455 lines
16 KiB
Python
455 lines
16 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from fractions import Fraction
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from typing import TYPE_CHECKING, Any
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import regex as re
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import torch
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from vllm.logger import init_logger
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from vllm.model_executor.layers.linear import LinearBase, UnquantizedLinearMethod
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from vllm.model_executor.layers.quantization import (
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QuantizationConfig,
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QuantizationMethods,
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)
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from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
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from vllm.platforms import current_platform
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from vllm.scalar_type import scalar_types
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if TYPE_CHECKING:
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from vllm.model_executor.models.utils import WeightsMapper
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logger = init_logger(__name__)
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class AutoRoundConfig(QuantizationConfig):
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"""Config class for AutoRound.
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Reference: https://arxiv.org/pdf/2309.05516
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"""
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SUPPORTED_BITS = {2, 3, 4, 8}
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SUPPORTED_DTYPES = {"int"}
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SUPPORTED_FORMATS = {"auto_round:auto_gptq", "auto_round:auto_awq"}
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SUPPORTED_BACKENDS = {
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"auto",
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"gptq",
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"gptq:marlin",
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"awq",
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"awq:marlin",
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"marlin",
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"ipex",
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}
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def __init__(
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self,
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weight_bits: int,
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group_size: int,
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sym: bool = True,
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packing_format: str = "auto_round:auto_gptq",
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block_name_to_quantize: str | list[str] | None = None,
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extra_config: dict[str, Any] | None = None,
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data_type: str = "int",
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backend: str = "auto",
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) -> None:
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super().__init__()
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if weight_bits not in self.SUPPORTED_BITS:
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raise ValueError(
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f"Unsupported weight_bits: {weight_bits}, "
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f"currently only support {self.SUPPORTED_BITS}"
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)
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if data_type not in self.SUPPORTED_DTYPES:
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raise ValueError(
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f"Unsupported data_type: {data_type},"
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f" currently only support {self.SUPPORTED_DTYPES}"
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)
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if packing_format not in self.SUPPORTED_FORMATS:
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raise ValueError(
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f"Unsupported packing_format: {packing_format}, "
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f"currently only support {self.SUPPORTED_FORMATS}"
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)
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if backend not in self.SUPPORTED_BACKENDS:
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raise ValueError(
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f"Unsupported backend: {backend}, "
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f"currently only support {self.SUPPORTED_BACKENDS}"
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)
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self.weight_bits = weight_bits
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self.group_size = group_size
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self.sym = sym
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self.packing_format = packing_format
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self.block_name_to_quantize = (
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block_name_to_quantize.split(",")
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if isinstance(block_name_to_quantize, str)
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else block_name_to_quantize
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)
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self.extra_config = extra_config
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self.data_type = data_type
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self.backend = backend
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self.pack_factor = Fraction(32, weight_bits)
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def __repr__(self) -> str:
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return (
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f"AutoRoundConfig(weight_bits={self.weight_bits}, "
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f"group_size={self.group_size}, sym={self.sym})"
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)
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@classmethod
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def get_name(cls) -> QuantizationMethods:
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return "auto-round"
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@classmethod
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def get_supported_act_dtypes(cls) -> list[torch.dtype]:
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return [torch.half, torch.bfloat16]
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@classmethod
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def get_min_capability(cls) -> int:
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return 60
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@classmethod
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def get_config_filenames(cls) -> list[str]:
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return ["quantization_config.json"]
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@classmethod
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def from_config(cls, config: dict[str, Any]) -> "AutoRoundConfig":
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return cls(
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weight_bits=cls.get_from_keys(config, ["bits"]),
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group_size=cls.get_from_keys(config, ["group_size"]),
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sym=cls.get_from_keys(config, ["sym"]),
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packing_format=cls.get_from_keys_or(
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config, ["packing_format"], "auto_round:auto_gptq"
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),
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block_name_to_quantize=cls.get_from_keys_or(
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config, ["block_name_to_quantize", "to_quant_block_names"], None
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),
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extra_config=cls.get_from_keys_or(config, ["extra_config"], None),
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data_type=cls.get_from_keys_or(config, ["data_type"], "int"),
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backend=cls.get_from_keys_or(config, ["backend", "vllm_backend"], "auto"),
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)
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def get_layer_config(self, layer, layer_name: str):
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def get_config(name: str, quantized: bool = True):
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if not self.extra_config:
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return (
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self.weight_bits if quantized else 16,
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self.group_size if quantized else -1,
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self.sym if quantized else True,
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)
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# exact match first
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if name in self.extra_config:
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cfg = self.extra_config[name]
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return (
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cfg.get("bits", self.weight_bits if quantized else 16),
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cfg.get("group_size", self.group_size if quantized else -1),
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cfg.get("sym", self.sym if quantized else True),
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)
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REGEX_SPECIAL_CHARS = set(r"*+?^$()[]{}|\\")
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for pattern, cfg in self.extra_config.items():
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if not isinstance(pattern, str) or not any(
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c in REGEX_SPECIAL_CHARS for c in pattern
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):
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continue
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try:
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if re.search(re.compile(pattern), name) is not None:
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return (
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cfg.get("bits", self.weight_bits if quantized else 16),
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cfg.get("group_size", self.group_size if quantized else -1),
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cfg.get("sym", self.sym if quantized else True),
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)
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except re.error:
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# Invalid regex, ignore.
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continue
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return (
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self.weight_bits if quantized else 16,
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self.group_size if quantized else -1,
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self.sym if quantized else True,
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)
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# 1. Exact match from config
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if self.extra_config and layer_name in self.extra_config:
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return get_config(layer_name)
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# 2. Determine whether layer should be quantized
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quantized = not isinstance(layer, ParallelLMHead)
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if self.block_name_to_quantize:
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quantized = any(
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layer_name.startswith(name) for name in self.block_name_to_quantize
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)
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# 3. Handle fused MoE
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if self.extra_config and "fusedmoe" in layer.__class__.__name__.lower():
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moe_configs = [
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get_config(name, quantized)
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for name in self.extra_config
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if name.startswith(layer_name)
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]
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if moe_configs:
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if len(set(moe_configs)) == 1:
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return moe_configs[0]
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raise ValueError(
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f"Fused MoE layer '{layer_name}' requires "
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f"consistent quant config for all sub-layers"
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)
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# 4. Handle fused QKV or other patterns
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if self.extra_config:
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for fusion_key, sub_keys in self.packed_modules_mapping.items():
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if fusion_key in layer_name and layer_name.count(fusion_key) == 1:
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sub_names = [
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layer_name.replace(fusion_key, sub_key) for sub_key in sub_keys
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]
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sub_configs = [get_config(name, quantized) for name in sub_names]
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if len(set(sub_configs)) == 1:
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return sub_configs[0]
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raise ValueError(
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f"Fused module '{layer_name}' requires "
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f"consistent quant config for {sub_names}"
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)
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# 5. Fallback or try a regular expression match
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return get_config(layer_name, quantized)
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def check_quantized(self, weight_bits: int) -> bool:
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return weight_bits < 16
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def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
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if self.block_name_to_quantize is not None:
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self.block_name_to_quantize = hf_to_vllm_mapper.apply_list(
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self.block_name_to_quantize
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)
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if self.extra_config is not None:
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self.extra_config = hf_to_vllm_mapper.apply_dict(self.extra_config)
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def apply_awq_quant_layer(self, layer, prefix: str, backend: str = "auto"):
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from vllm.model_executor.layers.fused_moe import FusedMoE
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from vllm.model_executor.layers.quantization.utils.marlin_utils import (
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check_marlin_supported,
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check_moe_marlin_supports_layer,
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)
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weight_bits, group_size, sym = self.get_layer_config(layer, prefix)
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if not self.check_quantized(weight_bits):
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if isinstance(layer, (LinearBase, ParallelLMHead)):
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return UnquantizedLinearMethod()
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else:
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return None
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logger.debug(
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"[%s] Type: %s, Bits: %s, Group Size: %s, Sym: %s",
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prefix,
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layer.__class__.__name__,
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weight_bits,
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group_size,
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sym,
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)
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if backend == "auto" or "marlin" in backend:
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AWQ_TYPE_MAP = {
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4: scalar_types.uint4,
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8: scalar_types.uint8,
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}
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use_marlin = (weight_bits in AWQ_TYPE_MAP) and check_marlin_supported(
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AWQ_TYPE_MAP[weight_bits], group_size, not sym
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)
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if isinstance(layer, FusedMoE):
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use_marlin = use_marlin and check_moe_marlin_supports_layer(
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layer, group_size
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)
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else:
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use_marlin = False
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if use_marlin:
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from vllm.model_executor.layers.quantization.awq_marlin import (
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AWQMarlinConfig,
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AWQMarlinLinearMethod,
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AWQMoEMethod,
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)
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quant_args_marlin = AWQMarlinConfig(
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weight_bits=weight_bits,
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group_size=group_size,
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zero_point=not sym,
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lm_head_quantized=False,
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full_config={},
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modules_to_not_convert=[],
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)
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else:
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from vllm.model_executor.layers.quantization.awq import (
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AWQConfig,
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AWQLinearMethod,
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)
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quant_args = AWQConfig(
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weight_bits=weight_bits,
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group_size=group_size,
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zero_point=not sym,
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)
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if isinstance(layer, FusedMoE):
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if use_marlin:
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return AWQMoEMethod(quant_args_marlin, layer.moe_config)
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from vllm.model_executor.layers.quantization.moe_wna16 import MoeWNA16Config
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config = {
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"quant_method": "awq",
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"bits": weight_bits,
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"group_size": group_size,
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"zero_point": not sym,
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"lm_head": False,
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}
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return MoeWNA16Config.from_config(config).get_quant_method(layer, prefix)
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if isinstance(layer, (LinearBase, ParallelLMHead)):
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if use_marlin:
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return AWQMarlinLinearMethod(quant_args_marlin)
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else:
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return AWQLinearMethod(quant_args)
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return None
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def apply_gptq_quant_layer(self, layer, prefix: str, backend: str = "auto"):
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from vllm.model_executor.layers.fused_moe import FusedMoE
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from vllm.model_executor.layers.quantization.utils.marlin_utils import (
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check_marlin_supported,
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check_moe_marlin_supports_layer,
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)
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weight_bits, group_size, sym = self.get_layer_config(layer, prefix)
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if not self.check_quantized(weight_bits):
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if isinstance(layer, (LinearBase, ParallelLMHead)):
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return UnquantizedLinearMethod()
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else:
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return None
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logger.debug(
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"[%s] Type: %s, Bits: %s, Group Size: %s, Sym: %s",
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prefix,
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layer.__class__.__name__,
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weight_bits,
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group_size,
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sym,
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)
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if backend == "auto" or "marlin" in backend:
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GPTQ_TYPE_MAP = {
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(4, True): scalar_types.uint4b8,
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(8, True): scalar_types.uint8b128,
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}
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use_marlin = (weight_bits, sym) in GPTQ_TYPE_MAP and check_marlin_supported(
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GPTQ_TYPE_MAP[(weight_bits, sym)], group_size, has_zp=not sym
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)
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if isinstance(layer, FusedMoE):
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use_marlin = use_marlin and check_moe_marlin_supports_layer(
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layer, group_size
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)
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else:
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use_marlin = False
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if use_marlin:
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from vllm.model_executor.layers.quantization.gptq_marlin import (
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GPTQMarlinConfig,
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GPTQMarlinLinearMethod,
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GPTQMarlinMoEMethod,
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)
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quant_args_marlin = GPTQMarlinConfig(
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weight_bits=weight_bits,
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group_size=group_size,
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is_sym=sym,
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lm_head_quantized=False,
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desc_act=False,
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dynamic={},
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full_config={},
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)
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else:
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from vllm.model_executor.layers.quantization.gptq import (
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GPTQConfig,
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GPTQLinearMethod,
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)
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quant_args = GPTQConfig(
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weight_bits=weight_bits,
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group_size=group_size,
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lm_head_quantized=False,
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desc_act=False,
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dynamic={},
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)
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if isinstance(layer, FusedMoE):
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if use_marlin:
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return GPTQMarlinMoEMethod(quant_args_marlin, layer.moe_config)
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else:
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from vllm.model_executor.layers.quantization.moe_wna16 import (
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MoeWNA16Config,
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)
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config = {
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"quant_method": "gptq",
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"bits": weight_bits,
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"group_size": group_size,
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"sym": sym,
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"lm_head": False,
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}
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return MoeWNA16Config.from_config(config).get_quant_method(
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layer, prefix
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)
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if isinstance(layer, (LinearBase, ParallelLMHead)):
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if use_marlin:
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return GPTQMarlinLinearMethod(quant_args_marlin)
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else:
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return GPTQLinearMethod(quant_args)
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return None
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def apply_ipex_quant_layer(self, layer, prefix: str):
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weight_bits, group_size, sym = self.get_layer_config(layer, prefix)
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if not self.check_quantized(weight_bits):
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if isinstance(layer, (LinearBase, ParallelLMHead)):
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return UnquantizedLinearMethod()
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else:
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return None
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from vllm.model_executor.layers.quantization.ipex_quant import (
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IPEXAWQLinearMethod,
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IPEXConfig,
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IPEXGPTQLinearMethod,
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)
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if isinstance(layer, (LinearBase, ParallelLMHead)):
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if "awq" in self.packing_format:
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config = IPEXConfig(
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method="awq", weight_bits=weight_bits, group_size=group_size
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)
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return IPEXAWQLinearMethod(config)
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elif "gptq" in self.packing_format:
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config = IPEXConfig(
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method="gptq", weight_bits=weight_bits, group_size=group_size
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)
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return IPEXGPTQLinearMethod(config)
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else:
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raise ValueError(
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f"ipex backend only supports awq "
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f"and gtpq format,but got {self.packing_format}"
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)
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else:
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return None
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def get_quant_method(self, layer: torch.nn.Module, prefix: str):
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if prefix and self.extra_config:
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for layer_name in self.extra_config:
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if (
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layer_name == prefix or layer_name == f"model.{prefix}"
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) and self.extra_config[layer_name].get("bits", 16) >= 16:
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return UnquantizedLinearMethod()
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if (
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current_platform.is_cpu()
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or current_platform.is_xpu()
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or self.backend == "ipex"
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):
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return self.apply_ipex_quant_layer(layer, prefix)
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if "gptq" in self.packing_format or "gptq" in self.backend:
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return self.apply_gptq_quant_layer(layer, prefix)
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if "awq" in self.packing_format or "awq" in self.backend:
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return self.apply_awq_quant_layer(layer, prefix)
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