diff --git a/README.md b/README.md index 6cbee78673c53cb479d8dcdd7b4ba55f50267003..c59d70a70d13213c0b2599154e8d255ce8d6eccc 100644 --- a/README.md +++ b/README.md @@ -146,6 +146,44 @@ qwen3 = load_dataset( ) ``` +## Gemma-4-12B recaptions + +`gemma-4-12b-recaptions/.parquet` has the same layout as `recaptions/` (same columns, same order, 1,000,000 rows), but both new captions were written by [google/gemma-4-12B-it](https://huggingface.co/google/gemma-4-12B-it). Join on `key`. + +```python +from datasets import load_dataset + +gemma = load_dataset( + "parquet", + data_files="hf://datasets/IntelligenceLab/BLIP3o-Recap/gemma-4-12b-recaptions/*.parquet", + split="train", +) +``` + +### Generation + +Same code, prompt, sampling (`n=2`, temperature 0.8, top-p 0.9, at most 160 new tokens), decode-time bans and reject/resample filter as the Qwen2.5-VL-3B recaptions above, with these differences: + +- `--model google/gemma-4-12B-it`; the prompt is rendered with Gemma's own chat template (thinking off), and images use Gemma's default processing (no 640×28×28 cap). +- Gemma follows "count first" literally and started 61% of captions with "One …", so a caption-initial "One" is also banned (the captions then start with "A …"). +- 12 A100 GPUs, about 9 images/s per GPU. Almost no samples were rejected. + +### Statistics + +| | | +| --- | ---: | +| captions that still violate a rule | 1 (a 54-word caption of a text-only image, below the 60-word minimum) | +| words per caption (p5 / median / p95) | 80 / 84 / 91 | +| `bert-large-uncased` tokens incl. [CLS]/[SEP] (p5 / median / p95 / p99 / max) | 99 / 106 / 117 / 126 / 188 | +| recap_1 vs recap_2, same image: bigram Jaccard / ROUGE-L | 0.246 / 0.522 | +| recap_1 vs recap_2, different images: bigram Jaccard / ROUGE-L | 0.039 / 0.211 | +| recap_1 vs original caption: bigram Jaccard / ROUGE-L | 0.060 / 0.242 | +| Gemma recap_1 vs Qwen2.5-VL-3B recap_1, same image: ROUGE-L | 0.288 | + +### Known limitations + +A manual check of 24 captions (12 images) found the main subject and its count, colors, setting, medium and legible text correct in all of them; 5 had one wrong secondary detail (an action, a clothing item, a person count, the spelling of a name, a left/right reference). No invented objects were found. Compared with the Qwen2.5-VL-3B captions, the two Gemma captions of an image are closer to each other (39% of the content words of `recap_2` are not in `recap_1`, against 57%): they mostly state the same facts in different words, with one or two different secondary details. Captions from different models (Gemma, Qwen2.5-VL-3B, the original Qwen2.5-VL-7B caption) differ more. Openings are formulaic: about 28% of captions start with "A single". + ## License Apache-2.0, same as the source dataset. diff --git a/code/launch.sh b/code/launch.sh index d6a474bd494993b4441d7935b9ceccc5edfa12d0..089c0447ca1cb70b7a23783184eead49de2bab0f 100644 --- a/code/launch.sh +++ b/code/launch.sh @@ -1,7 +1,9 @@ #!/bin/bash # One recaption.py worker per GPU inside the recap-vllm container; workers claim shards through lock files, # finished shards have .done (run) / .fixed (fix) markers, so rerunning this script resumes. -# docker exec recap-vllm bash /workspace/prh-vlm/blip3o_recap/launch.sh 0,1,2,3,4,5,6 [out_dir] [run|fix] +# docker exec recap-vllm bash /workspace/prh-vlm/blip3o_recap/launch.sh 0,1,2,3,4,5,6 [out_dir] [run|fix] [extra args] +# Several nodes may share one out_dir, so locks are kept by default; CLEAN_LOCKS=1 removes stale locks (only when no +# worker is alive on any node). set -e GPUS=${1:-0,1,2,3,4,5,6,7} OUT=${2:-/workspace/prh-vlm/data/blip3o_recap/recap} @@ -9,9 +11,9 @@ MODE=${3:-run} cd /workspace/prh-vlm/blip3o_recap if pgrep -f "^python3 recaption.py" >/dev/null; then echo "workers already running" >&2; exit 1; fi mkdir -p "$OUT/logs" -rm -f "$OUT"/*.lock "$OUT"/*.fixlock # no worker is alive, so every lock is stale +if [ "${CLEAN_LOCKS:-0}" = 1 ]; then rm -f "$OUT"/*.lock "$OUT"/*.fixlock; fi for g in ${GPUS//,/ }; do CUDA_VISIBLE_DEVICES=$g nohup python3 recaption.py $MODE --shards ../data/blip3o_recap/shards/*.tar --out "$OUT" \ - --gpu-mem 0.85 >> "$OUT/logs/${MODE}_gpu$g.log" 2>&1 & + --gpu-mem 0.85 "${@:4}" >> "$OUT/logs/${MODE}_$(hostname)_gpu$g.log" 2>&1 & done sleep 2; pgrep -f "^python3 recaption.py" | wc -l diff --git a/code/recaption.py b/code/recaption.py index ba355056be79d1f0ff0c3821835c12f82488197b..71670c2fe53f11cec6078cd2500057ca7b31dc92 100644 --- a/code/recaption.py +++ b/code/recaption.py @@ -60,10 +60,12 @@ _OPENERS = [f"{a} {b}" for a in ("The", "This", "These") for b in # caption-initial "The"/"This"/"These" (no leading space: a different token from mid-sentence " The"); the model # otherwise dodges every banned opener with "The main object in the image is ...", "The mainsubject ...", etc. _FIRST = ("The", "This", "These") +# Gemma obeys "count" first and opens 61% of captions with "One ..."; banning the caption-initial form gives "A ..." +_FIRST_GEMMA = _FIRST + ("One",) -def ban_bias(tokenizer) -> dict[int, float]: +def ban_bias(tokenizer, first: tuple[str, ...] = _FIRST) -> dict[int, float]: """logit_bias -100 on every single-token form of _WORDS (" vibrant", "Appear", ...). vLLM's ``bad_words`` cannot ban them: it keeps the space-prefixed form only when it tokenizes to as many tokens @@ -72,7 +74,7 @@ def ban_bias(tokenizer) -> dict[int, float]: """ forms = [p + c for x in _WORDS for c in (x, x.capitalize()) for p in ("", " ")] ids = [tokenizer.encode(f, add_special_tokens=False) for f in forms] - ids += [tokenizer.encode(f, add_special_tokens=False) for f in _FIRST] + ids += [tokenizer.encode(f, add_special_tokens=False) for f in first] return {i[0]: -100.0 for i in ids if len(i) == 1} @@ -123,11 +125,28 @@ def violations(text: str, finished: bool = True, strict: bool = True) -> list[st return out +_PROMPTS: dict[bool, str] = {} # filled by ``engine`` from the model's own chat template + + def prompt(focus: bool) -> str: - return ( - f"<|im_start|>system\n{SYSTEM}<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>" - f"{USER}{FOCUS if focus else ''}<|im_end|>\n<|im_start|>assistant\n" - ) + return _PROMPTS[focus] + + +def build_prompts(model: str) -> None: + """System + user turn with one image, rendered by the model's processor (its own image placeholder).""" + from transformers import AutoProcessor + + proc = AutoProcessor.from_pretrained(model) + for focus in (False, True): + msgs = [ + {"role": "system", "content": [{"type": "text", "text": SYSTEM}]}, + {"role": "user", "content": [{"type": "image"}, {"type": "text", "text": USER + (FOCUS if focus else "")}]}, + ] + text = proc.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True) + bos = proc.tokenizer.bos_token + if bos and text.startswith(bos) and proc.tokenizer("x").input_ids[0] == proc.tokenizer.bos_token_id: + text = text[len(bos) :] # vLLM tokenizes the prompt with special tokens: no double (Gemma) + _PROMPTS[focus] = text def read_shard(path: Path): @@ -200,6 +219,7 @@ def engine(args): from vllm import LLM, SamplingParams _patch_bad_words() + build_prompts(args.model) llm = LLM( model=args.model, @@ -207,11 +227,12 @@ def engine(args): gpu_memory_utilization=args.gpu_mem, enable_prefix_caching=True, limit_mm_per_prompt={"image": 1}, - mm_processor_kwargs={"max_pixels": MAX_PIXELS}, + mm_processor_kwargs={"max_pixels": MAX_PIXELS} if "qwen" in args.model.lower() else None, seed=args.seed, ) + first = _FIRST_GEMMA if "gemma" in args.model.lower() else _FIRST words = [c for x in _WORDS for c in (x, x.capitalize())] # multi-token forms; 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