[AMD][Spec] Fix aiter GQA packing + split-KV routing in NEXTN spec attention (verify & draft_extend) (#30105)

python/sglang/srt/environ.py

@@ -513,7 +513,13 @@ class Envs:
     # HND KV layout folds (page, head) into one paged index for per-kv-head sparse
     # page tables (DP attn); paged backends like trtllm_mha consume it directly.
     SGLANG_USE_HND_KVCACHE = EnvBool(False)
-    # Size the KV pool after CUDA-graph capture.
+
+    # Attention (aiter, ROCm): route NEXTN spec draft_extend (EAGLE-v2 KV
+    # catch-up) through aiter unified_attention (GQA-packed + split-KV) instead
+    # of the occupancy-starved mha_batch_prefill FMHA. Independent kill-switch
+    # for the new path; pairs with SGLANG_AITER_UNIFIED_VERIFY. Default on.
+    SGLANG_AITER_UNIFIED_DRAFT_EXTEND = EnvBool(True)
+    # size the KV pool after CUDA-graph capture
     SGLANG_ENABLE_POST_CAPTURE_KV_SIZING = EnvBool(False)
 
     # ===================================================================

python/sglang/srt/layers/attention/aiter_backend.py

@@ -70,6 +70,7 @@ from sglang.kernels.ops.quantization.fp8_kernel import (
     scaled_fp8_quant,
 )
 from sglang.srt.configs.model_config import AttentionArch
+from sglang.srt.environ import envs
 from sglang.srt.layers.attention.aiter_utils import (
     forward_decode_vectorized_5d,
     forward_extend_vectorized_5d,
@@ -2285,12 +2286,15 @@ class AiterAttnBackend(AttentionBackend):
                     v_unified = v_cache.view(
                         -1, self.page_size, layer.tp_v_head_num, layer.v_head_dim
                     )
-                    if layer.tp_k_head_num == 1 and layer.tp_q_head_num > 1:
+                    # GQA-packing fix: do NOT expand the single KV head to
-                        # Qwen3.5 can replicate one KV head across multiple TP ranks.
+                    # tp_q_head_num. Passing K/V with the true kv-head count (exactly
-                        # Present the local KV head as per-Q-head stride-0 views so
+                    # like forward_decode) lets unified_attention derive
-                        # target_verify uses the same local head mapping as the model.
+                    # num_queries_per_kv = tp_q_head_num (the GQA group) and pack all Q
-                        k_unified = k_unified.expand(-1, -1, layer.tp_q_head_num, -1)
+                    # heads against one KV load. The old stride-0 .expand() made the
-                        v_unified = v_unified.expand(-1, -1, layer.tp_q_head_num, -1)
+                    # wrapper see num_kv_heads=tp_q_head_num -> num_queries_per_kv=1 ->
+                    # full MHA tiling (~7x more KV traffic at long context; trace
+                    # signature num_query_heads_16/num_queries_per_kv_1). GQA head
+                    # mapping here is identical to the proven decode path.
 
                     # The seq_lens + draft_num add has to run INSIDE the graph
                     # region; a host-side pre-add would allocate a new tensor
@@ -2339,6 +2343,64 @@ class AiterAttnBackend(AttentionBackend):
                 )
                 return o.view(-1, layer.tp_q_head_num * layer.v_head_dim)
 
+            # draft_extend (EAGLE-v2 KV catch-up) is decode-shaped: short Q
+            # (accepted tokens) against long paged KV, GQA. The default
+            # mha_batch_prefill_func FMHA has no split-KV, so at short Q it is
+            # occupancy-starved (~0.2% HBM BW, ~400x over the memory floor).
+            # Route it through unified_attention (GQA-packed + split-KV), exactly
+            # like target_verify, so it runs near the memory floor.
+            if (
+                self._use_unified_verify
+                and forward_batch.forward_mode.is_draft_extend_v2()
+                and envs.SGLANG_AITER_UNIFIED_DRAFT_EXTEND.get()
+            ):
+                bs = forward_batch.batch_size
+                if layer.qk_head_dim != layer.v_head_dim:
+                    o = q.new_empty(
+                        (q.shape[0], layer.tp_q_head_num * layer.v_head_dim)
+                    )
+                else:
+                    o = torch.empty_like(q)
+                k_cache, v_cache = self.token_to_kv_pool.get_kv_buffer(layer.layer_id)
+                page_table, swa_page_table = self._build_unified_page_table_from_spec(
+                    self.forward_metadata, bs
+                )
+                pt = page_table
+                de_window = (-1, -1)
+                if (
+                    layer.sliding_window_size is not None
+                    and layer.sliding_window_size > -1
+                ):
+                    de_window = (layer.sliding_window_size - 1, 0)
+                    if swa_page_table is not None:
+                        pt = swa_page_table
+                kv_indptr = self.forward_metadata.kv_indptr
+                seqused_k = (kv_indptr[1 : bs + 1] - kv_indptr[:bs]).to(torch.int32)
+                unified_attention(
+                    q=q.view(-1, layer.tp_q_head_num, layer.qk_head_dim),
+                    k=k_cache.view(
+                        -1, self.page_size, layer.tp_k_head_num, layer.qk_head_dim
+                    ),
+                    v=v_cache.view(
+                        -1, self.page_size, layer.tp_v_head_num, layer.v_head_dim
+                    ),
+                    out=o.view(-1, layer.tp_q_head_num, layer.v_head_dim),
+                    cu_seqlens_q=self.qo_indptr[: bs + 1],
+                    seqused_k=seqused_k,
+                    max_seqlen_q=self.forward_metadata.max_q_len,
+                    max_seqlen_k=pt.shape[1] * self.page_size,
+                    softmax_scale=layer.scaling,
+                    causal=True,
+                    window_size=de_window,
+                    block_table=pt,
+                    softcap=layer.logit_cap,
+                    q_descale=None,
+                    k_descale=k_descale,
+                    v_descale=v_descale,
+                    sinks=sinks,
+                )
+                return o.view(-1, layer.tp_q_head_num * layer.v_head_dim)
+
             bs0 = forward_batch.batch_size + 1
             q_descale = None
 

test/manual/test_aiter_unified_draft_extend_env.py

@@ -0,0 +1,40 @@
+"""
+Unit test for the aiter NEXTN spec-decode draft_extend routing flag.
+
+aiter_backend.py routes EAGLE-v2 draft_extend (KV catch-up) through
+unified_attention (GQA-packed + split-KV) instead of the occupancy-starved
+mha_batch_prefill FMHA, gated behind SGLANG_AITER_UNIFIED_DRAFT_EXTEND. This
+guards the flag's registration and default so an accidental rename or default
+flip is caught in CI. The kernel path itself requires ROCm/gfx950 and is
+covered by the accuracy/speed evidence in the PR.
+
+Run:
+    python test/manual/test_aiter_unified_draft_extend_env.py
+"""
+
+import os
+import sys
+import unittest
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../../python"))
+
+
+class TestAiterUnifiedDraftExtendEnv(unittest.TestCase):
+    def setUp(self):
+        from sglang.srt.environ import envs
+
+        self.flag = envs.SGLANG_AITER_UNIFIED_DRAFT_EXTEND
+
+    def test_registered_and_default_on(self):
+        # Registered as an EnvBool defaulting to True (path enabled in prod).
+        self.assertTrue(self.flag.get())
+
+    def test_override_toggles_and_restores(self):
+        self.assertTrue(self.flag.get())
+        with self.flag.override(False):
+            self.assertFalse(self.flag.get())
+        self.assertTrue(self.flag.get())
+
+
+if __name__ == "__main__":
+    unittest.main()