Add Wall-X serving and Turtle2 TCP WebSocket bridge

tests/test_turtle2_inference_input.py

@@ -0,0 +1,47 @@
+"""Checkpoint-specific multimodal prompt contract, without loading model weights."""
+
+import logging
+import os
+from pathlib import Path
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+
+from wall_x._vendor.harrix.serving._wallx_infer.infer_config import InferConfig
+from wall_x._vendor.harrix.serving._wallx_infer.model_wrapper import WallxModelWrapper
+from wall_x.trainer.trainer_utils import load_wallx_processors
+
+
+def test_three_square_cameras_preserve_all_image_tokens():
+    """Truncating an image placeholder must never reach the VLA as valid input."""
+    if "WALLX_TEST_CHECKPOINT" not in os.environ:
+        pytest.skip("set WALLX_TEST_CHECKPOINT to run checkpoint-specific input tests")
+    checkpoint = Path(os.environ["WALLX_TEST_CHECKPOINT"])
+    config = InferConfig(
+        checkpoint_path=str(checkpoint),
+        train_config_path=str(checkpoint / "config.yml"),
+        model_device="cpu", action_horizon=32,
+    )
+    processor = load_wallx_processors(config.train_config, device="cpu")["processor"]
+    wrapper = WallxModelWrapper.__new__(WallxModelWrapper)
+    wrapper.config = config
+    wrapper.model = SimpleNamespace(processor=processor)
+    wrapper.cam_names = config.cam_names
+    wrapper.norm_key = "pick_paper_lerobot_v21_3cam"
+    wrapper.logger = logging.getLogger(__name__)
+    wrapper.role_start_symbol = "<|im_start|>"
+    wrapper.role_end_symbol = "<|im_end|>"
+    wrapper.vision_start_symbol = "<|vision_start|>"
+    wrapper.vision_end_symbol = "<|vision_end|>"
+    wrapper.image_pad_symbol = "<|image_pad|>"
+    wrapper.propri_symbol = "<|propri|>"
+    wrapper.action_symbol = "<|action|>"
+    prefix, postfix = wrapper.get_text_for_action("pick up the paper towel")
+    image = np.full((448, 448, 3), 127, dtype=np.uint8)
+    observation = [{name: image for name in wrapper.cam_names}]
+    inputs = wrapper.construct_model_input(observation, prefix, postfix)
+    merge = processor.image_processor.merge_size ** 2
+    expected = int((inputs["image_grid_thw"].prod(dim=1) // merge).sum())
+    actual = int((inputs["input_ids"] == processor.tokenizer.convert_tokens_to_ids("<|image_pad|>")).sum())
+    assert actual == expected == 768