| """Tests for harness/B/run.py -- result-record shape and result-file writing.""" |
|
|
| import json |
|
|
| from harness import B |
| from harness.B import run as harness_run |
|
|
| _FAKE_ANSWER = { |
| "prompt_text": "<rendered chat template>", |
| "answer_text": "4", |
| "answer_raw": "<|im_start|>assistant\n4<|im_end|>", |
| "input_token_count": 2558, |
| "vision_input_shapes": {"mm_token_type_ids": [1, 2558]}, |
| "output_token_ids": [19, 151645], |
| "output_token_count": 2, |
| "hit_token_limit": False, |
| "eos_token_ids": [151645], |
| "generation_seconds": 0.65, |
| "device": "cuda", |
| "dtype": "bfloat16", |
| "library_versions": {"transformers": "5.14.1", "torch": "2.13.0+cu130"}, |
| "generation_config": { |
| "max_new_tokens": 16, |
| "do_sample": False, |
| "temperature": 0.0, |
| "top_p": None, |
| "top_k": None, |
| "enable_thinking": False, |
| }, |
| } |
|
|
| _FAKE_ROW = { |
| "id": 7, |
| "scene_name": "scene0001_00", |
| "dataset": "scannet", |
| "question_type": "object_counting", |
| "question": "How many chairs?", |
| "options": None, |
| "ground_truth": "4", |
| } |
|
|
| _FAKE_CODE_INFO = { |
| "protocol": "thinking", |
| "spatial_code_format": "explicit", |
| "input_selection": "selective", |
| "frame_count": 64, |
| "depth": "metric", |
| "tracking": "tracking", |
| "spatial_code_path": "/workspace/data/spatial codes/.../scene0001_00.json", |
| } |
|
|
|
|
| def test_results_dir_for_matches_established_dimension_nesting(): |
| root = harness_run.results_dir_for( |
| "qwen3.5-4b", "thinking", "explicit", "metric", "tracking", "uniform", 32 |
| ) |
| assert root == ( |
| B.RESULTS_DIR |
| / "qwen3.5-4b" |
| / "explicit" |
| / "metric" |
| / "tracking" |
| / "uniform" |
| / "32" |
| ) |
|
|
|
|
| def test_results_dir_for_keeps_protocols_together(): |
| base = harness_run.results_dir_for( |
| "qwen3.5-4b", "base", "explicit", "metric", "tracking", "uniform", 32 |
| ) |
| extended = harness_run.results_dir_for( |
| "qwen3.5-4b", "thinking", "explicit", "metric", "tracking", "uniform", 32 |
| ) |
| assert base == extended |
|
|
|
|
| def test_results_dir_for_honors_explicit_override(tmp_path): |
| root = harness_run.results_dir_for( |
| "qwen3.5-4b", |
| "base", |
| "explicit", |
| "relative", |
| "no tracking", |
| "selective", |
| 16, |
| tmp_path, |
| ) |
| assert root == tmp_path |
|
|
|
|
| def test_build_record_preserves_every_field_untruncated(): |
| record = harness_run._build_record( |
| _FAKE_ROW, |
| "full prompt text", |
| _FAKE_ANSWER, |
| "MRA:.5:.95:.05", |
| 1.0, |
| "qwen3.5-4b", |
| "/root/models/qwen3.5-4b", |
| _FAKE_CODE_INFO, |
| ) |
| assert record["question"] == "How many chairs?" |
| assert record["full_prompt"] == "full prompt text" |
| assert record["rendered_prompt"] == _FAKE_ANSWER["prompt_text"] |
| assert record["answer_given"] == "4" |
| assert record["answer_raw"] == _FAKE_ANSWER["answer_raw"] |
| assert record["spatial_code_format"] == "explicit" |
| assert record["input_selection"] == "selective" |
| assert record["frame_count"] == 64 |
| assert record["depth"] == "metric" |
| assert record["tracking"] == "tracking" |
| assert record["spatial_code_path"] == _FAKE_CODE_INFO["spatial_code_path"] |
| assert record["condition"] == "thinking:explicit:metric:tracking:selective:64" |
| assert record["protocol"] == "thinking" |
| assert record["vision_input_shapes"] == {"mm_token_type_ids": [1, 2558]} |
| assert record["generation_config"] == _FAKE_ANSWER["generation_config"] |
| assert record["metric"] == "MRA:.5:.95:.05" |
| assert record["score"] == 1.0 |
| assert record["scene"] == "scene0001_00" |
| assert record["question_id"] == 7 |
| |
| assert "frame_selection" not in record |
| assert "video_path" not in record |
| assert "frame_indices" not in record |
|
|
|
|
| def test_write_question_result_writes_one_json_file_per_question(tmp_path): |
| path, record = harness_run.write_question_result( |
| _FAKE_ROW, |
| "full prompt text", |
| _FAKE_ANSWER, |
| "MRA:.5:.95:.05", |
| 1.0, |
| "qwen3.5-4b", |
| "/root/models/qwen3.5-4b", |
| _FAKE_CODE_INFO, |
| results_dir=tmp_path, |
| ) |
| assert path == tmp_path / "scene0001_00" / "7.json" |
| on_disk = json.loads(path.read_text()) |
| assert on_disk == record |
|
|
|
|
| def test_build_record_carries_reasoning_fields_when_forced(): |
| extended_answer = { |
| **_FAKE_ANSWER, |
| "reasoning_text": "long reasoning about the spatial code", |
| "reasoning_raw": "long reasoning about the spatial code<|im_end|>", |
| "reasoning_token_ids": list(range(50)), |
| "reasoning_token_count": 50, |
| "reasoning_hit_limit": True, |
| "forced": True, |
| "forced_input_token_count": 2510, |
| } |
| record = harness_run._build_record( |
| _FAKE_ROW, |
| "full prompt text", |
| extended_answer, |
| "MRA:.5:.95:.05", |
| 1.0, |
| "qwen3.5-4b", |
| "/root/models/qwen3.5-4b", |
| _FAKE_CODE_INFO, |
| ) |
| assert record["reasoning_text"] == "long reasoning about the spatial code" |
| assert record["reasoning_raw"] == "long reasoning about the spatial code<|im_end|>" |
| assert record["reasoning_token_ids"] == list(range(50)) |
| assert record["reasoning_token_count"] == 50 |
| assert record["reasoning_hit_limit"] is True |
| assert record["forced"] is True |
| assert record["forced_input_token_count"] == 2510 |
|
|
|
|
|
|
| def test_video_results_use_video_branch(): |
| assert ( |
| harness_run.results_dir_for( |
| "qwen3.5-4b", "thinking", "explicit", "metric", "tracking", "video", None |
| ) |
| == B.RESULTS_DIR / "qwen3.5-4b" / "explicit" / "metric" / "tracking" / "video" |
| ) |
|
|
|
|
| def test_video_record_has_no_frame_count_in_condition(): |
| info = dict(_FAKE_CODE_INFO, input_selection="video", frame_count=None) |
| record = harness_run._build_record( |
| _FAKE_ROW, "prompt", _FAKE_ANSWER, "metric", 1.0, "qwen3.5-4b", "/model", info |
| ) |
| assert record["condition"] == "thinking:explicit:metric:tracking:video" |
| assert record["frame_count"] is None |
|
|