feat: 真稀疏MoE(capacity分组,只算top-k,cutlass baddbmm,无host同步)

按expert排序token+固定capacity分桶,每桶dense baddbmm,减GEMM~3x。argsort/where/
scatter/index_add无.item()/bincount同步(不同于loop MoE)。超容量token丢弃(capacity_factor控)。
等价测试(大capacity无丢弃==dense)。bench --moe-sparse/--moe-cap。默认关待验证。

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

代码/code/tests/test_equiv.py

@@ -192,6 +192,25 @@ def test_varlen_matches_dense_attention():
     print(f"[PASS] varlen(嵌套张量) == 稠密SDPA (max err={err:.2e})")
 
 
+def test_sparse_moe_matches_dense():
+    # 大 capacity（无丢弃）下，稀疏 MoE 应与 dense 数学等价
+    torch.manual_seed(0)
+    dev = "cuda" if torch.cuda.is_available() else "cpu"
+    m = infer.SMoE(d_model=512, dim_ff=1024, num_experts=8, k=2).to(dev).eval()
+    x = torch.randn(1, 200, 512, device=dev)
+    with torch.no_grad():
+        infer.CONFIG["moe_sparse"] = False
+        ref, _ = m(x)
+        infer.CONFIG["moe_sparse"] = True
+        infer.CONFIG["moe_capacity"] = 8.0   # 足够大，不丢 token
+        new, _ = m(x)
+    infer.CONFIG["moe_sparse"] = False
+    infer.CONFIG["moe_capacity"] = 1.25
+    err = (ref - new).abs().max().item()
+    assert torch.allclose(ref, new, atol=1e-3, rtol=1e-3), f"sparse MoE 不等价 max err={err:.3e}"
+    print(f"[PASS] sparse MoE(大capacity) == dense (max err={err:.2e}, dev={dev})")
+
+
 def test_fused_embedding_matches_perslot():
     torch.manual_seed(0)
     dev = "cuda" if torch.cuda.is_available() else "cpu"
@@ -240,6 +259,7 @@ def test_flex_matches_dense_attention():
 
 if __name__ == "__main__":
     test_moe_dense_matches_loop()
+    test_sparse_moe_matches_dense()
     test_fused_embedding_matches_perslot()
     test_embedding_bag_matches()
     test_sparse_pool_matches()