Add Wall-X serving and Turtle2 TCP WebSocket bridge

tests/test_lora_scale.py

@@ -0,0 +1,45 @@
+"""Keep serving LoRA merge consistent with exported training parameters."""
+
+import json
+
+import pytest
+import torch
+
+from wall_x._vendor.harrix.utils import ckpt_load
+
+
+def _tiny_lora():
+    stem = "model.base_model.model.proj"
+    return {
+        stem + ".base_layer.weight": torch.zeros((2, 2)),
+        stem + ".lora_A.default.weight": torch.eye(2),
+        stem + ".lora_B.default.weight": torch.eye(2),
+    }
+
+
+def test_checkpoint_local_lora_config_controls_merge(tmp_path):
+    (tmp_path / "lora_config.json").write_text(
+        json.dumps({"lora_r": 2, "lora_alpha": 6}), encoding="utf-8"
+    )
+    tensors = _tiny_lora()
+    scale = ckpt_load.resolve_lora_scale(str(tmp_path), {}, tensors)
+    merged = ckpt_load.reshape_compatible_state_dict(
+        tensors, {"model.proj.weight": torch.zeros((2, 2))}, lora_scale=scale
+    )
+    assert scale == 3
+    torch.testing.assert_close(merged["model.proj.weight"], 3 * torch.eye(2))
+
+
+def test_checkpoint_lora_rank_mismatch_is_rejected(tmp_path):
+    (tmp_path / "lora_config.json").write_text(
+        json.dumps({"lora_r": 4, "lora_alpha": 8}), encoding="utf-8"
+    )
+    with pytest.raises(ValueError, match="rank"):
+        ckpt_load.resolve_lora_scale(str(tmp_path), {}, _tiny_lora())
+
+
+def test_legacy_checkpoint_without_lora_metadata_uses_previous_scale(tmp_path):
+    messages = []
+    scale = ckpt_load.resolve_lora_scale(str(tmp_path), {}, _tiny_lora(), log_fn=messages.append)
+    assert scale == 2
+    assert any("metadata" in message for message in messages)