diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..6c8ca901681a3e53314f8085c237f0c5051b2adf 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,24 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +examples/bagel_amazon/demo_0.png filter=lfs diff=lfs merge=lfs -text +examples/bagel_amazon/demo_1.png filter=lfs diff=lfs merge=lfs -text +examples/bagel_amazon/query.png filter=lfs diff=lfs merge=lfs -text +examples/bagel_infovqa_val_449/query.jpg filter=lfs diff=lfs merge=lfs -text +examples/bagel_ok_vqa_val2014_492_always/demo_0.png filter=lfs diff=lfs merge=lfs -text +examples/bagel_ok_vqa_val2014_492_always/demo_1.png filter=lfs diff=lfs merge=lfs -text +examples/bagel_vizwiz_vqa_val_345_always/demo_0.png filter=lfs diff=lfs merge=lfs -text +examples/bagel_vizwiz_vqa_val_345_always/demo_1.png filter=lfs diff=lfs merge=lfs -text +examples/bagel_vizwiz_vqa_val_345_always/query.jpg filter=lfs diff=lfs merge=lfs -text +examples/bagel_vizwiz_vqa_val_47_always/demo_0.png filter=lfs diff=lfs merge=lfs -text +examples/bagel_vizwiz_vqa_val_47_always/demo_1.png filter=lfs diff=lfs merge=lfs -text +examples/bagel_vizwiz_vqa_val_47_always/query.jpg filter=lfs diff=lfs merge=lfs -text +examples/bagel_vizwiz_vqa_val_87_always/demo_0.png filter=lfs diff=lfs merge=lfs -text +examples/bagel_vizwiz_vqa_val_87_always/demo_1.png filter=lfs diff=lfs merge=lfs -text +examples/bagel_vizwiz_vqa_val_87_always/query.jpg filter=lfs diff=lfs merge=lfs -text +examples/lance_ok_vqa_val2014_253/demo_0.png filter=lfs diff=lfs merge=lfs -text +examples/lance_ok_vqa_val2014_253/query.jpg filter=lfs diff=lfs merge=lfs -text +examples/lance_ok_vqa_val2014_8_always/demo_0.png filter=lfs diff=lfs merge=lfs -text +examples/lance_ok_vqa_val2014_8_always/demo_1.png filter=lfs diff=lfs merge=lfs -text +examples/lance_ok_vqa_val2014_8_always/demo_2.png filter=lfs diff=lfs merge=lfs -text +examples/lance_ok_vqa_val2014_8_always/demo_3.png filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md index b57c4de25f8089665b8619fb042a789ff9301bcf..ecafdcd1e618482801b1d5b3e6854fde47e4682e 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,77 @@ --- -title: Simit -emoji: 💻 -colorFrom: pink -colorTo: green +title: SIMIT - models that imagine their own practice examples +emoji: 🥯 +colorFrom: yellow +colorTo: red sdk: gradio sdk_version: 6.29.1 -python_version: '3.12' +python_version: "3.12" app_file: app.py pinned: false +license: apache-2.0 +startup_duration_timeout: 1h +short_description: A VLM imagines practice examples for your question +models: + - ByteDance-Seed/BAGEL-7B-MoT + - bytedance-research/Lance + - Qwen/Qwen3.8-27B-FP8 + - black-forest-labs/FLUX.2-klein-4B --- -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +# SIMIT demo + +Upload an image, ask a question, and compare the model's plain answer with its +SIMIT-ICL answer: before answering again, the model imagines a few practice +examples (question, answer decided first, an image made to fit), checks them, +and keeps them in context. + +Built on the [`simit`](https://github.com/monurcan/simit) library. See `tools/` for how the per-budget +hyperparameters were chosen and how the cached examples were produced. + +## Hardware notes (ZeroGPU) + +* Each request runs in a fresh `@spaces.GPU` process and uses the visitor's own + ZeroGPU quota for the selected time budget (20-180 s). +* BAGEL-7B-MoT and Lance run on the default 48 GB size; Qwen3.8-27B-FP8 + + FLUX.2-klein needs the 96 GB size (2x quota). +* All weights stay on CPU in the main process (~91 GB of RAM, ~92 GB of disk for + the model files). If the Space's disk is too small, attach persistent storage + and set `HF_HOME=/data/.huggingface`, or limit the models with + `SIMIT_DEMO_MODELS=bagel,lance`. +* The first request after start-up compiles and autotunes GPU kernels (Qwen: + ~25 s); later requests reuse the on-disk cache. + +## How the time budgets map to settings + +Each budget (20 / 40 / 60 / 90 / 180 s of GPU time per request) uses its own settings per model +(`presets.json`, built by `tools/build_presets.py`): + +1. **Accuracy per K_max** (`tools/tune_presets.py`): one synthesis cache with 4 candidates per query on a + mixed 64-question general-QA validation set: 8 each from VizWiz, VQAv2, OK-VQA, TextVQA, ChartQA, + DocVQA, MMBench and AI2D (LMMs-Eval-Lite rows 300-307), each scored with its own metric. Then 800 + Optuna trials over the ABA/DF thresholds and K_max ∈ {1,2,3,4}. Per K_max, the pick is regularized: + within one validation query of the best score, the widest difficulty-filter band. +2. **Latency** (`tools/latency.py`): per-request timelines on the demo's own code path in fresh forked + processes (as on ZeroGPU, weight transfer included). Measured on an H100 and scaled by spaces' + own factor for the ZeroGPU GPU sizes (1.5× on `large`, 1× on `xlarge`). +3. **Choice**: the tuned (best-quality) speed setting whenever some K fits the budget for ≥80% of + requests; among those, the K within one validation query of the best score with the widest band. + More budget also buys more attempts and verification rounds. If no K beats zero-shot on + validation, SIMIT keeps the base answer by default; "Imagine even when confident" still runs it. + +Validation scores (64 general questions): + +| model | zero-shot | K=1 | K=2 | K=3 | K=4 | used by default | +|---|---|---|---|---|---|---| +| BAGEL-7B-MoT | 0.703 | 0.708 | **0.750** | 0.760 | 0.755 | K=2 from 60 s (20-40 s: zero-shot) | +| Lance | 0.432 | 0.442 | 0.442 | 0.458 | **0.468** | K=1 at 20 s, K=4 from 40 s | +| Qwen3.8-27B-FP8 + FLUX | **0.854** | 0.839 | 0.823 | 0.839 | 0.844 | zero-shot (no gain found) | + +Lance's answers are sampled (greedy decoding degenerates on this checkpoint), so its outputs vary +between runs and SIMIT can also hurt a correct answer. Qwen3.8-27B is strong enough that imagined +examples did not improve it on these questions. + +The cached examples are real runs of this app (BAGEL-7B-MoT and Lance) on queries from the paper's +figures, plus one product-page screenshot (`examples/bagel_amazon`). Several of them used "Imagine even when confident" (shown in the example), because the base +model was confidently wrong (e.g. "Unanswerable"). Qwen3.8-27B already answered those queries +correctly, so it has no cached example. diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..470830a035491e9a1f292cbe17c053d4dd100600 --- /dev/null +++ b/app.py @@ -0,0 +1,297 @@ +"""SIMIT demo: a vision-language model imagines its own practice examples for +your question, then answers again with them in context. + +Runs on Hugging Face ZeroGPU (each request uses the visitor's own GPU quota) +and on any machine with a CUDA GPU (``python app.py``). +""" + +import os + +# ZeroGPU starts a fresh process per request: keep Triton's autotuning results on disk +# (otherwise every request re-tunes the FP8 / linear-attention kernels, ~20 s). +os.environ.setdefault("TRITON_CACHE_AUTOTUNING", "1") +os.environ.setdefault("TRANSFORMERS_DISABLE_DEEPGEMM_LINEAR", "1") + +import spaces # before anything CUDA-related: ZeroGPU patches torch # noqa: E402 # isort: skip + +import base64 # noqa: E402 +import html # noqa: E402 +import json # noqa: E402 +from pathlib import Path # noqa: E402 + +import gradio as gr # noqa: E402 +from PIL import Image # noqa: E402 + +from demo_models import BUDGETS, ENABLED, MODELS, code_snippet, run # noqa: E402 + +HERE = Path(__file__).parent +ASSETS = HERE / "assets" +EXAMPLES_DIR = HERE / "examples" + + +def _data_uri(path: Path) -> str: + return "data:image/webp;base64," + base64.b64encode(path.read_bytes()).decode() + + +MASCOT = {p.stem: _data_uri(p) for p in ASSETS.glob("*.webp")} +STAGES = { # stage -> (mascot, message) + "idle": ("initial", "Ask me anything about an image"), + "loading": ("initial", "Loading the model weights (first request only)"), + "warmup": ("initial", "Waking up the model on the GPU"), + "solving": ("initial", "Solving it on its own first"), + "synthesizing": ("synthesizer", "Writing practice questions it already knows the answers to"), + "routing": ("router", "Choosing how to draw each practice image"), + "drawing": ("artist", "Painting a practice image"), + "coding": ("architect", "Writing code for a chart, diagram or document"), + "checking": ("critic", "Checking that each picture matches its question"), + "improving": ("improved", "Answering again, with its own practice examples in context"), +} +LABELS = {MODELS[k].label: k for k in ENABLED} +BUDGET_LABELS = [f"{b} s" for b in BUDGETS] +GPU_OVERHEAD = 20 # seconds requested on top of the budget (worker start-up, final answer); weight transfer counts inside it + + +def _seconds(label: str) -> int: + return int(str(label).split()[0]) + + +# --------------------------------------------------------------------- HTML +def status_html(stage: str, elapsed: float = 0.0, limit: float = 0.0, n_demos: int = 0, note: str = "") -> str: + mascot, message = STAGES.get(stage, STAGES["solving"]) + dots = "" if stage == "idle" else '' + bar = "" + if limit: + pct = max(2.0, min(100.0, 100.0 * elapsed / limit)) + bar = (f'
' + f'
{elapsed:.0f} s of {limit:.0f} s' + + (f' · {n_demos} practice example{"s" if n_demos != 1 else ""} ready' if n_demos else "") + + "
") + return (f'
' + f'
{html.escape(message)}{dots}
' + f'{bar}{f"
{html.escape(note)}
" if note else ""}
') + + +def done_html(note: str) -> str: + return (f'
' + f'
Done
{html.escape(note)}
') + + +def error_html(note: str) -> str: + return (f'
' + f'
Something went wrong
{html.escape(note)}' + '
') + + +def answer_card(kind: str, answer: str = "", sub: str = "", mark: str = "") -> str: + title = "Base model (greedy)" if kind == "base" else "SIMIT-ICL (self-improved)" + body = html.escape(answer) if answer else '…' + size = " long" if len(answer) > 60 else "" + badge = {"right": '✓', "wrong": '✗'}.get(mark, "") + return (f'
{title}{badge}
' + f'
{body}
{html.escape(sub)}
') + + +def demo_caption(d) -> str: + bits = [f"Q: {d.question}", f"A: {d.answer}"] + meta = [d.skill] if d.skill else [] + if d.verify_score is not None: + meta.append(f"critic {d.verify_score}/100") + if d.confidence is not None: + meta.append(f"confidence {d.confidence:.2f}") + return "\n".join(bits) + (f"\n({', '.join(meta)})" if meta else "") + + +# ------------------------------------------------------------- GPU requests +def _duration(image, question, key, budget_label, always, *args, **kwargs): + return _seconds(budget_label) + GPU_OVERHEAD + + +def _session(image, question, key, budget_label, always): + """Runs inside the GPU call: turns pipeline events into UI updates.""" + spec, budget = MODELS[key], _seconds(budget_label) + try: + yield from _events(spec, image, question, budget, always) + except Exception as e: # show it in the page instead of a bare error toast + print(f"[demo] request failed: {type(e).__name__}: {e}", flush=True) + yield (error_html(f"{type(e).__name__}: {str(e)[:300]}"), answer_card("base"), answer_card("simit"), [], "") + + +def _events(spec, image, question, budget, always): + greedy, k_budget, demos, gallery = None, 0, [], [] + base, improved = answer_card("base"), answer_card("simit") + status = status_html("warmup") + for event in run(spec, image, question, budget, always): + kind = event[0] + if kind == "stage": + _, stage, info = event + status = status_html(stage, info.get("elapsed", 0), info.get("limit", 0), len(demos)) + elif kind == "greedy": + _, zs, k_budget = event + greedy = zs + sub = f"confidence p0 = {zs.confidence:.2f}" if zs.confidence is not None else "" + base = answer_card("base", zs.answer, sub) + elif kind == "demo": + demos.append(event[1]) + gallery = [(d.image, demo_caption(d)) for d in demos] + else: # final + _, answer, info = event + reason = info.get("skipped") + if reason == "confident": + sub = "The model was already confident, so SIMIT kept its answer (adaptive budget: 0 examples)." + elif reason == "policy": + sub = ("On general questions, our tuning found no gain from imagination for this model at this " + "budget, so SIMIT keeps the base answer. Tick 'Imagine even when confident' to try it anyway.") + elif reason == "no_time": + sub = "Not enough time budget left to imagine examples; kept the base answer." + elif reason == "none_passed": + sub = "No imagined example passed the checks in time; kept the base answer." + else: + changed = "changed" if answer.strip() != greedy.answer.strip() else "kept" + sub = f"{changed} the answer after {info['demos']} imagined example{'s' if info['demos'] > 1 else ''}" + improved = answer_card("simit", answer, sub) + status = done_html(f"{info['elapsed']:.0f} s on the GPU") + yield status, base, improved, gallery, _details(greedy, k_budget, demos, spec, budget) + + +@spaces.GPU(duration=_duration, size="large") +def gpu_large(image, question, key, budget_label, always): + yield from _session(image, question, key, budget_label, always) + + +@spaces.GPU(duration=_duration, size="xlarge") +def gpu_xlarge(image, question, key, budget_label, always): + yield from _session(image, question, key, budget_label, always) + + +def _details(greedy, k_budget, demos, spec, budget) -> str: + if greedy is None: + return "" + p0 = f"{greedy.confidence:.2f}" if greedy.confidence is not None else "n/a" + return (f"**{spec.label}**, {budget} s budget. Zero-shot confidence p0 = {p0}, so the adaptive budget asked " + f"for **{k_budget}** example{'s' if k_budget != 1 else ''}; **{len(demos)}** passed the checks. " + "Each example is a question the model wrote, whose answer it decided first, with an image made " + "to fit that answer.") + + +def submit(image, question, model_label, budget_label, always): + if image is None: + raise gr.Error("Please upload an image.") + if not question or not question.strip(): + raise gr.Error("Please type a question about the image.") + key = LABELS[model_label] + spec = MODELS[key] + image = image.convert("RGB") + if spec._weights is None: # main process, CPU: no GPU time is spent here + yield (status_html("loading"), answer_card("base"), answer_card("simit"), [], "") + spec.load() + fn = gpu_xlarge if spec.gpu_size == "xlarge" else gpu_large + yield from fn(image, question.strip(), key, budget_label, always) + + +# ------------------------------------------------------------------ cached +def _load_examples(): + index = EXAMPLES_DIR / "index.json" + return json.loads(index.read_text()) if index.exists() else [] + + +EXAMPLES = [e for e in _load_examples() if e["model"] in ENABLED] + + +def show_cached(image, question, model_label, budget_label, always=False): + """A cached example: the stored outputs of a real run, shown without using the GPU.""" + entry = next((e for e in EXAMPLES if e["question"] == question and MODELS[e["model"]].label == model_label + and f"{e['budget']} s" == budget_label and bool(e.get("always")) == bool(always)), None) + if entry is None: + return status_html("idle", note="Press Submit to run this example."), answer_card("base"), \ + answer_card("simit"), [], "" + d = EXAMPLES_DIR / entry["id"] + mark = lambda ok: "right" if ok else "wrong" # noqa: E731 + gt = f"reference answer: {entry['reference']}" + base = answer_card("base", entry["greedy"], f"confidence p0 = {entry['p0']:.2f} · {gt}", mark(entry["greedy_ok"])) + n = len(entry["demos"]) + improved = answer_card("simit", entry["simit"], f"after {n} imagined example{'s' if n != 1 else ''} · {gt}", + mark(entry["simit_ok"])) + gallery = [(str(d / x["image"]), "\n".join([f"Q: {x['question']}", f"A: {x['answer']}", + f"({x['skill']}" + (f", critic {x['verify_score']}/100" if + x.get("verify_score") is not None else "") + ")"])) + for x in entry["demos"]] + note = (f"Cached result from a real run ({entry['elapsed']:.0f} s on an H100, {entry['budget']} s budget" + + (", imagining even when confident" if entry.get("always") else "") + f"). Source: {entry['source']}.") + details = (f"**{MODELS[entry['model']].label}**, {entry['budget']} s budget. Zero-shot confidence " + f"p0 = {entry['p0']:.2f}; {n} imagined example{'s' if n != 1 else ''} passed the checks.") + return done_html(note), base, improved, gallery, details + + +# ---------------------------------------------------------------------- UI +CSS = (ASSETS / "style.css").read_text() +INTRO = """ +
+

SIMIT: models that imagine their own practice examples

+

Ask any question about an image. The model first answers on its own. Then, without any labels, it writes + similar practice questions whose answers it decides first, makes an image for each one, keeps only those it + can verify, and answers your question again with them in context.

+
+""" + +with gr.Blocks(title="SIMIT demo") as demo: + gr.HTML(INTRO) + with gr.Row(equal_height=False): + with gr.Column(scale=5): + image = gr.Image(type="pil", label="Image", height=360) + question = gr.Textbox(label="Question", placeholder="e.g. In what country would you find this hat?", + lines=2) + model = gr.Dropdown(list(LABELS), value=next(iter(LABELS)), label="Model") + budget = gr.Radio(BUDGET_LABELS, value="60 s", label="Max GPU time per request", + info="More time lets the model imagine and check more practice examples.") + with gr.Accordion("Advanced", open=False): + always = gr.Checkbox(False, label="Imagine even when the model is already confident", + info="By default SIMIT skips imagination for confident answers.") + run_btn = gr.Button("Submit", variant="primary") + with gr.Column(scale=6): + status = gr.HTML(status_html("idle", note="Upload an image and type a question, or pick an example below.")) + with gr.Row(): + base_out = gr.HTML(answer_card("base"), min_width=260) + simit_out = gr.HTML(answer_card("simit"), min_width=260) + with gr.Accordion("See the imagined practice examples", open=False): + details = gr.Markdown() + gallery = gr.Gallery(columns=4, height=300, object_fit="contain", show_label=False) + with gr.Accordion("Use SIMIT in your own code", open=False): + code = gr.Code(code_snippet(MODELS[next(iter(LABELS.values()))], 60, ""), language="python", + interactive=False) + + outputs = [status, base_out, simit_out, gallery, details] + # On ZeroGPU each request gets its own GPU process; run locally, requests share this process and GPU. + from spaces.config import Config as _SpacesConfig + run_btn.click(submit, [image, question, model, budget, always], outputs, + concurrency_limit=4 if _SpacesConfig.zero_gpu else 1) + + def _code(model_label, budget_label, q, force): + return code_snippet(MODELS[LABELS[model_label]], _seconds(budget_label), q or "", force) + for comp in (model, budget, question, always): + comp.change(_code, [model, budget, question, always], code, queue=False) + + if EXAMPLES: + gr.Markdown("### Examples\nCached results from real runs of this demo (click one; press Submit to " + "run it again live).") + examples = gr.Gallery( + [(str(EXAMPLES_DIR / e["id"] / e["query_file"]), + f"{e['question'].splitlines()[0]} ({MODELS[e['model']].label})") for e in EXAMPLES], + columns=4, height=300 * ((len(EXAMPLES) + 3) // 4) + 20, object_fit="cover", allow_preview=False, + show_label=False) + + def pick(evt: gr.SelectData): + e = EXAMPLES[evt.index] + inputs = [Image.open(EXAMPLES_DIR / e["id"] / e["query_file"]), e["question"], + MODELS[e["model"]].label, f"{e['budget']} s", bool(e.get("always"))] + return (*inputs, *show_cached(*inputs)) + examples.select(pick, None, [image, question, model, budget, always] + outputs) + gr.Markdown("Models: " + ", ".join(f"[{MODELS[k].label}](https://huggingface.co/{MODELS[k].repo})" + for k in ENABLED) + + ". Each request uses your own ZeroGPU quota for the selected time budget.") + +if os.environ.get("SIMIT_DEMO_PRELOAD", "1") == "1": + for key in ENABLED: # load every model on CPU before serving (main process, no CUDA) + MODELS[key].load() + +if __name__ == "__main__": + demo.queue(max_size=32).launch(css=CSS, theme=gr.themes.Soft(primary_hue="orange", secondary_hue="amber")) diff --git a/assets/architect.webp b/assets/architect.webp new file mode 100644 index 0000000000000000000000000000000000000000..0604d40ef57518f068ba27767e301c0b4e9f8029 Binary files /dev/null and b/assets/architect.webp differ diff --git a/assets/artist.webp b/assets/artist.webp new file mode 100644 index 0000000000000000000000000000000000000000..a27e6787ed4eeecfa1b932f170f1681f078e07cd Binary files /dev/null and b/assets/artist.webp differ diff --git a/assets/critic.webp b/assets/critic.webp new file mode 100644 index 0000000000000000000000000000000000000000..8c85c34a6956d6f608d6d6f77ae59e36dfeb5495 Binary files /dev/null and b/assets/critic.webp differ diff --git a/assets/improved.webp b/assets/improved.webp new file mode 100644 index 0000000000000000000000000000000000000000..65c036ee412a9a5576f4a3c8412131b82622538d Binary files /dev/null and b/assets/improved.webp differ diff --git a/assets/initial.webp b/assets/initial.webp new file mode 100644 index 0000000000000000000000000000000000000000..2c9b8ab0e3ea1af67ee607fc2682db3b6a4f3c0c Binary files /dev/null and b/assets/initial.webp differ diff --git a/assets/router.webp b/assets/router.webp new file mode 100644 index 0000000000000000000000000000000000000000..f15c8a755799afe138f82b9e4d0c6924bacc8587 Binary files /dev/null and b/assets/router.webp differ diff --git a/assets/solver.webp b/assets/solver.webp new file mode 100644 index 0000000000000000000000000000000000000000..f4a6132c27560dfa40f4e51699f733aac1993606 Binary files /dev/null and b/assets/solver.webp differ diff --git a/assets/style.css b/assets/style.css new file mode 100644 index 0000000000000000000000000000000000000000..5040aa30efa6296cd7bec6c46e83f8530cb34ef5 --- /dev/null +++ b/assets/style.css @@ -0,0 +1,32 @@ +.simit-hero h1 { font-size: 1.7rem; margin: 0.2rem 0 0.4rem; } +.simit-hero p { margin: 0 0 0.6rem; opacity: 0.85; max-width: 60rem; } + +.simit-status { display: flex; align-items: center; gap: 1rem; min-height: 132px; padding: 0.6rem 0.8rem; + border-radius: 14px; background: var(--block-background-fill); border: 1px solid var(--border-color-primary); } +.simit-mascot { height: 112px; width: auto; animation: simit-bob 1.6s ease-in-out infinite; transform-origin: 50% 90%; } +.simit-mascot.still { animation: simit-pop 0.5s ease-out 1; } +.simit-msg { font-size: 1.05rem; font-weight: 600; } +.simit-sub { font-size: 0.85rem; opacity: 0.75; margin-top: 0.3rem; } +.simit-dots::after { content: ""; animation: simit-dots 1.4s steps(4, end) infinite; } +.simit-bar { height: 8px; width: 16rem; max-width: 100%; border-radius: 4px; margin-top: 0.5rem; + background: var(--border-color-primary); overflow: hidden; } +.simit-bar > div { height: 100%; border-radius: 4px; transition: width 0.4s linear; + background: linear-gradient(90deg, #f2a65a, #e07a3f); } + +.simit-card { border-radius: 14px; padding: 0.8rem 1rem; min-height: 120px; + border: 1px solid var(--border-color-primary); background: var(--block-background-fill); } +.simit-card.base { border-top: 5px solid #c9c4bd; } +.simit-card.simit { border-top: 5px solid #e07a3f; } +.simit-card-title { font-size: 0.85rem; text-transform: uppercase; letter-spacing: 0.04em; opacity: 0.7; + display: flex; justify-content: space-between; } +.simit-answer { font-size: 1.35rem; font-weight: 650; margin: 0.4rem 0; white-space: pre-wrap; } +.simit-card-sub { font-size: 0.8rem; opacity: 0.7; } +.simit-wait { opacity: 0.4; } +.simit-ok { color: #2e9b5a; font-size: 1.1rem; } +.simit-no { color: #c94a3c; font-size: 1.1rem; } + +@keyframes simit-bob { 0%, 100% { transform: translateY(0) rotate(-2deg); } 50% { transform: translateY(-8px) rotate(2deg); } } +@keyframes simit-pop { 0% { transform: scale(0.8); } 70% { transform: scale(1.06); } 100% { transform: scale(1); } } +@keyframes simit-dots { 0% { content: ""; } 25% { content: "."; } 50% { content: ".."; } 75% { content: "..."; } } +@media (prefers-reduced-motion: reduce) { .simit-mascot, .simit-dots::after { animation: none; } } +.simit-answer.long { font-size: 1.0rem; font-weight: 550; line-height: 1.4; } diff --git a/assets/synthesizer.webp b/assets/synthesizer.webp new file mode 100644 index 0000000000000000000000000000000000000000..c8aa169d2951abce027cb9f6108687ecfd3f7361 Binary files /dev/null and b/assets/synthesizer.webp differ diff --git a/demo_models.py b/demo_models.py new file mode 100644 index 0000000000000000000000000000000000000000..783f5e2a76dcfe4fbf1acb79d38b50eadc8a19dd --- /dev/null +++ b/demo_models.py @@ -0,0 +1,251 @@ +"""Models of the demo and how one request runs on ZeroGPU. + +ZeroGPU forks the Space process for every ``@spaces.GPU`` call and kills the +fork afterwards. So the weights are loaded once, on CPU, in the main process +(no CUDA there), and each call moves only the chosen model to the GPU and +builds a fresh SIMIT engine around it. +""" + +from __future__ import annotations + +import contextlib +import json +import os +import queue +import threading +import time +from dataclasses import dataclass, field +from pathlib import Path +from typing import Optional + +import torch + +from simit import SIMIT, SIMITConfig + +HERE = Path(__file__).parent +PRESETS = json.loads((HERE / "presets.json").read_text()) +BUDGETS = [20, 40, 60, 90, 180] # seconds of GPU time per request +ANSWER_TOKENS = 128 +ANSWER_RESERVE = 5.0 # seconds kept for the final SIMIT-ICL answer + + +@dataclass +class ModelSpec: + key: str + label: str + repo: str + kind: str # "bagel" | "lance" | "hf" + gpu_size: str # ZeroGPU size: "large" (48 GB) or "xlarge" (96 GB) + image_generator: Optional[str] = None + blurb: str = "" + _weights: object = None + _lock: threading.Lock = field(default_factory=threading.Lock) + + # ----------------------------------------------------- main process (CPU) + def load(self): + """Load the weights on CPU (main process; nothing touches CUDA).""" + with self._lock: + if self._weights is None: + t = time.time() + self._weights = _LOADERS[self.kind](self) + print(f"[demo] loaded {self.label} on CPU in {time.time() - t:.0f}s", flush=True) + return self._weights + + # ------------------------------------------------- inside @spaces.GPU call + def build(self, preset: dict) -> SIMIT: + """Move the weights to the GPU and wrap them in a SIMIT engine.""" + w = self.load() + # Without ZeroGPU every request runs in this process: keep one model on the GPU at a time. + # (On ZeroGPU each request is a fresh fork in which nothing is on the GPU yet.) + if _ON_GPU[0] not in (None, self.key): + MODELS[_ON_GPU[0]]._to("cpu") + torch.cuda.empty_cache() + _ON_GPU[0] = self.key + cfg = SIMITConfig(**{k: v for k, v in preset.items() if k in SIMITConfig.__dataclass_fields__}) + if self.kind == "bagel": + from simit.backends.bagel import BagelBackend + backend = BagelBackend(w, device="cuda", image_steps=preset.get("image_steps"), use_cuda_graphs=False) + return SIMIT(backend, config=cfg, model_id=self.repo) + if self.kind == "lance": + from simit.backends.lance import LanceBackend + backend = LanceBackend(w, device="cuda", image_steps=preset.get("image_steps"), use_cuda_graphs=False) + return SIMIT(backend, config=cfg, model_id=self.repo) + from simit.backends.hf import HFBackend + from simit.backends.imagegen import DiffusersImageGenerator + model, processor, pipe = w + model.to("cuda") + pipe.to("cuda") + backend = HFBackend(model, processor=processor, max_batch_size=8, max_batch_tokens=32768) + return SIMIT(backend, image_generator=DiffusersImageGenerator(pipe), config=cfg, model_id=self.repo) + + def _to(self, device: str): + if self.kind in ("bagel", "lance"): + self._weights.to(device) + else: + model, _, pipe = self._weights + model.to(device) + pipe.to(device) + + def preset(self, budget: int) -> dict: + return dict(PRESETS[self.key][str(budget)]) + + +_ON_GPU = [None] # key of the model whose weights are on the GPU in this process + + +@contextlib.contextmanager +def _fp8_without_gpu(): + """transformers dequantizes FP8 checkpoints to bf16 when it sees no GPU at load + time. FP8 weights only need a GPU to *run*, so let the load keep them in FP8 + on CPU; they are moved to the GPU inside each request.""" + saved = torch.cuda.is_available, torch.cuda.get_device_capability + torch.cuda.is_available = lambda: True + torch.cuda.get_device_capability = lambda *a, **k: (9, 0) + try: + yield + finally: + torch.cuda.is_available, torch.cuda.get_device_capability = saved + + +def _load_bagel(spec): + from simit.backends.bagel import BagelModel + return BagelModel.load(spec.repo, device="cpu") + + +def _load_lance(spec): + from simit.backends.lance import LanceModel + return LanceModel.load(spec.repo, device="cpu") + + +def _load_hf(spec): + from diffusers import DiffusionPipeline + from transformers import AutoModelForMultimodalLM, AutoProcessor + + from simit.backends.hf import _dense_fp8_fix + os.environ.setdefault("TRANSFORMERS_DISABLE_DEEPGEMM_LINEAR", "1") # Triton FP8 kernels: no nvcc JIT at runtime + processor = AutoProcessor.from_pretrained(spec.repo) + kw = dict(device_map="cpu", dtype=torch.bfloat16) + _dense_fp8_fix(spec.repo, kw) # transformers would skip FP8 conversion of gate_proj for this checkpoint + with _fp8_without_gpu(): + model = AutoModelForMultimodalLM.from_pretrained(spec.repo, **kw) + model.eval() + pipe = DiffusionPipeline.from_pretrained(spec.image_generator, dtype=torch.bfloat16) + return model, processor, pipe + + +_LOADERS = {"bagel": _load_bagel, "lance": _load_lance, "hf": _load_hf} + +MODELS = {m.key: m for m in [ + ModelSpec("bagel", "BAGEL-7B-MoT", "ByteDance-Seed/BAGEL-7B-MoT", "bagel", "large", + blurb="Unified model: draws its own photos and writes code for charts and diagrams."), + ModelSpec("lance", "Lance", "bytedance-research/Lance", "lance", "large", + blurb="Small unified model: fast native image generation."), + ModelSpec("qwen", "Qwen3.8-27B (FP8) + FLUX.2-klein", "Qwen/Qwen3.8-27B-FP8", "hf", "xlarge", + image_generator="black-forest-labs/FLUX.2-klein-4B", + blurb="Standard VLM: FLUX.2-klein draws the photos; it writes code for charts and diagrams."), +]} +ENABLED = [k for k in os.environ.get("SIMIT_DEMO_MODELS", "bagel,lance,qwen").split(",") if k in MODELS] + + +# ---------------------------------------------------------------- a request +# Pipeline call tags (``on_progress``) -> what the loading screen shows. +STAGE_OF_TAG = {"synthesis": "synthesizing", "synthesis_fresh": "synthesizing", "revise": "synthesizing", + "route": "routing", "skill_spec": "coding", "render": "coding", "image": "drawing", + "critic": "checking", "critic_score": "checking"} + + +def run(spec: ModelSpec, image, question: str, budget: int, always: bool): + """Generator of progress events for one request (runs inside the GPU call): + ("stage", name, info) / ("greedy", ZeroShot, k) / ("demo", Demo) / ("final", answer, info).""" + t_start = time.time() + preset = spec.preset(budget) + yield ("stage", "warmup", {"elapsed": 0.0}) + sim = spec.build(preset) + try: + yield ("stage", "solving", {"elapsed": time.time() - t_start}) + zs = sim.zero_shot(image, question, max_new_tokens=ANSWER_TOKENS) # the greedy answer + p0 (ABA input) + k_budget = sim.config.budget(zs.confidence) + yield ("greedy", zs, k_budget) + + # what is left of the budget, keeping room for the final answer (measured: ~1-1.5 s after the last demo) + time_left = budget - (time.time() - t_start) - ANSWER_RESERVE + if (k_budget == 0 and not always) or time_left < 3: + if time_left < 3: + reason = "no_time" + else: + reason = "confident" if preset.get("imagine_by_default", True) else "policy" + yield ("final", zs.answer, {"demos": 0, "skipped": reason, "elapsed": time.time() - t_start}) + return + + events: "queue.Queue" = queue.Queue() + current = ["synthesis"] + result = {} + + def work(): + try: + result["det"] = sim.imagine(image, question, k=preset["k_max"] if always else None, + return_details=True, max_new_tokens=ANSWER_TOKENS, time_limit=time_left, + on_demo=events.put, on_progress=lambda tag: current.__setitem__(0, tag)) + except Exception as e: # surfaced below + result["error"] = e + th = threading.Thread(target=work, daemon=True) + th.start() + while th.is_alive() or not events.empty(): + try: + yield ("demo", events.get(timeout=0.4)) + continue + except queue.Empty: + pass + stage = STAGE_OF_TAG.get(current[0].split(":")[0], "synthesizing") + yield ("stage", stage, {"elapsed": time.time() - t_start, "limit": budget}) + if "error" in result: + raise result["error"] + det = result["det"] + if not det.demos: + yield ("final", zs.answer, {"demos": 0, "skipped": "none_passed", "elapsed": time.time() - t_start, + "stats": det.stats}) + return + yield ("stage", "improving", {"elapsed": time.time() - t_start}) + answer = sim.answer(image, question, det.demos, max_new_tokens=ANSWER_TOKENS) + yield ("final", answer, {"demos": len(det.demos), "elapsed": time.time() - t_start, "stats": det.stats}) + finally: + sim.close() + + +def code_snippet(spec: ModelSpec, budget: int, question: str, always: bool = False) -> str: + """Copy-paste code reproducing this request with the library.""" + p = spec.preset(budget) + force = always or not p.get("imagine_by_default", True) + keys = ["k_max", "epsilon", "A0", "A1", "B", "t_low", "t_high", "verify", "verify_think", "use_skills", + "attempts_per_slot", "verify_rounds"] + args = [f"{k}={round(p[k], 3) if isinstance(p[k], float) else p[k]!r}" for k in keys if k in p] + lines, line = [], "" + for a in args: # wrap the config arguments, a few per line + if line and len(line) + len(a) > 62: + lines.append(line.rstrip()) + line = "" + line += a + ", " + lines.append(line.rstrip(", ")) + cfg = "\n " + "\n ".join(lines) + ",\n " + gen = f',\n image_generator="{spec.image_generator}",' if spec.image_generator else "," + q = question.strip().replace('"""', "'''") or "What is shown in this image?" + steps = f"\n image_steps={p['image_steps']}," if p.get("image_steps") else "" + return f'''# pip install simit +from simit import SIMIT, SIMITConfig + +model = SIMIT.from_pretrained( + "{spec.repo}"{gen} + config=SIMITConfig({cfg}),{steps} +) + +image = "my_image.jpg" +question = """{q}""" + +greedy = model.greedy(image, question) # base model, zero-shot +demos = model.imagine(image, question{f", k={p['k_max']}" if force else ""}, time_limit={max(5, budget - 15)}) # imagined examples +improved = model.answer(image, question, demos) # SIMIT-ICL +print(greedy, "->", improved) + +from simit import save_demos +save_demos(demos, "imagined/") # PNGs + jsonl, e.g. to fine-tune on +''' diff --git a/examples/bagel_amazon/demo_0.png b/examples/bagel_amazon/demo_0.png new file mode 100644 index 0000000000000000000000000000000000000000..607461d31ed0817cbfef0ce3fceab26370616a81 --- /dev/null +++ b/examples/bagel_amazon/demo_0.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:802a7419911b15d848b369605e1dc5b4c90216038f7aaf73a550965289212b44 +size 242748 diff --git 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+[ + { + "id": "bagel_ok_vqa_val2014_492_always", + "query_file": "query.jpg", + "model": "bagel", + "budget": 60, + "always": true, + "question": "In what country would you find this hat?\nWhen the provided information is insufficient, respond with 'Unanswerable'.\nAnswer the question using a single word or phrase.", + "reference": "vietnam", + "greedy": "Unanswerable", + "p0": 0.8883122759482616, + "simit": "Vietnam", + "greedy_ok": false, + "simit_ok": true, + "demos": [ + { + "image": "demo_0.png", + "question": "How many wheels does the bicycle have?", + "answer": "Two", + "skill": "natural", + "verify_score": 100, + "confidence": null + }, + { + "image": "demo_1.png", + "question": "What color is the hat the person is wearing?", + "answer": "Brown", + "skill": "natural", + "verify_score": 70, + "confidence": null + } + ], + "elapsed": 16.20292091369629, + "source": "OK-VQA row 492, paper Fig. (qualitative samples)" + }, + { + "id": "bagel_infovqa_val_449", + "query_file": "query.jpg", + "model": "bagel", + "budget": 60, + "always": false, + "question": "How many points are listed under when to wash your hands?\nAnswer the question using a single word or phrase.", + "reference": "6", + "greedy": "7", + "p0": 0.6461464658834921, + "simit": "6", + "greedy_ok": false, + "simit_ok": true, + "demos": [ + { + "image": "demo_0.png", + "question": "How many steps are there in the handwashing process?", + "answer": "11", + "skill": "figure", + "verify_score": 70, + "confidence": 0.38085715656328817 + } + ], + "elapsed": 55.53464651107788, + "source": "InfographicVQA row 449, paper Fig. (qualitative samples)" + }, + { + "id": "bagel_amazon", + "query_file": "query.png", + "model": "bagel", + "budget": 60, + "always": false, + "question": "Does this contain any trans fat?", + "reference": "No", + "greedy": "Yes, the bagel contains trans fat. According to the nutrition facts label on the packaging, the bagel has 0.5 grams of trans fat per serving.", + "p0": 0.7576221457584613, + "simit": "No", + "greedy_ok": false, + "simit_ok": true, + "demos": [ + { + "image": "demo_0.png", + "question": "How many calories are in one serving of the Turkish Sesame Bagel?", + "answer": "290", + "skill": "natural", + "verify_score": 75, + "confidence": 0.3624771327791413 + }, + { + "image": "demo_1.png", + "question": "How many calories are listed for each serving of Turkish Sessame Bagel?", + "answer": "290", + "skill": "natural", + "verify_score": 100, + "confidence": 0.147100984163098 + } + ], + "elapsed": 27.87025022506714, + "source": "Amazon product page (screenshot)" + }, + { + "id": "bagel_vizwiz_vqa_val_345_always", + "query_file": "query.jpg", + "model": "bagel", + "budget": 60, + "always": true, + "question": "What color is this cat?\nWhen the provided information is insufficient, respond with 'Unanswerable'.\nAnswer the question using a single word or phrase.", + "reference": "brown", + "greedy": "Unanswerable", + "p0": 0.6977114977650781, + "simit": "Brown", + "greedy_ok": false, + "simit_ok": true, + "demos": [ + { + "image": "demo_0.png", + "question": "How many cats are in the image?", + "answer": "One", + "skill": "natural", + "verify_score": 70, + "confidence": null + }, + { + "image": "demo_1.png", + "question": "What type of furniture is visible in the background?", + "answer": "A chair", + "skill": "natural", + "verify_score": 70, + "confidence": null + } + ], + "elapsed": 10.399970293045044, + "source": "VizWiz row 345, paper teaser" + }, + { + "id": "bagel_vizwiz_vqa_val_47_always", + "query_file": "query.jpg", + "model": "bagel", + "budget": 60, + "always": true, + "question": "What is this item?\nWhen the provided information is insufficient, respond with 'Unanswerable'.\nAnswer the question using a single word or phrase.", + "reference": "angel", + "greedy": "Unanswerable.", + "p0": 0.8626286828795229, + "simit": "Angel", + "greedy_ok": false, + "simit_ok": true, + "demos": [ + { + "image": "demo_0.png", + "question": "How many cats are depicted in the image?", + "answer": "One", + "skill": "natural", + "verify_score": 80, + "confidence": null + }, + { + "image": "demo_1.png", + "question": "How many cats are featured in the image?", + "answer": "One", + "skill": "natural", + "verify_score": 70, + "confidence": null + } + ], + "elapsed": 15.055564641952515, + "source": "VizWiz row 47, paper appendix" + }, + { + "id": "bagel_vizwiz_vqa_val_87_always", + "query_file": "query.jpg", + "model": "bagel", + "budget": 60, + "always": true, + "question": "What is this a picture of?\nWhen the provided information is insufficient, respond with 'Unanswerable'.\nAnswer the question using a single word or phrase.", + "reference": "dog in lace dress", + "greedy": "Unanswerable.", + "p0": 0.8959448319074929, + "simit": "Dog", + "greedy_ok": false, + "simit_ok": true, + "demos": [ + { + "image": "demo_0.png", + "question": "How many legs does the dog have?", + "answer": "Four", + "skill": "natural", + "verify_score": 100, + "confidence": null + }, + { + "image": "demo_1.png", + "question": "What color is the dog's belly?", + "answer": "Pink", + "skill": "natural", + "verify_score": 100, + "confidence": null + } + ], + "elapsed": 9.160282850265503, + "source": "VizWiz row 87, paper appendix" + }, + { + "id": "lance_ok_vqa_val2014_253", + "query_file": "query.jpg", + "model": "lance", + "budget": 60, + "always": false, + "question": "What time period is the action pictured here based off of?\nAnswer the question using a single word or phrase.", + "reference": "medieval", + "greedy": "Polo", + "p0": 0.6023501950034147, + "simit": "Medieval", + "greedy_ok": false, + "simit_ok": true, + "demos": [ + { + "image": "demo_0.png", + "question": "What color does the umbrella look like?", + "answer": "Red", + "skill": "natural", + "verify_score": null, + "confidence": 0.36976529385784346 + } + ], + "elapsed": 16.72637391090393, + "source": "OK-VQA row 253, paper appendix" + }, + { + "id": "lance_ok_vqa_val2014_8_always", + "query_file": "query.jpg", + "model": "lance", + "budget": 60, + "always": true, + "question": "What kind of house is next to the stop sign?\nAnswer the question using a single word or phrase.", + "reference": "apartment", + "greedy": "Single story", + "p0": 0.5895590491401231, + "simit": "Apartment", + "greedy_ok": false, + "simit_ok": true, + "demos": [ + { + "image": "demo_0.png", + "question": "What are the colors of the umbrellas in the painting?", + "answer": "No", + "skill": "natural", + "verify_score": null, + "confidence": null + }, + { + "image": "demo_1.png", + "question": "What color does the door have?", + "answer": "Brown", + "skill": "natural", + "verify_score": null, + "confidence": null + }, + { + "image": "demo_2.png", + "question": "What color is the umbrella?", + "answer": "No", + "skill": "natural", + "verify_score": null, + "confidence": null + }, + { + "image": "demo_3.png", + "question": "What is the color of the stop sign?", + "answer": "Red", + "skill": "natural", + "verify_score": null, + "confidence": null + } + ], + "elapsed": 7.836957216262817, + "source": "OK-VQA row 8, paper appendix" + } +] \ No newline at end of file diff --git a/examples/lance_ok_vqa_val2014_253/demo_0.png b/examples/lance_ok_vqa_val2014_253/demo_0.png new file mode 100644 index 0000000000000000000000000000000000000000..c29955e9cb1bbcf161b336471c43c4148a8f179c --- /dev/null +++ b/examples/lance_ok_vqa_val2014_253/demo_0.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6704f5a226fe74337310684ae4abd162acf5549cb9fbecbcf9c9ea12e4d33a6d +size 196782 diff --git a/examples/lance_ok_vqa_val2014_253/query.jpg b/examples/lance_ok_vqa_val2014_253/query.jpg new file mode 100644 index 0000000000000000000000000000000000000000..d7f5553ee85eaec7601ea18e91e5d626bee93cee --- /dev/null +++ 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sha256:ca59133eb7a74dda75dd9b0244ada8a969497eba7ad5efd999c657bddaea4790 +size 208561 diff --git a/examples/lance_ok_vqa_val2014_8_always/demo_2.png b/examples/lance_ok_vqa_val2014_8_always/demo_2.png new file mode 100644 index 0000000000000000000000000000000000000000..212b9043c90d778491ceb9d2c9888a65139de020 --- /dev/null +++ b/examples/lance_ok_vqa_val2014_8_always/demo_2.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:42b888d177f87e5b48c8d4703353126ad0969af5cf12f9d8f85b7ba28ee2f50c +size 212452 diff --git a/examples/lance_ok_vqa_val2014_8_always/demo_3.png b/examples/lance_ok_vqa_val2014_8_always/demo_3.png new file mode 100644 index 0000000000000000000000000000000000000000..89a493bec3748afc405654af0e07bce0af05ca14 --- /dev/null +++ b/examples/lance_ok_vqa_val2014_8_always/demo_3.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd6ff8322a257d9adaf3ae63a345e1470b4a747e44a34ab259c3385cf333eeba +size 201596 diff --git a/examples/lance_ok_vqa_val2014_8_always/query.jpg b/examples/lance_ok_vqa_val2014_8_always/query.jpg new file mode 100644 index 0000000000000000000000000000000000000000..435f9ba4b48f04eefb52c0c57fe54f0668db622a Binary files /dev/null and b/examples/lance_ok_vqa_val2014_8_always/query.jpg differ diff --git a/packages.txt b/packages.txt new file mode 100644 index 0000000000000000000000000000000000000000..105542b680fec3e6030d8df336cb20cf9f7c0d30 --- /dev/null +++ b/packages.txt @@ -0,0 +1,18 @@ +libnss3 +libnspr4 +libatk1.0-0 +libatk-bridge2.0-0 +libcups2 +libdrm2 +libxkbcommon0 +libxcomposite1 +libxdamage1 +libxfixes3 +libxrandr2 +libgbm1 +libasound2 +libpango-1.0-0 +libcairo2 +libxrender1 +libxext6 +fonts-dejavu-core diff --git a/presets.json b/presets.json new file mode 100644 index 0000000000000000000000000000000000000000..c308fbb8c1782ece3a75b180b6c39762d36a4255 --- /dev/null +++ b/presets.json @@ -0,0 +1,233 @@ +{ + "_note": "built by tools/build_presets.py from tune_presets.py + latency.py runs", + "bagel": { + "20": { + "epsilon": 1.0, + "A0": 0.5631057319035666, + "A1": 0.6871778307441961, + "B": 0.3818684686169197, + "t_low": 0.01568469261109281, + "t_high": 0.9941888593223634, + "k_max": 1, + "attempts_per_slot": 2, + "repair_retries": 0, + "verify_rounds": 1, + "imagine_by_default": false, + "verify": false, + "image_steps": 25 + }, + "40": { + "epsilon": 1.0, + "A0": 0.5631057319035666, + "A1": 0.6871778307441961, + "B": 0.3818684686169197, + "t_low": 0.01568469261109281, + "t_high": 0.9941888593223634, + "k_max": 1, + "attempts_per_slot": 2, + "repair_retries": 1, + "verify_rounds": 1, + "imagine_by_default": false, + "verify": true, + "verify_think": true, + "image_steps": 50 + }, + "60": { + "epsilon": 0.13150801401314569, + "A0": 0.5697034469957714, + "A1": 0.6928285171478893, + "B": 0.4731007761788684, + "t_low": 0.012450289537920928, + "t_high": 0.702696037286609, + "k_max": 2, + "attempts_per_slot": 3, + "repair_retries": 1, + "verify_rounds": 2, + "imagine_by_default": true, + "verify": true, + "verify_think": true, + "image_steps": 50 + }, + "90": { + "epsilon": 0.13150801401314569, + "A0": 0.5697034469957714, + "A1": 0.6928285171478893, + "B": 0.4731007761788684, + "t_low": 0.012450289537920928, + "t_high": 0.702696037286609, + "k_max": 2, + "attempts_per_slot": 4, + "repair_retries": 2, + "verify_rounds": 3, + "imagine_by_default": true, + "verify": true, + "verify_think": true, + "image_steps": 50 + }, + "180": { + "epsilon": 0.13150801401314569, + "A0": 0.5697034469957714, + "A1": 0.6928285171478893, + "B": 0.4731007761788684, + "t_low": 0.012450289537920928, + "t_high": 0.702696037286609, + "k_max": 2, + "attempts_per_slot": 4, + "repair_retries": 2, + "verify_rounds": 5, + "imagine_by_default": true, + "verify": true, + "verify_think": true, + "image_steps": 50 + } + }, + "lance": { + "20": { + "epsilon": 0.10540046109011536, + "A0": 0.5284418351778891, + "A1": 0.7185259059153423, + "B": 0.20052864010313778, + "t_low": 0.24157862499461874, + "t_high": 0.8741991398657891, + "k_max": 1, + "attempts_per_slot": 2, + "repair_retries": 0, + "verify_rounds": 1, + "imagine_by_default": true, + "image_steps": 30 + }, + "40": { + "epsilon": 0.11989932190804062, + "A0": 0.35382385806255806, + "A1": 0.4638199725338076, + "B": 0.2584592356693373, + "t_low": 0.25642426481228886, + "t_high": 0.9450464322610527, + "k_max": 4, + "attempts_per_slot": 2, + "repair_retries": 1, + "verify_rounds": 1, + "imagine_by_default": true, + "image_steps": 30 + }, + "60": { + "epsilon": 0.11989932190804062, + "A0": 0.35382385806255806, + "A1": 0.4638199725338076, + "B": 0.2584592356693373, + "t_low": 0.25642426481228886, + "t_high": 0.9450464322610527, + "k_max": 4, + "attempts_per_slot": 3, + "repair_retries": 1, + "verify_rounds": 2, + "imagine_by_default": true, + "image_steps": 30 + }, + "90": { + "epsilon": 0.11989932190804062, + "A0": 0.35382385806255806, + "A1": 0.4638199725338076, + "B": 0.2584592356693373, + "t_low": 0.25642426481228886, + "t_high": 0.9450464322610527, + "k_max": 4, + "attempts_per_slot": 4, + "repair_retries": 2, + "verify_rounds": 3, + "imagine_by_default": true, + "image_steps": 30 + }, + "180": { + "epsilon": 0.11989932190804062, + "A0": 0.35382385806255806, + "A1": 0.4638199725338076, + "B": 0.2584592356693373, + "t_low": 0.25642426481228886, + "t_high": 0.9450464322610527, + "k_max": 4, + "attempts_per_slot": 4, + "repair_retries": 2, + "verify_rounds": 5, + "imagine_by_default": true, + "image_steps": 30 + } + }, + "qwen": { + "20": { + "epsilon": 1.0, + "A0": 0.29123083240427616, + "A1": 0.892922351072982, + "B": 0.051022405374014035, + "t_low": 0.10443837804741735, + "t_high": 0.24890099152089373, + "k_max": 1, + "attempts_per_slot": 2, + "repair_retries": 0, + "verify_rounds": 1, + "imagine_by_default": false, + "verify": false, + "use_skills": false + }, + "40": { + "epsilon": 1.0, + "A0": 0.29123083240427616, + "A1": 0.892922351072982, + "B": 0.051022405374014035, + "t_low": 0.10443837804741735, + "t_high": 0.24890099152089373, + "k_max": 1, + "attempts_per_slot": 2, + "repair_retries": 1, + "verify_rounds": 1, + "imagine_by_default": false, + "verify": false, + "use_skills": false + }, + "60": { + "epsilon": 1.0, + "A0": 0.4895372103191979, + "A1": 0.6789573167296538, + "B": 0.39026458814322773, + "t_low": 0.05913721293446661, + "t_high": 0.6612150885625033, + "k_max": 2, + "attempts_per_slot": 3, + "repair_retries": 1, + "verify_rounds": 2, + "imagine_by_default": false, + "verify": false, + "use_skills": true + }, + "90": { + "epsilon": 1.0, + "A0": 0.4895372103191979, + "A1": 0.6789573167296538, + "B": 0.39026458814322773, + "t_low": 0.05913721293446661, + "t_high": 0.6612150885625033, + "k_max": 2, + "attempts_per_slot": 4, + "repair_retries": 2, + "verify_rounds": 3, + "imagine_by_default": false, + "verify": false, + "use_skills": true + }, + "180": { + "epsilon": 1.0, + "A0": 0.29123083240427616, + "A1": 0.892922351072982, + "B": 0.051022405374014035, + "t_low": 0.10443837804741735, + "t_high": 0.24890099152089373, + "k_max": 1, + "attempts_per_slot": 4, + "repair_retries": 2, + "verify_rounds": 5, + "imagine_by_default": false, + "verify": true, + "use_skills": true + } + } +} \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..55b5b4a76866e596df14fa9c58bfb952d8b98411 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,4 @@ +# gradio, spaces and huggingface_hub are preinstalled and managed by the Spaces platform: don't list them. +simit>=0.1.1 +# simit needs torch >= 2.12 (torch.nn.attention.varlen); 2.13.0 is on ZeroGPU's supported list +torch==2.13.0 diff --git a/tools/build_presets.py b/tools/build_presets.py new file mode 100644 index 0000000000000000000000000000000000000000..81ab7f54702730589d50221f9e6a8e0734a971cc --- /dev/null +++ b/tools/build_presets.py @@ -0,0 +1,91 @@ +"""Combine accuracy (tune_presets.py) and latency (latency.py) into presets.json: +for every model and time budget, the largest K_max whose demos arrive in time +for most requests, at the best-quality speed setting that achieves it, with the +ABA/DF thresholds tuned for that K_max. + +python tools/build_presets.py RUN_DIR +""" +import json +import sys +from pathlib import Path + +HERE = Path(__file__).parent.parent +sys.path.insert(0, str(HERE)) +from demo_models import ANSWER_RESERVE, BUDGETS, MODELS # noqa: E402 + +run = Path(sys.argv[1]) +# ZeroGPU vs. our H100 (spaces' own duration_factor: 1.5 on the half GPU, none on the full one) +SLOWDOWN = {"large": 1.5, "xlarge": 1.0} +QUALITY = { # speed settings, best quality first (as timed by latency.py) + "bagel": ["think50", "nothink50", "nothink25", "nocritic25"], + "lance": ["steps30", "steps20"], + "qwen": ["critic", "skills", "natural"], +} +SETTING = { + "think50": dict(verify=True, verify_think=True, image_steps=50), + "nothink50": dict(verify=True, verify_think=False, image_steps=50), + "nothink25": dict(verify=True, verify_think=False, image_steps=25), + "nocritic25": dict(verify=False, image_steps=25), + "steps30": dict(image_steps=30), "steps20": dict(image_steps=20), + "critic": dict(verify=True, use_skills=True), "skills": dict(verify=False, use_skills=True), + "natural": dict(verify=False, use_skills=False), +} +SHARE = 0.8 # fraction of requests that must get all K demos in time + +out_file = HERE / "presets.json" +presets = json.loads(out_file.read_text()) if out_file.exists() else {} # models without data keep theirs +presets["_note"] = "built by tools/build_presets.py from tune_presets.py + latency.py runs" +report = [] +for key in MODELS: + tune_file, lat_file = run / f"presets_{key}.json", run / f"latency_{key}.json" + if not (tune_file.exists() and lat_file.exists()): + print(f"skip {key}: missing {tune_file.name if not tune_file.exists() else lat_file.name}") + continue + tuning = json.loads(tune_file.read_text()) + tuned, zero_shot = tuning["robust_per_k_max"], tuning["zero_shot"] + timelines = json.loads(lat_file.read_text()) + f = SLOWDOWN[MODELS[key].gpu_size] + scores = {int(k): v["score"] for k, v in tuned.items()} + presets[key] = {} + n_val = tuning["n_val"] + band = {int(k): v["params"]["t_high"] - v["params"]["t_low"] for k, v in tuned.items()} + + def fits(setting, k, deadline): + """Most requests have k demos before the deadline. Pooled over every run that asked for at least + k demos (when its k-th demo arrived): 6 requests per cell alone are too noisy.""" + runs = [r for r in timelines if r["setting"] == setting and r["k"] >= k] + if not runs: + return False + return sum(len(r.get("demos", [])) >= k and r["demos"][k - 1] <= deadline for r in runs) >= SHARE * len(runs) + + for budget in BUDGETS: + deadline = (budget - ANSWER_RESERVE) / f # the demo's imagination deadline, in H100 seconds + choice = None + # The tuned accuracies hold for the setting the tuning cache was made with (the first, best-quality + # one), so use it whenever any K fits; faster settings only when it cannot fit at all. + for setting in QUALITY[key]: + ks = [k for k in sorted(scores) if fits(setting, k, deadline)] + if not ks: + continue + top = max(scores[k] for k in ks) + near = [k for k in ks if scores[k] >= top - 1.0 / n_val] # within one validation query + k = min(near, key=lambda k: (-band[k], k)) # widest band, then fewest demos + choice = (k, setting) + break + if choice is None: # nothing fits: the fastest setting, one demo; the deadline decides + choice = (1, QUALITY[key][-1]) + k, setting = choice + # more time: more attempts and checking rounds (the paper's 4 / 2 / 5 at the largest budget) + effort = {20: (2, 0, 1), 40: (2, 1, 1), 60: (3, 1, 2), 90: (4, 2, 3), 180: (4, 2, 5)}[budget] + params = {p: v for p, v in tuned[str(k)]["params"].items() if p != "k_max"} + helps = scores[k] > zero_shot + 0.5 / n_val + if not helps: # imagination does not beat zero-shot here: ABA never imagines (the UI's checkbox still can) + params["epsilon"] = 1.0 + presets[key][str(budget)] = dict(params, k_max=k, attempts_per_slot=effort[0], repair_retries=effort[1], + verify_rounds=effort[2], imagine_by_default=helps, **SETTING[setting]) + report.append((key, budget, k, setting, round(scores[k], 3), round(zero_shot, 3), round(band[k], 2), + "" if helps else " -> no gain: zero-shot by default")) + +out_file.write_text(json.dumps(presets, indent=2)) +for row in report: + print("%-6s %4ss K_max=%d %-11s val %.3f (zero-shot %.3f) DF band %.2f%s" % row) diff --git a/tools/fork_test.py b/tools/fork_test.py new file mode 100644 index 0000000000000000000000000000000000000000..9a06966414281de5c8869465b58e1442137bb8a1 --- /dev/null +++ b/tools/fork_test.py @@ -0,0 +1,55 @@ +"""Simulate ZeroGPU locally: load weights on CPU in the parent (CUDA must stay +uninitialized there), then run whole requests in forked children, which is +what ``@spaces.GPU`` does on ZeroGPU. + +CUDA_VISIBLE_DEVICES=0 python tools/fork_test.py bagel [n_requests] +""" +import multiprocessing as mp +import sys +import time +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).parent.parent)) +sys.path.insert(0, str(Path(__file__).parent)) + +# ZeroGPU's own main-process emulation (fake CUDA queries, CUDA init forbidden); children undo it, +# as spaces' worker does before attaching the GPU. +from spaces.zero.torch import patching # noqa: E402 +import torch # noqa: E402 + +patching.patch() +from demo_models import MODELS, run # noqa: E402 +from general_qa import general_qa # noqa: E402 + +key = sys.argv[1] +n = int(sys.argv[2]) if len(sys.argv) > 2 else 2 +spec = MODELS[key] +t = time.time() +spec.load() +print(f"parent: loaded on CPU in {time.time() - t:.0f}s; CUDA initialized in parent: {torch.cuda.is_initialized()}", + flush=True) +assert not torch.cuda.is_initialized(), "the main process must not initialize CUDA (ZeroGPU forks it)" +queries = general_qa(per_subset=1, offset=420)[:n] + + +def child(i, q): + patching.unpatch() + t0 = time.time() + for ev in run(spec, q["image"], q["question"], 60, False): + if ev[0] == "greedy": + print(f" [{i}] greedy={ev[1].answer!r} p0={ev[1].confidence:.2f} k={ev[2]} at {time.time() - t0:.1f}s", + flush=True) + elif ev[0] == "demo": + print(f" [{i}] demo [{ev[1].skill}] {ev[1].question!r} -> {ev[1].answer!r} at {time.time() - t0:.1f}s", + flush=True) + elif ev[0] == "final": + print(f" [{i}] final={ev[1]!r} {ev[2]} refs={q['answers'][:3]} total {time.time() - t0:.1f}s", flush=True) + + +ctx = mp.get_context("fork") +for i, q in enumerate(queries): + p = ctx.Process(target=child, args=(i, q)) + t0 = time.time() + p.start() + p.join() + print(f"request {i} ({q['subset']}): exit={p.exitcode} in {time.time() - t0:.1f}s", flush=True) diff --git a/tools/general_qa.py b/tools/general_qa.py new file mode 100644 index 0000000000000000000000000000000000000000..2f7b54d02f1877e98691afc9a03acaaf41bfcac1 --- /dev/null +++ b/tools/general_qa.py @@ -0,0 +1,70 @@ +"""A mixed "general visual QA" set from LMMs-Eval-Lite, close to what demo +visitors ask: everyday photos (VizWiz, VQAv2), knowledge (OK-VQA), reading text +(TextVQA), charts and documents (ChartQA, DocVQA) and multiple-choice reasoning +(MMBench, AI2D). Each example carries its benchmark's own metric. +""" + +import ast + +from datasets import load_dataset + +VQA = "Answer the question using a single word or phrase." + + +def _list(v): + if isinstance(v, list): + return v + try: + v = ast.literal_eval(v) + except Exception: + pass + return v if isinstance(v, list) else [v] + + +def _options(row, keys): + opts = [(k, str(row[k])) for k in keys if str(row.get(k, "nan")) not in ("nan", "None", "")] + return "\n".join(f"{k}. {v}" for k, v in opts) + + +def _mmbench(r): + hint = str(r.get("hint", "nan")) + q = (hint + "\n" if hint not in ("nan", "None", "") else "") + r["question"] + return (q + "\n" + _options(r, "ABCD") + "\nAnswer with the option's letter from the given choices directly.", + [str(r["answer"])], "multiple_choice") + + +def _ai2d(r): + opts = _list(r["options"]) + q = r["question"] + "\n" + "\n".join(f"{chr(65 + i)}. {o}" for i, o in enumerate(opts)) + return q + "\nAnswer with the option's letter from the given choices directly.", [chr(65 + int(r["answer"]))], \ + "multiple_choice" + + +SUBSETS = { + "vizwiz_vqa_val": lambda r: (f"{r['question']}\nWhen the provided information is insufficient, respond with " + f"'Unanswerable'.\n{VQA}", [str(a) for a in _list(r["answers"])], "vqa_accuracy"), + "vqav2_val": lambda r: (f"{r['question']}\n{VQA}", + [a["answer"] if isinstance(a, dict) else str(a) for a in _list(r["answers"])], + "vqa_accuracy"), + "ok_vqa_val2014": lambda r: (f"{r['question']}\n{VQA}", [str(a) for a in _list(r["answers"])], "vqa_accuracy"), + "textvqa_val": lambda r: (f"{r['question']}\n{VQA}", [str(a) for a in _list(r["answers"])], "vqa_accuracy"), + "chartqa": lambda r: (f"{r['question']}\nAnswer the question with a single word.", + [str(a) for a in _list(r["answer"])], "relaxed_accuracy"), + "docvqa_val": lambda r: (f"{r['question']}\n{VQA}", [str(a) for a in _list(r["answers"])], "anls"), + "mmbench_en_dev": _mmbench, + "ai2d": _ai2d, +} + + +def general_qa(per_subset: int = 8, offset: int = 300) -> list[dict]: + """``per_subset`` examples from each subset, starting at row ``offset`` + (rows below 300 were used for the package benchmarks).""" + out = [] + for sub, fn in SUBSETS.items(): + ds = load_dataset("lmms-lab/LMMs-Eval-Lite", sub)["lite"] + for i in range(offset, offset + per_subset): + r = ds[i] + question, answers, metric = fn(r) + out.append({"image": r["image"].convert("RGB"), "question": question, "answers": answers, + "metric": metric, "subset": sub}) + return out diff --git a/tools/latency.py b/tools/latency.py new file mode 100644 index 0000000000000000000000000000000000000000..a61d81039ee0ceba572eee2c0513b3069cc57fad --- /dev/null +++ b/tools/latency.py @@ -0,0 +1,74 @@ +"""Per-request timelines on the demo's own code path, as ZeroGPU runs it (a fresh +forked process per request, weights moved to the GPU each time): when the greedy +answer and each imagined demo arrive, for forced K and a given speed setting. + +CUDA_VISIBLE_DEVICES=1 python tools/latency.py lance OUT.json +""" +import json +import multiprocessing as mp +import os +import sys +import time +from pathlib import Path + +os.environ.setdefault("TRITON_CACHE_AUTOTUNING", "1") +os.environ.setdefault("TRANSFORMERS_DISABLE_DEEPGEMM_LINEAR", "1") +sys.path.insert(0, str(Path(__file__).parent.parent)) +sys.path.insert(0, str(Path(__file__).parent)) +from spaces.zero.torch import patching # noqa: E402 +import torch # noqa: E402 + +patching.patch() +from demo_models import MODELS, run # noqa: E402 +from general_qa import general_qa # noqa: E402 + +ABA = dict(epsilon=0.06, A0=0.5, A1=0.85, B=0.4, t_low=0.0, t_high=1.0) +SETTINGS = { # speed settings to time (accuracy comes from tune_presets.py) + "bagel": {"think50": dict(verify=True, verify_think=True, image_steps=50), + "nothink50": dict(verify=True, verify_think=False, image_steps=50), + "nothink25": dict(verify=True, verify_think=False, image_steps=25), + "nocritic25": dict(verify=False, image_steps=25)}, + "lance": {"steps30": dict(image_steps=30), "steps20": dict(image_steps=20)}, + "qwen": {"natural": dict(verify=False, use_skills=False), "skills": dict(verify=False, use_skills=True), + "critic": dict(verify=True, use_skills=True)}, +} +KS = {"bagel": [1, 2, 4], "lance": [1, 2, 4], "qwen": [1, 2]} + +key, out = sys.argv[1], Path(sys.argv[2]) +n_q = int(sys.argv[3]) if len(sys.argv) > 3 else 6 +spec = MODELS[key] +spec.load() +assert not torch.cuda.is_initialized() +queries = general_qa(per_subset=1, offset=440)[:n_q] + + +def child(conn, q, preset): + patching.unpatch() + t0 = time.time() + rec = {"demos": []} + spec.preset = lambda budget: dict(preset) # this process only + for ev in run(spec, q["image"], q["question"], 1000, True): + t = time.time() - t0 + if ev[0] == "greedy": + rec["greedy"] = t + elif ev[0] == "demo": + rec["demos"].append(t) + elif ev[0] == "final": + rec["total"] = t + conn.send(rec) + + +results = [] +for sname, setting in SETTINGS[key].items(): + for k in KS[key]: + preset = dict(ABA, k_max=k, attempts_per_slot=4, repair_retries=1, verify_rounds=2, **setting) + for qi, q in enumerate(queries): + a, b = mp.get_context("fork").Pipe() + p = mp.get_context("fork").Process(target=child, args=(b, q, preset)) + p.start() + rec = a.recv() if a.poll(900) else {"error": "timeout"} + p.join(timeout=30) + rec.update(setting=sname, k=k, query=qi, subset=q["subset"]) + results.append(rec) + print(json.dumps(rec), flush=True) + out.write_text(json.dumps(results, indent=1)) diff --git a/tools/make_examples.py b/tools/make_examples.py new file mode 100644 index 0000000000000000000000000000000000000000..c30df4db707b44122537f68829a3744df21bc1a9 --- /dev/null +++ b/tools/make_examples.py @@ -0,0 +1,121 @@ +"""Cached demo examples: real runs of the demo pipeline on the paper's figure +queries (LMMs-Eval-Lite rows), stored with both answers, the imagined demos and +the reference answer. ``--keep improved`` keeps only runs where SIMIT-ICL fixed +the base answer. + +CUDA_VISIBLE_DEVICES=0 python tools/make_examples.py bagel 60 [--keep improved] +""" +import argparse +import json +import os +import shutil +import sys +import time +from pathlib import Path + +os.environ.setdefault("TRITON_CACHE_AUTOTUNING", "1") +os.environ.setdefault("TRANSFORMERS_DISABLE_DEEPGEMM_LINEAR", "1") +HERE = Path(__file__).parent.parent +sys.path.insert(0, str(HERE)) +sys.path.insert(0, str(HERE / "tools")) + +from datasets import load_dataset # noqa: E402 +from PIL import Image # noqa: E402 + +from demo_models import MODELS, run # noqa: E402 +from general_qa import _list # noqa: E402 +from simit.metrics import get_metric # noqa: E402 + +VQA = "Answer the question using a single word or phrase." +UNANSWERABLE = "When the provided information is insufficient, respond with 'Unanswerable'." +# (subset, row, prompt suffix, metric, where it appears in the paper) +CANDIDATES = [ + ("ok_vqa_val2014", 492, f"{UNANSWERABLE}\n{VQA}", "vqa_accuracy", "paper Fig. (qualitative samples)"), + ("infovqa_val", 449, VQA, "anls", "paper Fig. (qualitative samples)"), + ("vizwiz_vqa_val", 345, f"{UNANSWERABLE}\n{VQA}", "vqa_accuracy", "paper teaser"), + ("vizwiz_vqa_val", 47, f"{UNANSWERABLE}\n{VQA}", "vqa_accuracy", "paper appendix"), + ("vizwiz_vqa_val", 87, f"{UNANSWERABLE}\n{VQA}", "vqa_accuracy", "paper appendix"), +] + [("ok_vqa_val2014", i, VQA, "vqa_accuracy", "paper appendix") for i in (115, 151, 210, 22, 253, 272, 291, 381, + 477, 8)] +SUBSET_NAME = {"ok_vqa_val2014": "OK-VQA", "infovqa_val": "InfographicVQA", "vizwiz_vqa_val": "VizWiz"} + +ap = argparse.ArgumentParser() +ap.add_argument("model") +ap.add_argument("budget", type=int) +ap.add_argument("--keep", choices=["all", "improved"], default="all") +ap.add_argument("--always", action="store_true", help="imagine even when the model is confident (UI checkbox)") +ap.add_argument("--out", default=str(HERE / "examples")) +ap.add_argument("--only", nargs="*", default=None, help="subset:row items to run, e.g. ok_vqa_val2014:492") +ap.add_argument("--image", help="a custom query image (instead of the dataset candidates)") +ap.add_argument("--question", help="the question for --image") +ap.add_argument("--reference", nargs="+", help="reference answer(s) for --image") +ap.add_argument("--metric", default="contains", help="metric for --image (free-form answers: 'contains')") +ap.add_argument("--id", help="example id (folder name) for --image") +ap.add_argument("--source", default="custom example", help="source line shown in the UI for --image") +args = ap.parse_args() +out = Path(args.out) +out.mkdir(parents=True, exist_ok=True) +index_file = out / "index.json" +index = json.loads(index_file.read_text()) if index_file.exists() else [] +spec = MODELS[args.model] +spec.load() +suffix_id = "_always" if args.always else "" + + +def jobs(): + """(example id, image, question, references, metric name, source, keep the folder's own files)""" + if args.image: + img = Image.open(args.image) + img.load() + yield (args.id + suffix_id, img, args.question, args.reference, args.metric, args.source, True) + return + for sub, row, suffix, metric_name, where in CANDIDATES: + if args.only is not None and f"{sub}:{row}" not in args.only: + continue + r = load_dataset("lmms-lab/LMMs-Eval-Lite", sub)["lite"][row] + yield (f"{args.model}_{sub}_{row}{suffix_id}", r["image"].convert("RGB"), f"{r['question']}\n{suffix}", + [str(a) for a in _list(r.get("answers", r.get("answer")))], metric_name, + f"{SUBSET_NAME.get(sub, sub)} row {row}, {where}", False) + + +for ex_id, image, question, refs, metric_name, source, custom in jobs(): + metric = get_metric(metric_name) + t0 = time.time() + greedy, demos, final, info = None, [], None, {} + for ev in run(spec, image, question, args.budget, args.always): + if ev[0] == "greedy": + greedy = ev[1] + elif ev[0] == "demo": + demos.append(ev[1]) + elif ev[0] == "final": + final, info = ev[1], ev[2] + g_ok, s_ok = metric(greedy.answer, refs) >= 0.5, metric(final, refs) >= 0.5 + print(f"{ex_id}: greedy={greedy.answer!r} ({g_ok}) simit={final!r} ({s_ok}) demos={len(demos)} " + f"{time.time() - t0:.0f}s refs={refs[:3]}", flush=True) + if args.keep == "improved" and not (s_ok and not g_ok): + continue + d = out / ex_id + if custom and d.exists(): # the user's own files stay; only earlier demo images are replaced + for f in d.glob("demo_*.png"): + f.unlink() + else: + shutil.rmtree(d, ignore_errors=True) + d.mkdir() + if custom and Path(args.image).resolve().parent == d.resolve(): + query_file = Path(args.image).name + else: + shown = image.convert("RGB") + shown.thumbnail((1024, 1024)) # display copy (the run used the original) + shown.save(d / "query.jpg", quality=90) + query_file = "query.jpg" + stored = [] + for i, dm in enumerate(demos): + dm.image.save(d / f"demo_{i}.png") + stored.append({"image": f"demo_{i}.png", "question": dm.question, "answer": dm.answer, "skill": dm.skill, + "verify_score": dm.verify_score, "confidence": dm.confidence}) + entry = {"id": ex_id, "query_file": query_file, "model": args.model, "budget": args.budget, + "always": args.always, "question": question, "reference": refs[0], + "greedy": greedy.answer, "p0": greedy.confidence, "simit": final, "greedy_ok": g_ok, "simit_ok": s_ok, + "demos": stored, "elapsed": info.get("elapsed", time.time() - t0), "source": source} + index = [e for e in index if e["id"] != ex_id] + [entry] + index_file.write_text(json.dumps(index, indent=1)) diff --git a/tools/results/latency_bagel.json b/tools/results/latency_bagel.json new file mode 100644 index 0000000000000000000000000000000000000000..344cef95a56f322b138c71f03cad15a8b4168a2b --- /dev/null +++ b/tools/results/latency_bagel.json @@ -0,0 +1,882 @@ +[ + { + "demos": [ + 26.002389907836914 + ], + "greedy": 6.846283435821533, + "total": 26.74322533607483, + "setting": "think50", + "k": 1, + "query": 0, + "subset": "vizwiz_vqa_val" + }, + { + "demos": [ + 12.16827392578125 + ], + "greedy": 6.742192268371582, + "total": 12.760454177856445, + "setting": "think50", + "k": 1, + "query": 1, + "subset": "vqav2_val" + }, + { + "demos": [ + 17.263279914855957 + ], + "greedy": 6.47596001625061, + "total": 17.900477647781372, + "setting": "think50", + "k": 1, + "query": 2, + "subset": "ok_vqa_val2014" + }, + { + "demos": [ + 33.245251417160034 + ], + "greedy": 6.677622079849243, + "total": 34.04009461402893, + "setting": "think50", + "k": 1, + "query": 3, + "subset": "textvqa_val" + }, + { + "demos": [ + 21.499918460845947 + ], + "greedy": 6.547531366348267, + "total": 22.275030374526978, + "setting": "think50", + "k": 1, + "query": 4, + "subset": "chartqa" + }, + { + "demos": [], + "greedy": 6.649888515472412, + "total": 57.07210087776184, + "setting": "think50", + "k": 1, + "query": 5, + "subset": "docvqa_val" + }, + { + "demos": [ + 14.020853996276855, + 15.747642755508423 + ], + "greedy": 6.6902241706848145, + "total": 16.58476734161377, + "setting": "think50", + "k": 2, + "query": 0, + "subset": 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b/tools/tune_presets.py @@ -0,0 +1,54 @@ +"""Tune ABA + DF for the demo on the mixed general-QA set, separately for each +K_max: one synthesis cache (K_max = 4 candidates per query), one study over +K_max in {1, 2, 3, 4}, and the best trial for each K_max is kept. + +python tools/tune_presets.py bagel OUT_DIR +python tools/tune_presets.py lance OUT_DIR +python tools/tune_presets.py qwen OUT_DIR # vLLM engine for tuning speed (same FP8 weights as the demo) +""" +import json +import sys +import time +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).parent)) +from general_qa import general_qa # noqa: E402 + +from simit import SIMIT, SIMITConfig # noqa: E402 + +MODELS = { + "bagel": dict(model="ByteDance-Seed/BAGEL-7B-MoT"), + "lance": dict(model="bytedance-research/Lance"), + "qwen": dict(model="Qwen/Qwen3.8-27B-FP8", image_generator="black-forest-labs/FLUX.2-klein-4B", engine="vllm"), +} + +name, out = sys.argv[1], Path(sys.argv[2]) +out.mkdir(parents=True, exist_ok=True) +val = general_qa() +sim = SIMIT.from_pretrained(**MODELS[name], config=SIMITConfig(k_max=4)) +t0 = time.time() +res = sim.tune(val, n_trials=800, k_max_choices=[1, 2, 3, 4], cache_dir=out / f"cache_{name}", max_new_tokens=16, + apply=False) +print(f"tuned in {time.time() - t0:.0f}s: {res}", flush=True) + +best = {} +for params, value, cost in res.history: + k = params.get("k_max") + if value >= 0 and (k not in best or value > best[k]["score"]): + best[k] = {"score": value, "mean_candidates": cost, "params": params} +# Regularized pick per K_max: within one validation query's worth of the best score, the widest +# difficulty-filter band (small validation sets overfit narrow bands; a wide band also rejects fewer +# demos, which matters under the demo's time limit). +tol = 1.0 / len(val) +robust = {} +for k, b in best.items(): + near = [(p, v, c) for p, v, c in res.history if p.get("k_max") == k and v >= b["score"] - tol] + p, v, c = max(near, key=lambda t: (t[0]["t_high"] - t[0]["t_low"], t[1])) + robust[k] = {"score": v, "mean_candidates": c, "params": p} +report = {"model": name, "n_val": len(val), "zero_shot": res.zero_shot_score, "overall_best": res.best_params, + "best_per_k_max": {str(k): best[k] for k in sorted(best)}, + "robust_per_k_max": {str(k): robust[k] for k in sorted(robust)}, + "history": [{"params": p, "score": v, "mean_candidates": c} for p, v, c in res.history]} +(out / f"presets_{name}.json").write_text(json.dumps(report, indent=2)) +print(json.dumps(report, indent=2)) +sim.close()