[3/n] chore: decouple AWQ implementation from vLLM dependency (#8113)
Co-authored-by: AniZpZ <zhuangsen.zp@antgroup.com>
This commit is contained in:
Hongbo Xu
@@ -2,21 +2,52 @@
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from __future__ import annotations
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import logging
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from typing import Any, Dict, List, Optional
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import warnings
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from typing import Any, Callable, Dict, List, Optional
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import torch
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from sglang.srt.layers.linear import LinearBase, set_weight_attrs
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from sglang.srt.layers.parameter import GroupQuantScaleParameter, PackedvLLMParameter
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from sglang.srt.layers.quantization.base_config import (
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FusedMoEMethodBase,
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LinearMethodBase,
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QuantizationConfig,
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QuantizeMethodBase,
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)
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from sglang.srt.layers.quantization.marlin_utils import (
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apply_awq_marlin_linear,
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awq_to_marlin_zero_points,
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check_marlin_supported,
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check_marlin_supports_layer,
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check_moe_marlin_supports_layer,
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marlin_make_empty_g_idx,
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marlin_make_workspace,
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marlin_moe_permute_scales,
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marlin_permute_scales,
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moe_awq_to_marlin_zero_points,
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verify_marlin_supported,
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verify_marlin_supports_shape,
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)
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from sglang.srt.layers.quantization.scalar_type import scalar_types
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from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
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from sglang.srt.layers.quantization.utils import replace_parameter
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try:
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from vllm import _custom_ops as ops
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warnings.warn(
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f"Using kernels directly from vllm. This might lead to performance degradation or "
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f"missing functionalities as certain kernels may not be optimized. "
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)
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except ImportError:
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ops = None
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from sglang.srt.utils import is_cuda
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_is_cuda = is_cuda()
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if _is_cuda:
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from sgl_kernel import awq_dequantize
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from sgl_kernel import awq_dequantize, fused_marlin_moe
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logger = logging.getLogger(__name__)
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@@ -103,6 +134,176 @@ class AWQConfig(QuantizationConfig):
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return None
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class AWQMarlinConfig(QuantizationConfig):
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"""Config class for AWQ Marlin"""
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# num_bits -> type
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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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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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zero_point: bool,
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lm_head_quantized: bool,
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modules_to_not_convert: Optional[list[str]],
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full_config: dict[str, Any],
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) -> None:
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super().__init__()
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self.pack_factor = 32 // weight_bits # packed into int32
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self.group_size = group_size
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self.zero_point = zero_point
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self.lm_head_quantized = lm_head_quantized
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self.weight_bits = weight_bits
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self.modules_to_not_convert = modules_to_not_convert or []
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self.full_config = full_config
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if self.weight_bits not in self.TYPE_MAP:
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raise ValueError(
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f"Unsupported num_bits = {self.weight_bits}. "
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f"Supported num_bits = {self.TYPE_MAP.keys()}"
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)
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self.quant_type = self.TYPE_MAP[self.weight_bits]
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verify_marlin_supported(
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self.quant_type, group_size=self.group_size, has_zp=self.zero_point
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)
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def __repr__(self) -> str:
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return (
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f"AWQMarlinConfig(quant_type={self.quant_type}, "
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f"group_size={self.group_size}, "
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f"zero_point={self.zero_point}, "
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f"lm_head_quantized={self.lm_head_quantized}, "
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f"modules_to_not_convert={self.modules_to_not_convert})"
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)
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def get_scaled_act_names(self) -> List[str]:
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return []
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@classmethod
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def get_name(cls) -> str:
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return "awq_marlin"
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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 80
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@classmethod
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def get_config_filenames(cls) -> list[str]:
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return ["quantize_config.json"]
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@classmethod
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def from_config(cls, config: dict[str, Any]) -> AWQMarlinConfig:
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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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zero_point = cls.get_from_keys(config, ["zero_point"])
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lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"], default=False)
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modules_to_not_convert = cls.get_from_keys_or(
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config, ["modules_to_not_convert"], None
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)
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return cls(
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weight_bits,
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group_size,
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zero_point,
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lm_head_quantized,
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modules_to_not_convert,
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config,
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)
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@classmethod
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def override_quantization_method(cls, hf_quant_cfg, user_quant) -> Optional[str]:
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can_convert = cls.is_awq_marlin_compatible(hf_quant_cfg)
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is_valid_user_quant = (
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user_quant is None or user_quant == "marlin" or user_quant == "awq_marlin"
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)
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if can_convert and is_valid_user_quant:
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msg = (
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"The model is convertible to {} during runtime."
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" Using {} kernel.".format(cls.get_name(), cls.get_name())
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)
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logger.info(msg)
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return cls.get_name()
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if can_convert and user_quant == "awq":
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logger.info(
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"Detected that the model can run with awq_marlin"
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", however you specified quantization=awq explicitly,"
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" so forcing awq. Use quantization=awq_marlin for"
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" faster inference"
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)
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return None
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def get_quant_method(
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self, layer: torch.nn.Module, prefix: str
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) -> Optional[QuantizeMethodBase]:
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
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from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
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if isinstance(layer, LinearBase) or (
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isinstance(layer, ParallelLMHead) and self.lm_head_quantized
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):
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if is_layer_skipped_awq(prefix, self.modules_to_not_convert):
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return UnquantizedLinearMethod()
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# Check if the layer is supported by AWQMarlin.
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if not check_marlin_supports_layer(layer, self.group_size):
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logger.warning_once(
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"Layer '%s' is not supported by AWQMarlin. Falling back to unoptimized AWQ kernels.", # noqa: E501
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prefix,
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)
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return AWQConfig.from_config(self.full_config).get_quant_method(
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layer, prefix
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)
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return AWQMarlinLinearMethod(self)
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elif isinstance(layer, FusedMoE):
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from sglang.srt.layers.quantization.moe_wna16 import MoeWNA16Config
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if not check_moe_marlin_supports_layer(layer, self.group_size):
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logger.warning_once(
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f"Layer '{prefix}' is not supported by AWQMoeMarlin. "
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"Falling back to Moe WNA16 kernels."
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)
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return MoeWNA16Config.from_config(self.full_config).get_quant_method(
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layer, prefix
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)
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return AWQMoEMethod(self)
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return None
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@classmethod
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def is_awq_marlin_compatible(cls, quant_config: dict[str, Any]):
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# Extract data from quant config.
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quant_method = quant_config.get("quant_method", "").lower()
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num_bits = quant_config.get("bits")
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group_size = quant_config.get("group_size")
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zero_point = quant_config.get("zero_point")
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if not _is_cuda:
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return False
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if quant_method != "awq":
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return False
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# If we cannot find the info needed in the config, cannot convert.
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if num_bits is None or group_size is None or zero_point is None:
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return False
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if num_bits not in cls.TYPE_MAP:
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return False
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return check_marlin_supported(
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quant_type=cls.TYPE_MAP[num_bits], group_size=group_size, has_zp=zero_point
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)
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class AWQLinearMethod(LinearMethodBase):
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"""Linear method for AWQ.
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@@ -204,3 +405,382 @@ class AWQLinearMethod(LinearMethodBase):
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if bias is not None:
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out.add_(bias)
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return out.reshape(out_shape)
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class AWQMarlinLinearMethod(LinearMethodBase):
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"""Linear method for AWQ Marlin.
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Args:
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quant_config: The AWQ Marlin quantization config.
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"""
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def __init__(self, quant_config: AWQMarlinConfig) -> None:
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self.quant_config = quant_config
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def create_weights(
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self,
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layer: torch.nn.Module,
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input_size_per_partition: int,
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output_partition_sizes: list[int],
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input_size: int,
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output_size: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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) -> None:
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del output_size
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output_size_per_partition = sum(output_partition_sizes)
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weight_loader = extra_weight_attrs.get("weight_loader")
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# Normalize group_size
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if self.quant_config.group_size != -1:
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group_size = self.quant_config.group_size
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else:
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group_size = input_size
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verify_marlin_supports_shape(
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output_size_per_partition=output_size_per_partition,
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input_size_per_partition=input_size_per_partition,
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input_size=input_size,
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group_size=group_size,
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)
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qweight = PackedvLLMParameter(
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data=torch.empty(
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input_size_per_partition,
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output_size_per_partition // self.quant_config.pack_factor,
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dtype=torch.int32,
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),
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input_dim=0,
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output_dim=1,
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packed_dim=1,
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packed_factor=self.quant_config.pack_factor,
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weight_loader=weight_loader,
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)
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num_groups = input_size_per_partition // group_size
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qzeros = PackedvLLMParameter(
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data=torch.empty(
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num_groups,
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output_size_per_partition // self.quant_config.pack_factor,
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dtype=torch.int32,
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),
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input_dim=0,
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output_dim=1,
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packed_dim=1,
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packed_factor=self.quant_config.pack_factor,
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weight_loader=weight_loader,
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)
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scales = GroupQuantScaleParameter(
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data=torch.empty(
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num_groups,
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output_size_per_partition,
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dtype=params_dtype,
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),
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input_dim=0,
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output_dim=1,
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weight_loader=weight_loader,
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)
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layer.register_parameter("qweight", qweight)
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layer.register_parameter("qzeros", qzeros)
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layer.register_parameter("scales", scales)
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layer.input_size_per_partition = input_size_per_partition
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layer.output_size_per_partition = output_size_per_partition
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layer.num_groups = num_groups
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# TODO: Update this docs
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# Checkpoints are serialized in AutoAWQ format, which is different from the
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# marlin format. This function is called after the weights are loaded.
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# Here, we handle the repacking
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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device = layer.qweight.device
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layer.qweight = torch.nn.Parameter(layer.qweight.data, requires_grad=False)
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layer.qzeros = torch.nn.Parameter(layer.qzeros.data, requires_grad=False)
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layer.scales = torch.nn.Parameter(layer.scales.data, requires_grad=False)
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# Allocate marlin workspace
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layer.workspace = marlin_make_workspace(device)
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# Repack weights from AWQ format to marlin format.
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marlin_qweight = ops.awq_marlin_repack(
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layer.qweight,
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size_k=layer.input_size_per_partition,
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size_n=layer.output_size_per_partition,
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num_bits=self.quant_config.quant_type.size_bits,
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)
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replace_parameter(layer, "qweight", marlin_qweight)
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# Permute scales from AWQ format to marlin format.
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marlin_scales = marlin_permute_scales(
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layer.scales,
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size_k=layer.input_size_per_partition,
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size_n=layer.output_size_per_partition,
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group_size=self.quant_config.group_size,
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)
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replace_parameter(layer, "scales", marlin_scales)
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# Permute zero-points from AWQ format to marlin format.
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marlin_zp = awq_to_marlin_zero_points(
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layer.qzeros,
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size_k=layer.num_groups,
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size_n=layer.output_size_per_partition,
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num_bits=self.quant_config.quant_type.size_bits,
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)
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replace_parameter(layer, "qzeros", marlin_zp)
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# Not-used
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layer.g_idx = marlin_make_empty_g_idx(device)
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layer.g_idx_sort_indices = marlin_make_empty_g_idx(device)
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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bias: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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return apply_awq_marlin_linear(
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input=x,
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weight=layer.qweight,
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weight_scale=layer.scales,
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weight_zp=layer.qzeros,
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g_idx=layer.g_idx,
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g_idx_sort_indices=layer.g_idx_sort_indices,
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workspace=layer.workspace,
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quant_type=self.quant_config.quant_type,
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output_size_per_partition=layer.output_size_per_partition,
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input_size_per_partition=layer.input_size_per_partition,
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bias=bias,
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)
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class AWQMoEMethod(FusedMoEMethodBase):
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def __init__(self, quant_config: AWQMarlinConfig):
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self.quant_config = quant_config
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if self.quant_config.weight_bits != 4:
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raise ValueError("AWQMoEMethod only supports 4bit now.")
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self.quant_type = scalar_types.uint4
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def create_weights(
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self,
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layer: torch.nn.Module,
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num_experts: int,
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hidden_size: int,
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intermediate_size_per_partition: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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):
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# Delay the import to avoid circular dependency
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
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extra_weight_attrs.update(
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{
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"is_transposed": True,
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"quant_method": FusedMoeWeightScaleSupported.GROUP.value,
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}
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)
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w13_qweight = torch.nn.Parameter(
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torch.empty(
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num_experts,
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hidden_size,
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2 * intermediate_size_per_partition // self.quant_config.pack_factor,
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dtype=torch.int32,
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),
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requires_grad=False,
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)
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layer.register_parameter("w13_qweight", w13_qweight)
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set_weight_attrs(w13_qweight, extra_weight_attrs)
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w2_qweight = torch.nn.Parameter(
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torch.empty(
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num_experts,
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intermediate_size_per_partition,
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hidden_size // self.quant_config.pack_factor,
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dtype=torch.int32,
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),
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requires_grad=False,
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)
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layer.register_parameter("w2_qweight", w2_qweight)
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set_weight_attrs(w2_qweight, extra_weight_attrs)
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num_groups_w13 = hidden_size // self.quant_config.group_size
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num_groups_w2 = intermediate_size_per_partition // self.quant_config.group_size
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# WEIGHT_SCALES
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# Allocate 2 scales for w1 and w3 respectively.
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w13_scales = torch.nn.Parameter(
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torch.empty(
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num_experts,
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num_groups_w13,
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intermediate_size_per_partition * 2,
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dtype=params_dtype,
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),
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requires_grad=False,
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)
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layer.register_parameter("w13_scales", w13_scales)
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set_weight_attrs(w13_scales, extra_weight_attrs)
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w2_scales = torch.nn.Parameter(
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torch.empty(num_experts, num_groups_w2, hidden_size, dtype=params_dtype),
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requires_grad=False,
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)
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layer.register_parameter("w2_scales", w2_scales)
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set_weight_attrs(w2_scales, extra_weight_attrs)
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# WEIGHT_ZERO_POINT
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# Allocate 2 zero points for w1 and w3 respectively.
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w13_qzeros = torch.nn.Parameter(
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torch.empty(
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num_experts,
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num_groups_w13,
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2 * intermediate_size_per_partition // self.quant_config.pack_factor,
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||||
dtype=torch.int32,
|
||||
),
|
||||
requires_grad=False,
|
||||
)
|
||||
layer.register_parameter("w13_qzeros", w13_qzeros)
|
||||
set_weight_attrs(w13_qzeros, extra_weight_attrs)
|
||||
|
||||
w2_qzeros = torch.nn.Parameter(
|
||||
torch.empty(
|
||||
num_experts,
|
||||
num_groups_w2,
|
||||
hidden_size // self.quant_config.pack_factor,
|
||||
dtype=torch.int32,
|
||||
),
|
||||
requires_grad=False,
|
||||
)
|
||||
layer.register_parameter("w2_qzeros", w2_qzeros)
|
||||
set_weight_attrs(w2_qzeros, extra_weight_attrs)
|
||||
|
||||
device = layer.w13_qweight.device
|
||||
layer.workspace = marlin_make_workspace(device, 4)
|
||||
|
||||
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
|
||||
num_experts = layer.w13_qweight.shape[0]
|
||||
device = layer.w13_qweight.device
|
||||
|
||||
layer.w13_g_idx_sort_indices = torch.nn.Parameter(
|
||||
torch.empty((num_experts, 0), dtype=torch.int32, device=device),
|
||||
requires_grad=False,
|
||||
)
|
||||
layer.w2_g_idx_sort_indices = torch.nn.Parameter(
|
||||
torch.empty((num_experts, 0), dtype=torch.int32, device=device),
|
||||
requires_grad=False,
|
||||
)
|
||||
|
||||
marlin_w13_qweight = ops.awq_marlin_moe_repack(
|
||||
layer.w13_qweight,
|
||||
layer.w13_g_idx_sort_indices,
|
||||
size_k=layer.w13_qweight.shape[1],
|
||||
size_n=layer.w13_qweight.shape[2] * self.quant_config.pack_factor,
|
||||
num_bits=self.quant_config.weight_bits,
|
||||
)
|
||||
replace_parameter(layer, "w13_qweight", marlin_w13_qweight)
|
||||
|
||||
marlin_w2_qweight = ops.awq_marlin_moe_repack(
|
||||
layer.w2_qweight,
|
||||
layer.w2_g_idx_sort_indices,
|
||||
size_k=layer.w2_qweight.shape[1],
|
||||
size_n=layer.w2_qweight.shape[2] * self.quant_config.pack_factor,
|
||||
num_bits=self.quant_config.weight_bits,
|
||||
)
|
||||
replace_parameter(layer, "w2_qweight", marlin_w2_qweight)
|
||||
|
||||
# hidden_size->intermediate_size
|
||||
marlin_w13_scales = marlin_moe_permute_scales(
|
||||
s=layer.w13_scales,
|
||||
size_k=layer.intermediate_size_per_partition,
|
||||
size_n=layer.w13_scales.shape[2],
|
||||
group_size=self.quant_config.group_size,
|
||||
)
|
||||
|
||||
replace_parameter(layer, "w13_scales", marlin_w13_scales)
|
||||
|
||||
marlin_w2_scales = marlin_moe_permute_scales(
|
||||
s=layer.w2_scales,
|
||||
size_k=layer.intermediate_size_per_partition,
|
||||
size_n=layer.w2_scales.shape[2],
|
||||
group_size=self.quant_config.group_size,
|
||||
)
|
||||
replace_parameter(layer, "w2_scales", marlin_w2_scales)
|
||||
|
||||
marlin_w13_zp = moe_awq_to_marlin_zero_points(
|
||||
layer.w13_qzeros,
|
||||
size_k=layer.w13_qzeros.shape[1],
|
||||
size_n=layer.w13_qzeros.shape[2] * self.quant_config.pack_factor,
|
||||
num_bits=self.quant_config.weight_bits,
|
||||
)
|
||||
replace_parameter(layer, "w13_qzeros", marlin_w13_zp)
|
||||
|
||||
marlin_w2_zp = moe_awq_to_marlin_zero_points(
|
||||
layer.w2_qzeros,
|
||||
size_k=layer.w2_qzeros.shape[1],
|
||||
size_n=layer.w2_qzeros.shape[2] * self.quant_config.pack_factor,
|
||||
num_bits=self.quant_config.weight_bits,
|
||||
)
|
||||
replace_parameter(layer, "w2_qzeros", marlin_w2_zp)
|
||||
|
||||
def apply(
|
||||
self,
|
||||
layer: torch.nn.Module,
|
||||
x: torch.Tensor,
|
||||
router_logits: torch.Tensor,
|
||||
top_k: int,
|
||||
renormalize: bool,
|
||||
use_grouped_topk: bool = False,
|
||||
topk_group: Optional[int] = None,
|
||||
num_expert_group: Optional[int] = None,
|
||||
num_fused_shared_experts: int = 0,
|
||||
custom_routing_function: Optional[Callable] = None,
|
||||
scoring_func: str = "softmax",
|
||||
correction_bias: Optional[torch.Tensor] = None,
|
||||
apply_router_weight_on_input: bool = False,
|
||||
activation: str = "silu",
|
||||
routed_scaling_factor: Optional[float] = None,
|
||||
) -> torch.Tensor:
|
||||
# Delay the import to avoid circular dependency
|
||||
from sglang.srt.layers.moe.topk import select_experts
|
||||
|
||||
assert activation == "silu", "Only SiLU activation is supported."
|
||||
assert (
|
||||
scoring_func == "softmax"
|
||||
), "Only softmax score func is supported for now."
|
||||
|
||||
# The input must currently be float16
|
||||
orig_dtype = x.dtype
|
||||
x = x.half()
|
||||
|
||||
topk_weights, topk_ids = select_experts(
|
||||
hidden_states=x,
|
||||
router_logits=router_logits,
|
||||
top_k=top_k,
|
||||
use_grouped_topk=use_grouped_topk,
|
||||
renormalize=renormalize,
|
||||
topk_group=topk_group,
|
||||
num_expert_group=num_expert_group,
|
||||
num_fused_shared_experts=num_fused_shared_experts,
|
||||
custom_routing_function=custom_routing_function,
|
||||
correction_bias=correction_bias,
|
||||
routed_scaling_factor=routed_scaling_factor,
|
||||
)
|
||||
|
||||
return fused_marlin_moe(
|
||||
x,
|
||||
layer.w13_qweight,
|
||||
layer.w2_qweight,
|
||||
layer.w13_scales,
|
||||
layer.w2_scales,
|
||||
router_logits,
|
||||
topk_weights,
|
||||
topk_ids,
|
||||
sort_indices1=layer.w13_g_idx_sort_indices,
|
||||
sort_indices2=layer.w2_g_idx_sort_indices,
|
||||
w1_zeros=layer.w13_qzeros,
|
||||
w2_zeros=layer.w2_qzeros,
|
||||
num_bits=self.quant_config.weight_bits,
|
||||
).to(orig_dtype)
|
||||
|
||||
Reference in New Issue
Block a user