add qwen3
This commit is contained in:
Chranos
@@ -0,0 +1,565 @@
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from typing import Any, Callable, Dict, List, Optional, Set, Union
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import torch
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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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set_weight_attrs)
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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from vllm.model_executor.layers.quantization.kernels import (
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MPLinearLayerConfig, choose_mp_linear_kernel)
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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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check_marlin_supported, marlin_moe_permute_scales,
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marlin_repeat_scales_on_all_ranks, verify_marlin_supported)
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from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
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from vllm.model_executor.parameter import (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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from vllm.scalar_type import scalar_types
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logger = init_logger(__name__)
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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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) -> None:
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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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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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if (weight_bits, is_sym) not in self.TYPE_MAP:
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raise ValueError("Unsupported quantization config: "
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f"bits={weight_bits}, sym={is_sym}")
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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 (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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@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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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"],
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default=False)
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return cls(weight_bits, group_size, desc_act, is_sym,
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lm_head_quantized)
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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_gptq_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 == "gptq_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 == "gptq":
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logger.info("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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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[Union["GPTQMarlinLinearMethod", "GPTQMarlinMoEMethod"]]:
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if isinstance(layer, LinearBase) or (isinstance(layer, ParallelLMHead)
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and self.lm_head_quantized):
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return GPTQMarlinLinearMethod(self)
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elif isinstance(layer, FusedMoE):
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return GPTQMarlinMoEMethod(self)
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return None
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@classmethod
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def is_gptq_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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sym = quant_config.get("sym")
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desc_act = quant_config.get("desc_act")
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if quant_method != "gptq":
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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 sym is None
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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(quant_type=cls.TYPE_MAP[(num_bits, sym)],
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group_size=group_size)
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class GPTQMarlinLinearMethod(LinearMethodBase):
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"""Linear method for GPTQ Marlin.
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Args:
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quant_config: The GPTQ Marlin quantization config.
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"""
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_kernel_backends_being_used: Set[str] = set()
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def __init__(self, quant_config: GPTQMarlinConfig) -> None:
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self.quant_config = quant_config
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# Verify supported on platform.
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verify_marlin_supported(quant_type=self.quant_config.quant_type,
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group_size=self.quant_config.group_size)
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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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output_size_per_partition = sum(output_partition_sizes)
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is_row_parallel = input_size != input_size_per_partition
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weight_loader = extra_weight_attrs.get("weight_loader")
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mp_linear_kernel_config = MPLinearLayerConfig(
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full_weight_shape=(input_size, output_size),
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partition_weight_shape=\
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(input_size_per_partition, output_size_per_partition),
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weight_type=self.quant_config.quant_type,
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act_type=params_dtype,
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group_size=self.quant_config.group_size,
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zero_points=False,
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has_g_idx=self.quant_config.desc_act
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)
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kernel_type = choose_mp_linear_kernel(mp_linear_kernel_config)
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if kernel_type.__name__ not in self._kernel_backends_being_used:
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logger.info("Using %s for GPTQMarlinLinearMethod",
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kernel_type.__name__)
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self._kernel_backends_being_used.add(kernel_type.__name__)
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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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# Determine sharding
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if marlin_repeat_scales_on_all_ranks(self.quant_config.desc_act,
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self.quant_config.group_size,
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is_row_parallel):
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# By setting scale_dim == None, weight_loader will
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# repeat the scales on each GPU in TP>1 case.
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scales_and_zp_input_dim = None
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scales_and_zp_size = input_size // group_size
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else:
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# By setting scale_dim == 0, weight_loader will
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# shard the scales in TP>1 case.
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scales_and_zp_input_dim = 0
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scales_and_zp_size = input_size_per_partition // group_size
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# Quantized weights
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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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# Activation order
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g_idx = RowvLLMParameter(data=torch.empty(
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input_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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weight_loader=weight_loader)
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qzeros_args = {
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"data":
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torch.empty(
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scales_and_zp_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":
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weight_loader
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}
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weight_scale_args = {
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"data":
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torch.empty(
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scales_and_zp_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":
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weight_loader
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}
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if scales_and_zp_input_dim is None:
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scales = ChannelQuantScaleParameter(output_dim=1,
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**weight_scale_args)
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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,
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**qzeros_args)
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else:
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scales = GroupQuantScaleParameter(output_dim=1,
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input_dim=0,
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**weight_scale_args)
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qzeros = PackedvLLMParameter(
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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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**qzeros_args)
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layer.register_parameter("qweight", qweight)
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layer.register_parameter("g_idx", g_idx)
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layer.register_parameter("scales", scales)
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layer.register_parameter("qzeros", qzeros)
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self.kernel = kernel_type(mp_linear_kernel_config,
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w_q_param_name="qweight",
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w_s_param_name="scales",
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w_zp_param_name="qzeros",
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w_gidx_param_name="g_idx")
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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self.kernel.process_weights_after_loading(layer)
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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bias: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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return self.kernel.apply_weights(layer, x, bias)
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class GPTQMarlinMoEMethod(FusedMoEMethodBase):
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"""MoE Marlin method with quantization."""
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def __init__(self, quant_config: GPTQMarlinConfig) -> 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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num_experts: int,
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hidden_size: int,
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intermediate_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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# Currently assuming is_k_full is always True
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# (input size per partition is the same as full input size)
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# Supports only sym for now (no zp)
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if self.quant_config.group_size != -1:
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scales_size13 = hidden_size // self.quant_config.group_size
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scales_size2 = intermediate_size // self.quant_config.group_size
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strategy = FusedMoeWeightScaleSupported.GROUP.value
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else:
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scales_size13 = 1
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scales_size2 = 1
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strategy = FusedMoeWeightScaleSupported.CHANNEL.value
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extra_weight_attrs.update({
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"quant_method": strategy,
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"is_transposed": True
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})
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# Fused gate_up_proj (column parallel)
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w13_qweight = torch.nn.Parameter(
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torch.empty(
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num_experts,
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hidden_size // self.quant_config.pack_factor,
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2 * intermediate_size,
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dtype=torch.int32,
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),
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requires_grad=False,
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)
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layer.register_parameter("w13_qweight", w13_qweight)
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set_weight_attrs(w13_qweight, extra_weight_attrs)
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# down_proj (row parallel)
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w2_qweight = torch.nn.Parameter(
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torch.empty(
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num_experts,
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intermediate_size // self.quant_config.pack_factor,
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hidden_size,
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dtype=torch.int32,
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),
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requires_grad=False,
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)
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layer.register_parameter("w2_qweight", w2_qweight)
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set_weight_attrs(w2_qweight, extra_weight_attrs)
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# up_proj scales
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w13_scales = torch.nn.Parameter(
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torch.empty(num_experts,
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scales_size13,
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2 * intermediate_size,
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dtype=torch.half),
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requires_grad=False,
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)
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layer.register_parameter("w13_scales", w13_scales)
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set_weight_attrs(w13_scales, extra_weight_attrs)
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# down_proj scales
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w2_scales = torch.nn.Parameter(
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torch.empty(num_experts,
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scales_size2,
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hidden_size,
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dtype=torch.half),
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requires_grad=False,
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)
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layer.register_parameter("w2_scales", w2_scales)
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set_weight_attrs(w2_scales, extra_weight_attrs)
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# up_proj scales
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w13_qzeros = torch.nn.Parameter(
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torch.empty(num_experts,
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scales_size13,
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2 * intermediate_size // self.quant_config.pack_factor,
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dtype=params_dtype),
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requires_grad=False,
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)
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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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# down_proj scales
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w2_qzeros = torch.nn.Parameter(
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torch.empty(num_experts,
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scales_size2,
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hidden_size // self.quant_config.pack_factor,
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dtype=params_dtype),
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requires_grad=False,
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)
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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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w13_g_idx = torch.nn.Parameter(
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torch.empty(
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num_experts,
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hidden_size,
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dtype=torch.int32,
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),
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requires_grad=False,
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)
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layer.register_parameter("w13_g_idx", w13_g_idx)
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set_weight_attrs(w13_g_idx, extra_weight_attrs)
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w2_g_idx = torch.nn.Parameter(
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torch.empty(
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num_experts,
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intermediate_size,
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dtype=torch.int32,
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),
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requires_grad=False,
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)
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layer.register_parameter("w2_g_idx", w2_g_idx)
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set_weight_attrs(w2_g_idx, extra_weight_attrs)
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w13_g_idx_sort_indices = torch.nn.Parameter(
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torch.empty(
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num_experts,
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hidden_size,
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dtype=torch.int32,
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),
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requires_grad=False,
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)
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layer.register_parameter("w13_g_idx_sort_indices",
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w13_g_idx_sort_indices)
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set_weight_attrs(w13_g_idx_sort_indices, extra_weight_attrs)
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w2_g_idx_sort_indices = torch.nn.Parameter(
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torch.empty(
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num_experts,
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intermediate_size,
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dtype=torch.int32,
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),
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requires_grad=False,
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)
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layer.register_parameter("w2_g_idx_sort_indices",
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w2_g_idx_sort_indices)
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set_weight_attrs(w2_g_idx_sort_indices, extra_weight_attrs)
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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# Process act_order
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if self.quant_config.desc_act:
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# Get sorting based on g_idx
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num_experts = layer.w13_g_idx.shape[0]
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w13_g_idx_sort_indices = torch.empty_like(layer.w13_g_idx)
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w2_g_idx_sort_indices = torch.empty_like(layer.w2_g_idx)
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w13_sorted_g_idx = torch.empty_like(layer.w13_g_idx)
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w2_sorted_g_idx = torch.empty_like(layer.w2_g_idx)
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for e in range(num_experts):
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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.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 = True,
|
||||
use_grouped_topk: bool = False,
|
||||
num_expert_group: Optional[int] = None,
|
||||
topk_group: Optional[int] = None,
|
||||
custom_routing_function: Optional[Callable] = None,
|
||||
) -> torch.Tensor:
|
||||
# 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=None)
|
||||
|
||||
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,
|
||||
num_bits=self.quant_config.quant_type.size_bits,
|
||||
).to(orig_dtype)
|
||||
Reference in New Issue
Block a user