reoder config

Signed-off-by: czhu-cohere <conway.zhu@cohere.com>

Author: czhu-cohere

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -205,14 +205,14 @@ def get_moe_method(
  return CompressedTensorsW8A8Int8MoEMethod(
  weight_quant, input_quant, layer.moe_config
  )
- elif quant_config._is_dynamic_token_w4a8_int(weight_quant, input_quant):
- return CompressedTensorsW4A8Int8MoEMethod(
- weight_quant, input_quant, layer.moe_config
- )
  elif quant_config._is_fp8_w4a8_sm90(weight_quant, input_quant):
  logger.info_once("Using CompressedTensorsW4A8Fp8MoEMethod")
  return CompressedTensorsW4A8Fp8MoEMethod(
- quant_config, layer.moe_config, layer_name
+ weight_quant, input_quant, layer.moe_config
+ )
+ elif quant_config._is_dynamic_token_w4a8_int(weight_quant, input_quant):
+ return CompressedTensorsW4A8Int8MoEMethod(
+ weight_quant, input_quant, layer.moe_config
  )
  else:
  raise RuntimeError(
@@ -2436,16 +2436,15 @@ def _act_kind(s: str) -> int:
 class CompressedTensorsW4A8Fp8MoEMethod(CompressedTensorsMoEMethod):
  def __init__(
  self,
- quant_config: "CompressedTensorsConfig", # type: ignore # noqa E501
+ weight_quant: QuantizationArgs,
+ input_quant: QuantizationArgs,
  moe: FusedMoEConfig,
  layer_name: str | None = None,
  ):
  super().__init__(moe)
- self.quant_config = quant_config
- self.weight_quant = self.quant_config.target_scheme_map["Linear"].get("weights")
- self.input_quant = self.quant_config.target_scheme_map["Linear"].get(
- "input_activations"
- )
+ self.weight_quant = weight_quant
+ self.input_quant = input_quant
+
  self.group_size = self.weight_quant.group_size
  self.num_bits = self.weight_quant.num_bits
  self.packed_factor = 32 // self.num_bits