678 lines
27 KiB
Python
678 lines
27 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import Any, Callable, Dict, List, Optional
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import torch
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from torch.nn import Parameter
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import vllm.model_executor.layers.fused_moe # noqa
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from vllm import _custom_ops as ops
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from vllm.logger import init_logger
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from vllm.model_executor.layers.fused_moe.layer import (
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FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported)
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from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
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UnquantizedLinearMethod,
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set_weight_attrs)
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from vllm.model_executor.layers.quantization.awq import (AWQConfig,
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is_layer_skipped_awq)
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig, QuantizeMethodBase)
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from vllm.model_executor.layers.quantization.moe_wna16 import MoeWNA16Config
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from vllm.model_executor.layers.quantization.utils import replace_parameter
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from vllm.model_executor.layers.quantization.utils.marlin_utils import (
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apply_awq_marlin_linear, awq_to_marlin_zero_points, check_marlin_supported,
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check_marlin_supports_layer, marlin_make_empty_g_idx,
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marlin_make_workspace, marlin_moe_permute_scales, marlin_permute_scales,
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moe_awq_to_marlin_zero_points, verify_marlin_supported,
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verify_marlin_supports_shape)
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from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
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from vllm.model_executor.parameter import (GroupQuantScaleParameter,
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PackedvLLMParameter)
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from vllm.platforms import current_platform
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from vllm.scalar_type import scalar_types
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import ixformer.inference.functions as ixfops
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logger = init_logger(__name__)
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class AWQMarlinConfig(QuantizationConfig):
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"""Config class for AWQ Marlin"""
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# num_bits -> type
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TYPE_MAP = {
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4: scalar_types.uint4,
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8: scalar_types.uint8,
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}
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def __init__(self, weight_bits: int, group_size: int, zero_point: bool,
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lm_head_quantized: bool,
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modules_to_not_convert: Optional[List[str]],
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full_config: Dict[str, Any]) -> None:
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super().__init__()
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self.pack_factor = 32 // weight_bits # packed into int32
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self.group_size = group_size
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self.zero_point = zero_point
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self.lm_head_quantized = lm_head_quantized
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self.weight_bits = weight_bits
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self.modules_to_not_convert = modules_to_not_convert or []
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self.full_config = full_config
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if self.weight_bits not in self.TYPE_MAP:
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raise ValueError(f"Unsupported num_bits = {self.weight_bits}. "
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f"Supported num_bits = {self.TYPE_MAP.keys()}")
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self.quant_type = self.TYPE_MAP[self.weight_bits]
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verify_marlin_supported(self.quant_type,
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group_size=self.group_size,
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has_zp=self.zero_point)
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def __repr__(self) -> str:
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return (f"AWQMarlinConfig(quant_type={self.quant_type}, "
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f"group_size={self.group_size}, "
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f"zero_point={self.zero_point}, "
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f"lm_head_quantized={self.lm_head_quantized}, "
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f"modules_to_not_convert={self.modules_to_not_convert})")
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@classmethod
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def get_name(cls) -> str:
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return "awq_marlin"
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@classmethod
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def get_supported_act_dtypes(cls) -> List[torch.dtype]:
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return [torch.half, torch.bfloat16]
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@classmethod
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def get_min_capability(cls) -> int:
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return 80
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@classmethod
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def get_config_filenames(cls) -> List[str]:
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return ["quantize_config.json"]
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@classmethod
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def from_config(cls, config: Dict[str, Any]) -> "AWQMarlinConfig":
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weight_bits = cls.get_from_keys(config, ["bits"])
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group_size = cls.get_from_keys(config, ["group_size"])
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zero_point = cls.get_from_keys(config, ["zero_point"])
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lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"],
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default=False)
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modules_to_not_convert = cls.get_from_keys_or(
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config, ["modules_to_not_convert"], None)
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return cls(weight_bits, group_size, zero_point, lm_head_quantized,
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modules_to_not_convert, config)
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@classmethod
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def override_quantization_method(cls, hf_quant_cfg,
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user_quant) -> Optional[str]:
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can_convert = cls.is_awq_marlin_compatible(hf_quant_cfg)
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is_valid_user_quant = (user_quant is None or user_quant == "marlin"
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or user_quant == "awq_marlin")
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if can_convert and is_valid_user_quant:
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msg = ("The model is convertible to {} during runtime."
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" Using {} kernel.".format(cls.get_name(), cls.get_name()))
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logger.info(msg)
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return cls.get_name()
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if can_convert and user_quant == "awq":
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logger.info("Detected that the model can run with awq_marlin"
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", however you specified quantization=awq explicitly,"
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" so forcing awq. Use quantization=awq_marlin for"
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" faster inference")
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return None
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def get_quant_method(self, layer: torch.nn.Module,
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prefix: str) -> Optional["QuantizeMethodBase"]:
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if (isinstance(layer, LinearBase) or
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(isinstance(layer, ParallelLMHead) and self.lm_head_quantized)):
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if is_layer_skipped_awq(prefix, self.modules_to_not_convert):
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return UnquantizedLinearMethod()
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# Check if the layer is supported by AWQMarlin.
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if not check_marlin_supports_layer(layer, self.group_size):
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logger.warning_once(
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f"Layer '{prefix}' is not supported by AWQMarlin. "
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"Falling back to unoptimized AWQ kernels.")
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return AWQConfig.from_config(
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self.full_config).get_quant_method(layer, prefix)
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return AWQMarlinLinearMethod(self)
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elif isinstance(layer, FusedMoE):
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# if layer.local_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(
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# self.full_config).get_quant_method(layer, prefix)
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# else:
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# return AWQMoEMethod(self)
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return AWQMoEMethod(self)
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return None
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@classmethod
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def is_awq_marlin_compatible(cls, quant_config: Dict[str, Any]):
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# Extract data from quant config.
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quant_method = quant_config.get("quant_method", "").lower()
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num_bits = quant_config.get("bits")
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group_size = quant_config.get("group_size")
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zero_point = quant_config.get("zero_point")
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if not current_platform.is_cuda():
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return False
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if quant_method != "awq":
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return False
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# If we cannot find the info needed in the config, cannot convert.
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if (num_bits is None or group_size is None or zero_point is None):
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return False
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if num_bits not in cls.TYPE_MAP:
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return False
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return check_marlin_supported(quant_type=cls.TYPE_MAP[num_bits],
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group_size=group_size,
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has_zp=zero_point)
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class AWQMarlinLinearMethod(LinearMethodBase):
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"""Linear method for AWQ Marlin.
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Args:
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quant_config: The AWQ Marlin quantization config.
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"""
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def __init__(self, quant_config: AWQMarlinConfig) -> None:
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self.quant_config = quant_config
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def create_weights(
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self,
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layer: torch.nn.Module,
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input_size_per_partition: int,
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output_partition_sizes: List[int],
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input_size: int,
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output_size: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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) -> None:
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del output_size
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output_size_per_partition = sum(output_partition_sizes)
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weight_loader = extra_weight_attrs.get("weight_loader")
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# Normalize group_size
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if self.quant_config.group_size != -1:
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group_size = self.quant_config.group_size
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else:
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group_size = input_size
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verify_marlin_supports_shape(
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output_size_per_partition=output_size_per_partition,
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input_size_per_partition=input_size_per_partition,
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input_size=input_size,
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group_size=group_size)
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qweight = PackedvLLMParameter(
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data=torch.empty(
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input_size_per_partition,
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output_size_per_partition // self.quant_config.pack_factor,
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dtype=torch.int32,
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),
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input_dim=0,
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output_dim=1,
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packed_dim=1,
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packed_factor=self.quant_config.pack_factor,
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weight_loader=weight_loader)
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num_groups = input_size_per_partition // group_size
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qzeros = PackedvLLMParameter(
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data=torch.empty(
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num_groups,
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output_size_per_partition // self.quant_config.pack_factor,
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dtype=torch.int32,
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),
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input_dim=0,
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output_dim=1,
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packed_dim=1,
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packed_factor=self.quant_config.pack_factor,
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weight_loader=weight_loader)
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scales = GroupQuantScaleParameter(data=torch.empty(
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num_groups,
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output_size_per_partition,
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dtype=params_dtype,
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),
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input_dim=0,
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output_dim=1,
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weight_loader=weight_loader)
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layer.register_parameter("qweight", qweight)
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layer.register_parameter("qzeros", qzeros)
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layer.register_parameter("scales", scales)
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layer.input_size_per_partition = input_size_per_partition
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layer.output_size_per_partition = output_size_per_partition
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layer.num_groups = num_groups
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# TODO: Update this docs
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# Checkpoints are serialized in AutoAWQ format, which is different from the
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# marlin format. This function is called after the weights are loaded.
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# Here, we handle the repacking
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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layer.output_size_per_partition = layer.qweight.data.shape[1] * self.quant_config.pack_factor
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align_bits = 64 * 8
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align_size = align_bits // self.quant_config.weight_bits
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if layer.output_size_per_partition % align_size != 0:
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padding_output_size_per_partition = (layer.output_size_per_partition + align_size - 1) // align_size * align_size
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layer.output_padding_size = padding_output_size_per_partition - layer.output_size_per_partition
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device = layer.qweight.device
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pad_qweight = torch.zeros(
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layer.input_size_per_partition,
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padding_output_size_per_partition // self.quant_config.pack_factor,
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dtype=torch.int32,
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device=device,
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)
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pad_qzeros = torch.zeros(
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layer.num_groups,
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padding_output_size_per_partition // self.quant_config.pack_factor,
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dtype=torch.int32,
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device=device,
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)
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pad_scales = torch.zeros(
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layer.num_groups,
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padding_output_size_per_partition,
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dtype=layer.scales.data.dtype,
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device=device,
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)
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pad_qweight[..., :layer.output_size_per_partition // self.quant_config.pack_factor] = layer.qweight.data
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pad_qzeros[..., :layer.output_size_per_partition // self.quant_config.pack_factor] = layer.qzeros.data
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pad_scales[..., :layer.output_size_per_partition] = layer.scales.data
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replace_parameter(layer, "qweight", pad_qweight)
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replace_parameter(layer, "qzeros", pad_qzeros)
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replace_parameter(layer, "scales", pad_scales)
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return
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# TODO(gyf) Marlin format is not support for now..
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device = layer.qweight.device
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layer.qweight = torch.nn.Parameter(layer.qweight.data,
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requires_grad=False)
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layer.qzeros = torch.nn.Parameter(layer.qzeros.data,
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requires_grad=False)
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layer.scales = torch.nn.Parameter(layer.scales.data,
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requires_grad=False)
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# Allocate marlin workspace
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layer.workspace = marlin_make_workspace(
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layer.output_size_per_partition, device)
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# Repack weights from AWQ format to marlin format.
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marlin_qweight = ops.awq_marlin_repack(
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layer.qweight,
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size_k=layer.input_size_per_partition,
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size_n=layer.output_size_per_partition,
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num_bits=self.quant_config.quant_type.size_bits)
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replace_parameter(layer, "qweight", marlin_qweight)
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# Permute scales from AWQ format to marlin format.
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marlin_scales = marlin_permute_scales(
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layer.scales,
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size_k=layer.input_size_per_partition,
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size_n=layer.output_size_per_partition,
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group_size=self.quant_config.group_size)
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replace_parameter(layer, "scales", marlin_scales)
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# Permute zero-points from AWQ format to marlin format.
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marlin_zp = awq_to_marlin_zero_points(
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layer.qzeros,
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size_k=layer.num_groups,
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size_n=layer.output_size_per_partition,
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num_bits=self.quant_config.quant_type.size_bits)
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replace_parameter(layer, "qzeros", marlin_zp)
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# Not-used
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layer.g_idx = marlin_make_empty_g_idx(device)
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layer.g_idx_sort_indices = marlin_make_empty_g_idx(device)
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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bias: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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# TODO use awq kernel temporarily..
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qweight = layer.qweight
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scales = layer.scales
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qzeros = layer.qzeros
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pack_factor = self.quant_config.pack_factor
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out_shape = (x.shape[:-1] + (qweight.shape[-1] * pack_factor, ))
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reshaped_x = x.reshape(-1, x.shape[-1])
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out = ops.awq_gemm(reshaped_x, qweight, scales, qzeros,
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pack_factor, group_size=self.quant_config.group_size)
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if bias is not None:
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out.add_(bias)
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return out.reshape(out_shape)
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# return apply_awq_marlin_linear(
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# input=x,
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# weight=layer.qweight,
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# weight_scale=layer.scales,
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# weight_zp=layer.qzeros,
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# g_idx=layer.g_idx,
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# g_idx_sort_indices=layer.g_idx_sort_indices,
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# workspace=layer.workspace,
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# quant_type=self.quant_config.quant_type,
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# output_size_per_partition=layer.output_size_per_partition,
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# input_size_per_partition=layer.input_size_per_partition,
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# bias=bias)
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class AWQMoEMethod(FusedMoEMethodBase):
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def __init__(self, quant_config: AWQMarlinConfig):
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self.quant_config = quant_config
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def create_weights(self, layer: torch.nn.Module, num_experts: int,
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hidden_size: int, intermediate_size_per_partition: int,
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params_dtype: torch.dtype, **extra_weight_attrs):
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extra_weight_attrs.update({
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"is_transposed":
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True,
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"quant_method":
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FusedMoeWeightScaleSupported.GROUP.value,
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})
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w13_qweight = Parameter(
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torch.empty(num_experts,
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hidden_size,
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2 * intermediate_size_per_partition //
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self.quant_config.pack_factor,
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dtype=torch.int32),
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requires_grad=False)
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layer.register_parameter("w13_qweight", w13_qweight)
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set_weight_attrs(w13_qweight, extra_weight_attrs)
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w2_qweight = Parameter(torch.empty(num_experts,
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intermediate_size_per_partition,
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hidden_size //
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self.quant_config.pack_factor,
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dtype=torch.int32),
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requires_grad=False)
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layer.register_parameter("w2_qweight", w2_qweight)
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set_weight_attrs(w2_qweight, extra_weight_attrs)
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num_groups_w13 = hidden_size // self.quant_config.group_size
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num_groups_w2 = (intermediate_size_per_partition //
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self.quant_config.group_size)
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# WEIGHT_SCALES
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# Allocate 2 scales for w1 and w3 respectively.
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w13_scales = Parameter(torch.empty(num_experts,
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num_groups_w13,
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intermediate_size_per_partition * 2,
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dtype=params_dtype),
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requires_grad=False)
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layer.register_parameter("w13_scales", w13_scales)
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set_weight_attrs(w13_scales, extra_weight_attrs)
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w2_scales = Parameter(torch.empty(num_experts,
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num_groups_w2,
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hidden_size,
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dtype=params_dtype),
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requires_grad=False)
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layer.register_parameter("w2_scales", w2_scales)
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set_weight_attrs(w2_scales, extra_weight_attrs)
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# WEIGHT_ZERO_POINT
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# Allocate 2 zero points for w1 and w3 respectively.
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w13_qzeros = Parameter(
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torch.empty(num_experts,
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num_groups_w13,
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2 * intermediate_size_per_partition //
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self.quant_config.pack_factor,
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dtype=torch.int32),
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requires_grad=False)
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layer.register_parameter("w13_qzeros", w13_qzeros)
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set_weight_attrs(w13_qzeros, extra_weight_attrs)
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w2_qzeros = Parameter(torch.empty(num_experts,
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num_groups_w2,
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hidden_size //
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self.quant_config.pack_factor,
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dtype=torch.int32),
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requires_grad=False)
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layer.register_parameter("w2_qzeros", w2_qzeros)
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set_weight_attrs(w2_qzeros, extra_weight_attrs)
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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return
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# TODO(gyf) Marlin format is not support for now..
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num_experts = layer.w13_qweight.shape[0]
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device = layer.w13_qweight.device
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layer.w13_g_idx_sort_indices = torch.nn.Parameter(
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torch.empty((num_experts, 0), dtype=torch.int32, device=device),
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requires_grad=False,
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)
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layer.w2_g_idx_sort_indices = torch.nn.Parameter(
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torch.empty((num_experts, 0), dtype=torch.int32, device=device),
|
|
requires_grad=False,
|
|
)
|
|
|
|
marlin_w13_qweight = ops.awq_marlin_moe_repack(
|
|
layer.w13_qweight,
|
|
layer.w13_g_idx_sort_indices,
|
|
size_k=layer.w13_qweight.shape[1],
|
|
size_n=layer.w13_qweight.shape[2] * self.quant_config.pack_factor,
|
|
num_bits=self.quant_config.weight_bits,
|
|
)
|
|
replace_parameter(layer, "w13_qweight", marlin_w13_qweight)
|
|
|
|
marlin_w2_qweight = ops.awq_marlin_moe_repack(
|
|
layer.w2_qweight,
|
|
layer.w2_g_idx_sort_indices,
|
|
size_k=layer.w2_qweight.shape[1],
|
|
size_n=layer.w2_qweight.shape[2] * self.quant_config.pack_factor,
|
|
num_bits=self.quant_config.weight_bits,
|
|
)
|
|
replace_parameter(layer, "w2_qweight", marlin_w2_qweight)
|
|
|
|
# Why does this take the intermediate size for size_k?
|
|
marlin_w13_scales = marlin_moe_permute_scales(
|
|
s=layer.w13_scales,
|
|
size_k=layer.intermediate_size_per_partition,
|
|
size_n=layer.w13_scales.shape[2],
|
|
group_size=self.quant_config.group_size,
|
|
)
|
|
|
|
replace_parameter(layer, "w13_scales", marlin_w13_scales)
|
|
|
|
marlin_w2_scales = marlin_moe_permute_scales(
|
|
s=layer.w2_scales,
|
|
size_k=layer.intermediate_size_per_partition,
|
|
size_n=layer.w2_scales.shape[2],
|
|
group_size=self.quant_config.group_size,
|
|
)
|
|
replace_parameter(layer, "w2_scales", marlin_w2_scales)
|
|
|
|
marlin_w13_zp = moe_awq_to_marlin_zero_points(
|
|
layer.w13_qzeros,
|
|
size_k=layer.w13_qzeros.shape[1],
|
|
size_n=layer.w13_qzeros.shape[2] * self.quant_config.pack_factor,
|
|
num_bits=self.quant_config.weight_bits)
|
|
replace_parameter(layer, "w13_qzeros", marlin_w13_zp)
|
|
|
|
marlin_w2_zp = moe_awq_to_marlin_zero_points(
|
|
layer.w2_qzeros,
|
|
size_k=layer.w2_qzeros.shape[1],
|
|
size_n=layer.w2_qzeros.shape[2] * self.quant_config.pack_factor,
|
|
num_bits=self.quant_config.weight_bits)
|
|
replace_parameter(layer, "w2_qzeros", marlin_w2_zp)
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
top_k: int,
|
|
renormalize: bool,
|
|
use_grouped_topk: bool = False,
|
|
topk_group: Optional[int] = None,
|
|
num_expert_group: Optional[int] = None,
|
|
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,
|
|
apply_router_weight_on_input: bool = False,
|
|
activation: str = "silu",
|
|
extra_residual: torch.Tensor = None,
|
|
routed_scaling_factor: float = 1.0,
|
|
) -> torch.Tensor:
|
|
assert activation == "silu", "Only SiLU activation is supported."
|
|
use_ep = expert_map is not None
|
|
if use_ep:
|
|
start_eid = layer.ep_rank * layer.local_num_experts
|
|
end_eid = min((layer.ep_rank + 1) * layer.local_num_experts, global_num_experts)
|
|
if apply_router_weight_on_input:
|
|
raise NotImplementedError(
|
|
"Apply router weight on input is not supported for"
|
|
"fused Marlin MoE method.")
|
|
|
|
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)
|
|
|
|
num_tokens, num_experts = router_logits.shape
|
|
if use_ep:
|
|
hidden_size = x.shape[1]
|
|
(
|
|
src_to_dst,
|
|
sorted_token_ids,
|
|
expert_sizes_gpu,
|
|
expert_sizes_cpu,
|
|
expand_tokens,
|
|
) = ixfops.moe_compute_token_index_ep(
|
|
topk_ids=topk_ids,
|
|
num_experts=num_experts,
|
|
start_expert_id=start_eid,
|
|
end_expert_id=end_eid,
|
|
)
|
|
if expert_sizes_cpu.sum() == 0:
|
|
return torch.zeros(
|
|
(num_tokens, hidden_size),
|
|
device=x.device,
|
|
dtype=x.dtype,
|
|
)
|
|
else:
|
|
expand_tokens = num_tokens * top_k
|
|
(
|
|
src_to_dst,
|
|
sorted_token_ids,
|
|
expert_sizes_gpu,
|
|
expert_sizes_cpu,
|
|
) = ixfops.moe_compute_token_index(
|
|
topk_ids=topk_ids,
|
|
num_experts=num_experts,
|
|
)
|
|
expert_sizes_cpu = expert_sizes_gpu.cpu()
|
|
|
|
# expand + reorder
|
|
# TODO use kernel
|
|
expand_hidden_states = ixfops.moe_expand_input(
|
|
hidden_states=x,
|
|
dst_to_src=sorted_token_ids,
|
|
dst_tokens=expand_tokens,
|
|
topk=top_k,
|
|
src_to_dst=src_to_dst,
|
|
)
|
|
|
|
# w4a16 group gemm 1
|
|
# pt_output_1: (expand_tokens, 2n) dtype
|
|
pt_output_1 = ixfops.moe_w4a16_group_gemm(
|
|
input=expand_hidden_states,
|
|
weight=layer.w13_qweight,
|
|
w_scales=layer.w13_scales,
|
|
quant_type="awq",
|
|
tokens_per_experts=expert_sizes_cpu,
|
|
w_zeros=layer.w13_qzeros,
|
|
group_size=self.quant_config.group_size,
|
|
dst_to_src=None,
|
|
format="NN",
|
|
tokens_per_experts_gpu=expert_sizes_gpu,
|
|
)
|
|
|
|
# act
|
|
pt_output_2 = ixfops.silu_and_mul(pt_output_1)
|
|
|
|
# w4a16 group gemm 2 + reorder
|
|
# pt_output_3: (expand_tokens, k) dtype
|
|
if use_ep:
|
|
pt_output_3 = torch.empty(
|
|
(num_tokens * top_k, hidden_size),
|
|
device=x.device,
|
|
dtype=x.dtype,
|
|
)
|
|
|
|
ixfops.moe_w4a16_group_gemm(
|
|
input=pt_output_2,
|
|
weight=layer.w2_qweight,
|
|
w_scales=layer.w2_scales,
|
|
quant_type="awq",
|
|
tokens_per_experts=expert_sizes_cpu,
|
|
w_zeros=layer.w2_qzeros,
|
|
group_size=self.quant_config.group_size,
|
|
dst_to_src=sorted_token_ids,
|
|
format="NN",
|
|
output=pt_output_3,
|
|
)
|
|
|
|
reduce_mask = src_to_dst == -1
|
|
final_hidden_states = ixfops.moe_output_reduce_sum(
|
|
input=pt_output_3.view(num_tokens, top_k, -1),
|
|
topk_weight=topk_weights,
|
|
extra_residual=extra_residual,
|
|
scaling_factor=routed_scaling_factor,
|
|
mask=reduce_mask,
|
|
)
|
|
else:
|
|
pt_output_3 = ixfops.moe_w4a16_group_gemm(
|
|
input=pt_output_2,
|
|
weight=layer.w2_qweight,
|
|
w_scales=layer.w2_scales,
|
|
quant_type="awq",
|
|
tokens_per_experts=expert_sizes_cpu,
|
|
w_zeros=layer.w2_qzeros,
|
|
group_size=self.quant_config.group_size,
|
|
dst_to_src=sorted_token_ids,
|
|
format="NN",
|
|
tokens_per_experts_gpu=expert_sizes_gpu,
|
|
)
|
|
|
|
# mul + reduce_sum
|
|
# final_hidden_states: (num_tokens, k)
|
|
final_hidden_states = ixfops.moe_output_reduce_sum(
|
|
input=pt_output_3.view(num_tokens, top_k, -1),
|
|
topk_weight=topk_weights,
|
|
extra_residual=extra_residual,
|
|
scaling_factor=routed_scaling_factor
|
|
)
|
|
return final_hidden_states
|
|
# 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,
|
|
# w1_zeros=layer.w13_qzeros,
|
|
# w2_zeros=layer.w2_qzeros,
|
|
# num_bits=self.quant_config.weight_bits,
|
|
# )
|