[hotfix] fix mixtral with tensor-level compressed-tensor quantization (sgl-project#8721)

Author: ch-wan

python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -23,6 +23,7 @@
 from sglang.srt.utils import is_cpu, is_cuda, is_hip, is_npu, set_weight_attrs
 
 if TYPE_CHECKING:
+    from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
     from sglang.srt.layers.moe.topk import TopKOutput
     from sglang.srt.layers.quantization.compressed_tensors.compressed_tensors import (
         CompressedTensorsConfig,
@@ -189,7 +190,7 @@ def create_weights(
             layer.w13_input_scale = None
             layer.w2_input_scale = None
 
-    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+    def process_weights_after_loading(self, layer: FusedMoE) -> None:
         # Fp8 moe kernels require a single activation scale.
         # We take the max of all the scales in case they differ.
         if self.static_input_scales:
@@ -246,7 +247,7 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
             assert layer.w13_weight_scale is not None
             shard_size = layer.intermediate_size_per_partition
             max_w13_scales = layer.w13_weight_scale.max(dim=1).values
-            for expert_id in range(layer.local_num_experts):
+            for expert_id in range(layer.num_local_experts):
                 start = 0
                 for shard_id in range(2):
                     dq_weight = per_tensor_dequantize(