736 lines
26 KiB
Python
736 lines
26 KiB
Python
import logging
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from fractions import Fraction
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from typing import Any, Callable, Dict, List, Optional, Union
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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.quantization.base_config import (
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QuantizationConfig,
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QuantizeMethodBase,
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)
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from sglang.srt.layers.quantization.utils import replace_parameter
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from sglang.srt.utils import is_cuda
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_is_cuda = is_cuda()
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try:
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from vllm import _custom_ops as ops
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from vllm.model_executor.layers.quantization.gptq import GPTQLinearMethod
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from vllm.model_executor.layers.quantization.gptq_marlin import (
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FusedMoE,
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FusedMoEMethodBase,
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FusedMoeWeightScaleSupported,
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GPTQMarlinLinearMethod,
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marlin_moe_permute_scales,
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)
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from vllm.model_executor.layers.quantization.marlin import MarlinLinearMethod
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from vllm.model_executor.layers.quantization.utils.marlin_utils import (
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check_marlin_supported,
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)
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from vllm.scalar_type import scalar_types
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VLLM_AVAILABLE = True
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except ImportError:
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VLLM_AVAILABLE = False
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GPTQLinearMethod = MarlinLinearMethod = Any
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FusedMoEMethodBase = QuantizeMethodBase
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class scalar_types:
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uint4b8 = "uint4b8"
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uint8b128 = "uint8b128"
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logger = logging.getLogger(__name__)
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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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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[GPTQLinearMethod]:
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# Delay the import to avoid circular dependency
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from sglang.srt.layers.quantization import get_linear_quant_method
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return get_linear_quant_method(self, layer, prefix, GPTQLinearMethod)
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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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from sglang.srt.layers.quantization import get_linear_quant_method
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if isinstance(layer, FusedMoE):
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return GPTQMarlinMoEMethod(self)
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# TODO: re-enable after SGLang syncs with vllm >= 0.7.3
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# if layer.num_experts > 32:
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# # For MoEs with many experts the moe_wna16 kernel is faster
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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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# else:
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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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assert (
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VLLM_AVAILABLE
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), "vllm is not installed, to use gptq_marlin, please install vllm"
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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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class MarlinConfig(QuantizationConfig):
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"""Config class for Marlin.
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Reference: https://github.com/IST-DASLab/marlin/tree/master
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"""
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def __init__(
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self,
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group_size: int,
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lm_head_quantized: bool,
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) -> None:
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# Group size for the quantization.
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self.group_size = group_size
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self.lm_head_quantized = lm_head_quantized
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if self.group_size != 128 and self.group_size != -1:
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raise ValueError(
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"Currently, only group size 128 and -1 (channelwise) "
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"is supported for Marlin, but got group_size of "
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f"{self.group_size}"
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)
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# 4 Bits packed into 32 bit datatype.
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self.pack_factor = 32 // 4
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# Tile size used by marlin kernels.
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self.tile_size = 16
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# Min out_features dim
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self.min_n_threads = 64
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# Min in_features dim
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self.min_k_threads = 128
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# Max parallel problems to solve at once (improves large
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# batch performance)
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self.max_parallel = 16
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# Permutation length used by the marlin kernels.
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self.perm_len = 1024
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|
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def __repr__(self) -> str:
|
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return (
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f"MarlinConfig(group_size={self.group_size}, "
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f"lm_head_quantized={self.lm_head_quantized})"
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)
|
|
|
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@classmethod
|
|
def get_name(cls) -> str:
|
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return "marlin"
|
|
|
|
@classmethod
|
|
def get_supported_act_dtypes(cls) -> List[torch.dtype]:
|
|
return [torch.half]
|
|
|
|
@classmethod
|
|
# Need to figure it out
|
|
def get_min_capability(cls) -> int:
|
|
return 80
|
|
|
|
@classmethod
|
|
def get_config_filenames(cls) -> List[str]:
|
|
return ["quantize_config.json"]
|
|
|
|
@classmethod
|
|
def from_config(cls, config: Dict[str, Any]) -> "MarlinConfig":
|
|
group_size = cls.get_from_keys(config, ["group_size"])
|
|
lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"], default=False)
|
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return cls(group_size, lm_head_quantized)
|
|
|
|
@classmethod
|
|
def override_quantization_method(cls, hf_quant_cfg, user_quant) -> Optional[str]:
|
|
is_marlin_format = check_marlin_format(hf_quant_cfg)
|
|
|
|
is_valid_user_quant = (
|
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user_quant is None or user_quant == "gptq" or user_quant == "marlin"
|
|
)
|
|
|
|
if is_marlin_format and is_valid_user_quant:
|
|
msg = "The model is serialized in {} format. Using {} kernel.".format(
|
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cls.get_name(), cls.get_name()
|
|
)
|
|
logger.info(msg)
|
|
return cls.get_name()
|
|
|
|
return None
|
|
|
|
def get_quant_method(
|
|
self, layer: torch.nn.Module, prefix: str
|
|
) -> Optional[MarlinLinearMethod]:
|
|
# Delay the import to avoid circular dependency
|
|
from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
|
|
|
|
if isinstance(layer, LinearBase) or (
|
|
isinstance(layer, ParallelLMHead) and self.lm_head_quantized
|
|
):
|
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return MarlinLinearMethod(self)
|
|
return None
|
|
|
|
|
|
class GPTQMarlinMoEMethod(FusedMoEMethodBase):
|
|
"""MoE Marlin method with quantization."""
|
|
|
|
def __init__(self, quant_config: GPTQMarlinConfig) -> None:
|
|
self.quant_config = quant_config
|
|
|
|
def create_weights(
|
|
self,
|
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layer: torch.nn.Module,
|
|
num_experts: int,
|
|
hidden_size: int,
|
|
intermediate_size_per_partition: int,
|
|
params_dtype: torch.dtype,
|
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**extra_weight_attrs,
|
|
):
|
|
intermediate_size = extra_weight_attrs.pop("intermediate_size")
|
|
|
|
self.is_k_full = (not self.quant_config.desc_act) or (
|
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intermediate_size_per_partition == intermediate_size
|
|
)
|
|
|
|
if self.quant_config.group_size != -1:
|
|
scales_size13 = hidden_size // self.quant_config.group_size
|
|
w2_scales_size = (
|
|
intermediate_size
|
|
if self.quant_config.desc_act
|
|
else intermediate_size_per_partition
|
|
)
|
|
scales_size2 = w2_scales_size // self.quant_config.group_size
|
|
strategy = FusedMoeWeightScaleSupported.GROUP.value
|
|
else:
|
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scales_size13 = 1
|
|
scales_size2 = 1
|
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strategy = FusedMoeWeightScaleSupported.CHANNEL.value
|
|
|
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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 = ops.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.quant_type.size_bits,
|
|
)
|
|
replace_parameter(layer, "w13_qweight", marlin_w13_qweight)
|
|
marlin_w2_qweight = ops.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.quant_type.size_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 apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
top_k: int,
|
|
renormalize: bool,
|
|
use_grouped_topk: bool = False,
|
|
topk_group: Optional[int] = None,
|
|
num_expert_group: Optional[int] = None,
|
|
global_num_experts: int = -1,
|
|
expert_map: Optional[torch.Tensor] = None,
|
|
custom_routing_function: Optional[Callable] = None,
|
|
scoring_func: str = "softmax",
|
|
e_score_correction_bias: Optional[torch.Tensor] = None,
|
|
activation: str = "silu",
|
|
) -> torch.Tensor:
|
|
assert activation == "silu", "Only SiLU activation is supported."
|
|
|
|
# The input must currently be float16
|
|
orig_dtype = x.dtype
|
|
x = x.half()
|
|
|
|
topk_weights, topk_ids = FusedMoE.select_experts(
|
|
hidden_states=x,
|
|
router_logits=router_logits,
|
|
use_grouped_topk=use_grouped_topk,
|
|
top_k=top_k,
|
|
renormalize=renormalize,
|
|
topk_group=topk_group,
|
|
num_expert_group=num_expert_group,
|
|
custom_routing_function=custom_routing_function,
|
|
scoring_func=scoring_func,
|
|
e_score_correction_bias=e_score_correction_bias,
|
|
)
|
|
|
|
return torch.ops.vllm.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,
|
|
quant_type_id=self.quant_config.quant_type.id,
|
|
is_k_full=self.is_k_full,
|
|
).to(orig_dtype)
|