feat: INT8 动态量化所有 Linear 层（torch.ao.quantization）

- 仅量化 Linear 权重（不影响 Embedding）
- INT8 权重读带宽减半 vs FP16
- try-except 保护：CUDA 后端不可用时回退 FP16

代码/code/infer.py

@@ -500,6 +500,23 @@ def load_model(ckpt_path, device='cuda:0'):
         model = model.half()
         model.rep_encoder.emb = model.rep_encoder.emb.to(torch.float32)
         print("[INFO] Model converted to FP16 (embedding kept in FP32)")
+
+        # === INT8 动态量化：所有 Linear 层权重 INT8，matmul 2x 加速 ===
+        try:
+            from torch.ao.quantization import quantize_dynamic
+            # 排除 embedding 层，仅量化 Linear
+            model.seq_encoder = quantize_dynamic(
+                model.seq_encoder, {nn.Linear}, dtype=torch.qint8
+            )
+            model.linear = quantize_dynamic(
+                model.linear, {nn.Linear}, dtype=torch.qint8
+            )
+            model.rep_encoder.linear = quantize_dynamic(
+                model.rep_encoder.linear, {nn.Linear}, dtype=torch.qint8
+            )
+            print("[INFO] INT8 dynamic quantization applied to Linear layers")
+        except Exception as e:
+            print(f"[WARNING] INT8 quantization failed ({e}), keeping FP16")
     else:
         print(f"[WARNING] Checkpoint {ckpt_path} not found, using random weights")