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[Bugfix][Hardware][AMD] Fix device parameter in AITER topK metadata #31178

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[Bugfix][Hardware][AMD] Fix device parameter in AITER topK metadata #31178

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0e94517
[Bugfix][ROCm] Add device parameter to init_aiter_topK_meta_data
c0de128 Dec 22, 2025
7fae7ba
[Bugfix][Hardware][AMD] Guard AITER topK init with rocm_aiter_fmoe_en…
c0de128 Dec 26, 2025
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3 changes: 2 additions & 1 deletion vllm/model_executor/layers/fused_moe/layer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1066,7 +1066,7 @@ def _map_global_expert_id_to_local_expert_id(self, expert_id: int) -> int:
def _init_aiter_shared_experts_topK_buffer(
self, vllm_config: VllmConfig, dp_size: int
):
if self.num_fused_shared_experts > 0:
if self.num_fused_shared_experts > 0 and self.rocm_aiter_fmoe_enabled:
init_aiter_topK_meta_data(
n_routed_experts=self.global_num_experts,
n_shared_experts=self.num_fused_shared_experts,
Expand All @@ -1077,6 +1077,7 @@ def _init_aiter_shared_experts_topK_buffer(
max_num_tokens=vllm_config.scheduler_config.max_num_batched_tokens
* dp_size,
is_EP=self.use_ep,
device=torch.cuda.current_device(),
)
self.local_num_experts += self.num_fused_shared_experts

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7 changes: 4 additions & 3 deletions vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -52,6 +52,7 @@ def init_aiter_topK_meta_data(
shared_experts_score: float = 1.0,
max_num_tokens: int = 32768,
is_EP: bool = False,
device: int | str = "cuda",
):
global aiter_topK_meta_data
fake_expertid = n_routed_experts + n_shared_experts
Expand All @@ -64,7 +65,7 @@ def init_aiter_topK_meta_data(
total_topk_ids = torch.empty(
(max_num_tokens, top_k + n_shared_experts + is_EP),
dtype=torch.int32,
device="cuda",
device=device,
)
ns_topk_ids, s_topk_ids = total_topk_ids.split(
[top_k, n_shared_experts + is_EP], dim=1
Expand All @@ -80,12 +81,12 @@ def init_aiter_topK_meta_data(
s_topk_ids_list = [
list(range(n_routed_experts, fake_expertid))
] * max_num_tokens
s_topk_ids[:] = torch.tensor(s_topk_ids_list, dtype=torch.int32, device="cuda")
s_topk_ids[:] = torch.tensor(s_topk_ids_list, dtype=torch.int32, device=device)

total_topk_weights = torch.empty(
(max_num_tokens, top_k + n_shared_experts + is_EP),
dtype=torch.float32,
device="cuda",
device=device,
)
ns_topk_weights, s_topk_weights = total_topk_weights.split(
[top_k, n_shared_experts + is_EP], dim=1
Expand Down