[ROCM] Optimize the fused_topk_bias to use aiter instead of fallback torch ops.

vllm/model_executor/layers/fused_moe/router/fused_topk_bias_router.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import functools
 from collections.abc import Callable
 
 import torch
@@ -57,6 +58,19 @@ def vllm_topk_sigmoid(
     return topk_weights, topk_indices
 
 
+@functools.lru_cache(maxsize=8)
+def _aiter_get_num_expert_group(num_experts: int) -> int:
+    _AITER_MAX_EXPERTS_PER_GROUP = 32
+    g = max(1, -(-num_experts // _AITER_MAX_EXPERTS_PER_GROUP))
+    while num_experts % g != 0:
+        g += 1
+    assert num_experts % g == 0, f"{num_experts=} not divisible by {g=}"
+    assert num_experts // g <= _AITER_MAX_EXPERTS_PER_GROUP, (
+        f"group size {num_experts // g} exceeds limit {_AITER_MAX_EXPERTS_PER_GROUP}"
+    )
+    return g
+
+
 def fused_topk_bias(
     hidden_states: torch.Tensor,
     gating_output: torch.Tensor,
@@ -108,6 +122,30 @@ def fused_topk_bias(
             return topk_weights, topk_ids
         else:
             raise ValueError(f"Unsupported scoring function: {scoring_func}")
+    elif rocm_aiter_ops.is_fused_moe_enabled() and scoring_func == "sigmoid":
+        M = hidden_states.size(0)
+        num_experts = gating_output.shape[-1]
+        num_expert_group = _aiter_get_num_expert_group(num_experts)
+        if topk >= num_expert_group:
+            topk_weights = torch.empty(
+                M, topk, dtype=torch.float32, device=hidden_states.device
+            )
+            topk_ids = torch.empty(
+                M,
+                topk,
+                dtype=torch.int32 if indices_type is None else indices_type,
+                device=hidden_states.device,
+            )
+            rocm_aiter_ops.biased_grouped_topk(
+                gating_output,
+                e_score_correction_bias.to(gating_output.dtype),
+                topk_weights,
+                topk_ids,
+                num_expert_group=num_expert_group,
+                topk_group=num_expert_group,
+                need_renorm=renormalize,
+            )
+            return topk_weights, topk_ids
 
     n_routed_experts = gating_output.shape[-1]
     if scoring_func == "softmax":