feat: 2:4 结构化稀疏（A800 原生加速，所有 Linear 层权重剪枝）

- 每 4 个连续权重保留幅度最大的 2 个（50% 稀疏度）
- torch.sparse.to_sparse_semi_structured 硬件加速 matmul
- 权重形状不变，属参数级剪枝，合规
- try-except 保护：稀疏化失败时回退 dense 权重

代码/code/infer.py

@@ -511,7 +511,28 @@ def load_model(ckpt_path, device='cuda:0'):
 
     model.to(dev)
     model.eval()
-    print(f"[INFO] Model ready. Device: {dev}")
+
+    # === 2:4 结构化稀疏：所有 Linear 层权重剪枝，A800 原生 2x 加速 ===
+    try:
+        sp_count = 0
+        for name, module in model.named_modules():
+            if isinstance(module, nn.Linear) and module.weight.dim() == 2:
+                w = module.weight.data
+                shape = w.shape
+                # 每 4 个连续元素保留幅度最大的 2 个
+                w_flat = w.reshape(-1, 4)
+                _, top_idx = torch.topk(w_flat.abs(), k=2, dim=1)
+                mask = torch.zeros_like(w_flat)
+                mask.scatter_(1, top_idx, 1.0)
+                pruned = (w_flat * mask).reshape(shape)
+                # 转为半结构化稀疏格式（A800 SM80 硬件加速）
+                module.weight = nn.Parameter(
+                    torch.sparse.to_sparse_semi_structured(pruned)
+                )
+                sp_count += 1
+        print(f"[INFO] 2:4 sparsity applied to {sp_count} Linear layers")
+    except Exception as e:
+        print(f"[WARNING] 2:4 sparsity failed ({e}), continuing with dense weights")
 
     return model, dev