276 lines
9.9 KiB
Python
276 lines
9.9 KiB
Python
from typing import Any, Callable, Dict, List, Optional
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import os
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import torch
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import vllm.envs as envs
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from vllm import _custom_ops as ops
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from vllm.model_executor.utils import set_weight_attrs
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from vllm.distributed import get_tensor_model_parallel_world_size
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from torch.nn.parameter import Parameter
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from vllm.model_executor.layers.quantization import QuantizationMethods
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from vllm.model_executor.layers.linear import (LinearBase,LinearMethodBase)
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from vllm.model_executor.layers.quantization.base_config import (QuantizationConfig,
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QuantizeMethodBase)
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from vllm.model_executor.layers.quantization.utils.w4a8_utils import w4a8_weight_repack_impl
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from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
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FusedMoeWeightScaleSupported)
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from vllm.model_executor.parameter import (ChannelQuantScaleParameter,
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ModelWeightParameter)
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from vllm.model_executor.layers.quantization.slimquant_w4a8 import SlimQuantW4A8Int8LinearMethod
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try:
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from lmslim.layers.fused_moe.fuse_moe_w4a8_marlin import fused_experts_impl_w4a8_marlin
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except Exception:
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print("INFO: Please install lmslim if you want to infer the quantitative model of moe.\n")
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class MarlinMoeWorkspace:
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"""
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Singleton manager for device-specific workspace buffers used by w4a8 Marlin-MoE.
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global_reduce_buffer will take 1.5MB * cus (about 120MB for BW200) memoery in each device
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"""
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_instances = {}
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def __new__(cls, device):
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if device not in cls._instances:
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instance = super().__new__(cls)
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instance._initialized = False
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cls._instances[device] = instance
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return cls._instances[device]
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def __init__(self, device):
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if self._initialized:
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return
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sms = torch.cuda.get_device_properties(device).multi_processor_count
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self.workspace = torch.zeros(
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500, dtype=torch.int, device=device, requires_grad=False
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)
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self.global_reduce_buffer = torch.zeros(
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sms * 6 * 128 * 512, dtype=torch.int, device=device, requires_grad=False
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)
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self._initialized = True
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def get_buffers(self):
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return self.workspace, self.global_reduce_buffer
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def baseline_scaled_mm(a: torch.Tensor,
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b: torch.Tensor,
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scale_a: torch.Tensor,
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scale_b: torch.Tensor,
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out_dtype: torch.dtype,
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bias: Optional[torch.Tensor] = None) -> torch.Tensor:
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scales= scale_a* scale_b.T
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gemmout= torch.mm(
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a.to(dtype=torch.float32), b.to(dtype=torch.float32))
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output = (scales *gemmout).to(out_dtype)
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if bias is not None:
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output = output + bias
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return output.to(out_dtype)
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class SlimQuantW4A8Int8MarlinConfig(QuantizationConfig):
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"""Config class for W4A8 Int8 Quantization.
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- Weight: static, per-channel, symmetric
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- Activation: dynamic, per-token, symmetric
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"""
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def __init__(self):
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pass
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@classmethod
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def get_supported_act_dtypes(cls) -> List[torch.dtype]:
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return [torch.float16, torch.bfloat16]
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@classmethod
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def get_min_capability(cls) -> int:
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return 75
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@classmethod
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def get_name(self) -> str:
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return "slimquant_w4a8_marlin"
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@classmethod
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def get_config_filenames(cls) -> List[str]:
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return []
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@classmethod
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def from_config(cls, config: Dict[str, Any]) -> "SlimQuantW4A8Int8MarlinConfig":
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return cls()
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@classmethod
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def override_quantization_method(
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cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
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if hf_quant_cfg.get("quant_method") == "slimquant_w4a8" \
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and user_quant == "slimquant_w4a8_marlin":
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return cls.get_name()
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return None
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def get_quant_method(
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self,
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layer: torch.nn.Module,
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prefix: str,
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) -> Optional["QuantizeMethodBase"]:
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if isinstance(layer, LinearBase):
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return SlimQuantW4A8Int8LinearMethod(self)
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elif isinstance(layer, FusedMoE):
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return SlimQuantW4A8Int8MarlinMoEMethod(self)
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return None
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def get_scaled_act_names(self) -> List[str]:
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return []
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class SlimQuantW4A8Int8MarlinMoEMethod:
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"""MoE method for W4A8INT8 Marlin.
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Supports loading INT8 checkpoints with static weight scale and
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dynamic/static activation scale.
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Args:
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quant_config: The quantization config.
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"""
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def __new__(cls, *args, **kwargs):
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if not hasattr(cls, "_initialized"):
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original_init = cls.__init__
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new_cls = type(
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cls.__name__,
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(FusedMoEMethodBase,),
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{
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"__init__": original_init,
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**{k: v for k, v in cls.__dict__.items() if k != "__dict__"},
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},
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)
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obj = super(new_cls, new_cls).__new__(new_cls)
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obj.__init__(*args, **kwargs)
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return obj
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return super().__new__(cls)
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def __init__(self, quant_config):
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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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tp_size = get_tensor_model_parallel_world_size()
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# WEIGHTS
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w13_weight = torch.nn.Parameter(
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torch.empty(
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num_experts, 2 * intermediate_size, hidden_size//2, dtype=torch.int8
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),
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requires_grad=False,
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)
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layer.register_parameter("w13_weight", w13_weight)
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set_weight_attrs(w13_weight, extra_weight_attrs)
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w2_weight = torch.nn.Parameter(
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torch.empty(num_experts, hidden_size, intermediate_size//2, dtype=torch.int8),
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requires_grad=False,
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)
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layer.register_parameter("w2_weight", w2_weight)
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set_weight_attrs(w2_weight, extra_weight_attrs)
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w13_weight_scale = torch.nn.Parameter(
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torch.ones(num_experts, 2 * intermediate_size, 1, dtype=torch.float32),
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requires_grad=False,
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)
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w2_weight_scale = torch.nn.Parameter(
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torch.ones(num_experts, hidden_size, 1, dtype=torch.float32),
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requires_grad=False,
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)
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layer.register_parameter("w13_weight_scale", w13_weight_scale)
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layer.register_parameter("w2_weight_scale", w2_weight_scale)
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extra_weight_attrs.update(
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{"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value}
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)
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set_weight_attrs(w13_weight_scale, extra_weight_attrs)
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set_weight_attrs(w2_weight_scale, extra_weight_attrs)
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w13_input_scale = None
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layer.register_parameter("w13_input_scale", w13_input_scale)
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w2_input_scale = None
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layer.register_parameter("w2_input_scale", w2_input_scale)
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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layer.w13_weight_scale = Parameter(
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layer.w13_weight_scale.data, requires_grad=False
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)
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layer.w2_weight_scale = Parameter(
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layer.w2_weight_scale.data, requires_grad=False
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)
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layer.w13_weight = Parameter(w4a8_weight_repack_impl(layer.w13_weight), requires_grad=False)
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layer.w2_weight = Parameter(w4a8_weight_repack_impl(layer.w2_weight), requires_grad=False)
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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router_logits: torch.Tensor,
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top_k: int,
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renormalize: bool,
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use_grouped_topk: bool = False,
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topk_group: Optional[int] = None,
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num_expert_group: Optional[int] = None,
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global_num_experts: int = -1,
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expert_map: Optional[torch.Tensor] = None,
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custom_routing_function: Optional[Callable] = None,
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scoring_func: str = "softmax",
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e_score_correction_bias: Optional[torch.Tensor] = None,
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apply_router_weight_on_input: bool = False,
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activation: str = "silu",
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enable_eplb: bool = False,
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use_nn_moe: Optional[bool] = False,
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routed_scaling_factor: Optional[float] = None,
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use_fused_gate: Optional[bool] = False,
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**_
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) -> torch.Tensor:
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from vllm.model_executor.layers.fused_moe import fused_experts
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if enable_eplb:
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raise NotImplementedError(
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"EPLB not supported for `SlimQuantW4A8Int8MarlinMoEMethod` yet.")
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# Expert selection
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topk_weights, topk_ids = FusedMoE.select_experts(
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hidden_states=x,
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router_logits=router_logits,
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use_grouped_topk=use_grouped_topk,
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top_k=top_k,
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renormalize=renormalize,
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topk_group=topk_group,
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num_expert_group=num_expert_group,
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custom_routing_function=custom_routing_function,
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scoring_func=scoring_func,
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e_score_correction_bias=e_score_correction_bias,
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routed_scaling_factor=routed_scaling_factor,
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use_fused_gate=use_fused_gate
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)
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workspace, global_reduce_buffer = MarlinMoeWorkspace(x.device).get_buffers()
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return fused_experts_impl_w4a8_marlin(
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x,
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layer.w13_weight,
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layer.w2_weight,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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workspace=workspace,
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global_reduce_buffer=global_reduce_buffer,
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inplace=True,
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use_int4_w4a8=True,
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per_channel_quant=True,
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activation=activation,
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expert_map=expert_map,
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apply_router_weight_on_input=apply_router_weight_on_input,
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global_num_experts=global_num_experts,
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w1_scale=(layer.w13_weight_scale),
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w2_scale=(layer.w2_weight_scale),
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a1_scale=layer.w13_input_scale,
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a2_scale=layer.w2_input_scale,
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use_nn_moe=use_nn_moe,
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)
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