File size: 4,013 Bytes
e8055cf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | """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():
# Otherwise InternVL's default per-image dynamic tiling (~3300 tokens/frame) blows
# past this checkpoint's 40960-token context window at just 16 frames.
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?"},
]
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