1098 lines
38 KiB
Python
1098 lines
38 KiB
Python
from __future__ import annotations
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import logging
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from dataclasses import dataclass
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from fractions import Fraction
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from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union
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import torch
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from sglang.srt.layers.parameter import (
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BasevLLMParameter,
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ChannelQuantScaleParameter,
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GroupQuantScaleParameter,
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PackedColumnParameter,
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PackedvLLMParameter,
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RowvLLMParameter,
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permute_param_layout_,
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)
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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_gptq_marlin_linear,
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check_marlin_supported,
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check_marlin_supports_shape,
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marlin_is_k_full,
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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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marlin_repeat_scales_on_all_ranks,
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marlin_sort_g_idx,
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marlin_zero_points,
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verify_marlin_supported,
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)
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from sglang.srt.layers.quantization.utils import (
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get_linear_quant_method,
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get_scalar_types,
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replace_parameter,
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unpack_cols,
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)
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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
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_is_cuda = is_cuda()
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if _is_cuda:
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from sgl_kernel import fused_marlin_moe, gptq_gemm, gptq_marlin_repack, gptq_shuffle
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logger = logging.getLogger(__name__)
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ScalarType, scalar_types = get_scalar_types()
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def check_marlin_format(hf_quant_cfg: Dict[str, Any]) -> bool:
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# compat: gptqmodel and autogptq (eol) main use checkpoint_format: str
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# compat: autogptq <=0.7.1 is_marlin_format: bool
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return hf_quant_cfg.get("checkpoint_format") == "marlin" or hf_quant_cfg.get(
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"is_marlin_format", False
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)
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def gptq_marlin_moe_repack(
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b_q_weight: torch.Tensor,
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perm: torch.Tensor,
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size_k: int,
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size_n: int,
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num_bits: int,
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) -> torch.Tensor:
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num_experts = b_q_weight.shape[0]
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assert size_k % 16 == 0
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output = torch.empty(
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(num_experts, size_k // 16, size_n * (num_bits // 2)),
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device=b_q_weight.device,
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dtype=b_q_weight.dtype,
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)
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for e in range(num_experts):
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output[e] = gptq_marlin_repack(b_q_weight[e], perm[e], size_k, size_n, num_bits)
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return output
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@dataclass
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class MarlinLinearLayerConfig:
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full_weight_shape: tuple[int, int] # [in, out]
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partition_weight_shape: tuple[int, int]
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weight_type: ScalarType
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act_type: torch.dtype
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group_size: int
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zero_points: bool
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has_g_idx: bool
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class GPTQConfig(QuantizationConfig):
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"""Config class for GPTQ.
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Reference: https://arxiv.org/abs/2210.17323
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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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desc_act: bool,
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lm_head_quantized: bool,
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dynamic: Dict[str, Dict[str, Union[int, bool]]],
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) -> None:
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# GPTQModel use `dynamic` config property to allow per module
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# quantization config so each module can be individually optimized.
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# Format is Dict[str, Dict] where key is a regex string that can
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# perform both positive ("+:" prefixed) or negative ("-:" prefixed)
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# matching of a module.
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# Default to positive match, override base quant config mode, if no
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# prefix is used. Value is in dict format of field key and override
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# value.
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# Negative matching will skip quantization init for this module
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# entirely:
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# non-quantized inference. More details and quantization examples can be
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# found at: https://github.com/ModelCloud/GPTQModel
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# Example:
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# # last 1/2 of the layers 10-21 has 8bit vs 4bit for 0-9
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# # last 1/4 of the layers 16-21 has 8bit and group_size 64
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# dynamic = {
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# #`.*\.` matches the layers_node prefix
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# # positive match layer 10-15
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# r"+:.*\.(?:1[0-5])\..*": {"bits": 8,},
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# # positive match layer 16-21
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# r"+:.*\.(?:1[6-9]|20|21)\..*": {"bits": 8, "group_size": 64,},
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# r"-:.*\.moe\..*": {}, # negative match (skip) all `moe` layers
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# }
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super().__init__()
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self.dynamic = dynamic
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self.weight_bits = weight_bits
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self.group_size = group_size
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self.desc_act = desc_act
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self.lm_head_quantized = lm_head_quantized
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self.pack_factor = Fraction(32, self.weight_bits)
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if self.weight_bits not in [2, 3, 4, 8]:
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raise ValueError(
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"Currently, only 2/3/4/8-bit weight quantization is "
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f"supported for GPTQ, but got {self.weight_bits} bits."
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)
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def __repr__(self) -> str:
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return (
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f"GPTQConfig(weight_bits={self.weight_bits}, "
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f"group_size={self.group_size}, "
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f"desc_act={self.desc_act}),"
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f"lm_head_quantized={self.lm_head_quantized}), "
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f"dynamic={self.dynamic}"
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)
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def get_scaled_act_names(self) -> List[str]:
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"""Returns the activation function names that should be post-scaled.
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For now, this is only used by AWQ.
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"""
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raise NotImplementedError
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@classmethod
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def get_name(cls) -> str:
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return "gptq"
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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]
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@classmethod
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# Need to figure it out
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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 ["quantize_config.json"]
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@classmethod
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def from_config(cls, config: Dict[str, Any]) -> GPTQConfig:
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dynamic = cls.get_from_keys_or(config, ["dynamic"], default={})
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dynamic = {} if dynamic is None else dynamic
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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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desc_act = cls.get_from_keys(config, ["desc_act"])
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lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"], default=False)
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return cls(weight_bits, group_size, desc_act, lm_head_quantized, dynamic)
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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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# Delay the import to avoid circular dependency
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from sglang.srt.layers.linear import LinearBase
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
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if isinstance(layer, FusedMoE):
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raise TypeError("GPTQ Method does not support MoE, please use gptq_marlin")
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else:
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return get_linear_quant_method(
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self, layer, prefix=prefix, linear_method_cls=GPTQLinearMethod
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)
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class GPTQMarlinConfig(QuantizationConfig):
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"""Config class for GPTQ Marlin"""
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# (num_bits, is_sym) -> quant_type
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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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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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desc_act: bool,
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is_sym: bool,
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lm_head_quantized: bool,
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dynamic: Dict[str, Dict[str, Union[int, bool]]],
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full_config: Dict[str, Any],
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) -> None:
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super().__init__()
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if desc_act and group_size == -1:
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# In this case, act_order == True is the same as act_order == False
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# (since we have only one group per output channel)
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desc_act = False
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# GPTQModel use `dynamic` config property to allow per module
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# quantization config so each module can be individually optimized.
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# Format is Dict[str, Dict] where key is a regex string that can
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# perform both positive ("+:" prefixed) or negative ("-:" prefixed)
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# matching of a module.
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# Default to positive match, override base quant config mode, if no
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# prefix is used. Value is in dict format of field key and override
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# value.
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# Negative matching will skip quantization init for this module
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# entirely:
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# non-quantized inference. More details and quantization examples can be
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# found at: https://github.com/ModelCloud/GPTQModel
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# Example:
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# # last 1/2 of the layers 10-21 has 8bit vs 4bit for 0-9
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# # last 1/4 of the layers 16-21 has 8bit and group_size 64
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# dynamic = {
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# #`.*\.` matches the layers_node prefix
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# # positive match layer 10-15
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# r"+:.*\.(?:1[0-5])\..*": {"bits": 8,},
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# # positive match layer 16-21
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# r"+:.*\.(?:1[6-9]|20|21)\..*": {"bits": 8, "group_size": 64,},
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# r"-:.*\.moe\..*": {}, # negative match (skip) all `moe` layers
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# }
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self.dynamic = dynamic
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self.weight_bits = weight_bits
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self.is_sym = is_sym
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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.desc_act = desc_act
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self.lm_head_quantized = lm_head_quantized
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self.full_config = full_config
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if (weight_bits, is_sym) not in self.TYPE_MAP:
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raise ValueError(
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"Unsupported quantization config: " f"bits={weight_bits}, sym={is_sym}"
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)
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# (num_bits, is_sym) -> quant_type
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self.quant_type = self.TYPE_MAP[(weight_bits, is_sym)]
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def __repr__(self) -> str:
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return (
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f"GPTQMarlinConfig(quant_type={self.quant_type}, "
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f"group_size={self.group_size}, "
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f"desc_act={self.desc_act}, "
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f"lm_head_quantized={self.lm_head_quantized}), "
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f"dynamic={self.dynamic}"
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)
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def get_scaled_act_names(self) -> List[str]:
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"""Returns the activation function names that should be post-scaled.
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For now, this is only used by AWQ.
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"""
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raise NotImplementedError
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@classmethod
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def get_name(cls) -> str:
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return "gptq_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]) -> GPTQMarlinConfig:
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dynamic = cls.get_from_keys_or(config, ["dynamic"], default={})
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dynamic = {} if dynamic is None else dynamic
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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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desc_act = cls.get_from_keys(config, ["desc_act"])
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is_sym = cls.get_from_keys(config, ["sym"])
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lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"], default=False)
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return cls(
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weight_bits,
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group_size,
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desc_act,
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is_sym,
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lm_head_quantized,
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dynamic,
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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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is_marlin_format = check_marlin_format(hf_quant_cfg)
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can_convert = cls.is_gptq_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 == "gptq_marlin"
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)
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if not is_marlin_format and 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 not is_marlin_format and can_convert and user_quant == "gptq":
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logger.info(
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"Detected that the model can run with gptq_marlin"
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", however you specified quantization=gptq explicitly,"
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" so forcing gptq. Use quantization=gptq_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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# Delay the import to avoid circular dependency
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
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if isinstance(layer, FusedMoE):
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return GPTQMarlinMoEMethod(self)
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return get_linear_quant_method(self, layer, prefix, GPTQMarlinLinearMethod)
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@classmethod
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def is_gptq_marlin_compatible(cls, quant_config: Dict[str, Any]):
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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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sym = quant_config.get("sym")
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desc_act = quant_config.get("desc_act")
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if not _is_cuda:
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return False
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if quant_method != "gptq":
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return False
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# Marlin conversion is only valid if required properties are found
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if num_bits is None or group_size is None or sym is None or desc_act is None:
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return False
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if (num_bits, sym) 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, sym)], group_size=group_size
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)
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|
|
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class GPTQLinearMethod(LinearMethodBase):
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"""Linear method for GPTQ.
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|
Args:
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quant_config: The GPTQ quantization config.
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"""
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def __init__(self, quant_config: GPTQConfig):
|
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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,
|
|
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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del output_size # Unused.
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weight_loader = extra_weight_attrs.get("weight_loader")
|
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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.numerator != 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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|
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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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self.use_shuffle = True
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scale_and_zero_size = input_size // group_size
|
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scale_and_zero_input_dim = None
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if (
|
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input_size != input_size_per_partition
|
|
and self.quant_config.group_size != -1
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):
|
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if self.quant_config.desc_act:
|
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self.use_shuffle = False
|
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else:
|
|
# we need to partition qzeros and scales for exllama kernel
|
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scale_and_zero_size = input_size_per_partition // group_size
|
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scale_and_zero_input_dim = 0
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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 // self.quant_config.pack_factor,
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output_size_per_partition,
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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=0,
|
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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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|
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g_idx = RowvLLMParameter(
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data=torch.tensor(
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[
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i // self.quant_config.group_size
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for i in range(input_size_per_partition)
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],
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dtype=torch.int32,
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),
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input_dim=0,
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weight_loader=weight_loader,
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)
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qzeros_args = {
|
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"data": torch.empty(
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scale_and_zero_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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"weight_loader": weight_loader,
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}
|
|
weight_scale_args = {
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"data": torch.empty(
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scale_and_zero_size,
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output_size_per_partition,
|
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dtype=params_dtype,
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),
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"weight_loader": weight_loader,
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}
|
|
if scale_and_zero_input_dim is None:
|
|
scales = ChannelQuantScaleParameter(output_dim=1, **weight_scale_args)
|
|
qzeros = PackedColumnParameter(
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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,
|
|
**qzeros_args,
|
|
)
|
|
|
|
else:
|
|
scales = GroupQuantScaleParameter(
|
|
output_dim=1, input_dim=0, **weight_scale_args
|
|
)
|
|
qzeros = PackedvLLMParameter(
|
|
input_dim=0,
|
|
output_dim=1,
|
|
packed_dim=1,
|
|
packed_factor=self.quant_config.pack_factor,
|
|
**qzeros_args,
|
|
)
|
|
|
|
layer.register_parameter("qweight", qweight)
|
|
layer.register_parameter("g_idx", g_idx)
|
|
layer.register_parameter("qzeros", qzeros)
|
|
layer.register_parameter("scales", scales)
|
|
|
|
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
|
|
# for torch.compile
|
|
layer.qzeros = torch.nn.Parameter(layer.qzeros.data, requires_grad=False)
|
|
layer.qweight = torch.nn.Parameter(layer.qweight.data, requires_grad=False)
|
|
layer.g_idx = torch.nn.Parameter(layer.g_idx.data, requires_grad=False)
|
|
layer.scales = torch.nn.Parameter(layer.scales.data, requires_grad=False)
|
|
|
|
# exllama needs to shuffle the weight after the weight is loaded
|
|
# here we do the shuffle on first forward pass
|
|
if self.use_shuffle:
|
|
if self.quant_config.desc_act:
|
|
layer.g_idx.data = torch.argsort(layer.g_idx).to(torch.int)
|
|
else:
|
|
layer.g_idx.data = torch.empty(
|
|
(0,), dtype=torch.int, device=layer.g_idx.device
|
|
)
|
|
gptq_shuffle(layer.qweight, layer.g_idx, self.quant_config.weight_bits)
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
bias: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
out_shape = x.shape[:-1] + (layer.qweight.shape[-1],)
|
|
reshaped_x = x.reshape(-1, x.shape[-1])
|
|
|
|
output = gptq_gemm(
|
|
reshaped_x,
|
|
layer.qweight,
|
|
layer.qzeros,
|
|
layer.scales,
|
|
layer.g_idx,
|
|
self.use_shuffle,
|
|
self.quant_config.weight_bits,
|
|
)
|
|
if bias is not None:
|
|
output.add_(bias)
|
|
return output.reshape(out_shape)
|
|
|
|
|
|
class GPTQMarlinLinearMethod(LinearMethodBase):
|
|
"""Linear method for GPTQ Marlin.
|
|
|
|
Args:
|
|
quant_config: The GPTQ Marlin quantization config.
|
|
"""
|
|
|
|
_kernel_backends_being_used: set[str] = set()
|
|
|
|
def __init__(self, quant_config: GPTQMarlinConfig) -> None:
|
|
self.quant_config = quant_config
|
|
|
|
# Verify supported on platform.
|
|
verify_marlin_supported(
|
|
quant_type=self.quant_config.quant_type,
|
|
group_size=self.quant_config.group_size,
|
|
)
|
|
|
|
def create_weights(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
input_size_per_partition: int,
|
|
output_partition_sizes: list[int],
|
|
input_size: int,
|
|
output_size: int,
|
|
params_dtype: torch.dtype,
|
|
**extra_weight_attrs,
|
|
) -> None:
|
|
output_size_per_partition = sum(output_partition_sizes)
|
|
is_row_parallel = input_size != input_size_per_partition
|
|
weight_loader = extra_weight_attrs.get("weight_loader")
|
|
|
|
self.kernel_config = MarlinLinearLayerConfig(
|
|
full_weight_shape=(input_size, output_size),
|
|
partition_weight_shape=(
|
|
input_size_per_partition,
|
|
output_size_per_partition,
|
|
),
|
|
weight_type=self.quant_config.quant_type,
|
|
act_type=params_dtype,
|
|
group_size=self.quant_config.group_size,
|
|
zero_points=False,
|
|
has_g_idx=self.quant_config.desc_act,
|
|
)
|
|
# Normalize group_size
|
|
if self.quant_config.group_size != -1:
|
|
group_size = self.quant_config.group_size
|
|
else:
|
|
group_size = input_size
|
|
|
|
# Determine sharding
|
|
if marlin_repeat_scales_on_all_ranks(
|
|
self.quant_config.desc_act, self.quant_config.group_size, is_row_parallel
|
|
):
|
|
# By setting scale_dim == None, weight_loader will
|
|
# repeat the scales on each GPU in TP>1 case.
|
|
scales_and_zp_input_dim = None
|
|
scales_and_zp_size = input_size // group_size
|
|
else:
|
|
# By setting scale_dim == 0, weight_loader will
|
|
# shard the scales in TP>1 case.
|
|
scales_and_zp_input_dim = 0
|
|
scales_and_zp_size = input_size_per_partition // group_size
|
|
|
|
# Quantized weights
|
|
qweight = PackedvLLMParameter(
|
|
data=torch.empty(
|
|
input_size_per_partition // self.quant_config.pack_factor,
|
|
output_size_per_partition,
|
|
dtype=torch.int32,
|
|
),
|
|
input_dim=0,
|
|
output_dim=1,
|
|
packed_dim=0,
|
|
packed_factor=self.quant_config.pack_factor,
|
|
weight_loader=weight_loader,
|
|
)
|
|
|
|
# Activation order
|
|
g_idx = RowvLLMParameter(
|
|
data=torch.empty(
|
|
input_size_per_partition,
|
|
dtype=torch.int32,
|
|
),
|
|
input_dim=0,
|
|
weight_loader=weight_loader,
|
|
)
|
|
|
|
qzeros_args = {
|
|
"data": torch.empty(
|
|
scales_and_zp_size,
|
|
output_size_per_partition // self.quant_config.pack_factor,
|
|
dtype=torch.int32,
|
|
),
|
|
"weight_loader": weight_loader,
|
|
}
|
|
weight_scale_args = {
|
|
"data": torch.empty(
|
|
scales_and_zp_size,
|
|
output_size_per_partition,
|
|
dtype=params_dtype,
|
|
),
|
|
"weight_loader": weight_loader,
|
|
}
|
|
|
|
if scales_and_zp_input_dim is None:
|
|
scales = ChannelQuantScaleParameter(output_dim=1, **weight_scale_args)
|
|
qzeros = PackedColumnParameter(
|
|
output_dim=1,
|
|
packed_dim=1,
|
|
packed_factor=self.quant_config.pack_factor,
|
|
**qzeros_args,
|
|
)
|
|
|
|
else:
|
|
scales = GroupQuantScaleParameter(
|
|
output_dim=1, input_dim=0, **weight_scale_args
|
|
)
|
|
qzeros = PackedvLLMParameter(
|
|
input_dim=0,
|
|
output_dim=1,
|
|
packed_dim=1,
|
|
packed_factor=self.quant_config.pack_factor,
|
|
**qzeros_args,
|
|
)
|
|
|
|
layer.register_parameter("qweight", qweight)
|
|
layer.register_parameter("g_idx", g_idx)
|
|
layer.register_parameter("scales", scales)
|
|
layer.register_parameter("qzeros", qzeros)
|
|
|
|
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
|
|
device = getattr(layer, "qweight").device
|
|
c = self.kernel_config
|
|
|
|
check_marlin_supports_shape(
|
|
c.partition_weight_shape[1], # out_features
|
|
c.partition_weight_shape[0], # in_features
|
|
c.full_weight_shape[0], # in_features
|
|
c.group_size,
|
|
)
|
|
|
|
row_parallel = c.partition_weight_shape[0] != c.full_weight_shape[0]
|
|
self.is_k_full = marlin_is_k_full(c.has_g_idx, row_parallel)
|
|
|
|
# Allocate marlin workspace.
|
|
self.workspace = marlin_make_workspace(device)
|
|
|
|
# Default names since marlin requires empty parameters for these,
|
|
# TODO: remove this requirement from marlin (allow optional tensors)
|
|
self.w_q_name = "qweight"
|
|
self.w_s_name = "scales"
|
|
self.w_zp_name = "qzeros"
|
|
self.w_gidx_name = "g_idx"
|
|
|
|
def _transform_param(
|
|
layer: torch.nn.Module, name: Optional[str], fn: Callable
|
|
) -> None:
|
|
if name is not None and getattr(layer, name, None) is not None:
|
|
|
|
old_param = getattr(layer, name)
|
|
new_param = fn(old_param)
|
|
# replace the parameter with torch.nn.Parameter for TorchDynamo
|
|
# compatibility
|
|
replace_parameter(
|
|
layer, name, torch.nn.Parameter(new_param.data, requires_grad=False)
|
|
)
|
|
|
|
def transform_w_q(x):
|
|
assert isinstance(x, BasevLLMParameter)
|
|
permute_param_layout_(x, input_dim=0, output_dim=1, packed_dim=0)
|
|
x.data = gptq_marlin_repack(
|
|
x.data.contiguous(),
|
|
perm=layer.g_idx_sort_indices,
|
|
size_k=c.partition_weight_shape[0],
|
|
size_n=c.partition_weight_shape[1],
|
|
num_bits=c.weight_type.size_bits,
|
|
)
|
|
return x
|
|
|
|
def transform_w_s(x):
|
|
assert isinstance(x, BasevLLMParameter)
|
|
permute_param_layout_(x, input_dim=0, output_dim=1)
|
|
x.data = marlin_permute_scales(
|
|
x.data.contiguous(),
|
|
size_k=c.partition_weight_shape[0],
|
|
size_n=c.partition_weight_shape[1],
|
|
group_size=c.group_size,
|
|
)
|
|
return x
|
|
|
|
if c.has_g_idx:
|
|
g_idx, g_idx_sort_indices = marlin_sort_g_idx(
|
|
getattr(layer, self.w_gidx_name)
|
|
)
|
|
_transform_param(layer, self.w_gidx_name, lambda _: g_idx)
|
|
layer.g_idx_sort_indices = g_idx_sort_indices
|
|
else:
|
|
setattr(layer, self.w_gidx_name, marlin_make_empty_g_idx(device))
|
|
layer.g_idx_sort_indices = marlin_make_empty_g_idx(device)
|
|
|
|
if c.zero_points:
|
|
grouped_k = (
|
|
c.partition_weight_shape[0] // c.group_size if c.group_size != -1 else 1
|
|
)
|
|
_transform_param(
|
|
layer,
|
|
self.w_zp_name,
|
|
lambda x: marlin_zero_points(
|
|
unpack_cols(
|
|
x.t(),
|
|
c.weight_type.size_bits,
|
|
grouped_k,
|
|
c.partition_weight_shape[1],
|
|
),
|
|
size_k=grouped_k,
|
|
size_n=c.partition_weight_shape[1],
|
|
num_bits=c.weight_type.size_bits,
|
|
),
|
|
)
|
|
else:
|
|
setattr(layer, self.w_zp_name, marlin_make_empty_g_idx(device))
|
|
_transform_param(layer, self.w_q_name, transform_w_q)
|
|
_transform_param(layer, self.w_s_name, transform_w_s)
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
bias: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
c = self.kernel_config
|
|
|
|
def _get_weight_params(
|
|
layer: torch.nn.Module,
|
|
) -> tuple[
|
|
torch.Tensor, # w_q
|
|
torch.Tensor, # w_s
|
|
Optional[torch.Tensor], # w_zp,
|
|
Optional[torch.Tensor], # w_gidx
|
|
]:
|
|
return (
|
|
getattr(layer, self.w_q_name),
|
|
getattr(layer, self.w_s_name),
|
|
getattr(layer, self.w_zp_name or "", None),
|
|
getattr(layer, self.w_gidx_name or "", None),
|
|
)
|
|
|
|
w_q, w_s, w_zp, w_gidx = _get_weight_params(layer)
|
|
|
|
# `process_weights_after_loading` will ensure w_zp and w_gidx are not
|
|
# None for marlin
|
|
return apply_gptq_marlin_linear(
|
|
input=x,
|
|
weight=w_q,
|
|
weight_scale=w_s,
|
|
weight_zp=w_zp, # type: ignore
|
|
g_idx=w_gidx, # type: ignore
|
|
g_idx_sort_indices=layer.g_idx_sort_indices,
|
|
workspace=self.workspace,
|
|
wtype=c.weight_type,
|
|
input_size_per_partition=c.partition_weight_shape[0],
|
|
output_size_per_partition=c.partition_weight_shape[1],
|
|
is_k_full=self.is_k_full,
|
|
bias=bias,
|
|
)
|
|
|
|
|
|
class GPTQMarlinMoEMethod(FusedMoEMethodBase):
|
|
"""MoE Marlin method with quantization."""
|
|
|
|
def __init__(self, quant_config: GPTQMarlinConfig) -> None:
|
|
self.quant_config = quant_config
|
|
|
|
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.linear import set_weight_attrs
|
|
from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
|
|
|
|
self.is_k_full = (not self.quant_config.desc_act) or layer.moe_tp_size == 1
|
|
|
|
if self.quant_config.group_size != -1:
|
|
scales_size13 = hidden_size // self.quant_config.group_size
|
|
if self.quant_config.desc_act:
|
|
w2_scales_size = intermediate_size_per_partition
|
|
else:
|
|
w2_scales_size = intermediate_size_per_partition * layer.moe_tp_size
|
|
scales_size2 = w2_scales_size // self.quant_config.group_size
|
|
strategy = FusedMoeWeightScaleSupported.GROUP.value
|
|
else:
|
|
scales_size13 = 1
|
|
scales_size2 = 1
|
|
strategy = FusedMoeWeightScaleSupported.CHANNEL.value
|
|
|
|
extra_weight_attrs.update({"quant_method": strategy, "is_transposed": True})
|
|
# Fused gate_up_proj (column parallel)
|
|
w13_qweight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
hidden_size // self.quant_config.pack_factor,
|
|
2 * intermediate_size_per_partition,
|
|
dtype=torch.int32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_qweight", w13_qweight)
|
|
set_weight_attrs(w13_qweight, extra_weight_attrs)
|
|
# down_proj (row parallel)
|
|
w2_qweight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
intermediate_size_per_partition // self.quant_config.pack_factor,
|
|
hidden_size,
|
|
dtype=torch.int32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_qweight", w2_qweight)
|
|
set_weight_attrs(w2_qweight, extra_weight_attrs)
|
|
# up_proj scales
|
|
w13_scales = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
scales_size13,
|
|
2 * intermediate_size_per_partition,
|
|
dtype=torch.half,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_scales", w13_scales)
|
|
set_weight_attrs(w13_scales, extra_weight_attrs)
|
|
# down_proj scales
|
|
w2_scales = torch.nn.Parameter(
|
|
torch.empty(num_experts, scales_size2, hidden_size, dtype=torch.half),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_scales", w2_scales)
|
|
set_weight_attrs(w2_scales, extra_weight_attrs)
|
|
# dont shard the w2 scales when running act order
|
|
set_weight_attrs(w2_scales, {"load_full_w2": self.quant_config.desc_act})
|
|
# up_proj scales
|
|
w13_qzeros = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
scales_size13,
|
|
2 * intermediate_size_per_partition // self.quant_config.pack_factor,
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_qzeros", w13_qzeros)
|
|
set_weight_attrs(w13_qzeros, extra_weight_attrs)
|
|
# down_proj scales
|
|
w2_qzeros = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
scales_size2,
|
|
hidden_size // self.quant_config.pack_factor,
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_qzeros", w2_qzeros)
|
|
set_weight_attrs(w2_qzeros, extra_weight_attrs)
|
|
# dont shard the w2 scales when running act order
|
|
set_weight_attrs(w2_qzeros, {"load_full_w2": self.quant_config.desc_act})
|
|
w13_g_idx = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
hidden_size,
|
|
dtype=torch.int32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_g_idx", w13_g_idx)
|
|
set_weight_attrs(w13_g_idx, extra_weight_attrs)
|
|
w2_g_idx = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
intermediate_size_per_partition,
|
|
dtype=torch.int32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_g_idx", w2_g_idx)
|
|
set_weight_attrs(w2_g_idx, extra_weight_attrs)
|
|
w13_g_idx_sort_indices = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
hidden_size,
|
|
dtype=torch.int32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_g_idx_sort_indices", w13_g_idx_sort_indices)
|
|
set_weight_attrs(w13_g_idx_sort_indices, extra_weight_attrs)
|
|
w2_g_idx_sort_indices = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
intermediate_size_per_partition,
|
|
dtype=torch.int32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w2_g_idx_sort_indices", w2_g_idx_sort_indices)
|
|
set_weight_attrs(w2_g_idx_sort_indices, extra_weight_attrs)
|
|
|
|
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
|
|
|
|
# Process act_order
|
|
if self.quant_config.desc_act:
|
|
# Get sorting based on g_idx
|
|
num_experts = layer.w13_g_idx.shape[0]
|
|
w13_g_idx_sort_indices = torch.empty_like(layer.w13_g_idx)
|
|
w2_g_idx_sort_indices = torch.empty_like(layer.w2_g_idx)
|
|
w13_sorted_g_idx = torch.empty_like(layer.w13_g_idx)
|
|
w2_sorted_g_idx = torch.empty_like(layer.w2_g_idx)
|
|
for e in range(num_experts):
|
|
w13_g_idx_sort_indices[e] = torch.argsort(layer.w13_g_idx[e]).to(
|
|
torch.int32
|
|
)
|
|
w2_g_idx_sort_indices[e] = torch.argsort(layer.w2_g_idx[e]).to(
|
|
torch.int32
|
|
)
|
|
w13_sorted_g_idx[e] = layer.w13_g_idx[e][w13_g_idx_sort_indices[e]]
|
|
w2_sorted_g_idx[e] = layer.w2_g_idx[e][w2_g_idx_sort_indices[e]]
|
|
replace_parameter(layer, "w13_g_idx", w13_sorted_g_idx)
|
|
replace_parameter(layer, "w2_g_idx", w2_sorted_g_idx)
|
|
replace_parameter(layer, "w13_g_idx_sort_indices", w13_g_idx_sort_indices)
|
|
replace_parameter(layer, "w2_g_idx_sort_indices", w2_g_idx_sort_indices)
|
|
else:
|
|
# Reset g_idx related tensors
|
|
num_experts = layer.w13_g_idx.shape[0]
|
|
device = layer.w13_g_idx.device
|
|
layer.w13_g_idx = torch.nn.Parameter(
|
|
torch.empty((num_experts, 0), dtype=torch.int32, device=device),
|
|
requires_grad=False,
|
|
)
|
|
layer.w2_g_idx = torch.nn.Parameter(
|
|
torch.empty((num_experts, 0), dtype=torch.int32, device=device),
|
|
requires_grad=False,
|
|
)
|
|
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,
|
|
)
|
|
# Repack weights
|
|
marlin_w13_qweight = gptq_marlin_moe_repack(
|
|
layer.w13_qweight,
|
|
layer.w13_g_idx_sort_indices,
|
|
layer.w13_qweight.shape[1] * self.quant_config.pack_factor,
|
|
layer.w13_qweight.shape[2],
|
|
self.quant_config.weight_bits,
|
|
)
|
|
replace_parameter(layer, "w13_qweight", marlin_w13_qweight)
|
|
marlin_w2_qweight = gptq_marlin_moe_repack(
|
|
layer.w2_qweight,
|
|
layer.w2_g_idx_sort_indices,
|
|
layer.w2_qweight.shape[1] * self.quant_config.pack_factor,
|
|
layer.w2_qweight.shape[2],
|
|
self.quant_config.weight_bits,
|
|
)
|
|
replace_parameter(layer, "w2_qweight", marlin_w2_qweight)
|
|
# Repack scales
|
|
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.w2_scales.shape[1]
|
|
* (
|
|
self.quant_config.group_size
|
|
if self.quant_config.group_size != -1
|
|
else self.quant_config.pack_factor
|
|
),
|
|
size_n=layer.w2_scales.shape[2],
|
|
group_size=self.quant_config.group_size,
|
|
)
|
|
replace_parameter(layer, "w2_scales", marlin_w2_scales)
|
|
|
|
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
|
|
|
|
x = dispatch_output.hidden_states
|
|
topk_output = dispatch_output.topk_output
|
|
|
|
# Delay the import to avoid circular dependency
|
|
|
|
assert (
|
|
self.moe_runner_config.activation == "silu"
|
|
), "Only SiLU activation is supported."
|
|
|
|
# The input must currently be float16
|
|
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,
|
|
g_idx1=layer.w13_g_idx,
|
|
g_idx2=layer.w2_g_idx,
|
|
sort_indices1=layer.w13_g_idx_sort_indices,
|
|
sort_indices2=layer.w2_g_idx_sort_indices,
|
|
num_bits=self.quant_config.weight_bits,
|
|
is_k_full=self.is_k_full,
|
|
).to(orig_dtype)
|
|
return StandardCombineInput(hidden_states=output)
|