nixl_ep: Skip post-receive quantization for NVFP4 (#45606)

Signed-off-by: Itay Alroy <ialroy@nvidia.com>

vllm/model_executor/layers/fused_moe/experts/flashinfer_cutedsl_batched_moe.py

@@ -49,6 +49,9 @@ class FlashInferCuteDSLBatchedExperts(mk.FusedMoEExpertsModular):
             "Only nvfp4 quantization are currently supported."
         )
         self.out_dtype = moe_config.in_dtype
+        self.use_deep_ep_ll_nvfp4_dispatch = (
+            envs.VLLM_DEEPEPLL_NVFP4_DISPATCH and moe_config.use_deepep_ll_kernels
+        )
 
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         layer.w13_weight_scale_2.data.mul_(layer.w13_input_scale)
@@ -123,7 +126,7 @@ class FlashInferCuteDSLBatchedExperts(mk.FusedMoEExpertsModular):
 
         # We use global_num_experts due to how moe_align_block_size handles
         # expert_maps.
-        K_dim = K * 2 if envs.VLLM_DEEPEPLL_NVFP4_DISPATCH else K
+        K_dim = K * 2 if self.use_deep_ep_ll_nvfp4_dispatch else K
         output_shape = (local_num_experts, M, K_dim)
         workspace2 = (local_num_experts, M, N)
         workspace1 = output_shape
@@ -161,11 +164,11 @@ class FlashInferCuteDSLBatchedExperts(mk.FusedMoEExpertsModular):
         assert self.w2_scale.ndim == 3
 
         input_global_scale = (
-            None if envs.VLLM_DEEPEPLL_NVFP4_DISPATCH else self.a1_gscale
+            None if self.use_deep_ep_ll_nvfp4_dispatch else self.a1_gscale
         )
         flashinfer_hidden_states = (
             (hidden_states, a1q_scale)
-            if envs.VLLM_DEEPEPLL_NVFP4_DISPATCH
+            if self.use_deep_ep_ll_nvfp4_dispatch
             else hidden_states
         )
         flashinfer_cutedsl_moe_masked(

vllm/model_executor/layers/fused_moe/prepare_finalize/nixl_ep.py

@@ -6,7 +6,6 @@ import nixl_ep
 import torch
 
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
-from vllm.config import get_current_vllm_config
 from vllm.distributed import get_ep_group
 from vllm.distributed.device_communicators.all2all import NixlEPAll2AllManager
 from vllm.logger import init_logger
@@ -192,13 +191,9 @@ class NixlEPPrepareAndFinalize(mk.FusedMoEPrepareAndFinalizeModular):
         x = x.view((-1, hidden_dim))
         q_dtype = quant_config.quant_dtype
 
-        moe_backend = get_current_vllm_config().kernel_config.moe_backend
-        if moe_backend == "flashinfer_cutedsl":
-            logger.info_once(
-                "Skip quantization when using FlashInfer CUTEDSL "
-                "(--moe-backend flashinfer_cutedsl) for ModelOptNvFp4FusedMoE."
-            )
+        if q_dtype == "nvfp4":
             q_dtype = None
+            logger.debug_once("Using NIXL EP bfloat16 dispatch for NVFP4 MoE.")
 
         x, x_scales = moe_kernel_quantize_input(
             x,