[AMD] [GLM5] Fuse shared-expert append into aiter grouped-topk (skip per-layer append kernel) (#31323)

Co-authored-by: Thomas Wang <thomawan@amd.com>
Co-authored-by: HaiShaw <hixiao@gmail.com>

python/sglang/srt/layers/moe/topk.py

@@ -144,6 +144,78 @@ _RENORMALIZE_SUM_EPSILON = 1e-20
 # an accuracy run before becoming the default.
 _skip_hip_pad_mask = get_bool_env_var("SGLANG_MORI_NO_PAD_MASK", "False")
 
+# ATOM-style shared-expert fusion for the aiter grouped-topk path: keep a
+# persistent topk buffer whose shared-expert columns are pre-populated once (NOT
+# related to the LLM prefill phase — this applies to both prefill and decode), and
+# let the aiter kernel write only the routed columns (via row stride). This removes
+# the per-layer _fused_append_shared_experts kernel.
+# Auto-enabled (no env) when: non-EP aiter path (moe_ep_size == 1) and
+# num_fused_shared_experts > 0. Bit-identical to the plain append: the shared column
+# is filled with the same constant scale_factor the aiter append writes. The
+# persistent buffer is sized to the max prefill batch (chunked-prefill-size); for
+# token counts above it we fall back to the plain path (condition mirrored in
+# _post_process_topk_ids). Mirrors ATOM's init_aiter_topK_meta_data.
+# Hard upper bound on the persistent buffer (safety cap when chunked prefill is
+# disabled / unexpectedly huge); the buffer only costs ~[MAX, topk+n_shared] * 8B.
+_AITER_TOPK_FUSE_SHARED_MAX_TOKENS_CAP = 131072
+_aiter_topk_fuse_shared_max_tokens_cache = None
+_aiter_topk_fuse_shared_bufs: dict = {}
+
+
+def _get_aiter_topk_fuse_shared_max_tokens() -> int:
+    """Max per-forward token count the persistent buffer must cover. Sized to the
+    largest prefill batch (chunked-prefill-size / max-prefill-tokens); decode is
+    always tiny (bs * num_tokens_per_bs). Above this we fall back to the plain
+    path, so this only bounds the fast-path coverage, not correctness. Cached
+    (server args are fixed after startup)."""
+    global _aiter_topk_fuse_shared_max_tokens_cache
+    if _aiter_topk_fuse_shared_max_tokens_cache is None:
+        from sglang.srt.runtime_context import get_server_args
+
+        try:
+            sa = get_server_args()
+        except ValueError:
+            # Global server args not published yet (e.g. a unit test or offline
+            # init that reaches the aiter grouped-topk path before startup).
+            # Degrade gracefully instead of crashing -- this value only bounds
+            # the fast-path coverage, not correctness (see docstring). Return the
+            # safety cap WITHOUT caching, so a later call (once args are set)
+            # still computes and caches the real value.
+            return _AITER_TOPK_FUSE_SHARED_MAX_TOKENS_CAP
+        cps = getattr(sa, "chunked_prefill_size", None) or 0
+        mpt = getattr(sa, "max_prefill_tokens", None) or 0
+        m = max(int(cps), int(mpt), 8192)  # 8192 floor for tiny configs
+        if int(cps) <= 0 and int(mpt) <= 0:
+            # chunked prefill disabled -> use the safety cap
+            m = _AITER_TOPK_FUSE_SHARED_MAX_TOKENS_CAP
+        _aiter_topk_fuse_shared_max_tokens_cache = min(
+            m, _AITER_TOPK_FUSE_SHARED_MAX_TOKENS_CAP
+        )
+    return _aiter_topk_fuse_shared_max_tokens_cache
+
+
+def _get_aiter_topk_fuse_shared_buf(
+    topk_routed: int, n_shared: int, num_experts: int, shared_weight: float, device
+):
+    """Persistent [MAX, topk_routed + n_shared] weight/id buffers whose shared
+    columns are pre-filled once (id = num_experts + i, weight = shared_weight).
+    Fixed max size (>= max prefill batch) so the tensor address is stable across
+    CUDA-graph replays."""
+    key = (topk_routed, n_shared, num_experts, float(shared_weight), str(device))
+    buf = _aiter_topk_fuse_shared_bufs.get(key)
+    if buf is None:
+        total = topk_routed + n_shared
+        M = _get_aiter_topk_fuse_shared_max_tokens()
+        w = torch.empty((M, total), dtype=torch.float32, device=device)
+        ids = torch.empty((M, total), dtype=torch.int32, device=device)
+        ids[:, topk_routed:] = torch.arange(
+            num_experts, num_experts + n_shared, dtype=torch.int32, device=device
+        ).unsqueeze(0)
+        w[:, topk_routed:] = shared_weight
+        buf = (w, ids)
+        _aiter_topk_fuse_shared_bufs[key] = buf
+    return buf
+
 
 if _is_cuda:
     try:
@@ -1446,6 +1518,7 @@ def biased_grouped_topk_gpu(
     num_fused_shared_experts: int = 0,
     routed_scaling_factor: Optional[float] = None,
     apply_routed_scaling_factor_on_output: Optional[bool] = False,
+    fused_shared_experts_scaling_factor: Optional[float] = None,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
     num_tokens = gating_output.shape[0]
     num_experts = gating_output.shape[1]
@@ -1571,7 +1644,33 @@ def biased_grouped_topk_gpu(
         assert (
             hidden_states.shape[0] == gating_output.shape[0]
         ), f"Number of tokens mismatch: hidden_states.shape[0] = {hidden_states.shape[0]}, gating_output.shape[0] = {gating_output.shape[0]}"
-        topk_weights = torch.empty((token, topk), dtype=torch.float32, device=device)
+        _shared_fuse = (
+            num_fused_shared_experts > 0
+            and get_parallel().moe_ep_size == 1
+            and token <= _get_aiter_topk_fuse_shared_max_tokens()
+        )
+        if _shared_fuse:
+            # Persistent buffer with pre-populated shared columns. The weight is the
+            # constant scale_factor the aiter _post_process append writes (default
+            # 1.0), so this is bit-identical for any shared-expert scaling. aiter
+            # writes only the routed columns [:, :topk] via row stride; the shared
+            # column stays intact. If token exceeds the buffer size we drop to the
+            # plain path below (and _post_process appends shared experts as usual) —
+            # the same condition is mirrored there so the two paths stay consistent.
+            _shared_w = (
+                1.0
+                if fused_shared_experts_scaling_factor is None
+                else fused_shared_experts_scaling_factor
+            )
+            full_w, full_ids = _get_aiter_topk_fuse_shared_buf(
+                topk, num_fused_shared_experts, num_experts, _shared_w, device
+            )
+            topk_weights = full_w[:token, :topk]
+            topk_ids = full_ids[:token, :topk]
+        else:
+            topk_weights = torch.empty(
+                (token, topk), dtype=torch.float32, device=device
+            )
             topk_ids = torch.empty((token, topk), dtype=torch.int32, device=device)
         aiter_biased_grouped_topk(
             gating_output,
@@ -1583,6 +1682,10 @@ def biased_grouped_topk_gpu(
             renormalize,
             routed_scaling_factor if routed_scaling_factor is not None else 1.0,
         )
+        if _shared_fuse:
+            # Return the full [token, topk + n_shared] view (routed just written,
+            # shared pre-populated). _post_process_topk_ids skips its append.
+            return full_w[:token], full_ids[:token]
         return topk_weights, topk_ids
     elif _is_musa and (
         gating_output.shape[1] // num_expert_group <= 32
@@ -2036,6 +2139,24 @@ def _post_process_topk_ids(
             num_local_routed,
         )
     elif _aiter_append:
+        # Detect whether the shared experts were already fused/appended in
+        # biased_grouped_topk_gpu via the persistent pre-populated topk buffer.
+        # When fused, topk_ids already has the full width (routed + shared), i.e.
+        # topk_ids.shape[1] == topk_config.top_k; when the fast path fell back to
+        # the plain buffer (e.g. token > MAX during a large prefill), only the
+        # routed columns are present and we must append the shared experts here.
+        # Checking the tensor shape is robust to the exact fast-path conditions
+        # (no fragile mirroring of biased_grouped_topk_gpu's _shared_fuse check).
+        _shared_fused_in_topk = (
+            num_fused_shared_experts > 0
+            and get_parallel().moe_ep_size == 1
+            and topk_ids.shape[1] == topk_config.top_k
+        )
+        if _shared_fused_in_topk:
+            # Shared experts were already appended in biased_grouped_topk_gpu via
+            # the persistent pre-populated topk buffer; nothing to do here.
+            pass
+        else:
             M, N = router_logits.shape
             scale_factor = (
                 1.0
@@ -2149,6 +2270,7 @@ def select_experts(
                 num_fused_shared_experts=num_fused_shared_experts,
                 routed_scaling_factor=routed_scaling_factor,
                 apply_routed_scaling_factor_on_output=apply_routed_scaling_factor_on_output,
+                fused_shared_experts_scaling_factor=topk_config.fused_shared_experts_scaling_factor,
             )
     elif torch_native and custom_routing_function is None:
         assert (