Datasets:
File size: 12,914 Bytes
e1ced61 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 | """Deterministic Qwen3.5-VL prediction for frozen evaluation manifests.
GPU libraries remain lazy imports so manifest/scoring tests run on CPU-only
machines. The runner is append-resumable by eval ID and never re-hashes model,
adapter, or image trees; paths are checked structurally when they are used.
"""
from __future__ import annotations
import copy
import json
import os
import time
from collections.abc import Mapping, Sequence
from pathlib import Path
from typing import Any
from ..hashing import canonical_json
from ..paths import repo_root
from ..training.answers import parse_answer
from .core import EvaluationError
def resolve_asset(root: Path, raw_path: str) -> Path:
relative = Path(raw_path)
if not raw_path or relative.is_absolute() or ".." in relative.parts or "\\" in raw_path:
raise EvaluationError(f"unsafe evaluation image path: {raw_path!r}")
resolved_root = root.resolve()
resolved = (resolved_root / relative).resolve()
try:
resolved.relative_to(resolved_root)
except ValueError as exc:
raise EvaluationError(f"evaluation image escapes asset root: {raw_path!r}") from exc
if not resolved.is_file():
raise EvaluationError(f"evaluation image not found: {resolved}")
return resolved
def _question_text(question: str, choices: Sequence[Mapping[str, Any]]) -> str:
if not choices:
return question
rendered: list[str] = []
for choice in choices:
if "key" not in choice or "text" not in choice:
raise EvaluationError("evaluation choice requires key and text")
rendered.append(f"{choice['key']}. {choice['text']}")
return question + "\n\nChoices:\n" + "\n".join(rendered)
def build_messages(
row: Mapping[str, Any],
*,
images: Sequence[Any],
) -> list[dict[str, Any]]:
question = row.get("question")
choices = row.get("choices")
if not isinstance(question, str) or not question:
raise EvaluationError("evaluation row has no question")
if not isinstance(choices, list) or any(not isinstance(choice, Mapping) for choice in choices):
raise EvaluationError("evaluation row choices are malformed")
prompt_path = repo_root() / "prompts" / "common_system.txt"
try:
system = prompt_path.read_text(encoding="utf-8").strip()
except OSError as exc:
raise EvaluationError(f"cannot read common system prompt: {exc}") from exc
content = [{"type": "image", "image": image} for image in images]
content.append({"type": "text", "text": _question_text(question, choices)})
return [
{"role": "system", "content": [{"type": "text", "text": system}]},
{"role": "user", "content": content},
]
def _load_images(row: Mapping[str, Any], asset_root: Path) -> list[Any]:
from PIL import Image
raw_images = row.get("images")
if not isinstance(raw_images, list):
raise EvaluationError("evaluation row images must be a list")
images: list[Any] = []
for raw in raw_images:
if not isinstance(raw, Mapping) or not isinstance(raw.get("path"), str):
raise EvaluationError("evaluation image record is malformed")
path = resolve_asset(asset_root, str(raw["path"]))
try:
with Image.open(path) as image:
images.append(image.convert("RGB").copy())
except (OSError, ValueError) as exc:
raise EvaluationError(f"cannot decode evaluation image {path}: {exc}") from exc
return images
def _load_model(base_model: Path, adapter: Path | None) -> tuple[Any, Any]:
try:
import torch
from transformers import AutoModelForMultimodalLM, AutoProcessor
except ImportError as exc:
raise EvaluationError(f"GPU evaluation dependency is missing: {exc}") from exc
if not base_model.is_dir():
raise EvaluationError(f"base model directory not found: {base_model}")
processor = AutoProcessor.from_pretrained(str(base_model))
model = AutoModelForMultimodalLM.from_pretrained(
str(base_model),
dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
device_map="auto",
)
if adapter is not None:
if not adapter.is_dir():
raise EvaluationError(f"adapter directory not found: {adapter}")
try:
from peft import PeftModel
except ImportError as exc:
raise EvaluationError(f"PEFT is required for adapter evaluation: {exc}") from exc
model = PeftModel.from_pretrained(model, str(adapter), is_trainable=False)
model.eval()
return model, processor
def _model_device(model: Any) -> Any:
device = getattr(model, "device", None)
if device is not None:
return device
try:
return next(model.parameters()).device
except (AttributeError, StopIteration) as exc:
raise EvaluationError("cannot determine evaluation model device") from exc
def _predict_one(
row: Mapping[str, Any],
*,
asset_root: Path,
model: Any,
processor: Any,
max_prompt_tokens: int,
max_new_tokens: int,
image_row: Mapping[str, Any] | None = None,
question_only: bool = False,
) -> dict[str, Any]:
try:
import torch
from qwen_vl_utils import process_vision_info
except ImportError as exc:
raise EvaluationError(f"GPU evaluation dependency is missing: {exc}") from exc
images = [] if question_only else _load_images(image_row or row, asset_root)
messages = build_messages(row, images=images)
rendered = processor.apply_chat_template(
copy.deepcopy(messages),
tokenize=False,
add_generation_prompt=True,
enable_thinking=False,
)
image_inputs, video_inputs = process_vision_info(messages)
processor_kwargs: dict[str, Any] = {
"text": [rendered],
"padding": True,
"return_tensors": "pt",
}
if image_inputs:
processor_kwargs["images"] = image_inputs
if video_inputs:
processor_kwargs["videos"] = video_inputs
encoded = processor(**processor_kwargs)
prompt_tokens = int(encoded["input_ids"].shape[-1])
if prompt_tokens > max_prompt_tokens:
raise EvaluationError(
f"{row.get('eval_id')}: prompt has {prompt_tokens} tokens, limit is {max_prompt_tokens}"
)
encoded = encoded.to(_model_device(model))
started = time.monotonic()
with torch.inference_mode():
generated = model.generate(
**encoded,
do_sample=False,
max_new_tokens=max_new_tokens,
stop_strings=["</answer>"],
tokenizer=getattr(processor, "tokenizer", processor),
)
elapsed = time.monotonic() - started
completion_ids = generated[:, encoded["input_ids"].shape[-1] :]
response = processor.batch_decode(
completion_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False,
)[0]
token_count = int(completion_ids.shape[-1])
parsed = parse_answer(response)
return {
"response": response,
"prompt_tokens": prompt_tokens,
"completion_tokens": token_count,
"prompt_truncated": False,
"completion_truncated": bool(token_count >= max_new_tokens and not parsed.valid),
"latency_seconds": elapsed,
}
def _existing_predictions(path: Path, *, run_id: str) -> dict[str, dict[str, Any]]:
if not path.exists():
return {}
rows: dict[str, dict[str, Any]] = {}
try:
with path.open("r", encoding="utf-8") as handle:
for line_number, line in enumerate(handle, start=1):
if not line.strip():
continue
value = json.loads(line)
if not isinstance(value, dict):
raise EvaluationError(f"{path}:{line_number}: prediction is not an object")
if value.get("run_id") != run_id:
raise EvaluationError(f"{path}: existing predictions belong to another run")
eval_id = value.get("eval_id")
if not isinstance(eval_id, str) or not eval_id or eval_id in rows:
raise EvaluationError(f"{path}: duplicate or empty existing eval_id")
rows[eval_id] = value
except (OSError, json.JSONDecodeError) as exc:
raise EvaluationError(f"cannot resume prediction file {path}: {exc}") from exc
return rows
def predict_run(
rows: Sequence[Mapping[str, Any]],
*,
run_id: str,
base_model: Path,
adapter: Path | None,
asset_root: Path,
output_path: Path,
max_prompt_tokens: int = 4096,
max_new_tokens: int = 256,
limit: int | None = None,
input_mode: str = "standard",
constant_answer: str | None = None,
) -> dict[str, Any]:
"""Generate one exact, resumable prediction row per evaluation view."""
if not run_id:
raise EvaluationError("run_id must be non-empty")
if max_prompt_tokens <= 0 or max_new_tokens <= 0:
raise EvaluationError("evaluation token limits must be positive")
if input_mode not in {"standard", "question_only", "full_image_control"}:
raise EvaluationError(f"unsupported evaluation input mode: {input_mode!r}")
if constant_answer is not None and not parse_answer(
f"<answer>{constant_answer}</answer>"
).valid:
raise EvaluationError("constant answer cannot be encoded by the public answer schema")
selected = list(rows[:limit] if limit is not None else rows)
existing = _existing_predictions(output_path, run_id=run_id)
if any(row.get("input_mode", "standard") != input_mode for row in existing.values()):
raise EvaluationError("existing predictions use another input mode")
expected_ids = {str(row.get("eval_id", "")) for row in selected}
if not set(existing).issubset(expected_ids):
raise EvaluationError("existing prediction file contains IDs outside this evaluation")
pending = [row for row in selected if str(row.get("eval_id", "")) not in existing]
model: Any | None = None
processor: Any | None = None
if pending and constant_answer is None:
model, processor = _load_model(base_model, adapter)
full_by_group: dict[str, Mapping[str, Any]] = {}
if input_mode == "full_image_control":
for row in selected:
if row.get("state") == "FULL":
full_by_group[str(row.get("group_id", ""))] = row
missing_full = sorted(
{
str(row.get("group_id", ""))
for row in selected
if str(row.get("group_id", "")) not in full_by_group
}
)
if missing_full:
raise EvaluationError(
f"full-image control lacks FULL rows for groups: {missing_full[:5]}"
)
output_path.parent.mkdir(parents=True, exist_ok=True)
with output_path.open("a", encoding="utf-8") as handle:
for index, row in enumerate(pending, start=1):
if constant_answer is None:
assert model is not None and processor is not None
prediction = _predict_one(
row,
asset_root=asset_root,
model=model,
processor=processor,
max_prompt_tokens=max_prompt_tokens,
max_new_tokens=max_new_tokens,
image_row=full_by_group.get(str(row.get("group_id", ""))),
question_only=input_mode == "question_only",
)
else:
prediction = {
"response": f"<answer>{constant_answer}</answer>",
"prompt_tokens": 0,
"completion_tokens": 0,
"prompt_truncated": False,
"completion_truncated": False,
"latency_seconds": 0.0,
}
value = {
"schema_version": 1,
"run_id": run_id,
"eval_id": str(row["eval_id"]),
"input_mode": input_mode,
**prediction,
}
handle.write(canonical_json(value) + "\n")
handle.flush()
if index % 32 == 0:
os.fsync(handle.fileno())
if pending:
os.fsync(handle.fileno())
all_rows = _existing_predictions(output_path, run_id=run_id)
truncated = sum(bool(row.get("completion_truncated")) for row in all_rows.values())
return {
"run_id": run_id,
"output": str(output_path.resolve()),
"expected": len(selected),
"predicted": len(all_rows),
"new_predictions": len(pending),
"completion_truncation_count": truncated,
"input_mode": input_mode,
"constant_answer": constant_answer,
"complete": len(all_rows) == len(selected),
}
|