[Bug] Fix vLLM config is not set error (#29999)

Signed-off-by: yewentao256 <zhyanwentao@126.com>

vllm/model_executor/layers/fused_moe/cutlass_moe.py

@@ -460,6 +460,7 @@ def cutlass_moe_fp8(
     expert_map: torch.Tensor | None = None,
     apply_router_weight_on_input: bool = False,
     global_num_experts: int = -1,
+    parallel_config=None,
 ) -> torch.Tensor:
     """
     This function computes a a8w8-quantized Mixture of Experts (MoE) layer
@@ -537,6 +538,7 @@ def cutlass_moe_fp8(
             c_strides2=c_strides2,
             quant_config=quant_config,
         ),
+        parallel_config=parallel_config,
     )
 
     return fn(

vllm/model_executor/layers/fused_moe/fused_moe_modular_method.py

@@ -44,6 +44,11 @@ class FusedMoEModularMethod(FusedMoEMethodBase, CustomOp):
         prepare_finalize: FusedMoEPrepareAndFinalize,
         shared_experts: torch.nn.Module | None,
     ) -> "FusedMoEModularMethod":
+        parallel_config = getattr(
+            getattr(moe_layer, "vllm_config", None),
+            "parallel_config",
+            None,
+        )
         return FusedMoEModularMethod(
             old_quant_method,
             FusedMoEModularKernel(
@@ -51,6 +56,7 @@ class FusedMoEModularMethod(FusedMoEMethodBase, CustomOp):
                 old_quant_method.select_gemm_impl(prepare_finalize, moe_layer),
                 shared_experts,
                 getattr(moe_layer, "shared_experts_stream", None),
+                parallel_config=parallel_config,
             ),
         )
 

vllm/model_executor/layers/fused_moe/modular_kernel.py

@@ -10,7 +10,7 @@ from typing import final
 import torch
 
 import vllm.envs as envs
-from vllm.config import get_current_vllm_config
+from vllm.config import ParallelConfig, get_current_vllm_config
 from vllm.forward_context import get_forward_context, is_forward_context_available
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
@@ -716,6 +716,7 @@ class FusedMoEModularKernel(torch.nn.Module):
         fused_experts: FusedMoEPermuteExpertsUnpermute,
         shared_experts: torch.nn.Module | None = None,
         shared_experts_stream: torch.cuda.Stream | None = None,
+        parallel_config: ParallelConfig | None = None,
     ):
         super().__init__()
         self.prepare_finalize = prepare_finalize
@@ -723,6 +724,14 @@ class FusedMoEModularKernel(torch.nn.Module):
         self.shared_experts = shared_experts
         self.shared_experts_stream = shared_experts_stream
 
+        # cache whether this worker is using DP+EP
+        if parallel_config is None:
+            parallel_config = get_current_vllm_config().parallel_config
+        self.is_dp_ep = (
+            parallel_config.data_parallel_size > 1
+            and parallel_config.enable_expert_parallel
+        )
+
         self._post_init_setup()
         assert (
             prepare_finalize.activation_format == fused_experts.activation_formats[0]
@@ -811,33 +820,27 @@ class FusedMoEModularKernel(torch.nn.Module):
             is_forward_context_available()
             and get_forward_context().attn_metadata is None
         )
-        if is_profile_run and self.fused_experts.supports_chunking():
+        if is_profile_run and self.fused_experts.supports_chunking() and self.is_dp_ep:
-            parallel_config = get_current_vllm_config().parallel_config
+            max_workspace_13, max_workspace_2, max_fused_out_shape = (
-            is_dp_ep = (
+                self.fused_experts.workspace_shapes(
-                parallel_config.data_parallel_size > 1
+                    envs.VLLM_FUSED_MOE_CHUNK_SIZE,
-                and parallel_config.enable_expert_parallel
+                    N,
+                    K,
+                    top_k,
+                    global_num_experts,
+                    local_num_experts,
+                    expert_tokens_meta,
+                )
+            )
+            buffers.workspace13.get(
+                max_workspace_13, device=device, dtype=workspace_dtype
+            )
+            buffers.workspace2.get(
+                max_workspace_2, device=device, dtype=workspace_dtype
+            )
+            buffers.fused_out.get(
+                max_fused_out_shape, device=device, dtype=workspace_dtype
             )
-            if is_dp_ep:
-                max_workspace_13, max_workspace_2, max_fused_out_shape = (
-                    self.fused_experts.workspace_shapes(
-                        envs.VLLM_FUSED_MOE_CHUNK_SIZE,
-                        N,
-                        K,
-                        top_k,
-                        global_num_experts,
-                        local_num_experts,
-                        expert_tokens_meta,
-                    )
-                )
-                buffers.workspace13.get(
-                    max_workspace_13, device=device, dtype=workspace_dtype
-                )
-                buffers.workspace2.get(
-                    max_workspace_2, device=device, dtype=workspace_dtype
-                )
-                buffers.fused_out.get(
-                    max_fused_out_shape, device=device, dtype=workspace_dtype
-                )
 
         # Get intermediate workspace shapes based off the chunked M size.
         workspace13_shape, workspace2_shape, _ = self.fused_experts.workspace_shapes(

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

@@ -1287,6 +1287,9 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
                     ab_strides2=self.ab_strides2,
                     c_strides1=self.c_strides1,
                     c_strides2=self.ab_strides1_c_strides2,
+                    parallel_config=getattr(
+                        getattr(layer, "vllm_config", None), "parallel_config", None
+                    ),
                 )
 
         else:

vllm/model_executor/layers/quantization/utils/flashinfer_utils.py

@@ -247,6 +247,11 @@ def flashinfer_cutlass_moe_fp8(
     assert quant_config is not None
 
     # Construct modular kernel with block-scale support when requested.
+    parallel_config = getattr(
+        getattr(layer, "vllm_config", None),
+        "parallel_config",
+        None,
+    )
     fused_experts = mk.FusedMoEModularKernel(
         build_flashinfer_fp8_cutlass_moe_prepare_finalize(
             moe=moe, use_deepseek_fp8_block_scale=use_deepseek_fp8_block_scale
@@ -257,6 +262,7 @@ def flashinfer_cutlass_moe_fp8(
             out_dtype=hidden_states.dtype,
             use_deepseek_fp8_block_scale=use_deepseek_fp8_block_scale,
         ),
+        parallel_config=parallel_config,
     )
 
     return fused_experts(