783 lines
26 KiB
Python
783 lines
26 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from __future__ import annotations
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import logging
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import warnings
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from typing import TYPE_CHECKING, 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.unquant import UnquantizedLinearMethod
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from sglang.srt.layers.quantization.utils import get_scalar_types, replace_parameter
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if TYPE_CHECKING:
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from sglang.srt.layers.moe.moe_runner import MoeRunnerConfig
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from sglang.srt.layers.moe.token_dispatcher import (
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StandardDispatchOutput,
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CombineInput,
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)
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from sglang.srt.utils import is_cuda, is_hip
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_is_cuda = is_cuda()
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_is_hip = is_hip()
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if _is_cuda:
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from sgl_kernel import (
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awq_dequantize,
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awq_marlin_moe_repack,
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awq_marlin_repack,
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fused_marlin_moe,
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)
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elif _is_hip:
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from sglang.srt.layers.quantization.awq_triton import (
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awq_dequantize_triton as awq_dequantize,
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)
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warnings.warn(f"HIP does not support fused_marlin_moe currently.")
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else:
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warnings.warn(f"Only CUDA and HIP support AWQ currently.")
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logger = logging.getLogger(__name__)
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ScalarType, scalar_types = get_scalar_types()
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def is_layer_skipped_awq(prefix: str, modules_to_not_convert: List[str]):
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return any(module_name in prefix for module_name in modules_to_not_convert)
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class AWQConfig(QuantizationConfig):
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"""Config class for AWQ.
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Reference: https://arxiv.org/abs/2306.00978
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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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modules_to_not_convert: Optional[List[str]] = None,
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) -> None:
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super().__init__()
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self.weight_bits = weight_bits
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self.group_size = group_size
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self.zero_point = zero_point
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self.modules_to_not_convert = modules_to_not_convert or []
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if self.weight_bits != 4:
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raise ValueError(
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"Currently, only 4-bit weight quantization is supported for "
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f"AWQ, but got {self.weight_bits} bits."
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)
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self.pack_factor = 32 // self.weight_bits
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def __repr__(self) -> str:
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return (
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f"AWQConfig(weight_bits={self.weight_bits}, "
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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"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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def get_name(self) -> str:
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return "awq"
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def get_supported_act_dtypes(self) -> List[torch.dtype]:
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return [torch.half]
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@classmethod
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def get_min_capability(cls) -> int:
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# The AWQ kernel only supports Turing or newer GPUs.
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return 75
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@staticmethod
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def get_config_filenames() -> List[str]:
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return [
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"quant_config.json", # E.g., casperhansen/vicuna-7b-v1.5-awq
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# E.g., abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq
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"quantize_config.json",
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]
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@classmethod
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def from_config(cls, config: Dict[str, Any]) -> AWQConfig:
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weight_bits = cls.get_from_keys(config, ["w_bit", "bits"])
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group_size = cls.get_from_keys(config, ["q_group_size", "group_size"])
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zero_point = cls.get_from_keys(config, ["zero_point"])
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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(weight_bits, group_size, zero_point, modules_to_not_convert)
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def get_quant_method(
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self, layer: torch.nn.Module, prefix: str
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) -> Optional[LinearMethodBase]:
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from sglang.srt.layers.linear import LinearBase
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if isinstance(layer, LinearBase):
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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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return AWQLinearMethod(self)
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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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Args:
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quant_config: The AWQ quantization config.
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"""
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def __init__(self, quant_config: AWQConfig):
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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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):
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if input_size_per_partition % self.quant_config.group_size != 0:
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raise ValueError(
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"The input size is not aligned with the quantized "
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"weight shape. This can be caused by too large "
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"tensor parallel size."
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)
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output_size_per_partition = sum(output_partition_sizes)
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if output_size_per_partition % self.quant_config.pack_factor != 0:
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raise ValueError(
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"The output size is not aligned with the quantized "
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"weight shape. This can be caused by too large "
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"tensor parallel size."
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)
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weight_loader = extra_weight_attrs.get("weight_loader")
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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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qzeros = PackedvLLMParameter(
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data=torch.empty(
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input_size_per_partition // self.quant_config.group_size,
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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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input_size_per_partition // self.quant_config.group_size,
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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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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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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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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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qweight = layer.qweight
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scales = layer.scales
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qzeros = layer.qzeros
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pack_factor = self.quant_config.pack_factor
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out_shape = x.shape[:-1] + (qweight.shape[-1] * pack_factor,)
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reshaped_x = x.reshape(-1, x.shape[-1])
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out = awq_dequantize(qweight, scales, qzeros)
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out = torch.matmul(reshaped_x, out)
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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
|
|
# marlin format. This function is called after the weights are loaded.
|
|
# Here, we handle the repacking
|
|
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
|
|
device = layer.qweight.device
|
|
layer.qweight = torch.nn.Parameter(layer.qweight.data, requires_grad=False)
|
|
layer.qzeros = torch.nn.Parameter(layer.qzeros.data, requires_grad=False)
|
|
layer.scales = torch.nn.Parameter(layer.scales.data, requires_grad=False)
|
|
|
|
# Allocate marlin workspace
|
|
layer.workspace = marlin_make_workspace(device)
|
|
|
|
# Repack weights from AWQ format to marlin format.
|
|
marlin_qweight = awq_marlin_repack(
|
|
layer.qweight,
|
|
size_k=layer.input_size_per_partition,
|
|
size_n=layer.output_size_per_partition,
|
|
num_bits=self.quant_config.quant_type.size_bits,
|
|
)
|
|
replace_parameter(layer, "qweight", marlin_qweight)
|
|
|
|
# Permute scales from AWQ format to marlin format.
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|
marlin_scales = marlin_permute_scales(
|
|
layer.scales,
|
|
size_k=layer.input_size_per_partition,
|
|
size_n=layer.output_size_per_partition,
|
|
group_size=self.quant_config.group_size,
|
|
)
|
|
replace_parameter(layer, "scales", marlin_scales)
|
|
|
|
# Permute zero-points from AWQ format to marlin format.
|
|
marlin_zp = awq_to_marlin_zero_points(
|
|
layer.qzeros,
|
|
size_k=layer.num_groups,
|
|
size_n=layer.output_size_per_partition,
|
|
num_bits=self.quant_config.quant_type.size_bits,
|
|
)
|
|
replace_parameter(layer, "qzeros", marlin_zp)
|
|
|
|
# Not-used
|
|
layer.g_idx = marlin_make_empty_g_idx(device)
|
|
layer.g_idx_sort_indices = marlin_make_empty_g_idx(device)
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
bias: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
return apply_awq_marlin_linear(
|
|
input=x,
|
|
weight=layer.qweight,
|
|
weight_scale=layer.scales,
|
|
weight_zp=layer.qzeros,
|
|
g_idx=layer.g_idx,
|
|
g_idx_sort_indices=layer.g_idx_sort_indices,
|
|
workspace=layer.workspace,
|
|
quant_type=self.quant_config.quant_type,
|
|
output_size_per_partition=layer.output_size_per_partition,
|
|
input_size_per_partition=layer.input_size_per_partition,
|
|
bias=bias,
|
|
)
|
|
|
|
|
|
class AWQMoEMethod(FusedMoEMethodBase):
|
|
|
|
def __init__(self, quant_config: AWQMarlinConfig):
|
|
self.quant_config = quant_config
|
|
if self.quant_config.weight_bits != 4:
|
|
raise ValueError("AWQMoEMethod only supports 4bit now.")
|
|
self.quant_type = scalar_types.uint4
|
|
|
|
def create_weights(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
num_experts: int,
|
|
hidden_size: int,
|
|
intermediate_size_per_partition: int,
|
|
params_dtype: torch.dtype,
|
|
**extra_weight_attrs,
|
|
):
|
|
# Delay the import to avoid circular dependency
|
|
from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
|
|
|
|
extra_weight_attrs.update(
|
|
{
|
|
"is_transposed": True,
|
|
"quant_method": FusedMoeWeightScaleSupported.GROUP.value,
|
|
}
|
|
)
|
|
|
|
w13_qweight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
hidden_size,
|
|
2 * intermediate_size_per_partition // self.quant_config.pack_factor,
|
|
dtype=torch.int32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_qweight", w13_qweight)
|
|
set_weight_attrs(w13_qweight, extra_weight_attrs)
|
|
|
|
w2_qweight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
intermediate_size_per_partition,
|
|
hidden_size // self.quant_config.pack_factor,
|
|
dtype=torch.int32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_qweight", w2_qweight)
|
|
set_weight_attrs(w2_qweight, extra_weight_attrs)
|
|
|
|
num_groups_w13 = hidden_size // self.quant_config.group_size
|
|
num_groups_w2 = intermediate_size_per_partition // self.quant_config.group_size
|
|
|
|
# WEIGHT_SCALES
|
|
# Allocate 2 scales for w1 and w3 respectively.
|
|
w13_scales = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
num_groups_w13,
|
|
intermediate_size_per_partition * 2,
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_scales", w13_scales)
|
|
set_weight_attrs(w13_scales, extra_weight_attrs)
|
|
|
|
w2_scales = torch.nn.Parameter(
|
|
torch.empty(num_experts, num_groups_w2, hidden_size, dtype=params_dtype),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_scales", w2_scales)
|
|
set_weight_attrs(w2_scales, extra_weight_attrs)
|
|
|
|
# WEIGHT_ZERO_POINT
|
|
# Allocate 2 zero points for w1 and w3 respectively.
|
|
w13_qzeros = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
num_groups_w13,
|
|
2 * intermediate_size_per_partition // self.quant_config.pack_factor,
|
|
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 = 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 = 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 create_moe_runner(
|
|
self, layer: torch.nn.Module, moe_runner_config: MoeRunnerConfig
|
|
):
|
|
self.moe_runner_config = moe_runner_config
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
dispatch_output: StandardDispatchOutput,
|
|
) -> CombineInput:
|
|
from sglang.srt.layers.moe.token_dispatcher import StandardCombineInput
|
|
|
|
assert (
|
|
self.moe_runner_config.activation == "silu"
|
|
), "Only SiLU activation is supported."
|
|
|
|
# The input must currently be float16
|
|
x = dispatch_output.hidden_states
|
|
topk_output = dispatch_output.topk_output
|
|
|
|
orig_dtype = x.dtype
|
|
x = x.half()
|
|
|
|
topk_weights, topk_ids, router_logits = topk_output
|
|
|
|
output = 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)
|
|
return StandardCombineInput(hidden_states=output)
|