return MoeWNA16Config.from_config(config).get_quant_method(layer, prefix)
if isinstance(layer, (LinearBase, ParallelLMHead)):
if use_marlin:
return AWQMarlinLinearMethod(quant_args_marlin)
else:
return AWQLinearMethod(quant_args)
return None
def apply_gptq_quant_layer(self, layer, prefix: str, backend: str = "auto"):
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.quantization.utils.marlin_utils import (
check_marlin_supported,
check_moe_marlin_supports_layer,
)
weight_bits, group_size, sym = self.get_layer_config(layer, prefix)
if not self.check_quantized(weight_bits):
if isinstance(layer, (LinearBase, ParallelLMHead)):
return UnquantizedLinearMethod()
else:
return None
logger.debug(
"[%s] Type: %s, Bits: %s, Group Size: %s, Sym: %s",
prefix,
layer.__class__.__name__,
weight_bits,
group_size,
sym,
)
if backend == "auto" or "marlin" in backend:
GPTQ_TYPE_MAP = {
(4, True): scalar_types.uint4b8,
(8, True): scalar_types.uint8b128,
}
use_marlin = (weight_bits, sym) in GPTQ_TYPE_MAP and check_marlin_supported(
GPTQ_TYPE_MAP[(weight_bits, sym)], group_size, has_zp=not sym
)
if isinstance(layer, FusedMoE):
use_marlin = use_marlin and check_moe_marlin_supports_layer(
layer, group_size
)
else:
use_marlin = False
if use_marlin:
from vllm.model_executor.layers.quantization.gptq_marlin import (
GPTQMarlinConfig,
GPTQMarlinLinearMethod,
GPTQMarlinMoEMethod,
)
quant_args_marlin = GPTQMarlinConfig(
weight_bits=weight_bits,
group_size=group_size,
is_sym=sym,
lm_head_quantized=False,
desc_act=False,
dynamic={},
full_config={},
)
else:
from vllm.model_executor.layers.quantization.gptq import (
GPTQConfig,
GPTQLinearMethod,
)
quant_args = GPTQConfig(
weight_bits=weight_bits,
group_size=group_size,
lm_head_quantized=False,
desc_act=False,
dynamic={},
)
if isinstance(layer, FusedMoE):
if use_marlin:
return GPTQMarlinMoEMethod(quant_args_marlin, layer.moe_config)
else:
from vllm.model_executor.layers.quantization.moe_wna16 import (
MoeWNA16Config,
)
config = {
"quant_method": "gptq",
"bits": weight_bits,
"group_size": group_size,
"sym": sym,
"lm_head": False,
}
return MoeWNA16Config.from_config(config).get_quant_method(
layer, prefix
)
if isinstance(layer, (LinearBase, ParallelLMHead)):
if use_marlin:
return GPTQMarlinLinearMethod(quant_args_marlin)
else:
return GPTQLinearMethod(quant_args)
return None
def apply_ipex_quant_layer(self, layer, prefix: str):
weight_bits, group_size, sym = self.get_layer_config(layer, prefix)
if not self.check_quantized(weight_bits):
if isinstance(layer, (LinearBase, ParallelLMHead)):
return UnquantizedLinearMethod()
else:
return None
raise NotImplementedError(
"INC quantization is not supported during xpu kernel migration."
)
def get_quant_method(self, layer: torch.nn.Module, prefix: str):
if prefix and self.extra_config:
for layer_name in self.extra_config:
if (
layer_name == prefix or layer_name == f"model.{prefix}"
) and self.extra_config[layer_name].get("bits", 16) >= 16:
return UnquantizedLinearMethod()
if (
current_platform.is_cpu()
or current_platform.is_xpu()
or self.backend == "ipex"
):
return self.apply_ipex_quant_layer(layer, prefix)
if "gptq" in self.packing_format or "gptq" in self.backend:
return self.apply_gptq_quant_layer(layer, prefix)
if "awq" in self.packing_format or "awq" in self.backend:
return self.apply_awq_quant_layer(layer, prefix)
@classmethod
def override_quantization_method(
cls, hf_quant_cfg, user_quant
) -> "QuantizationMethods | None":
"""Override the `auto-round` method to `inc`."""
is_auto_round_format = hf_quant_cfg.get("quant_method", None) == "auto-round"
if is_auto_round_format:
return cls.get_name()
return None