workspace / tests /test_B /test_run.py
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"""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
# No frame-provenance fields -- B has no video frames.
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