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

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Signed-off-by: yewentao256 <zhyanwentao@126.com>
This commit is contained in:
Wentao Ye
2025-12-05 16:42:12 -05:00
committed by GitHub
parent 77e4472809
commit 7b5575fa7d
5 changed files with 47 additions and 27 deletions
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57
vllm/model_executor/layers/fused_moe/modular_kernel.py
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@@ -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():
parallel_config = get_current_vllm_config().parallel_config
is_dp_ep = (
parallel_config.data_parallel_size > 1
and parallel_config.enable_expert_parallel
if is_profile_run and self.fused_experts.supports_chunking() and self.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
)
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(