feat: 分块SDPA注意力(--attn chunked)，按用户边界切块降O(S²)

每块~chunk_users个用户、块内因果SDPA(评测端已验证、无嵌套开销)，sum(块S²)
远小于总S²。仅1次同步读切分边界。之前本地bs=16快13%被MoE同步吃掉，现MoE
同步已消除，切块红利应全露出。CONFIG.attn=chunked/chunk_users；等价测试已加。

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

代码/code/tests/test_equiv.py

@@ -64,6 +64,28 @@ def test_moe_dense_matches_loop():
     print(f"[PASS] MoE 稠密向量化 == 逐expert循环 (max err={err:.2e}, dev={dev})")
 
 
+def test_chunked_matches_dense_attention():
+    dev = "cuda" if torch.cuda.is_available() else "cpu"
+    rep = infer.RepEncoder(vocab_size=100, emb_dim=8, slot_num=28, d_model=8)
+    seq = infer.TransformerEncoder(d_model=8, n_heads=2, num_layers=1, dim_ff=16)
+    model = infer.CTRModel(rep, seq, d_model=8).to(dev)
+    torch.manual_seed(0)
+    H, Dh = 8, 64
+    offs = _offsets([10, 25, 7, 40, 18, 5, 33], dev)  # 7 个用户
+    S = int(offs[-1])
+    q = torch.randn(1, H, S, Dh, device=dev)
+    k = torch.randn(1, H, S, Dh, device=dev)
+    v = torch.randn(1, H, S, Dh, device=dev)
+    with torch.no_grad():
+        dense = infer.scaled_dot_product(q, k, v, {"mask": _dense_causal_mask(offs)[None, None]})
+        infer.CONFIG["chunk_users"] = 3  # 每块 3 个用户
+        chunks = model.build_chunks(offs, torch.device(dev))
+        chunked = infer.scaled_dot_product(q, k, v, {"chunks": chunks})
+    err = (dense - chunked).abs().max().item()
+    assert torch.allclose(dense, chunked, atol=1e-4, rtol=1e-4), f"chunked 不等价 max err={err:.3e}"
+    print(f"[PASS] chunked SDPA == 稠密SDPA (max err={err:.2e}, dev={dev})")
+
+
 def test_syncfree_mask_matches():
     dev = "cuda" if torch.cuda.is_available() else "cpu"
     rep = infer.RepEncoder(vocab_size=100, emb_dim=8, slot_num=28, d_model=8)
@@ -148,6 +170,7 @@ if __name__ == "__main__":
     test_moe_dense_matches_loop()
     test_fused_embedding_matches_perslot()
     test_syncfree_mask_matches()
+    test_chunked_matches_dense_attention()
     test_varlen_matches_dense_attention()
     test_flex_matches_dense_attention()
     print("[DONE] 等价测试结束")