[Deepseek V4] Keep fp32 routing weights in the mxfp4 trtllm MoE (#33608)

Co-authored-by: Brayden Zhong <brayden@radixark.ai>

python/sglang/srt/layers/quantization/mxfp4_flashinfer_trtllm_moe.py

@@ -7,7 +7,6 @@ import torch
 from torch.nn import Module
 from torch.nn.parameter import Parameter
 
-from sglang.kernels.ops.moe.pack_topk_ids import PackTopkIds
 from sglang.srt.distributed import get_tp_group
 from sglang.srt.distributed.device_communicators.pynccl_allocator import (
     use_symmetric_memory,
@@ -295,8 +294,6 @@ class Mxfp4FlashinferTrtllmMoEMethod:
         else:
             raise ValueError(f"Unsupported topk output format: {topk_output.format}")
 
-        packed_topk = PackTopkIds.execute(topk_ids, topk_weights)
-
         precision = self.flashinfer_mxfp4_moe_precision
         if precision == "bf16":
             assert hidden_states.dtype == torch.bfloat16
@@ -345,7 +342,7 @@ class Mxfp4FlashinferTrtllmMoEMethod:
             )
 
         output = trtllm_fp4_block_scale_routed_moe(
-            topk_ids=packed_topk,
+            topk_ids=(topk_ids, topk_weights),
             routing_bias=None,
             hidden_states=x_quant,
             hidden_states_scale=x_scale,
@@ -362,7 +359,7 @@ class Mxfp4FlashinferTrtllmMoEMethod:
             output1_scale_gate_scalar=layer.output1_scale_gate_scalar,
             output2_scale_scalar=layer.output2_scale_scalar,
             num_experts=layer.num_experts,
-            top_k=packed_topk.shape[1],
+            top_k=topk_ids.shape[1],
             n_group=1,
             topk_group=1,
             intermediate_size=intermediate_size,