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[Bugfix] Kimi-K2 grouped_topk usage for Flashinfer monolithic kernels. (#33858)

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Signed-off-by: Pavani Majety <pmajety@nvidia.com>
This commit is contained in:
Pavani Majety
2026-02-05 01:32:10 -08:00
committed by GitHub
parent 2abd97592f
commit d2f4a71cd5
1 changed files with 3 additions and 11 deletions
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14
vllm/model_executor/models/deepseek_v2.py
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@@ -295,14 +295,6 @@ class DeepseekV2MoE(nn.Module):
prefix=f"{prefix}.shared_experts",
)
n_group = getattr(config, "n_group", 1)
topk_group = getattr(config, "topk_group", 1)
use_grouped_topk = True
if (n_group, topk_group) == (1, 1):
n_group = None
topk_group = None
use_grouped_topk = False
self.experts = SharedFusedMoE(
shared_experts=self.shared_experts,
gate=self.gate,
@@ -313,9 +305,9 @@ class DeepseekV2MoE(nn.Module):
reduce_results=False,
renormalize=config.norm_topk_prob,
quant_config=quant_config,
use_grouped_topk=use_grouped_topk,
num_expert_group=n_group,
topk_group=topk_group,
use_grouped_topk=True,
num_expert_group=getattr(config, "n_group", 1),
topk_group=getattr(config, "topk_group", 1),
prefix=f"{prefix}.experts",
scoring_func=getattr(config, "scoring_func", "softmax"),
# we do scaling outside, set factor to 1.0 to avoid double mul