| """Tests for harness/A/models.py -- VLM adapter registry and prompt assembly. |
| |
| Deliberately excludes any test that loads real model weights or calls .generate() -- |
| those require the GPU and downloaded checkpoints and are exercised via harness.A.run |
| smoke runs instead, not the unit suite. |
| """ |
|
|
| import numpy as np |
| import pytest |
|
|
| from harness import A |
| from harness.A import models as vlm_models |
|
|
|
|
| def test_all_four_models_are_registered(): |
| assert vlm_models.available_models() == ( |
| "internvl3.5-2b", |
| "internvl3.5-4b", |
| "qwen3.5-2b", |
| "qwen3.5-4b", |
| ) |
|
|
|
|
| def test_get_adapter_binds_the_correct_checkpoint_path(): |
| adapter = vlm_models.get_adapter("qwen3.5-4b") |
| assert adapter.model_path == A.MODEL_PATHS["qwen3.5-4b"] |
| assert isinstance(adapter, vlm_models.QwenVLAdapter) |
|
|
|
|
| def test_get_adapter_returns_the_right_class_per_model(): |
| assert isinstance(vlm_models.get_adapter("qwen3.5-2b"), vlm_models.QwenVLAdapter) |
| assert isinstance( |
| vlm_models.get_adapter("internvl3.5-4b"), vlm_models.InternVLAdapter |
| ) |
|
|
|
|
| def test_get_adapter_rejects_unknown_model(): |
| with pytest.raises(KeyError): |
| vlm_models.get_adapter("not-a-real-model") |
|
|
|
|
| def test_internvl_adapter_disables_per_frame_tiling(): |
| |
| |
| assert vlm_models.InternVLAdapter.chat_template_kwargs == {"crop_to_patches": False} |
|
|
|
|
| def test_numbered_content_labels_every_frame_in_order(): |
| frames = ["frame0", "frame1", "frame2"] |
| content = vlm_models._numbered_content(frames, "What is in the room?") |
| assert content[0] == {"type": "text", "text": "Frame 1:"} |
| assert content[1] == {"type": "image", "image": "frame0"} |
| assert content[-1] == {"type": "text", "text": "What is in the room?"} |
| image_items = [item for item in content if item["type"] == "image"] |
| assert [item["image"] for item in image_items] == frames |
|
|
|
|
| def test_numbered_content_handles_zero_frames(): |
| content = vlm_models._numbered_content([], "question only") |
| assert content == [{"type": "text", "text": "question only"}] |
|
|
|
|
| def test_adapter_answer_before_load_model_raises(): |
| adapter = vlm_models.get_adapter("qwen3.5-2b") |
| with pytest.raises(RuntimeError): |
| adapter.answer(["frame"], "question") |
|
|
|
|
| def test_adapter_answer_extended_before_load_model_raises(): |
| adapter = vlm_models.get_adapter("qwen3.5-2b") |
| with pytest.raises(RuntimeError): |
| adapter.answer_extended(["frame"], "question") |
|
|
|
|
| def test_every_adapter_implements_answer_extended(): |
| for model in vlm_models.available_models(): |
| adapter = vlm_models.get_adapter(model) |
| assert callable(adapter.answer_extended) |
|
|
|
|
| def test_decode_new_tokens_preserves_clean_and_raw_text(): |
| adapter = vlm_models.get_adapter("qwen3.5-2b") |
|
|
| class Processor: |
| def decode(self, token_ids, skip_special_tokens): |
| if skip_special_tokens: |
| return "step one, step two, answer B" |
| return "<think>step one, step two</think>B<eos>" |
|
|
| adapter.processor = Processor() |
| token_ids, hit_limit, text, raw = adapter._decode_new_tokens( |
| np.array([[10, 11, 21, 22, 2]]), 2, 2048, [2] |
| ) |
| assert token_ids == [21, 22, 2] |
| assert hit_limit is False |
| assert text == "step one, step two, answer B" |
| assert raw == "<think>step one, step two</think>B<eos>" |
|
|
|
|
| def test_unload_clears_model_and_processor(): |
| adapter = vlm_models.get_adapter("qwen3.5-2b") |
| adapter.model = object() |
| adapter.processor = object() |
| adapter.unload() |
| assert adapter.model is None |
| assert adapter.processor is None |
|
|
|
|
| def test_native_video_content_uses_one_video_item(): |
| content = vlm_models._numbered_content("/data/scene.mp4", "What is in the room?") |
| assert content == [ |
| {"type": "video", "video": "/data/scene.mp4"}, |
| {"type": "text", "text": "What is in the room?"}, |
| ] |
|
|
