diff --git a/.github/workflows/add-target.yml b/.github/workflows/add-target.yml new file mode 100644 index 0000000..d805cdd --- /dev/null +++ b/.github/workflows/add-target.yml @@ -0,0 +1,61 @@ +name: add-target + +on: + workflow_dispatch: + inputs: + name: + description: "target name (map key, e.g. my-model)" + required: true + provider: + description: "provider" + required: true + type: choice + options: [fireworks, openrouter, openai, anthropic, gemini, interfaze] + model_id: + description: "provider model id (e.g. accounts/fireworks/models/my-model)" + required: true + capabilities_json: + description: 'optional capability overrides as JSON, e.g. {"reasoning":{"style":"thinking_budget","off_value":0,"on_value":-1,"true_off":true}}' + required: false + default: "" + ci_regression: + description: "include in the nightly regression run" + type: boolean + default: false + +permissions: + contents: write + pull-requests: write + +jobs: + add: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + - uses: astral-sh/setup-uv@v9.0.0 + with: + enable-cache: true + - run: uv sync + - name: Validate + append target + env: + T_NAME: ${{ github.event.inputs.name }} + T_PROVIDER: ${{ github.event.inputs.provider }} + T_MODEL: ${{ github.event.inputs.model_id }} + T_CAPS: ${{ github.event.inputs.capabilities_json }} + T_CI: ${{ github.event.inputs.ci_regression }} + run: | + ARGS=(--name "$T_NAME" --provider "$T_PROVIDER" --model-id "$T_MODEL" --capabilities-json "$T_CAPS") + [ "$T_CI" = "true" ] && ARGS+=(--ci-regression) + uv run python scripts/add_target.py "${ARGS[@]}" + - name: Open PR + uses: peter-evans/create-pull-request@v7 + with: + branch: add-target/${{ github.event.inputs.name }} + title: "Add benchmark target: ${{ github.event.inputs.name }}" + commit-message: "feat(targets): add ${{ github.event.inputs.name }}" + body: | + Adds `${{ github.event.inputs.name }}` (${{ github.event.inputs.provider }} / `${{ github.event.inputs.model_id }}`) to `src/targets.yaml`. + + The entry was validated (loads, resolves capabilities, builds its adapter). + **Merging this PR triggers the benchmark run for the new target** (benchmark.yml push trigger). + add-paths: src/targets.yaml diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml new file mode 100644 index 0000000..245ede8 --- /dev/null +++ b/.github/workflows/benchmark.yml @@ -0,0 +1,137 @@ +name: benchmark + +on: + workflow_dispatch: + inputs: + target: + description: "target name — required (see src/targets.yaml / list-targets)" + required: true + benchmarks: + description: "comma-separated benchmarks, or 'all' for the CI set" + default: all + sample_size: + description: "samples per benchmark (blank = full run — slow/expensive)" + default: "5" + reasoning: + description: "reasoning override: off|low|medium|high (blank = benchmark default)" + default: "" + schedule: + - cron: "0 6 * * 1" + push: + branches: [main] + paths: [src/targets.yaml] # a merged target change runs a full benchmark for it + +# never run two paid benchmark passes for the same ref at once (queue, don't cancel +# — a half-finished expensive run's spend shouldn't be thrown away) +concurrency: + group: benchmark-${{ github.ref }} + cancel-in-progress: false + +permissions: + contents: read + +jobs: + plan: + runs-on: ubuntu-latest + outputs: + matrix: ${{ steps.m.outputs.matrix }} + targets: ${{ steps.m.outputs.targets }} + has_work: ${{ steps.m.outputs.has_work }} + sample: ${{ steps.m.outputs.sample }} + reasoning: ${{ steps.m.outputs.reasoning }} + steps: + - uses: actions/checkout@v6 + with: + fetch-depth: 0 # need the merge's parent to diff targets.yaml on push + - uses: astral-sh/setup-uv@v9.0.0 + with: + enable-cache: true + - run: uv sync + - id: m + env: + EVENT_NAME: ${{ github.event_name }} + INPUT_TARGET: ${{ github.event.inputs.target }} + INPUT_BENCHMARKS: ${{ github.event.inputs.benchmarks }} + run: | + if [ "${{ github.event_name }}" = "push" ]; then + git show "${{ github.event.before }}:src/targets.yaml" > /tmp/old_targets.yaml 2>/dev/null || : > /tmp/old_targets.yaml + export OLD_TARGETS_FILE=/tmp/old_targets.yaml + SAMPLE="" # a merged target change gets a full benchmark run + else + SAMPLE="${{ github.event.inputs.sample_size || '5' }}" + fi + MATRIX="$(uv run python scripts/ci_matrix.py)" + echo "matrix=$MATRIX" >> "$GITHUB_OUTPUT" + echo "targets=$(printf '%s' "$MATRIX" | uv run python -c 'import json,sys; print(" ".join(sorted({i["target"] for i in json.load(sys.stdin)["include"]})))')" >> "$GITHUB_OUTPUT" + echo "has_work=$(printf '%s' "$MATRIX" | uv run python -c 'import json,sys; print(str(bool(json.load(sys.stdin)["include"])).lower())')" >> "$GITHUB_OUTPUT" + echo "sample=$SAMPLE" >> "$GITHUB_OUTPUT" + echo "reasoning=${{ github.event.inputs.reasoning }}" >> "$GITHUB_OUTPUT" + + run: + needs: plan + if: needs.plan.outputs.has_work == 'true' # no target selected/flagged -> nothing runs + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: ${{ fromJSON(needs.plan.outputs.matrix) }} + env: + OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} + ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} + GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }} + FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }} + OPENROUTER_API_KEY: ${{ secrets.OPENROUTER_API_KEY }} + INTERFAZE_API_KEY: ${{ secrets.INTERFAZE_API_KEY }} + HF_TOKEN: ${{ secrets.HF_TOKEN }} + steps: + - uses: actions/checkout@v6 + - uses: astral-sh/setup-uv@v9.0.0 + with: + enable-cache: true + - run: uv sync + - name: Run ${{ matrix.benchmark }} on ${{ matrix.target }} + run: | + SAMPLE="${{ needs.plan.outputs.sample }}" + REASONING="${{ needs.plan.outputs.reasoning }}" + uv run python -m src run \ + --target "${{ matrix.target }}" --benchmark "${{ matrix.benchmark }}" \ + ${SAMPLE:+--sample "$SAMPLE"} ${REASONING:+--reasoning "$REASONING"} + - name: Upload metrics + uses: actions/upload-artifact@v4 + with: + name: metrics-${{ matrix.target }}-${{ matrix.benchmark }} + path: results/${{ matrix.benchmark }}*/${{ matrix.target }}/metrics.json + if-no-files-found: ignore + + report: + needs: [plan, run] + if: always() && needs.plan.outputs.has_work == 'true' + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + - uses: astral-sh/setup-uv@v9.0.0 + with: + enable-cache: true + - run: uv sync + - name: Fetch run metrics + uses: actions/download-artifact@v4 + with: + path: incoming + merge-multiple: true + - name: Overlay fresh metrics onto the contract + run: | + # artifacts store their original results///metrics.json path + if [ -d incoming/results ]; then cp -r incoming/results/* results/; fi + - name: Compare vs baseline + write summary + run: | + for t in ${{ needs.plan.outputs.targets }}; do + uv run python scripts/ci_compare.py --target "$t" >> "$GITHUB_STEP_SUMMARY" + done + { + echo "" + echo '
Full score tables' + echo "" + echo '```' + uv run python scripts/report_scores.py --all 2>&1 || true + echo '```' + echo '
' + } >> "$GITHUB_STEP_SUMMARY" diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml new file mode 100644 index 0000000..ec3f45d --- /dev/null +++ b/.github/workflows/tests.yml @@ -0,0 +1,28 @@ +name: tests + +on: + push: + branches: [main] # post-merge sanity; feature branches are covered by their PR + pull_request: + +concurrency: + group: tests-${{ github.ref }} + cancel-in-progress: true + +permissions: + contents: read + +jobs: + test: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + - uses: astral-sh/setup-uv@v9.0.0 + with: + enable-cache: true + - name: Install deps + run: uv sync --group dev + - name: Lint (new code) + run: uv run ruff check src tests + - name: Tests (offline) + run: uv run pytest -q diff --git a/.gitignore b/.gitignore index 3c57a4e..98374b7 100644 --- a/.gitignore +++ b/.gitignore @@ -208,3 +208,18 @@ __marimo__/ # olmOCR-Bench dataset + per-candidate prediction outputs (large, downloaded from HF) benchmarks/olmocr_bench/olmocr_bench/ + +# Downloaded benchmark datasets (large; fetched at runtime, not source) +benchmarks/spider2_lite/data/ +benchmarks/olmocr/bench/full_data/ + +# Benchmark outputs are never tracked — they are run artifacts, not source, and +# large (hundreds of MB incl. Spider2/OCRBench responses). Kept on disk locally; +# regenerate with a run. +results/ + +# OCRBench spotting-eval scratch (generated during scoring) +benchmarks/ocrbench_v2/eval_scripts/spotting_eval/gt/ +benchmarks/ocrbench_v2/eval_scripts/spotting_eval/submit/ +benchmarks/ocrbench_v2/eval_scripts/spotting_eval/*.zip +benchmarks/ocrbench_v2/results.zip diff --git a/README.md b/README.md index 736167d..dda5ff1 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,8 @@ -# Interfaze complete benchmark scripts +# Interfaze complete benchmark -[Full break down blog](https://interfaze.ai/blog/interfaze-a-new-model-architecture-built-for-high-accuracy-at-scale) | [Leaderboard](https://interfaze.ai/leaderboards) +[Blog](https://interfaze.ai/blog/interfaze-a-new-model-architecture-built-for-high-accuracy-at-scale) · [Leaderboard](https://interfaze.ai/leaderboards) -Runner scripts for the public benchmarks Interfaze is evaluated on. Each -benchmark lives in its own directory under `benchmarks/`. +Runner scripts for the public benchmarks Interfaze is evaluated on. ## Setup @@ -11,196 +10,67 @@ benchmark lives in its own directory under `benchmarks/`. uv sync ``` -Create a `.env` in the repo root with whichever provider keys you plan to use: +Add a `.env` with the provider keys you'll use: -``` -INTERFAZE_API_KEY=... -OPENAI_API_KEY=... -ANTHROPIC_API_KEY=... -GEMINI_KEY=... -OPENROUTER_API_KEY=... -JIGSAWSTACK_API_KEY=... # only for benchmarks/obj_detection/ob_det_api.py -``` - -Every runner accepts `--limit N` for a smoke test and `--evaluate-only` / -`--predict-only` to split prediction and scoring. Re-running a benchmark -resumes from its checkpoint file — already-completed samples are skipped. - ---- - -## OCRBench v2 - -Links: [paper](https://arxiv.org/abs/2501.00321) · [repo](https://github.com/Yuliang-Liu/MultimodalOCR/tree/main/OCRBench_v2) - -```bash -# Interfaze -uv run -m benchmarks.ocrbench_v2.ocrbench_v2 - -# Per-provider runners -uv run -m benchmarks.ocrbench_v2.ocrbench_v2_openai -uv run -m benchmarks.ocrbench_v2.ocrbench_v2_openai_mini -uv run -m benchmarks.ocrbench_v2.ocrbench_v2_anthropic -uv run -m benchmarks.ocrbench_v2.ocrbench_v2_gemini -uv run -m benchmarks.ocrbench_v2.ocrbench_v2_gemini_pro_31 -uv run -m benchmarks.ocrbench_v2.ocrbench_v2_grok -uv run -m benchmarks.ocrbench_v2.ocrbench_v2_kimi # via OpenRouter - -# Text-spotting EN subset only -uv run -m benchmarks.ocrbench_v2.ocrbench_v2_text_spotting_en - -# Evaluate without re-running predictions -uv run -m benchmarks.ocrbench_v2.ocrbench_v2 --evaluate-only -``` - ---- - -## olmOCR-Bench - -Links: [repo](https://github.com/allenai/olmocr/tree/main/olmocr/bench) · [dataset](https://huggingface.co/datasets/allenai/olmOCR-bench) - -```bash -# Interfaze -uv run -m benchmarks.olmocr.olmocr_bench - -# Per-provider runners -uv run -m benchmarks.olmocr.olmocr_bench_openai_mini -uv run -m benchmarks.olmocr.olmocr_bench_gemini_pro_31 -uv run -m benchmarks.olmocr.olmocr_bench_grok - -# Useful flags -uv run -m benchmarks.olmocr.olmocr_bench --sample # tiny sample dataset -uv run -m benchmarks.olmocr.olmocr_bench --generate-only # predictions only -uv run -m benchmarks.olmocr.olmocr_bench --skip-generation # evaluation only -``` - ---- - -## RefCOCO (Object Detection) - -Links: [RefCOCO/RefCOCO+ paper](https://arxiv.org/abs/1608.00272) · [RefCOCOg paper](https://arxiv.org/abs/1511.02283) · [dataset](https://huggingface.co/datasets/lmms-lab/RefCOCO) - -Metric: Acc@IoU=0.5 on the referring-expression bounding box. - -```bash -# Interfaze (RefCOCO val by default) -uv run -m benchmarks.obj_detection.refcoco -uv run -m benchmarks.obj_detection.refcoco --split testA -uv run -m benchmarks.obj_detection.refcoco --dataset lmms-lab/RefCOCO+ --split testB - -# Any provider via the multi runner -uv run -m benchmarks.obj_detection.refcoco_multi --provider openai --model gpt-5.4 -uv run -m benchmarks.obj_detection.refcoco_multi --provider anthropic --model claude-sonnet-4-6 -uv run -m benchmarks.obj_detection.refcoco_multi --provider gemini --model gemini-3-flash-preview - -# JigsawStack object_detection API (instead of a VLM) -uv run -m benchmarks.obj_detection.ob_det_api - -# Evaluate only -uv run -m benchmarks.obj_detection.refcoco --evaluate-only +```env +INTERFAZE_API_KEY=… +OPENAI_API_KEY=… +ANTHROPIC_API_KEY=… +GEMINI_KEY=… +OPENROUTER_API_KEY=… +FIREWORKS_API_KEY=… ``` ---- +## Run a benchmark -## VoxPopuli-Cleaned-AA (ASR) - -Links: [dataset](https://huggingface.co/datasets/ArtificialAnalysis/VoxPopuli-Cleaned-AA) - -Metric: WER with Whisper-style text normalization. - -```bash -# Interfaze -uv run -m benchmarks.asr.voxpopuli_aa - -# Other providers (audio-capable) -uv run -m benchmarks.asr.voxpopuli_aa_multi --provider gemini --model gemini-3-flash-preview -uv run -m benchmarks.asr.voxpopuli_aa_multi --provider openai --model gpt-4o-audio-preview -uv run -m benchmarks.asr.voxpopuli_aa_multi --provider anthropic --model claude-sonnet-4-6 - -# Evaluate only -uv run -m benchmarks.asr.voxpopuli_aa --evaluate-only -``` - ---- - -## MMMLU (Multilingual MMLU) - -Links: [dataset](https://huggingface.co/datasets/openai/MMMLU) - -14 languages, exact-match accuracy macro-averaged across languages. +Everything runs through one CLI (`python -m src`). Models are defined in +`src/targets.yaml`; add one there to benchmark a new model — no code. ```bash -# Interfaze -uv run -m benchmarks.mmmlu.mmmlu -uv run -m benchmarks.mmmlu.mmmlu --languages DE_DE FR_FR # subset of languages - -# Any provider -uv run -m benchmarks.mmmlu.mmmlu_multi --provider openai --model gpt-5.4-mini -uv run -m benchmarks.mmmlu.mmmlu_multi --provider gemini --model gemini-3.1-pro-preview -uv run -m benchmarks.mmmlu.mmmlu_multi --provider anthropic --model claude-sonnet-4-6 -uv run -m benchmarks.mmmlu.mmmlu_multi --provider interfaze --model interfaze-beta - -# Evaluate only -uv run -m benchmarks.mmmlu.mmmlu --evaluate-only +uv run python -m src list-targets # what's available +uv run python -m src run --target inkling --benchmark gpqa # a full run +uv run python -m src run --target gpt-5.5 --benchmark ocrbench_v2 --sample 20 # smoke +uv run python scripts/report_scores.py # view scores ``` ---- - -## MMMU-Pro +`--sample N` runs the first N samples — a *smoke*. It **streams** only those N rows, so smoking a heavy image benchmark (e.g. `ocrbench_v2`, ~10k images) no longer downloads the whole split first. Smokes write to `results/_smoke/…`, isolated from full runs and hidden from `report_scores.py`, so they can't clobber a real score. Omit `--sample` for the full benchmark. Re-running resumes from the checkpoint. Add `--reasoning off|low|medium|high` to override the target's default. -Links: [paper](https://arxiv.org/abs/2409.02813) · [dataset](https://huggingface.co/datasets/MMMU/MMMU_Pro) +> A **full** image-benchmark run loads the whole split into RAM (RefCOCO `val` ≈ +> 8.8k images → several GB). Smoke with `--sample` first; if a full local run runs +> out of memory, see the lazy-by-index loader `ocrbench_v2` already uses. -Two settings: `standard` (text + inline images) and `vision` (rendered question image). +| Benchmark | `--benchmark` | Notes | Links | +|---|---|---|---| +| GPQA Diamond | `gpqa` | dataset is gated → `hf auth login` first | [paper](https://arxiv.org/abs/2311.12022) · [data](https://huggingface.co/datasets/Idavidrein/gpqa) | +| MMMLU | `mmmlu` | `--variant lite` (default) or `full` | [data](https://huggingface.co/datasets/openai/MMMLU) | +| MMMU-Pro | `mmmu_pro` | `--variant standard` or `vision` | [paper](https://arxiv.org/abs/2409.02813) · [data](https://huggingface.co/datasets/MMMU/MMMU_Pro) | +| OCRBench v2 | `ocrbench_v2` | 10k tasks; needs NLTK corpora † | [paper](https://arxiv.org/abs/2501.00321) · [repo](https://github.com/Yuliang-Liu/MultimodalOCR/tree/main/OCRBench_v2) | +| olmOCR | `olmocr` | needs poppler + chromium † | [repo](https://github.com/allenai/olmocr/tree/main/olmocr/bench) · [data](https://huggingface.co/datasets/allenai/olmOCR-bench) | +| RefCOCO | `refcoco` | `--variant val\|testA\|testB\|test\|plus-*\|g-*` | [paper](https://arxiv.org/abs/1608.00272) · [data](https://huggingface.co/datasets/lmms-lab/RefCOCO) | +| ASR (VoxPopuli) | `asr` | WER, any audio-capable target | [data](https://huggingface.co/datasets/ArtificialAnalysis/VoxPopuli-Cleaned-AA) | +| Spider2-Lite | `spider2` | run `fetch_data` first (~4GB) † | [repo](https://github.com/xlang-ai/Spider2) · [paper](https://arxiv.org/abs/2411.07763) | -```bash -# Any provider, standard or vision -uv run -m benchmarks.mmmu_pro.mmmu_pro_multi --provider gemini --model gemini-3.1-pro-preview --setting standard -uv run -m benchmarks.mmmu_pro.mmmu_pro_multi --provider gemini --model gemini-3.1-pro-preview --setting vision -uv run -m benchmarks.mmmu_pro.mmmu_pro_multi --provider openai --model gpt-5.5 --setting standard -uv run -m benchmarks.mmmu_pro.mmmu_pro_multi --provider anthropic --model claude-sonnet-4-6 --setting vision -uv run -m benchmarks.mmmu_pro.mmmu_pro_multi --provider interfaze --model interfaze-beta --setting standard - -# Run on Modal instead of locally -bash benchmarks/mmmu_pro/run_full.sh -bash benchmarks/mmmu_pro/run_smoke.sh -``` - ---- - -## GPQA Diamond - -Links: [paper](https://arxiv.org/abs/2311.12022) · [dataset](https://huggingface.co/datasets/Idavidrein/gpqa) (config: `gpqa_diamond`) +
+† one-time setup for some benchmarks ```bash -# OpenAI -uv run -m benchmarks.gpqa.gpqa_openai -uv run -m benchmarks.gpqa.gpqa_openai --model gpt-5.4-mini - -# Gemini -uv run -m benchmarks.gpqa.gpqa_gemini -uv run -m benchmarks.gpqa.gpqa_gemini --model gemini-3.1-pro-preview +# olmOCR — headless chromium for equation rendering +uv run python -m playwright install chromium -# Any model via OpenRouter (Grok, Kimi, Anthropic, etc.) -uv run -m benchmarks.gpqa.gpqa_openrouter --model x-ai/grok-4.3 --thinking on -uv run -m benchmarks.gpqa.gpqa_openrouter --model moonshotai/kimi-k2.6 +# OCRBench v2 — NLTK corpora. Run from OUTSIDE the repo (nltk trips on a CWD .venv): +(cd /tmp && uv run --project "$OLDPWD" python -c \ + "import nltk; [nltk.download(p, quiet=True) for p in ('wordnet','omw-1.4','punkt','punkt_tab')]") -# Evaluate only -uv run -m benchmarks.gpqa.gpqa_openai --evaluate-only +# Spider2 — clone + download the SQLite databases +uv run -m benchmarks.spider2_lite.fetch_data ``` ---- - -## Spider 2.0-Lite (SQLite subset, N=135) - -Links: [repo](https://github.com/xlang-ai/Spider2) · [paper](https://arxiv.org/abs/2411.07763) +
-Text-to-SQL with execution-accuracy scoring against per-example SQLite databases. +## Automation (GitHub Actions) -```bash -# One-time setup: clone Spider2 + download SQLite databases -uv run -m benchmarks.spider2_lite.fetch_data +- **`tests.yml`** — offline suite + lint on every push/PR (validates new `targets.yaml` entries). +- **`benchmark.yml`** — runs a benchmark: manual dispatch (pick target/benchmarks), weekly smoke of `ci_regression` targets, and a **full run on merge** of a `targets.yaml` change. +- **`add-target.yml`** — a UI form to add a model that opens a PR (doesn't run anything). -# Run -uv run -m benchmarks.spider2_lite.spider2_lite -uv run -m benchmarks.spider2_lite.spider2_lite --predict-only -uv run -m benchmarks.spider2_lite.spider2_lite --evaluate-only -``` +Add a model via the form or by editing `targets.yaml` in a PR; the benchmark runs **only once the PR is merged**. Needs the provider secrets + `HF_TOKEN` set in the repo. `olmocr` and `spider2` aren't in CI (heavy deps / ~4GB data). diff --git a/benchmarks/asr/_probe_voxpopuli_missing.py b/benchmarks/asr/_probe_voxpopuli_missing.py deleted file mode 100644 index 8ebfe05..0000000 --- a/benchmarks/asr/_probe_voxpopuli_missing.py +++ /dev/null @@ -1,100 +0,0 @@ -"""One-off probe: run the single VoxPopuli-AA sample that all three Gemini runs -dropped, and dump the raw response (text + finish reason + prompt/safety -feedback) so we can see why it was rejected.""" -import sys -from pathlib import Path - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -sys.path.insert(0, str(PROJECT_ROOT)) - -from datasets import load_dataset -from google import genai -from google.genai import types - -from benchmarks.asr.voxpopuli_aa import PROMPT, DATASET_ID, SPLIT, build_sample, fetch_audio_bytes -from benchmarks.asr.voxpopuli_aa_multi import _load_interfaze_env - -TARGET_ID = "20150527-0900-PLENARY-14-en_20150527-21:41:31_1" -MODELS = ["gemini-2.5-pro", "gemini-3-flash-preview", "gemini-3.1-pro-preview"] - - -def thinking_for(model: str) -> types.ThinkingConfig: - m = model.lower() - if m.startswith("gemini-2.5-pro"): - return types.ThinkingConfig(thinking_budget=128) - if m.startswith("gemini-2.5-flash"): - return types.ThinkingConfig(thinking_budget=0) - if "pro" in m: - return types.ThinkingConfig(thinking_level="low") - return types.ThinkingConfig(thinking_level="minimal") - - -def main(): - env = _load_interfaze_env() - client = genai.Client(api_key=env["GEMINI_KEY"]) - - print(f"Loading dataset {DATASET_ID} split={SPLIT}...") - ds = load_dataset(DATASET_ID, split=SPLIT) - sample = None - for row in ds: - s = build_sample(dict(row)) - if s["id"] == TARGET_ID: - sample = s - break - if sample is None: - print(f"!! sample {TARGET_ID} not found in dataset"); sys.exit(1) - - print(f"\nSample: id={sample['id']} dur={sample['duration']}s lang={sample['language']}") - print(f"GT transcript: {sample['transcript']!r}\n") - - audio_bytes = fetch_audio_bytes(sample["file_name"]) - print(f"Audio fetched: {len(audio_bytes)} bytes\n") - - for model in MODELS: - print("=" * 80) - print(f"MODEL: {model}") - print("=" * 80) - config = types.GenerateContentConfig( - thinking_config=thinking_for(model), - temperature=0.0, - ) - try: - resp = client.models.generate_content( - model=model, - contents=[ - types.Part.from_bytes(data=audio_bytes, mime_type="audio/wav"), - PROMPT, - ], - config=config, - ) - except Exception as e: - print(f" EXCEPTION: {type(e).__name__}: {e}\n") - continue - - text = (resp.text or "").strip() if hasattr(resp, "text") else "" - print(f" resp.text : {text!r}") - print(f" response_id : {getattr(resp, 'response_id', None)}") - - pf = getattr(resp, "prompt_feedback", None) - print(f" prompt_feedback : {pf}") - - cands = getattr(resp, "candidates", None) or [] - print(f" num candidates : {len(cands)}") - for i, c in enumerate(cands): - fr = getattr(c, "finish_reason", None) - fm = getattr(c, "finish_message", None) - sr = getattr(c, "safety_ratings", None) - content = getattr(c, "content", None) - parts = getattr(content, "parts", None) if content else None - part_texts = [getattr(p, "text", None) for p in (parts or [])] - print(f" candidate[{i}].finish_reason : {fr}") - print(f" candidate[{i}].finish_message : {fm}") - print(f" candidate[{i}].safety_ratings : {sr}") - print(f" candidate[{i}].part texts : {part_texts}") - - usage = getattr(resp, "usage_metadata", None) - print(f" usage_metadata : {usage}\n") - - -if __name__ == "__main__": - main() diff --git a/benchmarks/asr/bench.py b/benchmarks/asr/bench.py new file mode 100644 index 0000000..83458f5 --- /dev/null +++ b/benchmarks/asr/bench.py @@ -0,0 +1,137 @@ +# VoxPopuli-Cleaned-AA ASR: transcribe audio, score corpus + time-weighted WER. + +from __future__ import annotations + +import re +import unicodedata +from typing import Any + +from jiwer import cer, wer + +from src.request import AudioPart, Message, ReasoningSpec, Request, TextPart + +NAME = "voxpopuli_aa" +ID_KEY = "id" +PRIMARY_METRIC = "corpus_wer" +DEFAULTS = {"reasoning": "off", "rate_limit": 25, "max_in_flight": 8} + +_DATASET_ID = "ArtificialAnalysis/VoxPopuli-Cleaned-AA" +_SPLIT = "test" + +PROMPT = ( + "Transcribe the following audio. Fix anything that needs fixing — " + "disfluencies, stutters, obvious misspeaks, garbled words, or misheard " + "named entities — so the transcription reads as the speaker clearly " + "intended. Output ONLY the cleaned transcription, no commentary, labels, " + "speaker tags, or timestamps." +) + +_NON_ALNUM_SPACE = re.compile(r"[^a-z0-9' ]+") +_WHITESPACE = re.compile(r"\s+") + + +def normalize_text(text: str) -> str: + # Whisper-style: NFKC, lowercase, strip punctuation (keep apostrophes), collapse ws. + if not text: + return "" + text = unicodedata.normalize("NFKC", text).lower() + text = _NON_ALNUM_SPACE.sub(" ", text) + return _WHITESPACE.sub(" ", text).strip() + + +def load_samples(sample_size: int | None = None) -> list[dict]: + from datasets import load_dataset + from huggingface_hub import hf_hub_download + from tqdm import tqdm + + ds = load_dataset(_DATASET_ID, split=_SPLIT) + rows = list(ds)[:sample_size] if sample_size else list(ds) + samples = [] + for row in tqdm(rows, desc="fetch audio"): + row = dict(row) + # Warm the HF cache; build_request reads the local file (fast, no memory held). + path = hf_hub_download( + repo_id=_DATASET_ID, + repo_type="dataset", + filename=f"audio/{row['file_name']}", + ) + samples.append( + { + "id": str(row["id"]), + "file_name": row["file_name"], + "audio_path": path, + "transcript": row["transcript"], + "duration": row.get("duration"), + "language": row.get("language"), + } + ) + return samples + + +def build_request(sample: dict, mode: str) -> Request: + with open(sample["audio_path"], "rb") as f: + audio = f.read() + return Request( + messages=[Message("user", [TextPart(PROMPT), AudioPart(audio, "audio/wav")])], + reasoning=ReasoningSpec(mode), + temperature=0.0, + ) + + +def parse(response, sample) -> str: + return (response.text or "").strip() + + +def _sample_metric(fn, gt_norm: str, hyp_norm: str) -> float: + if not gt_norm: + return float("inf") + try: + return float(fn(gt_norm, hyp_norm)) + except (ValueError, ZeroDivisionError): + return float("inf") + + +def score(records: list[dict], samples: list[dict]) -> dict: + by_id = {s["id"]: s for s in samples} + rows: list[dict[str, Any]] = [] + for r in records: + s = by_id.get(r["id"]) + if s is None: + continue + gt = normalize_text(s["transcript"]) + hyp = normalize_text(r.get("prediction") or "") + rows.append( + { + "gt": gt, + "hyp": hyp, + "wer": _sample_metric(wer, gt, hyp), + "cer": _sample_metric(cer, gt, hyp), + "duration": s.get("duration"), + } + ) + if not rows: + return {} + + refs = [x["gt"] for x in rows if x["gt"]] + hyps = [x["hyp"] for x in rows if x["gt"]] + total_dur = sum(x["duration"] or 0 for x in rows) + finite_wer = [x["wer"] for x in rows if x["wer"] != float("inf")] + finite_cer = [x["cer"] for x in rows if x["cer"] != float("inf")] + + return { + "corpus_wer": float(wer(refs, hyps)) if refs else float("inf"), + "corpus_cer": float(cer(refs, hyps)) if refs else float("inf"), + "mean_sample_wer": sum(finite_wer) / max(1, len(rows)), + "mean_sample_cer": sum(finite_cer) / max(1, len(rows)), + "time_weighted_wer": ( + sum( + (x["wer"] if x["wer"] != float("inf") else 0) * (x["duration"] or 0) + for x in rows + ) + / total_dur + if total_dur > 0 + else float("inf") + ), + "num_samples": len(rows), + "total_duration_s": total_dur, + } diff --git a/benchmarks/asr/voxpopuli_aa.py b/benchmarks/asr/voxpopuli_aa.py deleted file mode 100644 index e3127bf..0000000 --- a/benchmarks/asr/voxpopuli_aa.py +++ /dev/null @@ -1,406 +0,0 @@ -""" -VoxPopuli-Cleaned-AA ASR benchmark for Interfaze. - -628 English speech samples drawn from European Parliament recordings, with -re-cleaned ground-truth transcripts curated by Artificial Analysis. This is -one of the three datasets that make up AA-WER v2.0 (the ASR benchmark Gemini 3 -Pro and other frontier audio models report on). The other two are -Earnings22-Cleaned-AA (open, 6 samples) and AA-AgentTalk (proprietary). - -Dataset: https://huggingface.co/datasets/ArtificialAnalysis/VoxPopuli-Cleaned-AA - -Metric: WER (Word Error Rate) with Whisper-style text normalization — - lowercase, strip punctuation, NFKC, collapse whitespace. - -Usage: - uv run -m benchmarks.asr.voxpopuli_aa - uv run -m benchmarks.asr.voxpopuli_aa --limit 5 - uv run -m benchmarks.asr.voxpopuli_aa --evaluate-only -""" - -import sys -import os -import re -import json -import time -import base64 -import asyncio -import argparse -import traceback -import unicodedata -from pathlib import Path - -from datasets import load_dataset -from huggingface_hub import hf_hub_download -from jiwer import wer, cer -from tqdm import tqdm -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -sys.path.insert(0, str(PROJECT_ROOT)) - -from src.commons import invoke_interfaze # noqa: E402 - -RESULTS_DIR = PROJECT_ROOT / "results" -DATASET_ID = "ArtificialAnalysis/VoxPopuli-Cleaned-AA" -SPLIT = "test" -REASONING_EFFORT = None # off -TEMPERATURE = 0.0 # deterministic -RATE_LIMIT = 25 -MAX_RETRIES = 3 - -PROMPT = ( - "Transcribe the following audio. Fix anything that needs fixing — " - "disfluencies, stutters, obvious misspeaks, garbled words, or misheard " - "named entities — so the transcription reads as the speaker clearly " - "intended. Output ONLY the cleaned transcription, no commentary, labels, " - "speaker tags, or timestamps." -) - - -class RateLimiter: - def __init__(self, rate: int): - self.rate = rate - self.tokens = rate - self.last_refill = 0.0 - self._lock = asyncio.Lock() - - async def acquire(self): - while True: - async with self._lock: - now = asyncio.get_running_loop().time() - elapsed = now - self.last_refill - self.tokens = min(self.rate, self.tokens + elapsed * self.rate) - self.last_refill = now - if self.tokens >= 1: - self.tokens -= 1 - return - await asyncio.sleep(1 / self.rate) - - -class JsonlWriter: - def __init__(self, path: Path): - self.path = path - self.path.parent.mkdir(parents=True, exist_ok=True) - self._lock = asyncio.Lock() - - async def append(self, record: dict): - line = json.dumps(record, ensure_ascii=False) - async with self._lock: - with open(self.path, "a", encoding="utf-8") as f: - f.write(line + "\n") - f.flush() - os.fsync(f.fileno()) - - -def load_completed_ids(path: Path) -> set[str]: - if not path.exists(): - return set() - done: set[str] = set() - with open(path, encoding="utf-8") as f: - for line_no, line in enumerate(f, 1): - line = line.strip() - if not line: - continue - try: - rec = json.loads(line) - except json.JSONDecodeError: - tqdm.write(f"[resume] skipping malformed line {line_no} in {path}") - continue - if rec.get("response"): - done.add(str(rec["id"])) - return done - - -def load_records(path: Path) -> list[dict]: - records: list[dict] = [] - if not path.exists(): - return records - with open(path, encoding="utf-8") as f: - for line in f: - line = line.strip() - if not line: - continue - try: - records.append(json.loads(line)) - except json.JSONDecodeError: - continue - by_id: dict[str, dict] = {} - for r in records: - by_id[str(r["id"])] = r - return list(by_id.values()) - - -_NON_ALNUM_SPACE = re.compile(r"[^a-z0-9' ]+") -_WHITESPACE = re.compile(r"\s+") - - -def normalize_text(text: str) -> str: - """Whisper-style light normalization for WER: NFKC, lowercase, strip - punctuation (keeping apostrophes for contractions), collapse whitespace.""" - if not text: - return "" - text = unicodedata.normalize("NFKC", text) - text = text.lower() - text = _NON_ALNUM_SPACE.sub(" ", text) - text = _WHITESPACE.sub(" ", text).strip() - return text - - -def fetch_audio_bytes(file_name: str) -> bytes: - """Pull a single audio/*.wav from the HF dataset repo (cached locally).""" - path = hf_hub_download( - repo_id=DATASET_ID, repo_type="dataset", - filename=f"audio/{file_name}", - ) - with open(path, "rb") as f: - return f.read() - - -def build_sample(row: dict) -> dict: - return { - "id": str(row["id"]), - "file_name": row["file_name"], - "transcript": row["transcript"], - "duration": row.get("duration"), - "gender": row.get("gender"), - "language": row.get("language"), - } - - -async def process_sample(sample: dict, rate_limiter, writer: JsonlWriter, - progress: dict) -> dict | None: - last_error: str | None = None - - # Fetch audio once (outside the retry loop — it's cached by hf_hub_download). - try: - audio_bytes = await asyncio.to_thread(fetch_audio_bytes, sample["file_name"]) - except Exception as e: - tqdm.write(f"[fetch error] id={sample['id']}: {type(e).__name__}: {e}") - progress["failed"] += 1 - return None - - b64 = base64.b64encode(audio_bytes).decode("ascii") - data_url = f"data:audio/wav;base64,{b64}" - - messages = [{ - "role": "user", - "content": [ - {"type": "text", "text": PROMPT}, - {"type": "file", "file": {"filename": sample["file_name"], "file_data": data_url}}, - ], - }] - - for attempt in range(1, MAX_RETRIES + 1): - await rate_limiter.acquire() - start = time.perf_counter() - try: - response = await asyncio.to_thread( - invoke_interfaze, - messages, - reasoning_effort=REASONING_EFFORT, - temperature=TEMPERATURE, - ) - latency_ms = int((time.perf_counter() - start) * 1000) - content = (response.choices[0].message.content or "").strip() - request_id = getattr(response, "id", None) - if not content: - last_error = "empty response content" - raise RuntimeError(last_error) - - pred_norm = normalize_text(content) - gt_norm = normalize_text(sample["transcript"]) - try: - sample_wer = float(wer(gt_norm, pred_norm)) if gt_norm else float("inf") - except Exception: - sample_wer = float("inf") - try: - sample_cer = float(cer(gt_norm, pred_norm)) if gt_norm else float("inf") - except Exception: - sample_cer = float("inf") - - record = { - "id": sample["id"], - "file_name": sample["file_name"], - "duration": sample["duration"], - "gender": sample["gender"], - "language": sample["language"], - "transcript": sample["transcript"], - "transcript_normalized": gt_norm, - "prediction": content, - "prediction_normalized": pred_norm, - "wer": sample_wer, - "cer": sample_cer, - "response": content, - "request_id": request_id, - "latency_ms": latency_ms, - "attempts": attempt, - } - await writer.append(record) - - progress["done"] += 1 - progress["sum_wer"] += sample_wer if sample_wer != float("inf") else 0 - tqdm.write( - f"[{progress['done']}/{progress['total']}] WER={sample_wer:.3f} " - f"CER={sample_cer:.3f} id={sample['id']} dur={sample['duration']}s " - f"latency={latency_ms}ms req_id={request_id}" - ) - return record - - except Exception as e: - latency_ms = int((time.perf_counter() - start) * 1000) - last_error = f"{type(e).__name__}: {e}" - tqdm.write( - f"[error] id={sample['id']} attempt={attempt}/{MAX_RETRIES} " - f"latency={latency_ms}ms error={last_error}" - ) - if attempt < MAX_RETRIES: - await asyncio.sleep(2 ** (attempt - 1)) - - progress["failed"] += 1 - tqdm.write(f"[FAILED] id={sample['id']} after {MAX_RETRIES} attempts: {last_error}") - return None - - -def compute_metrics(results: list[dict]) -> dict: - if not results: - return {} - # Aggregate WER/CER the correct way: per-ref length weighting — not a simple - # mean of per-sample WERs (long clips shouldn't count the same as short ones). - refs = [r["transcript_normalized"] for r in results if r.get("transcript_normalized")] - hyps = [r["prediction_normalized"] for r in results if r.get("transcript_normalized")] - corpus_wer = float(wer(refs, hyps)) if refs else float("inf") - corpus_cer = float(cer(refs, hyps)) if refs else float("inf") - - # Simple per-sample mean for reference. - mean_wer = sum(r["wer"] for r in results if r["wer"] != float("inf")) / max(1, len(results)) - mean_cer = sum(r["cer"] for r in results if r["cer"] != float("inf")) / max(1, len(results)) - - # Time-weighted WER (the AA-WER convention within a dataset) - total_dur = sum(r.get("duration", 0) or 0 for r in results) - time_weighted_wer = ( - sum((r["wer"] if r["wer"] != float("inf") else 0) * (r.get("duration", 0) or 0) - for r in results) / total_dur if total_dur > 0 else float("inf") - ) - - latencies = [r["latency_ms"] for r in results if isinstance(r.get("latency_ms"), int)] - latency_stats = {} - if latencies: - lats = sorted(latencies) - n = len(lats) - latency_stats = { - "count": n, "mean_ms": sum(lats) / n, - "p50_ms": lats[n // 2], - "p90_ms": lats[min(n - 1, int(n * 0.9))], - "p99_ms": lats[min(n - 1, int(n * 0.99))], - "max_ms": lats[-1], - } - - return { - "corpus_wer": corpus_wer, - "corpus_cer": corpus_cer, - "mean_sample_wer": mean_wer, - "mean_sample_cer": mean_cer, - "time_weighted_wer": time_weighted_wer, - "num_samples": len(results), - "total_duration_s": total_dur, - "latency": latency_stats, - } - - -def print_summary(metrics: dict): - print(f"\n{'=' * 60}") - print(f"VoxPopuli-Cleaned-AA Results (Interfaze, reasoning={REASONING_EFFORT}, temp={TEMPERATURE})") - print(f"{'=' * 60}") - print(f"Samples : {metrics['num_samples']}") - print(f"Total audio duration : {metrics['total_duration_s']:.1f}s") - print(f"Corpus WER : {metrics['corpus_wer']:.4f} ← primary metric") - print(f"Corpus CER : {metrics['corpus_cer']:.4f}") - print(f"Time-weighted WER : {metrics['time_weighted_wer']:.4f} ← AA-WER convention") - print(f"Mean per-sample WER : {metrics['mean_sample_wer']:.4f}") - print(f"Mean per-sample CER : {metrics['mean_sample_cer']:.4f}") - if metrics.get("latency"): - lat = metrics["latency"] - print(f"Latency : mean={lat['mean_ms']:.0f}ms p50={lat['p50_ms']}ms " - f"p90={lat['p90_ms']}ms p99={lat['p99_ms']}ms max={lat['max_ms']}ms") - - -async def run_predictions(pred_path: Path, limit: int | None = None): - print(f"Loading {DATASET_ID}, split={SPLIT}...") - dataset = load_dataset(DATASET_ID, split=SPLIT) - print(f"Loaded {len(dataset)} samples") - - samples = [build_sample(dict(row)) for row in dataset] - done_ids = load_completed_ids(pred_path) - pending = [s for s in samples if s["id"] not in done_ids] - if limit is not None: - pending = pending[:limit] - print(f"--limit applied: will run at most {limit} sample(s)") - print(f"Resume: {len(done_ids)} already completed, {len(pending)} remaining " - f"(checkpoint: {pred_path})") - if not pending: - return - - writer = JsonlWriter(pred_path) - rate_limiter = RateLimiter(RATE_LIMIT) - progress = {"total": len(pending), "done": 0, "failed": 0, "sum_wer": 0.0} - - tasks = [process_sample(s, rate_limiter, writer, progress) for s in pending] - try: - await tqdm_asyncio.gather(*tasks, desc=f"{DATASET_ID.split('/')[-1]}/{SPLIT}") - except Exception: - traceback.print_exc() - print(f"\nRun finished: {progress['done']}/{progress['total']} answered, " - f"{progress['failed']} failed.") - - -def run_evaluation(pred_path: Path, metrics_path: Path): - if not pred_path.exists(): - print(f"No predictions found at {pred_path}") - sys.exit(1) - results = load_records(pred_path) - if not results: - print(f"No records in {pred_path}") - sys.exit(1) - - for r in results: - if r.get("prediction_normalized") is None and r.get("response"): - r["prediction_normalized"] = normalize_text(r["response"]) - if r.get("transcript_normalized") is None and r.get("transcript"): - r["transcript_normalized"] = normalize_text(r["transcript"]) - - metrics = compute_metrics(results) - print_summary(metrics) - output = { - **metrics, - "dataset": DATASET_ID, "split": SPLIT, - "reasoning_effort": REASONING_EFFORT, "temperature": TEMPERATURE, - "rate_limit": RATE_LIMIT, "model": "interfaze-beta", - } - metrics_path.parent.mkdir(parents=True, exist_ok=True) - with open(metrics_path, "w") as f: - json.dump(output, f, indent=2) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - parser = argparse.ArgumentParser(description="VoxPopuli-Cleaned-AA ASR benchmark") - parser.add_argument("--predict-only", action="store_true") - parser.add_argument("--evaluate-only", action="store_true") - parser.add_argument("--limit", type=int, default=None) - args = parser.parse_args() - - pred_path = RESULTS_DIR / "voxpopuli_aa_responses.jsonl" - metrics_path = RESULTS_DIR / "voxpopuli_aa_metrics.json" - - if args.evaluate_only: - run_evaluation(pred_path, metrics_path) - elif args.predict_only: - asyncio.run(run_predictions(pred_path, limit=args.limit)) - else: - asyncio.run(run_predictions(pred_path, limit=args.limit)) - run_evaluation(pred_path, metrics_path) - - -if __name__ == "__main__": - main() diff --git a/benchmarks/asr/voxpopuli_aa_multi.py b/benchmarks/asr/voxpopuli_aa_multi.py deleted file mode 100644 index e04f8a4..0000000 --- a/benchmarks/asr/voxpopuli_aa_multi.py +++ /dev/null @@ -1,263 +0,0 @@ -""" -VoxPopuli-Cleaned-AA ASR benchmark — multi-provider edition. - -Runs the same prompt + dataset + scoring as benchmarks.asr.voxpopuli_aa, but -against non-interfaze providers (Gemini, Anthropic, OpenAI) for head-to-head -WER comparison. Designed to run in parallel with the interfaze run without -stepping on its checkpoint file. - -Output: results/voxpopuli_aa___responses.jsonl - -Usage: - uv run -m benchmarks.asr.voxpopuli_aa_multi --provider gemini --model gemini-3-flash-preview - uv run -m benchmarks.asr.voxpopuli_aa_multi --provider gemini --model gemini-3-flash-preview --limit 5 -""" - -import sys -import json -import time -import asyncio -import argparse -import traceback -from pathlib import Path - -from datasets import load_dataset -from jiwer import wer, cer -from tqdm import tqdm -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -sys.path.insert(0, str(PROJECT_ROOT)) - -# Reuse everything shared: prompt, WER normalization, JSONL writer, etc. -from benchmarks.asr.voxpopuli_aa import ( # noqa: E402 - PROMPT, DATASET_ID, SPLIT, RATE_LIMIT, MAX_RETRIES, - RateLimiter, JsonlWriter, normalize_text, fetch_audio_bytes, - build_sample, load_completed_ids, load_records, - compute_metrics, print_summary, -) - -RESULTS_DIR = PROJECT_ROOT / "results" - - -def _load_interfaze_env() -> dict: - env = {} - for line in (Path.home() / "interfaze" / ".env.local").read_text().splitlines(): - line = line.strip() - if not line or line.startswith("#") or "=" not in line: - continue - k, v = line.split("=", 1) - env[k.strip()] = v.strip().strip('"').strip("'") - return env - - -# -------- provider adapters: (audio_bytes, prompt, model, client) -> (content, req_id) -------- - -def call_gemini(audio_bytes: bytes, prompt_text: str, model: str, client): - """Thinking OFF (or as close as the model allows): - - Gemini 3.x Pro: thinking_level="low" (Pro rejects 'minimal'; min is 'low'). - - Gemini 3.x Flash: thinking_level="minimal" (true disable not supported). - - Gemini 2.5 Pro: thinking_budget=128 (Pro can't go lower than 128). - - Gemini 2.5 Flash: thinking_budget=0 (true disable). - Input audio as inline bytes (Gemini accepts up to ~20 MB inline).""" - from google.genai import types - m = model.lower() - if m.startswith("gemini-2.5-pro"): - thinking = types.ThinkingConfig(thinking_budget=128) - elif m.startswith("gemini-2.5-flash"): - thinking = types.ThinkingConfig(thinking_budget=0) - elif "pro" in m: - thinking = types.ThinkingConfig(thinking_level="low") - else: - thinking = types.ThinkingConfig(thinking_level="minimal") - config = types.GenerateContentConfig( - thinking_config=thinking, - temperature=0.0, - ) - resp = client.models.generate_content( - model=model, - contents=[ - types.Part.from_bytes(data=audio_bytes, mime_type="audio/wav"), - prompt_text, - ], - config=config, - ) - content = (resp.text or "").strip() - request_id = getattr(resp, "response_id", None) or "" - return content, request_id - - -def build_client(provider: str, env: dict): - if provider == "gemini": - from google import genai - return genai.Client(api_key=env["GEMINI_KEY"]) - raise ValueError(f"Provider not yet supported here: {provider}") - - -def get_call_fn(provider: str): - return {"gemini": call_gemini}[provider] - - -# -------- pipeline (mirrors voxpopuli_aa.process_sample, but routed via adapter) -------- - -async def process_sample(sample: dict, call_fn, model: str, rate_limiter, - writer: JsonlWriter, progress: dict, provider: str, - client) -> dict | None: - last_error: str | None = None - - try: - audio_bytes = await asyncio.to_thread(fetch_audio_bytes, sample["file_name"]) - except Exception as e: - tqdm.write(f"[{provider} fetch error] id={sample['id']}: {type(e).__name__}: {e}") - progress["failed"] += 1 - return None - - for attempt in range(1, MAX_RETRIES + 1): - await rate_limiter.acquire() - start = time.perf_counter() - try: - content, request_id = await asyncio.to_thread( - call_fn, audio_bytes, PROMPT, model, client - ) - latency_ms = int((time.perf_counter() - start) * 1000) - if not content: - last_error = "empty response content" - raise RuntimeError(last_error) - - pred_norm = normalize_text(content) - gt_norm = normalize_text(sample["transcript"]) - sample_wer = float(wer(gt_norm, pred_norm)) if gt_norm else float("inf") - sample_cer = float(cer(gt_norm, pred_norm)) if gt_norm else float("inf") - - record = { - "id": sample["id"], - "file_name": sample["file_name"], - "duration": sample["duration"], - "gender": sample["gender"], - "language": sample["language"], - "transcript": sample["transcript"], - "transcript_normalized": gt_norm, - "prediction": content, - "prediction_normalized": pred_norm, - "wer": sample_wer, - "cer": sample_cer, - "response": content, - "request_id": request_id, - "latency_ms": latency_ms, - "attempts": attempt, - "provider": provider, - "model": model, - } - await writer.append(record) - - progress["done"] += 1 - tqdm.write( - f"[{provider} {progress['done']}/{progress['total']}] WER={sample_wer:.3f} " - f"CER={sample_cer:.3f} id={sample['id']} dur={sample['duration']}s " - f"latency={latency_ms}ms req_id={request_id}" - ) - return record - - except Exception as e: - latency_ms = int((time.perf_counter() - start) * 1000) - last_error = f"{type(e).__name__}: {e}" - tqdm.write( - f"[{provider} error] id={sample['id']} attempt={attempt}/{MAX_RETRIES} " - f"latency={latency_ms}ms error={last_error}" - ) - if attempt < MAX_RETRIES: - await asyncio.sleep(2 ** (attempt - 1)) - - progress["failed"] += 1 - tqdm.write(f"[{provider} FAILED] id={sample['id']} after {MAX_RETRIES} attempts: {last_error}") - return None - - -def build_tag(provider: str, model: str) -> str: - return f"voxpopuli_aa_{provider}_{model.replace('/', '_').replace(':', '_')}" - - -async def run(provider: str, model: str, pred_path: Path, limit: int | None): - env = _load_interfaze_env() - client = build_client(provider, env) - call_fn = get_call_fn(provider) - - print(f"[{provider}/{model}] Loading {DATASET_ID}, split={SPLIT}...") - dataset = load_dataset(DATASET_ID, split=SPLIT) - samples = [build_sample(dict(row)) for row in dataset] - - done_ids = load_completed_ids(pred_path) - pending = [s for s in samples if s["id"] not in done_ids] - if limit is not None: - pending = pending[:limit] - print(f"--limit applied: will run at most {limit} sample(s)") - print(f"Resume: {len(done_ids)} completed, {len(pending)} remaining " - f"(checkpoint: {pred_path})") - if not pending: - return - - writer = JsonlWriter(pred_path) - rate_limiter = RateLimiter(RATE_LIMIT) - progress = {"total": len(pending), "done": 0, "failed": 0} - - tasks = [process_sample(s, call_fn, model, rate_limiter, writer, progress, provider, client) - for s in pending] - try: - await tqdm_asyncio.gather(*tasks, desc=f"{provider}/{model}") - except Exception: - traceback.print_exc() - print(f"\n[{provider}/{model}] Run finished: {progress['done']}/{progress['total']}, " - f"{progress['failed']} failed.") - - -def run_evaluation(pred_path: Path, metrics_path: Path, provider: str, model: str): - if not pred_path.exists(): - print(f"No predictions found at {pred_path}") - sys.exit(1) - results = load_records(pred_path) - if not results: - print(f"No records in {pred_path}") - sys.exit(1) - for r in results: - if r.get("prediction_normalized") is None and r.get("response"): - r["prediction_normalized"] = normalize_text(r["response"]) - if r.get("transcript_normalized") is None and r.get("transcript"): - r["transcript_normalized"] = normalize_text(r["transcript"]) - metrics = compute_metrics(results) - print_summary(metrics) - output = { - **metrics, - "dataset": DATASET_ID, "split": SPLIT, - "provider": provider, "model": model, - "rate_limit": RATE_LIMIT, - } - metrics_path.parent.mkdir(parents=True, exist_ok=True) - with open(metrics_path, "w") as f: - json.dump(output, f, indent=2) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - parser = argparse.ArgumentParser(description="Multi-provider VoxPopuli-Cleaned-AA eval") - parser.add_argument("--provider", required=True, choices=["gemini"]) - parser.add_argument("--model", required=True) - parser.add_argument("--predict-only", action="store_true") - parser.add_argument("--evaluate-only", action="store_true") - parser.add_argument("--limit", type=int, default=None) - args = parser.parse_args() - - tag = build_tag(args.provider, args.model) - pred_path = RESULTS_DIR / f"{tag}_responses.jsonl" - metrics_path = RESULTS_DIR / f"{tag}_metrics.json" - - if args.evaluate_only: - run_evaluation(pred_path, metrics_path, args.provider, args.model) - elif args.predict_only: - asyncio.run(run(args.provider, args.model, pred_path, limit=args.limit)) - else: - asyncio.run(run(args.provider, args.model, pred_path, limit=args.limit)) - run_evaluation(pred_path, metrics_path, args.provider, args.model) - - -if __name__ == "__main__": - main() diff --git a/benchmarks/gpqa/bench.py b/benchmarks/gpqa/bench.py new file mode 100644 index 0000000..0161e8c --- /dev/null +++ b/benchmarks/gpqa/bench.py @@ -0,0 +1,120 @@ +# GPQA Diamond: 198 expert MCQs; deterministic choice shuffle; letter-match accuracy. + +from __future__ import annotations + +import random +import re +from collections import defaultdict + +from src.request import Message, ReasoningSpec, Request, TextPart + +NAME = "gpqa" +ID_KEY = "id" +PRIMARY_METRIC = "accuracy" +DEFAULTS = {"reasoning": "off", "rate_limit": 10, "max_in_flight": 8} + +_DATASET_ID = "Idavidrein/gpqa" +_CONFIG = "gpqa_diamond" +_SPLIT = "train" # GPQA Diamond ships as a single 'train' split (198 rows) + +_PROMPT = ( + "The following is a multiple choice question (with answers). Respond with " + "only the single letter (A, B, C, or D) corresponding to the correct " + "answer. Do not show your work.\n\n" + "Question: {question}\n" + "A. {a}\nB. {b}\nC. {c}\nD. {d}\n\n" + "Answer:" +) + +_LETTER_RE = re.compile(r"\b([ABCD])\b") +_FIRST_LETTER_RE = re.compile(r"[ABCD]") + + +def _clean(s) -> str: + return str(s).strip() if s is not None else "" + + +def _build_sample(row: dict) -> dict: + # Shuffle choices deterministically by Record ID (avoids position bias and + # eval-to-eval drift); track which letter holds the correct answer. + record_id = str(row.get("Record ID") or row.get("Question", "")) + correct = _clean(row["Correct Answer"]) + incorrect = [ + _clean(row["Incorrect Answer 1"]), + _clean(row["Incorrect Answer 2"]), + _clean(row["Incorrect Answer 3"]), + ] + choices = [(correct, True)] + [(x, False) for x in incorrect] + random.Random(record_id).shuffle(choices) + letters = ["A", "B", "C", "D"] + correct_letter = letters[next(i for i, (_, c) in enumerate(choices) if c)] + return { + "id": record_id, + "question": _clean(row["Question"]), + "a": choices[0][0], + "b": choices[1][0], + "c": choices[2][0], + "d": choices[3][0], + "correct_letter": correct_letter, + "domain": _clean(row.get("High-level domain")), + } + + +def load_samples(sample_size: int | None = None) -> list[dict]: + from datasets import load_dataset + + ds = load_dataset(_DATASET_ID, _CONFIG, split=_SPLIT) + samples = [_build_sample(dict(row)) for row in ds] + return samples[:sample_size] if sample_size else samples + + +def build_request(sample: dict, mode: str) -> Request: + prompt = _PROMPT.format( + question=sample["question"], + a=sample["a"], + b=sample["b"], + c=sample["c"], + d=sample["d"], + ) + return Request( + messages=[Message("user", [TextPart(prompt)])], + reasoning=ReasoningSpec(mode), + temperature=0.0, + ) + + +def parse(response, sample) -> str | None: + text = (response.text or "").strip() + if not text: + return None + if len(text) == 1 and text.upper() in "ABCD": + return text.upper() + up = text.upper() + m = _LETTER_RE.search(up) + if m: + return m.group(1) + m = _FIRST_LETTER_RE.search(up) + return m.group(0) if m else None + + +def score(records: list[dict], samples: list[dict]) -> dict: + by_id = {s["id"]: s for s in samples} + rows = [(r.get("prediction"), by_id[r["id"]]) for r in records if r["id"] in by_id] + total = len(rows) + correct = sum(1 for pred, s in rows if pred == s["correct_letter"]) + unparseable = sum(1 for pred, _ in rows if pred is None) + + by_domain: dict[str, list[bool]] = defaultdict(list) + for pred, s in rows: + by_domain[s["domain"] or "Unknown"].append(pred == s["correct_letter"]) + per_domain = { + d: {"n": len(v), "accuracy": sum(v) / len(v)} for d, v in by_domain.items() + } + + return { + "accuracy": correct / total if total else 0.0, + "correct": correct, + "total": total, + "unparseable": unparseable, + "per_domain": per_domain, + } diff --git a/benchmarks/gpqa/gpqa_gemini.py b/benchmarks/gpqa/gpqa_gemini.py deleted file mode 100644 index 9eb5c70..0000000 --- a/benchmarks/gpqa/gpqa_gemini.py +++ /dev/null @@ -1,277 +0,0 @@ -""" -GPQA Diamond benchmark for Gemini. - -198 graduate-level multiple-choice questions in physics, chemistry, and biology. -Mirrors `gpqa_openai.py` exactly — same prompt, same deterministic shuffle of -choices, same scoring — only the inference call differs. - -Default model: gemini-3.1-pro-preview. Thinking is left ON at the model's -default level (the Pro family is reasoning-first; we don't pass a -thinking_config). Temperature pinned to 0.0. - -Usage: - uv run -m benchmarks.gpqa.gpqa_gemini - uv run -m benchmarks.gpqa.gpqa_gemini --model gemini-3.1-pro-preview - uv run -m benchmarks.gpqa.gpqa_gemini --thinking-level minimal - uv run -m benchmarks.gpqa.gpqa_gemini --limit 5 - uv run -m benchmarks.gpqa.gpqa_gemini --evaluate-only - -Env: GEMINI_KEY must be set in .env. -""" - -import os -import re -import sys -import json -import time -import asyncio -import argparse -import traceback -from pathlib import Path -from collections import defaultdict - -from datasets import load_dataset -from dotenv import load_dotenv -from google import genai -from google.genai import types -from tqdm import tqdm -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -sys.path.insert(0, str(PROJECT_ROOT)) - -# Reuse parsing/sample-builder/scoring from the OpenAI variant — guarantees -# identical prompts and answer-letter parsing across providers. -from benchmarks.gpqa.gpqa_openai import ( # noqa: E402 - DATASET_ID, - CONFIG, - SPLIT, - PROMPT_TEMPLATE, - JsonlWriter, - build_sample, - compute_metrics, - load_completed_ids, - load_records, - parse_letter, -) - -load_dotenv() - -RESULTS_DIR = PROJECT_ROOT / "results" -DEFAULT_MODEL = "gemini-3.1-pro-preview" -DEFAULT_THINKING_LEVEL: str | None = None # None => model default (Pro: thinking on) -TEMPERATURE = 0.0 -CONCURRENCY = 30 # Flash has much higher headroom than Pro. -MAX_RETRIES = 6 -RETRY_BACKOFF_CAP_S = 30.0 - -MODEL = DEFAULT_MODEL -THINKING_LEVEL: str | None = DEFAULT_THINKING_LEVEL - -GEMINI_KEY = ( - os.getenv("GEMINI_KEY") or os.getenv("GEMINI_API_KEY") or os.getenv("GOOGLE_API_KEY") -) -if not GEMINI_KEY: - raise RuntimeError( - "GEMINI_KEY is not set. Add it to .env " - "(get one from https://aistudio.google.com/app/apikey)." - ) - -gemini_client = genai.Client(api_key=GEMINI_KEY) - - -def model_slug(model: str) -> str: - return re.sub(r"[^a-z0-9]", "", model.lower()) - - -def invoke_gemini(prompt: str): - config_kwargs = {"temperature": TEMPERATURE} - if THINKING_LEVEL is not None: - config_kwargs["thinking_config"] = types.ThinkingConfig(thinking_level=THINKING_LEVEL) - config = types.GenerateContentConfig(**config_kwargs) - return gemini_client.models.generate_content( - model=MODEL, - contents=[types.Part.from_text(text=prompt)], - config=config, - ) - - -async def process_sample(sample: dict, semaphore: asyncio.Semaphore, - writer: JsonlWriter, progress: dict) -> dict | None: - prompt = PROMPT_TEMPLATE.format( - question=sample["question"], - a=sample["a"], b=sample["b"], c=sample["c"], d=sample["d"], - ) - last_error: str | None = None - - for attempt in range(1, MAX_RETRIES + 1): - start = time.perf_counter() - try: - async with semaphore: - start = time.perf_counter() - response = await asyncio.to_thread(invoke_gemini, prompt) - latency_ms = int((time.perf_counter() - start) * 1000) - content = (response.text or "").strip() - request_id = getattr(response, "response_id", None) - if not content: - last_error = "empty response content" - raise RuntimeError(last_error) - - predicted = parse_letter(content) - correct = predicted == sample["correct_letter"] - - record = { - "id": sample["id"], - "domain": sample["domain"], - "subdomain": sample["subdomain"], - "correct_letter": sample["correct_letter"], - "prediction": predicted, - "correct": correct, - "response": content, - "request_id": request_id, - "latency_ms": latency_ms, - "attempts": attempt, - } - await writer.append(record) - - progress["done"] += 1 - if correct: - progress["correct"] += 1 - mark = "OK" if correct else "X " - tqdm.write( - f"[{progress['done']}/{progress['total']}] {mark} " - f"id={sample['id']} domain={sample['domain']:10} " - f"gold={sample['correct_letter']} pred={predicted or '?'} " - f"latency={latency_ms}ms attempt={attempt}" - ) - return record - - except Exception as e: - latency_ms = int((time.perf_counter() - start) * 1000) - last_error = f"{type(e).__name__}: {e}" - tqdm.write( - f"[error] id={sample['id']} attempt={attempt}/{MAX_RETRIES} " - f"latency={latency_ms}ms error={last_error}" - ) - if attempt < MAX_RETRIES: - await asyncio.sleep(min(2 ** (attempt - 1), RETRY_BACKOFF_CAP_S)) - - progress["failed"] += 1 - tqdm.write(f"[FAILED] id={sample['id']} after {MAX_RETRIES} attempts: {last_error}") - return None - - -def print_summary(metrics: dict): - print(f"\n{'=' * 60}") - print( - f"GPQA Diamond — {DATASET_ID}/{CONFIG} ({MODEL}, " - f"thinking={'default' if THINKING_LEVEL is None else THINKING_LEVEL}, " - f"temp={TEMPERATURE})" - ) - print(f"{'=' * 60}") - print(f"Accuracy : {metrics['accuracy']:.4f} ({metrics['correct']}/{metrics['total']})") - print(f"Unparseable: {metrics['unparseable']}") - print("\nPer high-level domain:") - for d in sorted(metrics["per_domain"]): - v = metrics["per_domain"][d] - print(f" {d:12} n={v['n']:>3} acc={v['accuracy']:.4f}") - if metrics.get("latency"): - lat = metrics["latency"] - print(f"\nLatency : mean={lat['mean_ms']:.0f}ms p50={lat['p50_ms']}ms " - f"p90={lat['p90_ms']}ms p99={lat['p99_ms']}ms max={lat['max_ms']}ms") - - -async def run_predictions(pred_path: Path, limit: int | None): - print(f"Loading {DATASET_ID}/{CONFIG} (split={SPLIT})...") - ds = load_dataset(DATASET_ID, CONFIG, split=SPLIT) - print(f"Loaded {len(ds)} rows") - samples = [build_sample(dict(row)) for row in ds] - - done_ids = load_completed_ids(pred_path) - pending = [s for s in samples if s["id"] not in done_ids] - if limit is not None: - pending = pending[:limit] - print(f"--limit applied: will run at most {limit} sample(s)") - print(f"Resume: {len(done_ids)} already completed, {len(pending)} remaining " - f"(checkpoint: {pred_path})") - if not pending: - return - - writer = JsonlWriter(pred_path) - semaphore = asyncio.Semaphore(CONCURRENCY) - progress = {"total": len(pending), "done": 0, "correct": 0, "failed": 0} - - tasks = [process_sample(s, semaphore, writer, progress) for s in pending] - try: - await tqdm_asyncio.gather(*tasks, desc=f"GPQA Diamond / {MODEL}") - except Exception: - traceback.print_exc() - acc = progress["correct"] / progress["done"] if progress["done"] else 0.0 - print(f"\nRun finished: {progress['done']}/{progress['total']} answered, " - f"{progress['correct']} correct (acc={acc:.4f}), {progress['failed']} failed.") - - -def run_evaluation(pred_path: Path, metrics_path: Path): - if not pred_path.exists(): - print(f"No predictions found at {pred_path}") - sys.exit(1) - results = load_records(pred_path) - if not results: - print(f"No records in {pred_path}") - sys.exit(1) - for r in results: - if r.get("prediction") is None and r.get("response"): - r["prediction"] = parse_letter(r["response"]) - if r.get("correct") is None and r.get("prediction") is not None: - r["correct"] = r["prediction"] == r.get("correct_letter") - - metrics = compute_metrics(results) - print_summary(metrics) - output = { - **metrics, - "dataset": DATASET_ID, - "config": CONFIG, - "split": SPLIT, - "model": MODEL, - "thinking_level": THINKING_LEVEL, - "temperature": TEMPERATURE, - "concurrency": CONCURRENCY, - } - metrics_path.parent.mkdir(parents=True, exist_ok=True) - with open(metrics_path, "w") as f: - json.dump(output, f, indent=2, ensure_ascii=False) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - global MODEL, THINKING_LEVEL - parser = argparse.ArgumentParser(description="GPQA Diamond benchmark for Gemini") - parser.add_argument("--model", default=DEFAULT_MODEL, - help="Gemini model id (e.g. gemini-3.1-pro-preview)") - parser.add_argument("--thinking-level", default=DEFAULT_THINKING_LEVEL, - help="thinking_level ('minimal'|'low'|'high'); omit for model default") - parser.add_argument("--predict-only", action="store_true") - parser.add_argument("--evaluate-only", action="store_true") - parser.add_argument("--limit", type=int, default=None, - help="Only run the first N unanswered samples") - args = parser.parse_args() - - MODEL = args.model - THINKING_LEVEL = args.thinking_level - - thinking_slug = THINKING_LEVEL or "default" - tag = f"{model_slug(MODEL)}_thinking{thinking_slug}_gpqa_diamond" - pred_path = RESULTS_DIR / f"{tag}_responses.jsonl" - metrics_path = RESULTS_DIR / f"{tag}_metrics.json" - - if args.evaluate_only: - run_evaluation(pred_path, metrics_path) - elif args.predict_only: - asyncio.run(run_predictions(pred_path, limit=args.limit)) - else: - asyncio.run(run_predictions(pred_path, limit=args.limit)) - run_evaluation(pred_path, metrics_path) - - -if __name__ == "__main__": - main() diff --git a/benchmarks/gpqa/gpqa_openai.py b/benchmarks/gpqa/gpqa_openai.py deleted file mode 100644 index 199dc04..0000000 --- a/benchmarks/gpqa/gpqa_openai.py +++ /dev/null @@ -1,434 +0,0 @@ -""" -GPQA Diamond benchmark for OpenAI GPT-5.x. - -198 graduate-level multiple-choice questions in physics, chemistry, and biology -written and validated by domain experts. The "Diamond" subset is the hardest -slice of GPQA — questions where both expert validators answered correctly and -the majority of non-experts answered incorrectly. - -Dataset: https://huggingface.co/datasets/Idavidrein/gpqa (config: gpqa_diamond) -Paper: https://arxiv.org/abs/2311.12022 - -Methodology: - - For each question we deterministically shuffle the 4 answer choices using - Record ID as the seed. This avoids both position bias (always-A) and - pure-random eval-to-eval drift. - - Single-shot, pass@1, no chain-of-thought prompted. The model is asked to - output a single letter A/B/C/D. - - Metric: exact-match accuracy on the predicted letter, reported overall and - per high-level domain. - -Default model: gpt-5.5. Run gpt-5.4-mini with `--model gpt-5.4-mini`. -Reasoning defaults to fully off (`reasoning_effort="none"`); temperature 0.0. - -Usage: - uv run -m benchmarks.gpqa.gpqa_openai - uv run -m benchmarks.gpqa.gpqa_openai --model gpt-5.4-mini - uv run -m benchmarks.gpqa.gpqa_openai --limit 5 - uv run -m benchmarks.gpqa.gpqa_openai --evaluate-only - -Env: OPENAI_API_KEY must be set. -""" - -import os -import re -import sys -import json -import time -import random -import asyncio -import argparse -import traceback -from pathlib import Path -from collections import defaultdict - -from datasets import load_dataset -from dotenv import load_dotenv -from openai import OpenAI -from tqdm import tqdm -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -sys.path.insert(0, str(PROJECT_ROOT)) - -load_dotenv() - -RESULTS_DIR = PROJECT_ROOT / "results" -DATASET_ID = "Idavidrein/gpqa" -CONFIG = "gpqa_diamond" -SPLIT = "train" # GPQA Diamond ships as a single 'train' split with 198 rows. - -DEFAULT_MODEL = "gpt-5.5" -DEFAULT_REASONING_EFFORT = "none" -TEMPERATURE = 0.0 -CONCURRENCY = 10 -MAX_RETRIES = 5 -RETRY_BACKOFF_CAP_S = 30.0 - -MODEL = DEFAULT_MODEL -REASONING_EFFORT = DEFAULT_REASONING_EFFORT - -PROMPT_TEMPLATE = ( - "The following is a multiple choice question (with answers). Respond with " - "only the single letter (A, B, C, or D) corresponding to the correct " - "answer. Do not show your work.\n\n" - "Question: {question}\n" - "A. {a}\n" - "B. {b}\n" - "C. {c}\n" - "D. {d}\n\n" - "Answer:" -) - -OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") -if not OPENAI_API_KEY: - raise RuntimeError( - "OPENAI_API_KEY is not set. Add it to .env " - "(get one from https://platform.openai.com/api-keys)." - ) - -# Bypass the .env's interfaze base_url override. -openai_client = OpenAI( - base_url="https://api.openai.com/v1", - api_key=OPENAI_API_KEY, -) - - -def model_slug(model: str) -> str: - return re.sub(r"[^a-z0-9]", "", model.lower()) - - -def invoke_openai(messages: list[dict]): - # GPT-5.x rejects temperature!=default when reasoning is engaged - # ("Unsupported value: 'temperature' does not support 0.0 with this model. - # Only the default (1) value is supported."). With reasoning off ('none'), - # temperature=0 is accepted and gives us deterministic decoding. - kwargs = { - "model": MODEL, - "messages": messages, - "reasoning_effort": REASONING_EFFORT, - } - if REASONING_EFFORT == "none": - kwargs["temperature"] = TEMPERATURE - return openai_client.chat.completions.create(**kwargs) - - -class JsonlWriter: - def __init__(self, path: Path): - self.path = path - self.path.parent.mkdir(parents=True, exist_ok=True) - self._lock = asyncio.Lock() - - async def append(self, record: dict): - line = json.dumps(record, ensure_ascii=False) - async with self._lock: - with open(self.path, "a", encoding="utf-8") as f: - f.write(line + "\n") - f.flush() - os.fsync(f.fileno()) - - -def load_completed_ids(path: Path) -> set[str]: - if not path.exists(): - return set() - done: set[str] = set() - with open(path, encoding="utf-8") as f: - for line_no, line in enumerate(f, 1): - line = line.strip() - if not line: - continue - try: - rec = json.loads(line) - except json.JSONDecodeError: - tqdm.write(f"[resume] skipping malformed line {line_no} in {path}") - continue - if rec.get("response") is not None: - done.add(str(rec["id"])) - return done - - -def load_records(path: Path) -> list[dict]: - by_id: dict[str, dict] = {} - if not path.exists(): - return [] - with open(path, encoding="utf-8") as f: - for line in f: - line = line.strip() - if not line: - continue - try: - rec = json.loads(line) - by_id[str(rec["id"])] = rec - except json.JSONDecodeError: - continue - return list(by_id.values()) - - -_LETTER_RE = re.compile(r"\b([ABCD])\b") -_FIRST_LETTER_RE = re.compile(r"[ABCD]") - - -def parse_letter(text: str) -> str | None: - if not text: - return None - s = text.strip() - if len(s) == 1 and s.upper() in "ABCD": - return s.upper() - m = _LETTER_RE.search(s.upper()) - if m: - return m.group(1) - m = _FIRST_LETTER_RE.search(s.upper()) - if m: - return m.group(0) - return None - - -def _clean(s) -> str: - return str(s).strip() if s is not None else "" - - -def build_sample(row: dict) -> dict: - """Shuffle the 4 choices deterministically using Record ID as seed; track - which letter ended up holding the correct answer.""" - record_id = str(row.get("Record ID") or row.get("Question", "")) - correct = _clean(row["Correct Answer"]) - incorrect = [ - _clean(row["Incorrect Answer 1"]), - _clean(row["Incorrect Answer 2"]), - _clean(row["Incorrect Answer 3"]), - ] - choices = [(correct, True)] + [(x, False) for x in incorrect] - rng = random.Random(record_id) - rng.shuffle(choices) - letters = ["A", "B", "C", "D"] - correct_letter = letters[next(i for i, (_, is_c) in enumerate(choices) if is_c)] - return { - "id": record_id, - "question": _clean(row["Question"]), - "a": choices[0][0], - "b": choices[1][0], - "c": choices[2][0], - "d": choices[3][0], - "correct_letter": correct_letter, - "correct_answer_text": correct, - "domain": _clean(row.get("High-level domain")), - "subdomain": _clean(row.get("Subdomain")), - } - - -async def process_sample(sample: dict, semaphore: asyncio.Semaphore, - writer: JsonlWriter, progress: dict) -> dict | None: - prompt = PROMPT_TEMPLATE.format( - question=sample["question"], - a=sample["a"], b=sample["b"], c=sample["c"], d=sample["d"], - ) - messages = [{"role": "user", "content": prompt}] - last_error: str | None = None - - for attempt in range(1, MAX_RETRIES + 1): - start = time.perf_counter() - try: - async with semaphore: - start = time.perf_counter() - response = await asyncio.to_thread(invoke_openai, messages) - latency_ms = int((time.perf_counter() - start) * 1000) - content = (response.choices[0].message.content or "").strip() - request_id = getattr(response, "id", None) - if not content: - last_error = "empty response content" - raise RuntimeError(last_error) - - predicted = parse_letter(content) - correct = predicted == sample["correct_letter"] - - record = { - "id": sample["id"], - "domain": sample["domain"], - "subdomain": sample["subdomain"], - "correct_letter": sample["correct_letter"], - "prediction": predicted, - "correct": correct, - "response": content, - "request_id": request_id, - "latency_ms": latency_ms, - "attempts": attempt, - } - await writer.append(record) - - progress["done"] += 1 - if correct: - progress["correct"] += 1 - mark = "OK" if correct else "X " - tqdm.write( - f"[{progress['done']}/{progress['total']}] {mark} " - f"id={sample['id']} domain={sample['domain']:10} " - f"gold={sample['correct_letter']} pred={predicted or '?'} " - f"latency={latency_ms}ms attempt={attempt}" - ) - return record - - except Exception as e: - latency_ms = int((time.perf_counter() - start) * 1000) - last_error = f"{type(e).__name__}: {e}" - tqdm.write( - f"[error] id={sample['id']} attempt={attempt}/{MAX_RETRIES} " - f"latency={latency_ms}ms error={last_error}" - ) - if attempt < MAX_RETRIES: - await asyncio.sleep(min(2 ** (attempt - 1), RETRY_BACKOFF_CAP_S)) - - progress["failed"] += 1 - tqdm.write(f"[FAILED] id={sample['id']} after {MAX_RETRIES} attempts: {last_error}") - return None - - -def compute_metrics(results: list[dict]) -> dict: - if not results: - return {} - total = len(results) - n_correct = sum(1 for r in results if r.get("correct")) - n_unparseable = sum(1 for r in results if r.get("prediction") is None) - - by_domain: dict[str, list[dict]] = defaultdict(list) - for r in results: - by_domain[r.get("domain") or "Unknown"].append(r) - per_domain = { - d: { - "n": len(rows), - "accuracy": sum(1 for r in rows if r.get("correct")) / len(rows), - } - for d, rows in by_domain.items() - } - - latencies = [r["latency_ms"] for r in results if isinstance(r.get("latency_ms"), int)] - latency_stats = {} - if latencies: - lats = sorted(latencies) - n = len(lats) - latency_stats = { - "count": n, "mean_ms": sum(lats) / n, - "p50_ms": lats[n // 2], - "p90_ms": lats[min(n - 1, int(n * 0.9))], - "p99_ms": lats[min(n - 1, int(n * 0.99))], - "max_ms": lats[-1], - } - - return { - "accuracy": n_correct / total, - "correct": n_correct, - "total": total, - "unparseable": n_unparseable, - "per_domain": per_domain, - "latency": latency_stats, - } - - -def print_summary(metrics: dict): - print(f"\n{'=' * 60}") - print(f"GPQA Diamond — {DATASET_ID}/{CONFIG} ({MODEL}, reasoning={REASONING_EFFORT})") - print(f"{'=' * 60}") - print(f"Accuracy : {metrics['accuracy']:.4f} ({metrics['correct']}/{metrics['total']})") - print(f"Unparseable: {metrics['unparseable']}") - print("\nPer high-level domain:") - for d in sorted(metrics["per_domain"]): - v = metrics["per_domain"][d] - print(f" {d:12} n={v['n']:>3} acc={v['accuracy']:.4f}") - if metrics.get("latency"): - lat = metrics["latency"] - print(f"\nLatency : mean={lat['mean_ms']:.0f}ms p50={lat['p50_ms']}ms " - f"p90={lat['p90_ms']}ms p99={lat['p99_ms']}ms max={lat['max_ms']}ms") - - -async def run_predictions(pred_path: Path, limit: int | None): - print(f"Loading {DATASET_ID}/{CONFIG} (split={SPLIT})...") - ds = load_dataset(DATASET_ID, CONFIG, split=SPLIT) - print(f"Loaded {len(ds)} rows") - samples = [build_sample(dict(row)) for row in ds] - - done_ids = load_completed_ids(pred_path) - pending = [s for s in samples if s["id"] not in done_ids] - if limit is not None: - pending = pending[:limit] - print(f"--limit applied: will run at most {limit} sample(s)") - print(f"Resume: {len(done_ids)} already completed, {len(pending)} remaining " - f"(checkpoint: {pred_path})") - if not pending: - return - - writer = JsonlWriter(pred_path) - semaphore = asyncio.Semaphore(CONCURRENCY) - progress = {"total": len(pending), "done": 0, "correct": 0, "failed": 0} - - tasks = [process_sample(s, semaphore, writer, progress) for s in pending] - try: - await tqdm_asyncio.gather(*tasks, desc=f"GPQA Diamond / {MODEL}") - except Exception: - traceback.print_exc() - acc = progress["correct"] / progress["done"] if progress["done"] else 0.0 - print(f"\nRun finished: {progress['done']}/{progress['total']} answered, " - f"{progress['correct']} correct (acc={acc:.4f}), {progress['failed']} failed.") - - -def run_evaluation(pred_path: Path, metrics_path: Path): - if not pred_path.exists(): - print(f"No predictions found at {pred_path}") - sys.exit(1) - results = load_records(pred_path) - if not results: - print(f"No records in {pred_path}") - sys.exit(1) - for r in results: - if r.get("prediction") is None and r.get("response"): - r["prediction"] = parse_letter(r["response"]) - if r.get("correct") is None and r.get("prediction") is not None: - r["correct"] = r["prediction"] == r.get("correct_letter") - - metrics = compute_metrics(results) - print_summary(metrics) - output = { - **metrics, - "dataset": DATASET_ID, - "config": CONFIG, - "split": SPLIT, - "model": MODEL, - "reasoning_effort": REASONING_EFFORT, - "temperature": TEMPERATURE, - "concurrency": CONCURRENCY, - } - metrics_path.parent.mkdir(parents=True, exist_ok=True) - with open(metrics_path, "w") as f: - json.dump(output, f, indent=2, ensure_ascii=False) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - global MODEL, REASONING_EFFORT - parser = argparse.ArgumentParser(description="GPQA Diamond benchmark for OpenAI GPT-5.x") - parser.add_argument("--model", default=DEFAULT_MODEL, - help="OpenAI model id (e.g. gpt-5.5, gpt-5.4-mini)") - parser.add_argument("--reasoning-effort", default=DEFAULT_REASONING_EFFORT, - help="reasoning_effort param ('none', 'minimal', 'low', 'medium', 'high', etc.)") - parser.add_argument("--predict-only", action="store_true") - parser.add_argument("--evaluate-only", action="store_true") - parser.add_argument("--limit", type=int, default=None, - help="Only run the first N unanswered samples") - args = parser.parse_args() - - MODEL = args.model - REASONING_EFFORT = args.reasoning_effort - - tag = f"{model_slug(MODEL)}_reasoning{REASONING_EFFORT}_gpqa_diamond" - pred_path = RESULTS_DIR / f"{tag}_responses.jsonl" - metrics_path = RESULTS_DIR / f"{tag}_metrics.json" - - if args.evaluate_only: - run_evaluation(pred_path, metrics_path) - elif args.predict_only: - asyncio.run(run_predictions(pred_path, limit=args.limit)) - else: - asyncio.run(run_predictions(pred_path, limit=args.limit)) - run_evaluation(pred_path, metrics_path) - - -if __name__ == "__main__": - main() diff --git a/benchmarks/gpqa/gpqa_openrouter.py b/benchmarks/gpqa/gpqa_openrouter.py deleted file mode 100644 index 3603c74..0000000 --- a/benchmarks/gpqa/gpqa_openrouter.py +++ /dev/null @@ -1,274 +0,0 @@ -""" -GPQA Diamond benchmark via OpenRouter (default: x-ai/grok-4.3 with thinking on). - -Mirrors benchmarks.gpqa.gpqa_openai exactly — same dataset, same prompt, same -deterministic 4-choice shuffle, same parser, same metrics — but routes calls -through OpenRouter's OpenAI-compatible endpoint so we can target any model -they host (xAI Grok, Anthropic, etc.). - -Thinking is toggled via OpenRouter's `reasoning` body field, passed through -the OpenAI SDK's extra_body. For Grok 4.x reasoning is on by default; we set -it explicitly so the run is reproducible regardless of provider defaults. - -Usage: - uv run -m benchmarks.gpqa.gpqa_openrouter - uv run -m benchmarks.gpqa.gpqa_openrouter --model x-ai/grok-4.3 --thinking on - uv run -m benchmarks.gpqa.gpqa_openrouter --limit 5 - uv run -m benchmarks.gpqa.gpqa_openrouter --evaluate-only - -Env: OPENROUTER_API_KEY must be set. -""" - -import os -import re -import sys -import json -import time -import asyncio -import argparse -import traceback -from pathlib import Path - -from datasets import load_dataset -from dotenv import load_dotenv -from openai import OpenAI -from tqdm import tqdm -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -sys.path.insert(0, str(PROJECT_ROOT)) - -# Reuse all the dataset/parsing/metrics plumbing from the OpenAI runner. -from benchmarks.gpqa.gpqa_openai import ( # noqa: E402 - DATASET_ID, CONFIG, SPLIT, PROMPT_TEMPLATE, - JsonlWriter, load_completed_ids, load_records, - parse_letter, build_sample, compute_metrics, -) - -load_dotenv() - -RESULTS_DIR = PROJECT_ROOT / "results" - -DEFAULT_MODEL = "x-ai/grok-4.3" -DEFAULT_THINKING = "on" # 'on' or 'off' -DEFAULT_EFFORT: str | None = None # None | 'low' | 'medium' | 'high' -TEMPERATURE = 0.0 -CONCURRENCY = 10 -MAX_RETRIES = 5 -RETRY_BACKOFF_CAP_S = 30.0 - -MODEL = DEFAULT_MODEL -THINKING = DEFAULT_THINKING -EFFORT: str | None = DEFAULT_EFFORT - -OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY") -if not OPENROUTER_API_KEY: - raise RuntimeError( - "OPENROUTER_API_KEY is not set. Add it to .env " - "(get one from https://openrouter.ai/keys)." - ) - -openrouter_client = OpenAI( - base_url="https://openrouter.ai/api/v1", - api_key=OPENROUTER_API_KEY, -) - - -def model_slug(model: str) -> str: - return re.sub(r"[^a-z0-9]", "", model.lower()) - - -def invoke_openrouter(messages: list[dict]): - reasoning: dict = {"enabled": THINKING == "on"} - if THINKING == "on" and EFFORT: - reasoning["effort"] = EFFORT - extra_body = {"reasoning": reasoning} - return openrouter_client.chat.completions.create( - model=MODEL, - messages=messages, - temperature=TEMPERATURE, - extra_body=extra_body, - ) - - -async def process_sample(sample: dict, semaphore: asyncio.Semaphore, - writer: JsonlWriter, progress: dict) -> dict | None: - prompt = PROMPT_TEMPLATE.format( - question=sample["question"], - a=sample["a"], b=sample["b"], c=sample["c"], d=sample["d"], - ) - messages = [{"role": "user", "content": prompt}] - last_error: str | None = None - - for attempt in range(1, MAX_RETRIES + 1): - start = time.perf_counter() - try: - async with semaphore: - start = time.perf_counter() - response = await asyncio.to_thread(invoke_openrouter, messages) - latency_ms = int((time.perf_counter() - start) * 1000) - content = (response.choices[0].message.content or "").strip() - request_id = getattr(response, "id", None) - if not content: - last_error = "empty response content" - raise RuntimeError(last_error) - - predicted = parse_letter(content) - correct = predicted == sample["correct_letter"] - - record = { - "id": sample["id"], - "domain": sample["domain"], - "subdomain": sample["subdomain"], - "correct_letter": sample["correct_letter"], - "prediction": predicted, - "correct": correct, - "response": content, - "request_id": request_id, - "latency_ms": latency_ms, - "attempts": attempt, - } - await writer.append(record) - - progress["done"] += 1 - if correct: - progress["correct"] += 1 - mark = "OK" if correct else "X " - tqdm.write( - f"[{progress['done']}/{progress['total']}] {mark} " - f"id={sample['id']} domain={sample['domain']:10} " - f"gold={sample['correct_letter']} pred={predicted or '?'} " - f"latency={latency_ms}ms attempt={attempt}" - ) - return record - - except Exception as e: - latency_ms = int((time.perf_counter() - start) * 1000) - last_error = f"{type(e).__name__}: {e}" - tqdm.write( - f"[error] id={sample['id']} attempt={attempt}/{MAX_RETRIES} " - f"latency={latency_ms}ms error={last_error}" - ) - if attempt < MAX_RETRIES: - await asyncio.sleep(min(2 ** (attempt - 1), RETRY_BACKOFF_CAP_S)) - - progress["failed"] += 1 - tqdm.write(f"[FAILED] id={sample['id']} after {MAX_RETRIES} attempts: {last_error}") - return None - - -def print_summary(metrics: dict): - print(f"\n{'=' * 60}") - print(f"GPQA Diamond — {DATASET_ID}/{CONFIG} ({MODEL}, thinking={THINKING})") - print(f"{'=' * 60}") - print(f"Accuracy : {metrics['accuracy']:.4f} ({metrics['correct']}/{metrics['total']})") - print(f"Unparseable: {metrics['unparseable']}") - print("\nPer high-level domain:") - for d in sorted(metrics["per_domain"]): - v = metrics["per_domain"][d] - print(f" {d:12} n={v['n']:>3} acc={v['accuracy']:.4f}") - if metrics.get("latency"): - lat = metrics["latency"] - print(f"\nLatency : mean={lat['mean_ms']:.0f}ms p50={lat['p50_ms']}ms " - f"p90={lat['p90_ms']}ms p99={lat['p99_ms']}ms max={lat['max_ms']}ms") - - -async def run_predictions(pred_path: Path, limit: int | None): - print(f"Loading {DATASET_ID}/{CONFIG} (split={SPLIT})...") - ds = load_dataset(DATASET_ID, CONFIG, split=SPLIT) - print(f"Loaded {len(ds)} rows") - samples = [build_sample(dict(row)) for row in ds] - - done_ids = load_completed_ids(pred_path) - pending = [s for s in samples if s["id"] not in done_ids] - if limit is not None: - pending = pending[:limit] - print(f"--limit applied: will run at most {limit} sample(s)") - print(f"Resume: {len(done_ids)} already completed, {len(pending)} remaining " - f"(checkpoint: {pred_path})") - if not pending: - return - - writer = JsonlWriter(pred_path) - semaphore = asyncio.Semaphore(CONCURRENCY) - progress = {"total": len(pending), "done": 0, "correct": 0, "failed": 0} - - tasks = [process_sample(s, semaphore, writer, progress) for s in pending] - try: - await tqdm_asyncio.gather(*tasks, desc=f"GPQA Diamond / {MODEL}") - except Exception: - traceback.print_exc() - acc = progress["correct"] / progress["done"] if progress["done"] else 0.0 - print(f"\nRun finished: {progress['done']}/{progress['total']} answered, " - f"{progress['correct']} correct (acc={acc:.4f}), {progress['failed']} failed.") - - -def run_evaluation(pred_path: Path, metrics_path: Path): - if not pred_path.exists(): - print(f"No predictions found at {pred_path}") - sys.exit(1) - results = load_records(pred_path) - if not results: - print(f"No records in {pred_path}") - sys.exit(1) - for r in results: - if r.get("prediction") is None and r.get("response"): - r["prediction"] = parse_letter(r["response"]) - if r.get("correct") is None and r.get("prediction") is not None: - r["correct"] = r["prediction"] == r.get("correct_letter") - - metrics = compute_metrics(results) - print_summary(metrics) - output = { - **metrics, - "dataset": DATASET_ID, - "config": CONFIG, - "split": SPLIT, - "model": MODEL, - "thinking": THINKING, - "effort": EFFORT, - "temperature": TEMPERATURE, - "concurrency": CONCURRENCY, - "provider": "openrouter", - } - metrics_path.parent.mkdir(parents=True, exist_ok=True) - with open(metrics_path, "w") as f: - json.dump(output, f, indent=2, ensure_ascii=False) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - global MODEL, THINKING, EFFORT - parser = argparse.ArgumentParser(description="GPQA Diamond benchmark via OpenRouter") - parser.add_argument("--model", default=DEFAULT_MODEL, - help="OpenRouter model id (e.g. x-ai/grok-4.3)") - parser.add_argument("--thinking", default=DEFAULT_THINKING, choices=["on", "off"], - help="Toggle reasoning via OpenRouter's `reasoning.enabled`") - parser.add_argument("--effort", default=DEFAULT_EFFORT, choices=["low","medium","high"], - help="OpenRouter `reasoning.effort` (low|medium|high). Only applied when thinking=on.") - parser.add_argument("--predict-only", action="store_true") - parser.add_argument("--evaluate-only", action="store_true") - parser.add_argument("--limit", type=int, default=None, - help="Only run the first N unanswered samples") - args = parser.parse_args() - - MODEL = args.model - THINKING = args.thinking - EFFORT = args.effort - - effort_suffix = f"_effort{EFFORT}" if (THINKING == "on" and EFFORT) else "" - tag = f"{model_slug(MODEL)}_thinking{THINKING}{effort_suffix}_gpqa_diamond" - pred_path = RESULTS_DIR / f"{tag}_responses.jsonl" - metrics_path = RESULTS_DIR / f"{tag}_metrics.json" - - if args.evaluate_only: - run_evaluation(pred_path, metrics_path) - elif args.predict_only: - asyncio.run(run_predictions(pred_path, limit=args.limit)) - else: - asyncio.run(run_predictions(pred_path, limit=args.limit)) - run_evaluation(pred_path, metrics_path) - - -if __name__ == "__main__": - main() diff --git a/benchmarks/mmmlu/bench.py b/benchmarks/mmmlu/bench.py new file mode 100644 index 0000000..134d8e0 --- /dev/null +++ b/benchmarks/mmmlu/bench.py @@ -0,0 +1,171 @@ +"""MMMLU: 14-language MCQ; per-language accuracy, macro-averaged headline. + +Two variants (as in the original): `lite` (opencompass/mmmlu_lite, ~20k, the +default and what the archived leaderboard numbers use) and `full` +(openai/MMMLU, ~196k). Pick with --variant. +""" + +from __future__ import annotations + +import re +from collections import defaultdict + +from src.request import Message, ReasoningSpec, Request, TextPart + +NAME = "mmmlu" +ID_KEY = "id" +PRIMARY_METRIC = "macro_accuracy" +VARIANTS = ["lite", "full"] +DEFAULTS = {"reasoning": "off", "rate_limit": 50, "max_in_flight": 8} + +_DATASET_LITE_ID = "opencompass/mmmlu_lite" +_DATASET_FULL_ID = "openai/MMMLU" +_SPLIT = "test" +LANGUAGES = [ + "AR_XY", + "BN_BD", + "DE_DE", + "ES_LA", + "FR_FR", + "HI_IN", + "ID_ID", + "IT_IT", + "JA_JP", + "KO_KR", + "PT_BR", + "SW_KE", + "YO_NG", + "ZH_CN", +] + +# English meta-instruction held constant across all languages so the parser can +# rely on Latin A-D output. +_PROMPT = ( + "The following is a multiple choice question. Respond with only a single " + "letter (A, B, C, or D) corresponding to the correct answer. Do not " + "explain your reasoning.\n\n" + "Question: {question}\n" + "A. {a}\nB. {b}\nC. {c}\nD. {d}\n\n" + "Answer:" +) + +_LETTER_RE = re.compile(r"\b([ABCD])\b") +_FIRST_LETTER_RE = re.compile(r"[ABCD]") + + +def parse_answer(text: str) -> str | None: + if not text: + return None + s = text.strip() + if len(s) == 1 and s.upper() in "ABCD": + return s.upper() + m = _LETTER_RE.search(s.upper()) + if m: + return m.group(1) + m = _FIRST_LETTER_RE.search(s.upper()) + return m.group(0) if m else None + + +def _build_sample(row: dict, lang: str, i: int, variant: str) -> dict: + # lite (opencompass) and full (openai/MMMLU) use different column names + + # id sources; the prompt/answer semantics are identical. + if variant == "lite": + return { + "id": f"{lang}:{i}", + "language": lang, + "subject": row["subject"], + "question": row["input"], + "a": row["A"], + "b": row["B"], + "c": row["C"], + "d": row["D"], + "answer": str(row["target"]).strip().upper(), + } + return { + "id": f"{lang}:{row.get('Unnamed: 0')}", + "language": lang, + "subject": row["Subject"], + "question": row["Question"], + "a": row["A"], + "b": row["B"], + "c": row["C"], + "d": row["D"], + "answer": str(row["Answer"]).strip().upper(), + } + + +def load_samples(sample_size: int | None = None, variant: str = "lite") -> list[dict]: + from datasets import load_dataset + + dataset_id = _DATASET_LITE_ID if variant == "lite" else _DATASET_FULL_ID + samples = [] + for lang in LANGUAGES: + ds = load_dataset(dataset_id, lang, split=_SPLIT) + for i, row in enumerate(ds): + samples.append(_build_sample(dict(row), lang, i, variant)) + return samples[:sample_size] if sample_size else samples + + +def build_request(sample: dict, mode: str) -> Request: + prompt = _PROMPT.format( + question=sample["question"], + a=sample["a"], + b=sample["b"], + c=sample["c"], + d=sample["d"], + ) + return Request( + messages=[Message("user", [TextPart(prompt)])], + reasoning=ReasoningSpec(mode), + temperature=0.0, + ) + + +def parse(response, sample) -> str | None: + return parse_answer(response.text or "") + + +def score(records: list[dict], samples: list[dict]) -> dict: + by_id = {s["id"]: s for s in samples} + # de-dupe records by id (last-wins), matching the old load_records + latest = {r["id"]: r for r in records if r["id"] in by_id} + + by_lang: dict[str, list[bool]] = defaultdict(list) + by_subject: dict[str, list[bool]] = defaultdict(list) + unparse: dict[str, int] = defaultdict(int) + for rid, r in latest.items(): + s = by_id[rid] + pred = r.get("prediction") + correct = pred == s["answer"] + by_lang[s["language"]].append(correct) + by_subject[s["subject"]].append(correct) + if pred is None: + unparse[s["language"]] += 1 + + per_language = { + lang: { + "n": len(v), + "accuracy": sum(v) / len(v) if v else 0.0, + "unparseable": unparse[lang], + } + for lang, v in by_lang.items() + } + per_subject = { + subj: {"n": len(v), "accuracy": sum(v) / len(v) if v else 0.0} + for subj, v in by_subject.items() + } + + lang_accs = [v["accuracy"] for v in per_language.values()] + macro = sum(lang_accs) / len(lang_accs) if lang_accs else 0.0 + n_total = sum(v["n"] for v in per_language.values()) + # micro reconstructed the same lossy way the original did (float->int per lang) + n_correct = sum(int(v["accuracy"] * v["n"]) for v in per_language.values()) + micro = n_correct / n_total if n_total else 0.0 + + return { + "macro_accuracy": macro, + "micro_accuracy": micro, + "num_samples": n_total, + "per_language": per_language, + "per_subject": per_subject, + } diff --git a/benchmarks/mmmlu/mmmlu.py b/benchmarks/mmmlu/mmmlu.py deleted file mode 100644 index af5e035..0000000 --- a/benchmarks/mmmlu/mmmlu.py +++ /dev/null @@ -1,472 +0,0 @@ -""" -MMMLU (Multilingual MMLU) benchmark for Interfaze. - -OpenAI's professional-translation version of the MMLU test set across 14 -languages — the same benchmark Gemini 3 Pro reports as "Multilingual Q&A" -(91.8% in their Nov 2025 model card). Each language subset is the full MMLU -test split (~14,042 four-choice questions across 57 subjects) translated by -human translators. - -Dataset: https://huggingface.co/datasets/openai/MMMLU - -Languages (14): - AR_XY Arabic - BN_BD Bengali - DE_DE German - ES_LA Spanish (Latin America) - FR_FR French - HI_IN Hindi - ID_ID Indonesian - IT_IT Italian - JA_JP Japanese - KO_KR Korean - PT_BR Brazilian Portuguese - SW_KE Swahili - YO_NG Yoruba - ZH_CN Simplified Chinese - -Methodology (matches Gemini 3 Pro model card): - pass@1 — single attempt, no majority voting, no parallel test-time compute. - Default sampling, single trial (large benchmark, no trial averaging). - Headline number = macro-average accuracy across the 14 languages. - -Metric: exact-match accuracy on the predicted answer letter (A/B/C/D). - Reported per-language, per-subject, and macro-averaged. - -Usage: - uv run -m benchmarks.mmmlu.mmmlu # full run, all 14 langs - uv run -m benchmarks.mmmlu.mmmlu --limit 50 # smoke test: 50 per lang - uv run -m benchmarks.mmmlu.mmmlu --languages DE_DE FR_FR # subset of langs - uv run -m benchmarks.mmmlu.mmmlu --evaluate-only # rescore existing preds -""" - -import sys -import os -import re -import json -import time -import asyncio -import argparse -import traceback -from pathlib import Path -from collections import defaultdict - -from datasets import load_dataset -from tqdm import tqdm -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -sys.path.insert(0, str(PROJECT_ROOT)) - -from src.commons import invoke_interfaze # noqa: E402 - -RESULTS_DIR = PROJECT_ROOT / "results" -DATASET_ID = "openai/MMMLU" -SPLIT = "test" - -LANGUAGES = [ - "AR_XY", "BN_BD", "DE_DE", "ES_LA", "FR_FR", "HI_IN", "ID_ID", - "IT_IT", "JA_JP", "KO_KR", "PT_BR", "SW_KE", "YO_NG", "ZH_CN", -] - -REASONING_EFFORT = None # off — Gemini 3 Pro reports MMMLU as a non-thinking eval -TEMPERATURE = 0.0 # deterministic; repo convention (Gemini uses default sampling) -RATE_LIMIT = 25 -MAX_RETRIES = 3 - -# English instruction is intentional and standard for MMMLU evaluation: the -# question + options are in the target language, but the meta-instruction -# ("answer with a single letter") is held constant so the parser can rely on -# Latin A-D output regardless of the language of the question. -PROMPT_TEMPLATE = ( - "The following is a multiple choice question. Respond with only a single " - "letter (A, B, C, or D) corresponding to the correct answer. Do not " - "explain your reasoning.\n\n" - "Question: {question}\n" - "A. {a}\n" - "B. {b}\n" - "C. {c}\n" - "D. {d}\n\n" - "Answer:" -) - - -class RateLimiter: - def __init__(self, rate: int): - self.rate = rate - self.tokens = rate - self.last_refill = 0.0 - self._lock = asyncio.Lock() - - async def acquire(self): - while True: - async with self._lock: - now = asyncio.get_running_loop().time() - elapsed = now - self.last_refill - self.tokens = min(self.rate, self.tokens + elapsed * self.rate) - self.last_refill = now - if self.tokens >= 1: - self.tokens -= 1 - return - await asyncio.sleep(1 / self.rate) - - -class JsonlWriter: - def __init__(self, path: Path): - self.path = path - self.path.parent.mkdir(parents=True, exist_ok=True) - self._lock = asyncio.Lock() - - async def append(self, record: dict): - line = json.dumps(record, ensure_ascii=False) - async with self._lock: - with open(self.path, "a", encoding="utf-8") as f: - f.write(line + "\n") - f.flush() - os.fsync(f.fileno()) - - -def load_completed_ids(path: Path) -> set[str]: - if not path.exists(): - return set() - done: set[str] = set() - with open(path, encoding="utf-8") as f: - for line_no, line in enumerate(f, 1): - line = line.strip() - if not line: - continue - try: - rec = json.loads(line) - except json.JSONDecodeError: - tqdm.write(f"[resume] skipping malformed line {line_no} in {path}") - continue - if rec.get("response") is not None: - done.add(str(rec["id"])) - return done - - -def load_records(path: Path) -> list[dict]: - records: list[dict] = [] - if not path.exists(): - return records - with open(path, encoding="utf-8") as f: - for line in f: - line = line.strip() - if not line: - continue - try: - records.append(json.loads(line)) - except json.JSONDecodeError: - continue - by_id: dict[str, dict] = {} - for r in records: - by_id[str(r["id"])] = r - return list(by_id.values()) - - -_LETTER_RE = re.compile(r"\b([ABCD])\b") -_FIRST_LETTER_RE = re.compile(r"[ABCD]") - - -def parse_answer(text: str) -> str | None: - """Extract a single A/B/C/D letter from the model's response. - - Try a word-boundary match first (handles "A", "A.", "(A)", "Answer: A"). - Fall back to the first standalone-looking letter; return None if nothing matches.""" - if not text: - return None - stripped = text.strip() - # Direct hit: response is just the letter. - if len(stripped) == 1 and stripped.upper() in "ABCD": - return stripped.upper() - m = _LETTER_RE.search(stripped.upper()) - if m: - return m.group(1) - m = _FIRST_LETTER_RE.search(stripped.upper()) - if m: - return m.group(0) - return None - - -def build_sample(row: dict, language: str) -> dict: - idx = row.get("Unnamed: 0") - return { - "id": f"{language}:{idx}", - "language": language, - "row_index": idx, - "subject": row["Subject"], - "question": row["Question"], - "a": row["A"], - "b": row["B"], - "c": row["C"], - "d": row["D"], - "answer": str(row["Answer"]).strip().upper(), - } - - -async def process_sample(sample: dict, rate_limiter: RateLimiter, - writer: JsonlWriter, progress: dict) -> dict | None: - last_error: str | None = None - - prompt = PROMPT_TEMPLATE.format( - question=sample["question"], - a=sample["a"], b=sample["b"], c=sample["c"], d=sample["d"], - ) - messages = [{"role": "user", "content": prompt}] - - for attempt in range(1, MAX_RETRIES + 1): - await rate_limiter.acquire() - start = time.perf_counter() - try: - response = await asyncio.to_thread( - invoke_interfaze, - messages, - reasoning_effort=REASONING_EFFORT, - temperature=TEMPERATURE, - ) - latency_ms = int((time.perf_counter() - start) * 1000) - content = (response.choices[0].message.content or "").strip() - request_id = getattr(response, "id", None) - if not content: - last_error = "empty response content" - raise RuntimeError(last_error) - - predicted = parse_answer(content) - correct = predicted == sample["answer"] - - record = { - "id": sample["id"], - "language": sample["language"], - "row_index": sample["row_index"], - "subject": sample["subject"], - "answer": sample["answer"], - "prediction": predicted, - "correct": correct, - "response": content, - "request_id": request_id, - "latency_ms": latency_ms, - "attempts": attempt, - } - await writer.append(record) - - progress["done"] += 1 - if correct: - progress["correct"] += 1 - if predicted is None: - progress["unparseable"] += 1 - tqdm.write( - f"[{progress['done']}/{progress['total']}] " - f"{sample['language']} subj={sample['subject'][:18]:18} " - f"gold={sample['answer']} pred={predicted or '?'} " - f"{'OK' if correct else 'X '} latency={latency_ms}ms" - ) - return record - - except Exception as e: - latency_ms = int((time.perf_counter() - start) * 1000) - last_error = f"{type(e).__name__}: {e}" - tqdm.write( - f"[error] id={sample['id']} attempt={attempt}/{MAX_RETRIES} " - f"latency={latency_ms}ms error={last_error}" - ) - if attempt < MAX_RETRIES: - await asyncio.sleep(2 ** (attempt - 1)) - - progress["failed"] += 1 - tqdm.write(f"[FAILED] id={sample['id']} after {MAX_RETRIES} attempts: {last_error}") - return None - - -def compute_metrics(results: list[dict]) -> dict: - if not results: - return {} - - by_lang: dict[str, list[dict]] = defaultdict(list) - by_subject: dict[str, list[dict]] = defaultdict(list) - for r in results: - by_lang[r["language"]].append(r) - by_subject[r["subject"]].append(r) - - per_language: dict[str, dict] = {} - for lang, rows in by_lang.items(): - n = len(rows) - n_correct = sum(1 for r in rows if r.get("correct")) - n_unparseable = sum(1 for r in rows if r.get("prediction") is None) - per_language[lang] = { - "n": n, - "accuracy": n_correct / n if n else 0.0, - "unparseable": n_unparseable, - } - - per_subject: dict[str, dict] = {} - for subj, rows in by_subject.items(): - n = len(rows) - n_correct = sum(1 for r in rows if r.get("correct")) - per_subject[subj] = { - "n": n, - "accuracy": n_correct / n if n else 0.0, - } - - # Macro-average across languages — this is the headline MMMLU number. - lang_accs = [v["accuracy"] for v in per_language.values()] - macro_accuracy = sum(lang_accs) / len(lang_accs) if lang_accs else 0.0 - - # Micro-accuracy = pooled across all samples (depends on per-lang counts). - n_total = sum(v["n"] for v in per_language.values()) - n_total_correct = sum(int(v["accuracy"] * v["n"]) for v in per_language.values()) - micro_accuracy = n_total_correct / n_total if n_total else 0.0 - - latencies = [r["latency_ms"] for r in results if isinstance(r.get("latency_ms"), int)] - latency_stats = {} - if latencies: - lats = sorted(latencies) - n = len(lats) - latency_stats = { - "count": n, - "mean_ms": sum(lats) / n, - "p50_ms": lats[n // 2], - "p90_ms": lats[min(n - 1, int(n * 0.9))], - "p99_ms": lats[min(n - 1, int(n * 0.99))], - "max_ms": lats[-1], - } - - return { - "macro_accuracy": macro_accuracy, - "micro_accuracy": micro_accuracy, - "num_samples": n_total, - "per_language": per_language, - "per_subject": per_subject, - "latency": latency_stats, - } - - -def print_summary(metrics: dict): - print(f"\n{'=' * 68}") - print(f"MMMLU Results (Interfaze, reasoning={REASONING_EFFORT}, temp={TEMPERATURE})") - print(f"{'=' * 68}") - print(f"Samples : {metrics['num_samples']}") - print(f"Macro-avg accuracy : {metrics['macro_accuracy']:.4f} ← headline (Gemini 3 Pro: 0.918)") - print(f"Micro-avg accuracy : {metrics['micro_accuracy']:.4f}") - print() - print("Per-language accuracy:") - print(f" {'lang':6} {'n':>6} {'acc':>8} {'unparseable':>12}") - for lang in sorted(metrics["per_language"].keys()): - v = metrics["per_language"][lang] - print(f" {lang:6} {v['n']:>6} {v['accuracy']:>8.4f} {v['unparseable']:>12}") - if metrics.get("latency"): - lat = metrics["latency"] - print(f"\nLatency : mean={lat['mean_ms']:.0f}ms p50={lat['p50_ms']}ms " - f"p90={lat['p90_ms']}ms p99={lat['p99_ms']}ms max={lat['max_ms']}ms") - - -def load_all_samples(languages: list[str], limit: int | None) -> list[dict]: - """Load samples from each language subset, optionally capping per language.""" - all_samples: list[dict] = [] - for lang in languages: - print(f"Loading {DATASET_ID}/{lang} (split={SPLIT})...") - ds = load_dataset(DATASET_ID, lang, split=SPLIT) - rows = [build_sample(dict(row), lang) for row in ds] - if limit is not None: - rows = rows[:limit] - print(f" -> {len(rows)} samples") - all_samples.extend(rows) - return all_samples - - -async def run_predictions(pred_path: Path, languages: list[str], limit: int | None): - samples = load_all_samples(languages, limit) - done_ids = load_completed_ids(pred_path) - pending = [s for s in samples if s["id"] not in done_ids] - print(f"Total samples: {len(samples)}") - print(f"Resume: {len(done_ids)} already completed, {len(pending)} remaining " - f"(checkpoint: {pred_path})") - if not pending: - return - - writer = JsonlWriter(pred_path) - rate_limiter = RateLimiter(RATE_LIMIT) - progress = { - "total": len(pending), - "done": 0, - "correct": 0, - "unparseable": 0, - "failed": 0, - } - - tasks = [process_sample(s, rate_limiter, writer, progress) for s in pending] - try: - await tqdm_asyncio.gather(*tasks, desc="MMMLU") - except Exception: - traceback.print_exc() - - acc = progress["correct"] / progress["done"] if progress["done"] else 0.0 - print( - f"\nRun finished: {progress['done']}/{progress['total']} answered " - f"({progress['failed']} failed, {progress['unparseable']} unparseable). " - f"Pooled accuracy on this run: {acc:.4f}" - ) - - -def run_evaluation(pred_path: Path, metrics_path: Path): - if not pred_path.exists(): - print(f"No predictions found at {pred_path}") - sys.exit(1) - results = load_records(pred_path) - if not results: - print(f"No records in {pred_path}") - sys.exit(1) - - # Re-derive `correct` and `prediction` if missing (e.g., re-scoring an old run). - for r in results: - if r.get("prediction") is None and r.get("response"): - r["prediction"] = parse_answer(r["response"]) - if r.get("correct") is None and r.get("prediction") is not None: - r["correct"] = r["prediction"] == r.get("answer") - - metrics = compute_metrics(results) - print_summary(metrics) - output = { - **metrics, - "dataset": DATASET_ID, - "split": SPLIT, - "languages": sorted({r["language"] for r in results}), - "reasoning_effort": REASONING_EFFORT, - "temperature": TEMPERATURE, - "rate_limit": RATE_LIMIT, - "model": "interfaze-beta", - } - metrics_path.parent.mkdir(parents=True, exist_ok=True) - with open(metrics_path, "w") as f: - json.dump(output, f, indent=2, ensure_ascii=False) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - parser = argparse.ArgumentParser(description="MMMLU benchmark for Interfaze") - parser.add_argument("--predict-only", action="store_true") - parser.add_argument("--evaluate-only", action="store_true") - parser.add_argument( - "--languages", nargs="+", default=LANGUAGES, - choices=LANGUAGES, - help="Subset of languages to run (default: all 14).", - ) - parser.add_argument( - "--limit", type=int, default=None, - help="Cap samples per language (smoke test).", - ) - args = parser.parse_args() - - pred_path = RESULTS_DIR / "mmmlu_responses.jsonl" - metrics_path = RESULTS_DIR / "mmmlu_metrics.json" - - if args.evaluate_only: - run_evaluation(pred_path, metrics_path) - elif args.predict_only: - asyncio.run(run_predictions(pred_path, args.languages, args.limit)) - else: - asyncio.run(run_predictions(pred_path, args.languages, args.limit)) - run_evaluation(pred_path, metrics_path) - - -if __name__ == "__main__": - main() diff --git a/benchmarks/mmmlu/mmmlu_multi.py b/benchmarks/mmmlu/mmmlu_multi.py deleted file mode 100644 index bc11c9e..0000000 --- a/benchmarks/mmmlu/mmmlu_multi.py +++ /dev/null @@ -1,586 +0,0 @@ -""" -MMMLU benchmark — multi-provider runner. - -Same dataset, prompt, parser, and scoring as benchmarks.mmmlu.mmmlu (the -interfaze run), but routed through other providers (OpenAI, Anthropic, Google) -for head-to-head comparison. - -Methodology mirrors Gemini 3 Pro's published MMMLU setup: - - pass@1, single trial, no majority voting - - reasoning OFF where the model supports it (lowest available level) - - temperature=0 where the API allows it (some providers reject t=0 + thinking) - - macro-average accuracy across the 14 translated languages = headline number - -Output (per provider+model, so parallel runs don't clash): - results/mmmlu___responses.jsonl - results/mmmlu___metrics.json - -Usage: - uv run -m benchmarks.mmmlu.mmmlu_multi --provider interfaze --model interfaze-beta - uv run -m benchmarks.mmmlu.mmmlu_multi --provider openai --model gpt-5.4-mini - uv run -m benchmarks.mmmlu.mmmlu_multi --provider gemini --model gemini-3.1-pro-preview - uv run -m benchmarks.mmmlu.mmmlu_multi --provider anthropic --model claude-sonnet-4-6 - # Smoke (1 sample per language = 14 total): - uv run -m benchmarks.mmmlu.mmmlu_multi --provider gemini --model gemini-3-flash-preview --limit 1 - -Env: OPENAI_API_KEY, GEMINI_KEY, ANTHROPIC_API_KEY, INTERFAZE_API_KEY (loaded from .env). -""" - -import os -import re -import sys -import json -import time -import asyncio -import argparse -import traceback -from pathlib import Path - -from datasets import load_dataset -from dotenv import load_dotenv -from tqdm import tqdm -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -sys.path.insert(0, str(PROJECT_ROOT)) - -# Reuse helpers from the interfaze base script — guarantees identical prompt / -# parser / scoring across providers. -from benchmarks.mmmlu.mmmlu import ( # noqa: E402 - DATASET_ID, - SPLIT, - LANGUAGES, - PROMPT_TEMPLATE, - JsonlWriter, - RateLimiter, - build_sample, - compute_metrics, - load_completed_ids, - load_records, - parse_answer, - print_summary as _base_print_summary, -) - -load_dotenv() - - -def _load_interfaze_env_fallback() -> None: - """Pull any keys from ~/interfaze/.env.local that aren't already in os.environ. - Lets us run providers whose keys live there (e.g. ANTHROPIC_API_KEY) without - requiring users to duplicate them into the project .env.""" - path = Path.home() / "interfaze" / ".env.local" - if not path.exists(): - return - for line in path.read_text().splitlines(): - line = line.strip() - if not line or line.startswith("#") or "=" not in line: - continue - k, v = line.split("=", 1) - k = k.strip() - v = v.strip().strip('"').strip("'") - os.environ.setdefault(k, v) - - -_load_interfaze_env_fallback() - -RESULTS_DIR = PROJECT_ROOT / "results" -RATE_LIMIT = 50 -MAX_RETRIES = 3 -DEFAULT_TEMPERATURE = 0.0 - -# Dataset variant — "full" (openai/MMMLU, ~196k) or "lite" (opencompass/mmmlu_lite, -# ~20k stratified). Lite uses a different schema (input/target vs Question/Answer). -DATASET_VARIANT = "lite" - -DATASET_FULL_ID = "openai/MMMLU" -DATASET_LITE_ID = "opencompass/mmmlu_lite" - -# Reasoning mode — "off" (each model at its floor) or "high" (each model at -# max). Mutated by CLI before any inference runs. -REASONING_MODE = "off" - -# Anthropic thinking budget (only used when REASONING_MODE == "high"). max_tokens -# must be > budget_tokens; we set 16k cap with 10k thinking budget. -ANTHROPIC_HIGH_BUDGET_TOKENS = 10_000 -ANTHROPIC_HIGH_MAX_TOKENS = 16_000 -ANTHROPIC_OFF_MAX_TOKENS = 512 - - -# --------------------------------------------------------------------------- -# Dataset loading + sample building -# --------------------------------------------------------------------------- - -def build_sample_lite(row: dict, language: str, row_index: int) -> dict: - """opencompass/mmmlu_lite uses input/target/A-D/subject (no Unnamed: 0).""" - return { - "id": f"{language}:{row_index}", - "language": language, - "row_index": row_index, - "subject": row["subject"], - "question": row["input"], - "a": row["A"], - "b": row["B"], - "c": row["C"], - "d": row["D"], - "answer": str(row["target"]).strip().upper(), - } - - -def load_dataset_for_variant(variant: str, lang: str): - if variant == "lite": - return load_dataset(DATASET_LITE_ID, lang, split="test") - return load_dataset(DATASET_FULL_ID, lang, split="test") - - -def build_sample_for_variant(variant: str, row: dict, language: str, row_index: int) -> dict: - if variant == "lite": - return build_sample_lite(row, language, row_index) - return build_sample(row, language) - - -# --------------------------------------------------------------------------- -# Provider adapters: each takes (prompt, model, client) and returns -# (content: str, request_id: str | None, usage: dict) -# `usage` keys: input_tokens, output_tokens, reasoning_tokens (or None if -# the provider doesn't report it). Lets us verify whether reasoning is -# actually OFF rather than just configured off. -# Run synchronously inside asyncio.to_thread. -# --------------------------------------------------------------------------- - -def _safe_int(x): - try: - return int(x) if x is not None else None - except (TypeError, ValueError): - return None - -def _openai_usage(resp) -> dict: - """Extract input/output/reasoning tokens from an OpenAI chat.completions response.""" - u = getattr(resp, "usage", None) - if u is None: - return {"input_tokens": None, "output_tokens": None, "reasoning_tokens": None} - details = getattr(u, "completion_tokens_details", None) - reasoning = getattr(details, "reasoning_tokens", None) if details else None - return { - "input_tokens": _safe_int(getattr(u, "prompt_tokens", None)), - "output_tokens": _safe_int(getattr(u, "completion_tokens", None)), - "reasoning_tokens": _safe_int(reasoning), - } - - -def _gemini_usage(resp) -> dict: - """Extract usage from a google-genai response. Gemini reports thinking - tokens as `thoughts_token_count` — 0 confirms reasoning fully off.""" - u = getattr(resp, "usage_metadata", None) - if u is None: - return {"input_tokens": None, "output_tokens": None, "reasoning_tokens": None} - return { - "input_tokens": _safe_int(getattr(u, "prompt_token_count", None)), - "output_tokens": _safe_int(getattr(u, "candidates_token_count", None)), - "reasoning_tokens": _safe_int(getattr(u, "thoughts_token_count", None)), - } - - -def _anthropic_usage(resp) -> dict: - """Anthropic — `output_tokens` includes thinking tokens when extended - thinking is on. With thinking disabled, no thinking content blocks - appear, so output_tokens is purely the visible answer.""" - u = getattr(resp, "usage", None) - if u is None: - return {"input_tokens": None, "output_tokens": None, "reasoning_tokens": None} - return { - "input_tokens": _safe_int(getattr(u, "input_tokens", None)), - "output_tokens": _safe_int(getattr(u, "output_tokens", None)), - "reasoning_tokens": None, # not separately reported - } - - -def call_interfaze(prompt: str, model: str, client) -> tuple[str, str | None, dict]: - """Interfaze uses 'off'|'high' for reasoning_effort (NOT 'none'; that's - OpenAI's vocabulary). Valid values per the API: minimal|low|medium|high|on|off|auto.""" - kwargs = { - "model": model, - "messages": [{"role": "user", "content": prompt}], - "temperature": DEFAULT_TEMPERATURE, - "reasoning_effort": "off" if REASONING_MODE == "off" else "high", - } - resp = client.chat.completions.create(**kwargs) - return ( - (resp.choices[0].message.content or "").strip(), - getattr(resp, "id", None), - _openai_usage(resp), - ) - - -def call_openai(prompt: str, model: str, client) -> tuple[str, str | None, dict]: - """GPT-5.x — reasoning off vs high. With reasoning engaged the API rejects - temperature!=default, so we omit temperature in 'high' mode.""" - kwargs = { - "model": model, - "messages": [{"role": "user", "content": prompt}], - } - if REASONING_MODE == "off": - kwargs["reasoning_effort"] = "none" - kwargs["temperature"] = DEFAULT_TEMPERATURE - else: - kwargs["reasoning_effort"] = "high" - resp = client.chat.completions.create(**kwargs) - return ( - (resp.choices[0].message.content or "").strip(), - getattr(resp, "id", None), - _openai_usage(resp), - ) - - -def call_anthropic(prompt: str, model: str, client) -> tuple[str, str | None, dict]: - """Claude — thinking explicitly disabled vs enabled with a high budget. - With thinking enabled, Anthropic requires temperature=1, so we omit it.""" - kwargs = { - "model": model, - "messages": [{"role": "user", "content": prompt}], - } - if REASONING_MODE == "off": - kwargs["thinking"] = {"type": "disabled"} - kwargs["temperature"] = DEFAULT_TEMPERATURE - kwargs["max_tokens"] = ANTHROPIC_OFF_MAX_TOKENS - else: - kwargs["thinking"] = {"type": "enabled", "budget_tokens": ANTHROPIC_HIGH_BUDGET_TOKENS} - kwargs["max_tokens"] = ANTHROPIC_HIGH_MAX_TOKENS - resp = client.messages.create(**kwargs) - parts = [b.text for b in resp.content if getattr(b, "type", None) == "text"] - return "\n".join(parts).strip(), resp.id, _anthropic_usage(resp) - - -def _openrouter_extra_body(model: str) -> dict: - """Per-model OpenRouter extras. Models differ on what `reasoning` shapes - they accept and which underlying provider should serve them. - - - x-ai/grok-4.3: rejects `enabled=false`, so `off` maps to the lowest - accepted tier (`effort=minimal`); `high` = default thinking on. - - moonshotai/kimi-k2.6: supports `enabled=false`; pin provider to Moonshot - so we benchmark Moonshot's own deployment, not a downstream reseller. - - default: assume the model accepts the unified `enabled` toggle. - """ - m = model.lower() - if m.startswith("x-ai/grok-4.3"): - if REASONING_MODE == "off": - return {"reasoning": {"effort": "minimal"}} - return {"reasoning": {"enabled": True}} - if m.startswith("moonshotai/"): - body = {"reasoning": {"enabled": REASONING_MODE != "off"}} - body["provider"] = {"only": ["moonshotai"]} - return body - return {"reasoning": {"enabled": REASONING_MODE != "off"}} - - -def call_openrouter(prompt: str, model: str, client) -> tuple[str, str | None, dict]: - """OpenRouter dispatch — see _openrouter_extra_body for per-model knobs. - temperature=0 is accepted across the models we currently route here.""" - resp = client.chat.completions.create( - model=model, - messages=[{"role": "user", "content": prompt}], - temperature=DEFAULT_TEMPERATURE, - extra_body=_openrouter_extra_body(model), - ) - return ( - (resp.choices[0].message.content or "").strip(), - getattr(resp, "id", None), - _openai_usage(resp), - ) - - -def call_gemini(prompt: str, model: str, client) -> tuple[str, str | None, dict]: - """Gemini — off goes to each model's floor; high goes to max thinking. - 3.x Pro: 'low' floor / 'high' max. - 3.x Flash: 'minimal' floor / 'high' max. - 2.5 Pro: budget=128 floor / budget=-1 (dynamic, model-decides) for high. - 2.5 Flash: budget=0 floor / budget=-1 for high. - Temperature 0 is fine with thinking on for Gemini.""" - from google.genai import types - m = model.lower() - if REASONING_MODE == "off": - if m.startswith("gemini-2.5-pro"): - thinking = types.ThinkingConfig(thinking_budget=128) - elif m.startswith("gemini-2.5-flash"): - thinking = types.ThinkingConfig(thinking_budget=0) - elif "pro" in m: - thinking = types.ThinkingConfig(thinking_level="low") - else: - thinking = types.ThinkingConfig(thinking_level="minimal") - else: - if m.startswith("gemini-2.5"): - thinking = types.ThinkingConfig(thinking_budget=-1) - else: - thinking = types.ThinkingConfig(thinking_level="high") - config = types.GenerateContentConfig( - temperature=DEFAULT_TEMPERATURE, - thinking_config=thinking, - ) - resp = client.models.generate_content( - model=model, - contents=[types.Part.from_text(text=prompt)], - config=config, - ) - return ( - (resp.text or "").strip(), - getattr(resp, "response_id", None), - _gemini_usage(resp), - ) - - -def build_client(provider: str): - if provider == "interfaze": - from openai import OpenAI - api_key = os.getenv("INTERFAZE_API_KEY") - if not api_key: - raise RuntimeError("INTERFAZE_API_KEY missing from .env") - # Force the production endpoint — the project .env's OPENAI_BASE_URL - # may point at a dev/staging Cloudflare Worker, which we explicitly do - # not want for benchmark numbers. - return OpenAI(base_url="https://api.interfaze.ai/v1", api_key=api_key) - if provider == "openai": - from openai import OpenAI - api_key = os.getenv("OPENAI_API_KEY") - if not api_key: - raise RuntimeError("OPENAI_API_KEY missing from .env") - # Force the real OpenAI endpoint — .env's OPENAI_BASE_URL points at interfaze. - return OpenAI(base_url="https://api.openai.com/v1", api_key=api_key) - if provider == "anthropic": - from anthropic import Anthropic - api_key = os.getenv("ANTHROPIC_API_KEY") - if not api_key: - raise RuntimeError("ANTHROPIC_API_KEY missing from .env") - return Anthropic(api_key=api_key) - if provider == "gemini": - from google import genai - api_key = ( - os.getenv("GEMINI_KEY") or os.getenv("GEMINI_API_KEY") or os.getenv("GOOGLE_API_KEY") - ) - if not api_key: - raise RuntimeError("GEMINI_KEY missing from .env") - return genai.Client(api_key=api_key) - if provider == "openrouter": - from openai import OpenAI - api_key = os.getenv("OPENROUTER_API_KEY") or os.getenv("OPENROUTER_KEY") - if not api_key: - raise RuntimeError("OPENROUTER_API_KEY missing from .env") - return OpenAI(base_url="https://openrouter.ai/api/v1", api_key=api_key) - raise ValueError(f"unknown provider: {provider}") - - -def get_call_fn(provider: str): - return { - "interfaze": call_interfaze, - "openai": call_openai, - "anthropic": call_anthropic, - "gemini": call_gemini, - "openrouter": call_openrouter, - }[provider] - - -def model_slug(model: str) -> str: - return re.sub(r"[^a-z0-9]+", "-", model.lower()).strip("-") - - -# --------------------------------------------------------------------------- -# Pipeline -# --------------------------------------------------------------------------- - -async def process_sample(sample: dict, call_fn, model: str, rate_limiter: RateLimiter, - writer: JsonlWriter, progress: dict, provider: str, - client) -> dict | None: - prompt = PROMPT_TEMPLATE.format( - question=sample["question"], - a=sample["a"], b=sample["b"], c=sample["c"], d=sample["d"], - ) - last_error: str | None = None - - for attempt in range(1, MAX_RETRIES + 1): - await rate_limiter.acquire() - start = time.perf_counter() - try: - content, request_id, usage = await asyncio.to_thread(call_fn, prompt, model, client) - latency_ms = int((time.perf_counter() - start) * 1000) - if not content: - last_error = "empty response content" - raise RuntimeError(last_error) - - predicted = parse_answer(content) - correct = predicted == sample["answer"] - - record = { - "id": sample["id"], - "language": sample["language"], - "row_index": sample["row_index"], - "subject": sample["subject"], - "answer": sample["answer"], - "prediction": predicted, - "correct": correct, - "response": content, - "request_id": request_id, - "latency_ms": latency_ms, - "attempts": attempt, - "provider": provider, - "model": model, - "input_tokens": usage.get("input_tokens"), - "output_tokens": usage.get("output_tokens"), - "reasoning_tokens": usage.get("reasoning_tokens"), - } - await writer.append(record) - - progress["done"] += 1 - if correct: - progress["correct"] += 1 - if predicted is None: - progress["unparseable"] += 1 - rt = usage.get("reasoning_tokens") - ot = usage.get("output_tokens") - tqdm.write( - f"[{provider}/{model} {progress['done']}/{progress['total']}] " - f"{sample['language']} subj={sample['subject'][:18]:18} " - f"gold={sample['answer']} pred={predicted or '?'} " - f"{'OK' if correct else 'X '} latency={latency_ms}ms " - f"reasoning_tok={rt if rt is not None else '?'} out_tok={ot if ot is not None else '?'}" - ) - return record - - except Exception as e: - latency_ms = int((time.perf_counter() - start) * 1000) - last_error = f"{type(e).__name__}: {e}" - tqdm.write( - f"[{provider}/{model} error] id={sample['id']} attempt={attempt}/{MAX_RETRIES} " - f"latency={latency_ms}ms error={last_error}" - ) - if attempt < MAX_RETRIES: - await asyncio.sleep(2 ** (attempt - 1)) - - progress["failed"] += 1 - tqdm.write(f"[{provider}/{model} FAILED] id={sample['id']} after {MAX_RETRIES} attempts: {last_error}") - return None - - -def load_all_samples(languages: list[str], limit: int | None) -> list[dict]: - """Loads from openai/MMMLU (full) or opencompass/mmmlu_lite based on - DATASET_VARIANT global.""" - ds_id = DATASET_LITE_ID if DATASET_VARIANT == "lite" else DATASET_FULL_ID - all_samples: list[dict] = [] - for lang in languages: - print(f"Loading {ds_id}/{lang} (split=test)...") - ds = load_dataset_for_variant(DATASET_VARIANT, lang) - rows = [ - build_sample_for_variant(DATASET_VARIANT, dict(row), lang, i) - for i, row in enumerate(ds) - ] - if limit is not None: - rows = rows[:limit] - all_samples.extend(rows) - return all_samples - - -async def run(provider: str, model: str, languages: list[str], pred_path: Path, - limit: int | None): - client = build_client(provider) - call_fn = get_call_fn(provider) - - samples = load_all_samples(languages, limit) - done_ids = load_completed_ids(pred_path) - pending = [s for s in samples if s["id"] not in done_ids] - print(f"[{provider}/{model}] Total samples: {len(samples)}") - print(f"[{provider}/{model}] Resume: {len(done_ids)} done, {len(pending)} pending " - f"(checkpoint: {pred_path})") - if not pending: - return - - writer = JsonlWriter(pred_path) - rate_limiter = RateLimiter(RATE_LIMIT) - progress = { - "total": len(pending), - "done": 0, - "correct": 0, - "unparseable": 0, - "failed": 0, - } - - tasks = [process_sample(s, call_fn, model, rate_limiter, writer, progress, provider, client) - for s in pending] - try: - await tqdm_asyncio.gather(*tasks, desc=f"{provider}/{model}") - except Exception: - traceback.print_exc() - - acc = progress["correct"] / progress["done"] if progress["done"] else 0.0 - print( - f"\n[{provider}/{model}] Run finished: " - f"{progress['done']}/{progress['total']} answered " - f"({progress['failed']} failed, {progress['unparseable']} unparseable). " - f"Pooled accuracy on this run: {acc:.4f}" - ) - - -def run_evaluation(pred_path: Path, metrics_path: Path, provider: str, model: str): - if not pred_path.exists(): - print(f"No predictions found at {pred_path}") - sys.exit(1) - results = load_records(pred_path) - if not results: - print(f"No records in {pred_path}") - sys.exit(1) - for r in results: - if r.get("prediction") is None and r.get("response"): - r["prediction"] = parse_answer(r["response"]) - if r.get("correct") is None and r.get("prediction") is not None: - r["correct"] = r["prediction"] == r.get("answer") - - metrics = compute_metrics(results) - _base_print_summary(metrics) - output = { - **metrics, - "dataset": DATASET_ID, - "split": SPLIT, - "languages": sorted({r["language"] for r in results}), - "provider": provider, - "model": model, - "rate_limit": RATE_LIMIT, - "temperature": DEFAULT_TEMPERATURE, - } - metrics_path.parent.mkdir(parents=True, exist_ok=True) - with open(metrics_path, "w") as f: - json.dump(output, f, indent=2, ensure_ascii=False) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - global REASONING_MODE, DATASET_VARIANT - parser = argparse.ArgumentParser(description="Multi-provider MMMLU runner") - parser.add_argument("--provider", required=True, - choices=["interfaze", "openai", "anthropic", "gemini", "openrouter"]) - parser.add_argument("--model", required=True, help="Provider-specific model id") - parser.add_argument("--reasoning", default="off", choices=["off", "high"], - help="off = each model at its floor; high = each at max thinking") - parser.add_argument("--dataset-variant", default="lite", choices=["lite", "full"], - help="lite = opencompass/mmmlu_lite (~20k); full = openai/MMMLU (~196k)") - parser.add_argument("--languages", nargs="+", default=LANGUAGES, choices=LANGUAGES, - help="Subset of languages (default: all 14)") - parser.add_argument("--limit", type=int, default=None, - help="Cap samples per language (smoke test)") - parser.add_argument("--predict-only", action="store_true") - parser.add_argument("--evaluate-only", action="store_true") - args = parser.parse_args() - - REASONING_MODE = args.reasoning - DATASET_VARIANT = args.dataset_variant - variant_slug = "lite" if DATASET_VARIANT == "lite" else "full" - tag = f"mmmlu{variant_slug}_{args.provider}_{model_slug(args.model)}_reasoning{REASONING_MODE}" - pred_path = RESULTS_DIR / f"{tag}_responses.jsonl" - metrics_path = RESULTS_DIR / f"{tag}_metrics.json" - - if args.evaluate_only: - run_evaluation(pred_path, metrics_path, args.provider, args.model) - elif args.predict_only: - asyncio.run(run(args.provider, args.model, args.languages, pred_path, limit=args.limit)) - else: - asyncio.run(run(args.provider, args.model, args.languages, pred_path, limit=args.limit)) - run_evaluation(pred_path, metrics_path, args.provider, args.model) - - -if __name__ == "__main__": - main() diff --git a/benchmarks/mmmu_pro/bench.py b/benchmarks/mmmu_pro/bench.py new file mode 100644 index 0000000..5a120b4 --- /dev/null +++ b/benchmarks/mmmu_pro/bench.py @@ -0,0 +1,176 @@ +"""MMMU-Pro: multimodal MCQ (A-J), standard (text+images) and vision settings.""" + +from __future__ import annotations + +import ast +import re +from collections import defaultdict +from typing import Any + +from src.media import encode_image +from src.request import Message, ReasoningSpec, Request, TextPart + +NAME = "mmmu_pro" +ID_KEY = "id" +PRIMARY_METRIC = "accuracy" +VARIANTS = ["standard", "vision"] +DEFAULTS = {"reasoning": "off", "rate_limit": 25, "max_in_flight": 8} + +_DATASET_REPO = "MMMU/MMMU_Pro" +_SPLIT = "test" +_CONFIGS = {"standard": "standard (10 options)", "vision": "vision"} +_MAX_IMAGE_SIDE = 1536 +_DATASET: Any = None # full-run split; images read lazily by idx to bound memory +_LETTERS = list("ABCDEFGHIJ") + +_PROMPT_STANDARD = ( + "Answer the following multiple-choice question. The question may reference " + 'images via tags like "", "". The corresponding images ' + "are attached in order.\n\n" + "Respond with ONLY a single letter (A through J) corresponding to the correct " + "option. Do not explain.\n\n" + "Question: {question}\n\n" + "Options:\n{options}\n\n" + "Answer:" +) +_PROMPT_VISION = ( + "The attached image renders a multiple-choice question with its options. " + "Respond with ONLY a single letter (A through J) corresponding to the correct " + "option. Do not explain.\n\n" + "Answer:" +) + +_LETTER_RE = re.compile(r"\b([A-J])\b") +_FALLBACK_RE = re.compile(r"[A-J]") + + +def parse_answer(text: str) -> str | None: + if not text: + return None + s = text.strip() + if len(s) == 1 and s.upper() in _LETTERS: + return s.upper() + m = _LETTER_RE.search(s.upper()) + if m: + return m.group(1) + m = _FALLBACK_RE.search(s.upper()) + return m.group(0) if m else None + + +def _options(raw) -> list[str]: + return list(raw) if isinstance(raw, list) else list(ast.literal_eval(raw)) + + +def _options_block(options: list[str]) -> str: + return "\n".join(f"{ltr}. {opt}" for ltr, opt in zip(_LETTERS, options)) + + +def _img_cols(variant: str) -> list[str]: + return ["image"] if variant == "vision" else [f"image_{i}" for i in range(1, 8)] + + +def _row_images(row, setting: str) -> list: + if setting == "vision": + return [row["image"]] + imgs = [row.get(f"image_{i}") for i in range(1, 8)] + return [im for im in imgs if im is not None] + + +def _mk_sample(row, variant: str, idx: int | None = None) -> dict: + setting = "vision" if variant == "vision" else "standard" + s = { + "id": row["id"], + "setting": setting, + "options": _options(row["options"]), + "answer": str(row["answer"]).strip().upper(), + "subject": row.get("subject"), + "topic_difficulty": None + if setting == "vision" + else row.get("topic_difficulty"), + } + if setting == "standard": + s["question"] = row["question"] + if idx is None: + s["images"] = _row_images(row, setting) # smoke embeds streamed images + else: + s["idx"] = idx # full run reads images lazily from _DATASET + return s + + +def load_samples( + sample_size: int | None = None, variant: str = "standard" +) -> list[dict]: + global _DATASET + + if sample_size: + from src.datautil import load_rows + + rows = load_rows(_DATASET_REPO, _SPLIT, sample_size, config=_CONFIGS[variant]) + return [_mk_sample(dict(r), variant) for r in rows] + + # full run: keep the split memory-mapped and read images lazily by idx. + # Build samples from an image-free view so the load pass doesn't decode + # every image (materializing all ~1730 rows of images OOMs CI). + from datasets import load_dataset + + _DATASET = load_dataset(_DATASET_REPO, _CONFIGS[variant], split=_SPLIT) + present = [c for c in _img_cols(variant) if c in _DATASET.column_names] + meta = _DATASET.remove_columns(present) + return [_mk_sample(meta[i], variant, idx=i) for i in range(len(meta))] + + +def build_request(sample: dict, mode: str) -> Request: + if "images" in sample: + pil_images = sample["images"] # streamed smoke: embedded + elif "idx" in sample: + pil_images = _row_images(_DATASET[sample["idx"]], sample["setting"]) # lazy + else: + raise KeyError("MMMU-Pro sample missing both 'images' and 'idx'") + if sample["setting"] == "vision": + prompt = _PROMPT_VISION + else: + prompt = _PROMPT_STANDARD.format( + question=sample["question"], options=_options_block(sample["options"]) + ) + parts: list = [TextPart(prompt)] + parts += [ + encode_image(im, "image/jpeg", max_side=_MAX_IMAGE_SIDE) for im in pil_images + ] + return Request([Message("user", parts)], ReasoningSpec(mode), temperature=0.0) + + +def parse(response, sample) -> str | None: + return parse_answer(response.text or "") + + +def score(records: list[dict], samples: list[dict]) -> dict: + by_id = {s["id"]: s for s in samples} + latest = {r["id"]: r for r in records if r["id"] in by_id} + + total = correct = unparse = 0 + by_subject: dict[str, list[bool]] = defaultdict(list) + by_difficulty: dict[str, list[bool]] = defaultdict(list) + for rid, r in latest.items(): + s = by_id[rid] + pred = r.get("prediction") + ok = pred == s["answer"] + total += 1 + correct += int(ok) + unparse += int(pred is None) + if s.get("subject") is not None: + by_subject[s["subject"]].append(ok) + if s.get("topic_difficulty") is not None: + by_difficulty[s["topic_difficulty"]].append(ok) + + return { + "accuracy": correct / total if total else 0.0, + "num_samples": total, + "unparseable": unparse, + "per_subject": { + k: {"n": len(v), "accuracy": sum(v) / len(v)} for k, v in by_subject.items() + }, + "per_difficulty": { + k: {"n": len(v), "accuracy": sum(v) / len(v)} + for k, v in by_difficulty.items() + }, + } diff --git a/benchmarks/mmmu_pro/mmmu_pro_multi.py b/benchmarks/mmmu_pro/mmmu_pro_multi.py deleted file mode 100644 index 907e152..0000000 --- a/benchmarks/mmmu_pro/mmmu_pro_multi.py +++ /dev/null @@ -1,775 +0,0 @@ -""" -MMMU Pro benchmark — multi-provider runner. - -Two settings, both 10-option MCQ (A-J): - - standard: text question + up to 7 inline images (image_1..image_7) - - vision: single rendered image of the entire question (image) - -Methodology mirrors the MMMU Pro paper: - - pass@1, single attempt, no majority voting - - reasoning OFF (provider floor) by default; --reasoning high also supported - - temperature=0 where the API allows it (some providers reject t=0 + thinking) - - headline numbers are per-setting accuracy; the published "MMMU Pro" score - is the average of (standard, vision) - -Output (per provider+model+setting+reasoning): - results/mmmupro____reasoning_responses.jsonl - results/mmmupro____reasoning_metrics.json - -Usage: - uv run -m benchmarks.mmmu_pro.mmmu_pro_multi --provider gemini \\ - --model gemini-3.1-pro-preview --setting standard - uv run -m benchmarks.mmmu_pro.mmmu_pro_multi --provider openai \\ - --model gpt-5.5 --setting vision --limit 5 - -Env: OPENAI_API_KEY, GEMINI_KEY, ANTHROPIC_API_KEY, INTERFAZE_API_KEY (.env). -""" - -import os -import re -import io -import sys -import ast -import json -import time -import base64 -import asyncio -import argparse -import traceback -from pathlib import Path -from collections import defaultdict - -from datasets import load_dataset -from dotenv import load_dotenv -from PIL import Image -from tqdm import tqdm -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -sys.path.insert(0, str(PROJECT_ROOT)) - -load_dotenv() - - -def _load_interfaze_env_fallback() -> None: - path = Path.home() / "interfaze" / ".env.local" - if not path.exists(): - return - for line in path.read_text().splitlines(): - line = line.strip() - if not line or line.startswith("#") or "=" not in line: - continue - k, v = line.split("=", 1) - os.environ.setdefault(k.strip(), v.strip().strip('"').strip("'")) - - -_load_interfaze_env_fallback() - - -RESULTS_DIR = PROJECT_ROOT / "results" -DATASET_REPO = "MMMU/MMMU_Pro" -SETTINGS = { - "standard": "standard (10 options)", - "vision": "vision", -} -SPLIT = "test" - -REASONING_MODE = "off" -DEFAULT_TEMPERATURE = 0.0 -RATE_LIMIT = 25 -MAX_RETRIES = 3 - -ANTHROPIC_OFF_MAX_TOKENS = 1024 -ANTHROPIC_HIGH_BUDGET_TOKENS = 10_000 -ANTHROPIC_HIGH_MAX_TOKENS = 16_000 - -# Cap any individual image side to keep token cost in check; questions occasionally -# include large diagrams. 1536 keeps detail without paying for >2k-side renders. -MAX_IMAGE_SIDE = 1536 - -LETTERS_10 = list("ABCDEFGHIJ") - - -# --------------------------------------------------------------------------- -# IO helpers -# --------------------------------------------------------------------------- - -class JsonlWriter: - def __init__(self, path: Path): - self.path = path - self.path.parent.mkdir(parents=True, exist_ok=True) - self._lock = asyncio.Lock() - - async def append(self, record: dict): - line = json.dumps(record, ensure_ascii=False) - async with self._lock: - with open(self.path, "a", encoding="utf-8") as f: - f.write(line + "\n") - f.flush() - os.fsync(f.fileno()) - - -class RateLimiter: - def __init__(self, rate: int): - self.rate = rate - self.tokens = rate - self.last_refill = 0.0 - self._lock = asyncio.Lock() - - async def acquire(self): - while True: - async with self._lock: - now = asyncio.get_running_loop().time() - elapsed = now - self.last_refill - self.tokens = min(self.rate, self.tokens + elapsed * self.rate) - self.last_refill = now - if self.tokens >= 1: - self.tokens -= 1 - return - await asyncio.sleep(1 / self.rate) - - -def load_completed_ids(path: Path) -> set[str]: - if not path.exists(): - return set() - done: set[str] = set() - with open(path, encoding="utf-8") as f: - for line in f: - line = line.strip() - if not line: - continue - try: - rec = json.loads(line) - if rec.get("response") is not None: - done.add(str(rec["id"])) - except json.JSONDecodeError: - continue - return done - - -def load_records(path: Path) -> list[dict]: - if not path.exists(): - return [] - by_id: dict[str, dict] = {} - with open(path, encoding="utf-8") as f: - for line in f: - line = line.strip() - if not line: - continue - try: - rec = json.loads(line) - by_id[str(rec["id"])] = rec - except json.JSONDecodeError: - continue - return list(by_id.values()) - - -# --------------------------------------------------------------------------- -# Dataset / sample building -# --------------------------------------------------------------------------- - -def parse_options(raw: str) -> list[str]: - """`options` is a stringified Python list. Use literal_eval (safe).""" - if isinstance(raw, list): - return list(raw) - return list(ast.literal_eval(raw)) - - -def build_standard_sample(row: dict) -> dict: - images: list[Image.Image] = [] - for i in range(1, 8): - img = row.get(f"image_{i}") - if img is not None: - images.append(img) - return { - "id": row["id"], - "setting": "standard", - "question": row["question"], - "options": parse_options(row["options"]), - "images": images, - "answer": str(row["answer"]).strip().upper(), - "subject": row.get("subject"), - "topic_difficulty": row.get("topic_difficulty"), - } - - -def build_vision_sample(row: dict) -> dict: - return { - "id": row["id"], - "setting": "vision", - "image": row["image"], - "options": parse_options(row["options"]), - "answer": str(row["answer"]).strip().upper(), - "subject": row.get("subject"), - } - - -def load_samples(setting: str, limit: int | None) -> list[dict]: - cfg = SETTINGS[setting] - print(f"Loading {DATASET_REPO} config={cfg!r} split={SPLIT}...") - ds = load_dataset(DATASET_REPO, cfg, split=SPLIT) - rows = list(ds) - if limit is not None: - rows = rows[:limit] - if setting == "standard": - return [build_standard_sample(dict(r)) for r in rows] - return [build_vision_sample(dict(r)) for r in rows] - - -# --------------------------------------------------------------------------- -# Image preprocessing -# --------------------------------------------------------------------------- - -def image_to_jpeg_bytes(image: Image.Image) -> bytes: - if image.mode != "RGB": - image = image.convert("RGB") - w, h = image.size - scale = min(1.0, MAX_IMAGE_SIDE / max(w, h)) - if scale < 1.0: - image = image.resize((int(round(w * scale)), int(round(h * scale)))) - buf = io.BytesIO() - image.save(buf, format="JPEG", quality=92) - return buf.getvalue() - - -# --------------------------------------------------------------------------- -# Prompt construction -# --------------------------------------------------------------------------- - -def build_options_block(options: list[str]) -> str: - lines = [] - for letter, opt in zip(LETTERS_10, options): - lines.append(f"{letter}. {opt}") - return "\n".join(lines) - - -PROMPT_STANDARD = ( - "Answer the following multiple-choice question. The question may reference " - "images via tags like \"\", \"\". The corresponding images " - "are attached in order.\n\n" - "Respond with ONLY a single letter (A through J) corresponding to the correct " - "option. Do not explain.\n\n" - "Question: {question}\n\n" - "Options:\n{options}\n\n" - "Answer:" -) - -PROMPT_VISION = ( - "The attached image renders a multiple-choice question with its options. " - "Respond with ONLY a single letter (A through J) corresponding to the correct " - "option. Do not explain.\n\n" - "Answer:" -) - - -# --------------------------------------------------------------------------- -# Output parsing -# --------------------------------------------------------------------------- - -_LETTER_RE = re.compile(r"\b([A-J])\b") -_FALLBACK_RE = re.compile(r"[A-J]") - - -def parse_answer(text: str) -> str | None: - if not text: - return None - s = text.strip() - if len(s) == 1 and s.upper() in LETTERS_10: - return s.upper() - m = _LETTER_RE.search(s.upper()) - if m: - return m.group(1) - m = _FALLBACK_RE.search(s.upper()) - if m: - return m.group(0) - return None - - -# --------------------------------------------------------------------------- -# Provider adapters -# --------------------------------------------------------------------------- - -def _safe_int(x): - try: - return int(x) if x is not None else None - except (TypeError, ValueError): - return None - - -def _openai_usage(resp) -> dict: - u = getattr(resp, "usage", None) - if u is None: - return {"input_tokens": None, "output_tokens": None, "reasoning_tokens": None} - details = getattr(u, "completion_tokens_details", None) - rt = getattr(details, "reasoning_tokens", None) if details else None - return { - "input_tokens": _safe_int(getattr(u, "prompt_tokens", None)), - "output_tokens": _safe_int(getattr(u, "completion_tokens", None)), - "reasoning_tokens": _safe_int(rt), - } - - -def _gemini_usage(resp) -> dict: - u = getattr(resp, "usage_metadata", None) - if u is None: - return {"input_tokens": None, "output_tokens": None, "reasoning_tokens": None} - return { - "input_tokens": _safe_int(getattr(u, "prompt_token_count", None)), - "output_tokens": _safe_int(getattr(u, "candidates_token_count", None)), - "reasoning_tokens": _safe_int(getattr(u, "thoughts_token_count", None)), - } - - -def _anthropic_usage(resp) -> dict: - u = getattr(resp, "usage", None) - if u is None: - return {"input_tokens": None, "output_tokens": None, "reasoning_tokens": None} - return { - "input_tokens": _safe_int(getattr(u, "input_tokens", None)), - "output_tokens": _safe_int(getattr(u, "output_tokens", None)), - "reasoning_tokens": None, - } - - -def _images_for_sample(sample: dict) -> list[Image.Image]: - if sample["setting"] == "vision": - return [sample["image"]] - return sample["images"] - - -def _build_prompt(sample: dict) -> str: - if sample["setting"] == "vision": - return PROMPT_VISION - return PROMPT_STANDARD.format( - question=sample["question"], - options=build_options_block(sample["options"]), - ) - - -def _openai_style_content(prompt: str, images: list[Image.Image]) -> list[dict]: - """OpenAI/Interfaze chat content blocks: text + image_url(data URL).""" - content: list[dict] = [{"type": "text", "text": prompt}] - for img in images: - b64 = base64.b64encode(image_to_jpeg_bytes(img)).decode("utf-8") - content.append({ - "type": "image_url", - "image_url": {"url": f"data:image/jpeg;base64,{b64}"}, - }) - return content - - -def call_interfaze(sample: dict, model: str, client) -> tuple[str, str | None, dict]: - content = _openai_style_content(_build_prompt(sample), _images_for_sample(sample)) - kwargs = { - "model": model, - "messages": [{"role": "user", "content": content}], - "temperature": DEFAULT_TEMPERATURE, - "reasoning_effort": "off" if REASONING_MODE == "off" else "high", - } - resp = client.chat.completions.create(**kwargs) - return ( - (resp.choices[0].message.content or "").strip(), - getattr(resp, "id", None), - _openai_usage(resp), - ) - - -def call_openai(sample: dict, model: str, client) -> tuple[str, str | None, dict]: - content = _openai_style_content(_build_prompt(sample), _images_for_sample(sample)) - kwargs = { - "model": model, - "messages": [{"role": "user", "content": content}], - } - if REASONING_MODE == "off": - kwargs["reasoning_effort"] = "none" - kwargs["temperature"] = DEFAULT_TEMPERATURE - else: - kwargs["reasoning_effort"] = "high" - resp = client.chat.completions.create(**kwargs) - return ( - (resp.choices[0].message.content or "").strip(), - getattr(resp, "id", None), - _openai_usage(resp), - ) - - -def call_anthropic(sample: dict, model: str, client) -> tuple[str, str | None, dict]: - prompt = _build_prompt(sample) - parts: list[dict] = [] - for img in _images_for_sample(sample): - b64 = base64.b64encode(image_to_jpeg_bytes(img)).decode("utf-8") - parts.append({ - "type": "image", - "source": {"type": "base64", "media_type": "image/jpeg", "data": b64}, - }) - parts.append({"type": "text", "text": prompt}) - kwargs = { - "model": model, - "messages": [{"role": "user", "content": parts}], - } - if REASONING_MODE == "off": - kwargs["thinking"] = {"type": "disabled"} - kwargs["temperature"] = DEFAULT_TEMPERATURE - kwargs["max_tokens"] = ANTHROPIC_OFF_MAX_TOKENS - else: - kwargs["thinking"] = {"type": "enabled", "budget_tokens": ANTHROPIC_HIGH_BUDGET_TOKENS} - kwargs["max_tokens"] = ANTHROPIC_HIGH_MAX_TOKENS - resp = client.messages.create(**kwargs) - text_parts = [b.text for b in resp.content if getattr(b, "type", None) == "text"] - return "\n".join(text_parts).strip(), resp.id, _anthropic_usage(resp) - - -def _openrouter_extra_body(model: str) -> dict: - """Per-model OpenRouter extras. See benchmarks.mmmlu.mmmlu_multi for details. - Vision-capable models we currently route: x-ai/grok-4.3, moonshotai/kimi-k2.6.""" - m = model.lower() - if m.startswith("x-ai/grok-4.3"): - if REASONING_MODE == "off": - return {"reasoning": {"effort": "minimal"}} - return {"reasoning": {"enabled": True}} - if m.startswith("moonshotai/"): - body = {"reasoning": {"enabled": REASONING_MODE != "off"}} - body["provider"] = {"only": ["moonshotai"]} - return body - return {"reasoning": {"enabled": REASONING_MODE != "off"}} - - -def call_openrouter(sample: dict, model: str, client) -> tuple[str, str | None, dict]: - """OpenRouter dispatch (vision-aware). Vision: pass images as data: URLs - (OpenAI-shaped). See _openrouter_extra_body for per-model knobs.""" - content = _openai_style_content(_build_prompt(sample), _images_for_sample(sample)) - resp = client.chat.completions.create( - model=model, - messages=[{"role": "user", "content": content}], - temperature=DEFAULT_TEMPERATURE, - extra_body=_openrouter_extra_body(model), - ) - return ( - (resp.choices[0].message.content or "").strip(), - getattr(resp, "id", None), - _openai_usage(resp), - ) - - -def call_gemini(sample: dict, model: str, client) -> tuple[str, str | None, dict]: - from google.genai import types - prompt = _build_prompt(sample) - m = model.lower() - if REASONING_MODE == "off": - if m.startswith("gemini-2.5-pro"): - thinking = types.ThinkingConfig(thinking_budget=128) - elif m.startswith("gemini-2.5-flash"): - thinking = types.ThinkingConfig(thinking_budget=0) - elif "pro" in m: - thinking = types.ThinkingConfig(thinking_level="low") - else: - thinking = types.ThinkingConfig(thinking_level="minimal") - else: - if m.startswith("gemini-2.5"): - thinking = types.ThinkingConfig(thinking_budget=-1) - else: - thinking = types.ThinkingConfig(thinking_level="high") - config = types.GenerateContentConfig( - temperature=DEFAULT_TEMPERATURE, - thinking_config=thinking, - ) - contents: list = [] - for img in _images_for_sample(sample): - contents.append(types.Part.from_bytes( - data=image_to_jpeg_bytes(img), mime_type="image/jpeg" - )) - contents.append(prompt) - resp = client.models.generate_content(model=model, contents=contents, config=config) - return ( - (resp.text or "").strip(), - getattr(resp, "response_id", None), - _gemini_usage(resp), - ) - - -def build_client(provider: str): - if provider == "interfaze": - from openai import OpenAI - api_key = os.getenv("INTERFAZE_API_KEY") - if not api_key: - raise RuntimeError("INTERFAZE_API_KEY missing") - return OpenAI(base_url="https://api.interfaze.ai/v1", api_key=api_key) - if provider == "openai": - from openai import OpenAI - api_key = os.getenv("OPENAI_API_KEY") - if not api_key: - raise RuntimeError("OPENAI_API_KEY missing") - return OpenAI(base_url="https://api.openai.com/v1", api_key=api_key) - if provider == "anthropic": - from anthropic import Anthropic - api_key = os.getenv("ANTHROPIC_API_KEY") - if not api_key: - raise RuntimeError("ANTHROPIC_API_KEY missing") - return Anthropic(api_key=api_key) - if provider == "gemini": - from google import genai - api_key = os.getenv("GEMINI_KEY") or os.getenv("GEMINI_API_KEY") or os.getenv("GOOGLE_API_KEY") - if not api_key: - raise RuntimeError("GEMINI_KEY missing") - return genai.Client(api_key=api_key) - if provider == "openrouter": - from openai import OpenAI - api_key = os.getenv("OPENROUTER_API_KEY") or os.getenv("OPENROUTER_KEY") - if not api_key: - raise RuntimeError("OPENROUTER_API_KEY missing") - return OpenAI(base_url="https://openrouter.ai/api/v1", api_key=api_key) - raise ValueError(f"unknown provider: {provider}") - - -def get_call_fn(provider: str): - return { - "interfaze": call_interfaze, - "openai": call_openai, - "anthropic": call_anthropic, - "gemini": call_gemini, - "openrouter": call_openrouter, - }[provider] - - -def model_slug(model: str) -> str: - return re.sub(r"[^a-z0-9]+", "-", model.lower()).strip("-") - - -# --------------------------------------------------------------------------- -# Pipeline -# --------------------------------------------------------------------------- - -async def process_sample(sample: dict, call_fn, model: str, rate_limiter: RateLimiter, - writer: JsonlWriter, progress: dict, provider: str, - client) -> dict | None: - last_error: str | None = None - - for attempt in range(1, MAX_RETRIES + 1): - await rate_limiter.acquire() - start = time.perf_counter() - try: - content, request_id, usage = await asyncio.to_thread(call_fn, sample, model, client) - latency_ms = int((time.perf_counter() - start) * 1000) - if not content: - last_error = "empty response content" - raise RuntimeError(last_error) - - predicted = parse_answer(content) - correct = predicted == sample["answer"] - - record = { - "id": sample["id"], - "setting": sample["setting"], - "subject": sample.get("subject"), - "topic_difficulty": sample.get("topic_difficulty"), - "answer": sample["answer"], - "prediction": predicted, - "correct": correct, - "response": content, - "request_id": request_id, - "latency_ms": latency_ms, - "attempts": attempt, - "provider": provider, - "model": model, - "input_tokens": usage.get("input_tokens"), - "output_tokens": usage.get("output_tokens"), - "reasoning_tokens": usage.get("reasoning_tokens"), - } - await writer.append(record) - - progress["done"] += 1 - if correct: - progress["correct"] += 1 - if predicted is None: - progress["unparseable"] += 1 - rt = usage.get("reasoning_tokens") - ot = usage.get("output_tokens") - tqdm.write( - f"[{provider}/{model} {sample['setting']} {progress['done']}/{progress['total']}] " - f"id={sample['id']} subj={(sample.get('subject') or '')[:18]:18} " - f"gold={sample['answer']} pred={predicted or '?'} " - f"{'OK' if correct else 'X '} latency={latency_ms}ms " - f"reasoning_tok={rt if rt is not None else '?'} out_tok={ot if ot is not None else '?'}" - ) - return record - - except Exception as e: - latency_ms = int((time.perf_counter() - start) * 1000) - last_error = f"{type(e).__name__}: {e}" - tqdm.write( - f"[{provider}/{model} error] id={sample['id']} attempt={attempt}/{MAX_RETRIES} " - f"latency={latency_ms}ms error={last_error}" - ) - if attempt < MAX_RETRIES: - await asyncio.sleep(2 ** (attempt - 1)) - - progress["failed"] += 1 - tqdm.write(f"[{provider}/{model} FAILED] id={sample['id']} after {MAX_RETRIES} attempts: {last_error}") - return None - - -async def run(provider: str, model: str, setting: str, pred_path: Path, limit: int | None): - client = build_client(provider) - call_fn = get_call_fn(provider) - - samples = load_samples(setting, limit) - done_ids = load_completed_ids(pred_path) - pending = [s for s in samples if s["id"] not in done_ids] - print(f"[{provider}/{model} {setting}] Total: {len(samples)}, " - f"resume: {len(done_ids)} done / {len(pending)} pending " - f"(checkpoint: {pred_path})") - if not pending: - return - - writer = JsonlWriter(pred_path) - rate_limiter = RateLimiter(RATE_LIMIT) - progress = {"total": len(pending), "done": 0, "correct": 0, "unparseable": 0, "failed": 0} - - tasks = [process_sample(s, call_fn, model, rate_limiter, writer, progress, provider, client) - for s in pending] - try: - await tqdm_asyncio.gather(*tasks, desc=f"{provider}/{model}/{setting}") - except Exception: - traceback.print_exc() - - acc = progress["correct"] / progress["done"] if progress["done"] else 0.0 - print( - f"\n[{provider}/{model} {setting}] Run finished: " - f"{progress['done']}/{progress['total']} answered " - f"({progress['failed']} failed, {progress['unparseable']} unparseable). " - f"Pooled accuracy on this run: {acc:.4f}" - ) - - -# --------------------------------------------------------------------------- -# Metrics -# --------------------------------------------------------------------------- - -def compute_metrics(results: list[dict]) -> dict: - if not results: - return {} - by_subject: dict[str, list[dict]] = defaultdict(list) - by_difficulty: dict[str, list[dict]] = defaultdict(list) - for r in results: - if r.get("subject") is not None: - by_subject[r["subject"]].append(r) - if r.get("topic_difficulty") is not None: - by_difficulty[r["topic_difficulty"]].append(r) - - n_total = len(results) - n_correct = sum(1 for r in results if r.get("correct")) - n_unparseable = sum(1 for r in results if r.get("prediction") is None) - - per_subject = {} - for subj, rows in by_subject.items(): - n = len(rows) - c = sum(1 for r in rows if r.get("correct")) - per_subject[subj] = {"n": n, "accuracy": c / n if n else 0.0} - - per_difficulty = {} - for diff, rows in by_difficulty.items(): - n = len(rows) - c = sum(1 for r in rows if r.get("correct")) - per_difficulty[diff] = {"n": n, "accuracy": c / n if n else 0.0} - - latencies = [r["latency_ms"] for r in results if isinstance(r.get("latency_ms"), int)] - latency_stats = {} - if latencies: - lats = sorted(latencies) - n = len(lats) - latency_stats = { - "count": n, - "mean_ms": sum(lats) / n, - "p50_ms": lats[n // 2], - "p90_ms": lats[min(n - 1, int(n * 0.9))], - "p99_ms": lats[min(n - 1, int(n * 0.99))], - "max_ms": lats[-1], - } - - return { - "accuracy": n_correct / n_total if n_total else 0.0, - "num_samples": n_total, - "unparseable": n_unparseable, - "per_subject": per_subject, - "per_difficulty": per_difficulty, - "latency": latency_stats, - } - - -def print_summary(metrics: dict, setting: str, provider: str, model: str): - print(f"\n{'=' * 68}") - print(f"MMMU Pro [{setting}] — {provider}/{model} (reasoning={REASONING_MODE}, temp={DEFAULT_TEMPERATURE})") - print(f"{'=' * 68}") - print(f"Samples : {metrics['num_samples']}") - print(f"Accuracy : {metrics['accuracy']:.4f}") - print(f"Unparseable : {metrics['unparseable']}") - if metrics.get("latency"): - lat = metrics["latency"] - print(f"Latency : mean={lat['mean_ms']:.0f}ms p50={lat['p50_ms']}ms " - f"p90={lat['p90_ms']}ms p99={lat['p99_ms']}ms") - - -def run_evaluation(pred_path: Path, metrics_path: Path, provider: str, model: str, setting: str): - if not pred_path.exists(): - print(f"No predictions found at {pred_path}") - return - results = load_records(pred_path) - if not results: - print(f"No records in {pred_path}") - return - for r in results: - if r.get("prediction") is None and r.get("response"): - r["prediction"] = parse_answer(r["response"]) - if r.get("correct") is None and r.get("prediction") is not None: - r["correct"] = r["prediction"] == r.get("answer") - - metrics = compute_metrics(results) - print_summary(metrics, setting, provider, model) - output = { - **metrics, - "dataset": DATASET_REPO, - "config": SETTINGS[setting], - "setting": setting, - "split": SPLIT, - "provider": provider, - "model": model, - "reasoning_mode": REASONING_MODE, - "rate_limit": RATE_LIMIT, - "temperature": DEFAULT_TEMPERATURE, - } - metrics_path.parent.mkdir(parents=True, exist_ok=True) - with open(metrics_path, "w") as f: - json.dump(output, f, indent=2, ensure_ascii=False) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - global REASONING_MODE - parser = argparse.ArgumentParser(description="MMMU Pro multi-provider runner") - parser.add_argument("--provider", required=True, - choices=["interfaze", "openai", "anthropic", "gemini", "openrouter"]) - parser.add_argument("--model", required=True) - parser.add_argument("--setting", required=True, choices=list(SETTINGS.keys())) - parser.add_argument("--reasoning", default="off", choices=["off", "high"]) - parser.add_argument("--limit", type=int, default=None, - help="Cap samples (smoke test)") - parser.add_argument("--predict-only", action="store_true") - parser.add_argument("--evaluate-only", action="store_true") - args = parser.parse_args() - - REASONING_MODE = args.reasoning - tag = f"mmmupro_{args.setting}_{args.provider}_{model_slug(args.model)}_reasoning{REASONING_MODE}" - pred_path = RESULTS_DIR / f"{tag}_responses.jsonl" - metrics_path = RESULTS_DIR / f"{tag}_metrics.json" - - if args.evaluate_only: - run_evaluation(pred_path, metrics_path, args.provider, args.model, args.setting) - elif args.predict_only: - asyncio.run(run(args.provider, args.model, args.setting, pred_path, limit=args.limit)) - else: - asyncio.run(run(args.provider, args.model, args.setting, pred_path, limit=args.limit)) - run_evaluation(pred_path, metrics_path, args.provider, args.model, args.setting) - - -if __name__ == "__main__": - main() diff --git a/benchmarks/mmmu_pro/modal_app.py b/benchmarks/mmmu_pro/modal_app.py deleted file mode 100644 index 2721305..0000000 --- a/benchmarks/mmmu_pro/modal_app.py +++ /dev/null @@ -1,259 +0,0 @@ -""" -Modal runner for the MMMU Pro benchmark. - -Persists JSONL results + HF dataset cache to Modal Volumes so runs survive -local-machine death and can be inspected mid-flight without disturbing the -running container. - -Two settings, both 1730 samples each: - - standard: text question + up to 7 inline images - - vision: single rendered image of the entire question - -Quick reference (run from repo root): - - # smoke test (5 samples, attached so you see logs): - uv run modal run benchmarks/mmmu_pro/modal_app.py::run \\ - --provider gemini --model gemini-2.5-flash --setting standard --limit 5 - - # full detached run (survives Ctrl-C / laptop closing): - uv run modal run --detach benchmarks/mmmu_pro/modal_app.py::run \\ - --provider gemini --model gemini-3.1-pro-preview --setting standard - - # check progress (reads volume; does NOT touch the running container): - uv run modal run benchmarks/mmmu_pro/modal_app.py::check \\ - --provider gemini --model gemini-3.1-pro-preview --setting standard - - # pull results back to ./results/ when done: - uv run modal run benchmarks/mmmu_pro/modal_app.py::download \\ - --provider gemini --model gemini-3.1-pro-preview --setting standard - - # one-off prefetch of both dataset configs into the HF cache volume - # (recommended before launching parallel runs to avoid HF 429s): - uv run modal run benchmarks/mmmu_pro/modal_app.py::prefetch -""" - -from __future__ import annotations - -import json -from pathlib import Path - -import modal - -APP_NAME = "interfaze-mmmu-pro" -SECRET_NAME = "screenspot-bench" # same provider keys; reused -RESULTS_VOLUME = "mmmu-pro-results" -HF_CACHE_VOLUME = "mmmu-pro-hf-cache" - -REPO_ROOT = Path(__file__).resolve().parent.parent.parent - -image = ( - modal.Image.debian_slim(python_version="3.12") - .pip_install( - "openai>=2.26.0", - "anthropic>=0.96.0", - "google-genai>=1.73.1", - "huggingface_hub>=0.30.0", - "datasets>=4.7.0", - "pillow>=10.0.0", - "python-dotenv>=1.2.2", - "tqdm>=4.66.0", - ) - .env({"PYTHONPATH": "/app", "HF_HOME": "/hf_cache"}) - # add_local_* must come last (Modal injects these at container startup, - # so they can't be followed by build steps). - .add_local_dir( - str(REPO_ROOT / "benchmarks" / "mmmu_pro"), - remote_path="/app/benchmarks/mmmu_pro", - ) - .add_local_file( - str(REPO_ROOT / "benchmarks" / "__init__.py"), - remote_path="/app/benchmarks/__init__.py", - ) -) - -results_vol = modal.Volume.from_name(RESULTS_VOLUME, create_if_missing=True) -hf_cache_vol = modal.Volume.from_name(HF_CACHE_VOLUME, create_if_missing=True) -secret = modal.Secret.from_name(SECRET_NAME) - -app = modal.App(APP_NAME) - - -def _slug(model: str) -> str: - import re - return re.sub(r"[^a-z0-9]+", "-", model.lower()).strip("-") - - -def _tag(provider: str, model: str, setting: str, reasoning: str) -> str: - return f"mmmupro_{setting}_{provider}_{_slug(model)}_reasoning{reasoning}" - - -# --------------------------------------------------------------------------- -# Prefetch: cache both dataset configs into the volume sequentially, so the -# parallel benchmark runs hit the local cache rather than HF directly. -# --------------------------------------------------------------------------- - -@app.function( - image=image, - volumes={"/hf_cache": hf_cache_vol}, - timeout=60 * 60 * 2, -) -def prefetch() -> None: - from datasets import load_dataset - for cfg in ["standard (10 options)", "vision"]: - print(f"Loading MMMU/MMMU_Pro config={cfg!r} split=test...") - ds = load_dataset("MMMU/MMMU_Pro", cfg, split="test") - print(f" cached {len(ds)} rows") - hf_cache_vol.commit() - - -# --------------------------------------------------------------------------- -# Run: execute the benchmark. Streams JSONL into the results volume, with -# fsync after every record (already done in mmmu_pro_multi.JsonlWriter), -# so a separate reader function sees up-to-date progress on volume.reload(). -# --------------------------------------------------------------------------- - -@app.function( - image=image, - volumes={"/results": results_vol, "/hf_cache": hf_cache_vol}, - secrets=[secret], - timeout=60 * 60 * 24, # 24h ceiling - cpu=4.0, - memory=8192, -) -def _run_remote(provider: str, model: str, setting: str, reasoning: str, - limit: int | None) -> dict: - import asyncio - import sys - sys.path.insert(0, "/app") - - # Override RESULTS_DIR to point at the mounted volume before importing. - import benchmarks.mmmu_pro.mmmu_pro_multi as bench - bench.RESULTS_DIR = Path("/results") - bench.REASONING_MODE = reasoning - - tag = _tag(provider, model, setting, reasoning) - pred_path = Path("/results") / f"{tag}_responses.jsonl" - metrics_path = Path("/results") / f"{tag}_metrics.json" - - # Periodic volume commits so the reader function sees progress. - async def _committer(): - while True: - await asyncio.sleep(20) - try: - results_vol.commit() - except Exception: - pass - - async def _main(): - committer = asyncio.create_task(_committer()) - try: - await bench.run(provider, model, setting, pred_path, limit=limit) - finally: - committer.cancel() - results_vol.commit() - - asyncio.run(_main()) - bench.run_evaluation(pred_path, metrics_path, provider, model, setting) - results_vol.commit() - - metrics = json.loads(metrics_path.read_text()) if metrics_path.exists() else {} - return {"tag": tag, "metrics_summary": { - k: metrics.get(k) for k in ("accuracy", "num_samples", "unparseable") - }} - - -@app.local_entrypoint() -def run(provider: str, model: str, setting: str = "standard", - reasoning: str = "off", limit: int | None = None): - """Run the benchmark. Use `modal run --detach ...` for unattended runs.""" - if setting not in ("standard", "vision"): - raise SystemExit(f"--setting must be 'standard' or 'vision', got {setting!r}") - result = _run_remote.remote(provider, model, setting, reasoning, limit) - print(json.dumps(result, indent=2)) - - -# --------------------------------------------------------------------------- -# Check: ephemeral, read-only progress probe. Does NOT touch the run container. -# --------------------------------------------------------------------------- - -@app.function( - image=image, - volumes={"/results": results_vol}, - timeout=120, -) -def _check_remote(provider: str, model: str, setting: str, reasoning: str) -> dict: - import sys - sys.path.insert(0, "/app") - from benchmarks.mmmu_pro.mmmu_pro_multi import ( - load_records, compute_metrics, - ) - - # Force-refresh the local view of the volume so we see whatever the - # running writer has flushed so far. - results_vol.reload() - - tag = _tag(provider, model, setting, reasoning) - pred_path = Path("/results") / f"{tag}_responses.jsonl" - if not pred_path.exists(): - return {"tag": tag, "status": "no-file-yet", "path": str(pred_path)} - - records = load_records(pred_path) - metrics = compute_metrics(records) if records else {} - - mtime = pred_path.stat().st_mtime - return { - "tag": tag, - "records": len(records), - "accuracy": metrics.get("accuracy"), - "unparseable": metrics.get("unparseable"), - "total_target": 1730, - "latency_p50_ms": (metrics.get("latency") or {}).get("p50_ms"), - "latency_p90_ms": (metrics.get("latency") or {}).get("p90_ms"), - "file_mtime_epoch": mtime, - } - - -@app.local_entrypoint() -def check(provider: str, model: str, setting: str = "standard", - reasoning: str = "off"): - """Print live progress for a (running or finished) benchmark, non-disruptively.""" - result = _check_remote.remote(provider, model, setting, reasoning) - print(json.dumps(result, indent=2)) - - -# --------------------------------------------------------------------------- -# Download: pull the JSONL + metrics back to ./results/ on the local machine. -# --------------------------------------------------------------------------- - -@app.function( - image=image, - volumes={"/results": results_vol}, - timeout=300, -) -def _list_remote() -> list[str]: - return sorted(p.name for p in Path("/results").iterdir() if p.is_file()) - - -@app.local_entrypoint() -def download(provider: str, model: str, setting: str = "standard", - reasoning: str = "off"): - """Stream the JSONL + metrics for one run back to local ./results/.""" - tag = _tag(provider, model, setting, reasoning) - out_dir = REPO_ROOT / "results" - out_dir.mkdir(parents=True, exist_ok=True) - - files = [f"{tag}_responses.jsonl", f"{tag}_metrics.json"] - for fname in files: - local = out_dir / fname - with local.open("wb") as f: - for chunk in results_vol.read_file(fname): - f.write(chunk) - print(f"wrote {local} ({local.stat().st_size} bytes)") - - -@app.local_entrypoint() -def ls(): - """List files currently in the results volume.""" - files = _list_remote.remote() - for f in files: - print(f) diff --git a/benchmarks/obj_detection/_probe_grok_reasoning.py b/benchmarks/obj_detection/_probe_grok_reasoning.py deleted file mode 100644 index cb20f4f..0000000 --- a/benchmarks/obj_detection/_probe_grok_reasoning.py +++ /dev/null @@ -1,52 +0,0 @@ -"""Probe what reasoning settings x-ai/grok-4.3 accepts on OpenRouter.""" -import os -import sys -import json -from pathlib import Path - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -sys.path.insert(0, str(PROJECT_ROOT)) - -from dotenv import load_dotenv -from openai import OpenAI - -load_dotenv() - -key = os.getenv("OPENROUTER_API_KEY") or os.getenv("OPENROUTER_KEY") -client = OpenAI(base_url="https://openrouter.ai/api/v1", api_key=key) -MODEL = "x-ai/grok-4.3" -PROMPT = "What is 2+2? Reply with only the number." - -trials = [ - ("no_reasoning_field", {}), - ("reasoning.enabled=False", {"reasoning": {"enabled": False}}), - ("reasoning.enabled=True", {"reasoning": {"enabled": True}}), - ("reasoning.effort=minimal", {"reasoning": {"effort": "minimal"}}), - ("reasoning.effort=low", {"reasoning": {"effort": "low"}}), - ("reasoning.effort=medium", {"reasoning": {"effort": "medium"}}), - ("reasoning.effort=high", {"reasoning": {"effort": "high"}}), - ("reasoning.max_tokens=1", {"reasoning": {"max_tokens": 1}}), - ("reasoning.max_tokens=128", {"reasoning": {"max_tokens": 128}}), -] - -for label, extra in trials: - print("=" * 70) - print(f"trial: {label} body={extra}") - try: - r = client.chat.completions.create( - model=MODEL, - messages=[{"role": "user", "content": PROMPT}], - temperature=0.0, - extra_body=extra, - ) - msg = r.choices[0].message - content = (getattr(msg, "content", None) or "").strip() - reasoning = getattr(msg, "reasoning", None) - usage = getattr(r, "usage", None) - print(f" OK content={content!r}") - print(f" reasoning preview: {(reasoning or '')[:120]!r}") - if usage is not None: - ud = usage.model_dump() if hasattr(usage, "model_dump") else dict(usage) - print(f" usage: {json.dumps(ud, default=str)}") - except Exception as e: - print(f" ERROR {type(e).__name__}: {e}") diff --git a/benchmarks/obj_detection/bench.py b/benchmarks/obj_detection/bench.py new file mode 100644 index 0000000..2093e03 --- /dev/null +++ b/benchmarks/obj_detection/bench.py @@ -0,0 +1,336 @@ +"""RefCOCO grounding: predict one box per referring expression; Acc@IoU=0.5. + +Reports a strict single-interpretation score plus a format-tolerant `oracle` +sub-score that tries {pixel, pixel2x, norm1000, norm1} x {xyxy, yxyx} against GT +(pixel2x covers models like Inkling that report in a 2x-upscaled image space). +""" + +from __future__ import annotations + +import re +from collections import defaultdict +from typing import Any + +from src.media import encode_image +from src.request import Message, ReasoningSpec, Request, TextPart + +NAME = "refcoco" +ID_KEY = "id" +PRIMARY_METRIC = "accuracy" +# variant = split for base RefCOCO, or "plus-" / "g-" for +# RefCOCO+ / RefCOCOg (same underlying task, lmms-lab packaging). +VARIANTS = [ + "val", + "testA", + "testB", + "test", + "plus-val", + "plus-testA", + "plus-testB", + "g-val", + "g-test", +] +DEFAULTS = {"reasoning": "off", "rate_limit": 25, "max_in_flight": 8} + +_DATASETS = { + "": "lmms-lab/RefCOCO", + "plus": "lmms-lab/RefCOCO+", + "g": "lmms-lab/RefCOCOg", +} +_MAX_SIDE = 1024 +_DATASET: Any = None # full-run split; images read lazily by idx to bound memory +IOU_THRESHOLD = 0.5 +_THRESHOLDS = [0.3, 0.5, 0.7, 0.75, 0.9] + +_PROMPT = ( + "Please provide the bounding box coordinate of the region this sentence describes: " + "{expression}\n\n" + "Output the coordinates in the format [x_min, y_min, x_max, y_max]." +) + +# --- production single-interpretation parser (from refcoco.py) --- +BOX_LINE_PATTERN = re.compile( + r"(?im)^\s*(?:box|answer|bounding\s*box)[\s:]*" + r"\[?\s*(-?\d+(?:\.\d+)?)\s*[,\s]\s*(-?\d+(?:\.\d+)?)\s*[,\s]\s*" + r"(-?\d+(?:\.\d+)?)\s*[,\s]\s*(-?\d+(?:\.\d+)?)\s*\]?" +) +BOXED_PATTERN = re.compile( + r"\\boxed\{\s*\[?\s*(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*,\s*" + r"(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*\]?\s*\}" +) +BARE_4TUPLE = re.compile( + r"\[\s*(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*,\s*" + r"(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*\]" +) +JSON_TLBR_PATTERN = re.compile( + r'"top_left"\s*:\s*\{[^}]*?"x"\s*:\s*(-?\d+(?:\.\d+)?)[^}]*?"y"\s*:\s*(-?\d+(?:\.\d+)?)' + r'[^}]*?\}[^}]*?"bottom_right"\s*:\s*\{[^}]*?"x"\s*:\s*(-?\d+(?:\.\d+)?)' + r'[^}]*?"y"\s*:\s*(-?\d+(?:\.\d+)?)', + re.DOTALL, +) +JSON_BOX2D_PATTERN = re.compile( + r'"box_2d"\s*:\s*\[\s*(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*,\s*' + r"(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*\]" +) + + +def _normalize_to_pixels(box, width, height, order): + max_coord = max(abs(c) for c in box) + if max_coord <= 1.0: + sx, sy = float(width), float(height) + elif max_coord <= 1000 and ( + max(width, height) > 1000 + or box[0] > width + or box[2] > width + or box[1] > height + or box[3] > height + ): + sx, sy = width / 1000.0, height / 1000.0 + else: + sx = sy = 1.0 + if order == "xyxy": + return [box[0] * sx, box[1] * sy, box[2] * sx, box[3] * sy] + return [box[1] * sx, box[0] * sy, box[3] * sx, box[2] * sy] + + +def parse_box(text: str, width: int, height: int): + if not text: + return None + m = None + for match in JSON_TLBR_PATTERN.finditer(text): + m = match + if m is not None: + return _normalize_to_pixels( + [float(m.group(i)) for i in range(1, 5)], width, height, "xyxy" + ) + m = None + for match in JSON_BOX2D_PATTERN.finditer(text): + m = match + if m is not None: + return _normalize_to_pixels( + [float(m.group(i)) for i in range(1, 5)], width, height, "yxyx" + ) + for pattern in (BOX_LINE_PATTERN, BOXED_PATTERN): + matches = list(pattern.finditer(text)) + if matches: + m = matches[-1] + return _normalize_to_pixels( + [float(m.group(i)) for i in range(1, 5)], width, height, "xyxy" + ) + matches = list(BARE_4TUPLE.finditer(text[-800:])) + if matches: + m = matches[-1] + return _normalize_to_pixels( + [float(m.group(i)) for i in range(1, 5)], width, height, "xyxy" + ) + return None + + +def compute_iou(a, b) -> float: + ax1, ay1, ax2, ay2 = a + bx1, by1, bx2, by2 = b + ax1, ax2 = min(ax1, ax2), max(ax1, ax2) + ay1, ay2 = min(ay1, ay2), max(ay1, ay2) + bx1, bx2 = min(bx1, bx2), max(bx1, bx2) + by1, by2 = min(by1, by2), max(by1, by2) + ix1, iy1, ix2, iy2 = max(ax1, bx1), max(ay1, by1), min(ax2, bx2), min(ay2, by2) + if ix1 >= ix2 or iy1 >= iy2: + return 0.0 + inter = (ix2 - ix1) * (iy2 - iy1) + area_a = max(0.0, ax2 - ax1) * max(0.0, ay2 - ay1) + area_b = max(0.0, bx2 - bx1) * max(0.0, by2 - by1) + union = area_a + area_b - inter + return inter / union if union > 0 else 0.0 + + +def coco_bbox_to_xyxy(bbox, sx=1.0, sy=1.0): + x, y, w, h = bbox + return [x * sx, y * sy, (x + w) * sx, (y + h) * sy] + + +# --- format-tolerant oracle (from reeval_format_tolerant.py) --- +_TUPLE = BARE_4TUPLE +_PAREN_PAIRS = re.compile( + r"\(\s*(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*\)\s*[^()]*?\s*" + r"\(\s*(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*\)" +) + + +def _extract_tuples(text: str): + out = [] + for m in _TUPLE.finditer(text): + out.append(tuple(float(g) for g in m.groups())) + for m in JSON_TLBR_PATTERN.finditer(text): + out.append(tuple(float(g) for g in m.groups())) + for m in _PAREN_PAIRS.finditer(text): + out.append(tuple(float(g) for g in m.groups())) + return out + + +def _all_interpretations(nums, w, h): + n0, n1, n2, n3 = nums + mx = max(abs(c) for c in nums) + yield "pixel-xyxy", [n0, n1, n2, n3] + yield "pixel-yxyx", [n1, n0, n3, n2] + if mx > min(w, h): + yield "pixel2x-xyxy", [n0 / 2, n1 / 2, n2 / 2, n3 / 2] + yield "pixel2x-yxyx", [n1 / 2, n0 / 2, n3 / 2, n2 / 2] + if mx <= 1000: + yield ( + "norm1000-xyxy", + [n0 * w / 1000, n1 * h / 1000, n2 * w / 1000, n3 * h / 1000], + ) + yield ( + "norm1000-yxyx", + [n1 * w / 1000, n0 * h / 1000, n3 * w / 1000, n2 * h / 1000], + ) + if mx <= 1.0: + yield "norm1-xyxy", [n0 * w, n1 * h, n2 * w, n3 * h] + yield "norm1-yxyx", [n1 * w, n0 * h, n3 * w, n2 * h] + + +def _best_box(response: str, w: int, h: int, gt): + best_iou, best_label = 0.0, None + for nums in _extract_tuples(response): + for label, box in _all_interpretations(nums, w, h): + v = compute_iou(box, gt) + if v > best_iou: + best_iou, best_label = v, label + return best_iou, best_label + + +# --- benchmark interface --- +def _sent_dims(w, h, max_side=_MAX_SIDE): + longest = max(w, h) + if longest <= max_side: + return w, h + scale = max_side / longest + return round(w * scale), round(h * scale) + + +def _parse_variant(variant: str) -> tuple[str, str]: + if variant.startswith("plus-"): + return _DATASETS["plus"], variant[len("plus-") :] + if variant.startswith("g-"): + return _DATASETS["g"], variant[len("g-") :] + return _DATASETS[""], variant + + +def _mk_sample(row, i, image, idx=None) -> dict | None: + answers = row.get("answer") + if isinstance(answers, str): + exprs = [answers] + elif isinstance(answers, list): + exprs = [str(a) for a in answers if str(a).strip()] + else: + exprs = [] + if not exprs: + return None + ow, oh = image.size # header-only; not retained on the full path + sw, sh = _sent_dims(ow, oh) + gt = coco_bbox_to_xyxy(list(row["bbox"]), sw / ow, sh / oh) + s = { + "id": f"{row.get('question_id', i)}_{i}", + "expression": exprs[0], + "sent_w": sw, + "sent_h": sh, + "gt_bbox_xyxy": gt, + } + if idx is None: + s["image"] = image # smoke embeds the streamed image + else: + s["idx"] = idx # full run reads it lazily from _DATASET + return s + + +def load_samples(sample_size: int | None = None, variant: str = "val") -> list[dict]: + global _DATASET + dataset_id, split = _parse_variant(variant) + + if sample_size: + from src.datautil import load_rows + + rows = load_rows(dataset_id, split, sample_size) + out = (_mk_sample(r, i, r["image"]) for i, r in enumerate(rows)) + return [s for s in out if s] + + # full run: keep the split memory-mapped and read images lazily by idx + # (list(ds) materializes all ~8.8k decoded images and OOMs CI). + from datasets import load_dataset + + _DATASET = load_dataset(dataset_id, split=split) + samples = [] + for i in range(len(_DATASET)): + row = _DATASET[i] + s = _mk_sample(row, i, row["image"], idx=i) + if s: + samples.append(s) + return samples + + +def build_request(sample: dict, mode: str) -> Request: + prompt = _PROMPT.format(expression=sample["expression"]) + if "image" in sample: + image = sample["image"] # streamed smoke: embedded + elif "idx" in sample: + image = _DATASET[sample["idx"]]["image"] # full run: decoded lazily + else: + raise KeyError("RefCOCO sample missing both 'image' and 'idx'") + img = encode_image(image, "image/jpeg", max_side=_MAX_SIDE) + return Request( + [Message("user", [TextPart(prompt), img])], ReasoningSpec(mode), temperature=0.0 + ) + + +def parse(response, sample): + return parse_box(response.text or "", sample["sent_w"], sample["sent_h"]) + + +def _sweep(ious): + n = len(ious) + return { + f"acc@{t}": (sum(1 for i in ious if i >= t) / n if n else 0) + for t in _THRESHOLDS + } + + +def score(records: list[dict], samples: list[dict]) -> dict: + by_id = {s["id"]: s for s in samples} + latest = {r["id"]: r for r in records if r["id"] in by_id} + + strict_ious, oracle_ious = [], [] + strict_correct = oracle_correct = unparsed = total = 0 + labels: dict[str, int] = defaultdict(int) + for rid, r in latest.items(): + s = by_id[rid] + gt, w, h = s["gt_bbox_xyxy"], s["sent_w"], s["sent_h"] + total += 1 + pred = r.get("prediction") + iou = compute_iou(pred, gt) if pred else 0.0 + strict_ious.append(iou) + if pred is not None and iou >= IOU_THRESHOLD: + strict_correct += 1 + if pred is None: + unparsed += 1 + oiou, olabel = _best_box(r.get("response") or "", w, h, gt) + oracle_ious.append(oiou) + if oiou >= IOU_THRESHOLD: + oracle_correct += 1 + if olabel: + labels[olabel] += 1 + + return { + "accuracy": strict_correct / total if total else 0.0, + "correct": strict_correct, + "total": total, + "unparsed": unparsed, + "mean_iou": sum(strict_ious) / len(strict_ious) if strict_ious else 0.0, + "iou_thresholds": _sweep(strict_ious), + "oracle": { + "accuracy": oracle_correct / total if total else 0.0, + "correct": oracle_correct, + "total": total, + "mean_iou": sum(oracle_ious) / len(oracle_ious) if oracle_ious else 0.0, + "interpretation_counts": dict(labels), + }, + } diff --git a/benchmarks/obj_detection/ob_det_api.py b/benchmarks/obj_detection/ob_det_api.py deleted file mode 100644 index 778c358..0000000 --- a/benchmarks/obj_detection/ob_det_api.py +++ /dev/null @@ -1,448 +0,0 @@ -""" -RefCOCO benchmark for the JigsawStack /v1/object_detection API. - -Mirrors the protocol used by `refcoco.py` (interfaze-beta) so numbers are -directly comparable on the same splits at Acc@IoU=0.5. The only thing that -changes is the inference call: instead of asking a VLM to emit a `Box: [...]` -line, we hit JigsawStack's object_detection endpoint with the referring -expression as a prompt and parse `objects[0].bounds`. - -Per-sample we record both: - - acc / iou — IoU of the FIRST returned object vs. the GT box. - This is the honest, deployable number. - - oracle_iou / oracle_correct — best IoU across ALL returned objects. - Diagnostic upper bound — tells us whether the - right box was in the response and we just picked - wrong. - -Datasets (lmms-lab packaging — same data as standard RefCOCO splits): - lmms-lab/RefCOCO val 8811, test 5000, testA 1975, testB 1810 - lmms-lab/RefCOCO+ val 8823, testA 1975, testB 1798 - lmms-lab/RefCOCOg val 7573, test 9602 (UMD split) - -Usage: - uv run -m benchmarks.obj_detection.ob_det_api # RefCOCO val - uv run -m benchmarks.obj_detection.ob_det_api --split testA - uv run -m benchmarks.obj_detection.ob_det_api --dataset lmms-lab/RefCOCO+ --split testB - uv run -m benchmarks.obj_detection.ob_det_api --limit 20 - uv run -m benchmarks.obj_detection.ob_det_api --evaluate-only - -Env: JIGSAWSTACK_API_KEY must be set (loaded from .env). - -Checkpointing: results/jigsawstack___responses.jsonl — -each successful sample is appended as it finishes; reruns skip completed ids. -""" - -import sys -import json -import os -import time -import base64 -import asyncio -import argparse -import traceback -import concurrent.futures -from io import BytesIO -from pathlib import Path - -import httpx -from datasets import load_dataset -from dotenv import load_dotenv -from tqdm import tqdm -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -sys.path.insert(0, str(PROJECT_ROOT)) - -# Reuse sample construction, IoU, and resume logic from refcoco.py — keeps -# scoring byte-for-byte identical to the interfaze run. -from benchmarks.obj_detection.refcoco import ( # noqa: E402 - JsonlWriter, - build_samples, - coco_bbox_to_xyxy, - compute_iou, - load_completed_ids, - load_records, - pil_to_data_url, -) - -load_dotenv() - -RESULTS_DIR = PROJECT_ROOT / "results" -DEFAULT_DATASET = "lmms-lab/RefCOCO" -DEFAULT_SPLIT = "val" -CONCURRENCY = 10 # JigsawStack rate-limits more aggressively than interfaze -MAX_RETRIES = 3 -IOU_THRESHOLD = 0.5 -REQUEST_TIMEOUT = 120.0 - -JIGSAWSTACK_URL = "http://localhost:3000/api/v1/object_detection" -JIGSAWSTACK_API_KEY = os.getenv("JIGSAWSTACK_API_KEY") -if not JIGSAWSTACK_API_KEY: - raise RuntimeError( - "JIGSAWSTACK_API_KEY is not set. Add it to .env " - "(get one from https://jigsawstack.com/dashboard)." - ) - - -def invoke_jigsawstack_obj_detection(image_data_url: str, expression: str) -> dict: - """POST to /v1/object_detection with the referring expression as a prompt. - - `annotated_image=False` skips the rendered overlay we don't need, which - saves both bandwidth and server-side time. `x-jigsaw-skip-cache: true` - matches the example curl and ensures we measure real inference latency - rather than cache hits.""" - payload = { - "url": image_data_url, - "annotated_image": False, - "features": ["object"], - "prompts": [expression], - } - headers = { - "x-api-key": JIGSAWSTACK_API_KEY, - "x-jigsaw-skip-cache": "true", - "Content-Type": "application/json", - } - with httpx.Client(timeout=REQUEST_TIMEOUT) as client: - resp = client.post(JIGSAWSTACK_URL, json=payload, headers=headers) - if resp.status_code != 200: - raise RuntimeError(f"HTTP {resp.status_code}: {resp.text[:500]}") - data = resp.json() - if not data.get("success", False): - raise RuntimeError(f"API error: {str(data)[:500]}") - return data - - -def bounds_to_xyxy(bounds: dict) -> list[float] | None: - """Convert JigsawStack's `bounds` object to [x1, y1, x2, y2]. - - The API returns four corners (top_left/top_right/bottom_left/bottom_right); - we only need the diagonal pair. Falls back to width/height if a corner is - missing.""" - tl = bounds.get("top_left") or {} - br = bounds.get("bottom_right") or {} - if "x" in tl and "y" in tl and "x" in br and "y" in br: - return [float(tl["x"]), float(tl["y"]), float(br["x"]), float(br["y"])] - if "x" in tl and "y" in tl and "width" in bounds and "height" in bounds: - x1, y1 = float(tl["x"]), float(tl["y"]) - return [x1, y1, x1 + float(bounds["width"]), y1 + float(bounds["height"])] - return None - - -def extract_boxes(api_response: dict) -> list[list[float]]: - """Return all detected boxes as [x1,y1,x2,y2], in the order returned.""" - boxes: list[list[float]] = [] - for obj in api_response.get("objects") or []: - bounds = obj.get("bounds") - if not bounds: - continue - xyxy = bounds_to_xyxy(bounds) - if xyxy is not None: - boxes.append(xyxy) - return boxes - - -async def process_sample( - sample: dict, semaphore: asyncio.Semaphore, writer: JsonlWriter, progress: dict -) -> dict | None: - orig_w, orig_h = sample["image"].size - data_url, sent_w, sent_h = pil_to_data_url(sample["image"]) - sx = sent_w / orig_w - sy = sent_h / orig_h - gt_xyxy = coco_bbox_to_xyxy(sample["bbox_xywh"], sx, sy) - - last_error: str | None = None - - for attempt in range(1, MAX_RETRIES + 1): - start = time.perf_counter() - try: - async with semaphore: - start = time.perf_counter() - api_response = await asyncio.to_thread( - invoke_jigsawstack_obj_detection, - data_url, - sample["expression"], - ) - latency_ms = int((time.perf_counter() - start) * 1000) - - boxes = extract_boxes(api_response) - num_objects = len(boxes) - - # Honest pick: first object, matching how a downstream caller - # would use the API without ground truth. - pred_box = boxes[0] if boxes else None - iou = compute_iou(pred_box, gt_xyxy) if pred_box else 0.0 - correct = pred_box is not None and iou >= IOU_THRESHOLD - - # Oracle: best IoU across all returned boxes — tells us how often - # the right answer was in the response but we picked wrong. - if boxes: - oracle_iou = max(compute_iou(b, gt_xyxy) for b in boxes) - else: - oracle_iou = 0.0 - oracle_correct = oracle_iou >= IOU_THRESHOLD - - log_id = api_response.get("log_id") - usage = api_response.get("_usage") - - record = { - "id": sample["id"], - "question_id": sample["question_id"], - "file_name": sample["file_name"], - "expression": sample["expression"], - "all_expressions": sample["all_expressions"], - "image_width": sent_w, - "image_height": sent_h, - "gt_bbox_xyxy": gt_xyxy, - "pred_bbox_xyxy": pred_box, - "all_pred_boxes": boxes, - "num_objects": num_objects, - "iou": iou, - "correct": correct, - "oracle_iou": oracle_iou, - "oracle_correct": oracle_correct, - "response": api_response, - "log_id": log_id, - "usage": usage, - "latency_ms": latency_ms, - "attempts": attempt, - } - await writer.append(record) - - progress["done"] += 1 - if correct: - progress["correct"] += 1 - mark = "OK" - elif oracle_correct: - mark = "o " # right box was returned, just not first - else: - mark = "X " - tqdm.write( - f"[{progress['done']}/{progress['total']}] {mark} " - f"id={sample['id']} iou={iou:.3f} oracle={oracle_iou:.3f} " - f"n={num_objects} pred={pred_box} " - f"gt={[round(x, 1) for x in gt_xyxy]} " - f"latency={latency_ms}ms log_id={log_id} attempt={attempt}" - ) - return record - - except Exception as e: - latency_ms = int((time.perf_counter() - start) * 1000) - last_error = f"{type(e).__name__}: {e}" - tqdm.write( - f"[error] id={sample['id']} attempt={attempt}/{MAX_RETRIES} " - f"latency={latency_ms}ms error={last_error}" - ) - if attempt < MAX_RETRIES: - await asyncio.sleep(2 ** (attempt - 1)) - - progress["failed"] += 1 - tqdm.write(f"[FAILED] id={sample['id']} after {MAX_RETRIES} attempts: {last_error}") - return None - - -def compute_metrics(results: list[dict]) -> dict: - total = len(results) - correct = sum(1 for r in results if r.get("correct")) - oracle_correct = sum(1 for r in results if r.get("oracle_correct")) - no_objects = sum(1 for r in results if not r.get("num_objects")) - ious = [r["iou"] for r in results if isinstance(r.get("iou"), (int, float))] - oracle_ious = [ - r["oracle_iou"] - for r in results - if isinstance(r.get("oracle_iou"), (int, float)) - ] - latencies = [ - r["latency_ms"] for r in results if isinstance(r.get("latency_ms"), int) - ] - n_objects = [ - r["num_objects"] for r in results if isinstance(r.get("num_objects"), int) - ] - - thresholds = [0.3, 0.5, 0.7, 0.75, 0.9] - at_threshold = { - f"acc@{t}": (sum(1 for iou in ious if iou >= t) / len(ious) if ious else 0) - for t in thresholds - } - oracle_at_threshold = { - f"oracle_acc@{t}": ( - sum(1 for iou in oracle_ious if iou >= t) / len(oracle_ious) - if oracle_ious - else 0 - ) - for t in thresholds - } - - latency_stats = {} - if latencies: - lats = sorted(latencies) - n = len(lats) - latency_stats = { - "count": n, - "mean_ms": sum(lats) / n, - "p50_ms": lats[n // 2], - "p90_ms": lats[min(n - 1, int(n * 0.9))], - "p99_ms": lats[min(n - 1, int(n * 0.99))], - "max_ms": lats[-1], - } - - return { - "accuracy": correct / total if total else 0.0, - "oracle_accuracy": oracle_correct / total if total else 0.0, - "correct": correct, - "oracle_correct": oracle_correct, - "total": total, - "no_objects_returned": no_objects, - "mean_iou": sum(ious) / len(ious) if ious else 0.0, - "mean_oracle_iou": sum(oracle_ious) / len(oracle_ious) if oracle_ious else 0.0, - "mean_objects_per_response": sum(n_objects) / len(n_objects) - if n_objects - else 0.0, - "iou_thresholds": at_threshold, - "oracle_iou_thresholds": oracle_at_threshold, - "latency": latency_stats, - } - - -def print_summary(metrics: dict, dataset_name: str, split: str): - print(f"\n{'=' * 60}") - print( - f"Grounding Results — {dataset_name} / {split} (JigsawStack object_detection)" - ) - print(f"{'=' * 60}") - print( - f"Acc@IoU=0.5 : {metrics['accuracy']:.4f} ({metrics['correct']}/{metrics['total']})" - ) - print( - f"Oracle Acc@IoU=0.5 : {metrics['oracle_accuracy']:.4f} ({metrics['oracle_correct']}/{metrics['total']})" - ) - print(f"Mean IoU : {metrics['mean_iou']:.4f}") - print(f"Mean Oracle IoU : {metrics['mean_oracle_iou']:.4f}") - print(f"Avg objects / resp : {metrics['mean_objects_per_response']:.2f}") - print(f"No-object responses: {metrics['no_objects_returned']}") - if metrics.get("latency"): - lat = metrics["latency"] - print( - f"Latency : mean={lat['mean_ms']:.0f}ms p50={lat['p50_ms']}ms " - f"p90={lat['p90_ms']}ms p99={lat['p99_ms']}ms max={lat['max_ms']}ms" - ) - print("\nIoU thresholds (first-object pick):") - for t, acc in metrics["iou_thresholds"].items(): - print(f" {t}: {acc:.4f}") - print("\nIoU thresholds (oracle / best-of-N):") - for t, acc in metrics["oracle_iou_thresholds"].items(): - print(f" {t}: {acc:.4f}") - - -def build_tag(dataset: str, split: str) -> str: - ds_slug = dataset.split("/")[-1].lower().replace("+", "plus") - return f"jigsawstack_{ds_slug}_{split}" - - -async def run_predictions( - dataset_name: str, split: str, pred_path: Path, limit: int | None -): - print(f"Loading {dataset_name}, split={split}...") - dataset = load_dataset(dataset_name, split=split) - print(f"Loaded {len(dataset)} rows") - - samples = build_samples(dataset) - done_ids = load_completed_ids(pred_path) - pending = [s for s in samples if s["id"] not in done_ids] - if limit is not None: - pending = pending[:limit] - print(f"--limit applied: will run at most {limit} sample(s)") - print( - f"Resume: {len(done_ids)} already completed, {len(pending)} remaining " - f"(checkpoint: {pred_path})" - ) - if not pending: - return - - asyncio.get_running_loop().set_default_executor( - concurrent.futures.ThreadPoolExecutor(max_workers=CONCURRENCY) - ) - - writer = JsonlWriter(pred_path) - semaphore = asyncio.Semaphore(CONCURRENCY) - progress = {"total": len(pending), "done": 0, "correct": 0, "failed": 0} - tasks = [process_sample(s, semaphore, writer, progress) for s in pending] - try: - await tqdm_asyncio.gather(*tasks, desc=f"{dataset_name}/{split}") - except Exception: - traceback.print_exc() - print( - f"\nRun finished: {progress['done']}/{progress['total']} answered, " - f"{progress['correct']} correct, {progress['failed']} failed." - ) - - -def run_evaluation(dataset_name: str, split: str, pred_path: Path, metrics_path: Path): - if not pred_path.exists(): - print(f"No predictions found at {pred_path}") - sys.exit(1) - results = load_records(pred_path) - if not results: - print(f"No records in {pred_path}") - sys.exit(1) - - metrics = compute_metrics(results) - print_summary(metrics, dataset_name, split) - output = { - **metrics, - "dataset": dataset_name, - "split": split, - "concurrency": CONCURRENCY, - "iou_threshold": IOU_THRESHOLD, - "model": "jigsawstack:object_detection", - } - metrics_path.parent.mkdir(parents=True, exist_ok=True) - with open(metrics_path, "w") as f: - json.dump(output, f, indent=2) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - parser = argparse.ArgumentParser( - description="RefCOCO / RefCOCO+ / RefCOCOg benchmark for the JigsawStack " - "object_detection API (Acc@IoU=0.5, comparable to refcoco.py)" - ) - parser.add_argument( - "--dataset", - default=DEFAULT_DATASET, - help="lmms-lab/RefCOCO | lmms-lab/RefCOCO+ | lmms-lab/RefCOCOg", - ) - parser.add_argument( - "--split", - default=DEFAULT_SPLIT, - help="val | testA | testB | test (availability varies by dataset)", - ) - parser.add_argument("--predict-only", action="store_true") - parser.add_argument("--evaluate-only", action="store_true") - parser.add_argument( - "--limit", - type=int, - default=None, - help="Only run the first N unanswered samples", - ) - args = parser.parse_args() - - tag = build_tag(args.dataset, args.split) - pred_path = RESULTS_DIR / f"{tag}_responses.jsonl" - metrics_path = RESULTS_DIR / f"{tag}_metrics.json" - - if args.evaluate_only: - run_evaluation(args.dataset, args.split, pred_path, metrics_path) - elif args.predict_only: - asyncio.run( - run_predictions(args.dataset, args.split, pred_path, limit=args.limit) - ) - else: - asyncio.run( - run_predictions(args.dataset, args.split, pred_path, limit=args.limit) - ) - run_evaluation(args.dataset, args.split, pred_path, metrics_path) - - -if __name__ == "__main__": - main() diff --git a/benchmarks/obj_detection/reeval_format_tolerant.py b/benchmarks/obj_detection/reeval_format_tolerant.py deleted file mode 100644 index c928a44..0000000 --- a/benchmarks/obj_detection/reeval_format_tolerant.py +++ /dev/null @@ -1,174 +0,0 @@ -""" -Format-tolerant re-evaluation of RefCOCO grounding runs. - -Rationale: under the canonical eval prompt, different models emit boxes in -different conventions — xyxy vs yxyx ordering, 0-1.0 floats / 0-1000 -normalized / raw pixels. The sample-by-sample correct-vs-wrong split under -a single-interpretation parser conflates two very different things: - - 1. Did the model find the right region? (grounding capability — what we - actually want to measure) - 2. Did the model emit it in the format we happened to parse? (format - compliance — a separate, prompt-dependent concern) - -This script re-scores existing `*_responses.jsonl` files by, for each -sample, trying every reasonable interpretation of every 4-tuple in the -response and keeping the one whose IoU with GT is highest. That isolates -(1) by taking (2) out of the equation — applied uniformly to every model -so the comparison stays apples-to-apples. - -This is oracle parsing — it uses GT to disambiguate, so the resulting -numbers are *upper bounds* on production performance. Single-interpretation -numbers stay in the originals. - -Usage: - uv run -m benchmarks.obj_detection.reeval_format_tolerant -""" - -import json -import re -import sys -from pathlib import Path - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -sys.path.insert(0, str(PROJECT_ROOT)) - -from benchmarks.obj_detection.refcoco import ( # noqa: E402 - compute_iou, compute_metrics, -) - -RESULTS_DIR = PROJECT_ROOT / "results" -TUPLE_PATTERN = re.compile( - r'\[\s*(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*,\s*' - r'(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*\]' -) -PAREN_PAIRS = re.compile( - r'\(\s*(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*\)\s*[^()]*?\s*' - r'\(\s*(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*\)' -) -TLBR_JSON = re.compile( - r'"top_left"\s*:\s*\{[^}]*?"x"\s*:\s*(-?\d+(?:\.\d+)?)[^}]*?"y"\s*:\s*' - r'(-?\d+(?:\.\d+)?)[^}]*?\}[^}]*?"bottom_right"\s*:\s*\{[^}]*?"x"\s*:\s*' - r'(-?\d+(?:\.\d+)?)[^}]*?"y"\s*:\s*(-?\d+(?:\.\d+)?)', - re.DOTALL, -) - - -def extract_tuples(text: str) -> list[tuple[float, float, float, float]]: - """Pull every plausible 4-tuple out of the response — bracket lists, - parenthesized (x,y) pairs, and JSON top_left/bottom_right blocks. - Order is preserved (so callers can prefer the last match if needed).""" - out = [] - for m in TUPLE_PATTERN.finditer(text): - out.append(tuple(float(g) for g in m.groups())) - for m in TLBR_JSON.finditer(text): - # already xyxy in pixel space — emit as-is. - out.append(tuple(float(g) for g in m.groups())) - for m in PAREN_PAIRS.finditer(text): - out.append(tuple(float(g) for g in m.groups())) - return out - - -def all_interpretations(nums, w, h): - """Yield (label, [x1,y1,x2,y2]) for every plausible interpretation - of a 4-tuple under the canonical RefCOCO eval — covers xyxy/yxyx - order × {raw pixel, 0-1000 normalized, 0-1.0 float} scale.""" - n0, n1, n2, n3 = nums - mx = max(abs(c) for c in nums) - yield "pixel-xyxy", [n0, n1, n2, n3] - yield "pixel-yxyx", [n1, n0, n3, n2] - if mx <= 1000: - yield "norm1000-xyxy", [n0 * w / 1000, n1 * h / 1000, n2 * w / 1000, n3 * h / 1000] - yield "norm1000-yxyx", [n1 * w / 1000, n0 * h / 1000, n3 * w / 1000, n2 * h / 1000] - if mx <= 1.0: - yield "norm1-xyxy", [n0 * w, n1 * h, n2 * w, n3 * h] - yield "norm1-yxyx", [n1 * w, n0 * h, n3 * w, n2 * h] - - -def best_box(response: str, w: int, h: int, gt: list[float]): - """Return (best_box_xyxy, best_iou, label) — picks the interpretation - of any 4-tuple in the response that maximizes IoU vs GT.""" - best_iou = 0.0 - best_box = None - best_label = None - for nums in extract_tuples(response): - for label, box in all_interpretations(nums, w, h): - v = compute_iou(box, gt) - if v > best_iou: - best_iou, best_box, best_label = v, box, label - return best_box, best_iou, best_label - - -def reeval_file(path: Path) -> tuple[list[dict], dict]: - records = [] - label_counts = {} - n_changed = 0 - with open(path) as f: - for line in f: - line = line.strip() - if not line: - continue - r = json.loads(line) - gt = r["gt_bbox_xyxy"] - w = r["image_width"] - h = r["image_height"] - box, iou, label = best_box(r["response"], w, h, gt) - new_correct = box is not None and iou >= 0.5 - if r.get("correct") != new_correct: - n_changed += 1 - r["pred_bbox_xyxy"] = box - r["iou"] = iou - r["correct"] = new_correct - r["interpretation"] = label - records.append(r) - if box is not None: - label_counts[label] = label_counts.get(label, 0) + 1 - return records, {"n_changed": n_changed, "label_counts": label_counts} - - -def write_records(records: list[dict], path: Path) -> None: - path.parent.mkdir(parents=True, exist_ok=True) - with open(path, "w") as f: - for r in records: - f.write(json.dumps(r, ensure_ascii=False) + "\n") - - -def main(): - targets = [ - ("interfaze", "refcoco_testA"), - ("gpt-5.5", "gpt55_refcoco_testA"), - ("kimi-k2.6", "kimi_k26_refcoco_testA"), - ] - print(f"{'model':<14} {'orig_acc':>10} {'oracle_acc':>11} {'mean_iou':>10} {'changed':>9}") - print("-" * 60) - for label, tag in targets: - src = RESULTS_DIR / f"{tag}_responses.jsonl" - if not src.exists(): - print(f"{label:<14} (missing {src.name})") - continue - # Original score (before re-eval) - with open(src) as f: - orig_records = [json.loads(l) for l in f if l.strip()] - orig_correct = sum(1 for r in orig_records if r.get("correct")) - orig_total = len(orig_records) - - records, info = reeval_file(src) - out_responses = RESULTS_DIR / f"{tag}_oracle_responses.jsonl" - out_metrics = RESULTS_DIR / f"{tag}_oracle_metrics.json" - write_records(records, out_responses) - - metrics = compute_metrics(records) - metrics["interpretation_counts"] = info["label_counts"] - metrics["records_changed_from_orig"] = info["n_changed"] - metrics["model"] = label - with open(out_metrics, "w") as f: - json.dump(metrics, f, indent=2) - - print(f"{label:<14} {orig_correct/orig_total:>10.4f} " - f"{metrics['accuracy']:>11.4f} {metrics['mean_iou']:>10.4f} " - f"{info['n_changed']:>9d}") - print(f" interpretations used: {dict(sorted(info['label_counts'].items(), key=lambda x: -x[1]))}") - - -if __name__ == "__main__": - main() diff --git a/benchmarks/obj_detection/refcoco.py b/benchmarks/obj_detection/refcoco.py deleted file mode 100644 index ecd39c3..0000000 --- a/benchmarks/obj_detection/refcoco.py +++ /dev/null @@ -1,536 +0,0 @@ -""" -RefCOCO (+ optionally RefCOCO+ / RefCOCOg) benchmark for Interfaze. - -This is the canonical VLM grounding benchmark: given an image and a referring -expression, the model outputs a bounding box. A prediction is considered -correct if its IoU with the ground-truth box > 0.5 (Acc@IoU=0.5). This is -exactly the metric reported by Qwen3-VL, DeepSeek-VL2, InternVL 2.5/3, -GLM-4.x-V, CogVLM-Grounding, Kosmos-2 and essentially every VLM paper. - -Datasets (all lmms-lab packaging — same underlying RefCOCO data as the -original Kazemzadeh et al. 2014 / Mao et al. 2016 splits): - lmms-lab/RefCOCO val 8811, test 5000, testA 1975, testB 1810 - lmms-lab/RefCOCO+ val 8823, testA 1975, testB 1798 - lmms-lab/RefCOCOg val 7573, test 9602 (UMD split) - -Usage: - uv run -m benchmarks.obj_detection.refcoco # RefCOCO val - uv run -m benchmarks.obj_detection.refcoco --split testA - uv run -m benchmarks.obj_detection.refcoco --dataset lmms-lab/RefCOCO+ --split testB - uv run -m benchmarks.obj_detection.refcoco --limit 20 - uv run -m benchmarks.obj_detection.refcoco --evaluate-only - -Checkpointing: - Each successful sample is appended to results/_responses.jsonl - as it finishes. Reruns only query samples still missing. Failed-after- - retries samples are NOT written, so they retry on next run. -""" - -import sys -import json -import os -import re -import time -import base64 -import asyncio -import argparse -import traceback -import concurrent.futures -from io import BytesIO -from pathlib import Path - -from datasets import load_dataset -from tqdm import tqdm -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -sys.path.insert(0, str(PROJECT_ROOT)) - -from src.commons import invoke_interfaze # noqa: E402 - -RESULTS_DIR = PROJECT_ROOT / "results" -DEFAULT_DATASET = "lmms-lab/RefCOCO" -DEFAULT_SPLIT = "val" -REASONING_EFFORT = None # None => omit the reasoning_effort param entirely (interfaze default = off) -TEMPERATURE = 0.0 # deterministic decoding — matches standard VLM grounding eval protocol -CONCURRENCY = 25 -MAX_RETRIES = 3 -IOU_THRESHOLD = 0.5 - -# Canonical RefCOCO grounding prompt — wording from lmms-eval's `refcoco_rec` -# task (InternVL / Qwen-VL convention) plus the single output-format line that -# published "general VLM on RefCOCO" tables (LLaVA, GPT-4V, Claude) use to -# disambiguate xyxy vs yxyx ordering. No coord-space pinning, no step-by-step. -# `{width}` and `{height}` are accepted but unused so callers don't break. -PROMPT_TEMPLATE = ( - "Please provide the bounding box coordinate of the region this sentence describes: " - "{expression}\n\n" - "Output the coordinates in the format [x_min, y_min, x_max, y_max]." -) - - -BOX_LINE_PATTERN = re.compile( - r"(?im)^\s*(?:box|answer|bounding\s*box)[\s:]*" - r"\[?\s*(-?\d+(?:\.\d+)?)\s*[,\s]\s*(-?\d+(?:\.\d+)?)\s*[,\s]\s*" - r"(-?\d+(?:\.\d+)?)\s*[,\s]\s*(-?\d+(?:\.\d+)?)\s*\]?" -) -BOXED_PATTERN = re.compile( - r"\\boxed\{\s*\[?\s*(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*,\s*" - r"(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*\]?\s*\}" -) -BARE_4TUPLE = re.compile( - r"\[\s*(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*,\s*" - r"(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*\]" -) - - -class JsonlWriter: - def __init__(self, path: Path): - self.path = path - self.path.parent.mkdir(parents=True, exist_ok=True) - self._lock = asyncio.Lock() - - async def append(self, record: dict): - line = json.dumps(record, ensure_ascii=False) - async with self._lock: - with open(self.path, "a", encoding="utf-8") as f: - f.write(line + "\n") - f.flush() - os.fsync(f.fileno()) - - -def pil_to_data_url(image, max_side: int = 1024) -> tuple[str, int, int]: - """Encode PIL image as data URL. Returns (url, width, height) of the - image as sent to the model — coords are interpreted in this space.""" - if image.mode != "RGB": - image = image.convert("RGB") - w, h = image.size - scale = min(1.0, max_side / max(w, h)) - if scale < 1.0: - new_w, new_h = int(round(w * scale)), int(round(h * scale)) - image = image.resize((new_w, new_h)) - w, h = new_w, new_h - buf = BytesIO() - image.save(buf, format="JPEG", quality=92) - b64 = base64.b64encode(buf.getvalue()).decode("utf-8") - return f"data:image/jpeg;base64,{b64}", w, h - - -def coco_bbox_to_xyxy(bbox, scale_x: float = 1.0, scale_y: float = 1.0) -> list[float]: - """RefCOCO ground truth is COCO format [x, y, w, h]. Convert to - [x1, y1, x2, y2] and optionally rescale into a resized image space.""" - x, y, w, h = bbox - return [x * scale_x, y * scale_y, (x + w) * scale_x, (y + h) * scale_y] - - -JSON_TLBR_PATTERN = re.compile( - r'"top_left"\s*:\s*\{[^}]*?"x"\s*:\s*(-?\d+(?:\.\d+)?)[^}]*?"y"\s*:\s*(-?\d+(?:\.\d+)?)' - r'[^}]*?\}[^}]*?"bottom_right"\s*:\s*\{[^}]*?"x"\s*:\s*(-?\d+(?:\.\d+)?)' - r'[^}]*?"y"\s*:\s*(-?\d+(?:\.\d+)?)', - re.DOTALL, -) -JSON_BOX2D_PATTERN = re.compile( - r'"box_2d"\s*:\s*\[\s*(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*,\s*' - r'(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*\]' -) - - -def parse_box(text: str, width: int, height: int) -> list[float] | None: - """Extract a [x1, y1, x2, y2] bounding box in pixel space from the model - response. Robust to the formats general VLMs commonly emit on RefCOCO: - - - "Box: [a,b,c,d]" / "\\boxed{[a,b,c,d]}" → [x1,y1,x2,y2] - - bare last [a,b,c,d] in tail → [x1,y1,x2,y2] - - {"top_left":{"x","y"}, "bottom_right":{"x","y"}} - - {"box_2d":[ymin,xmin,ymax,xmax]} (Gemini convention, 0-1000) - - Coordinate space is auto-detected: 0-1.0 floats, 0-1000 normalized, or - raw pixels — picks whichever fits the image dimensions. - """ - if not text: - return None - - # 1. JSON object with top_left / bottom_right corners — already xyxy. - m = None - for match in JSON_TLBR_PATTERN.finditer(text): - m = match - if m is not None: - return _normalize_to_pixels( - [float(m.group(i)) for i in range(1, 5)], width, height, order="xyxy" - ) - - # 2. Gemini-style {"box_2d": [...]} — [ymin, xmin, ymax, xmax]. - m = None - for match in JSON_BOX2D_PATTERN.finditer(text): - m = match - if m is not None: - return _normalize_to_pixels( - [float(m.group(i)) for i in range(1, 5)], width, height, order="yxyx" - ) - - # 3. Box: / Answer: / \boxed{} prefixed line — assume xyxy. - for pattern in (BOX_LINE_PATTERN, BOXED_PATTERN): - matches = list(pattern.finditer(text)) - if matches: - m = matches[-1] - return _normalize_to_pixels( - [float(m.group(i)) for i in range(1, 5)], width, height, order="xyxy" - ) - - # 4. Fallback — last bare 4-tuple in the tail. - tail = text[-800:] - matches = list(BARE_4TUPLE.finditer(tail)) - if matches: - m = matches[-1] - return _normalize_to_pixels( - [float(m.group(i)) for i in range(1, 5)], width, height, order="xyxy" - ) - - return None - - -def _normalize_to_pixels( - box: list[float], width: int, height: int, order: str -) -> list[float]: - """Convert a 4-tuple to pixel-space [x1, y1, x2, y2] given its order - ('xyxy' or 'yxyx') and auto-detected coordinate scale (0-1.0 floats / - 0-1000 normalized / raw pixels). - """ - max_coord = max(abs(c) for c in box) - # Scale detection: pick the smallest scale whose interpretation fits - # the image, preferring 0-1 then 0-1000 then pixel. - if max_coord <= 1.0: - sx, sy = float(width), float(height) - elif max_coord <= 1000 and ( - max(width, height) > 1000 - or box[0] > width or box[2] > width - or box[1] > height or box[3] > height - ): - sx, sy = width / 1000.0, height / 1000.0 - else: - sx = sy = 1.0 - - if order == "xyxy": - return [box[0] * sx, box[1] * sy, box[2] * sx, box[3] * sy] - # yxyx (Gemini box_2d) → swap to xyxy and apply per-axis scale - return [box[1] * sx, box[0] * sy, box[3] * sx, box[2] * sy] - - -def compute_iou(a: list[float], b: list[float]) -> float: - ax1, ay1, ax2, ay2 = a - bx1, by1, bx2, by2 = b - # Normalize in case of (x1,y1,x2,y2) with swapped corners. - ax1, ax2 = min(ax1, ax2), max(ax1, ax2) - ay1, ay2 = min(ay1, ay2), max(ay1, ay2) - bx1, bx2 = min(bx1, bx2), max(bx1, bx2) - by1, by2 = min(by1, by2), max(by1, by2) - ix1 = max(ax1, bx1) - iy1 = max(ay1, by1) - ix2 = min(ax2, bx2) - iy2 = min(ay2, by2) - if ix1 >= ix2 or iy1 >= iy2: - return 0.0 - inter = (ix2 - ix1) * (iy2 - iy1) - area_a = max(0.0, (ax2 - ax1)) * max(0.0, (ay2 - ay1)) - area_b = max(0.0, (bx2 - bx1)) * max(0.0, (by2 - by1)) - union = area_a + area_b - inter - return inter / union if union > 0 else 0.0 - - -def build_samples(dataset) -> list[dict]: - """One sample per row, using the first referring expression. Matches - the convention used by lmms-eval / InternVL grounding evaluation.""" - samples = [] - for i, row in enumerate(dataset): - answers = row.get("answer") - if isinstance(answers, str): - expressions = [answers] - elif isinstance(answers, list) and answers: - expressions = [str(a) for a in answers if str(a).strip()] - else: - expressions = [] - if not expressions: - continue - samples.append({ - "id": f"{row.get('question_id', i)}_{i}", - "question_id": row.get("question_id"), - "file_name": row.get("file_name"), - "image": row["image"], - "expression": expressions[0], - "all_expressions": expressions, - "bbox_xywh": list(row["bbox"]), - }) - return samples - - -def load_completed_ids(path: Path) -> set[str]: - if not path.exists(): - return set() - done: set[str] = set() - with open(path, encoding="utf-8") as f: - for line_no, line in enumerate(f, 1): - line = line.strip() - if not line: - continue - try: - rec = json.loads(line) - except json.JSONDecodeError: - tqdm.write(f"[resume] skipping malformed line {line_no} in {path}") - continue - if rec.get("response"): - done.add(str(rec["id"])) - return done - - -def load_records(path: Path) -> list[dict]: - records: list[dict] = [] - if not path.exists(): - return records - with open(path, encoding="utf-8") as f: - for line in f: - line = line.strip() - if not line: - continue - try: - records.append(json.loads(line)) - except json.JSONDecodeError: - continue - by_id: dict[str, dict] = {} - for r in records: - by_id[str(r["id"])] = r - return list(by_id.values()) - - -async def process_sample(sample: dict, semaphore: asyncio.Semaphore, - writer: JsonlWriter, progress: dict) -> dict | None: - orig_w, orig_h = sample["image"].size - data_url, sent_w, sent_h = pil_to_data_url(sample["image"]) - sx = sent_w / orig_w - sy = sent_h / orig_h - gt_xyxy = coco_bbox_to_xyxy(sample["bbox_xywh"], sx, sy) - - prompt = PROMPT_TEMPLATE.format( - width=sent_w, height=sent_h, expression=sample["expression"] - ) - messages = [{ - "role": "user", - "content": [ - {"type": "text", "text": prompt}, - {"type": "image_url", "image_url": {"url": data_url}}, - ], - }] - last_error: str | None = None - - for attempt in range(1, MAX_RETRIES + 1): - start = time.perf_counter() - try: - async with semaphore: - start = time.perf_counter() - response = await asyncio.to_thread( - invoke_interfaze, - messages, - reasoning_effort=REASONING_EFFORT, - temperature=TEMPERATURE, - ) - latency_ms = int((time.perf_counter() - start) * 1000) - content = (response.choices[0].message.content or "").strip() - request_id = getattr(response, "id", None) - if not content: - last_error = "empty response content" - raise RuntimeError(last_error) - - pred_box = parse_box(content, sent_w, sent_h) - iou = compute_iou(pred_box, gt_xyxy) if pred_box else 0.0 - correct = pred_box is not None and iou >= IOU_THRESHOLD - - record = { - "id": sample["id"], - "question_id": sample["question_id"], - "file_name": sample["file_name"], - "expression": sample["expression"], - "all_expressions": sample["all_expressions"], - "image_width": sent_w, - "image_height": sent_h, - "gt_bbox_xyxy": gt_xyxy, - "pred_bbox_xyxy": pred_box, - "iou": iou, - "correct": correct, - "response": content, - "request_id": request_id, - "latency_ms": latency_ms, - "attempts": attempt, - } - await writer.append(record) - - progress["done"] += 1 - if correct: - progress["correct"] += 1 - mark = "OK" - else: - mark = "X " - tqdm.write( - f"[{progress['done']}/{progress['total']}] {mark} " - f"id={sample['id']} iou={iou:.3f} " - f"pred={pred_box} gt={[round(x,1) for x in gt_xyxy]} " - f"latency={latency_ms}ms req_id={request_id} attempt={attempt}" - ) - return record - - except Exception as e: - latency_ms = int((time.perf_counter() - start) * 1000) - last_error = f"{type(e).__name__}: {e}" - tqdm.write( - f"[error] id={sample['id']} attempt={attempt}/{MAX_RETRIES} " - f"latency={latency_ms}ms error={last_error}" - ) - if attempt < MAX_RETRIES: - await asyncio.sleep(2 ** (attempt - 1)) - - progress["failed"] += 1 - tqdm.write(f"[FAILED] id={sample['id']} after {MAX_RETRIES} attempts: {last_error}") - return None - - -def compute_metrics(results: list[dict]) -> dict: - total = len(results) - correct = sum(1 for r in results if r.get("correct")) - unparsed = sum(1 for r in results if r.get("pred_bbox_xyxy") is None) - ious = [r["iou"] for r in results if isinstance(r.get("iou"), (int, float))] - latencies = [r["latency_ms"] for r in results if isinstance(r.get("latency_ms"), int)] - - # Threshold sweep for sanity - thresholds = [0.3, 0.5, 0.7, 0.75, 0.9] - at_threshold = {f"acc@{t}": sum(1 for iou in ious if iou >= t) / len(ious) if ious else 0 - for t in thresholds} - - latency_stats = {} - if latencies: - lats = sorted(latencies) - n = len(lats) - latency_stats = { - "count": n, "mean_ms": sum(lats) / n, - "p50_ms": lats[n // 2], - "p90_ms": lats[min(n - 1, int(n * 0.9))], - "p99_ms": lats[min(n - 1, int(n * 0.99))], - "max_ms": lats[-1], - } - - return { - "accuracy": correct / total if total else 0.0, - "correct": correct, - "total": total, - "unparsed": unparsed, - "mean_iou": sum(ious) / len(ious) if ious else 0.0, - "iou_thresholds": at_threshold, - "latency": latency_stats, - } - - -def print_summary(metrics: dict, dataset_name: str, split: str): - print(f"\n{'=' * 60}") - print(f"Grounding Results — {dataset_name} / {split} (Interfaze, reasoning={REASONING_EFFORT})") - print(f"{'=' * 60}") - print(f"Acc@IoU=0.5 : {metrics['accuracy']:.4f} ({metrics['correct']}/{metrics['total']})") - print(f"Mean IoU : {metrics['mean_iou']:.4f}") - print(f"Unparsed : {metrics['unparsed']}") - if metrics.get("latency"): - lat = metrics["latency"] - print(f"Latency : mean={lat['mean_ms']:.0f}ms p50={lat['p50_ms']}ms " - f"p90={lat['p90_ms']}ms p99={lat['p99_ms']}ms max={lat['max_ms']}ms") - print("\nIoU thresholds:") - for t, acc in metrics["iou_thresholds"].items(): - print(f" {t}: {acc:.4f}") - - -def build_tag(dataset: str, split: str) -> str: - ds_slug = dataset.split("/")[-1].lower().replace("+", "plus") - return f"{ds_slug}_{split}" - - -async def run_predictions(dataset_name: str, split: str, pred_path: Path, limit: int | None): - print(f"Loading {dataset_name}, split={split}...") - dataset = load_dataset(dataset_name, split=split) - print(f"Loaded {len(dataset)} rows") - - samples = build_samples(dataset) - done_ids = load_completed_ids(pred_path) - pending = [s for s in samples if s["id"] not in done_ids] - if limit is not None: - pending = pending[:limit] - print(f"--limit applied: will run at most {limit} sample(s)") - print(f"Resume: {len(done_ids)} already completed, {len(pending)} remaining " - f"(checkpoint: {pred_path})") - if not pending: - return - - # Default asyncio thread pool is min(32, cpu_count + 4) — typically 12-16 - # on macOS, which would cap real in-flight HTTP calls below CONCURRENCY. - asyncio.get_running_loop().set_default_executor( - concurrent.futures.ThreadPoolExecutor(max_workers=CONCURRENCY) - ) - - writer = JsonlWriter(pred_path) - semaphore = asyncio.Semaphore(CONCURRENCY) - progress = {"total": len(pending), "done": 0, "correct": 0, "failed": 0} - tasks = [process_sample(s, semaphore, writer, progress) for s in pending] - try: - await tqdm_asyncio.gather(*tasks, desc=f"{dataset_name}/{split}") - except Exception: - traceback.print_exc() - print(f"\nRun finished: {progress['done']}/{progress['total']} answered, " - f"{progress['correct']} correct, {progress['failed']} failed.") - - -def run_evaluation(dataset_name: str, split: str, pred_path: Path, metrics_path: Path): - if not pred_path.exists(): - print(f"No predictions found at {pred_path}") - sys.exit(1) - results = load_records(pred_path) - if not results: - print(f"No records in {pred_path}") - sys.exit(1) - - metrics = compute_metrics(results) - print_summary(metrics, dataset_name, split) - output = { - **metrics, - "dataset": dataset_name, "split": split, - "reasoning_effort": REASONING_EFFORT, "concurrency": CONCURRENCY, - "iou_threshold": IOU_THRESHOLD, "model": "interfaze-beta", - } - metrics_path.parent.mkdir(parents=True, exist_ok=True) - with open(metrics_path, "w") as f: - json.dump(output, f, indent=2) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - parser = argparse.ArgumentParser( - description="RefCOCO / RefCOCO+ / RefCOCOg benchmark for Interfaze " - "(Acc@IoU=0.5, comparable to Qwen3-VL / DeepSeek-VL2 / InternVL / GLM-V)" - ) - parser.add_argument("--dataset", default=DEFAULT_DATASET, - help="lmms-lab/RefCOCO | lmms-lab/RefCOCO+ | lmms-lab/RefCOCOg") - parser.add_argument("--split", default=DEFAULT_SPLIT, - help="val | testA | testB | test (availability varies by dataset)") - parser.add_argument("--predict-only", action="store_true") - parser.add_argument("--evaluate-only", action="store_true") - parser.add_argument("--limit", type=int, default=None, - help="Only run the first N unanswered samples") - args = parser.parse_args() - - tag = build_tag(args.dataset, args.split) - pred_path = RESULTS_DIR / f"{tag}_responses.jsonl" - metrics_path = RESULTS_DIR / f"{tag}_metrics.json" - - if args.evaluate_only: - run_evaluation(args.dataset, args.split, pred_path, metrics_path) - elif args.predict_only: - asyncio.run(run_predictions(args.dataset, args.split, pred_path, limit=args.limit)) - else: - asyncio.run(run_predictions(args.dataset, args.split, pred_path, limit=args.limit)) - run_evaluation(args.dataset, args.split, pred_path, metrics_path) - - -if __name__ == "__main__": - main() diff --git a/benchmarks/obj_detection/refcoco_gemini.py b/benchmarks/obj_detection/refcoco_gemini.py deleted file mode 100644 index cf55bb7..0000000 --- a/benchmarks/obj_detection/refcoco_gemini.py +++ /dev/null @@ -1,351 +0,0 @@ -""" -RefCOCO benchmark for Gemini 2.5 Pro. - -Mirrors the protocol used by `refcoco.py` (interfaze-beta) so numbers are -directly comparable on the same splits at Acc@IoU=0.5. Only the inference -call changes — same prompt, same parser, same IoU, same checkpointing. - -Reasoning: Gemini 2.5 Pro cannot fully disable thinking (min budget = 128). -We use thinking_budget=128 to approximate the interfaze "reasoning off" -default. Temperature pinned to 0.0 to match. - -Datasets (lmms-lab packaging — same as standard RefCOCO splits): - lmms-lab/RefCOCO val 8811, test 5000, testA 1975, testB 1810 - lmms-lab/RefCOCO+ val 8823, testA 1975, testB 1798 - lmms-lab/RefCOCOg val 7573, test 9602 (UMD split) - -Usage: - uv run -m benchmarks.obj_detection.refcoco_gemini --split testA - uv run -m benchmarks.obj_detection.refcoco_gemini --split testA --limit 20 - uv run -m benchmarks.obj_detection.refcoco_gemini --split testA --evaluate-only - -Env: GEMINI_KEY must be set (loaded from .env). -""" - -import sys -import json -import os -import re -import time -import asyncio -import argparse -import traceback -import concurrent.futures -from pathlib import Path - -from datasets import load_dataset -from dotenv import load_dotenv -from google import genai -from google.genai import types -from tqdm import tqdm -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -sys.path.insert(0, str(PROJECT_ROOT)) - -from benchmarks.obj_detection.refcoco import ( # noqa: E402 - JsonlWriter, - PROMPT_TEMPLATE, - build_samples, - coco_bbox_to_xyxy, - compute_iou, - compute_metrics, - load_completed_ids, - load_records, - parse_box, - pil_to_data_url, -) - -load_dotenv() - -RESULTS_DIR = PROJECT_ROOT / "results" -DEFAULT_DATASET = "lmms-lab/RefCOCO" -DEFAULT_SPLIT = "val" -MODEL = "gemini-2.5-pro" -THINKING_BUDGET = 128 # Pro min — Pro API rejects 0 ("only works in thinking mode") -TEMPERATURE = 0.0 -CONCURRENCY = 5 # Pro 503s ("model overloaded") under heavier load -MAX_RETRIES = 6 -RETRY_BACKOFF_CAP_S = 30.0 -IOU_THRESHOLD = 0.5 - -GEMINI_KEY = os.getenv("GEMINI_KEY") or os.getenv("GEMINI_API_KEY") or os.getenv("GOOGLE_API_KEY") -if not GEMINI_KEY: - raise RuntimeError( - "GEMINI_KEY is not set. Add it to .env " - "(get one from https://aistudio.google.com/app/apikey)." - ) - -# google-genai's Client is thread-safe; one shared instance is fine. -gemini_client = genai.Client(api_key=GEMINI_KEY) - - -def invoke_gemini(prompt: str, jpeg_bytes: bytes): - """Single Gemini 2.5 Pro generate_content call. Returns the SDK response.""" - contents = [ - types.Content( - role="user", - parts=[ - types.Part.from_text(text=prompt), - types.Part.from_bytes(data=jpeg_bytes, mime_type="image/jpeg"), - ], - ), - ] - config = types.GenerateContentConfig( - temperature=TEMPERATURE, - thinking_config=types.ThinkingConfig(thinking_budget=THINKING_BUDGET), - ) - return gemini_client.models.generate_content( - model=MODEL, - contents=contents, - config=config, - ) - - -def data_url_to_jpeg_bytes(data_url: str) -> bytes: - """Strip the `data:image/jpeg;base64,` prefix and decode.""" - import base64 - _, b64 = data_url.split(",", 1) - return base64.b64decode(b64) - - -# Gemini's native bounding-box convention: [ymin, xmin, ymax, xmax] normalized -# to 0-1000 — documented in https://ai.google.dev/gemini-api/docs/image-understanding -# (and the format every Gemini grounding example uses). Asking for this format -# is the fair head-to-head equivalent of the Qwen / InternVL / interfaze prompts: -# each model is queried in the coordinate convention it was trained on. -PROMPT_GEMINI_TEMPLATE = """Locate the single region in the image described by the following expression and return its bounding box. - -Expression: "{expression}" - -Output ONLY a JSON object on the last line in this exact format: -{{"box_2d": [ymin, xmin, ymax, xmax]}} - -Coordinates must be normalized to 0-1000 where (0,0) is the top-left corner and (1000,1000) is the bottom-right corner of the image. Think step by step before answering.""" - - -JSON_BOX_PATTERN = re.compile( - r'"box_2d"\s*:\s*\[\s*(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*,\s*' - r'(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*\]' -) -BARE_4TUPLE_PATTERN = re.compile( - r'\[\s*(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*,\s*' - r'(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)\s*\]' -) - - -def parse_box_gemini(text: str, width: int, height: int) -> list[float] | None: - """Parse Gemini's native [ymin, xmin, ymax, xmax] in 0-1000 → pixel xyxy. - - Tries `"box_2d": [...]` first, falls back to the last bare 4-tuple in the - response. Returns [x1, y1, x2, y2] in pixel space, or None if unparseable. - """ - if not text: - return None - m = None - for match in JSON_BOX_PATTERN.finditer(text): - m = match - if m is None: - for match in BARE_4TUPLE_PATTERN.finditer(text): - m = match - if m is None: - return None - ymin, xmin, ymax, xmax = (float(m.group(i)) for i in range(1, 5)) - # Gemini almost always emits 0-1000 normalized; guard against the rare - # case where it emits raw pixels by checking whether values fit in [0, 1000]. - max_coord = max(abs(ymin), abs(xmin), abs(ymax), abs(xmax)) - if max_coord <= 1000: - x1 = xmin * width / 1000.0 - y1 = ymin * height / 1000.0 - x2 = xmax * width / 1000.0 - y2 = ymax * height / 1000.0 - else: - x1, y1, x2, y2 = xmin, ymin, xmax, ymax - return [x1, y1, x2, y2] - - -async def process_sample(sample: dict, semaphore: asyncio.Semaphore, - writer: JsonlWriter, progress: dict) -> dict | None: - orig_w, orig_h = sample["image"].size - data_url, sent_w, sent_h = pil_to_data_url(sample["image"]) - jpeg_bytes = data_url_to_jpeg_bytes(data_url) - sx = sent_w / orig_w - sy = sent_h / orig_h - gt_xyxy = coco_bbox_to_xyxy(sample["bbox_xywh"], sx, sy) - - prompt = PROMPT_TEMPLATE.format( - width=sent_w, height=sent_h, expression=sample["expression"] - ) - last_error: str | None = None - - for attempt in range(1, MAX_RETRIES + 1): - start = time.perf_counter() - try: - async with semaphore: - start = time.perf_counter() - response = await asyncio.to_thread(invoke_gemini, prompt, jpeg_bytes) - latency_ms = int((time.perf_counter() - start) * 1000) - content = (response.text or "").strip() - request_id = getattr(response, "response_id", None) - if not content: - last_error = "empty response content" - raise RuntimeError(last_error) - - pred_box = parse_box(content, sent_w, sent_h) - iou = compute_iou(pred_box, gt_xyxy) if pred_box else 0.0 - correct = pred_box is not None and iou >= IOU_THRESHOLD - - record = { - "id": sample["id"], - "question_id": sample["question_id"], - "file_name": sample["file_name"], - "expression": sample["expression"], - "all_expressions": sample["all_expressions"], - "image_width": sent_w, - "image_height": sent_h, - "gt_bbox_xyxy": gt_xyxy, - "pred_bbox_xyxy": pred_box, - "iou": iou, - "correct": correct, - "response": content, - "request_id": request_id, - "latency_ms": latency_ms, - "attempts": attempt, - } - await writer.append(record) - - progress["done"] += 1 - if correct: - progress["correct"] += 1 - mark = "OK" - else: - mark = "X " - tqdm.write( - f"[{progress['done']}/{progress['total']}] {mark} " - f"id={sample['id']} iou={iou:.3f} " - f"pred={pred_box} gt={[round(x,1) for x in gt_xyxy]} " - f"latency={latency_ms}ms req_id={request_id} attempt={attempt}" - ) - return record - - except Exception as e: - latency_ms = int((time.perf_counter() - start) * 1000) - last_error = f"{type(e).__name__}: {e}" - tqdm.write( - f"[error] id={sample['id']} attempt={attempt}/{MAX_RETRIES} " - f"latency={latency_ms}ms error={last_error}" - ) - if attempt < MAX_RETRIES: - await asyncio.sleep(min(2 ** (attempt - 1), RETRY_BACKOFF_CAP_S)) - - progress["failed"] += 1 - tqdm.write(f"[FAILED] id={sample['id']} after {MAX_RETRIES} attempts: {last_error}") - return None - - -def print_summary(metrics: dict, dataset_name: str, split: str): - print(f"\n{'=' * 60}") - print(f"Grounding Results — {dataset_name} / {split} ({MODEL}, thinking_budget={THINKING_BUDGET})") - print(f"{'=' * 60}") - print(f"Acc@IoU=0.5 : {metrics['accuracy']:.4f} ({metrics['correct']}/{metrics['total']})") - print(f"Mean IoU : {metrics['mean_iou']:.4f}") - print(f"Unparsed : {metrics['unparsed']}") - if metrics.get("latency"): - lat = metrics["latency"] - print(f"Latency : mean={lat['mean_ms']:.0f}ms p50={lat['p50_ms']}ms " - f"p90={lat['p90_ms']}ms p99={lat['p99_ms']}ms max={lat['max_ms']}ms") - print("\nIoU thresholds:") - for t, acc in metrics["iou_thresholds"].items(): - print(f" {t}: {acc:.4f}") - - -def build_tag(dataset: str, split: str) -> str: - ds_slug = dataset.split("/")[-1].lower().replace("+", "plus") - return f"gemini25pro_{ds_slug}_{split}" - - -async def run_predictions(dataset_name: str, split: str, pred_path: Path, limit: int | None): - print(f"Loading {dataset_name}, split={split}...") - dataset = load_dataset(dataset_name, split=split) - print(f"Loaded {len(dataset)} rows") - - samples = build_samples(dataset) - done_ids = load_completed_ids(pred_path) - pending = [s for s in samples if s["id"] not in done_ids] - if limit is not None: - pending = pending[:limit] - print(f"--limit applied: will run at most {limit} sample(s)") - print(f"Resume: {len(done_ids)} already completed, {len(pending)} remaining " - f"(checkpoint: {pred_path})") - if not pending: - return - - asyncio.get_running_loop().set_default_executor( - concurrent.futures.ThreadPoolExecutor(max_workers=CONCURRENCY) - ) - - writer = JsonlWriter(pred_path) - semaphore = asyncio.Semaphore(CONCURRENCY) - progress = {"total": len(pending), "done": 0, "correct": 0, "failed": 0} - tasks = [process_sample(s, semaphore, writer, progress) for s in pending] - try: - await tqdm_asyncio.gather(*tasks, desc=f"{dataset_name}/{split}") - except Exception: - traceback.print_exc() - print(f"\nRun finished: {progress['done']}/{progress['total']} answered, " - f"{progress['correct']} correct, {progress['failed']} failed.") - - -def run_evaluation(dataset_name: str, split: str, pred_path: Path, metrics_path: Path): - if not pred_path.exists(): - print(f"No predictions found at {pred_path}") - sys.exit(1) - results = load_records(pred_path) - if not results: - print(f"No records in {pred_path}") - sys.exit(1) - - metrics = compute_metrics(results) - print_summary(metrics, dataset_name, split) - output = { - **metrics, - "dataset": dataset_name, "split": split, - "thinking_budget": THINKING_BUDGET, "temperature": TEMPERATURE, - "concurrency": CONCURRENCY, "iou_threshold": IOU_THRESHOLD, "model": MODEL, - } - metrics_path.parent.mkdir(parents=True, exist_ok=True) - with open(metrics_path, "w") as f: - json.dump(output, f, indent=2) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - parser = argparse.ArgumentParser( - description="RefCOCO / RefCOCO+ / RefCOCOg benchmark for Gemini 2.5 Pro " - "(Acc@IoU=0.5, comparable to refcoco.py interfaze run)" - ) - parser.add_argument("--dataset", default=DEFAULT_DATASET, - help="lmms-lab/RefCOCO | lmms-lab/RefCOCO+ | lmms-lab/RefCOCOg") - parser.add_argument("--split", default=DEFAULT_SPLIT, - help="val | testA | testB | test (availability varies by dataset)") - parser.add_argument("--predict-only", action="store_true") - parser.add_argument("--evaluate-only", action="store_true") - parser.add_argument("--limit", type=int, default=None, - help="Only run the first N unanswered samples") - args = parser.parse_args() - - tag = build_tag(args.dataset, args.split) - pred_path = RESULTS_DIR / f"{tag}_responses.jsonl" - metrics_path = RESULTS_DIR / f"{tag}_metrics.json" - - if args.evaluate_only: - run_evaluation(args.dataset, args.split, pred_path, metrics_path) - elif args.predict_only: - asyncio.run(run_predictions(args.dataset, args.split, pred_path, limit=args.limit)) - else: - asyncio.run(run_predictions(args.dataset, args.split, pred_path, limit=args.limit)) - run_evaluation(args.dataset, args.split, pred_path, metrics_path) - - -if __name__ == "__main__": - main() diff --git a/benchmarks/obj_detection/refcoco_grok.py b/benchmarks/obj_detection/refcoco_grok.py deleted file mode 100644 index 6973c06..0000000 --- a/benchmarks/obj_detection/refcoco_grok.py +++ /dev/null @@ -1,288 +0,0 @@ -""" -RefCOCO benchmark for xAI Grok 4.3 via OpenRouter. - -Strict head-to-head with `refcoco.py` (interfaze): same prompt, same parser, -same IoU, same checkpointing — only the inference call changes. Mirrors -`refcoco_kimi.py` but routed at xAI's Grok 4.3. - -Reasoning: Grok 4.3's OpenRouter endpoint rejects `reasoning.enabled=false` -("Reasoning is mandatory for this endpoint"), so the closest analog to the -other refcoco runs' "reasoning off" is the lowest accepted effort tier, -`reasoning.effort="minimal"`. The reasoning_effort knob is exposed via ---reasoning-effort. Temperature pinned to 0.0. - -Usage: - uv run -m benchmarks.obj_detection.refcoco_grok --split testA - uv run -m benchmarks.obj_detection.refcoco_grok --split testA --limit 20 - uv run -m benchmarks.obj_detection.refcoco_grok --split testA --evaluate-only - -Env: OPENROUTER_API_KEY must be set (loaded from .env). -""" - -import sys -import json -import os -import time -import asyncio -import argparse -import traceback -import concurrent.futures -from pathlib import Path - -from datasets import load_dataset -from dotenv import load_dotenv -from openai import OpenAI -from tqdm import tqdm -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -sys.path.insert(0, str(PROJECT_ROOT)) - -from benchmarks.obj_detection.refcoco import ( # noqa: E402 - JsonlWriter, - PROMPT_TEMPLATE, - build_samples, - coco_bbox_to_xyxy, - compute_iou, - compute_metrics, - load_completed_ids, - load_records, - parse_box, - pil_to_data_url, -) - -load_dotenv() - -RESULTS_DIR = PROJECT_ROOT / "results" -DEFAULT_DATASET = "lmms-lab/RefCOCO" -DEFAULT_SPLIT = "val" -MODEL = "x-ai/grok-4.3" -DEFAULT_REASONING_EFFORT = "minimal" -TEMPERATURE = 0.0 -CONCURRENCY = 10 -MAX_RETRIES = 5 -RETRY_BACKOFF_CAP_S = 30.0 -IOU_THRESHOLD = 0.5 - -REASONING_EFFORT = DEFAULT_REASONING_EFFORT - -OPENROUTER_KEY = os.getenv("OPENROUTER_API_KEY") or os.getenv("OPENROUTER_KEY") -if not OPENROUTER_KEY: - raise RuntimeError( - "OPENROUTER_API_KEY is not set. Add it to .env " - "(get one from https://openrouter.ai/keys)." - ) - -openrouter_client = OpenAI( - base_url="https://openrouter.ai/api/v1", - api_key=OPENROUTER_KEY, -) - -def invoke_grok(messages: list[dict]): - """Single Grok 4.3 chat.completions call via OpenRouter.""" - return openrouter_client.chat.completions.create( - model=MODEL, - messages=messages, - temperature=TEMPERATURE, - extra_body={"reasoning": {"effort": REASONING_EFFORT}}, - ) - - -async def process_sample(sample: dict, semaphore: asyncio.Semaphore, - writer: JsonlWriter, progress: dict) -> dict | None: - orig_w, orig_h = sample["image"].size - data_url, sent_w, sent_h = pil_to_data_url(sample["image"]) - sx = sent_w / orig_w - sy = sent_h / orig_h - gt_xyxy = coco_bbox_to_xyxy(sample["bbox_xywh"], sx, sy) - - prompt = PROMPT_TEMPLATE.format( - width=sent_w, height=sent_h, expression=sample["expression"] - ) - messages = [{ - "role": "user", - "content": [ - {"type": "text", "text": prompt}, - {"type": "image_url", "image_url": {"url": data_url}}, - ], - }] - last_error: str | None = None - - for attempt in range(1, MAX_RETRIES + 1): - start = time.perf_counter() - try: - async with semaphore: - start = time.perf_counter() - response = await asyncio.to_thread(invoke_grok, messages) - latency_ms = int((time.perf_counter() - start) * 1000) - content = (response.choices[0].message.content or "").strip() - request_id = getattr(response, "id", None) - if not content: - last_error = "empty response content" - raise RuntimeError(last_error) - - pred_box = parse_box(content, sent_w, sent_h) - iou = compute_iou(pred_box, gt_xyxy) if pred_box else 0.0 - correct = pred_box is not None and iou >= IOU_THRESHOLD - - record = { - "id": sample["id"], - "question_id": sample["question_id"], - "file_name": sample["file_name"], - "expression": sample["expression"], - "all_expressions": sample["all_expressions"], - "image_width": sent_w, - "image_height": sent_h, - "gt_bbox_xyxy": gt_xyxy, - "pred_bbox_xyxy": pred_box, - "iou": iou, - "correct": correct, - "response": content, - "request_id": request_id, - "latency_ms": latency_ms, - "attempts": attempt, - } - await writer.append(record) - - progress["done"] += 1 - if correct: - progress["correct"] += 1 - mark = "OK" - else: - mark = "X " - tqdm.write( - f"[{progress['done']}/{progress['total']}] {mark} " - f"id={sample['id']} iou={iou:.3f} " - f"pred={pred_box} gt={[round(x,1) for x in gt_xyxy]} " - f"latency={latency_ms}ms req_id={request_id} attempt={attempt}" - ) - return record - - except Exception as e: - latency_ms = int((time.perf_counter() - start) * 1000) - last_error = f"{type(e).__name__}: {e}" - tqdm.write( - f"[error] id={sample['id']} attempt={attempt}/{MAX_RETRIES} " - f"latency={latency_ms}ms error={last_error}" - ) - if attempt < MAX_RETRIES: - await asyncio.sleep(min(2 ** (attempt - 1), RETRY_BACKOFF_CAP_S)) - - progress["failed"] += 1 - tqdm.write(f"[FAILED] id={sample['id']} after {MAX_RETRIES} attempts: {last_error}") - return None - - -def print_summary(metrics: dict, dataset_name: str, split: str): - print(f"\n{'=' * 60}") - print(f"Grounding Results — {dataset_name} / {split} ({MODEL}, reasoning_effort={REASONING_EFFORT})") - print(f"{'=' * 60}") - print(f"Acc@IoU=0.5 : {metrics['accuracy']:.4f} ({metrics['correct']}/{metrics['total']})") - print(f"Mean IoU : {metrics['mean_iou']:.4f}") - print(f"Unparsed : {metrics['unparsed']}") - if metrics.get("latency"): - lat = metrics["latency"] - print(f"Latency : mean={lat['mean_ms']:.0f}ms p50={lat['p50_ms']}ms " - f"p90={lat['p90_ms']}ms p99={lat['p99_ms']}ms max={lat['max_ms']}ms") - print("\nIoU thresholds:") - for t, acc in metrics["iou_thresholds"].items(): - print(f" {t}: {acc:.4f}") - - -def build_tag(dataset: str, split: str) -> str: - ds_slug = dataset.split("/")[-1].lower().replace("+", "plus") - return f"grok43_reasoning{REASONING_EFFORT}_{ds_slug}_{split}" - - -async def run_predictions(dataset_name: str, split: str, pred_path: Path, limit: int | None): - print(f"Loading {dataset_name}, split={split}...") - dataset = load_dataset(dataset_name, split=split) - print(f"Loaded {len(dataset)} rows") - - samples = build_samples(dataset) - done_ids = load_completed_ids(pred_path) - pending = [s for s in samples if s["id"] not in done_ids] - if limit is not None: - pending = pending[:limit] - print(f"--limit applied: will run at most {limit} sample(s)") - print(f"Resume: {len(done_ids)} already completed, {len(pending)} remaining " - f"(checkpoint: {pred_path})") - if not pending: - return - - asyncio.get_running_loop().set_default_executor( - concurrent.futures.ThreadPoolExecutor(max_workers=CONCURRENCY) - ) - - writer = JsonlWriter(pred_path) - semaphore = asyncio.Semaphore(CONCURRENCY) - progress = {"total": len(pending), "done": 0, "correct": 0, "failed": 0} - tasks = [process_sample(s, semaphore, writer, progress) for s in pending] - try: - await tqdm_asyncio.gather(*tasks, desc=f"{dataset_name}/{split}") - except Exception: - traceback.print_exc() - print(f"\nRun finished: {progress['done']}/{progress['total']} answered, " - f"{progress['correct']} correct, {progress['failed']} failed.") - - -def run_evaluation(dataset_name: str, split: str, pred_path: Path, metrics_path: Path): - if not pred_path.exists(): - print(f"No predictions found at {pred_path}") - sys.exit(1) - results = load_records(pred_path) - if not results: - print(f"No records in {pred_path}") - sys.exit(1) - - metrics = compute_metrics(results) - print_summary(metrics, dataset_name, split) - output = { - **metrics, - "dataset": dataset_name, "split": split, - "reasoning_effort": REASONING_EFFORT, "temperature": TEMPERATURE, - "concurrency": CONCURRENCY, "iou_threshold": IOU_THRESHOLD, - "model": MODEL, "provider": "openrouter", - } - metrics_path.parent.mkdir(parents=True, exist_ok=True) - with open(metrics_path, "w") as f: - json.dump(output, f, indent=2) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - global REASONING_EFFORT - parser = argparse.ArgumentParser( - description="RefCOCO benchmark for xAI Grok 4.3 via OpenRouter " - "(strict head-to-head with refcoco.py — same prompt, parser, IoU)" - ) - parser.add_argument("--dataset", default=DEFAULT_DATASET, - help="lmms-lab/RefCOCO | lmms-lab/RefCOCO+ | lmms-lab/RefCOCOg") - parser.add_argument("--split", default=DEFAULT_SPLIT, - help="val | testA | testB | test (availability varies by dataset)") - parser.add_argument("--reasoning-effort", default=DEFAULT_REASONING_EFFORT, - choices=["minimal", "low", "medium", "high"], - help="OpenRouter reasoning.effort (Grok 4.3 cannot fully disable; " - "minimal is the lowest accepted tier)") - parser.add_argument("--predict-only", action="store_true") - parser.add_argument("--evaluate-only", action="store_true") - parser.add_argument("--limit", type=int, default=None, - help="Only run the first N unanswered samples") - args = parser.parse_args() - REASONING_EFFORT = args.reasoning_effort - - tag = build_tag(args.dataset, args.split) - pred_path = RESULTS_DIR / f"{tag}_responses.jsonl" - metrics_path = RESULTS_DIR / f"{tag}_metrics.json" - - if args.evaluate_only: - run_evaluation(args.dataset, args.split, pred_path, metrics_path) - elif args.predict_only: - asyncio.run(run_predictions(args.dataset, args.split, pred_path, limit=args.limit)) - else: - asyncio.run(run_predictions(args.dataset, args.split, pred_path, limit=args.limit)) - run_evaluation(args.dataset, args.split, pred_path, metrics_path) - - -if __name__ == "__main__": - main() diff --git a/benchmarks/obj_detection/refcoco_kimi.py b/benchmarks/obj_detection/refcoco_kimi.py deleted file mode 100644 index 9dd4f61..0000000 --- a/benchmarks/obj_detection/refcoco_kimi.py +++ /dev/null @@ -1,290 +0,0 @@ -""" -RefCOCO benchmark for Kimi K2.6 via OpenRouter (provider pinned to Moonshot). - -Strict head-to-head with `refcoco.py` (interfaze): same prompt, same parser, -same IoU, same checkpointing — only the inference call changes. - -Model: `moonshotai/kimi-k2.6` (Apr 20, 2026 release — Moonshot's flagship -multimodal MoE, 1T total / 32B active, 256K context). OpenRouter exposes an -OpenAI-compatible API; we pin provider to `moonshotai` so Moonshot's own -inference is used (not a third-party reseller). - -Reasoning: passed via OpenRouter's `reasoning` extra_body parameter -({"enabled": false}) — equivalent to interfaze's `reasoning_effort=None`. -Temperature pinned to 0.0. - -Usage: - uv run -m benchmarks.obj_detection.refcoco_kimi --split testA - uv run -m benchmarks.obj_detection.refcoco_kimi --split testA --limit 20 - uv run -m benchmarks.obj_detection.refcoco_kimi --split testA --evaluate-only - -Env: OPENROUTER_KEY must be set (loaded from .env). -""" - -import sys -import json -import os -import time -import asyncio -import argparse -import traceback -import concurrent.futures -from pathlib import Path - -from datasets import load_dataset -from dotenv import load_dotenv -from openai import OpenAI -from tqdm import tqdm -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -sys.path.insert(0, str(PROJECT_ROOT)) - -from benchmarks.obj_detection.refcoco import ( # noqa: E402 - JsonlWriter, - PROMPT_TEMPLATE, - build_samples, - coco_bbox_to_xyxy, - compute_iou, - compute_metrics, - load_completed_ids, - load_records, - parse_box, - pil_to_data_url, -) - -load_dotenv() - -RESULTS_DIR = PROJECT_ROOT / "results" -DEFAULT_DATASET = "lmms-lab/RefCOCO" -DEFAULT_SPLIT = "val" -MODEL = "moonshotai/kimi-k2.6" -TEMPERATURE = 0.0 -CONCURRENCY = 10 -MAX_RETRIES = 5 -RETRY_BACKOFF_CAP_S = 30.0 -IOU_THRESHOLD = 0.5 - -OPENROUTER_KEY = os.getenv("OPENROUTER_KEY") or os.getenv("OPENROUTER_API_KEY") -if not OPENROUTER_KEY: - raise RuntimeError( - "OPENROUTER_KEY is not set. Add it to .env " - "(get one from https://openrouter.ai/keys)." - ) - -openrouter_client = OpenAI( - base_url="https://openrouter.ai/api/v1", - api_key=OPENROUTER_KEY, -) - -# `reasoning.enabled=false` = thinking off (OpenRouter unified reasoning API). -# `provider.only=["moonshotai"]` pins routing to Moonshot's own inference so -# we're benchmarking Moonshot's deployment, not a downstream reseller's. -EXTRA_BODY = { - "reasoning": {"enabled": False}, - "provider": {"only": ["moonshotai"]}, -} - - -def invoke_kimi(messages: list[dict]): - """Single Kimi K2.6 chat.completions call via OpenRouter.""" - return openrouter_client.chat.completions.create( - model=MODEL, - messages=messages, - temperature=TEMPERATURE, - extra_body=EXTRA_BODY, - ) - - -async def process_sample(sample: dict, semaphore: asyncio.Semaphore, - writer: JsonlWriter, progress: dict) -> dict | None: - orig_w, orig_h = sample["image"].size - data_url, sent_w, sent_h = pil_to_data_url(sample["image"]) - sx = sent_w / orig_w - sy = sent_h / orig_h - gt_xyxy = coco_bbox_to_xyxy(sample["bbox_xywh"], sx, sy) - - prompt = PROMPT_TEMPLATE.format( - width=sent_w, height=sent_h, expression=sample["expression"] - ) - messages = [{ - "role": "user", - "content": [ - {"type": "text", "text": prompt}, - {"type": "image_url", "image_url": {"url": data_url}}, - ], - }] - last_error: str | None = None - - for attempt in range(1, MAX_RETRIES + 1): - start = time.perf_counter() - try: - async with semaphore: - start = time.perf_counter() - response = await asyncio.to_thread(invoke_kimi, messages) - latency_ms = int((time.perf_counter() - start) * 1000) - content = (response.choices[0].message.content or "").strip() - request_id = getattr(response, "id", None) - if not content: - last_error = "empty response content" - raise RuntimeError(last_error) - - pred_box = parse_box(content, sent_w, sent_h) - iou = compute_iou(pred_box, gt_xyxy) if pred_box else 0.0 - correct = pred_box is not None and iou >= IOU_THRESHOLD - - record = { - "id": sample["id"], - "question_id": sample["question_id"], - "file_name": sample["file_name"], - "expression": sample["expression"], - "all_expressions": sample["all_expressions"], - "image_width": sent_w, - "image_height": sent_h, - "gt_bbox_xyxy": gt_xyxy, - "pred_bbox_xyxy": pred_box, - "iou": iou, - "correct": correct, - "response": content, - "request_id": request_id, - "latency_ms": latency_ms, - "attempts": attempt, - } - await writer.append(record) - - progress["done"] += 1 - if correct: - progress["correct"] += 1 - mark = "OK" - else: - mark = "X " - tqdm.write( - f"[{progress['done']}/{progress['total']}] {mark} " - f"id={sample['id']} iou={iou:.3f} " - f"pred={pred_box} gt={[round(x,1) for x in gt_xyxy]} " - f"latency={latency_ms}ms req_id={request_id} attempt={attempt}" - ) - return record - - except Exception as e: - latency_ms = int((time.perf_counter() - start) * 1000) - last_error = f"{type(e).__name__}: {e}" - tqdm.write( - f"[error] id={sample['id']} attempt={attempt}/{MAX_RETRIES} " - f"latency={latency_ms}ms error={last_error}" - ) - if attempt < MAX_RETRIES: - await asyncio.sleep(min(2 ** (attempt - 1), RETRY_BACKOFF_CAP_S)) - - progress["failed"] += 1 - tqdm.write(f"[FAILED] id={sample['id']} after {MAX_RETRIES} attempts: {last_error}") - return None - - -def print_summary(metrics: dict, dataset_name: str, split: str): - print(f"\n{'=' * 60}") - print(f"Grounding Results — {dataset_name} / {split} ({MODEL}, reasoning=off)") - print(f"{'=' * 60}") - print(f"Acc@IoU=0.5 : {metrics['accuracy']:.4f} ({metrics['correct']}/{metrics['total']})") - print(f"Mean IoU : {metrics['mean_iou']:.4f}") - print(f"Unparsed : {metrics['unparsed']}") - if metrics.get("latency"): - lat = metrics["latency"] - print(f"Latency : mean={lat['mean_ms']:.0f}ms p50={lat['p50_ms']}ms " - f"p90={lat['p90_ms']}ms p99={lat['p99_ms']}ms max={lat['max_ms']}ms") - print("\nIoU thresholds:") - for t, acc in metrics["iou_thresholds"].items(): - print(f" {t}: {acc:.4f}") - - -def build_tag(dataset: str, split: str) -> str: - ds_slug = dataset.split("/")[-1].lower().replace("+", "plus") - return f"kimi_k26_{ds_slug}_{split}" - - -async def run_predictions(dataset_name: str, split: str, pred_path: Path, limit: int | None): - print(f"Loading {dataset_name}, split={split}...") - dataset = load_dataset(dataset_name, split=split) - print(f"Loaded {len(dataset)} rows") - - samples = build_samples(dataset) - done_ids = load_completed_ids(pred_path) - pending = [s for s in samples if s["id"] not in done_ids] - if limit is not None: - pending = pending[:limit] - print(f"--limit applied: will run at most {limit} sample(s)") - print(f"Resume: {len(done_ids)} already completed, {len(pending)} remaining " - f"(checkpoint: {pred_path})") - if not pending: - return - - asyncio.get_running_loop().set_default_executor( - concurrent.futures.ThreadPoolExecutor(max_workers=CONCURRENCY) - ) - - writer = JsonlWriter(pred_path) - semaphore = asyncio.Semaphore(CONCURRENCY) - progress = {"total": len(pending), "done": 0, "correct": 0, "failed": 0} - tasks = [process_sample(s, semaphore, writer, progress) for s in pending] - try: - await tqdm_asyncio.gather(*tasks, desc=f"{dataset_name}/{split}") - except Exception: - traceback.print_exc() - print(f"\nRun finished: {progress['done']}/{progress['total']} answered, " - f"{progress['correct']} correct, {progress['failed']} failed.") - - -def run_evaluation(dataset_name: str, split: str, pred_path: Path, metrics_path: Path): - if not pred_path.exists(): - print(f"No predictions found at {pred_path}") - sys.exit(1) - results = load_records(pred_path) - if not results: - print(f"No records in {pred_path}") - sys.exit(1) - - metrics = compute_metrics(results) - print_summary(metrics, dataset_name, split) - output = { - **metrics, - "dataset": dataset_name, "split": split, - "reasoning": "off", "temperature": TEMPERATURE, - "concurrency": CONCURRENCY, "iou_threshold": IOU_THRESHOLD, - "model": MODEL, "provider": "moonshotai (via openrouter)", - } - metrics_path.parent.mkdir(parents=True, exist_ok=True) - with open(metrics_path, "w") as f: - json.dump(output, f, indent=2) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - parser = argparse.ArgumentParser( - description="RefCOCO benchmark for Kimi K2.6 via OpenRouter " - "(strict head-to-head with refcoco.py — same prompt, parser, IoU)" - ) - parser.add_argument("--dataset", default=DEFAULT_DATASET, - help="lmms-lab/RefCOCO | lmms-lab/RefCOCO+ | lmms-lab/RefCOCOg") - parser.add_argument("--split", default=DEFAULT_SPLIT, - help="val | testA | testB | test (availability varies by dataset)") - parser.add_argument("--predict-only", action="store_true") - parser.add_argument("--evaluate-only", action="store_true") - parser.add_argument("--limit", type=int, default=None, - help="Only run the first N unanswered samples") - args = parser.parse_args() - - tag = build_tag(args.dataset, args.split) - pred_path = RESULTS_DIR / f"{tag}_responses.jsonl" - metrics_path = RESULTS_DIR / f"{tag}_metrics.json" - - if args.evaluate_only: - run_evaluation(args.dataset, args.split, pred_path, metrics_path) - elif args.predict_only: - asyncio.run(run_predictions(args.dataset, args.split, pred_path, limit=args.limit)) - else: - asyncio.run(run_predictions(args.dataset, args.split, pred_path, limit=args.limit)) - run_evaluation(args.dataset, args.split, pred_path, metrics_path) - - -if __name__ == "__main__": - main() diff --git a/benchmarks/obj_detection/refcoco_multi.py b/benchmarks/obj_detection/refcoco_multi.py deleted file mode 100644 index 9239abe..0000000 --- a/benchmarks/obj_detection/refcoco_multi.py +++ /dev/null @@ -1,390 +0,0 @@ -""" -RefCOCO benchmark against multiple VLM providers (OpenAI, Anthropic, Google). - -Designed for head-to-head comparison with the interfaze-beta numbers from -`refcoco.py` on the same splits (Acc@IoU=0.5). Each provider runs with -thinking/reasoning DISABLED to match the interfaze run's `reasoning_effort=None`. - -Usage: - # Single-model run: - uv run -m benchmarks.obj_detection.refcoco_multi --provider openai --model gpt-5.4 - uv run -m benchmarks.obj_detection.refcoco_multi --provider anthropic --model claude-sonnet-4-6 - uv run -m benchmarks.obj_detection.refcoco_multi --provider gemini --model gemini-3.0-flash - - # Smoke test with --limit 1: - uv run -m benchmarks.obj_detection.refcoco_multi --provider openai --model gpt-5.4 --limit 1 - -Keys are loaded from ~/interfaze/.env.local (OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_KEY). -""" - -import sys -import json -import time -import base64 -import asyncio -import argparse -import traceback -from io import BytesIO -from pathlib import Path - -from datasets import load_dataset -from tqdm import tqdm -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -sys.path.insert(0, str(PROJECT_ROOT)) - -# Reuse parsing/IoU from the interfaze script — guarantees identical scoring. -from benchmarks.obj_detection.refcoco import ( # noqa: E402 - parse_box, compute_iou, coco_bbox_to_xyxy, - JsonlWriter, build_samples, load_completed_ids, load_records, - compute_metrics, print_summary, PROMPT_TEMPLATE, IOU_THRESHOLD, -) - - -class RateLimiter: - """Simple async token-bucket. Local to this script — refcoco.py does not - export one, so we don't try to import it.""" - def __init__(self, rate: int): - self.rate = rate - self.tokens = rate - self.last_refill = 0.0 - self._lock = asyncio.Lock() - - async def acquire(self): - while True: - async with self._lock: - now = asyncio.get_running_loop().time() - elapsed = now - self.last_refill - self.tokens = min(self.rate, self.tokens + elapsed * self.rate) - self.last_refill = now - if self.tokens >= 1: - self.tokens -= 1 - return - await asyncio.sleep(1 / self.rate) - -RESULTS_DIR = PROJECT_ROOT / "results" -DEFAULT_DATASET = "lmms-lab/RefCOCO" -DEFAULT_SPLIT = "testA" -RATE_LIMIT = 25 -MAX_RETRIES = 3 - - -def _load_interfaze_env() -> dict: - """Parse ~/interfaze/.env.local and return a dict of keys.""" - path = Path.home() / "interfaze" / ".env.local" - if not path.exists(): - raise FileNotFoundError(f"Expected keys at {path}") - env = {} - for line in path.read_text().splitlines(): - line = line.strip() - if not line or line.startswith("#") or "=" not in line: - continue - k, v = line.split("=", 1) - v = v.strip().strip('"').strip("'") - env[k.strip()] = v - return env - - -# -------------------------------------------------------------------------- -# Provider adapters -# -------------------------------------------------------------------------- -# Each adapter is a function: (image_pil, prompt, model) -> (content, request_id) -# Runs synchronously inside asyncio.to_thread. - -def _image_to_jpeg_bytes(image, max_side: int = 1024) -> tuple[bytes, int, int]: - if image.mode != "RGB": - image = image.convert("RGB") - w, h = image.size - scale = min(1.0, max_side / max(w, h)) - if scale < 1.0: - new_w, new_h = int(round(w * scale)), int(round(h * scale)) - image = image.resize((new_w, new_h)) - w, h = new_w, new_h - buf = BytesIO() - image.save(buf, format="JPEG", quality=92) - return buf.getvalue(), w, h - - -def call_openai(image, prompt: str, model: str, client) -> tuple[str, str, int, int]: - """Returns (content, request_id, width, height). - - For GPT-5.4 (and 5.2+) reasoning is OFF by default ("none"). We still pass - it explicitly to defend against future default changes. NOTE: "minimal" - is NOT a valid value on GPT-5.4 — valid are none/low/medium/high/xhigh. - """ - img_bytes, w, h = _image_to_jpeg_bytes(image) - b64 = base64.b64encode(img_bytes).decode("utf-8") - data_url = f"data:image/jpeg;base64,{b64}" - kwargs = { - "model": model, - "messages": [{ - "role": "user", - "content": [ - {"type": "text", "text": prompt}, - {"type": "image_url", "image_url": {"url": data_url}}, - ], - }], - } - if model.startswith("gpt-5") or model.startswith("o"): - kwargs["reasoning_effort"] = "none" - resp = client.chat.completions.create(**kwargs) - content = (resp.choices[0].message.content or "").strip() - return content, resp.id, w, h - - -def call_anthropic(image, prompt: str, model: str, client) -> tuple[str, str, int, int]: - """Extended thinking explicitly disabled — belt-and-suspenders even though - omitting `thinking` is off by default for Sonnet 4.6.""" - img_bytes, w, h = _image_to_jpeg_bytes(image) - b64 = base64.b64encode(img_bytes).decode("utf-8") - resp = client.messages.create( - model=model, - max_tokens=1024, - thinking={"type": "disabled"}, - messages=[{ - "role": "user", - "content": [ - {"type": "image", "source": {"type": "base64", "media_type": "image/jpeg", "data": b64}}, - {"type": "text", "text": prompt}, - ], - }], - ) - parts = [b.text for b in resp.content if getattr(b, "type", None) == "text"] - content = "\n".join(parts).strip() - return content, resp.id, w, h - - -def call_gemini(image, prompt: str, model: str, client) -> tuple[str, str, int, int]: - """Gemini 3.x has thinking ON by default (HIGH for Pro, can't disable). - We always override to the LOWEST supported level for the chosen model: - gemini-3*-pro*: 'low' (Pro rejects 'minimal'; min is 'low') - gemini-3*-flash*: 'minimal' (Flash supports 'minimal' as the floor) - Gemini 2.5 used thinking_budget; we don't support that path here.""" - from google.genai import types - img_bytes, w, h = _image_to_jpeg_bytes(image) - m = model.lower() - if "pro" in m: - thinking_level = "low" - else: - thinking_level = "minimal" - config = types.GenerateContentConfig( - temperature=0.0, - thinking_config=types.ThinkingConfig(thinking_level=thinking_level), - ) - resp = client.models.generate_content( - model=model, - contents=[ - types.Part.from_bytes(data=img_bytes, mime_type="image/jpeg"), - prompt, - ], - config=config, - ) - content = (resp.text or "").strip() - request_id = getattr(resp, "response_id", None) or "" - return content, request_id, w, h - - -# -------------------------------------------------------------------------- -# Provider-agnostic pipeline (mirrors refcoco.py's process_sample) -# -------------------------------------------------------------------------- - -async def process_sample(sample: dict, call_fn, model: str, rate_limiter, - writer: JsonlWriter, progress: dict, provider: str, - client) -> dict | None: - """Pre-resize the image to know the exact dims that will be sent, embed - those dims in the prompt, then call the provider adapter.""" - tmp_bytes, sent_w, sent_h = _image_to_jpeg_bytes(sample["image"]) - del tmp_bytes - orig_w, orig_h = sample["image"].size - sx = sent_w / orig_w - sy = sent_h / orig_h - gt_xyxy = coco_bbox_to_xyxy(sample["bbox_xywh"], sx, sy) - - prompt = PROMPT_TEMPLATE.format( - width=sent_w, height=sent_h, expression=sample["expression"] - ) - - last_error: str | None = None - for attempt in range(1, MAX_RETRIES + 1): - await rate_limiter.acquire() - start = time.perf_counter() - try: - content, request_id, _, _ = await asyncio.to_thread( - call_fn, sample["image"], prompt, model, client - ) - latency_ms = int((time.perf_counter() - start) * 1000) - if not content: - last_error = "empty response content" - raise RuntimeError(last_error) - - pred_box = parse_box(content, sent_w, sent_h) - iou = compute_iou(pred_box, gt_xyxy) if pred_box else 0.0 - correct = pred_box is not None and iou >= IOU_THRESHOLD - - record = { - "id": sample["id"], - "question_id": sample["question_id"], - "file_name": sample["file_name"], - "expression": sample["expression"], - "all_expressions": sample["all_expressions"], - "image_width": sent_w, - "image_height": sent_h, - "gt_bbox_xyxy": gt_xyxy, - "pred_bbox_xyxy": pred_box, - "iou": iou, - "correct": correct, - "response": content, - "request_id": request_id, - "latency_ms": latency_ms, - "attempts": attempt, - "provider": provider, - "model": model, - } - await writer.append(record) - progress["done"] += 1 - if correct: - progress["correct"] += 1 - mark = "OK" - else: - mark = "X " - tqdm.write( - f"[{provider} {progress['done']}/{progress['total']}] {mark} " - f"id={sample['id']} iou={iou:.3f} " - f"pred={pred_box} gt={[round(x,1) for x in gt_xyxy]} " - f"latency={latency_ms}ms req_id={request_id} attempt={attempt}" - ) - return record - - except Exception as e: - latency_ms = int((time.perf_counter() - start) * 1000) - last_error = f"{type(e).__name__}: {e}" - tqdm.write( - f"[{provider} error] id={sample['id']} attempt={attempt}/{MAX_RETRIES} " - f"latency={latency_ms}ms error={last_error}" - ) - if attempt < MAX_RETRIES: - await asyncio.sleep(2 ** (attempt - 1)) - - progress["failed"] += 1 - tqdm.write(f"[{provider} FAILED] id={sample['id']} after {MAX_RETRIES} attempts: {last_error}") - return None - - -# -------------------------------------------------------------------------- -# CLI -# -------------------------------------------------------------------------- - -def build_tag(dataset: str, split: str, provider: str, model: str) -> str: - ds_slug = dataset.split("/")[-1].lower().replace("+", "plus") - model_slug = model.replace("/", "_").replace(":", "_") - return f"{ds_slug}_{split}_{provider}_{model_slug}" - - -def build_client(provider: str, env: dict): - if provider == "openai": - from openai import OpenAI - return OpenAI(api_key=env["OPENAI_API_KEY"]) - if provider == "anthropic": - from anthropic import Anthropic - return Anthropic(api_key=env["ANTHROPIC_API_KEY"]) - if provider == "gemini": - from google import genai - return genai.Client(api_key=env["GEMINI_KEY"]) - raise ValueError(f"unknown provider: {provider}") - - -def get_call_fn(provider: str): - return { - "openai": call_openai, - "anthropic": call_anthropic, - "gemini": call_gemini, - }[provider] - - -async def run(provider: str, model: str, dataset_name: str, split: str, - pred_path: Path, limit: int | None): - env = _load_interfaze_env() - client = build_client(provider, env) - call_fn = get_call_fn(provider) - - print(f"[{provider}/{model}] Loading {dataset_name}, split={split}...") - dataset = load_dataset(dataset_name, split=split) - samples = build_samples(dataset) - - done_ids = load_completed_ids(pred_path) - pending = [s for s in samples if s["id"] not in done_ids] - if limit is not None: - pending = pending[:limit] - print(f"--limit applied: will run at most {limit} sample(s)") - print(f"Resume: {len(done_ids)} already completed, {len(pending)} remaining " - f"(checkpoint: {pred_path})") - if not pending: - return - - writer = JsonlWriter(pred_path) - rate_limiter = RateLimiter(RATE_LIMIT) - progress = {"total": len(pending), "done": 0, "correct": 0, "failed": 0} - tasks = [process_sample(s, call_fn, model, rate_limiter, writer, progress, provider, client) - for s in pending] - try: - await tqdm_asyncio.gather(*tasks, desc=f"{provider}/{model}") - except Exception: - traceback.print_exc() - print(f"\n[{provider}/{model}] Run finished: {progress['done']}/{progress['total']} answered, " - f"{progress['correct']} correct, {progress['failed']} failed.") - - -def run_evaluation(dataset_name: str, split: str, pred_path: Path, metrics_path: Path, - provider: str, model: str): - if not pred_path.exists(): - print(f"No predictions found at {pred_path}") - sys.exit(1) - results = load_records(pred_path) - if not results: - print(f"No records in {pred_path}") - sys.exit(1) - metrics = compute_metrics(results) - print_summary(metrics, dataset_name, split) - output = { - **metrics, - "dataset": dataset_name, "split": split, - "provider": provider, "model": model, - "rate_limit": RATE_LIMIT, "iou_threshold": IOU_THRESHOLD, - } - metrics_path.parent.mkdir(parents=True, exist_ok=True) - with open(metrics_path, "w") as f: - json.dump(output, f, indent=2) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - parser = argparse.ArgumentParser(description="Multi-provider RefCOCO eval") - parser.add_argument("--provider", required=True, choices=["openai", "anthropic", "gemini"]) - parser.add_argument("--model", required=True, help="Provider-specific model id") - parser.add_argument("--dataset", default=DEFAULT_DATASET) - parser.add_argument("--split", default=DEFAULT_SPLIT) - parser.add_argument("--predict-only", action="store_true") - parser.add_argument("--evaluate-only", action="store_true") - parser.add_argument("--limit", type=int, default=None) - args = parser.parse_args() - - tag = build_tag(args.dataset, args.split, args.provider, args.model) - pred_path = RESULTS_DIR / f"{tag}_responses.jsonl" - metrics_path = RESULTS_DIR / f"{tag}_metrics.json" - - if args.evaluate_only: - run_evaluation(args.dataset, args.split, pred_path, metrics_path, - args.provider, args.model) - elif args.predict_only: - asyncio.run(run(args.provider, args.model, args.dataset, args.split, - pred_path, limit=args.limit)) - else: - asyncio.run(run(args.provider, args.model, args.dataset, args.split, - pred_path, limit=args.limit)) - run_evaluation(args.dataset, args.split, pred_path, metrics_path, - args.provider, args.model) - - -if __name__ == "__main__": - main() diff --git a/benchmarks/obj_detection/refcoco_openai.py b/benchmarks/obj_detection/refcoco_openai.py deleted file mode 100644 index 3f9d071..0000000 --- a/benchmarks/obj_detection/refcoco_openai.py +++ /dev/null @@ -1,306 +0,0 @@ -""" -RefCOCO benchmark for OpenAI GPT-5 series. - -Strict head-to-head with `refcoco.py` (interfaze): same prompt, same parser, -same IoU, same checkpointing — only the inference call changes. - -Default model: `gpt-5.5`. Other models can be selected with `--model`, e.g. -`--model gpt-5.4-mini`. Reasoning defaults to fully off (`reasoning_effort="none"`), -which is accepted by the GPT-5.x family. Temperature pinned to 0.0. - -Usage: - uv run -m benchmarks.obj_detection.refcoco_openai --split testA - uv run -m benchmarks.obj_detection.refcoco_openai --split testA --model gpt-5.4-mini - uv run -m benchmarks.obj_detection.refcoco_openai --split testA --limit 20 - uv run -m benchmarks.obj_detection.refcoco_openai --split testA --evaluate-only - -Env: OPENAI_API_KEY must be set (loaded from .env). -""" - -import re -import sys -import json -import os -import time -import asyncio -import argparse -import traceback -import concurrent.futures -from pathlib import Path - -from datasets import load_dataset -from dotenv import load_dotenv -from openai import OpenAI -from tqdm import tqdm -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -sys.path.insert(0, str(PROJECT_ROOT)) - -from benchmarks.obj_detection.refcoco import ( # noqa: E402 - JsonlWriter, - PROMPT_TEMPLATE, - build_samples, - coco_bbox_to_xyxy, - compute_iou, - compute_metrics, - load_completed_ids, - load_records, - parse_box, - pil_to_data_url, -) - -load_dotenv() - -RESULTS_DIR = PROJECT_ROOT / "results" -DEFAULT_DATASET = "lmms-lab/RefCOCO" -DEFAULT_SPLIT = "val" -DEFAULT_MODEL = "gpt-5.5" -DEFAULT_REASONING_EFFORT = "none" -TEMPERATURE = 0.0 -CONCURRENCY = 10 -MAX_RETRIES = 5 -RETRY_BACKOFF_CAP_S = 30.0 -IOU_THRESHOLD = 0.5 - -# Mutated by CLI before any inference runs. -MODEL = DEFAULT_MODEL -REASONING_EFFORT = DEFAULT_REASONING_EFFORT - - -def model_slug(model: str) -> str: - """Compact model id used in output filenames. Matches existing convention: - 'gpt-5.5' -> 'gpt55', 'gpt-5.4-mini' -> 'gpt54mini'.""" - return re.sub(r"[^a-z0-9]", "", model.lower()) - -OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") -if not OPENAI_API_KEY: - raise RuntimeError( - "OPENAI_API_KEY is not set. Add it to .env " - "(get one from https://platform.openai.com/api-keys)." - ) - -# Override OPENAI_BASE_URL — `.env` points it at the interfaze worker for the -# main benchmark; we need to hit the real OpenAI API here. -openai_client = OpenAI( - base_url="https://api.openai.com/v1", - api_key=OPENAI_API_KEY, -) - - -def invoke_openai(messages: list[dict]): - """Single chat.completions call against the selected GPT-5.x model. - - GPT-5.x rejects non-default temperature when reasoning is engaged. We pass - `temperature=0.0` only when reasoning is fully off ('none'); otherwise we - omit it and let the API default apply.""" - kwargs = { - "model": MODEL, - "messages": messages, - "reasoning_effort": REASONING_EFFORT, - } - if REASONING_EFFORT == "none": - kwargs["temperature"] = TEMPERATURE - return openai_client.chat.completions.create(**kwargs) - - -async def process_sample(sample: dict, semaphore: asyncio.Semaphore, - writer: JsonlWriter, progress: dict) -> dict | None: - orig_w, orig_h = sample["image"].size - data_url, sent_w, sent_h = pil_to_data_url(sample["image"]) - sx = sent_w / orig_w - sy = sent_h / orig_h - gt_xyxy = coco_bbox_to_xyxy(sample["bbox_xywh"], sx, sy) - - prompt = PROMPT_TEMPLATE.format( - width=sent_w, height=sent_h, expression=sample["expression"] - ) - messages = [{ - "role": "user", - "content": [ - {"type": "text", "text": prompt}, - {"type": "image_url", "image_url": {"url": data_url}}, - ], - }] - last_error: str | None = None - - for attempt in range(1, MAX_RETRIES + 1): - start = time.perf_counter() - try: - async with semaphore: - start = time.perf_counter() - response = await asyncio.to_thread(invoke_openai, messages) - latency_ms = int((time.perf_counter() - start) * 1000) - content = (response.choices[0].message.content or "").strip() - request_id = getattr(response, "id", None) - if not content: - last_error = "empty response content" - raise RuntimeError(last_error) - - pred_box = parse_box(content, sent_w, sent_h) - iou = compute_iou(pred_box, gt_xyxy) if pred_box else 0.0 - correct = pred_box is not None and iou >= IOU_THRESHOLD - - record = { - "id": sample["id"], - "question_id": sample["question_id"], - "file_name": sample["file_name"], - "expression": sample["expression"], - "all_expressions": sample["all_expressions"], - "image_width": sent_w, - "image_height": sent_h, - "gt_bbox_xyxy": gt_xyxy, - "pred_bbox_xyxy": pred_box, - "iou": iou, - "correct": correct, - "response": content, - "request_id": request_id, - "latency_ms": latency_ms, - "attempts": attempt, - } - await writer.append(record) - - progress["done"] += 1 - if correct: - progress["correct"] += 1 - mark = "OK" - else: - mark = "X " - tqdm.write( - f"[{progress['done']}/{progress['total']}] {mark} " - f"id={sample['id']} iou={iou:.3f} " - f"pred={pred_box} gt={[round(x,1) for x in gt_xyxy]} " - f"latency={latency_ms}ms req_id={request_id} attempt={attempt}" - ) - return record - - except Exception as e: - latency_ms = int((time.perf_counter() - start) * 1000) - last_error = f"{type(e).__name__}: {e}" - tqdm.write( - f"[error] id={sample['id']} attempt={attempt}/{MAX_RETRIES} " - f"latency={latency_ms}ms error={last_error}" - ) - if attempt < MAX_RETRIES: - await asyncio.sleep(min(2 ** (attempt - 1), RETRY_BACKOFF_CAP_S)) - - progress["failed"] += 1 - tqdm.write(f"[FAILED] id={sample['id']} after {MAX_RETRIES} attempts: {last_error}") - return None - - -def print_summary(metrics: dict, dataset_name: str, split: str): - print(f"\n{'=' * 60}") - print(f"Grounding Results — {dataset_name} / {split} ({MODEL}, reasoning={REASONING_EFFORT})") - print(f"{'=' * 60}") - print(f"Acc@IoU=0.5 : {metrics['accuracy']:.4f} ({metrics['correct']}/{metrics['total']})") - print(f"Mean IoU : {metrics['mean_iou']:.4f}") - print(f"Unparsed : {metrics['unparsed']}") - if metrics.get("latency"): - lat = metrics["latency"] - print(f"Latency : mean={lat['mean_ms']:.0f}ms p50={lat['p50_ms']}ms " - f"p90={lat['p90_ms']}ms p99={lat['p99_ms']}ms max={lat['max_ms']}ms") - print("\nIoU thresholds:") - for t, acc in metrics["iou_thresholds"].items(): - print(f" {t}: {acc:.4f}") - - -def build_tag(dataset: str, split: str) -> str: - ds_slug = dataset.split("/")[-1].lower().replace("+", "plus") - return f"{model_slug(MODEL)}_{ds_slug}_{split}" - - -async def run_predictions(dataset_name: str, split: str, pred_path: Path, limit: int | None): - print(f"Loading {dataset_name}, split={split}...") - dataset = load_dataset(dataset_name, split=split) - print(f"Loaded {len(dataset)} rows") - - samples = build_samples(dataset) - done_ids = load_completed_ids(pred_path) - pending = [s for s in samples if s["id"] not in done_ids] - if limit is not None: - pending = pending[:limit] - print(f"--limit applied: will run at most {limit} sample(s)") - print(f"Resume: {len(done_ids)} already completed, {len(pending)} remaining " - f"(checkpoint: {pred_path})") - if not pending: - return - - asyncio.get_running_loop().set_default_executor( - concurrent.futures.ThreadPoolExecutor(max_workers=CONCURRENCY) - ) - - writer = JsonlWriter(pred_path) - semaphore = asyncio.Semaphore(CONCURRENCY) - progress = {"total": len(pending), "done": 0, "correct": 0, "failed": 0} - tasks = [process_sample(s, semaphore, writer, progress) for s in pending] - try: - await tqdm_asyncio.gather(*tasks, desc=f"{dataset_name}/{split}") - except Exception: - traceback.print_exc() - print(f"\nRun finished: {progress['done']}/{progress['total']} answered, " - f"{progress['correct']} correct, {progress['failed']} failed.") - - -def run_evaluation(dataset_name: str, split: str, pred_path: Path, metrics_path: Path): - if not pred_path.exists(): - print(f"No predictions found at {pred_path}") - sys.exit(1) - results = load_records(pred_path) - if not results: - print(f"No records in {pred_path}") - sys.exit(1) - - metrics = compute_metrics(results) - print_summary(metrics, dataset_name, split) - output = { - **metrics, - "dataset": dataset_name, "split": split, - "reasoning_effort": REASONING_EFFORT, "temperature": TEMPERATURE, - "concurrency": CONCURRENCY, "iou_threshold": IOU_THRESHOLD, - "model": MODEL, - } - metrics_path.parent.mkdir(parents=True, exist_ok=True) - with open(metrics_path, "w") as f: - json.dump(output, f, indent=2) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - global MODEL, REASONING_EFFORT - parser = argparse.ArgumentParser( - description="RefCOCO benchmark for OpenAI GPT-5.x " - "(strict head-to-head with refcoco.py — same prompt, parser, IoU)" - ) - parser.add_argument("--dataset", default=DEFAULT_DATASET, - help="lmms-lab/RefCOCO | lmms-lab/RefCOCO+ | lmms-lab/RefCOCOg") - parser.add_argument("--split", default=DEFAULT_SPLIT, - help="val | testA | testB | test (availability varies by dataset)") - parser.add_argument("--model", default=DEFAULT_MODEL, - help="OpenAI model id (e.g. gpt-5.5, gpt-5.4-mini)") - parser.add_argument("--reasoning-effort", default=DEFAULT_REASONING_EFFORT, - help="reasoning_effort param ('none', 'minimal', 'low', 'medium', 'high', etc.)") - parser.add_argument("--predict-only", action="store_true") - parser.add_argument("--evaluate-only", action="store_true") - parser.add_argument("--limit", type=int, default=None, - help="Only run the first N unanswered samples") - args = parser.parse_args() - - MODEL = args.model - REASONING_EFFORT = args.reasoning_effort - - tag = build_tag(args.dataset, args.split) - pred_path = RESULTS_DIR / f"{tag}_responses.jsonl" - metrics_path = RESULTS_DIR / f"{tag}_metrics.json" - - if args.evaluate_only: - run_evaluation(args.dataset, args.split, pred_path, metrics_path) - elif args.predict_only: - asyncio.run(run_predictions(args.dataset, args.split, pred_path, limit=args.limit)) - else: - asyncio.run(run_predictions(args.dataset, args.split, pred_path, limit=args.limit)) - run_evaluation(args.dataset, args.split, pred_path, metrics_path) - - -if __name__ == "__main__": - main() diff --git a/benchmarks/ocrbench_v2/bench.py b/benchmarks/ocrbench_v2/bench.py new file mode 100644 index 0000000..0ac6520 --- /dev/null +++ b/benchmarks/ocrbench_v2/bench.py @@ -0,0 +1,222 @@ +"""OCRBench v2: 10k OCR/vision tasks across 30 types; macro-of-category-means. + +The per-sample scorer (eval_scripts/eval.py) is reused verbatim — it is file- +based, needs CWD=benchmarks/ocrbench_v2 and eval_scripts on sys.path, and shells +out for text-spotting. Only the category aggregation is ported here. +""" + +from __future__ import annotations + +import json +import os +import sys +import tempfile +from collections import defaultdict +from pathlib import Path +from typing import Any + +from src.media import encode_image +from src.request import Message, ReasoningSpec, Request, TextPart + +NAME = "ocrbench_v2" +ID_KEY = "id" +PRIMARY_METRIC = "en_overall" +DEFAULTS = {"reasoning": "off", "rate_limit": 25, "max_in_flight": 8} + +_BENCH_DIR = Path(__file__).resolve().parent +_DATASET_ID = "lmms-lab/OCRBench-v2" +_SPLIT = "test" + +TEXT_SPOTTING_PROMPT_TEMPLATE = """Use OCR on this image to spot all text at {level}. The OCR tool returns each detected text region with its text content and four corner coordinates: top_left, top_right, bottom_left, bottom_right (each as an x,y pixel pair). + +Then use run code to write a Python script that takes those OCR results and: +1. For each text region, compute the axis-aligned bounding box from the four corners: + - x1 = min of all x coordinates (leftmost) + - y1 = min of all y coordinates (topmost) + - x2 = max of all x coordinates (rightmost) + - y2 = max of all y coordinates (bottommost) +2. Normalize each coordinate to the range 0-1000 by dividing by the image width (for x) or height (for y) and multiplying by 1000, then rounding to an integer. +3. Print the results as a Python list. + +Your final answer must be ONLY a Python list in this exact format, with no markdown, no code fences, no explanation: +[(x1, y1, x2, y2, "text"), (x1, y1, x2, y2, "text"), ...]""" + +TYPE_TO_EN = { + "text recognition en": "text_recognition", + "fine-grained text recognition en": "text_recognition", + "full-page OCR en": "text_recognition", + "text grounding en": "text_detection", + "VQA with position en": "text_detection", + "text spotting en": "text_spotting", + "key information extraction en": "relationship_extraction", + "key information mapping en": "relationship_extraction", + "document parsing en": "element_parsing", + "chart parsing en": "element_parsing", + "table parsing en": "element_parsing", + "formula recognition en": "element_parsing", + "math QA en": "mathematical_calculation", + "text counting en": "mathematical_calculation", + "document classification en": "visual_text_understanding", + "cognition VQA en": "visual_text_understanding", + "diagram QA en": "visual_text_understanding", + "reasoning VQA en": "knowledge_reasoning", + "science QA en": "knowledge_reasoning", + "APP agent en": "knowledge_reasoning", + "ASCII art classification en": "knowledge_reasoning", +} +TYPE_TO_CN = { + "full-page OCR cn": "text_recognition", + "key information extraction cn": "relationship_extraction", + "handwritten answer extraction cn": "relationship_extraction", + "document parsing cn": "element_parsing", + "table parsing cn": "element_parsing", + "formula recognition cn": "element_parsing", + "cognition VQA cn": "visual_text_understanding", + "reasoning VQA cn": "knowledge_reasoning", + "text translation cn": "knowledge_reasoning", +} +# distinct categories in first-seen order +EN_CATEGORIES = list(dict.fromkeys(TYPE_TO_EN.values())) +CN_CATEGORIES = list(dict.fromkeys(TYPE_TO_CN.values())) + +_DATASET: Any = None # lazily-held so images decode per request, not all 10k upfront + + +def get_spotting_prompt(question: str) -> str: + level = "line-level" if "line-level" in question else "word-level" + return TEXT_SPOTTING_PROMPT_TEMPLATE.format(level=level) + + +def _mk_sample(row, idx=None, image=None) -> dict: + question = row["question"] + if row["type"] == "text spotting en": + question = get_spotting_prompt(question) + s = { + "id": row["id"], + "dataset_name": row["dataset_name"], + "type": row["type"], + "question": question, + "answers": row["answers"], + } + # full runs carry only an index (image read lazily); smokes embed the image + if image is not None: + s["image"] = image + else: + s["idx"] = idx + return s + + +def load_samples(sample_size: int | None = None) -> list[dict]: + global _DATASET + from datasets import load_dataset + + if sample_size: + # stream the first N so a smoke doesn't download all 10k images + ds = load_dataset(_DATASET_ID, split=_SPLIT, streaming=True) + return [_mk_sample(dict(r), image=r["image"]) for r in ds.take(sample_size)] + + # full run: keep the split memory-mapped and read images lazily by index + # (materializing 10k decoded images would OOM) + ds = load_dataset(_DATASET_ID, split=_SPLIT) + _DATASET = ds + meta = ds.select_columns(["id", "dataset_name", "type", "question", "answers"]) + return [_mk_sample(meta[i], idx=i) for i in range(len(meta))] + + +def build_request(sample: dict, mode: str) -> Request: + if "image" in sample: + image = sample["image"] # streamed smoke: embedded + elif "idx" in sample: + image = _DATASET[sample["idx"]]["image"] # full run: decoded lazily + else: + raise KeyError("OCRBench sample missing both 'image' and 'idx'") + img = encode_image( + image, "image/jpeg" + ) # RGB + JPEG q95, no resize (matches runner) + return Request( + [Message("user", [TextPart(sample["question"]), img])], + reasoning=ReasoningSpec(mode), + temperature=0.0, + ) + + +def parse(response, sample) -> str: + return response.text or "" + + +def _run_per_sample_scorer(preds: list[dict]) -> list[dict]: + """Reuse eval_scripts/eval.py verbatim: it takes file paths, needs + CWD=benchmarks/ocrbench_v2 and eval_scripts on sys.path (text-spotting shells + out to relative dirs there).""" + eval_dir = _BENCH_DIR / "eval_scripts" + if str(eval_dir) not in sys.path: + sys.path.insert(0, str(eval_dir)) + from benchmarks.ocrbench_v2.eval_scripts.eval import process_predictions + + with tempfile.TemporaryDirectory() as td: + pred_path = Path(td) / "pred.json" + scored_path = Path(td) / "scored.json" + pred_path.write_text(json.dumps(preds, ensure_ascii=False)) + cwd = os.getcwd() + os.chdir(_BENCH_DIR) + try: + process_predictions(str(pred_path), str(scored_path)) + finally: + os.chdir(cwd) + return json.loads(scored_path.read_text()) + + +def aggregate(scored: list[dict]) -> dict: + """Macro-of-category-means: each category = mean of its per-sample scores, + overall = mean of the non-empty category means.""" + en: dict[str, list[float]] = defaultdict(list) + cn: dict[str, list[float]] = defaultdict(list) + for item in scored: + if "ignore" in item: + continue + t = item["type"] + if t in TYPE_TO_EN: + en[TYPE_TO_EN[t]].append(item["score"]) + elif t in TYPE_TO_CN: + cn[TYPE_TO_CN[t]].append(item["score"]) + + def cat_scores(buckets, categories): + return { + c: { + "avg": (sum(buckets[c]) / len(buckets[c]) if buckets[c] else 0.0), + "count": len(buckets[c]), + } + for c in categories + } + + en_scores = cat_scores(en, EN_CATEGORIES) + cn_scores = cat_scores(cn, CN_CATEGORIES) + en_avgs = [en_scores[c]["avg"] for c in EN_CATEGORIES if en_scores[c]["count"]] + cn_avgs = [cn_scores[c]["avg"] for c in CN_CATEGORIES if cn_scores[c]["count"]] + return { + "en_scores": en_scores, + "cn_scores": cn_scores, + "en_overall": sum(en_avgs) / len(en_avgs) if en_avgs else 0.0, + "cn_overall": sum(cn_avgs) / len(cn_avgs) if cn_avgs else 0.0, + } + + +def score(records: list[dict], samples: list[dict]) -> dict: + by_id = {s["id"]: s for s in samples} + preds = [] + for r in records: + s = by_id.get(r["id"]) + if s is None: + continue + preds.append( + { + "id": r["id"], + "dataset_name": s["dataset_name"], + "type": s["type"], + "question": s["question"], + "answers": s["answers"], + "predict": r.get("prediction") or "", + } + ) + scored = _run_per_sample_scorer(preds) + return aggregate(scored) diff --git a/benchmarks/ocrbench_v2/eval_scripts/eval.py b/benchmarks/ocrbench_v2/eval_scripts/eval.py index c6fd162..4e8bc97 100644 --- a/benchmarks/ocrbench_v2/eval_scripts/eval.py +++ b/benchmarks/ocrbench_v2/eval_scripts/eval.py @@ -107,9 +107,11 @@ def process_predictions(input_path, output_path): # Infer eval method when missing eval_type = data_item.get("eval") if not eval_type: - if (len(data_item["answers"]) == 1 + if ( + len(data_item["answers"]) == 1 and len(data_item["answers"][0]) <= 2 - and data_item["answers"][0].isalpha()): + and data_item["answers"][0].isalpha() + ): eval_type = "multiple choice" if eval_type == "multiple choice": if not isinstance(data_item["answers"], list): @@ -119,9 +121,7 @@ def process_predictions(input_path, output_path): if not isinstance(data_item["predict"], str): data_item["score"] = 0 else: - predict = "".join( - c for c in data_item["predict"] if c.isalpha() - ) + predict = "".join(c for c in data_item["predict"] if c.isalpha()) if predict == data_item["answers"][0]: data_item["score"] = 1 @@ -468,7 +468,11 @@ def process_predictions(input_path, output_path): elif data_item["type"] == "text spotting en": # Parse bbox/content from answers if not present (HF dataset format) # GT format: variable-length polygons (8, 12, 16, 28+ coords) followed by text - if "bbox" not in data_item and "answers" in data_item and data_item["answers"]: + if ( + "bbox" not in data_item + and "answers" in data_item + and data_item["answers"] + ): bboxes, contents = [], [] for line in data_item["answers"][0].strip().split("\n"): parts = line.split(",") @@ -489,9 +493,13 @@ def process_predictions(input_path, output_path): x2, y2 = max(x_coords), max(y_coords) bboxes.append([x1, y1, x2, y1, x2, y2, x1, y2]) contents.append(text) - elif num_coords >= 8 and num_coords == len(parts) and num_coords % 2 == 1: + elif ( + num_coords >= 8 + and num_coords == len(parts) + and num_coords % 2 == 1 + ): # All-numeric text (e.g. "1700"): odd coord count means last is text - coords = [int(p.strip()) for p in parts[:num_coords - 1]] + coords = [int(p.strip()) for p in parts[: num_coords - 1]] text = parts[num_coords - 1].strip() x_coords = coords[0::2] y_coords = coords[1::2] @@ -510,7 +518,9 @@ def process_predictions(input_path, output_path): if not predict_bbox: data_item["score"] = 0 else: - data_item["score"] = spotting_evaluation_normalized(predict_bbox, data_item) + data_item["score"] = spotting_evaluation_normalized( + predict_bbox, data_item + ) else: raise ValueError("Unknown task type!") diff --git a/benchmarks/ocrbench_v2/eval_scripts/eval_text_spotting_en.py b/benchmarks/ocrbench_v2/eval_scripts/eval_text_spotting_en.py index e7821f2..1fbc210 100644 --- a/benchmarks/ocrbench_v2/eval_scripts/eval_text_spotting_en.py +++ b/benchmarks/ocrbench_v2/eval_scripts/eval_text_spotting_en.py @@ -47,11 +47,7 @@ def extract_bounding_boxes(predict_str): continue text_content = ( - str(item[4]) - .replace("\n", "") - .strip() - .strip('"') - .strip("'") + str(item[4]).replace("\n", "").strip().strip('"').strip("'") ) # Normalize inverted coordinates @@ -186,7 +182,7 @@ def process_predictions(input_path, output_path): # Polygons always have even number of coords (pairs of x,y) if num_coords % 2 == 1: # Odd count means last "coord" is actually the text - coords = [int(p.strip()) for p in parts[:num_coords - 1]] + coords = [int(p.strip()) for p in parts[: num_coords - 1]] text = parts[num_coords - 1].strip() x_coords = coords[0::2] y_coords = coords[1::2] diff --git a/benchmarks/ocrbench_v2/ocrbench_v2.py b/benchmarks/ocrbench_v2/ocrbench_v2.py deleted file mode 100644 index 58c8a10..0000000 --- a/benchmarks/ocrbench_v2/ocrbench_v2.py +++ /dev/null @@ -1,350 +0,0 @@ -""" -OCRBench v2 benchmark for Interfaze. -10,000 QA pairs across 30 task types (EN + CN). - -Usage: - uv run python benchmarks/OCRBench_v2/ocrbench_v2.py - uv run python benchmarks/OCRBench_v2/ocrbench_v2.py --predict-only - uv run python benchmarks/OCRBench_v2/ocrbench_v2.py --evaluate-only -""" - -import sys -import json -import asyncio -import argparse -import base64 -from pathlib import Path -from io import BytesIO - -from datasets import load_dataset -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -BENCHMARK_DIR = Path(__file__).resolve().parent -RESULTS_DIR = PROJECT_ROOT / "results" -PRED_OUTPUT = RESULTS_DIR / "ocrbench_v2_predictions.json" -EVAL_OUTPUT = RESULTS_DIR / "ocrbench_v2_scored.json" - -sys.path.insert(0, str(PROJECT_ROOT)) -from src.commons import invoke_interfaze # noqa: E402 - -RATE_LIMIT = 25 -MAX_RETRIES = 3 - -TEXT_SPOTTING_PROMPT_TEMPLATE = """Use OCR on this image to spot all text at {level}. The OCR tool returns each detected text region with its text content and four corner coordinates: top_left, top_right, bottom_left, bottom_right (each as an x,y pixel pair). - -Then use run code to write a Python script that takes those OCR results and: -1. For each text region, compute the axis-aligned bounding box from the four corners: - - x1 = min of all x coordinates (leftmost) - - y1 = min of all y coordinates (topmost) - - x2 = max of all x coordinates (rightmost) - - y2 = max of all y coordinates (bottommost) -2. Normalize each coordinate to the range 0-1000 by dividing by the image width (for x) or height (for y) and multiplying by 1000, then rounding to an integer. -3. Print the results as a Python list. - -Your final answer must be ONLY a Python list in this exact format, with no markdown, no code fences, no explanation: -[(x1, y1, x2, y2, "text"), (x1, y1, x2, y2, "text"), ...]""" - - -def get_spotting_prompt(original_question: str) -> str: - """Preserve word-level vs line-level from the original question.""" - if "line-level" in original_question: - return TEXT_SPOTTING_PROMPT_TEMPLATE.format(level="line-level") - return TEXT_SPOTTING_PROMPT_TEMPLATE.format(level="word-level") - - -class RateLimiter: - def __init__(self, rate: int): - self.rate = rate - self.tokens = rate - self.last_refill = 0.0 - self._lock = asyncio.Lock() - - async def acquire(self): - while True: - async with self._lock: - now = asyncio.get_running_loop().time() - elapsed = now - self.last_refill - self.tokens = min(self.rate, self.tokens + elapsed * self.rate) - self.last_refill = now - if self.tokens >= 1: - self.tokens -= 1 - return - await asyncio.sleep(1 / self.rate) - - -def pil_to_data_url(image) -> str: - buffer = BytesIO() - image.save(buffer, format="JPEG", quality=95) - b64 = base64.b64encode(buffer.getvalue()).decode("utf-8") - return f"data:image/jpeg;base64,{b64}" - - -def build_messages(question: str, image_url: str) -> list[dict]: - return [ - { - "role": "user", - "content": [ - {"type": "text", "text": question}, - {"type": "image_url", "image_url": {"url": image_url}}, - ], - } - ] - - -async def process_sample(sample_meta: dict, rate_limiter): - messages = build_messages(sample_meta["question"], sample_meta["image_url"]) - - for attempt in range(MAX_RETRIES): - await rate_limiter.acquire() - try: - response = await asyncio.to_thread(invoke_interfaze, messages) - return response.choices[0].message.content - except Exception as e: - if attempt < MAX_RETRIES - 1: - await asyncio.sleep(2**attempt) - else: - print( - f"Failed after {MAX_RETRIES} attempts for id={sample_meta['id']}: {e}" - ) - return "" - - -BATCH_SIZE = 100 - - -async def run_predictions(): - print("Loading OCRBench v2 from HuggingFace...") - dataset = load_dataset("lmms-lab/OCRBench-v2", split="test") - total = len(dataset) - print(f"Loaded {total} samples") - - RESULTS_DIR.mkdir(parents=True, exist_ok=True) - - # Resume: load existing predictions and only re-run failures - existing = {} - if PRED_OUTPUT.exists(): - with open(PRED_OUTPUT) as f: - for item in json.load(f): - if item.get("predict", "") != "": - existing[item["id"]] = item - print(f"Resuming: {len(existing)} successful predictions found, skipping them") - - rate_limiter = RateLimiter(RATE_LIMIT) - output_data = {} - output_data.update(existing) - num_retried = 0 - - from tqdm import tqdm - - for batch_start in tqdm(range(0, total, BATCH_SIZE), desc="Batches"): - batch_end = min(batch_start + BATCH_SIZE, total) - batch = dataset[batch_start:batch_end] - - # Encode images to base64 and extract metadata, skip already-done samples - samples = [] - for i in range(len(batch["id"])): - sample_id = batch["id"][i] - if sample_id in existing: - continue - image_url = pil_to_data_url(batch["image"][i]) - question = batch["question"][i] - # Use custom prompt for text spotting EN - if batch["type"][i] == "text spotting en": - question = get_spotting_prompt(question) - samples.append( - { - "id": sample_id, - "dataset_name": batch["dataset_name"][i], - "type": batch["type"][i], - "question": question, - "answers": batch["answers"][i], - "image_url": image_url, - } - ) - del batch - - if not samples: - continue - - num_retried += len(samples) - tasks = [process_sample(s, rate_limiter) for s in samples] - predictions = await tqdm_asyncio.gather( - *tasks, desc=f"Predicting {batch_start}-{batch_end}", leave=False - ) - - for sample, pred in zip(samples, predictions): - output_data[sample["id"]] = { - "id": sample["id"], - "dataset_name": sample["dataset_name"], - "type": sample["type"], - "question": sample["question"], - "answers": sample["answers"], - "predict": pred, - } - - del samples, predictions - - # Sort by id and save - final_data = [output_data[i] for i in sorted(output_data.keys())] - with open(PRED_OUTPUT, "w", encoding="utf-8") as f: - json.dump(final_data, f, ensure_ascii=False, indent=2) - - num_failures = sum(1 for d in final_data if d.get("predict", "") == "") - print(f"\nPredictions saved to {PRED_OUTPUT}") - print( - f"Total: {total} | Retried: {num_retried} | Remaining failures: {num_failures}" - ) - - -def run_evaluation(): - if not PRED_OUTPUT.exists(): - print(f"No predictions found at {PRED_OUTPUT}") - print("Run with --predict-only first, or without flags to do both.") - sys.exit(1) - - eval_scripts_dir = BENCHMARK_DIR / "eval_scripts" - sys.path.insert(0, str(eval_scripts_dir)) - - # spotting_metric.py uses relative paths, so chdir to benchmark dir - import os - original_cwd = os.getcwd() - os.chdir(BENCHMARK_DIR) - - # Step 1: Score each sample using eval.py - print("Step 1: Scoring individual samples...") - from eval import process_predictions # noqa: E402 - - EVAL_OUTPUT.parent.mkdir(parents=True, exist_ok=True) - process_predictions(str(PRED_OUTPUT), str(EVAL_OUTPUT)) - - os.chdir(original_cwd) - print(f"Scored results saved to {EVAL_OUTPUT}") - - # Step 2: Compute overall metrics using get_score.py logic - print("\nStep 2: Computing overall metrics...") - with open(EVAL_OUTPUT) as f: - scored_data = json.load(f) - - en_scores = { - "text_recognition": [], - "text_detection": [], - "text_spotting": [], - "relationship_extraction": [], - "element_parsing": [], - "mathematical_calculation": [], - "visual_text_understanding": [], - "knowledge_reasoning": [], - } - cn_scores = { - "text_recognition": [], - "relationship_extraction": [], - "element_parsing": [], - "visual_text_understanding": [], - "knowledge_reasoning": [], - } - - type_to_en = { - "text recognition en": "text_recognition", - "fine-grained text recognition en": "text_recognition", - "full-page OCR en": "text_recognition", - "text grounding en": "text_detection", - "VQA with position en": "text_detection", - "text spotting en": "text_spotting", - "key information extraction en": "relationship_extraction", - "key information mapping en": "relationship_extraction", - "document parsing en": "element_parsing", - "chart parsing en": "element_parsing", - "table parsing en": "element_parsing", - "formula recognition en": "element_parsing", - "math QA en": "mathematical_calculation", - "text counting en": "mathematical_calculation", - "document classification en": "visual_text_understanding", - "cognition VQA en": "visual_text_understanding", - "diagram QA en": "visual_text_understanding", - "reasoning VQA en": "knowledge_reasoning", - "science QA en": "knowledge_reasoning", - "APP agent en": "knowledge_reasoning", - "ASCII art classification en": "knowledge_reasoning", - } - type_to_cn = { - "full-page OCR cn": "text_recognition", - "key information extraction cn": "relationship_extraction", - "handwritten answer extraction cn": "relationship_extraction", - "document parsing cn": "element_parsing", - "table parsing cn": "element_parsing", - "formula recognition cn": "element_parsing", - "cognition VQA cn": "visual_text_understanding", - "reasoning VQA cn": "knowledge_reasoning", - "text translation cn": "knowledge_reasoning", - } - - for item in scored_data: - if "ignore" in item: - continue - t = item["type"] - if t in type_to_en: - en_scores[type_to_en[t]].append(item["score"]) - elif t in type_to_cn: - cn_scores[type_to_cn[t]].append(item["score"]) - - def avg(lst): - return sum(lst) / len(lst) if lst else 0.0 - - en_avgs = {k: avg(v) for k, v in en_scores.items() if v} - cn_avgs = {k: avg(v) for k, v in cn_scores.items() if v} - en_overall = avg(list(en_avgs.values())) - cn_overall = avg(list(cn_avgs.values())) - - print(f"\n{'=' * 60}") - print("OCRBench v2 Results (Interfaze)") - print(f"{'=' * 60}") - print(f"\n{'Category':<30} {'EN':>8} {'CN':>8}") - print("-" * 48) - all_cats = sorted(set(list(en_scores.keys()) + list(cn_scores.keys()))) - for cat in all_cats: - en_val = f"{en_avgs[cat]:.3f}" if cat in en_avgs else " -" - cn_val = f"{cn_avgs[cat]:.3f}" if cat in cn_avgs else " -" - print(f"{cat:<30} {en_val:>8} {cn_val:>8}") - print("-" * 48) - print(f"{'OVERALL':<30} {en_overall:>8.3f} {cn_overall:>8.3f}") - - # Save metrics - metrics = { - "en_scores": { - k: {"avg": avg(v), "count": len(v)} for k, v in en_scores.items() - }, - "cn_scores": { - k: {"avg": avg(v), "count": len(v)} for k, v in cn_scores.items() - }, - "en_overall": en_overall, - "cn_overall": cn_overall, - "model": "interfaze-beta", - } - metrics_path = RESULTS_DIR / "ocrbench_v2_metrics.json" - with open(metrics_path, "w") as f: - json.dump(metrics, f, indent=2) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - parser = argparse.ArgumentParser(description="OCRBench v2 benchmark for Interfaze") - parser.add_argument( - "--predict-only", action="store_true", help="Only generate predictions" - ) - parser.add_argument( - "--evaluate-only", action="store_true", help="Only run evaluation" - ) - args = parser.parse_args() - - if args.evaluate_only: - run_evaluation() - elif args.predict_only: - asyncio.run(run_predictions()) - else: - asyncio.run(run_predictions()) - run_evaluation() - - -if __name__ == "__main__": - main() diff --git a/benchmarks/ocrbench_v2/ocrbench_v2_anthropic.py b/benchmarks/ocrbench_v2/ocrbench_v2_anthropic.py deleted file mode 100644 index 0527640..0000000 --- a/benchmarks/ocrbench_v2/ocrbench_v2_anthropic.py +++ /dev/null @@ -1,355 +0,0 @@ -""" -OCRBench v2 benchmark for Anthropic Claude. -10,000 QA pairs across 30 task types (EN + CN). - -Usage: - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_anthropic - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_anthropic --predict-only - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_anthropic --evaluate-only -""" - -import sys -import json -import asyncio -import argparse -import base64 -from pathlib import Path -from io import BytesIO - -from datasets import load_dataset -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -BENCHMARK_DIR = Path(__file__).resolve().parent -RESULTS_DIR = PROJECT_ROOT / "results" -PRED_OUTPUT = RESULTS_DIR / "ocrbench_v2_anthropic_predictions.json" -EVAL_OUTPUT = RESULTS_DIR / "ocrbench_v2_anthropic_scored.json" - -sys.path.insert(0, str(PROJECT_ROOT)) -from src.commons_anthropic import invoke_anthropic # noqa: E402 - -MODEL = "claude-sonnet-4-6" -RATE_LIMIT = 25 -MAX_RETRIES = 3 - -TEXT_SPOTTING_PROMPT_TEMPLATE = """Use OCR on this image to spot all text at {level}. The OCR tool returns each detected text region with its text content and four corner coordinates: top_left, top_right, bottom_left, bottom_right (each as an x,y pixel pair). - -Then use run code to write a Python script that takes those OCR results and: -1. For each text region, compute the axis-aligned bounding box from the four corners: - - x1 = min of all x coordinates (leftmost) - - y1 = min of all y coordinates (topmost) - - x2 = max of all x coordinates (rightmost) - - y2 = max of all y coordinates (bottommost) -2. Normalize each coordinate to the range 0-1000 by dividing by the image width (for x) or height (for y) and multiplying by 1000, then rounding to an integer. -3. Print the results as a Python list. - -Your final answer must be ONLY a Python list in this exact format, with no markdown, no code fences, no explanation: -[(x1, y1, x2, y2, "text"), (x1, y1, x2, y2, "text"), ...]""" - - -def get_spotting_prompt(original_question: str) -> str: - if "line-level" in original_question: - return TEXT_SPOTTING_PROMPT_TEMPLATE.format(level="line-level") - return TEXT_SPOTTING_PROMPT_TEMPLATE.format(level="word-level") - - -class RateLimiter: - def __init__(self, rate: int): - self.rate = rate - self.tokens = rate - self.last_refill = 0.0 - self._lock = asyncio.Lock() - - async def acquire(self): - while True: - async with self._lock: - now = asyncio.get_running_loop().time() - elapsed = now - self.last_refill - self.tokens = min(self.rate, self.tokens + elapsed * self.rate) - self.last_refill = now - if self.tokens >= 1: - self.tokens -= 1 - return - await asyncio.sleep(1 / self.rate) - - -def pil_to_b64(image) -> str: - buffer = BytesIO() - image.save(buffer, format="JPEG", quality=95) - return base64.b64encode(buffer.getvalue()).decode("utf-8") - - -def build_messages(question: str, image_b64: str) -> list[dict]: - return [ - { - "role": "user", - "content": [ - { - "type": "image", - "source": { - "type": "base64", - "media_type": "image/jpeg", - "data": image_b64, - }, - }, - {"type": "text", "text": question}, - ], - } - ] - - -def extract_text(response) -> str: - for block in response.content: - if block.type == "text": - return block.text - return "" - - -async def process_sample(sample_meta: dict, rate_limiter): - messages = build_messages(sample_meta["question"], sample_meta["image_b64"]) - - for attempt in range(MAX_RETRIES): - await rate_limiter.acquire() - try: - response = await asyncio.to_thread(invoke_anthropic, messages, MODEL) - return extract_text(response) - except Exception as e: - if attempt < MAX_RETRIES - 1: - await asyncio.sleep(2**attempt) - else: - print( - f"Failed after {MAX_RETRIES} attempts for id={sample_meta['id']}: {e}" - ) - return "" - - -BATCH_SIZE = 100 - - -async def run_predictions(): - print("Loading OCRBench v2 from HuggingFace...") - dataset = load_dataset("lmms-lab/OCRBench-v2", split="test") - total = len(dataset) - print(f"Loaded {total} samples") - - RESULTS_DIR.mkdir(parents=True, exist_ok=True) - - existing = {} - if PRED_OUTPUT.exists(): - with open(PRED_OUTPUT) as f: - for item in json.load(f): - if item.get("predict", "") != "": - existing[item["id"]] = item - print(f"Resuming: {len(existing)} successful predictions found, skipping them") - - rate_limiter = RateLimiter(RATE_LIMIT) - output_data = {} - output_data.update(existing) - num_retried = 0 - - from tqdm import tqdm - - for batch_start in tqdm(range(0, total, BATCH_SIZE), desc="Batches"): - batch_end = min(batch_start + BATCH_SIZE, total) - batch = dataset[batch_start:batch_end] - - samples = [] - for i in range(len(batch["id"])): - sample_id = batch["id"][i] - if sample_id in existing: - continue - image_b64 = pil_to_b64(batch["image"][i]) - question = batch["question"][i] - if batch["type"][i] == "text spotting en": - question = get_spotting_prompt(question) - samples.append( - { - "id": sample_id, - "dataset_name": batch["dataset_name"][i], - "type": batch["type"][i], - "question": question, - "answers": batch["answers"][i], - "image_b64": image_b64, - } - ) - del batch - - if not samples: - continue - - num_retried += len(samples) - tasks = [process_sample(s, rate_limiter) for s in samples] - predictions = await tqdm_asyncio.gather( - *tasks, desc=f"Predicting {batch_start}-{batch_end}", leave=False - ) - - for sample, pred in zip(samples, predictions): - output_data[sample["id"]] = { - "id": sample["id"], - "dataset_name": sample["dataset_name"], - "type": sample["type"], - "question": sample["question"], - "answers": sample["answers"], - "predict": pred, - } - - del samples, predictions - - final_data = [output_data[i] for i in sorted(output_data.keys())] - with open(PRED_OUTPUT, "w", encoding="utf-8") as f: - json.dump(final_data, f, ensure_ascii=False, indent=2) - - num_failures = sum(1 for d in final_data if d.get("predict", "") == "") - print(f"\nPredictions saved to {PRED_OUTPUT}") - print( - f"Total: {total} | Retried: {num_retried} | Remaining failures: {num_failures}" - ) - - -def run_evaluation(): - if not PRED_OUTPUT.exists(): - print(f"No predictions found at {PRED_OUTPUT}") - print("Run with --predict-only first, or without flags to do both.") - sys.exit(1) - - eval_scripts_dir = BENCHMARK_DIR / "eval_scripts" - sys.path.insert(0, str(eval_scripts_dir)) - - import os - original_cwd = os.getcwd() - os.chdir(BENCHMARK_DIR) - - print("Step 1: Scoring individual samples...") - from benchmarks.ocrbench_v2.eval_scripts.eval import process_predictions # noqa: E402 - - EVAL_OUTPUT.parent.mkdir(parents=True, exist_ok=True) - process_predictions(str(PRED_OUTPUT), str(EVAL_OUTPUT)) - - os.chdir(original_cwd) - print(f"Scored results saved to {EVAL_OUTPUT}") - - print("\nStep 2: Computing overall metrics...") - with open(EVAL_OUTPUT) as f: - scored_data = json.load(f) - - en_scores = { - "text_recognition": [], - "text_detection": [], - "text_spotting": [], - "relationship_extraction": [], - "element_parsing": [], - "mathematical_calculation": [], - "visual_text_understanding": [], - "knowledge_reasoning": [], - } - cn_scores = { - "text_recognition": [], - "relationship_extraction": [], - "element_parsing": [], - "visual_text_understanding": [], - "knowledge_reasoning": [], - } - - type_to_en = { - "text recognition en": "text_recognition", - "fine-grained text recognition en": "text_recognition", - "full-page OCR en": "text_recognition", - "text grounding en": "text_detection", - "VQA with position en": "text_detection", - "text spotting en": "text_spotting", - "key information extraction en": "relationship_extraction", - "key information mapping en": "relationship_extraction", - "document parsing en": "element_parsing", - "chart parsing en": "element_parsing", - "table parsing en": "element_parsing", - "formula recognition en": "element_parsing", - "math QA en": "mathematical_calculation", - "text counting en": "mathematical_calculation", - "document classification en": "visual_text_understanding", - "cognition VQA en": "visual_text_understanding", - "diagram QA en": "visual_text_understanding", - "reasoning VQA en": "knowledge_reasoning", - "science QA en": "knowledge_reasoning", - "APP agent en": "knowledge_reasoning", - "ASCII art classification en": "knowledge_reasoning", - } - type_to_cn = { - "full-page OCR cn": "text_recognition", - "key information extraction cn": "relationship_extraction", - "handwritten answer extraction cn": "relationship_extraction", - "document parsing cn": "element_parsing", - "table parsing cn": "element_parsing", - "formula recognition cn": "element_parsing", - "cognition VQA cn": "visual_text_understanding", - "reasoning VQA cn": "knowledge_reasoning", - "text translation cn": "knowledge_reasoning", - } - - for item in scored_data: - if "ignore" in item: - continue - t = item["type"] - if t in type_to_en: - en_scores[type_to_en[t]].append(item["score"]) - elif t in type_to_cn: - cn_scores[type_to_cn[t]].append(item["score"]) - - def avg(lst): - return sum(lst) / len(lst) if lst else 0.0 - - en_avgs = {k: avg(v) for k, v in en_scores.items() if v} - cn_avgs = {k: avg(v) for k, v in cn_scores.items() if v} - en_overall = avg(list(en_avgs.values())) - cn_overall = avg(list(cn_avgs.values())) - - print(f"\n{'=' * 60}") - print(f"OCRBench v2 Results ({MODEL})") - print(f"{'=' * 60}") - print(f"\n{'Category':<30} {'EN':>8} {'CN':>8}") - print("-" * 48) - all_cats = sorted(set(list(en_scores.keys()) + list(cn_scores.keys()))) - for cat in all_cats: - en_val = f"{en_avgs[cat]:.3f}" if cat in en_avgs else " -" - cn_val = f"{cn_avgs[cat]:.3f}" if cat in cn_avgs else " -" - print(f"{cat:<30} {en_val:>8} {cn_val:>8}") - print("-" * 48) - print(f"{'OVERALL':<30} {en_overall:>8.3f} {cn_overall:>8.3f}") - - metrics = { - "en_scores": { - k: {"avg": avg(v), "count": len(v)} for k, v in en_scores.items() - }, - "cn_scores": { - k: {"avg": avg(v), "count": len(v)} for k, v in cn_scores.items() - }, - "en_overall": en_overall, - "cn_overall": cn_overall, - "model": MODEL, - } - metrics_path = RESULTS_DIR / "ocrbench_v2_anthropic_metrics.json" - with open(metrics_path, "w") as f: - json.dump(metrics, f, indent=2) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - parser = argparse.ArgumentParser(description="OCRBench v2 benchmark for Anthropic Claude") - parser.add_argument( - "--predict-only", action="store_true", help="Only generate predictions" - ) - parser.add_argument( - "--evaluate-only", action="store_true", help="Only run evaluation" - ) - args = parser.parse_args() - - if args.evaluate_only: - run_evaluation() - elif args.predict_only: - asyncio.run(run_predictions()) - else: - asyncio.run(run_predictions()) - run_evaluation() - - -if __name__ == "__main__": - main() diff --git a/benchmarks/ocrbench_v2/ocrbench_v2_gemini.py b/benchmarks/ocrbench_v2/ocrbench_v2_gemini.py deleted file mode 100644 index 224f49a..0000000 --- a/benchmarks/ocrbench_v2/ocrbench_v2_gemini.py +++ /dev/null @@ -1,342 +0,0 @@ -""" -OCRBench v2 benchmark for Google Gemini. -10,000 QA pairs across 30 task types (EN + CN). - -Usage: - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_gemini - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_gemini --predict-only - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_gemini --evaluate-only -""" - -import sys -import json -import asyncio -import argparse -from pathlib import Path -from io import BytesIO - -from datasets import load_dataset -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -BENCHMARK_DIR = Path(__file__).resolve().parent -RESULTS_DIR = PROJECT_ROOT / "results" -PRED_OUTPUT = RESULTS_DIR / "ocrbench_v2_gemini_predictions.json" -EVAL_OUTPUT = RESULTS_DIR / "ocrbench_v2_gemini_scored.json" - -sys.path.insert(0, str(PROJECT_ROOT)) -from src.commons_gemini import invoke_gemini # noqa: E402 - -MODEL = "gemini-3-flash-preview" -RATE_LIMIT = 25 -MAX_RETRIES = 3 - -TEXT_SPOTTING_PROMPT_TEMPLATE = """Use OCR on this image to spot all text at {level}. The OCR tool returns each detected text region with its text content and four corner coordinates: top_left, top_right, bottom_left, bottom_right (each as an x,y pixel pair). - -Then use run code to write a Python script that takes those OCR results and: -1. For each text region, compute the axis-aligned bounding box from the four corners: - - x1 = min of all x coordinates (leftmost) - - y1 = min of all y coordinates (topmost) - - x2 = max of all x coordinates (rightmost) - - y2 = max of all y coordinates (bottommost) -2. Normalize each coordinate to the range 0-1000 by dividing by the image width (for x) or height (for y) and multiplying by 1000, then rounding to an integer. -3. Print the results as a Python list. - -Your final answer must be ONLY a Python list in this exact format, with no markdown, no code fences, no explanation: -[(x1, y1, x2, y2, "text"), (x1, y1, x2, y2, "text"), ...]""" - - -def get_spotting_prompt(original_question: str) -> str: - if "line-level" in original_question: - return TEXT_SPOTTING_PROMPT_TEMPLATE.format(level="line-level") - return TEXT_SPOTTING_PROMPT_TEMPLATE.format(level="word-level") - - -class RateLimiter: - def __init__(self, rate: int): - self.rate = rate - self.tokens = rate - self.last_refill = 0.0 - self._lock = asyncio.Lock() - - async def acquire(self): - while True: - async with self._lock: - now = asyncio.get_running_loop().time() - elapsed = now - self.last_refill - self.tokens = min(self.rate, self.tokens + elapsed * self.rate) - self.last_refill = now - if self.tokens >= 1: - self.tokens -= 1 - return - await asyncio.sleep(1 / self.rate) - - -def pil_to_jpeg_bytes(image) -> bytes: - buffer = BytesIO() - image.save(buffer, format="JPEG", quality=95) - return buffer.getvalue() - - -def build_contents(question: str, image_bytes: bytes) -> list: - from google.genai import types - - return [ - types.Part.from_bytes(data=image_bytes, mime_type="image/jpeg"), - question, - ] - - -def extract_text(response) -> str: - text = getattr(response, "text", None) - return text or "" - - -async def process_sample(sample_meta: dict, rate_limiter): - contents = build_contents(sample_meta["question"], sample_meta["image_bytes"]) - - for attempt in range(MAX_RETRIES): - await rate_limiter.acquire() - try: - response = await asyncio.to_thread(invoke_gemini, contents, MODEL) - return extract_text(response) - except Exception as e: - if attempt < MAX_RETRIES - 1: - await asyncio.sleep(2**attempt) - else: - print( - f"Failed after {MAX_RETRIES} attempts for id={sample_meta['id']}: {e}" - ) - return "" - - -BATCH_SIZE = 100 - - -async def run_predictions(): - print("Loading OCRBench v2 from HuggingFace...") - dataset = load_dataset("lmms-lab/OCRBench-v2", split="test") - total = len(dataset) - print(f"Loaded {total} samples") - - RESULTS_DIR.mkdir(parents=True, exist_ok=True) - - existing = {} - if PRED_OUTPUT.exists(): - with open(PRED_OUTPUT) as f: - for item in json.load(f): - if item.get("predict", "") != "": - existing[item["id"]] = item - print(f"Resuming: {len(existing)} successful predictions found, skipping them") - - rate_limiter = RateLimiter(RATE_LIMIT) - output_data = {} - output_data.update(existing) - num_retried = 0 - - from tqdm import tqdm - - for batch_start in tqdm(range(0, total, BATCH_SIZE), desc="Batches"): - batch_end = min(batch_start + BATCH_SIZE, total) - batch = dataset[batch_start:batch_end] - - samples = [] - for i in range(len(batch["id"])): - sample_id = batch["id"][i] - if sample_id in existing: - continue - image_bytes = pil_to_jpeg_bytes(batch["image"][i]) - question = batch["question"][i] - if batch["type"][i] == "text spotting en": - question = get_spotting_prompt(question) - samples.append( - { - "id": sample_id, - "dataset_name": batch["dataset_name"][i], - "type": batch["type"][i], - "question": question, - "answers": batch["answers"][i], - "image_bytes": image_bytes, - } - ) - del batch - - if not samples: - continue - - num_retried += len(samples) - tasks = [process_sample(s, rate_limiter) for s in samples] - predictions = await tqdm_asyncio.gather( - *tasks, desc=f"Predicting {batch_start}-{batch_end}", leave=False - ) - - for sample, pred in zip(samples, predictions): - output_data[sample["id"]] = { - "id": sample["id"], - "dataset_name": sample["dataset_name"], - "type": sample["type"], - "question": sample["question"], - "answers": sample["answers"], - "predict": pred, - } - - del samples, predictions - - final_data = [output_data[i] for i in sorted(output_data.keys())] - with open(PRED_OUTPUT, "w", encoding="utf-8") as f: - json.dump(final_data, f, ensure_ascii=False, indent=2) - - num_failures = sum(1 for d in final_data if d.get("predict", "") == "") - print(f"\nPredictions saved to {PRED_OUTPUT}") - print( - f"Total: {total} | Retried: {num_retried} | Remaining failures: {num_failures}" - ) - - -def run_evaluation(): - if not PRED_OUTPUT.exists(): - print(f"No predictions found at {PRED_OUTPUT}") - print("Run with --predict-only first, or without flags to do both.") - sys.exit(1) - - eval_scripts_dir = BENCHMARK_DIR / "eval_scripts" - sys.path.insert(0, str(eval_scripts_dir)) - - import os - original_cwd = os.getcwd() - os.chdir(BENCHMARK_DIR) - - print("Step 1: Scoring individual samples...") - from benchmarks.ocrbench_v2.eval_scripts.eval import process_predictions # noqa: E402 - - EVAL_OUTPUT.parent.mkdir(parents=True, exist_ok=True) - process_predictions(str(PRED_OUTPUT), str(EVAL_OUTPUT)) - - os.chdir(original_cwd) - print(f"Scored results saved to {EVAL_OUTPUT}") - - print("\nStep 2: Computing overall metrics...") - with open(EVAL_OUTPUT) as f: - scored_data = json.load(f) - - en_scores = { - "text_recognition": [], - "text_detection": [], - "text_spotting": [], - "relationship_extraction": [], - "element_parsing": [], - "mathematical_calculation": [], - "visual_text_understanding": [], - "knowledge_reasoning": [], - } - cn_scores = { - "text_recognition": [], - "relationship_extraction": [], - "element_parsing": [], - "visual_text_understanding": [], - "knowledge_reasoning": [], - } - - type_to_en = { - "text recognition en": "text_recognition", - "fine-grained text recognition en": "text_recognition", - "full-page OCR en": "text_recognition", - "text grounding en": "text_detection", - "VQA with position en": "text_detection", - "text spotting en": "text_spotting", - "key information extraction en": "relationship_extraction", - "key information mapping en": "relationship_extraction", - "document parsing en": "element_parsing", - "chart parsing en": "element_parsing", - "table parsing en": "element_parsing", - "formula recognition en": "element_parsing", - "math QA en": "mathematical_calculation", - "text counting en": "mathematical_calculation", - "document classification en": "visual_text_understanding", - "cognition VQA en": "visual_text_understanding", - "diagram QA en": "visual_text_understanding", - "reasoning VQA en": "knowledge_reasoning", - "science QA en": "knowledge_reasoning", - "APP agent en": "knowledge_reasoning", - "ASCII art classification en": "knowledge_reasoning", - } - type_to_cn = { - "full-page OCR cn": "text_recognition", - "key information extraction cn": "relationship_extraction", - "handwritten answer extraction cn": "relationship_extraction", - "document parsing cn": "element_parsing", - "table parsing cn": "element_parsing", - "formula recognition cn": "element_parsing", - "cognition VQA cn": "visual_text_understanding", - "reasoning VQA cn": "knowledge_reasoning", - "text translation cn": "knowledge_reasoning", - } - - for item in scored_data: - if "ignore" in item: - continue - t = item["type"] - if t in type_to_en: - en_scores[type_to_en[t]].append(item["score"]) - elif t in type_to_cn: - cn_scores[type_to_cn[t]].append(item["score"]) - - def avg(lst): - return sum(lst) / len(lst) if lst else 0.0 - - en_avgs = {k: avg(v) for k, v in en_scores.items() if v} - cn_avgs = {k: avg(v) for k, v in cn_scores.items() if v} - en_overall = avg(list(en_avgs.values())) - cn_overall = avg(list(cn_avgs.values())) - - print(f"\n{'=' * 60}") - print(f"OCRBench v2 Results ({MODEL})") - print(f"{'=' * 60}") - print(f"\n{'Category':<30} {'EN':>8} {'CN':>8}") - print("-" * 48) - all_cats = sorted(set(list(en_scores.keys()) + list(cn_scores.keys()))) - for cat in all_cats: - en_val = f"{en_avgs[cat]:.3f}" if cat in en_avgs else " -" - cn_val = f"{cn_avgs[cat]:.3f}" if cat in cn_avgs else " -" - print(f"{cat:<30} {en_val:>8} {cn_val:>8}") - print("-" * 48) - print(f"{'OVERALL':<30} {en_overall:>8.3f} {cn_overall:>8.3f}") - - metrics = { - "en_scores": { - k: {"avg": avg(v), "count": len(v)} for k, v in en_scores.items() - }, - "cn_scores": { - k: {"avg": avg(v), "count": len(v)} for k, v in cn_scores.items() - }, - "en_overall": en_overall, - "cn_overall": cn_overall, - "model": MODEL, - } - metrics_path = RESULTS_DIR / "ocrbench_v2_gemini_metrics.json" - with open(metrics_path, "w") as f: - json.dump(metrics, f, indent=2) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - parser = argparse.ArgumentParser(description="OCRBench v2 benchmark for Google Gemini") - parser.add_argument( - "--predict-only", action="store_true", help="Only generate predictions" - ) - parser.add_argument( - "--evaluate-only", action="store_true", help="Only run evaluation" - ) - args = parser.parse_args() - - if args.evaluate_only: - run_evaluation() - elif args.predict_only: - asyncio.run(run_predictions()) - else: - asyncio.run(run_predictions()) - run_evaluation() - - -if __name__ == "__main__": - main() diff --git a/benchmarks/ocrbench_v2/ocrbench_v2_gemini_pro_31.py b/benchmarks/ocrbench_v2/ocrbench_v2_gemini_pro_31.py deleted file mode 100644 index e53c652..0000000 --- a/benchmarks/ocrbench_v2/ocrbench_v2_gemini_pro_31.py +++ /dev/null @@ -1,342 +0,0 @@ -""" -OCRBench v2 benchmark for Google Gemini. -10,000 QA pairs across 30 task types (EN + CN). - -Usage: - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_gemini_pro_31 - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_gemini_pro_31 --predict-only - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_gemini_pro_31 --evaluate-only -""" - -import sys -import json -import asyncio -import argparse -from pathlib import Path -from io import BytesIO - -from datasets import load_dataset -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -BENCHMARK_DIR = Path(__file__).resolve().parent -RESULTS_DIR = PROJECT_ROOT / "results" -PRED_OUTPUT = RESULTS_DIR / "ocrbench_v2_gemini_pro_31_predictions.json" -EVAL_OUTPUT = RESULTS_DIR / "ocrbench_v2_gemini_pro_31_scored.json" - -sys.path.insert(0, str(PROJECT_ROOT)) -from src.commons_gemini import invoke_gemini # noqa: E402 - -MODEL = "gemini-3.1-pro-preview" -RATE_LIMIT = 50 -MAX_RETRIES = 3 - -TEXT_SPOTTING_PROMPT_TEMPLATE = """Use OCR on this image to spot all text at {level}. The OCR tool returns each detected text region with its text content and four corner coordinates: top_left, top_right, bottom_left, bottom_right (each as an x,y pixel pair). - -Then use run code to write a Python script that takes those OCR results and: -1. For each text region, compute the axis-aligned bounding box from the four corners: - - x1 = min of all x coordinates (leftmost) - - y1 = min of all y coordinates (topmost) - - x2 = max of all x coordinates (rightmost) - - y2 = max of all y coordinates (bottommost) -2. Normalize each coordinate to the range 0-1000 by dividing by the image width (for x) or height (for y) and multiplying by 1000, then rounding to an integer. -3. Print the results as a Python list. - -Your final answer must be ONLY a Python list in this exact format, with no markdown, no code fences, no explanation: -[(x1, y1, x2, y2, "text"), (x1, y1, x2, y2, "text"), ...]""" - - -def get_spotting_prompt(original_question: str) -> str: - if "line-level" in original_question: - return TEXT_SPOTTING_PROMPT_TEMPLATE.format(level="line-level") - return TEXT_SPOTTING_PROMPT_TEMPLATE.format(level="word-level") - - -class RateLimiter: - def __init__(self, rate: int): - self.rate = rate - self.tokens = rate - self.last_refill = 0.0 - self._lock = asyncio.Lock() - - async def acquire(self): - while True: - async with self._lock: - now = asyncio.get_running_loop().time() - elapsed = now - self.last_refill - self.tokens = min(self.rate, self.tokens + elapsed * self.rate) - self.last_refill = now - if self.tokens >= 1: - self.tokens -= 1 - return - await asyncio.sleep(1 / self.rate) - - -def pil_to_jpeg_bytes(image) -> bytes: - buffer = BytesIO() - image.save(buffer, format="JPEG", quality=95) - return buffer.getvalue() - - -def build_contents(question: str, image_bytes: bytes) -> list: - from google.genai import types - - return [ - types.Part.from_bytes(data=image_bytes, mime_type="image/jpeg"), - question, - ] - - -def extract_text(response) -> str: - text = getattr(response, "text", None) - return text or "" - - -async def process_sample(sample_meta: dict, rate_limiter): - contents = build_contents(sample_meta["question"], sample_meta["image_bytes"]) - - for attempt in range(MAX_RETRIES): - await rate_limiter.acquire() - try: - response = await asyncio.to_thread(invoke_gemini, contents, MODEL) - return extract_text(response) - except Exception as e: - if attempt < MAX_RETRIES - 1: - await asyncio.sleep(2**attempt) - else: - print( - f"Failed after {MAX_RETRIES} attempts for id={sample_meta['id']}: {e}" - ) - return "" - - -BATCH_SIZE = 200 - - -async def run_predictions(): - print("Loading OCRBench v2 from HuggingFace...") - dataset = load_dataset("lmms-lab/OCRBench-v2", split="test") - total = len(dataset) - print(f"Loaded {total} samples") - - RESULTS_DIR.mkdir(parents=True, exist_ok=True) - - existing = {} - if PRED_OUTPUT.exists(): - with open(PRED_OUTPUT) as f: - for item in json.load(f): - if item.get("predict", "") != "": - existing[item["id"]] = item - print(f"Resuming: {len(existing)} successful predictions found, skipping them") - - rate_limiter = RateLimiter(RATE_LIMIT) - output_data = {} - output_data.update(existing) - num_retried = 0 - - from tqdm import tqdm - - for batch_start in tqdm(range(0, total, BATCH_SIZE), desc="Batches"): - batch_end = min(batch_start + BATCH_SIZE, total) - batch = dataset[batch_start:batch_end] - - samples = [] - for i in range(len(batch["id"])): - sample_id = batch["id"][i] - if sample_id in existing: - continue - image_bytes = pil_to_jpeg_bytes(batch["image"][i]) - question = batch["question"][i] - if batch["type"][i] == "text spotting en": - question = get_spotting_prompt(question) - samples.append( - { - "id": sample_id, - "dataset_name": batch["dataset_name"][i], - "type": batch["type"][i], - "question": question, - "answers": batch["answers"][i], - "image_bytes": image_bytes, - } - ) - del batch - - if not samples: - continue - - num_retried += len(samples) - tasks = [process_sample(s, rate_limiter) for s in samples] - predictions = await tqdm_asyncio.gather( - *tasks, desc=f"Predicting {batch_start}-{batch_end}", leave=False - ) - - for sample, pred in zip(samples, predictions): - output_data[sample["id"]] = { - "id": sample["id"], - "dataset_name": sample["dataset_name"], - "type": sample["type"], - "question": sample["question"], - "answers": sample["answers"], - "predict": pred, - } - - del samples, predictions - - final_data = [output_data[i] for i in sorted(output_data.keys())] - with open(PRED_OUTPUT, "w", encoding="utf-8") as f: - json.dump(final_data, f, ensure_ascii=False, indent=2) - - num_failures = sum(1 for d in final_data if d.get("predict", "") == "") - print(f"\nPredictions saved to {PRED_OUTPUT}") - print( - f"Total: {total} | Retried: {num_retried} | Remaining failures: {num_failures}" - ) - - -def run_evaluation(): - if not PRED_OUTPUT.exists(): - print(f"No predictions found at {PRED_OUTPUT}") - print("Run with --predict-only first, or without flags to do both.") - sys.exit(1) - - eval_scripts_dir = BENCHMARK_DIR / "eval_scripts" - sys.path.insert(0, str(eval_scripts_dir)) - - import os - original_cwd = os.getcwd() - os.chdir(BENCHMARK_DIR) - - print("Step 1: Scoring individual samples...") - from benchmarks.ocrbench_v2.eval_scripts.eval import process_predictions # noqa: E402 - - EVAL_OUTPUT.parent.mkdir(parents=True, exist_ok=True) - process_predictions(str(PRED_OUTPUT), str(EVAL_OUTPUT)) - - os.chdir(original_cwd) - print(f"Scored results saved to {EVAL_OUTPUT}") - - print("\nStep 2: Computing overall metrics...") - with open(EVAL_OUTPUT) as f: - scored_data = json.load(f) - - en_scores = { - "text_recognition": [], - "text_detection": [], - "text_spotting": [], - "relationship_extraction": [], - "element_parsing": [], - "mathematical_calculation": [], - "visual_text_understanding": [], - "knowledge_reasoning": [], - } - cn_scores = { - "text_recognition": [], - "relationship_extraction": [], - "element_parsing": [], - "visual_text_understanding": [], - "knowledge_reasoning": [], - } - - type_to_en = { - "text recognition en": "text_recognition", - "fine-grained text recognition en": "text_recognition", - "full-page OCR en": "text_recognition", - "text grounding en": "text_detection", - "VQA with position en": "text_detection", - "text spotting en": "text_spotting", - "key information extraction en": "relationship_extraction", - "key information mapping en": "relationship_extraction", - "document parsing en": "element_parsing", - "chart parsing en": "element_parsing", - "table parsing en": "element_parsing", - "formula recognition en": "element_parsing", - "math QA en": "mathematical_calculation", - "text counting en": "mathematical_calculation", - "document classification en": "visual_text_understanding", - "cognition VQA en": "visual_text_understanding", - "diagram QA en": "visual_text_understanding", - "reasoning VQA en": "knowledge_reasoning", - "science QA en": "knowledge_reasoning", - "APP agent en": "knowledge_reasoning", - "ASCII art classification en": "knowledge_reasoning", - } - type_to_cn = { - "full-page OCR cn": "text_recognition", - "key information extraction cn": "relationship_extraction", - "handwritten answer extraction cn": "relationship_extraction", - "document parsing cn": "element_parsing", - "table parsing cn": "element_parsing", - "formula recognition cn": "element_parsing", - "cognition VQA cn": "visual_text_understanding", - "reasoning VQA cn": "knowledge_reasoning", - "text translation cn": "knowledge_reasoning", - } - - for item in scored_data: - if "ignore" in item: - continue - t = item["type"] - if t in type_to_en: - en_scores[type_to_en[t]].append(item["score"]) - elif t in type_to_cn: - cn_scores[type_to_cn[t]].append(item["score"]) - - def avg(lst): - return sum(lst) / len(lst) if lst else 0.0 - - en_avgs = {k: avg(v) for k, v in en_scores.items() if v} - cn_avgs = {k: avg(v) for k, v in cn_scores.items() if v} - en_overall = avg(list(en_avgs.values())) - cn_overall = avg(list(cn_avgs.values())) - - print(f"\n{'=' * 60}") - print(f"OCRBench v2 Results ({MODEL})") - print(f"{'=' * 60}") - print(f"\n{'Category':<30} {'EN':>8} {'CN':>8}") - print("-" * 48) - all_cats = sorted(set(list(en_scores.keys()) + list(cn_scores.keys()))) - for cat in all_cats: - en_val = f"{en_avgs[cat]:.3f}" if cat in en_avgs else " -" - cn_val = f"{cn_avgs[cat]:.3f}" if cat in cn_avgs else " -" - print(f"{cat:<30} {en_val:>8} {cn_val:>8}") - print("-" * 48) - print(f"{'OVERALL':<30} {en_overall:>8.3f} {cn_overall:>8.3f}") - - metrics = { - "en_scores": { - k: {"avg": avg(v), "count": len(v)} for k, v in en_scores.items() - }, - "cn_scores": { - k: {"avg": avg(v), "count": len(v)} for k, v in cn_scores.items() - }, - "en_overall": en_overall, - "cn_overall": cn_overall, - "model": MODEL, - } - metrics_path = RESULTS_DIR / "ocrbench_v2_gemini_pro_31_metrics.json" - with open(metrics_path, "w") as f: - json.dump(metrics, f, indent=2) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - parser = argparse.ArgumentParser(description="OCRBench v2 benchmark for Google Gemini") - parser.add_argument( - "--predict-only", action="store_true", help="Only generate predictions" - ) - parser.add_argument( - "--evaluate-only", action="store_true", help="Only run evaluation" - ) - args = parser.parse_args() - - if args.evaluate_only: - run_evaluation() - elif args.predict_only: - asyncio.run(run_predictions()) - else: - asyncio.run(run_predictions()) - run_evaluation() - - -if __name__ == "__main__": - main() diff --git a/benchmarks/ocrbench_v2/ocrbench_v2_grok.py b/benchmarks/ocrbench_v2/ocrbench_v2_grok.py deleted file mode 100644 index bd9cc25..0000000 --- a/benchmarks/ocrbench_v2/ocrbench_v2_grok.py +++ /dev/null @@ -1,347 +0,0 @@ -""" -OCRBench v2 benchmark for Grok 4.3 via OpenRouter. -10,000 QA pairs across 30 task types (EN + CN). - -Usage: - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_grok - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_grok --predict-only - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_grok --evaluate-only -""" - -import sys -import json -import asyncio -import argparse -import base64 -from pathlib import Path -from io import BytesIO - -from datasets import load_dataset -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -BENCHMARK_DIR = Path(__file__).resolve().parent -RESULTS_DIR = PROJECT_ROOT / "results" -PRED_OUTPUT = RESULTS_DIR / "ocrbench_v2_grok_predictions.json" -EVAL_OUTPUT = RESULTS_DIR / "ocrbench_v2_grok_scored.json" - -sys.path.insert(0, str(PROJECT_ROOT)) -from src.commons_openrouter import openrouter_client # noqa: E402 - -MODEL = "x-ai/grok-4.3" -RATE_LIMIT = 50 -MAX_RETRIES = 3 - -TEXT_SPOTTING_PROMPT_TEMPLATE = """Use OCR on this image to spot all text at {level}. The OCR tool returns each detected text region with its text content and four corner coordinates: top_left, top_right, bottom_left, bottom_right (each as an x,y pixel pair). - -Then use run code to write a Python script that takes those OCR results and: -1. For each text region, compute the axis-aligned bounding box from the four corners: - - x1 = min of all x coordinates (leftmost) - - y1 = min of all y coordinates (topmost) - - x2 = max of all x coordinates (rightmost) - - y2 = max of all y coordinates (bottommost) -2. Normalize each coordinate to the range 0-1000 by dividing by the image width (for x) or height (for y) and multiplying by 1000, then rounding to an integer. -3. Print the results as a Python list. - -Your final answer must be ONLY a Python list in this exact format, with no markdown, no code fences, no explanation: -[(x1, y1, x2, y2, "text"), (x1, y1, x2, y2, "text"), ...]""" - - -def get_spotting_prompt(original_question: str) -> str: - if "line-level" in original_question: - return TEXT_SPOTTING_PROMPT_TEMPLATE.format(level="line-level") - return TEXT_SPOTTING_PROMPT_TEMPLATE.format(level="word-level") - - -class RateLimiter: - def __init__(self, rate: int): - self.rate = rate - self.tokens = rate - self.last_refill = 0.0 - self._lock = asyncio.Lock() - - async def acquire(self): - while True: - async with self._lock: - now = asyncio.get_running_loop().time() - elapsed = now - self.last_refill - self.tokens = min(self.rate, self.tokens + elapsed * self.rate) - self.last_refill = now - if self.tokens >= 1: - self.tokens -= 1 - return - await asyncio.sleep(1 / self.rate) - - -def pil_to_data_url(image) -> str: - buffer = BytesIO() - image.save(buffer, format="JPEG", quality=95) - b64 = base64.b64encode(buffer.getvalue()).decode("utf-8") - return f"data:image/jpeg;base64,{b64}" - - -def build_messages(question: str, image_url: str) -> list[dict]: - return [ - { - "role": "user", - "content": [ - {"type": "text", "text": question}, - {"type": "image_url", "image_url": {"url": image_url}}, - ], - } - ] - - -async def process_sample(sample_meta: dict, rate_limiter): - messages = build_messages(sample_meta["question"], sample_meta["image_url"]) - - for attempt in range(MAX_RETRIES): - await rate_limiter.acquire() - try: - response = await asyncio.to_thread( - openrouter_client.chat.completions.create, - model=MODEL, - messages=messages, - reasoning_effort="low", - ) - return response.choices[0].message.content or "" - except Exception as e: - if attempt < MAX_RETRIES - 1: - await asyncio.sleep(2**attempt) - else: - print( - f"Failed after {MAX_RETRIES} attempts for id={sample_meta['id']}: {e}" - ) - return "" - - -BATCH_SIZE = 200 - - -async def run_predictions(): - print("Loading OCRBench v2 from HuggingFace...") - dataset = load_dataset("lmms-lab/OCRBench-v2", split="test") - total = len(dataset) - print(f"Loaded {total} samples") - - RESULTS_DIR.mkdir(parents=True, exist_ok=True) - - existing = {} - if PRED_OUTPUT.exists(): - with open(PRED_OUTPUT) as f: - for item in json.load(f): - if item.get("predict", "") != "": - existing[item["id"]] = item - print(f"Resuming: {len(existing)} successful predictions found, skipping them") - - rate_limiter = RateLimiter(RATE_LIMIT) - output_data = {} - output_data.update(existing) - num_retried = 0 - - from tqdm import tqdm - - for batch_start in tqdm(range(0, total, BATCH_SIZE), desc="Batches"): - batch_end = min(batch_start + BATCH_SIZE, total) - batch = dataset[batch_start:batch_end] - - samples = [] - for i in range(len(batch["id"])): - sample_id = batch["id"][i] - if sample_id in existing: - continue - image_url = pil_to_data_url(batch["image"][i]) - question = batch["question"][i] - if batch["type"][i] == "text spotting en": - question = get_spotting_prompt(question) - samples.append( - { - "id": sample_id, - "dataset_name": batch["dataset_name"][i], - "type": batch["type"][i], - "question": question, - "answers": batch["answers"][i], - "image_url": image_url, - } - ) - del batch - - if not samples: - continue - - num_retried += len(samples) - tasks = [process_sample(s, rate_limiter) for s in samples] - predictions = await tqdm_asyncio.gather( - *tasks, desc=f"Predicting {batch_start}-{batch_end}", leave=False - ) - - for sample, pred in zip(samples, predictions): - output_data[sample["id"]] = { - "id": sample["id"], - "dataset_name": sample["dataset_name"], - "type": sample["type"], - "question": sample["question"], - "answers": sample["answers"], - "predict": pred, - } - - del samples, predictions - - final_data = [output_data[i] for i in sorted(output_data.keys())] - with open(PRED_OUTPUT, "w", encoding="utf-8") as f: - json.dump(final_data, f, ensure_ascii=False, indent=2) - - num_failures = sum(1 for d in final_data if d.get("predict", "") == "") - print(f"\nPredictions saved to {PRED_OUTPUT}") - print( - f"Total: {total} | Retried: {num_retried} | Remaining failures: {num_failures}" - ) - - -def run_evaluation(): - if not PRED_OUTPUT.exists(): - print(f"No predictions found at {PRED_OUTPUT}") - print("Run with --predict-only first, or without flags to do both.") - sys.exit(1) - - eval_scripts_dir = BENCHMARK_DIR / "eval_scripts" - sys.path.insert(0, str(eval_scripts_dir)) - - import os - original_cwd = os.getcwd() - os.chdir(BENCHMARK_DIR) - - print("Step 1: Scoring individual samples...") - from benchmarks.ocrbench_v2.eval_scripts.eval import process_predictions # noqa: E402 - - EVAL_OUTPUT.parent.mkdir(parents=True, exist_ok=True) - process_predictions(str(PRED_OUTPUT), str(EVAL_OUTPUT)) - - os.chdir(original_cwd) - print(f"Scored results saved to {EVAL_OUTPUT}") - - print("\nStep 2: Computing overall metrics...") - with open(EVAL_OUTPUT) as f: - scored_data = json.load(f) - - en_scores = { - "text_recognition": [], - "text_detection": [], - "text_spotting": [], - "relationship_extraction": [], - "element_parsing": [], - "mathematical_calculation": [], - "visual_text_understanding": [], - "knowledge_reasoning": [], - } - cn_scores = { - "text_recognition": [], - "relationship_extraction": [], - "element_parsing": [], - "visual_text_understanding": [], - "knowledge_reasoning": [], - } - - type_to_en = { - "text recognition en": "text_recognition", - "fine-grained text recognition en": "text_recognition", - "full-page OCR en": "text_recognition", - "text grounding en": "text_detection", - "VQA with position en": "text_detection", - "text spotting en": "text_spotting", - "key information extraction en": "relationship_extraction", - "key information mapping en": "relationship_extraction", - "document parsing en": "element_parsing", - "chart parsing en": "element_parsing", - "table parsing en": "element_parsing", - "formula recognition en": "element_parsing", - "math QA en": "mathematical_calculation", - "text counting en": "mathematical_calculation", - "document classification en": "visual_text_understanding", - "cognition VQA en": "visual_text_understanding", - "diagram QA en": "visual_text_understanding", - "reasoning VQA en": "knowledge_reasoning", - "science QA en": "knowledge_reasoning", - "APP agent en": "knowledge_reasoning", - "ASCII art classification en": "knowledge_reasoning", - } - type_to_cn = { - "full-page OCR cn": "text_recognition", - "key information extraction cn": "relationship_extraction", - "handwritten answer extraction cn": "relationship_extraction", - "document parsing cn": "element_parsing", - "table parsing cn": "element_parsing", - "formula recognition cn": "element_parsing", - "cognition VQA cn": "visual_text_understanding", - "reasoning VQA cn": "knowledge_reasoning", - "text translation cn": "knowledge_reasoning", - } - - for item in scored_data: - if "ignore" in item: - continue - t = item["type"] - if t in type_to_en: - en_scores[type_to_en[t]].append(item["score"]) - elif t in type_to_cn: - cn_scores[type_to_cn[t]].append(item["score"]) - - def avg(lst): - return sum(lst) / len(lst) if lst else 0.0 - - en_avgs = {k: avg(v) for k, v in en_scores.items() if v} - cn_avgs = {k: avg(v) for k, v in cn_scores.items() if v} - en_overall = avg(list(en_avgs.values())) - cn_overall = avg(list(cn_avgs.values())) - - print(f"\n{'=' * 60}") - print(f"OCRBench v2 Results ({MODEL})") - print(f"{'=' * 60}") - print(f"\n{'Category':<30} {'EN':>8} {'CN':>8}") - print("-" * 48) - all_cats = sorted(set(list(en_scores.keys()) + list(cn_scores.keys()))) - for cat in all_cats: - en_val = f"{en_avgs[cat]:.3f}" if cat in en_avgs else " -" - cn_val = f"{cn_avgs[cat]:.3f}" if cat in cn_avgs else " -" - print(f"{cat:<30} {en_val:>8} {cn_val:>8}") - print("-" * 48) - print(f"{'OVERALL':<30} {en_overall:>8.3f} {cn_overall:>8.3f}") - - metrics = { - "en_scores": { - k: {"avg": avg(v), "count": len(v)} for k, v in en_scores.items() - }, - "cn_scores": { - k: {"avg": avg(v), "count": len(v)} for k, v in cn_scores.items() - }, - "en_overall": en_overall, - "cn_overall": cn_overall, - "model": MODEL, - } - metrics_path = RESULTS_DIR / "ocrbench_v2_grok_metrics.json" - with open(metrics_path, "w") as f: - json.dump(metrics, f, indent=2) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - parser = argparse.ArgumentParser(description="OCRBench v2 benchmark for Grok 4.3 via OpenRouter") - parser.add_argument( - "--predict-only", action="store_true", help="Only generate predictions" - ) - parser.add_argument( - "--evaluate-only", action="store_true", help="Only run evaluation" - ) - args = parser.parse_args() - - if args.evaluate_only: - run_evaluation() - elif args.predict_only: - asyncio.run(run_predictions()) - else: - asyncio.run(run_predictions()) - run_evaluation() - - -if __name__ == "__main__": - main() diff --git a/benchmarks/ocrbench_v2/ocrbench_v2_kimi.py b/benchmarks/ocrbench_v2/ocrbench_v2_kimi.py deleted file mode 100644 index 49a1f6e..0000000 --- a/benchmarks/ocrbench_v2/ocrbench_v2_kimi.py +++ /dev/null @@ -1,346 +0,0 @@ -""" -OCRBench v2 benchmark for Kimi K2.6 via OpenRouter. -10,000 QA pairs across 30 task types (EN + CN). - -Usage: - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_kimi - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_kimi --predict-only - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_kimi --evaluate-only -""" - -import sys -import json -import asyncio -import argparse -import base64 -from pathlib import Path -from io import BytesIO - -from datasets import load_dataset -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -BENCHMARK_DIR = Path(__file__).resolve().parent -RESULTS_DIR = PROJECT_ROOT / "results" -PRED_OUTPUT = RESULTS_DIR / "ocrbench_v2_kimi_predictions.json" -EVAL_OUTPUT = RESULTS_DIR / "ocrbench_v2_kimi_scored.json" - -sys.path.insert(0, str(PROJECT_ROOT)) -from src.commons_openrouter import openrouter_client # noqa: E402 - -MODEL = "moonshotai/kimi-k2.6" -RATE_LIMIT = 50 -MAX_RETRIES = 3 - -TEXT_SPOTTING_PROMPT_TEMPLATE = """Use OCR on this image to spot all text at {level}. The OCR tool returns each detected text region with its text content and four corner coordinates: top_left, top_right, bottom_left, bottom_right (each as an x,y pixel pair). - -Then use run code to write a Python script that takes those OCR results and: -1. For each text region, compute the axis-aligned bounding box from the four corners: - - x1 = min of all x coordinates (leftmost) - - y1 = min of all y coordinates (topmost) - - x2 = max of all x coordinates (rightmost) - - y2 = max of all y coordinates (bottommost) -2. Normalize each coordinate to the range 0-1000 by dividing by the image width (for x) or height (for y) and multiplying by 1000, then rounding to an integer. -3. Print the results as a Python list. - -Your final answer must be ONLY a Python list in this exact format, with no markdown, no code fences, no explanation: -[(x1, y1, x2, y2, "text"), (x1, y1, x2, y2, "text"), ...]""" - - -def get_spotting_prompt(original_question: str) -> str: - if "line-level" in original_question: - return TEXT_SPOTTING_PROMPT_TEMPLATE.format(level="line-level") - return TEXT_SPOTTING_PROMPT_TEMPLATE.format(level="word-level") - - -class RateLimiter: - def __init__(self, rate: int): - self.rate = rate - self.tokens = rate - self.last_refill = 0.0 - self._lock = asyncio.Lock() - - async def acquire(self): - while True: - async with self._lock: - now = asyncio.get_running_loop().time() - elapsed = now - self.last_refill - self.tokens = min(self.rate, self.tokens + elapsed * self.rate) - self.last_refill = now - if self.tokens >= 1: - self.tokens -= 1 - return - await asyncio.sleep(1 / self.rate) - - -def pil_to_data_url(image) -> str: - buffer = BytesIO() - image.save(buffer, format="JPEG", quality=95) - b64 = base64.b64encode(buffer.getvalue()).decode("utf-8") - return f"data:image/jpeg;base64,{b64}" - - -def build_messages(question: str, image_url: str) -> list[dict]: - return [ - { - "role": "user", - "content": [ - {"type": "text", "text": question}, - {"type": "image_url", "image_url": {"url": image_url}}, - ], - } - ] - - -async def process_sample(sample_meta: dict, rate_limiter): - messages = build_messages(sample_meta["question"], sample_meta["image_url"]) - - for attempt in range(MAX_RETRIES): - await rate_limiter.acquire() - try: - response = await asyncio.to_thread( - openrouter_client.chat.completions.create, - model=MODEL, - messages=messages, - ) - return response.choices[0].message.content or "" - except Exception as e: - if attempt < MAX_RETRIES - 1: - await asyncio.sleep(2**attempt) - else: - print( - f"Failed after {MAX_RETRIES} attempts for id={sample_meta['id']}: {e}" - ) - return "" - - -BATCH_SIZE = 200 - - -async def run_predictions(): - print("Loading OCRBench v2 from HuggingFace...") - dataset = load_dataset("lmms-lab/OCRBench-v2", split="test") - total = len(dataset) - print(f"Loaded {total} samples") - - RESULTS_DIR.mkdir(parents=True, exist_ok=True) - - existing = {} - if PRED_OUTPUT.exists(): - with open(PRED_OUTPUT) as f: - for item in json.load(f): - if item.get("predict", "") != "": - existing[item["id"]] = item - print(f"Resuming: {len(existing)} successful predictions found, skipping them") - - rate_limiter = RateLimiter(RATE_LIMIT) - output_data = {} - output_data.update(existing) - num_retried = 0 - - from tqdm import tqdm - - for batch_start in tqdm(range(0, total, BATCH_SIZE), desc="Batches"): - batch_end = min(batch_start + BATCH_SIZE, total) - batch = dataset[batch_start:batch_end] - - samples = [] - for i in range(len(batch["id"])): - sample_id = batch["id"][i] - if sample_id in existing: - continue - image_url = pil_to_data_url(batch["image"][i]) - question = batch["question"][i] - if batch["type"][i] == "text spotting en": - question = get_spotting_prompt(question) - samples.append( - { - "id": sample_id, - "dataset_name": batch["dataset_name"][i], - "type": batch["type"][i], - "question": question, - "answers": batch["answers"][i], - "image_url": image_url, - } - ) - del batch - - if not samples: - continue - - num_retried += len(samples) - tasks = [process_sample(s, rate_limiter) for s in samples] - predictions = await tqdm_asyncio.gather( - *tasks, desc=f"Predicting {batch_start}-{batch_end}", leave=False - ) - - for sample, pred in zip(samples, predictions): - output_data[sample["id"]] = { - "id": sample["id"], - "dataset_name": sample["dataset_name"], - "type": sample["type"], - "question": sample["question"], - "answers": sample["answers"], - "predict": pred, - } - - del samples, predictions - - final_data = [output_data[i] for i in sorted(output_data.keys())] - with open(PRED_OUTPUT, "w", encoding="utf-8") as f: - json.dump(final_data, f, ensure_ascii=False, indent=2) - - num_failures = sum(1 for d in final_data if d.get("predict", "") == "") - print(f"\nPredictions saved to {PRED_OUTPUT}") - print( - f"Total: {total} | Retried: {num_retried} | Remaining failures: {num_failures}" - ) - - -def run_evaluation(): - if not PRED_OUTPUT.exists(): - print(f"No predictions found at {PRED_OUTPUT}") - print("Run with --predict-only first, or without flags to do both.") - sys.exit(1) - - eval_scripts_dir = BENCHMARK_DIR / "eval_scripts" - sys.path.insert(0, str(eval_scripts_dir)) - - import os - original_cwd = os.getcwd() - os.chdir(BENCHMARK_DIR) - - print("Step 1: Scoring individual samples...") - from benchmarks.ocrbench_v2.eval_scripts.eval import process_predictions # noqa: E402 - - EVAL_OUTPUT.parent.mkdir(parents=True, exist_ok=True) - process_predictions(str(PRED_OUTPUT), str(EVAL_OUTPUT)) - - os.chdir(original_cwd) - print(f"Scored results saved to {EVAL_OUTPUT}") - - print("\nStep 2: Computing overall metrics...") - with open(EVAL_OUTPUT) as f: - scored_data = json.load(f) - - en_scores = { - "text_recognition": [], - "text_detection": [], - "text_spotting": [], - "relationship_extraction": [], - "element_parsing": [], - "mathematical_calculation": [], - "visual_text_understanding": [], - "knowledge_reasoning": [], - } - cn_scores = { - "text_recognition": [], - "relationship_extraction": [], - "element_parsing": [], - "visual_text_understanding": [], - "knowledge_reasoning": [], - } - - type_to_en = { - "text recognition en": "text_recognition", - "fine-grained text recognition en": "text_recognition", - "full-page OCR en": "text_recognition", - "text grounding en": "text_detection", - "VQA with position en": "text_detection", - "text spotting en": "text_spotting", - "key information extraction en": "relationship_extraction", - "key information mapping en": "relationship_extraction", - "document parsing en": "element_parsing", - "chart parsing en": "element_parsing", - "table parsing en": "element_parsing", - "formula recognition en": "element_parsing", - "math QA en": "mathematical_calculation", - "text counting en": "mathematical_calculation", - "document classification en": "visual_text_understanding", - "cognition VQA en": "visual_text_understanding", - "diagram QA en": "visual_text_understanding", - "reasoning VQA en": "knowledge_reasoning", - "science QA en": "knowledge_reasoning", - "APP agent en": "knowledge_reasoning", - "ASCII art classification en": "knowledge_reasoning", - } - type_to_cn = { - "full-page OCR cn": "text_recognition", - "key information extraction cn": "relationship_extraction", - "handwritten answer extraction cn": "relationship_extraction", - "document parsing cn": "element_parsing", - "table parsing cn": "element_parsing", - "formula recognition cn": "element_parsing", - "cognition VQA cn": "visual_text_understanding", - "reasoning VQA cn": "knowledge_reasoning", - "text translation cn": "knowledge_reasoning", - } - - for item in scored_data: - if "ignore" in item: - continue - t = item["type"] - if t in type_to_en: - en_scores[type_to_en[t]].append(item["score"]) - elif t in type_to_cn: - cn_scores[type_to_cn[t]].append(item["score"]) - - def avg(lst): - return sum(lst) / len(lst) if lst else 0.0 - - en_avgs = {k: avg(v) for k, v in en_scores.items() if v} - cn_avgs = {k: avg(v) for k, v in cn_scores.items() if v} - en_overall = avg(list(en_avgs.values())) - cn_overall = avg(list(cn_avgs.values())) - - print(f"\n{'=' * 60}") - print(f"OCRBench v2 Results ({MODEL})") - print(f"{'=' * 60}") - print(f"\n{'Category':<30} {'EN':>8} {'CN':>8}") - print("-" * 48) - all_cats = sorted(set(list(en_scores.keys()) + list(cn_scores.keys()))) - for cat in all_cats: - en_val = f"{en_avgs[cat]:.3f}" if cat in en_avgs else " -" - cn_val = f"{cn_avgs[cat]:.3f}" if cat in cn_avgs else " -" - print(f"{cat:<30} {en_val:>8} {cn_val:>8}") - print("-" * 48) - print(f"{'OVERALL':<30} {en_overall:>8.3f} {cn_overall:>8.3f}") - - metrics = { - "en_scores": { - k: {"avg": avg(v), "count": len(v)} for k, v in en_scores.items() - }, - "cn_scores": { - k: {"avg": avg(v), "count": len(v)} for k, v in cn_scores.items() - }, - "en_overall": en_overall, - "cn_overall": cn_overall, - "model": MODEL, - } - metrics_path = RESULTS_DIR / "ocrbench_v2_kimi_metrics.json" - with open(metrics_path, "w") as f: - json.dump(metrics, f, indent=2) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - parser = argparse.ArgumentParser(description="OCRBench v2 benchmark for Kimi K2.6 via OpenRouter") - parser.add_argument( - "--predict-only", action="store_true", help="Only generate predictions" - ) - parser.add_argument( - "--evaluate-only", action="store_true", help="Only run evaluation" - ) - args = parser.parse_args() - - if args.evaluate_only: - run_evaluation() - elif args.predict_only: - asyncio.run(run_predictions()) - else: - asyncio.run(run_predictions()) - run_evaluation() - - -if __name__ == "__main__": - main() diff --git a/benchmarks/ocrbench_v2/ocrbench_v2_openai.py b/benchmarks/ocrbench_v2/ocrbench_v2_openai.py deleted file mode 100644 index d23b756..0000000 --- a/benchmarks/ocrbench_v2/ocrbench_v2_openai.py +++ /dev/null @@ -1,342 +0,0 @@ -""" -OCRBench v2 benchmark for OpenAI. -10,000 QA pairs across 30 task types (EN + CN). - -Usage: - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_openai - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_openai --predict-only - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_openai --evaluate-only -""" - -import sys -import json -import asyncio -import argparse -import base64 -from pathlib import Path -from io import BytesIO - -from datasets import load_dataset -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -BENCHMARK_DIR = Path(__file__).resolve().parent -RESULTS_DIR = PROJECT_ROOT / "results" -PRED_OUTPUT = RESULTS_DIR / "ocrbench_v2_openai_predictions.json" -EVAL_OUTPUT = RESULTS_DIR / "ocrbench_v2_openai_scored.json" - -sys.path.insert(0, str(PROJECT_ROOT)) -from src.commons_openai import invoke_openai # noqa: E402 - -MODEL = "gpt-5.4" -RATE_LIMIT = 25 -MAX_RETRIES = 3 - -TEXT_SPOTTING_PROMPT_TEMPLATE = """Use OCR on this image to spot all text at {level}. The OCR tool returns each detected text region with its text content and four corner coordinates: top_left, top_right, bottom_left, bottom_right (each as an x,y pixel pair). - -Then use run code to write a Python script that takes those OCR results and: -1. For each text region, compute the axis-aligned bounding box from the four corners: - - x1 = min of all x coordinates (leftmost) - - y1 = min of all y coordinates (topmost) - - x2 = max of all x coordinates (rightmost) - - y2 = max of all y coordinates (bottommost) -2. Normalize each coordinate to the range 0-1000 by dividing by the image width (for x) or height (for y) and multiplying by 1000, then rounding to an integer. -3. Print the results as a Python list. - -Your final answer must be ONLY a Python list in this exact format, with no markdown, no code fences, no explanation: -[(x1, y1, x2, y2, "text"), (x1, y1, x2, y2, "text"), ...]""" - - -def get_spotting_prompt(original_question: str) -> str: - if "line-level" in original_question: - return TEXT_SPOTTING_PROMPT_TEMPLATE.format(level="line-level") - return TEXT_SPOTTING_PROMPT_TEMPLATE.format(level="word-level") - - -class RateLimiter: - def __init__(self, rate: int): - self.rate = rate - self.tokens = rate - self.last_refill = 0.0 - self._lock = asyncio.Lock() - - async def acquire(self): - while True: - async with self._lock: - now = asyncio.get_running_loop().time() - elapsed = now - self.last_refill - self.tokens = min(self.rate, self.tokens + elapsed * self.rate) - self.last_refill = now - if self.tokens >= 1: - self.tokens -= 1 - return - await asyncio.sleep(1 / self.rate) - - -def pil_to_data_url(image) -> str: - buffer = BytesIO() - image.save(buffer, format="JPEG", quality=95) - b64 = base64.b64encode(buffer.getvalue()).decode("utf-8") - return f"data:image/jpeg;base64,{b64}" - - -def build_messages(question: str, image_url: str) -> list[dict]: - return [ - { - "role": "user", - "content": [ - {"type": "text", "text": question}, - {"type": "image_url", "image_url": {"url": image_url}}, - ], - } - ] - - -async def process_sample(sample_meta: dict, rate_limiter): - messages = build_messages(sample_meta["question"], sample_meta["image_url"]) - - for attempt in range(MAX_RETRIES): - await rate_limiter.acquire() - try: - response = await asyncio.to_thread(invoke_openai, messages, MODEL) - return response.choices[0].message.content or "" - except Exception as e: - if attempt < MAX_RETRIES - 1: - await asyncio.sleep(2**attempt) - else: - print( - f"Failed after {MAX_RETRIES} attempts for id={sample_meta['id']}: {e}" - ) - return "" - - -BATCH_SIZE = 100 - - -async def run_predictions(): - print("Loading OCRBench v2 from HuggingFace...") - dataset = load_dataset("lmms-lab/OCRBench-v2", split="test") - total = len(dataset) - print(f"Loaded {total} samples") - - RESULTS_DIR.mkdir(parents=True, exist_ok=True) - - existing = {} - if PRED_OUTPUT.exists(): - with open(PRED_OUTPUT) as f: - for item in json.load(f): - if item.get("predict", "") != "": - existing[item["id"]] = item - print(f"Resuming: {len(existing)} successful predictions found, skipping them") - - rate_limiter = RateLimiter(RATE_LIMIT) - output_data = {} - output_data.update(existing) - num_retried = 0 - - from tqdm import tqdm - - for batch_start in tqdm(range(0, total, BATCH_SIZE), desc="Batches"): - batch_end = min(batch_start + BATCH_SIZE, total) - batch = dataset[batch_start:batch_end] - - samples = [] - for i in range(len(batch["id"])): - sample_id = batch["id"][i] - if sample_id in existing: - continue - image_url = pil_to_data_url(batch["image"][i]) - question = batch["question"][i] - if batch["type"][i] == "text spotting en": - question = get_spotting_prompt(question) - samples.append( - { - "id": sample_id, - "dataset_name": batch["dataset_name"][i], - "type": batch["type"][i], - "question": question, - "answers": batch["answers"][i], - "image_url": image_url, - } - ) - del batch - - if not samples: - continue - - num_retried += len(samples) - tasks = [process_sample(s, rate_limiter) for s in samples] - predictions = await tqdm_asyncio.gather( - *tasks, desc=f"Predicting {batch_start}-{batch_end}", leave=False - ) - - for sample, pred in zip(samples, predictions): - output_data[sample["id"]] = { - "id": sample["id"], - "dataset_name": sample["dataset_name"], - "type": sample["type"], - "question": sample["question"], - "answers": sample["answers"], - "predict": pred, - } - - del samples, predictions - - final_data = [output_data[i] for i in sorted(output_data.keys())] - with open(PRED_OUTPUT, "w", encoding="utf-8") as f: - json.dump(final_data, f, ensure_ascii=False, indent=2) - - num_failures = sum(1 for d in final_data if d.get("predict", "") == "") - print(f"\nPredictions saved to {PRED_OUTPUT}") - print( - f"Total: {total} | Retried: {num_retried} | Remaining failures: {num_failures}" - ) - - -def run_evaluation(): - if not PRED_OUTPUT.exists(): - print(f"No predictions found at {PRED_OUTPUT}") - print("Run with --predict-only first, or without flags to do both.") - sys.exit(1) - - eval_scripts_dir = BENCHMARK_DIR / "eval_scripts" - sys.path.insert(0, str(eval_scripts_dir)) - - import os - original_cwd = os.getcwd() - os.chdir(BENCHMARK_DIR) - - print("Step 1: Scoring individual samples...") - from benchmarks.ocrbench_v2.eval_scripts.eval import process_predictions # noqa: E402 - - EVAL_OUTPUT.parent.mkdir(parents=True, exist_ok=True) - process_predictions(str(PRED_OUTPUT), str(EVAL_OUTPUT)) - - os.chdir(original_cwd) - print(f"Scored results saved to {EVAL_OUTPUT}") - - print("\nStep 2: Computing overall metrics...") - with open(EVAL_OUTPUT) as f: - scored_data = json.load(f) - - en_scores = { - "text_recognition": [], - "text_detection": [], - "text_spotting": [], - "relationship_extraction": [], - "element_parsing": [], - "mathematical_calculation": [], - "visual_text_understanding": [], - "knowledge_reasoning": [], - } - cn_scores = { - "text_recognition": [], - "relationship_extraction": [], - "element_parsing": [], - "visual_text_understanding": [], - "knowledge_reasoning": [], - } - - type_to_en = { - "text recognition en": "text_recognition", - "fine-grained text recognition en": "text_recognition", - "full-page OCR en": "text_recognition", - "text grounding en": "text_detection", - "VQA with position en": "text_detection", - "text spotting en": "text_spotting", - "key information extraction en": "relationship_extraction", - "key information mapping en": "relationship_extraction", - "document parsing en": "element_parsing", - "chart parsing en": "element_parsing", - "table parsing en": "element_parsing", - "formula recognition en": "element_parsing", - "math QA en": "mathematical_calculation", - "text counting en": "mathematical_calculation", - "document classification en": "visual_text_understanding", - "cognition VQA en": "visual_text_understanding", - "diagram QA en": "visual_text_understanding", - "reasoning VQA en": "knowledge_reasoning", - "science QA en": "knowledge_reasoning", - "APP agent en": "knowledge_reasoning", - "ASCII art classification en": "knowledge_reasoning", - } - type_to_cn = { - "full-page OCR cn": "text_recognition", - "key information extraction cn": "relationship_extraction", - "handwritten answer extraction cn": "relationship_extraction", - "document parsing cn": "element_parsing", - "table parsing cn": "element_parsing", - "formula recognition cn": "element_parsing", - "cognition VQA cn": "visual_text_understanding", - "reasoning VQA cn": "knowledge_reasoning", - "text translation cn": "knowledge_reasoning", - } - - for item in scored_data: - if "ignore" in item: - continue - t = item["type"] - if t in type_to_en: - en_scores[type_to_en[t]].append(item["score"]) - elif t in type_to_cn: - cn_scores[type_to_cn[t]].append(item["score"]) - - def avg(lst): - return sum(lst) / len(lst) if lst else 0.0 - - en_avgs = {k: avg(v) for k, v in en_scores.items() if v} - cn_avgs = {k: avg(v) for k, v in cn_scores.items() if v} - en_overall = avg(list(en_avgs.values())) - cn_overall = avg(list(cn_avgs.values())) - - print(f"\n{'=' * 60}") - print(f"OCRBench v2 Results ({MODEL})") - print(f"{'=' * 60}") - print(f"\n{'Category':<30} {'EN':>8} {'CN':>8}") - print("-" * 48) - all_cats = sorted(set(list(en_scores.keys()) + list(cn_scores.keys()))) - for cat in all_cats: - en_val = f"{en_avgs[cat]:.3f}" if cat in en_avgs else " -" - cn_val = f"{cn_avgs[cat]:.3f}" if cat in cn_avgs else " -" - print(f"{cat:<30} {en_val:>8} {cn_val:>8}") - print("-" * 48) - print(f"{'OVERALL':<30} {en_overall:>8.3f} {cn_overall:>8.3f}") - - metrics = { - "en_scores": { - k: {"avg": avg(v), "count": len(v)} for k, v in en_scores.items() - }, - "cn_scores": { - k: {"avg": avg(v), "count": len(v)} for k, v in cn_scores.items() - }, - "en_overall": en_overall, - "cn_overall": cn_overall, - "model": MODEL, - } - metrics_path = RESULTS_DIR / "ocrbench_v2_openai_metrics.json" - with open(metrics_path, "w") as f: - json.dump(metrics, f, indent=2) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - parser = argparse.ArgumentParser(description="OCRBench v2 benchmark for OpenAI") - parser.add_argument( - "--predict-only", action="store_true", help="Only generate predictions" - ) - parser.add_argument( - "--evaluate-only", action="store_true", help="Only run evaluation" - ) - args = parser.parse_args() - - if args.evaluate_only: - run_evaluation() - elif args.predict_only: - asyncio.run(run_predictions()) - else: - asyncio.run(run_predictions()) - run_evaluation() - - -if __name__ == "__main__": - main() diff --git a/benchmarks/ocrbench_v2/ocrbench_v2_openai_mini.py b/benchmarks/ocrbench_v2/ocrbench_v2_openai_mini.py deleted file mode 100644 index 8c5329b..0000000 --- a/benchmarks/ocrbench_v2/ocrbench_v2_openai_mini.py +++ /dev/null @@ -1,342 +0,0 @@ -""" -OCRBench v2 benchmark for OpenAI gpt-5.4-mini. -10,000 QA pairs across 30 task types (EN + CN). - -Usage: - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_openai_mini - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_openai_mini --predict-only - uv run -m benchmarks.ocrbench_v2.ocrbench_v2_openai_mini --evaluate-only -""" - -import sys -import json -import asyncio -import argparse -import base64 -from pathlib import Path -from io import BytesIO - -from datasets import load_dataset -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -BENCHMARK_DIR = Path(__file__).resolve().parent -RESULTS_DIR = PROJECT_ROOT / "results" -PRED_OUTPUT = RESULTS_DIR / "ocrbench_v2_openai_mini_predictions.json" -EVAL_OUTPUT = RESULTS_DIR / "ocrbench_v2_openai_mini_scored.json" - -sys.path.insert(0, str(PROJECT_ROOT)) -from src.commons_openai import invoke_openai # noqa: E402 - -MODEL = "gpt-5.4-mini" -RATE_LIMIT = 50 -MAX_RETRIES = 3 - -TEXT_SPOTTING_PROMPT_TEMPLATE = """Use OCR on this image to spot all text at {level}. The OCR tool returns each detected text region with its text content and four corner coordinates: top_left, top_right, bottom_left, bottom_right (each as an x,y pixel pair). - -Then use run code to write a Python script that takes those OCR results and: -1. For each text region, compute the axis-aligned bounding box from the four corners: - - x1 = min of all x coordinates (leftmost) - - y1 = min of all y coordinates (topmost) - - x2 = max of all x coordinates (rightmost) - - y2 = max of all y coordinates (bottommost) -2. Normalize each coordinate to the range 0-1000 by dividing by the image width (for x) or height (for y) and multiplying by 1000, then rounding to an integer. -3. Print the results as a Python list. - -Your final answer must be ONLY a Python list in this exact format, with no markdown, no code fences, no explanation: -[(x1, y1, x2, y2, "text"), (x1, y1, x2, y2, "text"), ...]""" - - -def get_spotting_prompt(original_question: str) -> str: - if "line-level" in original_question: - return TEXT_SPOTTING_PROMPT_TEMPLATE.format(level="line-level") - return TEXT_SPOTTING_PROMPT_TEMPLATE.format(level="word-level") - - -class RateLimiter: - def __init__(self, rate: int): - self.rate = rate - self.tokens = rate - self.last_refill = 0.0 - self._lock = asyncio.Lock() - - async def acquire(self): - while True: - async with self._lock: - now = asyncio.get_running_loop().time() - elapsed = now - self.last_refill - self.tokens = min(self.rate, self.tokens + elapsed * self.rate) - self.last_refill = now - if self.tokens >= 1: - self.tokens -= 1 - return - await asyncio.sleep(1 / self.rate) - - -def pil_to_data_url(image) -> str: - buffer = BytesIO() - image.save(buffer, format="JPEG", quality=95) - b64 = base64.b64encode(buffer.getvalue()).decode("utf-8") - return f"data:image/jpeg;base64,{b64}" - - -def build_messages(question: str, image_url: str) -> list[dict]: - return [ - { - "role": "user", - "content": [ - {"type": "text", "text": question}, - {"type": "image_url", "image_url": {"url": image_url}}, - ], - } - ] - - -async def process_sample(sample_meta: dict, rate_limiter): - messages = build_messages(sample_meta["question"], sample_meta["image_url"]) - - for attempt in range(MAX_RETRIES): - await rate_limiter.acquire() - try: - response = await asyncio.to_thread(invoke_openai, messages, MODEL) - return response.choices[0].message.content or "" - except Exception as e: - if attempt < MAX_RETRIES - 1: - await asyncio.sleep(2**attempt) - else: - print( - f"Failed after {MAX_RETRIES} attempts for id={sample_meta['id']}: {e}" - ) - return "" - - -BATCH_SIZE = 200 - - -async def run_predictions(): - print("Loading OCRBench v2 from HuggingFace...") - dataset = load_dataset("lmms-lab/OCRBench-v2", split="test") - total = len(dataset) - print(f"Loaded {total} samples") - - RESULTS_DIR.mkdir(parents=True, exist_ok=True) - - existing = {} - if PRED_OUTPUT.exists(): - with open(PRED_OUTPUT) as f: - for item in json.load(f): - if item.get("predict", "") != "": - existing[item["id"]] = item - print(f"Resuming: {len(existing)} successful predictions found, skipping them") - - rate_limiter = RateLimiter(RATE_LIMIT) - output_data = {} - output_data.update(existing) - num_retried = 0 - - from tqdm import tqdm - - for batch_start in tqdm(range(0, total, BATCH_SIZE), desc="Batches"): - batch_end = min(batch_start + BATCH_SIZE, total) - batch = dataset[batch_start:batch_end] - - samples = [] - for i in range(len(batch["id"])): - sample_id = batch["id"][i] - if sample_id in existing: - continue - image_url = pil_to_data_url(batch["image"][i]) - question = batch["question"][i] - if batch["type"][i] == "text spotting en": - question = get_spotting_prompt(question) - samples.append( - { - "id": sample_id, - "dataset_name": batch["dataset_name"][i], - "type": batch["type"][i], - "question": question, - "answers": batch["answers"][i], - "image_url": image_url, - } - ) - del batch - - if not samples: - continue - - num_retried += len(samples) - tasks = [process_sample(s, rate_limiter) for s in samples] - predictions = await tqdm_asyncio.gather( - *tasks, desc=f"Predicting {batch_start}-{batch_end}", leave=False - ) - - for sample, pred in zip(samples, predictions): - output_data[sample["id"]] = { - "id": sample["id"], - "dataset_name": sample["dataset_name"], - "type": sample["type"], - "question": sample["question"], - "answers": sample["answers"], - "predict": pred, - } - - del samples, predictions - - final_data = [output_data[i] for i in sorted(output_data.keys())] - with open(PRED_OUTPUT, "w", encoding="utf-8") as f: - json.dump(final_data, f, ensure_ascii=False, indent=2) - - num_failures = sum(1 for d in final_data if d.get("predict", "") == "") - print(f"\nPredictions saved to {PRED_OUTPUT}") - print( - f"Total: {total} | Retried: {num_retried} | Remaining failures: {num_failures}" - ) - - -def run_evaluation(): - if not PRED_OUTPUT.exists(): - print(f"No predictions found at {PRED_OUTPUT}") - print("Run with --predict-only first, or without flags to do both.") - sys.exit(1) - - eval_scripts_dir = BENCHMARK_DIR / "eval_scripts" - sys.path.insert(0, str(eval_scripts_dir)) - - import os - original_cwd = os.getcwd() - os.chdir(BENCHMARK_DIR) - - print("Step 1: Scoring individual samples...") - from benchmarks.ocrbench_v2.eval_scripts.eval import process_predictions # noqa: E402 - - EVAL_OUTPUT.parent.mkdir(parents=True, exist_ok=True) - process_predictions(str(PRED_OUTPUT), str(EVAL_OUTPUT)) - - os.chdir(original_cwd) - print(f"Scored results saved to {EVAL_OUTPUT}") - - print("\nStep 2: Computing overall metrics...") - with open(EVAL_OUTPUT) as f: - scored_data = json.load(f) - - en_scores = { - "text_recognition": [], - "text_detection": [], - "text_spotting": [], - "relationship_extraction": [], - "element_parsing": [], - "mathematical_calculation": [], - "visual_text_understanding": [], - "knowledge_reasoning": [], - } - cn_scores = { - "text_recognition": [], - "relationship_extraction": [], - "element_parsing": [], - "visual_text_understanding": [], - "knowledge_reasoning": [], - } - - type_to_en = { - "text recognition en": "text_recognition", - "fine-grained text recognition en": "text_recognition", - "full-page OCR en": "text_recognition", - "text grounding en": "text_detection", - "VQA with position en": "text_detection", - "text spotting en": "text_spotting", - "key information extraction en": "relationship_extraction", - "key information mapping en": "relationship_extraction", - "document parsing en": "element_parsing", - "chart parsing en": "element_parsing", - "table parsing en": "element_parsing", - "formula recognition en": "element_parsing", - "math QA en": "mathematical_calculation", - "text counting en": "mathematical_calculation", - "document classification en": "visual_text_understanding", - "cognition VQA en": "visual_text_understanding", - "diagram QA en": "visual_text_understanding", - "reasoning VQA en": "knowledge_reasoning", - "science QA en": "knowledge_reasoning", - "APP agent en": "knowledge_reasoning", - "ASCII art classification en": "knowledge_reasoning", - } - type_to_cn = { - "full-page OCR cn": "text_recognition", - "key information extraction cn": "relationship_extraction", - "handwritten answer extraction cn": "relationship_extraction", - "document parsing cn": "element_parsing", - "table parsing cn": "element_parsing", - "formula recognition cn": "element_parsing", - "cognition VQA cn": "visual_text_understanding", - "reasoning VQA cn": "knowledge_reasoning", - "text translation cn": "knowledge_reasoning", - } - - for item in scored_data: - if "ignore" in item: - continue - t = item["type"] - if t in type_to_en: - en_scores[type_to_en[t]].append(item["score"]) - elif t in type_to_cn: - cn_scores[type_to_cn[t]].append(item["score"]) - - def avg(lst): - return sum(lst) / len(lst) if lst else 0.0 - - en_avgs = {k: avg(v) for k, v in en_scores.items() if v} - cn_avgs = {k: avg(v) for k, v in cn_scores.items() if v} - en_overall = avg(list(en_avgs.values())) - cn_overall = avg(list(cn_avgs.values())) - - print(f"\n{'=' * 60}") - print(f"OCRBench v2 Results ({MODEL})") - print(f"{'=' * 60}") - print(f"\n{'Category':<30} {'EN':>8} {'CN':>8}") - print("-" * 48) - all_cats = sorted(set(list(en_scores.keys()) + list(cn_scores.keys()))) - for cat in all_cats: - en_val = f"{en_avgs[cat]:.3f}" if cat in en_avgs else " -" - cn_val = f"{cn_avgs[cat]:.3f}" if cat in cn_avgs else " -" - print(f"{cat:<30} {en_val:>8} {cn_val:>8}") - print("-" * 48) - print(f"{'OVERALL':<30} {en_overall:>8.3f} {cn_overall:>8.3f}") - - metrics = { - "en_scores": { - k: {"avg": avg(v), "count": len(v)} for k, v in en_scores.items() - }, - "cn_scores": { - k: {"avg": avg(v), "count": len(v)} for k, v in cn_scores.items() - }, - "en_overall": en_overall, - "cn_overall": cn_overall, - "model": MODEL, - } - metrics_path = RESULTS_DIR / "ocrbench_v2_openai_mini_metrics.json" - with open(metrics_path, "w") as f: - json.dump(metrics, f, indent=2) - print(f"\nMetrics saved to {metrics_path}") - - -def main(): - parser = argparse.ArgumentParser(description="OCRBench v2 benchmark for OpenAI gpt-5.4-mini") - parser.add_argument( - "--predict-only", action="store_true", help="Only generate predictions" - ) - parser.add_argument( - "--evaluate-only", action="store_true", help="Only run evaluation" - ) - args = parser.parse_args() - - if args.evaluate_only: - run_evaluation() - elif args.predict_only: - asyncio.run(run_predictions()) - else: - asyncio.run(run_predictions()) - run_evaluation() - - -if __name__ == "__main__": - main() diff --git a/benchmarks/ocrbench_v2/ocrbench_v2_text_spotting_en.py b/benchmarks/ocrbench_v2/ocrbench_v2_text_spotting_en.py deleted file mode 100644 index b0eeb09..0000000 --- a/benchmarks/ocrbench_v2/ocrbench_v2_text_spotting_en.py +++ /dev/null @@ -1,376 +0,0 @@ -""" -OCRBench v2 benchmark for Interfaze — Text Spotting EN only. -Runs predictions and evaluation exclusively for the "text spotting en" task type. - -Usage: - uv run python benchmarks/ocrbench_v2/ocrbench_v2_text_spotting_en.py - uv run python benchmarks/ocrbench_v2/ocrbench_v2_text_spotting_en.py --predict-only - uv run python benchmarks/ocrbench_v2/ocrbench_v2_text_spotting_en.py --evaluate-only -""" - -import sys -import json -import asyncio -import argparse -import base64 -from pathlib import Path -from io import BytesIO - -from datasets import load_dataset -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -BENCHMARK_DIR = Path(__file__).resolve().parent -RESULTS_DIR = PROJECT_ROOT / "results" -PRED_OUTPUT = RESULTS_DIR / "ocrbench_v2_text_spotting_en_predictions.json" -EVAL_OUTPUT = RESULTS_DIR / "ocrbench_v2_text_spotting_en_scored.json" - -sys.path.insert(0, str(PROJECT_ROOT)) -from src.commons import invoke_interfaze # noqa: E402 - -RATE_LIMIT = 25 -MAX_RETRIES = 3 -TARGET_TYPE = "text spotting en" - -TEXT_SPOTTING_PROMPT_TEMPLATE = """Use OCR on this image to spot all text at {level}. The OCR tool returns each detected text region with its text content and four corner coordinates: top_left, top_right, bottom_left, bottom_right (each as an x,y pixel pair). - -Then use run code to write a Python script that takes those OCR results and: -1. For each text region, compute the axis-aligned bounding box from the four corners: - - x1 = min of all x coordinates (leftmost) - - y1 = min of all y coordinates (topmost) - - x2 = max of all x coordinates (rightmost) - - y2 = max of all y coordinates (bottommost) -2. Normalize each coordinate to the range 0-1000 by dividing by the image width (for x) or height (for y) and multiplying by 1000, then rounding to an integer. -3. Print the results as a Python list. - -Your final answer must be ONLY a Python list in this exact format, with no markdown, no code fences, no explanation: -[(x1, y1, x2, y2, "text"), (x1, y1, x2, y2, "text"), ...]""" - - -def get_spotting_prompt(original_question: str) -> str: - """Preserve word-level vs line-level from the original question.""" - if "line-level" in original_question: - return TEXT_SPOTTING_PROMPT_TEMPLATE.format(level="line-level") - return TEXT_SPOTTING_PROMPT_TEMPLATE.format(level="word-level") - - -class RateLimiter: - def __init__(self, rate: int): - self.rate = rate - self.tokens = rate - self.last_refill = 0.0 - self._lock = asyncio.Lock() - - async def acquire(self): - while True: - async with self._lock: - now = asyncio.get_running_loop().time() - elapsed = now - self.last_refill - self.tokens = min(self.rate, self.tokens + elapsed * self.rate) - self.last_refill = now - if self.tokens >= 1: - self.tokens -= 1 - return - await asyncio.sleep(1 / self.rate) - - -def pil_to_data_url(image) -> str: - buffer = BytesIO() - image.save(buffer, format="JPEG", quality=95) - b64 = base64.b64encode(buffer.getvalue()).decode("utf-8") - return f"data:image/jpeg;base64,{b64}" - - -def build_messages(question: str, image_url: str) -> list[dict]: - return [ - { - "role": "user", - "content": [ - {"type": "text", "text": question}, - {"type": "image_url", "image_url": {"url": image_url}}, - ], - } - ] - - -async def process_sample(sample_meta: dict, rate_limiter): - messages = build_messages(sample_meta["question"], sample_meta["image_url"]) - - for attempt in range(MAX_RETRIES): - await rate_limiter.acquire() - try: - response = await asyncio.to_thread(invoke_interfaze, messages) - return response.choices[0].message.content - except Exception as e: - if attempt < MAX_RETRIES - 1: - await asyncio.sleep(2**attempt) - else: - print( - f"Failed after {MAX_RETRIES} attempts for id={sample_meta['id']}: {e}" - ) - return "" - - -BATCH_SIZE = 20 - - -async def run_predictions(): - print("Loading OCRBench v2 from HuggingFace...") - dataset = load_dataset("lmms-lab/OCRBench-v2", split="test") - total = len(dataset) - print(f"Loaded {total} samples") - - # Filter to only "text spotting en" samples - spotting_indices = [i for i in range(total) if dataset[i]["type"] == TARGET_TYPE] - print(f"Filtered to {len(spotting_indices)} '{TARGET_TYPE}' samples") - - RESULTS_DIR.mkdir(parents=True, exist_ok=True) - - # Always run fresh - if PRED_OUTPUT.exists(): - PRED_OUTPUT.unlink() - print("Cleared previous predictions — running fresh") - - rate_limiter = RateLimiter(RATE_LIMIT) - output_data = {} - num_retried = 0 - - # Import eval functions for realtime scoring - eval_scripts_dir = BENCHMARK_DIR / "eval_scripts" - sys.path.insert(0, str(eval_scripts_dir)) - import os - - original_cwd = os.getcwd() - os.chdir(BENCHMARK_DIR) - from eval_text_spotting_en import ( - extract_bounding_boxes, - spotting_evaluation_normalized, - ) - - def parse_gt(answers): - """Parse ground truth polygons into axis-aligned bboxes + text.""" - bboxes, contents = [], [] - for line in answers[0].strip().split("\n"): - parts = line.split(",") - num_coords = 0 - for p in parts: - try: - int(p.strip()) - num_coords += 1 - except ValueError: - break - if num_coords >= 8 and num_coords < len(parts): - coords = [int(p.strip()) for p in parts[:num_coords]] - text = ",".join(parts[num_coords:]) - x_coords = coords[0::2] - y_coords = coords[1::2] - x1, y1 = min(x_coords), min(y_coords) - x2, y2 = max(x_coords), max(y_coords) - bboxes.append([x1, y1, x2, y1, x2, y2, x1, y2]) - contents.append(text) - elif num_coords >= 8 and num_coords == len(parts) and num_coords % 2 == 1: - coords = [int(p.strip()) for p in parts[:num_coords - 1]] - text = parts[num_coords - 1].strip() - x_coords = coords[0::2] - y_coords = coords[1::2] - x1, y1 = min(x_coords), min(y_coords) - x2, y2 = max(x_coords), max(y_coords) - bboxes.append([x1, y1, x2, y1, x2, y2, x1, y2]) - contents.append(text) - return bboxes, contents - - def quick_score(predict_str, answers): - """Score a single prediction inline.""" - try: - bboxes, contents = parse_gt(answers) - if not bboxes: - return 0.0 - pred_boxes = extract_bounding_boxes(predict_str) - if not pred_boxes: - return 0.0 - return spotting_evaluation_normalized( - pred_boxes, {"bbox": bboxes, "content": contents} - ) - except Exception: - return 0.0 - - from tqdm import tqdm - - all_scores = [] - num_batches = (len(spotting_indices) + BATCH_SIZE - 1) // BATCH_SIZE - - for batch_num in range(num_batches): - batch_start = batch_num * BATCH_SIZE - batch_end = min(batch_start + BATCH_SIZE, len(spotting_indices)) - batch_indices = spotting_indices[batch_start:batch_end] - - # Prepare all samples in this batch - samples = [] - for idx in batch_indices: - row = dataset[idx] - image_url = pil_to_data_url(row["image"]) - samples.append({ - "id": row["id"], - "dataset_name": row["dataset_name"], - "type": row["type"], - "question": get_spotting_prompt(row["question"]), - "answers": row["answers"], - "image_url": image_url, - }) - - # Fire all requests in parallel - print(f"Batch {batch_num + 1}/{num_batches} — sending {len(samples)} requests in parallel...") - tasks = [process_sample(s, rate_limiter) for s in samples] - predictions = await tqdm_asyncio.gather( - *tasks, - desc=f"Batch {batch_num + 1}/{num_batches}", - leave=True, - ) - - # Score all results - batch_scores = [] - for sample, pred in zip(samples, predictions): - num_retried += 1 - score = quick_score(pred, sample["answers"]) - batch_scores.append(score) - all_scores.append(score) - - output_data[sample["id"]] = { - "id": sample["id"], - "dataset_name": sample["dataset_name"], - "type": sample["type"], - "question": sample["question"], - "answers": sample["answers"], - "predict": pred, - } - - # Save after each batch - final_data = [output_data[i] for i in sorted(output_data.keys())] - with open(PRED_OUTPUT, "w", encoding="utf-8") as f: - json.dump(final_data, f, ensure_ascii=False, indent=2) - - batch_avg = sum(batch_scores) / len(batch_scores) if batch_scores else 0.0 - overall_avg = sum(all_scores) / len(all_scores) if all_scores else 0.0 - print( - f" Batch {batch_num + 1} done — batch H-mean: {batch_avg:.4f} | overall H-mean: {overall_avg:.4f}" - ) - - os.chdir(original_cwd) - - final_data = [output_data[i] for i in sorted(output_data.keys())] - num_failures = sum(1 for d in final_data if d.get("predict", "") == "") - final_hmean = sum(all_scores) / len(all_scores) if all_scores else 0.0 - print(f"\nPredictions saved to {PRED_OUTPUT}") - print( - f"Total: {len(spotting_indices)} | Retried: {num_retried} | Remaining failures: {num_failures}" - ) - print(f"Final H-mean: {final_hmean:.4f}" - ) - - -def run_evaluation(): - if not PRED_OUTPUT.exists(): - print(f"No predictions found at {PRED_OUTPUT}") - print("Run with --predict-only first, or without flags to do both.") - sys.exit(1) - - eval_scripts_dir = BENCHMARK_DIR / "eval_scripts" - sys.path.insert(0, str(eval_scripts_dir)) - - # spotting_metric.py uses relative paths like ./eval_scripts/spotting_eval/submit - # so we must chdir to the benchmark dir for the RRC evaluation to work - import os - - original_cwd = os.getcwd() - os.chdir(BENCHMARK_DIR) - - # Step 1: Score each sample using eval_text_spotting_en.py - print("Step 1: Scoring text spotting EN samples...") - from eval_text_spotting_en import process_predictions # noqa: E402 - - EVAL_OUTPUT.parent.mkdir(parents=True, exist_ok=True) - process_predictions(str(PRED_OUTPUT), str(EVAL_OUTPUT)) - - os.chdir(original_cwd) - print(f"Scored results saved to {EVAL_OUTPUT}") - - # Step 2: Compute text spotting metrics - print("\nStep 2: Computing text spotting EN metrics...") - with open(EVAL_OUTPUT) as f: - scored_data = json.load(f) - - scores = [] - for item in scored_data: - if "ignore" in item: - continue - if item["type"] == TARGET_TYPE: - scores.append(item["score"]) - - avg_score = sum(scores) / len(scores) if scores else 0.0 - - print(f"\n{'=' * 60}") - print("OCRBench v2 — Text Spotting EN Results (Interfaze)") - print(f"{'=' * 60}") - print(f" Samples: {len(scores)}") - print(f" H-mean: {avg_score:.4f}") - print(f"{'=' * 60}") - - # Save metrics - metrics = { - "text_spotting_en": { - "avg": avg_score, - "count": len(scores), - }, - "model": "interfaze-beta", - } - metrics_path = RESULTS_DIR / "ocrbench_v2_text_spotting_en_metrics.json" - with open(metrics_path, "w") as f: - json.dump(metrics, f, indent=2) - print(f"\nMetrics saved to {metrics_path}") - - # Also update the main metrics file's text_spotting entry - main_metrics_path = RESULTS_DIR / "ocrbench_v2_metrics.json" - if main_metrics_path.exists(): - with open(main_metrics_path) as f: - main_metrics = json.load(f) - main_metrics["en_scores"]["text_spotting"] = { - "avg": avg_score, - "count": len(scores), - } - # Recompute en_overall - en_avgs = { - k: v["avg"] - for k, v in main_metrics["en_scores"].items() - if v.get("count", 0) > 0 - } - main_metrics["en_overall"] = ( - sum(en_avgs.values()) / len(en_avgs) if en_avgs else 0.0 - ) - with open(main_metrics_path, "w") as f: - json.dump(main_metrics, f, indent=2) - print(f"Updated text_spotting in {main_metrics_path}") - - -def main(): - parser = argparse.ArgumentParser( - description="OCRBench v2 — Text Spotting EN benchmark for Interfaze" - ) - parser.add_argument( - "--predict-only", action="store_true", help="Only generate predictions" - ) - parser.add_argument( - "--evaluate-only", action="store_true", help="Only run evaluation" - ) - args = parser.parse_args() - - if args.evaluate_only: - run_evaluation() - elif args.predict_only: - asyncio.run(run_predictions()) - else: - asyncio.run(run_predictions()) - run_evaluation() - - -if __name__ == "__main__": - main() diff --git a/benchmarks/olmocr/bench/benchmark.py b/benchmarks/olmocr/bench/benchmark.py index df17b03..acbb44e 100644 --- a/benchmarks/olmocr/bench/benchmark.py +++ b/benchmarks/olmocr/bench/benchmark.py @@ -31,8 +31,19 @@ def evaluate_candidate( - candidate_folder: str, all_tests: List[BasePDFTest], pdf_basenames: List[str], force: bool = False -) -> Tuple[float, int, List[str], List[str], Dict[str, List[float]], List[float], Dict[str, Dict[int, List[Tuple[BasePDFTest, bool, str]]]]]: + candidate_folder: str, + all_tests: List[BasePDFTest], + pdf_basenames: List[str], + force: bool = False, +) -> Tuple[ + float, + int, + List[str], + List[str], + Dict[str, List[float]], + List[float], + Dict[str, Dict[int, List[Tuple[BasePDFTest, bool, str]]]], +]: """ For the candidate folder (pipeline tool output), validate that it contains at least one .md file (i.e. repeated generations like _pg{page}_repeat{repeat}.md) for every PDF in the pdf folder. @@ -59,25 +70,40 @@ def evaluate_candidate( # Map each PDF to its corresponding MD repeats (e.g., doc1_pg1_repeat1.md, doc1_pg2_repeat2.md, etc.) pdf_to_md_files = {} - all_files = list(glob.glob(os.path.join(candidate_folder, "**/*.md"), recursive=True)) + all_files = list( + glob.glob(os.path.join(candidate_folder, "**/*.md"), recursive=True) + ) for pdf_name in pdf_basenames: md_base = os.path.splitext(pdf_name)[0] md_regex = re.compile(rf"^{re.escape(md_base)}_pg\d+_repeat\d+\.md$") - md_files = [f for f in all_files if md_regex.match(os.path.relpath(f, candidate_folder))] + md_files = [ + f for f in all_files if md_regex.match(os.path.relpath(f, candidate_folder)) + ] if not md_files and not force: candidate_errors.append( - f"Candidate '{candidate_name}' is missing MD repeats for {pdf_name} " f"(expected files matching {md_base}_pg{{page}}_repeat*.md)." + f"Candidate '{candidate_name}' is missing MD repeats for {pdf_name} " + f"(expected files matching {md_base}_pg{{page}}_repeat*.md)." ) else: pdf_to_md_files[pdf_name] = md_files if candidate_errors: - return (0.0, len(all_tests), candidate_errors, test_failures, test_type_breakdown, all_test_scores, test_results) + return ( + 0.0, + len(all_tests), + candidate_errors, + test_failures, + test_type_breakdown, + all_test_scores, + test_results, + ) # Define an inner function to evaluate a single test - def process_test(test: BasePDFTest) -> Tuple[float, str, str, List[str], Tuple[bool, str]]: + def process_test( + test: BasePDFTest, + ) -> Tuple[float, str, str, List[str], Tuple[bool, str]]: local_errors = [] test_failure = None pdf_name = test.pdf @@ -91,7 +117,9 @@ def process_test(test: BasePDFTest) -> Tuple[float, str, str, List[str], Tuple[b md_base = os.path.splitext(pdf_name)[0] md_files = pdf_to_md_files.get(pdf_name, []) # Filter MD files for the specific page corresponding to the test - page_md_files = [f for f in md_files if re.search(rf"_pg{test.page}_", os.path.basename(f))] + page_md_files = [ + f for f in md_files if re.search(rf"_pg{test.page}_", os.path.basename(f)) + ] if not page_md_files: local_errors.append( f"Candidate '{candidate_name}' is missing MD repeats for {pdf_name} page {test.page} " @@ -123,18 +151,28 @@ def process_test(test: BasePDFTest) -> Tuple[float, str, str, List[str], Tuple[b explanations.append(str(e)) test_avg = repeat_passes / num_repeats if num_repeats > 0 else 0.0 - final_passed = test_avg > 0.5 # Consider test passed if majority of repeats pass + final_passed = ( + test_avg > 0.5 + ) # Consider test passed if majority of repeats pass final_explanation = explanations[0] if explanations else "All repeats passed" # Store the test result for reporting - test_results[pdf_name][test.page].append((test, final_passed, final_explanation)) + test_results[pdf_name][test.page].append( + (test, final_passed, final_explanation) + ) if test_avg < 1.0: test_failure = ( f"Test {test.id} on {md_base} page {test.page} average pass ratio: {test_avg:.3f} " f"({repeat_passes}/{num_repeats} repeats passed). Ex: {explanations[0] if explanations else 'No explanation'}" ) - return (test_avg, test_failure, test.type, local_errors, (final_passed, final_explanation)) + return ( + test_avg, + test_failure, + test.type, + local_errors, + (final_passed, final_explanation), + ) total_test_score = 0.0 futures = [] @@ -142,7 +180,12 @@ def process_test(test: BasePDFTest) -> Tuple[float, str, str, List[str], Tuple[b with ThreadPoolExecutor(max_workers=min(os.cpu_count() or 1, 64)) as executor: futures = [executor.submit(process_test, test) for test in all_tests] # tqdm progress bar for this candidate's tests - for future in tqdm(as_completed(futures), total=len(futures), desc=f"Evaluating tests for {candidate_name}", unit="test"): + for future in tqdm( + as_completed(futures), + total=len(futures), + desc=f"Evaluating tests for {candidate_name}", + unit="test", + ): test_avg, test_failure, test_type, errors, _ = future.result() all_test_scores.append(test_avg) total_test_score += test_avg @@ -156,7 +199,15 @@ def process_test(test: BasePDFTest) -> Tuple[float, str, str, List[str], Tuple[b candidate_errors.extend(local_errors) overall_score = total_test_score / len(all_tests) if all_tests else 0.0 - return (overall_score, len(all_tests), candidate_errors, test_failures, test_type_breakdown, all_test_scores, test_results) + return ( + overall_score, + len(all_tests), + candidate_errors, + test_failures, + test_type_breakdown, + all_test_scores, + test_results, + ) def main(): @@ -171,8 +222,17 @@ def main(): action="store_true", help="Run benchmark even if some files are missing", ) - parser.add_argument("--candidate", type=str, default=None, help="Run test only for a single candidate") - parser.add_argument("--skip_baseline", action="store_true", help="Skip running baseline tests (ex. that check that basic content is present on each page)") + parser.add_argument( + "--candidate", + type=str, + default=None, + help="Run test only for a single candidate", + ) + parser.add_argument( + "--skip_baseline", + action="store_true", + help="Skip running baseline tests (ex. that check that basic content is present on each page)", + ) parser.add_argument( "--bootstrap_samples", type=int, @@ -186,10 +246,23 @@ def main(): help="Confidence level for interval calculation (default: 0.95 for 95% CI).", ) # New arguments - parser.add_argument("--sample", type=int, default=None, help="Randomly sample N tests to run instead of all tests.") - parser.add_argument("--test_report", type=str, default=None, help="Generate an HTML report of test results. Provide a filename (e.g., results.html).") parser.add_argument( - "--output_failed", type=str, default=None, help="Output a JSONL file containing tests that failed across all candidates. Provide a filename." + "--sample", + type=int, + default=None, + help="Randomly sample N tests to run instead of all tests.", + ) + parser.add_argument( + "--test_report", + type=str, + default=None, + help="Generate an HTML report of test results. Provide a filename (e.g., results.html).", + ) + parser.add_argument( + "--output_failed", + type=str, + default=None, + help="Output a JSONL file containing tests that failed across all candidates. Provide a filename.", ) args = parser.parse_args() @@ -202,7 +275,9 @@ def main(): print("Error: /pdfs folder must exist in your data directory.", file=sys.stderr) sys.exit(1) - all_pdf_files = list(glob.glob(os.path.join(pdf_folder, "**/*.pdf"), recursive=True)) + all_pdf_files = list( + glob.glob(os.path.join(pdf_folder, "**/*.pdf"), recursive=True) + ) if not all_pdf_files: print(f"Error: No PDF files found in {pdf_folder}", file=sys.stderr) @@ -234,13 +309,20 @@ def main(): for pdf in pdf_basenames: if not any(t.type == "baseline" for t in all_tests if t.pdf == pdf): - all_tests.append(BaselineTest(id=f"{pdf}_baseline", pdf=pdf, page=1, type="baseline")) + all_tests.append( + BaselineTest(id=f"{pdf}_baseline", pdf=pdf, page=1, type="baseline") + ) test_to_jsonl[all_tests[-1].id] = "baseline" for pdf in pdf_basenames: pdf_doc = PdfReader(os.path.join(pdf_folder, pdf)) for page in range(1, len(pdf_doc.pages) + 1): - if not any(test for test in all_tests if test.pdf == pdf and test.page == page) and not args.force: + if ( + not any( + test for test in all_tests if test.pdf == pdf and test.page == page + ) + and not args.force + ): print(f"No dataset entry found for pdf {pdf} page {page}") sys.exit(1) @@ -250,9 +332,13 @@ def main(): # Sample tests if requested if args.sample is not None and args.sample > 0: if args.sample >= len(all_tests): - print(f"Sample size {args.sample} is greater than or equal to the total number of tests ({len(all_tests)}). Using all tests.") + print( + f"Sample size {args.sample} is greater than or equal to the total number of tests ({len(all_tests)}). Using all tests." + ) else: - print(f"Randomly sampling {args.sample} tests out of {len(all_tests)} total tests.") + print( + f"Randomly sampling {args.sample} tests out of {len(all_tests)} total tests." + ) all_tests = random.sample(all_tests, args.sample) candidate_folders = [] @@ -266,7 +352,10 @@ def main(): candidate_folders.append(full_path) if not candidate_folders: - print("Error: No candidate pipeline folders found (subdirectories besides 'pdfs').", file=sys.stderr) + print( + "Error: No candidate pipeline folders found (subdirectories besides 'pdfs').", + file=sys.stderr, + ) sys.exit(1) candidate_folders.sort() @@ -278,9 +367,15 @@ def main(): for candidate in candidate_folders: candidate_name = os.path.basename(candidate) print(f"\nEvaluating candidate: {candidate_name}") - overall_score, total_tests, candidate_errors, test_failures, test_type_breakdown, all_test_scores, test_results = evaluate_candidate( - candidate, all_tests, pdf_basenames, args.force - ) + ( + overall_score, + total_tests, + candidate_errors, + test_failures, + test_type_breakdown, + all_test_scores, + test_results, + ) = evaluate_candidate(candidate, all_tests, pdf_basenames, args.force) # Always store test results for displaying jsonl file groupings test_results_by_candidate[candidate_name] = test_results @@ -321,10 +416,26 @@ def main(): # Calculate CI using the updated function with splits if jsonl_scores: - ci = calculate_bootstrap_ci(jsonl_scores, n_bootstrap=n_bootstrap, ci_level=ci_level, splits=jsonl_file_sizes) + ci = calculate_bootstrap_ci( + jsonl_scores, + n_bootstrap=n_bootstrap, + ci_level=ci_level, + splits=jsonl_file_sizes, + ) else: ci = (0.0, 0.0) - summary.append((candidate_name, overall_score, total_tests, candidate_errors, test_failures, test_type_breakdown, ci, all_test_scores)) + summary.append( + ( + candidate_name, + overall_score, + total_tests, + candidate_errors, + test_failures, + test_type_breakdown, + ci, + all_test_scores, + ) + ) print(f"\nCandidate: {candidate_name}") if candidate_errors: for err in candidate_errors: @@ -340,12 +451,27 @@ def main(): pass_rate = results["passed"] / results["total"] jsonl_pass_rates.append(pass_rate) - per_category_score = sum(jsonl_pass_rates) / len(jsonl_pass_rates) if jsonl_pass_rates else 0.0 - print(f" Average Score: {per_category_score * 100:.1f}% (95% CI: [{ci[0] * 100:.1f}%, {ci[1] * 100:.1f}%]) over {total_tests} tests.") + per_category_score = ( + sum(jsonl_pass_rates) / len(jsonl_pass_rates) + if jsonl_pass_rates + else 0.0 + ) + print( + f" Average Score: {per_category_score * 100:.1f}% (95% CI: [{ci[0] * 100:.1f}%, {ci[1] * 100:.1f}%]) over {total_tests} tests." + ) print("\n" + "=" * 60) print("Final Summary with 95% Confidence Intervals:") - for idx, (candidate_name, _, total_tests, candidate_errors, _, test_type_breakdown, ci, _) in enumerate(summary): + for idx, ( + candidate_name, + _, + total_tests, + candidate_errors, + _, + test_type_breakdown, + ci, + _, + ) in enumerate(summary): # Group results by jsonl file jsonl_results = {} for test in all_tests: @@ -362,8 +488,14 @@ def main(): if not candidate_errors and hasattr(test, "pdf") and hasattr(test, "page"): pdf_name = test.pdf page = test.page - if pdf_name in test_results_by_candidate.get(candidate_name, {}) and page in test_results_by_candidate[candidate_name].get(pdf_name, {}): - for t, passed, _ in test_results_by_candidate[candidate_name][pdf_name][page]: + if pdf_name in test_results_by_candidate.get( + candidate_name, {} + ) and page in test_results_by_candidate[candidate_name].get( + pdf_name, {} + ): + for t, passed, _ in test_results_by_candidate[candidate_name][ + pdf_name + ][page]: if t.id == test.id: test_result = passed break @@ -379,10 +511,21 @@ def main(): jsonl_pass_rates.append(pass_rate) # New overall score is average of per-JSONL pass rates - new_overall_score = sum(jsonl_pass_rates) / len(jsonl_pass_rates) if jsonl_pass_rates else 0.0 + new_overall_score = ( + sum(jsonl_pass_rates) / len(jsonl_pass_rates) if jsonl_pass_rates else 0.0 + ) # Update the overall_score in the summary list for later use (e.g., in permutation tests) - summary[idx] = (candidate_name, new_overall_score, total_tests, candidate_errors, summary[idx][4], test_type_breakdown, ci, summary[idx][7]) + summary[idx] = ( + candidate_name, + new_overall_score, + total_tests, + candidate_errors, + summary[idx][4], + test_type_breakdown, + ci, + summary[idx][7], + ) if candidate_errors: status = "FAILED (errors)" @@ -392,19 +535,25 @@ def main(): # Use the CI that was calculated with proper category-based bootstrap half_width = ((ci[1] - ci[0]) / 2) * 100 ciw_str = f"± {half_width:0.1f}%" - print(f"{candidate_name:20s} : Average Score: {status} {ciw_str} (average of per-JSONL scores)") + print( + f"{candidate_name:20s} : Average Score: {status} {ciw_str} (average of per-JSONL scores)" + ) # Sort the test types alphabetically for ttype in sorted(test_type_breakdown.keys()): scores = test_type_breakdown[ttype] avg = sum(scores) / len(scores) * 100 if scores else 0.0 - print(f" {ttype:8s}: {avg:0.1f}% average pass rate over {len(scores)} tests") + print( + f" {ttype:8s}: {avg:0.1f}% average pass rate over {len(scores)} tests" + ) print("\n Results by JSONL file:") for jsonl_file, results in sorted(jsonl_results.items()): if results["total"] > 0: pass_rate = (results["passed"] / results["total"]) * 100 - print(f" {jsonl_file:30s}: {pass_rate:0.1f}% ({results['passed']}/{results['total']} tests)") + print( + f" {jsonl_file:30s}: {pass_rate:0.1f}% ({results['passed']}/{results['total']} tests)" + ) print("") # Generate HTML report if requested @@ -415,7 +564,9 @@ def main(): if args.output_failed: # Identify tests that failed across all candidates all_failed_tests = [] - valid_candidates = [c for c in summary if not c[3]] # Skip candidates with errors + valid_candidates = [ + c for c in summary if not c[3] + ] # Skip candidates with errors for test in all_tests: # Track whether this test has any results @@ -428,8 +579,14 @@ def main(): if hasattr(test, "pdf") and hasattr(test, "page"): pdf_name = test.pdf page = test.page - if pdf_name in test_results_by_candidate.get(candidate_name, {}) and page in test_results_by_candidate[candidate_name].get(pdf_name, {}): - for t, passed, explanation in test_results_by_candidate[candidate_name][pdf_name][page]: + if pdf_name in test_results_by_candidate.get( + candidate_name, {} + ) and page in test_results_by_candidate[candidate_name].get( + pdf_name, {} + ): + for t, passed, explanation in test_results_by_candidate[ + candidate_name + ][pdf_name][page]: if t.id == test.id: has_results = True test_result = passed @@ -443,12 +600,18 @@ def main(): all_failed_tests.append(test) # If we have any failed tests, write them to the specified JSONL file - output_path = os.path.join(input_folder, args.output_failed) if not os.path.isabs(args.output_failed) else args.output_failed + output_path = ( + os.path.join(input_folder, args.output_failed) + if not os.path.isabs(args.output_failed) + else args.output_failed + ) if all_failed_tests: save_tests(all_failed_tests, output_path) - print(f"\nOutput {len(all_failed_tests)} tests that failed across all candidates to {output_path}") + print( + f"\nOutput {len(all_failed_tests)} tests that failed across all candidates to {output_path}" + ) else: print("\nNo tests failed across all candidates. No output file created.") diff --git a/benchmarks/olmocr/bench/katex/render.py b/benchmarks/olmocr/bench/katex/render.py index 109b877..b18fbdd 100644 --- a/benchmarks/olmocr/bench/katex/render.py +++ b/benchmarks/olmocr/bench/katex/render.py @@ -35,7 +35,9 @@ class EquationCache: def __init__(self, db_path: Optional[str] = None): if db_path is None: # Use the same cache directory as before - cache_dir = pathlib.Path.home() / ".cache" / "olmocr" / "bench" / "equations" + cache_dir = ( + pathlib.Path.home() / ".cache" / "olmocr" / "bench" / "equations" + ) cache_dir.mkdir(parents=True, exist_ok=True) db_path = str(cache_dir / "cache.db") self.db_path = db_path @@ -62,7 +64,10 @@ def load(self, eq_hash: str) -> Optional["RenderedEquation"]: with self.lock: conn = sqlite3.connect(self.db_path) c = conn.cursor() - c.execute("SELECT mathml, spans, error FROM equations WHERE eq_hash = ?", (eq_hash,)) + c.execute( + "SELECT mathml, spans, error FROM equations WHERE eq_hash = ?", + (eq_hash,), + ) row = c.fetchone() conn.close() if row: @@ -160,7 +165,9 @@ def get_equation_hash(equation, bg_color="white", text_color="black", font_size= # Global thread pool executor with a fixed number of threads # Each thread will maintain its own Playwright instance -_render_executor = ThreadPoolExecutor(max_workers=8, thread_name_prefix="playwright-render") +_render_executor = ThreadPoolExecutor( + max_workers=8, thread_name_prefix="playwright-render" +) def _cleanup_executor(): @@ -209,7 +216,9 @@ def _get_thread_local_browser(): return owner -def _render_in_executor(equation, bg_color, text_color, font_size, use_cache, debug_dom, eq_hash): +def _render_in_executor( + equation, bg_color, text_color, font_size, use_cache, debug_dom, eq_hash +): """ Function to be run in the executor thread pool. Each thread maintains its own Playwright instance. @@ -238,7 +247,9 @@ def _do_render(context, equation, bg_color, text_color, font_size, debug_dom): katex_js_path = os.path.join(script_dir, "katex.min.js") if not os.path.exists(katex_css_path) or not os.path.exists(katex_js_path): - raise FileNotFoundError(f"KaTeX files not found. Please ensure katex.min.css and katex.min.js are in {script_dir}") + raise FileNotFoundError( + f"KaTeX files not found. Please ensure katex.min.css and katex.min.js are in {script_dir}" + ) # Create a new page. page = context.new_page() @@ -277,7 +288,9 @@ def _do_render(context, equation, bg_color, text_color, font_size, debug_dom): katex_loaded = page.evaluate("typeof katex !== 'undefined'") if not katex_loaded: page.close() - raise RuntimeError("KaTeX library failed to load. Check your katex.min.js file.") + raise RuntimeError( + "KaTeX library failed to load. Check your katex.min.js file." + ) try: error_message = page.evaluate(f""" @@ -399,7 +412,16 @@ def render_equation( return cached # Submit the rendering task to the thread pool executor - future = _render_executor.submit(_render_in_executor, equation, bg_color, text_color, font_size, use_cache, debug_dom, eq_hash) + future = _render_executor.submit( + _render_in_executor, + equation, + bg_color, + text_color, + font_size, + use_cache, + debug_dom, + eq_hash, + ) # Wait for the result rendered_eq = future.result() @@ -411,7 +433,9 @@ def render_equation( return rendered_eq -def compare_rendered_equations(reference: RenderedEquation, hypothesis: RenderedEquation) -> bool: +def compare_rendered_equations( + reference: RenderedEquation, hypothesis: RenderedEquation +) -> bool: """ Compare two RenderedEquation objects. First, check if the normalized MathML of the hypothesis is contained within that of the reference. @@ -424,7 +448,11 @@ def extract_inner(mathml: str) -> str: soup = BeautifulSoup(mathml, "xml") semantics = soup.find("semantics") if semantics: - inner_parts = [str(child) for child in semantics.contents if getattr(child, "name", None) != "annotation"] + inner_parts = [ + str(child) + for child in semantics.contents + if getattr(child, "name", None) != "annotation" + ] return "".join(inner_parts) else: return str(soup) @@ -451,7 +479,8 @@ def expand_span_info(span_info: SpanInfo) -> list[SpanInfo]: SpanInfo( c, BoundingBox( - span_info.bounding_box.x + (span_info.bounding_box.width * index) / total_elems, + span_info.bounding_box.x + + (span_info.bounding_box.width * index) / total_elems, span_info.bounding_box.y, span_info.bounding_box.width / total_elems, span_info.bounding_box.height, diff --git a/benchmarks/olmocr/bench/report.py b/benchmarks/olmocr/bench/report.py index 939d38e..bf8bac2 100644 --- a/benchmarks/olmocr/bench/report.py +++ b/benchmarks/olmocr/bench/report.py @@ -10,7 +10,11 @@ def generate_html_report( - test_results_by_candidate: Dict[str, Dict[str, Dict[int, List[Tuple[BasePDFTest, bool, str]]]]], pdf_folder: str, output_file: str + test_results_by_candidate: Dict[ + str, Dict[str, Dict[int, List[Tuple[BasePDFTest, bool, str]]]] + ], + pdf_folder: str, + output_file: str, ) -> None: """ Generate a simple static HTML report of test results. @@ -153,7 +157,11 @@ def generate_html_report( elif test_type == "absent" and hasattr(test, "text"): text = getattr(test, "text", "") html += f"""

Text should not appear: "{text}"

\n""" - elif test_type == "order" and hasattr(test, "before") and hasattr(test, "after"): + elif ( + test_type == "order" + and hasattr(test, "before") + and hasattr(test, "after") + ): before = getattr(test, "before", "") after = getattr(test, "after", "") html += f"""

Text order: "{before}" should appear before "{after}"

\n""" @@ -198,9 +206,19 @@ def generate_html_report( md_content = None try: md_base = os.path.splitext(pdf_name)[0] - md_files = list(glob.glob(os.path.join(os.path.dirname(pdf_folder), candidate, f"{md_base}_pg{page}_repeat*.md"))) + md_files = list( + glob.glob( + os.path.join( + os.path.dirname(pdf_folder), + candidate, + f"{md_base}_pg{page}_repeat*.md", + ) + ) + ) if md_files: - md_file_path = md_files[0] # Use the first repeat as an example + md_file_path = md_files[ + 0 + ] # Use the first repeat as an example with open(md_file_path, "r", encoding="utf-8") as f: md_content = f.read() except Exception as e: diff --git a/benchmarks/olmocr/bench/runners/run_gemini_pro_31.py b/benchmarks/olmocr/bench/runners/run_gemini_pro_31.py deleted file mode 100644 index bdb83b5..0000000 --- a/benchmarks/olmocr/bench/runners/run_gemini_pro_31.py +++ /dev/null @@ -1,72 +0,0 @@ -import os - -from google import genai -from google.genai import types - -from benchmarks.olmocr.data.renderpdf import render_pdf_to_base64png - -_client = None - - -def _get_client(): - global _client - if _client is None: - api_key = os.getenv("GEMINI_API_KEY") - if not api_key: - raise SystemExit( - "GEMINI_API_KEY not set — get it from https://aistudio.google.com/apikey" - ) - _client = genai.Client(api_key=api_key) - return _client - - -def run_gemini_pro_31( - pdf_path: str, - page_num: int = 1, - model: str = "gemini-3.1-pro-preview", - target_longest_image_dim: int = 2048, -) -> str: - """Convert a PDF page to markdown via Gemini 3.1 Pro Preview (vision).""" - import base64 - - image_base64 = render_pdf_to_base64png( - pdf_path, page_num=page_num, target_longest_image_dim=target_longest_image_dim - ) - image_bytes = base64.b64decode(image_base64) - - client = _get_client() - - prompt = ( - "Below is the image of one page of a PDF document. " - "Just return the plain text representation of this document as if you were reading it naturally.\n" - "Turn equations into LaTeX using \\( \\) for inline math and \\[ \\] for display math. " - "Never describe equations in words — always use LaTeX notation. " - "Turn tables into markdown format.\n" - "Remove the headers and footers completely — do not include any text " - "that appears at the very top or very bottom of the page outside the main body content. " - "This includes page numbers, journal names, author names in running headers, " - "copyright lines, DOI lines, citation requests, institutional addresses in margins, " - "and download dates. Keep references and footnotes that are part of the body.\n" - "For multi-column layouts, read each column top to bottom before moving to the next.\n" - "Read any natural handwriting.\n" - "This is likely one page out of several in the document, so be sure to preserve " - "any sentences that come from the previous page, or continue onto the next page, exactly as they are.\n" - "If there is no text at all that you think you should read, you can output null.\n" - "Do not hallucinate." - ) - - image_part = types.Part.from_bytes(data=image_bytes, mime_type="image/png") - - # Pro models reject thinking_budget=0 — let API use default thinking. - config = types.GenerateContentConfig() - - response = client.models.generate_content( - model=model, - contents=[prompt, image_part], - config=config, - ) - - raw = response.text if hasattr(response, "text") else None - if raw is None or raw.strip().lower() in ("null", "none", "n/a", ""): - return "" - return raw diff --git a/benchmarks/olmocr/bench/runners/run_grok.py b/benchmarks/olmocr/bench/runners/run_grok.py deleted file mode 100644 index 64693df..0000000 --- a/benchmarks/olmocr/bench/runners/run_grok.py +++ /dev/null @@ -1,76 +0,0 @@ -import os - -from openai import OpenAI - -from benchmarks.olmocr.data.renderpdf import render_pdf_to_base64png - -_client = None - - -def _get_client() -> OpenAI: - global _client - if _client is None: - api_key = os.getenv("OPENROUTER_API_KEY") - if not api_key: - raise SystemExit( - "OPENROUTER_API_KEY not set — get it from https://openrouter.ai/keys" - ) - _client = OpenAI(api_key=api_key, base_url="https://openrouter.ai/api/v1") - return _client - - -def run_grok( - pdf_path: str, - page_num: int = 1, - model: str = "x-ai/grok-4.3", - target_longest_image_dim: int = 2048, -) -> str: - """Convert a PDF page to markdown via Grok 4.3 with reasoning_effort='low'.""" - image_base64 = render_pdf_to_base64png( - pdf_path, page_num=page_num, target_longest_image_dim=target_longest_image_dim - ) - - client = _get_client() - - prompt = ( - "Below is the image of one page of a PDF document. " - "Just return the plain text representation of this document as if you were reading it naturally.\n" - "Turn equations into LaTeX using \\( \\) for inline math and \\[ \\] for display math. " - "Never describe equations in words — always use LaTeX notation. " - "Turn tables into markdown format.\n" - "Remove the headers and footers completely — do not include any text " - "that appears at the very top or very bottom of the page outside the main body content. " - "This includes page numbers, journal names, author names in running headers, " - "copyright lines, DOI lines, citation requests, institutional addresses in margins, " - "and download dates. Keep references and footnotes that are part of the body.\n" - "For multi-column layouts, read each column top to bottom before moving to the next.\n" - "Read any natural handwriting.\n" - "This is likely one page out of several in the document, so be sure to preserve " - "any sentences that come from the previous page, or continue onto the next page, exactly as they are.\n" - "If there is no text at all that you think you should read, you can output null.\n" - "Do not hallucinate." - ) - - response = client.chat.completions.create( - model=model, - messages=[ - { - "role": "user", - "content": [ - {"type": "text", "text": prompt}, - { - "type": "image_url", - "image_url": {"url": f"data:image/png;base64,{image_base64}"}, - }, - ], - } - ], - reasoning_effort="low", - max_completion_tokens=20000, - ) - - assert len(response.choices) > 0 - raw = response.choices[0].message.content - if raw is None or raw.strip().lower() in ("null", "none", "n/a", ""): - return "" - return raw diff --git a/benchmarks/olmocr/bench/runners/run_interfaze.py b/benchmarks/olmocr/bench/runners/run_interfaze.py deleted file mode 100644 index 7506b6d..0000000 --- a/benchmarks/olmocr/bench/runners/run_interfaze.py +++ /dev/null @@ -1,93 +0,0 @@ -import os - -from openai import OpenAI - -from benchmarks.olmocr.data.renderpdf import render_pdf_to_base64png - -def run_interfaze( - pdf_path: str, - page_num: int = 1, - model: str = "interfaze-beta", - temperature: float = 0.1, - target_longest_image_dim: int = 2048, -) -> str: - """ - Convert a page of a PDF file to markdown using the Interfaze API. - - Args: - pdf_path: The local path to the PDF file. - page_num: The page number to process (starting from 1). - model: The Interfaze model to use. - temperature: The temperature parameter for generation. - target_longest_image_dim: Target longest image dimension for rendering. - - Returns: - The OCR result in markdown format. - """ - image_base64 = render_pdf_to_base64png( - pdf_path, page_num=page_num, target_longest_image_dim=target_longest_image_dim - ) - - api_key = os.getenv("INTERFAZE_API_KEY") - if not api_key: - raise SystemExit( - "You must set INTERFAZE_API_KEY - get it from https://interfaze.ai/dashboard" - ) - - client = OpenAI( - base_url=os.getenv("OPENAI_BASE_URL", "https://api.interfaze.ai/v1"), - api_key=api_key, - ) - - - prompt = ( - "Below is the image of one page of a PDF document. " - "Just return the plain text representation of this document as if you were reading it naturally.\n" - "Turn equations into LaTeX using \\( \\) for inline math and \\[ \\] for display math. " - "Never describe equations in words — always use LaTeX notation. " - "Turn tables into markdown format.\n" - "Remove the headers and footers completely — do not include any text " - "that appears at the very top or very bottom of the page outside the main body content. " - "This includes page numbers, journal names, author names in running headers, " - "copyright lines, DOI lines, citation requests, institutional addresses in margins, " - "and download dates. Keep references and footnotes that are part of the body.\n" - "For multi-column layouts, read each column top to bottom before moving to the next.\n" - "Read any natural handwriting.\n" - "This is likely one page out of several in the document, so be sure to preserve " - "any sentences that come from the previous page, or continue onto the next page, exactly as they are.\n" - "If there is no text at all that you think you should read, you can output null.\n" - "Do not hallucinate." - ) - - - - response = client.chat.completions.create( - model=model, - messages=[ - { - "role": "user", - "content": [ - {"type": "text", "text": prompt}, - { - "type": "image_url", - "image_url": { - "url": f"data:image/png;base64,{image_base64}" - }, - }, - ], - } - ], - temperature=temperature, - max_completion_tokens=20000, - ) - - assert len(response.choices) > 0 - assert response.choices[0].finish_reason == "stop" - - raw = response.choices[0].message.content - - # Handle blank pages - model may return "null" or similar for empty pages - if raw is None or raw.strip().lower() in ("null", "none", "n/a", ""): - return "" - - return raw diff --git a/benchmarks/olmocr/bench/runners/run_openai_mini.py b/benchmarks/olmocr/bench/runners/run_openai_mini.py deleted file mode 100644 index 2eb1a7f..0000000 --- a/benchmarks/olmocr/bench/runners/run_openai_mini.py +++ /dev/null @@ -1,79 +0,0 @@ -import os - -from openai import OpenAI - -from benchmarks.olmocr.data.renderpdf import render_pdf_to_base64png - -# Direct OpenAI client (not commons_openai, since commons module-level read of -# OPENAI_API_KEY would conflict with how this file is imported lazily). -_client = None - - -def _get_client() -> OpenAI: - global _client - if _client is None: - api_key = os.getenv("OPENAI_API_KEY") - if not api_key: - raise SystemExit( - "OPENAI_API_KEY not set — get it from https://platform.openai.com/api-keys" - ) - base_url = os.getenv("OPENAI_API_BASE_URL", "https://api.openai.com/v1") - _client = OpenAI(api_key=api_key, base_url=base_url) - return _client - - -def run_openai_mini( - pdf_path: str, - page_num: int = 1, - model: str = "gpt-5.4-mini", - target_longest_image_dim: int = 2048, -) -> str: - """Convert a PDF page to markdown via OpenAI gpt-5.4-mini (vision).""" - image_base64 = render_pdf_to_base64png( - pdf_path, page_num=page_num, target_longest_image_dim=target_longest_image_dim - ) - - client = _get_client() - - prompt = ( - "Below is the image of one page of a PDF document. " - "Just return the plain text representation of this document as if you were reading it naturally.\n" - "Turn equations into LaTeX using \\( \\) for inline math and \\[ \\] for display math. " - "Never describe equations in words — always use LaTeX notation. " - "Turn tables into markdown format.\n" - "Remove the headers and footers completely — do not include any text " - "that appears at the very top or very bottom of the page outside the main body content. " - "This includes page numbers, journal names, author names in running headers, " - "copyright lines, DOI lines, citation requests, institutional addresses in margins, " - "and download dates. Keep references and footnotes that are part of the body.\n" - "For multi-column layouts, read each column top to bottom before moving to the next.\n" - "Read any natural handwriting.\n" - "This is likely one page out of several in the document, so be sure to preserve " - "any sentences that come from the previous page, or continue onto the next page, exactly as they are.\n" - "If there is no text at all that you think you should read, you can output null.\n" - "Do not hallucinate." - ) - - response = client.chat.completions.create( - model=model, - messages=[ - { - "role": "user", - "content": [ - {"type": "text", "text": prompt}, - { - "type": "image_url", - "image_url": {"url": f"data:image/png;base64,{image_base64}"}, - }, - ], - } - ], - reasoning_effort="none", - max_completion_tokens=20000, - ) - - assert len(response.choices) > 0 - raw = response.choices[0].message.content - if raw is None or raw.strip().lower() in ("null", "none", "n/a", ""): - return "" - return raw diff --git a/benchmarks/olmocr/bench/runners/run_reducto.py b/benchmarks/olmocr/bench/runners/run_reducto.py index 5755a9e..f663ace 100644 --- a/benchmarks/olmocr/bench/runners/run_reducto.py +++ b/benchmarks/olmocr/bench/runners/run_reducto.py @@ -84,7 +84,8 @@ def run_reducto( timeout=timeout, ) try: - from src.commons_reducto import record_usage + from src.providers.reducto import record_usage + record_usage("parse", getattr(response, "usage", None)) except Exception: pass diff --git a/benchmarks/olmocr/bench/tests.py b/benchmarks/olmocr/bench/tests.py index 0045ea1..5a691c1 100644 --- a/benchmarks/olmocr/bench/tests.py +++ b/benchmarks/olmocr/bench/tests.py @@ -26,10 +26,18 @@ class TableData: """Class to hold table data and metadata about headers.""" data: np.ndarray # The actual table data - header_rows: Set[int] = field(default_factory=set) # Indices of rows that are headers - header_cols: Set[int] = field(default_factory=set) # Indices of columns that are headers - col_headers: dict = field(default_factory=dict) # Maps column index to header text, handling colspan - row_headers: dict = field(default_factory=dict) # Maps row index to header text, handling rowspan + header_rows: Set[int] = field( + default_factory=set + ) # Indices of rows that are headers + header_cols: Set[int] = field( + default_factory=set + ) # Indices of columns that are headers + col_headers: dict = field( + default_factory=dict + ) # Maps column index to header text, handling colspan + row_headers: dict = field( + default_factory=dict + ) # Maps row index to header text, handling rowspan def __repr__(self) -> str: """Returns a concise representation of the TableData object for debugging.""" @@ -40,7 +48,9 @@ def __str__(self) -> str: output = [] # Table dimensions - output.append(f"Table: {self.data.shape[0]} rows × {self.data.shape[1]} columns") + output.append( + f"Table: {self.data.shape[0]} rows × {self.data.shape[1]} columns" + ) # Header info output.append(f"Header rows: {sorted(self.header_rows)}") @@ -52,7 +62,11 @@ def __str__(self) -> str: # Add a header for row indices output.append(separator) headers = [""] + [f"Column {i}" for i in range(self.data.shape[1])] - output.append("| {:<5} | ".format("Row") + " | ".join(["{:<15}".format(h) for h in headers[1:]]) + " |") + output.append( + "| {:<5} | ".format("Row") + + " | ".join(["{:<15}".format(h) for h in headers[1:]]) + + " |" + ) output.append(separator) # Format each row @@ -68,7 +82,11 @@ def __str__(self) -> str: cell = f"*{cell}*" cells.append(cell) - row_str = "| {:<5} | ".format(i) + " | ".join(["{:<15}".format(c) for c in cells]) + " |" + row_str = ( + "| {:<5} | ".format(i) + + " | ".join(["{:<15}".format(c) for c in cells]) + + " |" + ) output.append(row_str) output.append(separator) @@ -137,7 +155,21 @@ def normalize_text(md_content: str) -> str: md_content = unicodedata.normalize("NFC", md_content) # Dictionary of characters to replace: keys are fancy characters, values are ASCII equivalents, unicode micro with greek mu comes up often enough too - replacements = {"‘": "'", "’": "'", "‚": "'", "“": '"', "”": '"', "„": '"', "＿": "_", "–": "-", "—": "-", "‑": "-", "‒": "-", "−": "-", "\u00b5": "\u03bc"} + replacements = { + "‘": "'", + "’": "'", + "‚": "'", + "“": '"', + "”": '"', + "„": '"', + "＿": "_", + "–": "-", + "—": "-", + "‑": "-", + "‒": "-", + "−": "-", + "\u00b5": "\u03bc", + } # Apply all replacements from the dictionary for fancy_char, ascii_char in replacements.items(): @@ -185,7 +217,9 @@ def parse_markdown_tables(md_content: str) -> List[TableData]: if table_data and len(table_data) > 0: # Convert to numpy array for easier manipulation max_cols = max(len(row) for row in table_data) - padded_data = [row + [""] * (max_cols - len(row)) for row in table_data] + padded_data = [ + row + [""] * (max_cols - len(row)) for row in table_data + ] table_array = np.array(padded_data) # In markdown tables, the first row is typically a header row @@ -196,20 +230,28 @@ def parse_markdown_tables(md_content: str) -> List[TableData]: if len(table_array) > 0: for col_idx in range(table_array.shape[1]): if col_idx < len(table_array[0]): - col_headers[col_idx] = [(0, table_array[0, col_idx])] + col_headers[col_idx] = [ + (0, table_array[0, col_idx]) + ] # Set up row_headers with first column headers for each row row_headers = {} if table_array.shape[1] > 0: - for row_idx in range(1, table_array.shape[0]): # Skip header row - row_headers[row_idx] = [(0, table_array[row_idx, 0])] # First column as heading + for row_idx in range( + 1, table_array.shape[0] + ): # Skip header row + row_headers[row_idx] = [ + (0, table_array[row_idx, 0]) + ] # First column as heading # Create TableData object parsed_tables.append( TableData( data=table_array, header_rows=header_rows, - header_cols={0} if table_array.shape[1] > 0 else set(), # First column as header + header_cols={0} + if table_array.shape[1] > 0 + else set(), # First column as header col_headers=col_headers, row_headers=row_headers, ) @@ -239,14 +281,18 @@ def parse_markdown_tables(md_content: str) -> List[TableData]: row_headers = {} if table_array.shape[1] > 0: for row_idx in range(1, table_array.shape[0]): # Skip header row - row_headers[row_idx] = [(0, table_array[row_idx, 0])] # First column as heading + row_headers[row_idx] = [ + (0, table_array[row_idx, 0]) + ] # First column as heading # Create TableData object parsed_tables.append( TableData( data=table_array, header_rows=header_rows, - header_cols={0} if table_array.shape[1] > 0 else set(), # First column as header + header_cols={0} + if table_array.shape[1] > 0 + else set(), # First column as header col_headers=col_headers, row_headers=row_headers, ) @@ -374,7 +420,9 @@ def parse_html_tables(html_content: str) -> List[TableData]: if j == 0 and i > 0: # Only for cells directly below cell_grid[(row_idx + i, col_idx + j)] = cell_text else: - cell_grid[(row_idx + i, col_idx + j)] = "" # Mark other spans as empty + cell_grid[(row_idx + i, col_idx + j)] = ( + "" # Mark other spans as empty + ) # If this is a header cell (th), mark it and its span if cell.name == "th": @@ -427,12 +475,19 @@ def parse_html_tables(html_content: str) -> List[TableData]: # Add this header to all columns it spans over for row_idx in range(len(table_data)): if row_idx not in header_rows: # Only apply to data rows - for j in range(col, len(table_data[row_idx]) if row_idx < len(table_data) else 0): + for j in range( + col, + len(table_data[row_idx]) + if row_idx < len(table_data) + else 0, + ): # Add header info to data cells in these columns if j not in col_headers: col_headers[j] = [] if not any(h[1] == header_text for h in col_headers[j]): - header_row = min([r for r, t in col_headers.get(col, [(0, "")])]) + header_row = min( + [r for r, t in col_headers.get(col, [(0, "")])] + ) col_headers[j].append((header_row, header_text)) # Handle row headers @@ -452,7 +507,9 @@ def parse_html_tables(html_content: str) -> List[TableData]: if col_idx < len(row) and row[col_idx].strip(): if row_idx not in row_headers: row_headers[row_idx] = [] - if not any(h[1] == row[col_idx] for h in row_headers.get(row_idx, [])): + if not any( + h[1] == row[col_idx] for h in row_headers.get(row_idx, []) + ): row_headers[row_idx].append((col_idx, row[col_idx])) # Calculate max columns for padding @@ -465,7 +522,13 @@ def parse_html_tables(html_content: str) -> List[TableData]: # Create TableData object with the table and header information parsed_tables.append( - TableData(data=table_array, header_rows=header_rows, header_cols=header_cols, col_headers=col_headers, row_headers=row_headers) + TableData( + data=table_array, + header_rows=header_rows, + header_cols=header_cols, + col_headers=col_headers, + row_headers=row_headers, + ) ) return parsed_tables @@ -556,20 +619,28 @@ def run(self, md_content: str) -> Tuple[bool, str]: md_content = md_content[-self.last_n :] # Threshold for fuzzy matching derived from max_diffs - threshold = 1.0 - (self.max_diffs / (len(reference_query) if len(reference_query) > 0 else 1)) + threshold = 1.0 - ( + self.max_diffs / (len(reference_query) if len(reference_query) > 0 else 1) + ) best_ratio = fuzz.partial_ratio(reference_query, md_content) / 100.0 if self.type == TestType.PRESENT.value: if best_ratio >= threshold: return True, "" else: - msg = f"Expected '{reference_query[:40]}...' with threshold {threshold} " f"but best match ratio was {best_ratio:.3f}" + msg = ( + f"Expected '{reference_query[:40]}...' with threshold {threshold} " + f"but best match ratio was {best_ratio:.3f}" + ) return False, msg else: # ABSENT if best_ratio < threshold: return True, "" else: - msg = f"Expected absence of '{reference_query[:40]}...' with threshold {threshold} " f"but best match ratio was {best_ratio:.3f}" + msg = ( + f"Expected absence of '{reference_query[:40]}...' with threshold {threshold} " + f"but best match ratio was {best_ratio:.3f}" + ) return False, msg @@ -596,25 +667,43 @@ def __post_init__(self): raise ValidationError("Before field cannot be empty") if not self.after.strip(): raise ValidationError("After field cannot be empty") - if self.max_diffs > len(self.before) // 2 or self.max_diffs > len(self.after) // 2: - raise ValidationError("Max diffs is too large for this test, greater than 50% of the search string") + if ( + self.max_diffs > len(self.before) // 2 + or self.max_diffs > len(self.after) // 2 + ): + raise ValidationError( + "Max diffs is too large for this test, greater than 50% of the search string" + ) def run(self, md_content: str) -> Tuple[bool, str]: md_content = normalize_text(md_content) - before_matches = find_near_matches(self.before, md_content, max_l_dist=self.max_diffs) - after_matches = find_near_matches(self.after, md_content, max_l_dist=self.max_diffs) + before_matches = find_near_matches( + self.before, md_content, max_l_dist=self.max_diffs + ) + after_matches = find_near_matches( + self.after, md_content, max_l_dist=self.max_diffs + ) if not before_matches: - return False, f"'before' text '{self.before[:40]}...' not found with max_l_dist {self.max_diffs}" + return ( + False, + f"'before' text '{self.before[:40]}...' not found with max_l_dist {self.max_diffs}", + ) if not after_matches: - return False, f"'after' text '{self.after[:40]}...' not found with max_l_dist {self.max_diffs}" + return ( + False, + f"'after' text '{self.after[:40]}...' not found with max_l_dist {self.max_diffs}", + ) for before_match in before_matches: for after_match in after_matches: if before_match.start < after_match.start: return True, "" - return False, (f"Could not find a location where '{self.before[:40]}...' appears before " f"'{self.after[:40]}...'.") + return False, ( + f"Could not find a location where '{self.before[:40]}...' appears before " + f"'{self.after[:40]}...'." + ) @dataclass @@ -671,7 +760,9 @@ def run(self, content: str) -> Tuple[bool, str]: failed_reasons = [] # Threshold for fuzzy matching derived from max_diffs - threshold = 1.0 - (self.max_diffs / (len(self.cell) if len(self.cell) > 0 else 1)) + threshold = 1.0 - ( + self.max_diffs / (len(self.cell) if len(self.cell) > 0 else 1) + ) threshold = max(0.5, threshold) # Parse tables based on content_type @@ -716,23 +807,45 @@ def run(self, content: str) -> Tuple[bool, str]: if self.up and row_idx > 0: up_cell = normalize_text(table_array[row_idx - 1, col_idx]) up_similarity = fuzz.ratio(self.up, up_cell) / 100.0 - if up_similarity < max(0.5, 1.0 - (self.max_diffs / (len(self.up) if len(self.up) > 0 else 1))): + if up_similarity < max( + 0.5, + 1.0 + - (self.max_diffs / (len(self.up) if len(self.up) > 0 else 1)), + ): all_relationships_satisfied = False - current_failed_reasons.append(f"Cell above '{up_cell}' doesn't match expected '{self.up}' (similarity: {up_similarity:.2f})") + current_failed_reasons.append( + f"Cell above '{up_cell}' doesn't match expected '{self.up}' (similarity: {up_similarity:.2f})" + ) # Check down relationship if self.down and row_idx < table_array.shape[0] - 1: down_cell = normalize_text(table_array[row_idx + 1, col_idx]) down_similarity = fuzz.ratio(self.down, down_cell) / 100.0 - if down_similarity < max(0.5, 1.0 - (self.max_diffs / (len(self.down) if len(self.down) > 0 else 1))): + if down_similarity < max( + 0.5, + 1.0 + - ( + self.max_diffs + / (len(self.down) if len(self.down) > 0 else 1) + ), + ): all_relationships_satisfied = False - current_failed_reasons.append(f"Cell below '{down_cell}' doesn't match expected '{self.down}' (similarity: {down_similarity:.2f})") + current_failed_reasons.append( + f"Cell below '{down_cell}' doesn't match expected '{self.down}' (similarity: {down_similarity:.2f})" + ) # Check left relationship if self.left and col_idx > 0: left_cell = normalize_text(table_array[row_idx, col_idx - 1]) left_similarity = fuzz.ratio(self.left, left_cell) / 100.0 - if left_similarity < max(0.5, 1.0 - (self.max_diffs / (len(self.left) if len(self.left) > 0 else 1))): + if left_similarity < max( + 0.5, + 1.0 + - ( + self.max_diffs + / (len(self.left) if len(self.left) > 0 else 1) + ), + ): all_relationships_satisfied = False current_failed_reasons.append( f"Cell to the left '{left_cell}' doesn't match expected '{self.left}' (similarity: {left_similarity:.2f})" @@ -742,7 +855,14 @@ def run(self, content: str) -> Tuple[bool, str]: if self.right and col_idx < table_array.shape[1] - 1: right_cell = normalize_text(table_array[row_idx, col_idx + 1]) right_similarity = fuzz.ratio(self.right, right_cell) / 100.0 - if right_similarity < max(0.5, 1.0 - (self.max_diffs / (len(self.right) if len(self.right) > 0 else 1))): + if right_similarity < max( + 0.5, + 1.0 + - ( + self.max_diffs + / (len(self.right) if len(self.right) > 0 else 1) + ), + ): all_relationships_satisfied = False current_failed_reasons.append( f"Cell to the right '{right_cell}' doesn't match expected '{self.right}' (similarity: {right_similarity:.2f})" @@ -759,11 +879,24 @@ def run(self, content: str) -> Tuple[bool, str]: if col_idx in table_data.col_headers: for _, header_text in table_data.col_headers[col_idx]: header_text = normalize_text(header_text) - similarity = fuzz.ratio(self.top_heading, header_text) / 100.0 + similarity = ( + fuzz.ratio(self.top_heading, header_text) / 100.0 + ) if similarity > best_similarity: best_similarity = similarity best_match = header_text - if best_similarity >= max(0.5, 1.0 - (self.max_diffs / (len(self.top_heading) if len(self.top_heading) > 0 else 1))): + if best_similarity >= max( + 0.5, + 1.0 + - ( + self.max_diffs + / ( + len(self.top_heading) + if len(self.top_heading) > 0 + else 1 + ) + ), + ): top_heading_found = True break @@ -772,11 +905,24 @@ def run(self, content: str) -> Tuple[bool, str]: for i in sorted(header_rows): if i < row_idx and table_array[i, col_idx].strip(): header_text = normalize_text(table_array[i, col_idx]) - similarity = fuzz.ratio(self.top_heading, header_text) / 100.0 + similarity = ( + fuzz.ratio(self.top_heading, header_text) / 100.0 + ) if similarity > best_similarity: best_similarity = similarity best_match = header_text - if best_similarity >= max(0.5, 1.0 - (self.max_diffs / (len(self.top_heading) if len(self.top_heading) > 0 else 1))): + if best_similarity >= max( + 0.5, + 1.0 + - ( + self.max_diffs + / ( + len(self.top_heading) + if len(self.top_heading) > 0 + else 1 + ) + ), + ): top_heading_found = True break @@ -785,15 +931,30 @@ def run(self, content: str) -> Tuple[bool, str]: for i in range(row_idx): if table_array[i, col_idx].strip(): header_text = normalize_text(table_array[i, col_idx]) - similarity = fuzz.ratio(self.top_heading, header_text) / 100.0 + similarity = ( + fuzz.ratio(self.top_heading, header_text) / 100.0 + ) if similarity > best_similarity: best_similarity = similarity best_match = header_text if not best_match: all_relationships_satisfied = False - current_failed_reasons.append(f"No top heading found for cell at ({row_idx}, {col_idx})") - elif best_similarity < max(0.5, 1.0 - (self.max_diffs / (len(self.top_heading) if len(self.top_heading) > 0 else 1))): + current_failed_reasons.append( + f"No top heading found for cell at ({row_idx}, {col_idx})" + ) + elif best_similarity < max( + 0.5, + 1.0 + - ( + self.max_diffs + / ( + len(self.top_heading) + if len(self.top_heading) > 0 + else 1 + ) + ), + ): all_relationships_satisfied = False current_failed_reasons.append( f"Top heading '{best_match}' doesn't match expected '{self.top_heading}' (similarity: {best_similarity:.2f})" @@ -810,11 +971,24 @@ def run(self, content: str) -> Tuple[bool, str]: if row_idx in table_data.row_headers: for _, header_text in table_data.row_headers[row_idx]: header_text = normalize_text(header_text) - similarity = fuzz.ratio(self.left_heading, header_text) / 100.0 + similarity = ( + fuzz.ratio(self.left_heading, header_text) / 100.0 + ) if similarity > best_similarity: best_similarity = similarity best_match = header_text - if best_similarity >= max(0.5, 1.0 - (self.max_diffs / (len(self.left_heading) if len(self.left_heading) > 0 else 1))): + if best_similarity >= max( + 0.5, + 1.0 + - ( + self.max_diffs + / ( + len(self.left_heading) + if len(self.left_heading) > 0 + else 1 + ) + ), + ): left_heading_found = True break @@ -823,11 +997,24 @@ def run(self, content: str) -> Tuple[bool, str]: for j in sorted(header_cols): if j < col_idx and table_array[row_idx, j].strip(): header_text = normalize_text(table_array[row_idx, j]) - similarity = fuzz.ratio(self.left_heading, header_text) / 100.0 + similarity = ( + fuzz.ratio(self.left_heading, header_text) / 100.0 + ) if similarity > best_similarity: best_similarity = similarity best_match = header_text - if best_similarity >= max(0.5, 1.0 - (self.max_diffs / (len(self.left_heading) if len(self.left_heading) > 0 else 1))): + if best_similarity >= max( + 0.5, + 1.0 + - ( + self.max_diffs + / ( + len(self.left_heading) + if len(self.left_heading) > 0 + else 1 + ) + ), + ): left_heading_found = True break @@ -836,15 +1023,30 @@ def run(self, content: str) -> Tuple[bool, str]: for j in range(col_idx): if table_array[row_idx, j].strip(): header_text = normalize_text(table_array[row_idx, j]) - similarity = fuzz.ratio(self.left_heading, header_text) / 100.0 + similarity = ( + fuzz.ratio(self.left_heading, header_text) / 100.0 + ) if similarity > best_similarity: best_similarity = similarity best_match = header_text if not best_match: all_relationships_satisfied = False - current_failed_reasons.append(f"No left heading found for cell at ({row_idx}, {col_idx})") - elif best_similarity < max(0.5, 1.0 - (self.max_diffs / (len(self.left_heading) if len(self.left_heading) > 0 else 1))): + current_failed_reasons.append( + f"No left heading found for cell at ({row_idx}, {col_idx})" + ) + elif best_similarity < max( + 0.5, + 1.0 + - ( + self.max_diffs + / ( + len(self.left_heading) + if len(self.left_heading) > 0 + else 1 + ) + ), + ): all_relationships_satisfied = False current_failed_reasons.append( f"Left heading '{best_match}' doesn't match expected '{self.left_heading}' (similarity: {best_similarity:.2f})" @@ -858,9 +1060,15 @@ def run(self, content: str) -> Tuple[bool, str]: # If we've gone through all tables and all matching cells and none satisfied all relationships if not failed_reasons: - return False, f"No cell matching '{self.cell}' found in any table with threshold {threshold}" + return ( + False, + f"No cell matching '{self.cell}' found in any table with threshold {threshold}", + ) else: - return False, f"Found cells matching '{self.cell}' but relationships were not satisfied: {'; '.join(failed_reasons)}" + return ( + False, + f"Found cells matching '{self.cell}' but relationships were not satisfied: {'; '.join(failed_reasons)}", + ) @dataclass @@ -887,10 +1095,15 @@ def run(self, content: str) -> Tuple[bool, str]: if self.max_length_skips_image_alt_tags: # Remove markdown image tags like ![alt text](image.png) from the text length count content_for_length_check = re.sub(r"!\[.*?\]\(.*?\)", "", content) - base_content_len = len("".join(c for c in content_for_length_check if c.isalnum()).strip()) + base_content_len = len( + "".join(c for c in content_for_length_check if c.isalnum()).strip() + ) if base_content_len > self.max_length: - return False, f"{base_content_len} characters were output for a page we expected to be blank" + return ( + False, + f"{base_content_len} characters were output for a page we expected to be blank", + ) else: return True, "" @@ -906,7 +1119,10 @@ def run(self, content: str) -> Tuple[bool, str]: for index, count in enumerate(repeats): if count > self.max_repeats: - return False, f"Text ends with {count} repeating {index+1}-grams, invalid" + return ( + False, + f"Text ends with {count} repeating {index + 1}-grams, invalid", + ) pattern = re.compile( r"[" @@ -970,7 +1186,9 @@ def run(self, content: str) -> Tuple[bool, str]: equations.extend([e.strip() for e in matches]) # Replace all instances of this pattern with empty strings - modified_content = re.sub(replace_pattern, "", modified_content, flags=re.DOTALL) + modified_content = re.sub( + replace_pattern, "", modified_content, flags=re.DOTALL + ) # If an equation in the markdown exactly matches our math string, then that's good enough # we don't have to do a more expensive comparison @@ -1046,7 +1264,9 @@ def load_tests(jsonl_file: str) -> List[BasePDFTest]: A list of test objects. """ - def process_line_with_number(line_tuple: Tuple[int, str]) -> Optional[Tuple[int, BasePDFTest]]: + def process_line_with_number( + line_tuple: Tuple[int, str], + ) -> Optional[Tuple[int, BasePDFTest]]: """ Process a single line from the JSONL file and return a tuple of (line_number, test object). Returns None for empty lines. @@ -1078,9 +1298,13 @@ def process_line_with_number(line_tuple: Tuple[int, str]) -> Optional[Tuple[int, # Use a ThreadPoolExecutor to process each line in parallel. with ThreadPoolExecutor(max_workers=min(os.cpu_count() or 1, 64)) as executor: # Submit all tasks concurrently. - futures = {executor.submit(process_line_with_number, item): item[0] for item in lines} + futures = { + executor.submit(process_line_with_number, item): item[0] for item in lines + } # Use tqdm to show progress as futures complete. - for future in tqdm(as_completed(futures), total=len(futures), desc="Loading tests"): + for future in tqdm( + as_completed(futures), total=len(futures), desc="Loading tests" + ): result = future.result() if result is not None: _, test = result @@ -1090,7 +1314,9 @@ def process_line_with_number(line_tuple: Tuple[int, str]) -> Optional[Tuple[int, unique_ids = set() for test in tests: if test.id in unique_ids: - raise ValidationError(f"Test with duplicate id {test.id} found, error loading tests.") + raise ValidationError( + f"Test with duplicate id {test.id} found, error loading tests." + ) unique_ids.add(test.id) return tests diff --git a/benchmarks/olmocr/bench/utils.py b/benchmarks/olmocr/bench/utils.py index 5fe5f38..768b8fe 100644 --- a/benchmarks/olmocr/bench/utils.py +++ b/benchmarks/olmocr/bench/utils.py @@ -3,7 +3,12 @@ import numpy as np -def calculate_bootstrap_ci(test_scores: List[float], n_bootstrap: int = 1000, ci_level: float = 0.95, splits: List[int] = None) -> Tuple[float, float]: +def calculate_bootstrap_ci( + test_scores: List[float], + n_bootstrap: int = 1000, + ci_level: float = 0.95, + splits: List[int] = None, +) -> Tuple[float, float]: """ Calculate bootstrap confidence interval for test scores, respecting category splits. @@ -35,7 +40,9 @@ def calculate_bootstrap_ci(test_scores: List[float], n_bootstrap: int = 1000, ci else: # Validate splits if sum(splits) != len(scores): - raise ValueError(f"Sum of splits ({sum(splits)}) must equal length of test_scores ({len(scores)})") + raise ValueError( + f"Sum of splits ({sum(splits)}) must equal length of test_scores ({len(scores)})" + ) # Convert flat scores list to a list of category scores category_scores = [] @@ -52,7 +59,9 @@ def calculate_bootstrap_ci(test_scores: List[float], n_bootstrap: int = 1000, ci for cat_scores in category_scores: if len(cat_scores) > 0: # Sample with replacement within this category - cat_sample = np.random.choice(cat_scores, size=len(cat_scores), replace=True) + cat_sample = np.random.choice( + cat_scores, size=len(cat_scores), replace=True + ) category_means.append(np.mean(cat_sample)) # Overall score is average of category means (if any categories have scores) @@ -68,7 +77,11 @@ def calculate_bootstrap_ci(test_scores: List[float], n_bootstrap: int = 1000, ci def perform_permutation_test( - scores_a: List[float], scores_b: List[float], n_permutations: int = 10000, splits_a: List[int] = None, splits_b: List[int] = None + scores_a: List[float], + scores_b: List[float], + n_permutations: int = 10000, + splits_a: List[int] = None, + splits_b: List[int] = None, ) -> Tuple[float, float]: """ Perform a permutation test to determine if there's a significant difference @@ -133,9 +146,13 @@ def mean_of_category_means(scores, splits=None): # For category-based permutation test, we need to maintain category structure # Validate that the splits match the score lengths if splits_a is not None and sum(splits_a) != len(scores_a): - raise ValueError(f"Sum of splits_a ({sum(splits_a)}) must equal length of scores_a ({len(scores_a)})") + raise ValueError( + f"Sum of splits_a ({sum(splits_a)}) must equal length of scores_a ({len(scores_a)})" + ) if splits_b is not None and sum(splits_b) != len(scores_b): - raise ValueError(f"Sum of splits_b ({sum(splits_b)}) must equal length of scores_b ({len(scores_b)})") + raise ValueError( + f"Sum of splits_b ({sum(splits_b)}) must equal length of scores_b ({len(scores_b)})" + ) # Create category structures categories_a = [] diff --git a/benchmarks/olmocr/data/renderpdf.py b/benchmarks/olmocr/data/renderpdf.py index d33a7ca..0c77a6a 100644 --- a/benchmarks/olmocr/data/renderpdf.py +++ b/benchmarks/olmocr/data/renderpdf.py @@ -6,7 +6,9 @@ from PIL import Image -def get_pdf_media_box_width_height(local_pdf_path: str, page_num: int) -> tuple[float, float]: +def get_pdf_media_box_width_height( + local_pdf_path: str, page_num: int +) -> tuple[float, float]: """ Get the MediaBox dimensions for a specific page in a PDF file using the pdfinfo command. @@ -15,10 +17,22 @@ def get_pdf_media_box_width_height(local_pdf_path: str, page_num: int) -> tuple[ :return: A dictionary containing MediaBox dimensions or None if not found """ # Construct the pdfinfo command to extract info for the specific page - command = ["pdfinfo", "-f", str(page_num), "-l", str(page_num), "-box", "-enc", "UTF-8", local_pdf_path] + command = [ + "pdfinfo", + "-f", + str(page_num), + "-l", + str(page_num), + "-box", + "-enc", + "UTF-8", + local_pdf_path, + ] # Run the command using subprocess - result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True) + result = subprocess.run( + command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True + ) # Check if there is any error in executing the command if result.returncode != 0: @@ -36,7 +50,9 @@ def get_pdf_media_box_width_height(local_pdf_path: str, page_num: int) -> tuple[ raise ValueError("MediaBox not found in the PDF info.") -def render_pdf_to_base64png(local_pdf_path: str, page_num: int, target_longest_image_dim: int = 2048) -> str: +def render_pdf_to_base64png( + local_pdf_path: str, page_num: int, target_longest_image_dim: int = 2048 +) -> str: longest_dim = max(get_pdf_media_box_width_height(local_pdf_path, page_num)) # Convert PDF page to PNG using pdftoppm @@ -49,7 +65,9 @@ def render_pdf_to_base64png(local_pdf_path: str, page_num: int, target_longest_i "-l", str(page_num), "-r", - str(target_longest_image_dim * 72 / longest_dim), # 72 pixels per point is the conversion factor + str( + target_longest_image_dim * 72 / longest_dim + ), # 72 pixels per point is the conversion factor local_pdf_path, ], timeout=120, @@ -60,7 +78,9 @@ def render_pdf_to_base64png(local_pdf_path: str, page_num: int, target_longest_i return base64.b64encode(pdftoppm_result.stdout).decode("utf-8") -def render_pdf_to_base64webp(local_pdf_path: str, page: int, target_longest_image_dim: int = 1024): +def render_pdf_to_base64webp( + local_pdf_path: str, page: int, target_longest_image_dim: int = 1024 +): base64_png = render_pdf_to_base64png(local_pdf_path, page, target_longest_image_dim) png_image = Image.open(io.BytesIO(base64.b64decode(base64_png))) diff --git a/benchmarks/olmocr/harness.py b/benchmarks/olmocr/harness.py new file mode 100644 index 0000000..d787234 --- /dev/null +++ b/benchmarks/olmocr/harness.py @@ -0,0 +1,213 @@ +"""olmOCR-bench: PDF page -> markdown, scored by the external olmocr.bench scorer. + +Special-cased: the scorer reads a directory of per-page .md files and prints its +table to stdout (no metrics.json), so `score` materializes the .md files from the +run records, invokes the scorer, tees stdout to logs/olmocr_.log (what +report_scores reads), and parses the overall out of it. +""" + +from __future__ import annotations + +import json +import os +import re +import sys +from pathlib import Path + +from src.request import ImagePart, Message, ReasoningSpec, Request, TextPart +from src.results import model_slug + +NAME = "olmocr" +ID_KEY = "id" +PRIMARY_METRIC = "overall" +DEFAULTS = {"reasoning": "off", "rate_limit": 25, "max_in_flight": 8} + +_HF_REPO = "allenai/olmOCR-bench" +_FULL_DATA_DIR = Path(__file__).resolve().parent / "bench" / "full_data" +_LOGS_DIR = Path(__file__).resolve().parent.parent.parent / "logs" +_SPLITS = [ + "arxiv_math", + "headers_footers", + "long_tiny_text", + "multi_column", + "old_scans", + "old_scans_math", + "table_tests", +] +_MAX_TOKENS = 20000 + +PROMPT = ( + "Below is the image of one page of a PDF document. " + "Just return the plain text representation of this document as if you were reading it naturally.\n" + "Turn equations into LaTeX using \\( \\) for inline math and \\[ \\] for display math. " + "Never describe equations in words — always use LaTeX notation. " + "Turn tables into markdown format.\n" + "Remove the headers and footers completely — do not include any text " + "that appears at the very top or very bottom of the page outside the main body content. " + "This includes page numbers, journal names, author names in running headers, " + "copyright lines, DOI lines, citation requests, institutional addresses in margins, " + "and download dates. Keep references and footnotes that are part of the body.\n" + "For multi-column layouts, read each column top to bottom before moving to the next.\n" + "Read any natural handwriting.\n" + "This is likely one page out of several in the document, so be sure to preserve " + "any sentences that come from the previous page, or continue onto the next page, exactly as they are.\n" + "If there is no text at all that you think you should read, you can output null.\n" + "Do not hallucinate." +) + +_NULL = ("null", "none", "n/a", "") + + +def _download() -> Path: + from huggingface_hub import hf_hub_download + + data_dir = _FULL_DATA_DIR + pdf_dir = data_dir / "pdfs" + all_pdfs = set() + for split in _SPLITS: + dest = data_dir / f"{split}.jsonl" + if not dest.exists(): + src = hf_hub_download( + _HF_REPO, f"bench_data/{split}.jsonl", repo_type="dataset" + ) + dest.parent.mkdir(parents=True, exist_ok=True) + dest.write_text(Path(src).read_text()) + for line in dest.read_text().splitlines(): + if line.strip(): + all_pdfs.add(json.loads(line)["pdf"]) + for pdf_rel in sorted(all_pdfs): + local = pdf_dir / pdf_rel + if local.exists(): + continue + local.parent.mkdir(parents=True, exist_ok=True) + src = hf_hub_download( + _HF_REPO, f"bench_data/pdfs/{pdf_rel}", repo_type="dataset" + ) + os.symlink(src, str(local)) + return data_dir + + +def load_samples(sample_size: int | None = None) -> list[dict]: + data_dir = _download() + pairs = set() + for jf in data_dir.glob("*.jsonl"): + for line in jf.read_text().splitlines(): + if line.strip(): + t = json.loads(line) + pairs.add((t["pdf"], t["page"])) + samples = [] + for pdf_rel, page in sorted(pairs): + pdf_path = data_dir / "pdfs" / pdf_rel + if not pdf_path.exists(): + continue + base = os.path.splitext(os.path.basename(pdf_rel))[0] + parent = os.path.dirname(pdf_rel) + out_rel = ( + f"{parent}/{base}_pg{page}_repeat1.md" + if parent + else f"{base}_pg{page}_repeat1.md" + ) + samples.append( + { + "id": f"{pdf_rel}#{page}", + "pdf_path": str(pdf_path), + "page": page, + "out_rel": out_rel, + } + ) + return samples[:sample_size] if sample_size else samples + + +def build_request(sample: dict, mode: str) -> Request: + from benchmarks.olmocr.data.renderpdf import render_pdf_to_base64png + + b64 = render_pdf_to_base64png( + sample["pdf_path"], page_num=sample["page"], target_longest_image_dim=2048 + ) + import base64 + + png = base64.b64decode(b64) + return Request( + [Message("user", [TextPart(PROMPT), ImagePart(png, "image/png")])], + reasoning=ReasoningSpec(mode), + temperature=0.0, + max_tokens=_MAX_TOKENS, + ) + + +def parse(response, sample) -> str: + raw = response.text or "" + return "" if raw.strip().lower() in _NULL else raw + + +class _Tee: + def __init__(self, *streams): + self.streams = streams + + def write(self, data): + for s in self.streams: + s.write(data) + s.flush() + + def flush(self): + for s in self.streams: + s.flush() + + +def score(records: list[dict], samples: list[dict], target=None) -> dict: + candidate = model_slug(target.name) if target is not None else "candidate" + by_id = {s["id"]: s for s in samples} + out_root = _FULL_DATA_DIR / candidate + # materialize per-page .md from the run records (the scorer reads these) + for r in records: + s = by_id.get(r["id"]) + if s is None: + continue + md_path = out_root / s["out_rel"] + md_path.parent.mkdir(parents=True, exist_ok=True) + md_path.write_text(r.get("prediction") or "", encoding="utf-8") + + from olmocr.bench.benchmark import main as bench_main + + _LOGS_DIR.mkdir(parents=True, exist_ok=True) + log_path = _LOGS_DIR / f"olmocr_{candidate}.log" + import io + + buf = io.StringIO() + argv, real = sys.argv, sys.stdout + sys.argv = [ + "benchmark", + "--dir", + str(_FULL_DATA_DIR), + "--candidate", + candidate, + "--force", + ] + with open(log_path, "w", encoding="utf-8") as fh: + sys.stdout = _Tee(real, fh, buf) + try: + bench_main() + finally: + sys.stdout, sys.argv = real, argv + + return { + "candidate": candidate, + "log": str(log_path), + **_parse_scores(buf.getvalue()), + } + + +def _parse_scores(text: str) -> dict: + text = text.replace("\r", "\n") + head = re.search( + r"^(\S+)\s*:\s*Average Score:\s*([\d.]+)%\s*±\s*([\d.]+)%", text, re.MULTILINE + ) + overall = float(head.group(2)) if head else None + splits = { + m.group(1): float(m.group(2)) + for m in re.finditer(r"^\s+(\w+)\.jsonl\s*:\s*([\d.]+)%", text, re.MULTILINE) + } + base = re.search(r"^\s+baseline\s*:\s*([\d.]+)%", text, re.MULTILINE) + if base: + splits["baseline"] = float(base.group(1)) + return {"overall": overall, "splits": splits} diff --git a/benchmarks/olmocr/olmocr_bench.py b/benchmarks/olmocr/olmocr_bench.py deleted file mode 100644 index fe48770..0000000 --- a/benchmarks/olmocr/olmocr_bench.py +++ /dev/null @@ -1,248 +0,0 @@ -""" -OlmOCR Benchmark for Interfaze. - -Downloads the full olmOCR-bench dataset from HuggingFace, processes all PDFs -through the Interfaze API, and evaluates against the test suite. - -Usage: - uv run -m benchmarks.olmocr.olmocr_bench # full run - uv run -m benchmarks.olmocr.olmocr_bench --sample # sample data only - uv run -m benchmarks.olmocr.olmocr_bench --skip-generation # evaluate only - uv run -m benchmarks.olmocr.olmocr_bench --generate-only # generate only -""" - -import argparse -import asyncio -import json -import os -import sys -from pathlib import Path - -from dotenv import load_dotenv -from huggingface_hub import hf_hub_download -from pypdf import PdfReader -from tqdm.asyncio import tqdm_asyncio - -load_dotenv() - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -SAMPLE_DATA_DIR = Path(__file__).resolve().parent / "bench" / "sample_data" -FULL_DATA_DIR = Path(__file__).resolve().parent / "bench" / "full_data" -CANDIDATE_NAME = "interfaze" -RATE_LIMIT = 25 -MAX_RETRIES = 3 - -HF_REPO = "allenai/olmOCR-bench" -SPLITS = [ - "arxiv_math", - "headers_footers", - "long_tiny_text", - "multi_column", - "old_scans", - "old_scans_math", - "table_tests", -] - -sys.path.insert(0, str(PROJECT_ROOT)) -sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) - - -class RateLimiter: - def __init__(self, rate: int): - self.rate = rate - self.tokens = rate - self.last_refill = 0.0 - self._lock = asyncio.Lock() - - async def acquire(self): - while True: - async with self._lock: - now = asyncio.get_running_loop().time() - elapsed = now - self.last_refill - self.tokens = min(self.rate, self.tokens + elapsed * self.rate) - self.last_refill = now - if self.tokens >= 1: - self.tokens -= 1 - return - await asyncio.sleep(1 / self.rate) - - -def download_full_dataset(): - """Download JSONL test files and PDFs from HuggingFace into full_data/.""" - data_dir = FULL_DATA_DIR - pdf_dir = data_dir / "pdfs" - - # Download JSONL files - all_pdfs = set() - for split in SPLITS: - jsonl_dest = data_dir / f"{split}.jsonl" - if jsonl_dest.exists(): - print(f" {split}.jsonl already exists, loading PDF list...") - with open(jsonl_dest) as f: - tests = [json.loads(l) for l in f if l.strip()] - else: - print(f" Downloading {split}.jsonl...") - src = hf_hub_download(HF_REPO, f"bench_data/{split}.jsonl", repo_type="dataset") - with open(src) as f: - tests = [json.loads(l) for l in f if l.strip()] - # Copy to our data dir - data_dir.mkdir(parents=True, exist_ok=True) - with open(jsonl_dest, "w") as f: - for t in tests: - f.write(json.dumps(t) + "\n") - - print(f" {split}: {len(tests)} tests") - for t in tests: - all_pdfs.add(t["pdf"]) - - print(f"\n Total unique PDFs to download: {len(all_pdfs)}") - - # Download PDFs - downloaded = 0 - skipped = 0 - for pdf_rel in sorted(all_pdfs): - local_path = pdf_dir / pdf_rel - if local_path.exists(): - skipped += 1 - continue - local_path.parent.mkdir(parents=True, exist_ok=True) - try: - src = hf_hub_download(HF_REPO, f"bench_data/pdfs/{pdf_rel}", repo_type="dataset") - # Symlink to HF cache to save disk space - os.symlink(src, str(local_path)) - downloaded += 1 - except Exception as e: - print(f" Failed to download {pdf_rel}: {e}") - - print(f" PDFs: {downloaded} downloaded, {skipped} already existed") - return data_dir - - -async def process_page( - pdf_path: str, - page_num: int, - output_path: str, - rate_limiter: RateLimiter, -) -> bool: - """Process a single PDF page through Interfaze and save the markdown output.""" - from olmocr.bench.runners.run_interfaze import run_interfaze - - for attempt in range(MAX_RETRIES): - await rate_limiter.acquire() - try: - result = await asyncio.to_thread(run_interfaze, pdf_path, page_num) - os.makedirs(os.path.dirname(output_path), exist_ok=True) - with open(output_path, "w", encoding="utf-8") as f: - f.write(result) - return True - except Exception as e: - if attempt < MAX_RETRIES - 1: - await asyncio.sleep(2**attempt) - else: - print(f"Failed after {MAX_RETRIES} attempts: {pdf_path} page {page_num}: {e}") - return False - - -async def generate_outputs(data_dir: Path): - """Generate markdown outputs for all PDFs using Interfaze.""" - pdf_folder = data_dir / "pdfs" - output_folder = data_dir / CANDIDATE_NAME - - # Collect all unique (pdf, page) pairs from JSONL test files - pdf_pages = set() - for jsonl_file in data_dir.glob("*.jsonl"): - with open(jsonl_file) as f: - for line in f: - line = line.strip() - if not line: - continue - t = json.loads(line) - pdf_pages.add((t["pdf"], t["page"])) - - print(f"Found {len(pdf_pages)} unique (pdf, page) pairs to process") - - rate_limiter = RateLimiter(RATE_LIMIT) - tasks = [] - - for pdf_rel, page in sorted(pdf_pages): - pdf_path = str(pdf_folder / pdf_rel) - if not os.path.exists(pdf_path): - continue - - base_name = os.path.splitext(os.path.basename(pdf_rel))[0] - parent_dir = os.path.dirname(pdf_rel) - md_filename = f"{base_name}_pg{page}_repeat1.md" - - if parent_dir: - out_path = str(output_folder / parent_dir / md_filename) - else: - out_path = str(output_folder / md_filename) - - if os.path.exists(out_path): - continue - - tasks.append(process_page(pdf_path, page, out_path, rate_limiter)) - - if not tasks: - print("All outputs already exist, skipping generation.") - return True - - print(f"Processing {len(tasks)} pages...") - results = await tqdm_asyncio.gather(*tasks, desc="Generating markdown outputs") - num_success = sum(1 for r in results if r) - num_failed = len(results) - num_success - print(f"Done: {num_success} succeeded, {num_failed} failed") - - return num_failed == 0 - - -def run_evaluation(data_dir: Path): - """Run the olmocr benchmark evaluation for the Interfaze candidate.""" - from olmocr.bench.benchmark import main as bench_main - - sys.argv = [ - "benchmark", - "--dir", str(data_dir), - "--candidate", CANDIDATE_NAME, - "--force", - ] - bench_main() - - -async def main(): - parser = argparse.ArgumentParser(description="Run OlmOCR benchmark with Interfaze") - parser.add_argument( - "--sample", - action="store_true", - help="Use sample data only (small subset for testing)", - ) - parser.add_argument( - "--skip-generation", - action="store_true", - help="Skip markdown generation, only run evaluation", - ) - parser.add_argument( - "--generate-only", - action="store_true", - help="Only generate markdown outputs, skip evaluation", - ) - args = parser.parse_args() - - if args.sample: - data_dir = SAMPLE_DATA_DIR - print("=== Using sample data ===") - else: - print("=== Downloading full olmOCR-bench dataset from HuggingFace ===") - data_dir = download_full_dataset() - - if not args.skip_generation: - print("\n=== Generating Interfaze outputs ===") - await generate_outputs(data_dir) - - if not args.generate_only: - print("\n=== Running OlmOCR Benchmark Evaluation ===") - run_evaluation(data_dir) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/benchmarks/olmocr/olmocr_bench_gemini_pro_31.py b/benchmarks/olmocr/olmocr_bench_gemini_pro_31.py deleted file mode 100644 index aae674d..0000000 --- a/benchmarks/olmocr/olmocr_bench_gemini_pro_31.py +++ /dev/null @@ -1,202 +0,0 @@ -""" -OlmOCR Benchmark for Gemini 3.1 Pro Preview. - -Mirrors olmocr_bench.py (interfaze) but uses gemini-3.1-pro-preview. - -Usage: - uv run -m benchmarks.olmocr.olmocr_bench_gemini_pro_31 - uv run -m benchmarks.olmocr.olmocr_bench_gemini_pro_31 --sample - uv run -m benchmarks.olmocr.olmocr_bench_gemini_pro_31 --skip-generation - uv run -m benchmarks.olmocr.olmocr_bench_gemini_pro_31 --generate-only -""" - -import argparse -import asyncio -import json -import os -import sys -from pathlib import Path - -from dotenv import load_dotenv -from huggingface_hub import hf_hub_download -from tqdm.asyncio import tqdm_asyncio - -load_dotenv() - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -SAMPLE_DATA_DIR = Path(__file__).resolve().parent / "bench" / "sample_data" -FULL_DATA_DIR = Path(__file__).resolve().parent / "bench" / "full_data" -CANDIDATE_NAME = "gemini_pro_31" -RATE_LIMIT = 25 -MAX_RETRIES = 3 - -HF_REPO = "allenai/olmOCR-bench" -SPLITS = [ - "arxiv_math", - "headers_footers", - "long_tiny_text", - "multi_column", - "old_scans", - "old_scans_math", - "table_tests", -] - -sys.path.insert(0, str(PROJECT_ROOT)) -sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) - - -class RateLimiter: - def __init__(self, rate: int): - self.rate = rate - self.tokens = rate - self.last_refill = 0.0 - self._lock = asyncio.Lock() - - async def acquire(self): - while True: - async with self._lock: - now = asyncio.get_running_loop().time() - elapsed = now - self.last_refill - self.tokens = min(self.rate, self.tokens + elapsed * self.rate) - self.last_refill = now - if self.tokens >= 1: - self.tokens -= 1 - return - await asyncio.sleep(1 / self.rate) - - -def download_full_dataset(): - data_dir = FULL_DATA_DIR - pdf_dir = data_dir / "pdfs" - all_pdfs = set() - for split in SPLITS: - jsonl_dest = data_dir / f"{split}.jsonl" - if jsonl_dest.exists(): - with open(jsonl_dest) as f: - tests = [json.loads(l) for l in f if l.strip()] - else: - print(f" Downloading {split}.jsonl...") - src = hf_hub_download(HF_REPO, f"bench_data/{split}.jsonl", repo_type="dataset") - with open(src) as f: - tests = [json.loads(l) for l in f if l.strip()] - data_dir.mkdir(parents=True, exist_ok=True) - with open(jsonl_dest, "w") as f: - for t in tests: - f.write(json.dumps(t) + "\n") - print(f" {split}: {len(tests)} tests") - for t in tests: - all_pdfs.add(t["pdf"]) - - print(f"\n Total unique PDFs to download: {len(all_pdfs)}") - downloaded = 0 - skipped = 0 - for pdf_rel in sorted(all_pdfs): - local_path = pdf_dir / pdf_rel - if local_path.exists(): - skipped += 1 - continue - local_path.parent.mkdir(parents=True, exist_ok=True) - try: - src = hf_hub_download(HF_REPO, f"bench_data/pdfs/{pdf_rel}", repo_type="dataset") - os.symlink(src, str(local_path)) - downloaded += 1 - except Exception as e: - print(f" Failed to download {pdf_rel}: {e}") - print(f" PDFs: {downloaded} downloaded, {skipped} already existed") - return data_dir - - -async def process_page(pdf_path, page_num, output_path, rate_limiter): - from olmocr.bench.runners.run_gemini_pro_31 import run_gemini_pro_31 - - for attempt in range(MAX_RETRIES): - await rate_limiter.acquire() - try: - result = await asyncio.to_thread(run_gemini_pro_31, pdf_path, page_num) - os.makedirs(os.path.dirname(output_path), exist_ok=True) - with open(output_path, "w", encoding="utf-8") as f: - f.write(result) - return True - except Exception as e: - if attempt < MAX_RETRIES - 1: - await asyncio.sleep(2**attempt) - else: - print(f"Failed after {MAX_RETRIES} attempts: {pdf_path} page {page_num}: {e}") - return False - - -async def generate_outputs(data_dir: Path): - pdf_folder = data_dir / "pdfs" - output_folder = data_dir / CANDIDATE_NAME - - pdf_pages = set() - for jsonl_file in data_dir.glob("*.jsonl"): - with open(jsonl_file) as f: - for line in f: - line = line.strip() - if not line: - continue - t = json.loads(line) - pdf_pages.add((t["pdf"], t["page"])) - - print(f"Found {len(pdf_pages)} unique (pdf, page) pairs to process") - - rate_limiter = RateLimiter(RATE_LIMIT) - tasks = [] - for pdf_rel, page in sorted(pdf_pages): - pdf_path = str(pdf_folder / pdf_rel) - if not os.path.exists(pdf_path): - continue - base_name = os.path.splitext(os.path.basename(pdf_rel))[0] - parent_dir = os.path.dirname(pdf_rel) - md_filename = f"{base_name}_pg{page}_repeat1.md" - if parent_dir: - out_path = str(output_folder / parent_dir / md_filename) - else: - out_path = str(output_folder / md_filename) - if os.path.exists(out_path): - continue - tasks.append(process_page(pdf_path, page, out_path, rate_limiter)) - - if not tasks: - print("All outputs already exist, skipping generation.") - return True - print(f"Processing {len(tasks)} pages...") - results = await tqdm_asyncio.gather(*tasks, desc=f"Generating {CANDIDATE_NAME} outputs") - num_success = sum(1 for r in results if r) - num_failed = len(results) - num_success - print(f"Done: {num_success} succeeded, {num_failed} failed") - return num_failed == 0 - - -def run_evaluation(data_dir: Path): - from olmocr.bench.benchmark import main as bench_main - sys.argv = ["benchmark", "--dir", str(data_dir), "--candidate", CANDIDATE_NAME, "--force"] - bench_main() - - -async def main(): - parser = argparse.ArgumentParser(description=f"Run OlmOCR benchmark with {CANDIDATE_NAME}") - parser.add_argument("--sample", action="store_true") - parser.add_argument("--skip-generation", action="store_true") - parser.add_argument("--generate-only", action="store_true") - args = parser.parse_args() - - if args.sample: - data_dir = SAMPLE_DATA_DIR - print("=== Using sample data ===") - else: - print("=== Downloading full olmOCR-bench dataset from HuggingFace ===") - data_dir = download_full_dataset() - - if not args.skip_generation: - print(f"\n=== Generating {CANDIDATE_NAME} outputs ===") - await generate_outputs(data_dir) - - if not args.generate_only: - print("\n=== Running OlmOCR Benchmark Evaluation ===") - run_evaluation(data_dir) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/benchmarks/olmocr/olmocr_bench_grok.py b/benchmarks/olmocr/olmocr_bench_grok.py deleted file mode 100644 index 6a214da..0000000 --- a/benchmarks/olmocr/olmocr_bench_grok.py +++ /dev/null @@ -1,202 +0,0 @@ -""" -OlmOCR Benchmark for OpenAI Grok 4.3. - -Mirrors olmocr_bench.py (interfaze) but uses Grok 4.3 via OpenAI. - -Usage: - uv run -m benchmarks.olmocr.olmocr_bench_grok - uv run -m benchmarks.olmocr.olmocr_bench_grok --sample - uv run -m benchmarks.olmocr.olmocr_bench_grok --skip-generation - uv run -m benchmarks.olmocr.olmocr_bench_grok --generate-only -""" - -import argparse -import asyncio -import json -import os -import sys -from pathlib import Path - -from dotenv import load_dotenv -from huggingface_hub import hf_hub_download -from tqdm.asyncio import tqdm_asyncio - -load_dotenv() - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -SAMPLE_DATA_DIR = Path(__file__).resolve().parent / "bench" / "sample_data" -FULL_DATA_DIR = Path(__file__).resolve().parent / "bench" / "full_data" -CANDIDATE_NAME = "grok" -RATE_LIMIT = 25 -MAX_RETRIES = 3 - -HF_REPO = "allenai/olmOCR-bench" -SPLITS = [ - "arxiv_math", - "headers_footers", - "long_tiny_text", - "multi_column", - "old_scans", - "old_scans_math", - "table_tests", -] - -sys.path.insert(0, str(PROJECT_ROOT)) -sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) - - -class RateLimiter: - def __init__(self, rate: int): - self.rate = rate - self.tokens = rate - self.last_refill = 0.0 - self._lock = asyncio.Lock() - - async def acquire(self): - while True: - async with self._lock: - now = asyncio.get_running_loop().time() - elapsed = now - self.last_refill - self.tokens = min(self.rate, self.tokens + elapsed * self.rate) - self.last_refill = now - if self.tokens >= 1: - self.tokens -= 1 - return - await asyncio.sleep(1 / self.rate) - - -def download_full_dataset(): - data_dir = FULL_DATA_DIR - pdf_dir = data_dir / "pdfs" - all_pdfs = set() - for split in SPLITS: - jsonl_dest = data_dir / f"{split}.jsonl" - if jsonl_dest.exists(): - with open(jsonl_dest) as f: - tests = [json.loads(l) for l in f if l.strip()] - else: - print(f" Downloading {split}.jsonl...") - src = hf_hub_download(HF_REPO, f"bench_data/{split}.jsonl", repo_type="dataset") - with open(src) as f: - tests = [json.loads(l) for l in f if l.strip()] - data_dir.mkdir(parents=True, exist_ok=True) - with open(jsonl_dest, "w") as f: - for t in tests: - f.write(json.dumps(t) + "\n") - print(f" {split}: {len(tests)} tests") - for t in tests: - all_pdfs.add(t["pdf"]) - - print(f"\n Total unique PDFs to download: {len(all_pdfs)}") - downloaded = 0 - skipped = 0 - for pdf_rel in sorted(all_pdfs): - local_path = pdf_dir / pdf_rel - if local_path.exists(): - skipped += 1 - continue - local_path.parent.mkdir(parents=True, exist_ok=True) - try: - src = hf_hub_download(HF_REPO, f"bench_data/pdfs/{pdf_rel}", repo_type="dataset") - os.symlink(src, str(local_path)) - downloaded += 1 - except Exception as e: - print(f" Failed to download {pdf_rel}: {e}") - print(f" PDFs: {downloaded} downloaded, {skipped} already existed") - return data_dir - - -async def process_page(pdf_path, page_num, output_path, rate_limiter): - from olmocr.bench.runners.run_grok import run_grok - - for attempt in range(MAX_RETRIES): - await rate_limiter.acquire() - try: - result = await asyncio.to_thread(run_grok, pdf_path, page_num) - os.makedirs(os.path.dirname(output_path), exist_ok=True) - with open(output_path, "w", encoding="utf-8") as f: - f.write(result) - return True - except Exception as e: - if attempt < MAX_RETRIES - 1: - await asyncio.sleep(2**attempt) - else: - print(f"Failed after {MAX_RETRIES} attempts: {pdf_path} page {page_num}: {e}") - return False - - -async def generate_outputs(data_dir: Path): - pdf_folder = data_dir / "pdfs" - output_folder = data_dir / CANDIDATE_NAME - - pdf_pages = set() - for jsonl_file in data_dir.glob("*.jsonl"): - with open(jsonl_file) as f: - for line in f: - line = line.strip() - if not line: - continue - t = json.loads(line) - pdf_pages.add((t["pdf"], t["page"])) - - print(f"Found {len(pdf_pages)} unique (pdf, page) pairs to process") - - rate_limiter = RateLimiter(RATE_LIMIT) - tasks = [] - for pdf_rel, page in sorted(pdf_pages): - pdf_path = str(pdf_folder / pdf_rel) - if not os.path.exists(pdf_path): - continue - base_name = os.path.splitext(os.path.basename(pdf_rel))[0] - parent_dir = os.path.dirname(pdf_rel) - md_filename = f"{base_name}_pg{page}_repeat1.md" - if parent_dir: - out_path = str(output_folder / parent_dir / md_filename) - else: - out_path = str(output_folder / md_filename) - if os.path.exists(out_path): - continue - tasks.append(process_page(pdf_path, page, out_path, rate_limiter)) - - if not tasks: - print("All outputs already exist, skipping generation.") - return True - print(f"Processing {len(tasks)} pages...") - results = await tqdm_asyncio.gather(*tasks, desc=f"Generating {CANDIDATE_NAME} outputs") - num_success = sum(1 for r in results if r) - num_failed = len(results) - num_success - print(f"Done: {num_success} succeeded, {num_failed} failed") - return num_failed == 0 - - -def run_evaluation(data_dir: Path): - from olmocr.bench.benchmark import main as bench_main - sys.argv = ["benchmark", "--dir", str(data_dir), "--candidate", CANDIDATE_NAME, "--force"] - bench_main() - - -async def main(): - parser = argparse.ArgumentParser(description=f"Run OlmOCR benchmark with {CANDIDATE_NAME}") - parser.add_argument("--sample", action="store_true") - parser.add_argument("--skip-generation", action="store_true") - parser.add_argument("--generate-only", action="store_true") - args = parser.parse_args() - - if args.sample: - data_dir = SAMPLE_DATA_DIR - print("=== Using sample data ===") - else: - print("=== Downloading full olmOCR-bench dataset from HuggingFace ===") - data_dir = download_full_dataset() - - if not args.skip_generation: - print(f"\n=== Generating {CANDIDATE_NAME} outputs ===") - await generate_outputs(data_dir) - - if not args.generate_only: - print("\n=== Running OlmOCR Benchmark Evaluation ===") - run_evaluation(data_dir) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/benchmarks/olmocr/olmocr_bench_openai_mini.py b/benchmarks/olmocr/olmocr_bench_openai_mini.py deleted file mode 100644 index bfbdc00..0000000 --- a/benchmarks/olmocr/olmocr_bench_openai_mini.py +++ /dev/null @@ -1,202 +0,0 @@ -""" -OlmOCR Benchmark for OpenAI gpt-5.4-mini. - -Mirrors olmocr_bench.py (interfaze) but uses gpt-5.4-mini via OpenAI. - -Usage: - uv run -m benchmarks.olmocr.olmocr_bench_openai_mini - uv run -m benchmarks.olmocr.olmocr_bench_openai_mini --sample - uv run -m benchmarks.olmocr.olmocr_bench_openai_mini --skip-generation - uv run -m benchmarks.olmocr.olmocr_bench_openai_mini --generate-only -""" - -import argparse -import asyncio -import json -import os -import sys -from pathlib import Path - -from dotenv import load_dotenv -from huggingface_hub import hf_hub_download -from tqdm.asyncio import tqdm_asyncio - -load_dotenv() - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -SAMPLE_DATA_DIR = Path(__file__).resolve().parent / "bench" / "sample_data" -FULL_DATA_DIR = Path(__file__).resolve().parent / "bench" / "full_data" -CANDIDATE_NAME = "openai_mini" -RATE_LIMIT = 25 -MAX_RETRIES = 3 - -HF_REPO = "allenai/olmOCR-bench" -SPLITS = [ - "arxiv_math", - "headers_footers", - "long_tiny_text", - "multi_column", - "old_scans", - "old_scans_math", - "table_tests", -] - -sys.path.insert(0, str(PROJECT_ROOT)) -sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) - - -class RateLimiter: - def __init__(self, rate: int): - self.rate = rate - self.tokens = rate - self.last_refill = 0.0 - self._lock = asyncio.Lock() - - async def acquire(self): - while True: - async with self._lock: - now = asyncio.get_running_loop().time() - elapsed = now - self.last_refill - self.tokens = min(self.rate, self.tokens + elapsed * self.rate) - self.last_refill = now - if self.tokens >= 1: - self.tokens -= 1 - return - await asyncio.sleep(1 / self.rate) - - -def download_full_dataset(): - data_dir = FULL_DATA_DIR - pdf_dir = data_dir / "pdfs" - all_pdfs = set() - for split in SPLITS: - jsonl_dest = data_dir / f"{split}.jsonl" - if jsonl_dest.exists(): - with open(jsonl_dest) as f: - tests = [json.loads(l) for l in f if l.strip()] - else: - print(f" Downloading {split}.jsonl...") - src = hf_hub_download(HF_REPO, f"bench_data/{split}.jsonl", repo_type="dataset") - with open(src) as f: - tests = [json.loads(l) for l in f if l.strip()] - data_dir.mkdir(parents=True, exist_ok=True) - with open(jsonl_dest, "w") as f: - for t in tests: - f.write(json.dumps(t) + "\n") - print(f" {split}: {len(tests)} tests") - for t in tests: - all_pdfs.add(t["pdf"]) - - print(f"\n Total unique PDFs to download: {len(all_pdfs)}") - downloaded = 0 - skipped = 0 - for pdf_rel in sorted(all_pdfs): - local_path = pdf_dir / pdf_rel - if local_path.exists(): - skipped += 1 - continue - local_path.parent.mkdir(parents=True, exist_ok=True) - try: - src = hf_hub_download(HF_REPO, f"bench_data/pdfs/{pdf_rel}", repo_type="dataset") - os.symlink(src, str(local_path)) - downloaded += 1 - except Exception as e: - print(f" Failed to download {pdf_rel}: {e}") - print(f" PDFs: {downloaded} downloaded, {skipped} already existed") - return data_dir - - -async def process_page(pdf_path, page_num, output_path, rate_limiter): - from olmocr.bench.runners.run_openai_mini import run_openai_mini - - for attempt in range(MAX_RETRIES): - await rate_limiter.acquire() - try: - result = await asyncio.to_thread(run_openai_mini, pdf_path, page_num) - os.makedirs(os.path.dirname(output_path), exist_ok=True) - with open(output_path, "w", encoding="utf-8") as f: - f.write(result) - return True - except Exception as e: - if attempt < MAX_RETRIES - 1: - await asyncio.sleep(2**attempt) - else: - print(f"Failed after {MAX_RETRIES} attempts: {pdf_path} page {page_num}: {e}") - return False - - -async def generate_outputs(data_dir: Path): - pdf_folder = data_dir / "pdfs" - output_folder = data_dir / CANDIDATE_NAME - - pdf_pages = set() - for jsonl_file in data_dir.glob("*.jsonl"): - with open(jsonl_file) as f: - for line in f: - line = line.strip() - if not line: - continue - t = json.loads(line) - pdf_pages.add((t["pdf"], t["page"])) - - print(f"Found {len(pdf_pages)} unique (pdf, page) pairs to process") - - rate_limiter = RateLimiter(RATE_LIMIT) - tasks = [] - for pdf_rel, page in sorted(pdf_pages): - pdf_path = str(pdf_folder / pdf_rel) - if not os.path.exists(pdf_path): - continue - base_name = os.path.splitext(os.path.basename(pdf_rel))[0] - parent_dir = os.path.dirname(pdf_rel) - md_filename = f"{base_name}_pg{page}_repeat1.md" - if parent_dir: - out_path = str(output_folder / parent_dir / md_filename) - else: - out_path = str(output_folder / md_filename) - if os.path.exists(out_path): - continue - tasks.append(process_page(pdf_path, page, out_path, rate_limiter)) - - if not tasks: - print("All outputs already exist, skipping generation.") - return True - print(f"Processing {len(tasks)} pages...") - results = await tqdm_asyncio.gather(*tasks, desc=f"Generating {CANDIDATE_NAME} outputs") - num_success = sum(1 for r in results if r) - num_failed = len(results) - num_success - print(f"Done: {num_success} succeeded, {num_failed} failed") - return num_failed == 0 - - -def run_evaluation(data_dir: Path): - from olmocr.bench.benchmark import main as bench_main - sys.argv = ["benchmark", "--dir", str(data_dir), "--candidate", CANDIDATE_NAME, "--force"] - bench_main() - - -async def main(): - parser = argparse.ArgumentParser(description=f"Run OlmOCR benchmark with {CANDIDATE_NAME}") - parser.add_argument("--sample", action="store_true") - parser.add_argument("--skip-generation", action="store_true") - parser.add_argument("--generate-only", action="store_true") - args = parser.parse_args() - - if args.sample: - data_dir = SAMPLE_DATA_DIR - print("=== Using sample data ===") - else: - print("=== Downloading full olmOCR-bench dataset from HuggingFace ===") - data_dir = download_full_dataset() - - if not args.skip_generation: - print(f"\n=== Generating {CANDIDATE_NAME} outputs ===") - await generate_outputs(data_dir) - - if not args.generate_only: - print("\n=== Running OlmOCR Benchmark Evaluation ===") - run_evaluation(data_dir) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/benchmarks/olmocr/olmocr_bench_reducto.py b/benchmarks/olmocr/olmocr_bench_reducto.py index d107e7d..039e25b 100644 --- a/benchmarks/olmocr/olmocr_bench_reducto.py +++ b/benchmarks/olmocr/olmocr_bench_reducto.py @@ -85,8 +85,7 @@ def download_full_dataset(): tests = [json.loads(l) for l in f if l.strip()] data_dir.mkdir(parents=True, exist_ok=True) with open(jsonl_dest, "w") as f: - for t in tests: - f.write(json.dumps(t) + "\n") + f.writelines(json.dumps(t) + "\n" for t in tests) print(f" {split}: {len(tests)} tests") for t in tests: all_pdfs.add(t["pdf"]) @@ -137,7 +136,6 @@ async def generate_outputs(data_dir: Path): pdf_folder = data_dir / "pdfs" output_folder = data_dir / CANDIDATE_NAME - pdf_pages = set() for jsonl_file in data_dir.glob("*.jsonl"): with open(jsonl_file) as f: @@ -179,7 +177,7 @@ async def generate_outputs(data_dir: Path): print(f"Done: {num_success} succeeded, {num_failed} failed") try: - from src.commons_reducto import write_usage_snapshot + from src.providers.reducto import write_usage_snapshot write_usage_snapshot(USAGE_OUTPUT) print(f"Usage written to {USAGE_OUTPUT}") diff --git a/benchmarks/olmocr/repeatdetect.py b/benchmarks/olmocr/repeatdetect.py index 03dcbe5..8ea5f2c 100644 --- a/benchmarks/olmocr/repeatdetect.py +++ b/benchmarks/olmocr/repeatdetect.py @@ -168,7 +168,7 @@ def testLargeRandom(self): end = time.perf_counter() - print(f"testLargeRandom took {end-start:0.0001f} seconds") + print(f"testLargeRandom took {end - start:0.0001f} seconds") if __name__ == "__main__": diff --git a/benchmarks/spider2_lite/bench.py b/benchmarks/spider2_lite/bench.py new file mode 100644 index 0000000..b727c73 --- /dev/null +++ b/benchmarks/spider2_lite/bench.py @@ -0,0 +1,305 @@ +"""Spider2-Lite (SQLite subset): text-to-SQL; execution accuracy over 135 local +instances. Scoring re-executes predicted SQL against the local .sqlite DBs and +compares result sets to gold — it needs the (gitignored) data/ directory. +""" + +from __future__ import annotations + +import csv +import math +import os +import re +import sqlite3 +import time +from pathlib import Path + +from src.request import Message, ReasoningSpec, Request, TextPart + +NAME = "spider2_lite" +ID_KEY = "instance_id" +PRIMARY_METRIC = "accuracy_of_local_135" +DEFAULTS = {"reasoning": "off", "rate_limit": 8, "max_in_flight": 8} + +_DATA_DIR = Path(__file__).resolve().parent / "data" +_SPIDER2_LITE = _DATA_DIR / "Spider2" / "spider2-lite" +_SQLITE_DB_DIR = _SPIDER2_LITE / "resource" / "databases" +_SCHEMA_DIR = _SQLITE_DB_DIR / "sqlite" +_DOCUMENTS_DIR = _SPIDER2_LITE / "resource" / "documents" +_GOLD_DIR = _SPIDER2_LITE / "evaluation_suite" / "gold" +_EVAL_STANDARD = _GOLD_DIR / "spider2lite_eval.jsonl" +_GOLD_EXEC_DIR = _GOLD_DIR / "exec_result" +_ALL_EXAMPLES = _SPIDER2_LITE / "spider2-lite.jsonl" + +QUERY_TIMEOUT_S = 120.0 +MAX_DDL_CHARS = 80_000 +MAX_EK_CHARS = 40_000 + +PROMPT_TEMPLATE = """You are an expert SQLite SQL developer. Write a SQL query that answers the user's question against the given database. Target dialect: SQLite. + +### Database Schema +{schema} +{external_knowledge_section} +### Question +{question} + +Return ONLY the final SQL query, wrapped in a fenced code block like: +```sql +SELECT ... +``` +Do not include any explanation before or after the code block.""" + +_SQL_FENCE = re.compile(r"```sql\s*\n(.*?)```", re.IGNORECASE | re.DOTALL) +_ANY_FENCE = re.compile(r"```\s*\n?(.*?)```", re.DOTALL) +# Unclosed fence: an opener with no terminator (some models never close the block). +_OPEN_FENCE = re.compile(r"```(?:sql)?[ \t]*\r?\n(.*)\Z", re.IGNORECASE | re.DOTALL) + + +def extract_sql(text: str) -> str: + if not text: + return "" + for pat in (_SQL_FENCE, _ANY_FENCE, _OPEN_FENCE): + m = pat.search(text) + if m: + return m.group(1).strip() + return text.strip() + + +# --- prompt assembly (needs data/) --- +def load_schema(db_name: str) -> str: + ddl_path = _SCHEMA_DIR / db_name / "DDL.csv" + if not ddl_path.exists(): + return f"-- schema file missing: {ddl_path}" + parts = [] + with open(ddl_path, encoding="utf-8") as f: + for row in csv.DictReader(f): + ddl = (row.get("DDL") or "").strip() + if ddl: + parts.append(ddl.rstrip(";") + ";") + schema = "\n\n".join(parts) + if len(schema) > MAX_DDL_CHARS: + schema = schema[:MAX_DDL_CHARS] + "\n-- [schema truncated]" + return schema + + +def load_external_knowledge(filename: str | None) -> str | None: + if not filename: + return None + path = _DOCUMENTS_DIR / filename + if not path.exists(): + return None + text = path.read_text(encoding="utf-8", errors="ignore") + if len(text) > MAX_EK_CHARS: + text = text[:MAX_EK_CHARS] + "\n... [truncated]" + return text + + +def build_prompt(example: dict) -> str: + ek = load_external_knowledge(example.get("external_knowledge")) + ek_section = f"\n### External Knowledge\n{ek}\n" if ek else "\n" + return PROMPT_TEMPLATE.format( + schema=load_schema(example["db"]), + external_knowledge_section=ek_section, + question=example["question"], + ) + + +def load_samples(sample_size: int | None = None) -> list[dict]: + if not _ALL_EXAMPLES.exists(): + raise FileNotFoundError( + f"Missing {_ALL_EXAMPLES}. The Spider2 data/ dir (gitignored, ~4GB) must " + "be present. Run: uv run -m benchmarks.spider2_lite.fetch_data" + ) + import json + + rows = [ + json.loads(line) + for line in _ALL_EXAMPLES.read_text().splitlines() + if line.strip() + ] + local = [r for r in rows if r["instance_id"].startswith("local")] + samples = [] + for ex in local: + samples.append( + { + "instance_id": ex["instance_id"], + "db": ex["db"], + "question": ex["question"], + "external_knowledge": ex.get("external_knowledge"), + "prompt": build_prompt(ex), + } + ) + return samples[:sample_size] if sample_size else samples + + +def build_request(sample: dict, mode: str) -> Request: + return Request( + [Message("user", [TextPart(sample["prompt"])])], + reasoning=ReasoningSpec(mode), + temperature=0.0, + ) + + +def parse(response, sample) -> str: + return extract_sql(response.text or "") + + +# --- execution + comparison (verbatim port of the official evaluator's quirks) --- +def execute_sqlite(db_path: Path, sql: str): + try: + disk = sqlite3.connect(str(db_path)) + mem = sqlite3.connect(":memory:") + try: + import pandas as pd + + disk.backup(mem) + deadline = time.monotonic() + QUERY_TIMEOUT_S + # Non-zero from the handler aborts the query (unbounded joins otherwise + # pin CPU forever and hang the whole scoring pass). + mem.set_progress_handler( + lambda: 1 if time.monotonic() > deadline else 0, 10_000 + ) + df = pd.read_sql_query(sql, mem) + mem.set_progress_handler(None, 0) + return True, df + finally: + mem.close() + disk.close() + except Exception as e: # noqa: BLE001 — any failure = failed execution (score 0) + return False, f"{type(e).__name__}: {e}" + + +def _normalize(v): + import pandas as pd + + return 0 if pd.isna(v) else v + + +def _sort_key(x): + return (x is None, str(x), isinstance(x, (int, float))) + + +def _vectors_match(v1, v2, ignore_order: bool, tol: float = 1e-2) -> bool: + import pandas as pd + + v1 = [_normalize(x) for x in v1] + v2 = [_normalize(x) for x in v2] + if ignore_order: + v1 = sorted(v1, key=_sort_key) + v2 = sorted(v2, key=_sort_key) + if len(v1) != len(v2): + return False + for a, b in zip(v1, v2): + if pd.isna(a) and pd.isna(b): + continue + if isinstance(a, (int, float)) and isinstance(b, (int, float)): + if not math.isclose(float(a), float(b), abs_tol=tol): + return False + elif a != b: + return False + return True + + +def compare_table(pred, gold, condition_cols, ignore_order: bool) -> int: + if condition_cols: + if not isinstance(condition_cols, (list, tuple)): + condition_cols = [condition_cols] + gold = gold.iloc[:, condition_cols] + t_gold = gold.transpose().values.tolist() + t_pred = pred.transpose().values.tolist() + for gv in t_gold: + if not any(_vectors_match(gv, pv, ignore_order) for pv in t_pred): + return 0 + return 1 + + +def compare_multi(pred, golds, multi_condition_cols, ignore_order: bool) -> int: + if not golds: + return 0 + if multi_condition_cols in (None, [], [[]], [None]): + multi_condition_cols = [[] for _ in golds] + elif len(golds) > 1 and not all(isinstance(s, list) for s in multi_condition_cols): + multi_condition_cols = [multi_condition_cols for _ in golds] + for gold, cc in zip(golds, multi_condition_cols): + if compare_table(pred, gold, cc, ignore_order): + return 1 + return 0 + + +def resolve_gold_paths(instance_id: str): + base = _GOLD_EXEC_DIR / f"{instance_id}.csv" + if base.exists(): + return [base], True + pattern = re.compile(rf"^{re.escape(instance_id)}(_[a-z])?\.csv$") + matches = sorted( + _GOLD_EXEC_DIR / name + for name in os.listdir(_GOLD_EXEC_DIR) + if pattern.match(name) + ) + return matches, False + + +def load_eval_standard() -> dict: + import json + + out = {} + with open(_EVAL_STANDARD, encoding="utf-8") as f: + for line in f: + if line.strip(): + rec = json.loads(line) + out[rec["instance_id"]] = rec + return out + + +def evaluate_record(instance_id: str, db: str, pred_sql: str, eval_std: dict) -> dict: + import pandas as pd + + db_path = _SQLITE_DB_DIR / f"{db}.sqlite" + if not db_path.exists(): + return { + "instance_id": instance_id, + "score": 0, + "error": f"missing sqlite db: {db_path}", + } + if not (pred_sql or "").strip(): + return {"instance_id": instance_id, "score": 0, "error": "empty pred_sql"} + ok, result = execute_sqlite(db_path, pred_sql) + if not ok: + return {"instance_id": instance_id, "score": 0, "error": f"sql error: {result}"} + gold_paths, is_single = resolve_gold_paths(instance_id) + if not gold_paths: + return {"instance_id": instance_id, "score": 0, "error": "no gold file"} + std = eval_std.get(instance_id, {}) + cc, ignore_order = std.get("condition_cols"), std.get("ignore_order", False) + try: + if is_single: + score = compare_table(result, pd.read_csv(gold_paths[0]), cc, ignore_order) + else: + score = compare_multi( + result, [pd.read_csv(p) for p in gold_paths], cc, ignore_order + ) + except Exception as e: # noqa: BLE001 + return {"instance_id": instance_id, "score": 0, "error": f"compare: {e}"} + return {"instance_id": instance_id, "score": score, "error": None} + + +def score(records: list[dict], samples: list[dict]) -> dict: + by_id = {s["instance_id"]: s for s in samples} + latest = {r["instance_id"]: r for r in records if r["instance_id"] in by_id} + eval_std = load_eval_standard() + per_example, correct = [], 0 + for iid, r in latest.items(): + res = evaluate_record( + iid, by_id[iid]["db"], r.get("prediction") or "", eval_std + ) + per_example.append(res) + correct += res["score"] + total = len(latest) + total_local = len(samples) + return { + "accuracy_of_local_135": correct / total_local if total_local else 0.0, + "accuracy_evaluated": correct / total if total else 0.0, + "correct": correct, + "total_evaluated": total, + "total_local_subset": total_local, + "per_example": per_example, + } diff --git a/benchmarks/spider2_lite/fetch_data.py b/benchmarks/spider2_lite/fetch_data.py index 2a8f1b4..5efdb91 100644 --- a/benchmarks/spider2_lite/fetch_data.py +++ b/benchmarks/spider2_lite/fetch_data.py @@ -39,7 +39,10 @@ def ensure_repo() -> None: print(f"[clone] xlang-ai/Spider2 (depth=1) -> {REPO_DIR}") subprocess.run( [ - "git", "clone", "--depth", "1", + "git", + "clone", + "--depth", + "1", "https://github.com/xlang-ai/Spider2.git", str(REPO_DIR), ], @@ -58,7 +61,10 @@ def ensure_sqlite_dbs() -> None: try: import gdown except ImportError: - print("ERROR: `gdown` is required to download from Google Drive.", file=sys.stderr) + print( + "ERROR: `gdown` is required to download from Google Drive.", + file=sys.stderr, + ) print(" Add it with `uv add gdown` and re-run.", file=sys.stderr) sys.exit(1) print(f"[download] Google Drive id={SQLITE_ZIP_DRIVE_ID} -> {zip_path}") diff --git a/benchmarks/spider2_lite/spider2_lite.py b/benchmarks/spider2_lite/spider2_lite.py deleted file mode 100644 index 5dd8840..0000000 --- a/benchmarks/spider2_lite/spider2_lite.py +++ /dev/null @@ -1,590 +0,0 @@ -""" -Spider 2.0-Lite — SQLite subset — text-to-SQL benchmark for Interfaze. - -This runs the 135 `local*` examples from Spider 2.0-Lite (the SQLite-backed -slice). Scoring is execution accuracy: predicted SQL is executed against the -per-example `.sqlite` file and the result DataFrame is compared to the gold -exec_result CSV(s). Comparison logic is ported from the official -`evaluation_suite/evaluate.py` so scores are directly consistent with the -reference implementation. - -Important caveat: this is ~25% of the full Spider 2.0-Lite benchmark (135/547). -The BigQuery and Snowflake subsets are excluded because they need external -warehouse access. A result here is reportable as "Spider 2.0-Lite (SQLite -subset, N=135)" — NOT as the headline Spider 2.0-Lite score. - -Setup (one-time): - uv run -m benchmarks.spider2_lite.fetch_data - -Usage: - # Full run (predict + evaluate) - uv run -m benchmarks.spider2_lite.spider2_lite - - # Prediction only - uv run -m benchmarks.spider2_lite.spider2_lite --predict-only - - # Evaluation only - uv run -m benchmarks.spider2_lite.spider2_lite --evaluate-only - - # Smoke test - uv run -m benchmarks.spider2_lite.spider2_lite --limit 1 - -Checkpointing: each successful prediction is appended to -`results/spider2_lite_local_responses.jsonl` AND written as -`results/spider2_lite_local_sql/.sql`. Reruns only query -examples still missing from the JSONL. -""" - -from __future__ import annotations - -import argparse -import asyncio -import csv -import json -import math -import os -import re -import sys -import sqlite3 -import time -import traceback -from pathlib import Path - -import pandas as pd -from tqdm import tqdm -from tqdm.asyncio import tqdm_asyncio - -PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent -sys.path.insert(0, str(PROJECT_ROOT)) - -from src.commons import invoke_interfaze # noqa: E402 - -# Paths ----------------------------------------------------------------------- - -DATA_DIR = Path(__file__).resolve().parent / "data" -SPIDER2_LITE = DATA_DIR / "Spider2" / "spider2-lite" -SQLITE_DB_DIR = SPIDER2_LITE / "resource" / "databases" -SCHEMA_DIR = SPIDER2_LITE / "resource" / "databases" / "sqlite" -DOCUMENTS_DIR = SPIDER2_LITE / "resource" / "documents" -GOLD_DIR = SPIDER2_LITE / "evaluation_suite" / "gold" -EVAL_STANDARD = GOLD_DIR / "spider2lite_eval.jsonl" -GOLD_EXEC_DIR = GOLD_DIR / "exec_result" -ALL_EXAMPLES = SPIDER2_LITE / "spider2-lite.jsonl" - -RESULTS_DIR = PROJECT_ROOT / "results" -TAG = "spider2_lite_local" -RESPONSES_PATH = RESULTS_DIR / f"{TAG}_responses.jsonl" -PRED_SQL_DIR = RESULTS_DIR / f"{TAG}_sql" -METRICS_PATH = RESULTS_DIR / f"{TAG}_metrics.json" - -# Config ---------------------------------------------------------------------- - -REASONING_EFFORT = None -TEMPERATURE = 0.0 -RATE_LIMIT = 8 -MAX_RETRIES = 3 -# Truncate huge DDLs / external-knowledge docs so a single monster schema -# doesn't blow the context. 80k chars leaves plenty of room for reasoning -# output on a 200k-token context model. -MAX_DDL_CHARS = 80_000 -MAX_EK_CHARS = 40_000 - -PROMPT_TEMPLATE = """You are an expert SQLite SQL developer. Write a SQL query that answers the user's question against the given database. Target dialect: SQLite. - -### Database Schema -{schema} -{external_knowledge_section} -### Question -{question} - -Return ONLY the final SQL query, wrapped in a fenced code block like: -```sql -SELECT ... -``` -Do not include any explanation before or after the code block.""" - - -# ----------------------------------------------------------------------------- -# Utilities shared with other benches -# ----------------------------------------------------------------------------- - -class RateLimiter: - def __init__(self, rate: int): - self.rate = rate - self.tokens = rate - self.last_refill = 0.0 - self._lock = asyncio.Lock() - - async def acquire(self): - while True: - async with self._lock: - now = asyncio.get_running_loop().time() - elapsed = now - self.last_refill - self.tokens = min(self.rate, self.tokens + elapsed * self.rate) - self.last_refill = now - if self.tokens >= 1: - self.tokens -= 1 - return - await asyncio.sleep(1 / self.rate) - - -class JsonlWriter: - def __init__(self, path: Path): - self.path = path - self.path.parent.mkdir(parents=True, exist_ok=True) - self._lock = asyncio.Lock() - - async def append(self, record: dict): - line = json.dumps(record, ensure_ascii=False) - async with self._lock: - with open(self.path, "a", encoding="utf-8") as f: - f.write(line + "\n") - f.flush() - os.fsync(f.fileno()) - - -# ----------------------------------------------------------------------------- -# Dataset loading -# ----------------------------------------------------------------------------- - -def load_local_examples() -> list[dict]: - if not ALL_EXAMPLES.exists(): - raise FileNotFoundError( - f"Missing {ALL_EXAMPLES}. Run: uv run -m benchmarks.spider2_lite.fetch_data" - ) - with open(ALL_EXAMPLES, encoding="utf-8") as f: - rows = [json.loads(line) for line in f if line.strip()] - return [r for r in rows if r["instance_id"].startswith("local")] - - -def load_schema(db_name: str) -> str: - """Concatenate every table's DDL from resource/databases/sqlite//DDL.csv. - DDL.csv is a 2-column file: `table_name,DDL`. Some DDL strings span lines, - so use csv.DictReader rather than hand-parsing.""" - ddl_path = SCHEMA_DIR / db_name / "DDL.csv" - if not ddl_path.exists(): - return f"-- schema file missing: {ddl_path}" - parts: list[str] = [] - with open(ddl_path, encoding="utf-8") as f: - for row in csv.DictReader(f): - ddl = (row.get("DDL") or "").strip() - if ddl: - parts.append(ddl.rstrip(";") + ";") - schema = "\n\n".join(parts) - if len(schema) > MAX_DDL_CHARS: - schema = schema[:MAX_DDL_CHARS] + "\n-- [schema truncated]" - return schema - - -def load_external_knowledge(filename: str | None) -> str | None: - if not filename: - return None - path = DOCUMENTS_DIR / filename - if not path.exists(): - return None - text = path.read_text(encoding="utf-8", errors="ignore") - if len(text) > MAX_EK_CHARS: - text = text[:MAX_EK_CHARS] + "\n... [truncated]" - return text - - -def build_prompt(example: dict) -> str: - schema = load_schema(example["db"]) - ek = load_external_knowledge(example.get("external_knowledge")) - ek_section = f"\n### External Knowledge\n{ek}\n" if ek else "\n" - return PROMPT_TEMPLATE.format( - schema=schema, - external_knowledge_section=ek_section, - question=example["question"], - ) - - -# ----------------------------------------------------------------------------- -# Response parsing -# ----------------------------------------------------------------------------- - -_SQL_FENCE = re.compile(r"```sql\s*\n(.*?)```", re.IGNORECASE | re.DOTALL) -_ANY_FENCE = re.compile(r"```\s*\n?(.*?)```", re.DOTALL) - - -def extract_sql(text: str) -> str: - """Mirrors the behavior of the official evaluate.py: prefer an ```sql - fenced block, otherwise treat the whole response as SQL. Also tolerate - a plain ``` fence without the `sql` tag.""" - if not text: - return "" - m = _SQL_FENCE.search(text) - if m: - return m.group(1).strip() - m = _ANY_FENCE.search(text) - if m: - return m.group(1).strip() - return text.strip() - - -# ----------------------------------------------------------------------------- -# Checkpoint helpers -# ----------------------------------------------------------------------------- - -def load_completed_ids(path: Path) -> set[str]: - if not path.exists(): - return set() - done: set[str] = set() - with open(path, encoding="utf-8") as f: - for line in f: - line = line.strip() - if not line: - continue - try: - rec = json.loads(line) - except json.JSONDecodeError: - continue - if rec.get("pred_sql"): - done.add(str(rec["instance_id"])) - return done - - -def load_records(path: Path) -> list[dict]: - if not path.exists(): - return [] - by_id: dict[str, dict] = {} - with open(path, encoding="utf-8") as f: - for line in f: - line = line.strip() - if not line: - continue - try: - rec = json.loads(line) - except json.JSONDecodeError: - continue - by_id[str(rec["instance_id"])] = rec - return list(by_id.values()) - - -# ----------------------------------------------------------------------------- -# Prediction -# ----------------------------------------------------------------------------- - -async def process_example(example: dict, rate_limiter: RateLimiter, - writer: JsonlWriter, progress: dict) -> dict | None: - instance_id = example["instance_id"] - prompt = build_prompt(example) - messages = [{"role": "user", "content": prompt}] - - last_error: str | None = None - for attempt in range(1, MAX_RETRIES + 1): - await rate_limiter.acquire() - start = time.perf_counter() - try: - response = await asyncio.to_thread( - invoke_interfaze, - messages, - reasoning_effort=REASONING_EFFORT, - temperature=TEMPERATURE, - ) - latency_ms = int((time.perf_counter() - start) * 1000) - content = (response.choices[0].message.content or "").strip() - request_id = getattr(response, "id", None) - if not content: - last_error = "empty response content" - raise RuntimeError(last_error) - - pred_sql = extract_sql(content) - # Persist per-instance .sql file so the official evaluate.py can - # consume the same directory if someone wants to cross-check. - PRED_SQL_DIR.mkdir(parents=True, exist_ok=True) - (PRED_SQL_DIR / f"{instance_id}.sql").write_text(pred_sql, encoding="utf-8") - - record = { - "instance_id": instance_id, - "db": example["db"], - "question": example["question"], - "external_knowledge": example.get("external_knowledge"), - "pred_sql": pred_sql, - "response": content, - "request_id": request_id, - "latency_ms": latency_ms, - "attempts": attempt, - } - await writer.append(record) - progress["done"] += 1 - tqdm.write( - f"[{progress['done']}/{progress['total']}] OK " - f"id={instance_id} db={example['db']} " - f"latency={latency_ms}ms sql_len={len(pred_sql)} " - f"req_id={request_id} attempt={attempt}" - ) - return record - - except Exception as e: - latency_ms = int((time.perf_counter() - start) * 1000) - last_error = f"{type(e).__name__}: {e}" - tqdm.write( - f"[error] id={instance_id} attempt={attempt}/{MAX_RETRIES} " - f"latency={latency_ms}ms error={last_error}" - ) - if attempt < MAX_RETRIES: - await asyncio.sleep(2 ** (attempt - 1)) - - progress["failed"] += 1 - tqdm.write(f"[FAILED] id={instance_id} after {MAX_RETRIES} attempts: {last_error}") - return None - - -async def run_prediction(examples: list[dict], limit: int | None): - done_ids = load_completed_ids(RESPONSES_PATH) - pending = [e for e in examples if e["instance_id"] not in done_ids] - if limit is not None: - pending = pending[:limit] - print(f"--limit applied: will run at most {limit} example(s)") - print(f"Resume: {len(done_ids)} already completed, {len(pending)} remaining " - f"(checkpoint: {RESPONSES_PATH})") - if not pending: - return - - writer = JsonlWriter(RESPONSES_PATH) - rate_limiter = RateLimiter(RATE_LIMIT) - progress = {"total": len(pending), "done": 0, "failed": 0} - tasks = [process_example(e, rate_limiter, writer, progress) for e in pending] - try: - await tqdm_asyncio.gather(*tasks, desc="spider2-lite/local") - except Exception: - traceback.print_exc() - print(f"\nPrediction finished: {progress['done']}/{progress['total']} answered, " - f"{progress['failed']} failed.") - - -# ----------------------------------------------------------------------------- -# Evaluation — SQLite execution + row-set comparison. -# -# Ported from spider2-lite/evaluation_suite/evaluate.py. Kept faithful to the -# original semantics (column-vector matching, float tolerance of 1e-2, -# condition_cols / ignore_order flags, multi-gold). -# ----------------------------------------------------------------------------- - -def _normalize(v): - return 0 if pd.isna(v) else v - - -def _sort_key(x): - return (x is None, str(x), isinstance(x, (int, float))) - - -def _vectors_match(v1, v2, ignore_order: bool, tol: float = 1e-2) -> bool: - v1 = [_normalize(x) for x in v1] - v2 = [_normalize(x) for x in v2] - if ignore_order: - v1 = sorted(v1, key=_sort_key) - v2 = sorted(v2, key=_sort_key) - if len(v1) != len(v2): - return False - for a, b in zip(v1, v2): - if pd.isna(a) and pd.isna(b): - continue - if isinstance(a, (int, float)) and isinstance(b, (int, float)): - if not math.isclose(float(a), float(b), abs_tol=tol): - return False - elif a != b: - return False - return True - - -def compare_table(pred: pd.DataFrame, gold: pd.DataFrame, - condition_cols, ignore_order: bool) -> int: - if condition_cols: - if not isinstance(condition_cols, (list, tuple)): - condition_cols = [condition_cols] - gold_cols = gold.iloc[:, condition_cols] - else: - gold_cols = gold - t_gold = gold_cols.transpose().values.tolist() - t_pred = pred.transpose().values.tolist() - for gv in t_gold: - if not any(_vectors_match(gv, pv, ignore_order) for pv in t_pred): - return 0 - return 1 - - -def compare_multi(pred: pd.DataFrame, golds: list[pd.DataFrame], - multi_condition_cols, ignore_order: bool) -> int: - if not golds: - return 0 - if multi_condition_cols in (None, [], [[]], [None]): - multi_condition_cols = [[] for _ in golds] - elif len(golds) > 1 and not all(isinstance(s, list) for s in multi_condition_cols): - multi_condition_cols = [multi_condition_cols for _ in golds] - for gold, cc in zip(golds, multi_condition_cols): - if compare_table(pred, gold, cc, ignore_order): - return 1 - return 0 - - -def resolve_gold_paths(instance_id: str) -> tuple[list[Path], bool]: - base = GOLD_EXEC_DIR / f"{instance_id}.csv" - if base.exists(): - return [base], True - pattern = re.compile(rf"^{re.escape(instance_id)}(_[a-z])?\.csv$") - matches = sorted( - GOLD_EXEC_DIR / name - for name in os.listdir(GOLD_EXEC_DIR) - if pattern.match(name) - ) - return matches, False - - -def execute_sqlite(db_path: Path, sql: str) -> tuple[bool, pd.DataFrame | str]: - """Run `sql` against `db_path`. Returns (ok, df-or-error-string). - - We copy the on-disk DB into :memory: (same pattern as the official - evaluate.py) — faster for repeated queries and isolates writes. - """ - try: - disk = sqlite3.connect(str(db_path)) - mem = sqlite3.connect(":memory:") - try: - disk.backup(mem) - df = pd.read_sql_query(sql, mem) - return True, df - finally: - mem.close() - disk.close() - except Exception as e: - return False, f"{type(e).__name__}: {e}" - - -def load_eval_standard() -> dict[str, dict]: - if not EVAL_STANDARD.exists(): - raise FileNotFoundError( - f"Missing {EVAL_STANDARD}. Run: uv run -m benchmarks.spider2_lite.fetch_data" - ) - out: dict[str, dict] = {} - with open(EVAL_STANDARD, encoding="utf-8") as f: - for line in f: - line = line.strip() - if not line: - continue - rec = json.loads(line) - out[rec["instance_id"]] = rec - return out - - -def evaluate_record(record: dict, eval_std: dict) -> dict: - instance_id = record["instance_id"] - db_path = SQLITE_DB_DIR / f"{record['db']}.sqlite" - if not db_path.exists(): - return { - "instance_id": instance_id, "score": 0, - "error": f"missing sqlite db: {db_path}", - } - pred_sql = record.get("pred_sql") or "" - if not pred_sql.strip(): - return {"instance_id": instance_id, "score": 0, "error": "empty pred_sql"} - - ok, result = execute_sqlite(db_path, pred_sql) - if not ok: - return {"instance_id": instance_id, "score": 0, "error": f"sql error: {result}"} - - pred_df: pd.DataFrame = result # type: ignore[assignment] - gold_paths, is_single = resolve_gold_paths(instance_id) - if not gold_paths: - return {"instance_id": instance_id, "score": 0, "error": "no gold file"} - - standard = eval_std.get(instance_id, {}) - condition_cols = standard.get("condition_cols") - ignore_order = standard.get("ignore_order", False) - - try: - if is_single: - gold_df = pd.read_csv(gold_paths[0]) - score = compare_table(pred_df, gold_df, condition_cols, ignore_order) - else: - gold_dfs = [pd.read_csv(p) for p in gold_paths] - score = compare_multi(pred_df, gold_dfs, condition_cols, ignore_order) - except Exception as e: - return {"instance_id": instance_id, "score": 0, "error": f"compare: {e}"} - - return {"instance_id": instance_id, "score": score, "error": None} - - -def run_evaluation(total_local: int) -> None: - records = load_records(RESPONSES_PATH) - if not records: - print(f"No predictions at {RESPONSES_PATH}") - sys.exit(1) - eval_std = load_eval_standard() - - results: list[dict] = [] - for rec in tqdm(records, desc="Evaluating"): - res = evaluate_record(rec, eval_std) - results.append(res) - mark = "OK" if res["score"] == 1 else "X " - extra = f" ({res['error']})" if res.get("error") else "" - tqdm.write(f" {mark} {res['instance_id']}{extra}") - - correct = sum(r["score"] for r in results) - total = len(results) - accuracy_of_evaluated = correct / total if total else 0.0 - accuracy_of_subset = correct / total_local if total_local else 0.0 - - print(f"\n{'=' * 60}") - print(f"Spider 2.0-Lite — SQLite subset (Interfaze, reasoning={REASONING_EFFORT})") - print(f"{'=' * 60}") - print(f"Correct : {correct}/{total}") - print(f"Accuracy (of predicted): {accuracy_of_evaluated:.4f}") - print(f"Accuracy (of local 135): {accuracy_of_subset:.4f}") - - # Top error categories for quick eyeballing. - errors = [r for r in results if r["score"] == 0 and r.get("error")] - if errors: - print("\nTop error kinds:") - kinds: dict[str, int] = {} - for r in errors: - kind = (r["error"] or "").split(":", 1)[0] - kinds[kind] = kinds.get(kind, 0) + 1 - for k, v in sorted(kinds.items(), key=lambda kv: -kv[1]): - print(f" {v:4d} {k}") - - out = { - "accuracy_evaluated": accuracy_of_evaluated, - "accuracy_of_local_135": accuracy_of_subset, - "correct": correct, - "total_evaluated": total, - "total_local_subset": total_local, - "subset": "local (SQLite)", - "benchmark": "Spider 2.0-Lite", - "reasoning_effort": REASONING_EFFORT, - "temperature": TEMPERATURE, - "per_example": results, - } - METRICS_PATH.parent.mkdir(parents=True, exist_ok=True) - with open(METRICS_PATH, "w") as f: - json.dump(out, f, indent=2) - print(f"\nMetrics saved to {METRICS_PATH}") - - -# ----------------------------------------------------------------------------- -# Entrypoint -# ----------------------------------------------------------------------------- - -def main() -> None: - parser = argparse.ArgumentParser(description="Spider 2.0-Lite SQLite subset (Interfaze)") - parser.add_argument("--predict-only", action="store_true") - parser.add_argument("--evaluate-only", action="store_true") - parser.add_argument("--limit", type=int, default=None, - help="Only predict the first N pending examples") - args = parser.parse_args() - - examples = load_local_examples() - print(f"Loaded {len(examples)} local (SQLite) examples from {ALL_EXAMPLES.name}") - - if args.evaluate_only: - run_evaluation(total_local=len(examples)) - elif args.predict_only: - asyncio.run(run_prediction(examples, limit=args.limit)) - else: - asyncio.run(run_prediction(examples, limit=args.limit)) - run_evaluation(total_local=len(examples)) - - -if __name__ == "__main__": - main() diff --git a/main.py b/main.py deleted file mode 100644 index cce44f8..0000000 --- a/main.py +++ /dev/null @@ -1,15 +0,0 @@ -# main.py -from src.commons import invoke_interfaze -from pydantic import BaseModel, Field - - -class ResponseModel(BaseModel): - capital: str = Field(..., description="The capital of the country", required=True) - - -response = invoke_interfaze( - messages=[{"role": "user", "content": "What is the capital of France?"}], - structured_response=True, - structure_definition=ResponseModel, -) -print(response.choices[0].message.content) diff --git a/pyproject.toml b/pyproject.toml index e3f5b95..9b2341b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -41,4 +41,31 @@ dependencies = [ "langchain>=1.2.11", "olmocr==0.4.27", "pymupdf>=1.27.2", + "jiwer>=3.0.0", + "fuzzysearch>=0.7.3", + "playwright>=1.44.0", + "gdown>=6.1.0", + "google-genai>=2.18.1", + "anthropic>=0.122.0", + "reducto>=0.22.0", ] + +[dependency-groups] +dev = [ + "pytest>=8.0.0", + "pytest-asyncio>=0.24.0", +] + +[tool.pytest.ini_options] +asyncio_mode = "auto" +testpaths = ["tests"] + +[tool.ruff] +extend-exclude = [ + "benchmarks/ocrbench_v2/eval_scripts", + "benchmarks/olmocr/bench", + "benchmarks/olmocr/data", +] + +[tool.ruff.lint.per-file-ignores] +"benchmarks/olmocr/olmocr_bench_reducto.py" = ["BLE001", "ASYNC230"] diff --git a/scripts/add_target.py b/scripts/add_target.py new file mode 100644 index 0000000..b799cbd --- /dev/null +++ b/scripts/add_target.py @@ -0,0 +1,108 @@ +"""Append a target to src/targets.yaml from UI/CLI inputs, then validate. + +Used by the add-target GitHub workflow (which opens a PR) and locally: + + uv run python scripts/add_target.py --name my-model --provider fireworks \\ + --model-id accounts/fireworks/models/my-model \\ + --capabilities-json '{"reasoning":{"style":"effort","off_value":"none","on_value":"high","true_off":false}}' \\ + --ci-regression + +The entry is appended as text (preserving the file's comments), then the whole +file is re-parsed and the new target must resolve to typed capabilities + build +its adapter — otherwise nothing is written and it exits non-zero. +""" + +from __future__ import annotations + +import argparse +import json +import sys +import tempfile +from pathlib import Path + +import yaml + +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) + +from src.config import ( + PROVIDERS, + build_adapter, + load_all_targets, + resolve_capabilities, +) + +TARGETS_FILE = ROOT / "src" / "targets.yaml" + + +def _entry_text(name, provider, model_id, capabilities, ci_regression) -> str: + lines = [f" {name}:", f" provider: {provider}", f" model_id: {model_id}"] + if ci_regression: + lines.append(" ci_regression: true") + if capabilities: + block = yaml.safe_dump( + {"capabilities": capabilities}, sort_keys=False, default_flow_style=False + ) + lines += [" " + ln for ln in block.rstrip("\n").splitlines()] + return "\n".join(lines) + "\n" + + +def add_target( + name, + provider, + model_id, + capabilities_json="", + ci_regression=False, + path=TARGETS_FILE, +) -> None: + path = Path(path) + if provider not in PROVIDERS: + raise SystemExit(f"unknown provider {provider!r}; known: {sorted(PROVIDERS)}") + existing = load_all_targets(path) + if name in existing: + raise SystemExit(f"target {name!r} already exists — edit it directly instead") + capabilities = json.loads(capabilities_json) if capabilities_json.strip() else None + + current = path.read_text() + new_text = ( + current.rstrip("\n") + + "\n" + + _entry_text(name, provider, model_id, capabilities, ci_regression) + ) + + # validate on a temp copy before touching the real file + with tempfile.NamedTemporaryFile("w", suffix=".yaml", delete=False) as tf: + tf.write(new_text) + tmp = Path(tf.name) + try: + targets = load_all_targets(tmp) + if name not in targets: + raise SystemExit(f"target {name!r} did not parse from the appended entry") + resolve_capabilities(targets[name]) # merges + type-checks capabilities + build_adapter(targets[name]) # provider resolves to an adapter + finally: + tmp.unlink(missing_ok=True) + + path.write_text(new_text) + print(f"added target {name!r} ({provider} / {model_id}) to {path}") + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--name", required=True) + ap.add_argument("--provider", required=True, choices=sorted(PROVIDERS)) + ap.add_argument("--model-id", required=True) + ap.add_argument("--capabilities-json", default="") + ap.add_argument("--ci-regression", action="store_true") + args = ap.parse_args() + add_target( + args.name, + args.provider, + args.model_id, + args.capabilities_json, + args.ci_regression, + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/ci_compare.py b/scripts/ci_compare.py new file mode 100644 index 0000000..fb7a7f2 --- /dev/null +++ b/scripts/ci_compare.py @@ -0,0 +1,116 @@ +"""Compare a fresh run's metrics against the committed baseline and emit a +markdown summary (for $GITHUB_STEP_SUMMARY). Regressions beyond a tolerance are +flagged as ::warning:: — non-blocking, since benchmark scores aren't +deterministic (reasoning models, SQL-timeout boundaries). + + uv run python scripts/ci_compare.py --target inkling [--tolerance 0.02] + +Baseline = the metrics.json at git HEAD for the same path; "fresh" = the working +tree after the run. Smoke runs (n far below baseline) are reported but not judged. +""" + +from __future__ import annotations + +import argparse +import json +import subprocess +from pathlib import Path + +ROOT = Path(__file__).resolve().parent.parent +RESULTS = ROOT / "results" + +# primary metric per benchmark: (key, higher_is_better) +_PRIMARY = [ + ("corpus_wer", False), + ("accuracy_of_local_135", True), + ("macro_accuracy", True), + ("en_overall", True), + ("accuracy", True), +] + + +def primary(metrics: dict): + for key, higher in _PRIMARY: + if key in metrics and isinstance(metrics[key], (int, float)): + return key, metrics[key], higher + return None, None, True + + +def _baseline(path: Path) -> dict | None: + rel = path.relative_to(ROOT) + try: + out = subprocess.run( + ["git", "show", f"HEAD:{rel.as_posix()}"], + cwd=ROOT, + capture_output=True, + text=True, + check=True, + ) + return json.loads(out.stdout) + except (subprocess.CalledProcessError, json.JSONDecodeError): + return None + + +def compare_target(target: str, tolerance: float) -> tuple[list[list], list[str]]: + rows, warnings = [], [] + for mpath in sorted(RESULTS.glob(f"*/{target}/metrics.json")): + fresh = json.loads(mpath.read_text()) + base = _baseline(mpath) + key, new, higher = primary(fresh) + bench = fresh.get("benchmark", mpath.parent.parent.name) + n = fresh.get("n") + if base is None: + rows.append([bench, key or "-", _fmt(new), "new", "—"]) + continue + _, old, _ = primary(base) + delta = (new - old) if (new is not None and old is not None) else None + base_n = base.get("n") + note = "" + if base_n and n and n < base_n * 0.5: + note = f"smoke (n={n} vs {base_n})" + elif delta is not None: + regressed = (delta < -tolerance) if higher else (delta > tolerance) + if regressed: + note = "REGRESSION" + warnings.append( + f"{bench}/{target}: {key} {old:.4f} -> {new:.4f} (Δ{delta:+.4f})" + ) + rows.append( + [ + bench, + key or "-", + _fmt(new), + _fmt(old), + _fmt(delta, sign=True) + (f" {note}" if note else ""), + ] + ) + return rows, warnings + + +def _fmt(v, sign=False): + if not isinstance(v, (int, float)): + return "—" + return f"{v:+.4f}" if sign else f"{v:.4f}" + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--target", required=True) + ap.add_argument("--tolerance", type=float, default=0.02) + args = ap.parse_args() + + rows, warnings = compare_target(args.target, args.tolerance) + print(f"### Benchmark results — `{args.target}` vs baseline\n") + if not rows: + print("_no metrics found for this target_") + return + print("| benchmark | metric | new | baseline | Δ |") + print("|---|---|---|---|---|") + for r in rows: + print("| " + " | ".join(str(c) for c in r) + " |") + for w in warnings: + print(f"\n::warning::regression — {w}") + + +if __name__ == "__main__": + main() diff --git a/scripts/ci_matrix.py b/scripts/ci_matrix.py new file mode 100644 index 0000000..27b4e9c --- /dev/null +++ b/scripts/ci_matrix.py @@ -0,0 +1,101 @@ +"""Emit the GitHub Actions job matrix ({"include": [{target, benchmark}, ...]}). + +- schedule -> every target flagged `ci_regression: true` x the CI benchmark set +- dispatch -> the chosen target x the chosen benchmarks (or the CI set for "all") +- push -> the target(s) whose entry changed in this merge x the CI set + +The CI set is the API-only, --sample-friendly benchmarks. olmocr (needs poppler + +playwright + the full dataset to score) and spider2 (needs the ~4GB data/) are +left to local/self-hosted runs. +""" + +from __future__ import annotations + +import json +import os +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) + +CI_BENCHMARKS = ["gpqa", "mmmlu", "mmmu_pro", "asr", "ocrbench_v2", "refcoco"] + + +def _matrix(targets, benches): + return { + "include": [{"target": t, "benchmark": b} for t in targets for b in benches] + } + + +def changed_targets(old_path: str | None, new_path: str) -> list[str]: + """Target names whose *run-affecting* spec was added or modified between two + targets.yaml files (the merge diff).""" + import yaml + + def load(p): + if not p or not Path(p).exists(): + return {} + return (yaml.safe_load(Path(p).read_text()) or {}).get("targets") or {} + + old, new = load(old_path), load(new_path) + if not old: + # No usable baseline (first push / force-push / unavailable). Don't + # assume everything changed and benchmark the whole registry — run + # nothing; the author can dispatch explicitly. + return [] + + def runspec(spec): + # ci_regression only controls nightly membership, not the run itself, + # so flipping it must NOT trigger a (full, paid) benchmark. + return {k: v for k, v in (spec or {}).items() if k != "ci_regression"} + + return [ + n for n, spec in new.items() if n not in old or runspec(old[n]) != runspec(spec) + ] + + +def build_matrix( + event: str, + target: str | None = None, + benchmarks: str | None = None, + targets: list[str] | None = None, +) -> dict: + if event == "schedule": + from src.config import load_all_targets + + flagged = [ + n for n, t in load_all_targets().items() if t.raw.get("ci_regression") + ] + return _matrix( + flagged, CI_BENCHMARKS + ) # explicit opt-in only; nothing flagged -> nothing runs + if event == "push": + return _matrix(targets or [], CI_BENCHMARKS) + # workflow_dispatch (or manual): no target -> nothing runs (no default model) + b = (benchmarks or "").strip() + benches = ( + CI_BENCHMARKS + if (not b or b == "all") + else [x.strip() for x in b.split(",") if x.strip()] + ) + return _matrix([target] if target else [], benches) + + +if __name__ == "__main__": + event = os.getenv("EVENT_NAME", "workflow_dispatch") + if event == "push": + tl = changed_targets( + os.getenv("OLD_TARGETS_FILE"), + os.getenv("NEW_TARGETS_FILE", "src/targets.yaml"), + ) + print(json.dumps(build_matrix("push", targets=tl))) + else: + print( + json.dumps( + build_matrix( + event, + os.getenv("INPUT_TARGET") or None, + os.getenv("INPUT_BENCHMARKS") or None, + ) + ) + ) diff --git a/scripts/report_scores.py b/scripts/report_scores.py new file mode 100644 index 0000000..0049224 --- /dev/null +++ b/scripts/report_scores.py @@ -0,0 +1,545 @@ +""" +Detailed benchmark scores for every model with results on disk. + + uv run python scripts/report_scores.py # interactive picker + uv run python scripts/report_scores.py --all # every model + uv run python scripts/report_scores.py --model inkling + uv run python scripts/report_scores.py --list # just the model names + uv run python scripts/report_scores.py --model inkling --tsv # paste into Sheets + +Reads the results///metrics.json contract (written by the CLI) +plus the olmOCR run logs (olmOCR-bench prints its per-split table to stdout +instead of persisting metrics, so the logs are the only source for those +numbers). One row per target; the contract keys by (benchmark, target). +""" + +from __future__ import annotations + +import argparse +import re +import sys +from pathlib import Path + +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) # so `src` is importable when run as a script +RESULTS = ROOT / "results" +LOGS = ROOT / "logs" + +# --------------------------------------------------------------------------- +# Column orders, exactly as the sheet expects them +# --------------------------------------------------------------------------- + +OCR_COLS = [ + ("Avg Real", "en_overall"), + ("Recog", "text_recognition"), + ("Refer", "text_detection"), + ("Spot", "text_spotting"), + ("Extract", "relationship_extraction"), + ("Parse", "element_parsing"), + ("Calc", "mathematical_calculation"), + ("Understand", "visual_text_understanding"), + ("Reason", "knowledge_reasoning"), +] + +OLMOCR_COLS = [ + ("Overall", None), + ("ArXiv", "arxiv_math"), + ("OldScansMath", "old_scans_math"), + ("Tables", "table_tests"), + ("OldScans", "old_scans"), + ("Headers", "headers_footers"), + ("MultiCol", "multi_column"), + ("LongTinyText", "long_tiny_text"), + ("Base", "baseline"), + ("Real Overall", None), +] + +GPQA_COLS = [ + ("Overall", None), + ("Physics (n=86)", "Physics"), + ("Chemistry (n=93)", "Chemistry"), + ("Biology (n=19)", "Biology"), +] + +MMMLU_LANGS = [ + "FR_FR", + "PT_BR", + "BN_BD", + "JA_JP", + "DE_DE", + "YO_NG", + "ES_LA", + "ID_ID", + "ZH_CN", + "SW_KE", + "IT_IT", + "AR_XY", + "KO_KR", + "HI_IN", +] + +MMMU_STD_SUBJECTS = [ + "overall", + "Art", + "Electronics", + "Economics", + "Marketing", + "Finance", + "Art_Theory", + "Public_Health", + "Basic_Medical_Science", + "Sociology", + "Literature", + "Physics", + "Energy_and_Power", + "History", + "Design", + "Materials", + "Biology", + "Psychology", + "Geography", + "Manage", + "Pharmacy", + "Agriculture", + "Clinical_Medicine", + "Accounting", + "Computer_Science", + "Architecture_and_Engineering", + "Chemistry", + "Math", + "Diagnostics_and_Laboratory_Medicine", + "Mechanical_Engineering", + "Music", +] + +MMMU_VIS_SUBJECTS = [ + "overall", + "Economics", + "Art_Theory", + "Basic_Medical_Science", + "Art", + "Literature", + "Pharmacy", + "Clinical_Medicine", + "Sociology", + "Public_Health", + "Design", + "Physics", + "History", + "Chemistry", + "Electronics", + "Marketing", + "Geography", + "Math", + "Biology", + "Computer_Science", + "Manage", + "Finance", + "Agriculture", + "Accounting", + "Psychology", + "Mechanical_Engineering", + "Diagnostics_and_Laboratory_Medicine", + "Energy_and_Power", + "Materials", + "Architecture_and_Engineering", + "Music", +] + + +# --------------------------------------------------------------------------- +# Loading +# --------------------------------------------------------------------------- + + +def canon(model: str) -> str: + """Bare model name, provider prefixes and slug mangling removed.""" + m = model.strip().rsplit("/", 1)[-1].lower() + m = re.sub(r"^accounts-fireworks-models-", "", m) + # filename slugs turn dots into dashes: gemini-3-7-flash -> gemini-3.7-flash + m = re.sub(r"^gemini-(\d)-(\d)-", r"gemini-\1.\2-", m) + m = re.sub(r"^inklingsmall$", "inkling-small", m) + m = re.sub(r"^thinkingmachinesinklingsmall$", "inkling-small", m) + m = re.sub(r"^gemini37flash$", "gemini-3.7-flash", m) + return m + + +def _pct(x): + return None if x is None else round(float(x) * 100, 2) + + +def load_metrics() -> dict: + """{model: {benchmark_key: payload}} from the results/// + metrics.json contract (written by the CLI).""" + from src.results import discover + + out: dict[str, dict] = {} + for d in discover(RESULTS): + benchmark, model = d.get("benchmark"), d.get("target") + if not benchmark or not model: + continue + b = out.setdefault(model, {}) + reasoning = (d.get("reasoning") or {}).get("mode", "off") + + if benchmark == "gpqa": + b["gpqa"] = { + "overall": _pct(d.get("accuracy")), + "n": d.get("total") or d.get("n"), + "domains": { + k: _pct(v["accuracy"]) + for k, v in (d.get("per_domain") or {}).items() + }, + } + elif benchmark == "voxpopuli_aa": + wer = d.get("corpus_wer") + b["asr"] = { + "wer": _pct(wer), + "inv": _pct(1 - wer) if wer is not None else None, + "cer": _pct(d.get("corpus_cer")), + "n": d.get("num_samples") or d.get("n"), + } + elif benchmark.startswith("mmmlu"): # mmmlu_lite / mmmlu_full + variant = benchmark[len("mmmlu_") :] if "_" in benchmark else "lite" + b[f"mmmlu_{variant}"] = { + "macro": _pct(d.get("macro_accuracy")), + "n": d.get("num_samples") or d.get("n"), + "langs": { + k: _pct(v["accuracy"]) + for k, v in (d.get("per_language") or {}).items() + }, + "reasoning": reasoning, + } + elif benchmark.startswith("mmmu_pro_"): + setting = benchmark[len("mmmu_pro_") :] + b[f"mmmupro_{setting}_{reasoning}"] = { + "overall": _pct(d.get("accuracy")), + "n": d.get("num_samples") or d.get("n"), + "subjects": { + k: _pct(v["accuracy"]) + for k, v in (d.get("per_subject") or {}).items() + }, + "reasoning": reasoning, + } + elif benchmark.startswith("refcoco_"): + split = benchmark[len("refcoco_") :] + b[f"refcoco_{split}"] = { + "acc": _pct(d.get("accuracy")), + "mean_iou": round(d.get("mean_iou", 0), 4), + "n": d.get("total") or d.get("n"), + "split": split, + } + orc = d.get("oracle") + if orc: + b[f"refcoco_{split}_oracle"] = { + "acc": _pct(orc.get("accuracy")), + "mean_iou": round(orc.get("mean_iou", 0), 4), + "n": orc.get("total") or d.get("total"), + "split": split, + } + elif benchmark == "ocrbench_v2": + en = d.get("en_scores", {}) + covered = sum(1 for v in en.values() if v.get("count")) + b["ocrbench"] = { + "en_overall": _pct(d.get("en_overall")), + "cn_overall": _pct(d.get("cn_overall")), + "cats": { + k: _pct(v["avg"]) if v.get("count") else None for k, v in en.items() + }, + "counts": {k: v.get("count", 0) for k, v in en.items()}, + "partial": covered < 8, + "covered": covered, + } + elif benchmark == "spider2_lite": + b["spider2"] = { + "acc": _pct(d.get("accuracy_of_local_135")), + "correct": d.get("correct"), + "n": d.get("total_local_subset"), + } + return out + + +def load_olmocr() -> dict: + """olmOCR-bench prints its table to stdout, so parse the run logs.""" + out: dict[str, dict] = {} + # olmOCR-bench prints its table to stdout and persists nothing, so scan any + # captured output: per-step suite logs, standalone runs, or a pasted summary. + candidates = [] + for pat in ("**/olmocr*.log", "**/*olmocr*.txt", "**/suite*.log"): + candidates += sorted(LOGS.glob(pat)) + for log in dict.fromkeys(candidates): + text = log.read_text(errors="ignore").replace("\r", "\n") + head = re.search( + r"^(\S+)\s*:\s*Average Score:\s*([\d.]+)%\s*±\s*([\d.]+)%", + text, + re.MULTILINE, + ) + if not head: + continue + model, overall, ci = head.group(1), float(head.group(2)), float(head.group(3)) + splits = { + m.group(1): float(m.group(2)) + for m in re.finditer( + r"^\s+(\w+)\.jsonl\s*:\s*([\d.]+)%", text, re.MULTILINE + ) + } + base = re.search(r"^\s+baseline\s*:\s*([\d.]+)%", text, re.MULTILINE) + if base: + splits["baseline"] = float(base.group(1)) + out[canon(model)] = { + "overall": overall, + "ci": ci, + "splits": splits, + "source": str(log.relative_to(ROOT)), + } + return out + + +# --------------------------------------------------------------------------- +# Rendering +# --------------------------------------------------------------------------- + + +def fmt(v, width=7): + return f"{v:>{width}.2f}" if isinstance(v, (int, float)) else f"{'-':>{width}}" + + +def table(headers, rows, tsv=False): + if tsv: + print("\t".join(headers)) + for r in rows: + print( + "\t".join( + "" + if c is None + else (f"{c:.2f}" if isinstance(c, float) else str(c)) + for c in r + ) + ) + return + widths = ( + [ + max( + len(str(h)), + *( + len(f"{c:.2f}") + if isinstance(c, float) + else len(str(c if c is not None else "-")) + for c in col + ), + ) + for h, col in zip(headers, zip(*rows)) + ] + if rows + else [len(h) for h in headers] + ) + print( + " ".join( + h.ljust(w) if i == 0 else h.rjust(w) + for i, (h, w) in enumerate(zip(headers, widths)) + ) + ) + print(" ".join("-" * w for w in widths)) + for r in rows: + cells = [] + for i, (c, w) in enumerate(zip(r, widths)): + s = f"{c:.2f}" if isinstance(c, float) else str(c if c is not None else "-") + cells.append(s.ljust(w) if i == 0 else s.rjust(w)) + print(" ".join(cells)) + + +def report(models: list[str], data: dict, olm: dict, tsv=False): + def get(model, key): + return data.get(model, {}).get(key) + + # ---- OCRBench V2 + rows, notes = [], [] + for m in models: + d = get(m, "ocrbench") + if not d: + continue + row = [m, d["en_overall"]] + [d["cats"].get(k) for _, k in OCR_COLS[1:]] + rows.append(row) + if d["partial"]: + missing = [k for k, c in d["counts"].items() if not c] + notes.append( + f" ! {m}: PARTIAL — only {d['covered']}/8 EN categories have samples " + f"(missing: {', '.join(missing)}). 'Avg Real' averages the covered ones, " + f"so it is NOT comparable to a full run." + ) + if rows: + print("\n=== OCRBench V2 (EN) ===") + table([c for c, _ in [("model", None)] + OCR_COLS], rows, tsv) + for n in notes: + print(n) + + # ---- olmOCR + rows = [] + for m in models: + d = olm.get(m) + if not d: + continue + row = [m, d["overall"]] + [d["splits"].get(k) for _, k in OLMOCR_COLS[1:-1]] + row.append(f"{d['overall']:.1f}±{d['ci']:.1f}") + rows.append(row) + if rows: + print("\n=== olmOCR-bench ===") + table([c for c, _ in [("model", None)] + OLMOCR_COLS], rows, tsv) + + # ---- RefCOCO (every split found, so a val number is never mistaken for TestA) + rows = [] + for m in models: + for key, d in sorted((data.get(m) or {}).items()): + if not key.startswith("refcoco"): + continue + label = ( + f"{d['split']}{' (oracle)' if key.endswith('_oracle') else ' (strict)'}" + ) + rows.append([m, label, d["acc"], d["mean_iou"], d["n"]]) + if rows: + print("\n=== RefCOCO ===") + table(["Model", "split/scoring", "Acc@0.5", "mean IoU", "n"], rows, tsv) + + # ---- ASR + rows = [ + [ + m, + get(m, "asr")["inv"], + get(m, "asr")["wer"], + get(m, "asr")["cer"], + get(m, "asr")["n"], + ] + for m in models + if get(m, "asr") + ] + if rows: + print("\n=== ASR (VoxPopuliCleaned-AA) ===") + table(["Model", "1-WER", "WER", "CER", "n"], rows, tsv) + print( + " (sheet convention is 1-WER; WER shown too since lower-is-better there)" + ) + + # ---- GPQA + rows = [] + for m in models: + d = get(m, "gpqa") + if d: + rows.append( + [m, d["overall"]] + [d["domains"].get(k) for _, k in GPQA_COLS[1:]] + ) + if rows: + print("\n=== GPQA Diamond ===") + table([c for c, _ in [("Model", None)] + GPQA_COLS], rows, tsv) + + # ---- MMMLU (lite and/or full) + for variant in ("lite", "full"): + rows = [] + for m in models: + d = get(m, f"mmmlu_{variant}") + if d: + rows.append( + [m, d["macro"]] + [d["langs"].get(lg) for lg in MMMLU_LANGS] + ) + if rows: + print(f"\n=== MMMLU ({variant}) ===") + table(["Model", "macro"] + MMMLU_LANGS, rows, tsv) + + # ---- MMMU-Pro, subjects as rows in the sheet's order + for setting, subjects in ( + ("standard", MMMU_STD_SUBJECTS), + ("vision", MMMU_VIS_SUBJECTS), + ): + cols, found = [], [] + for m in models: + for reasoning in ("off", "high"): + d = get(m, f"mmmupro_{setting}_{reasoning}") + if d: + cols.append((f"{m} ({reasoning})", d)) + found.append(m) + if not cols: + continue + print( + f"\n=== MMMU-Pro-{'Standard Split' if setting == 'standard' else 'Vision'} ===" + ) + rows = [] + for s in subjects: + row = [s] + for _, d in cols: + row.append(d["overall"] if s == "overall" else d["subjects"].get(s)) + rows.append(row) + table(["Subject"] + [c for c, _ in cols], rows, tsv) + + # ---- Spider2 + rows = [ + [ + m, + get(m, "spider2")["acc"], + f"{get(m, 'spider2')['correct']}/{get(m, 'spider2')['n']}", + ] + for m in models + if get(m, "spider2") + ] + if rows: + print("\n=== Spider-2.0-lite (SQLite subset) ===") + table(["Model", "Spider-2.0-lite", "correct"], rows, tsv) + for m in models: + d = get(m, "spider2") + if d and d["n"] and d["correct"] is not None and d["n"] < 135: + print(f" ! {m}: only {d['n']} examples scored — not a full run") + + +def main(): + ap = argparse.ArgumentParser(description="Detailed benchmark scores from results/") + ap.add_argument("--model", action="append", help="model name (repeatable)") + ap.add_argument("--all", action="store_true", help="every model found") + ap.add_argument("--list", action="store_true", help="list model names and exit") + ap.add_argument( + "--tsv", action="store_true", help="tab-separated, for pasting into Sheets" + ) + args = ap.parse_args() + + data, olm = load_metrics(), load_olmocr() + known = sorted(set(data) | set(olm)) + if not known: + sys.exit(f"No results found in {RESULTS}") + + if args.list: + for m in known: + n = len(data.get(m, {})) + (1 if m in olm else 0) + print(f"{m:24} {n} benchmark result(s)") + return + + if args.all: + chosen = known + elif args.model: + chosen = [] + for want in args.model: + hits = [m for m in known if want.lower() in m] + if not hits: + sys.exit(f"No model matching {want!r}. Known: {', '.join(known)}") + chosen += hits + else: + # Interactive by default. + print("Models with results:\n") + for i, m in enumerate(known, 1): + n = len(data.get(m, {})) + (1 if m in olm else 0) + print(f" {i:2}. {m:24} ({n} result(s))") + print(f" {len(known) + 1:2}. ALL") + try: + raw = input("\nPick a number (or name, blank = ALL): ").strip() + except EOFError: + raw = "" + if not raw or raw == str(len(known) + 1): + chosen = known + elif raw.isdigit() and 1 <= int(raw) <= len(known): + chosen = [known[int(raw) - 1]] + else: + chosen = [m for m in known if raw.lower() in m] or sys.exit( + f"No match for {raw!r}" + ) + + print(f"\nModels: {', '.join(chosen)}") + report(chosen, data, olm, tsv=args.tsv) + + +if __name__ == "__main__": + main() diff --git a/src/__main__.py b/src/__main__.py new file mode 100644 index 0000000..64c79f7 --- /dev/null +++ b/src/__main__.py @@ -0,0 +1,12 @@ +# Some provider SDKs (google-genai) leave non-daemon background threads that keep +# the process alive after the run finishes and every result is already fsync'd. + +import os +import sys + +from src.cli import main + +main() +sys.stdout.flush() +sys.stderr.flush() +os._exit(0) diff --git a/src/capabilities.py b/src/capabilities.py new file mode 100644 index 0000000..906e864 --- /dev/null +++ b/src/capabilities.py @@ -0,0 +1,110 @@ +# Declared model capabilities + the merge that resolves them. + +from __future__ import annotations + +import copy +from collections.abc import Mapping +from dataclasses import dataclass, field +from enum import Enum +from typing import Any + + +class ReasoningStyle(str, Enum): + EFFORT = "effort" # OpenAI/Fireworks/Interfaze: reasoning_effort + THINKING_LEVEL = "thinking_level" # Gemini 3.x: thinking_config.thinking_level + THINKING_BUDGET = "thinking_budget" # Gemini 2.5: thinking_budget int + DISABLED_BLOCK = "disabled_block" # Anthropic: thinking type disabled/enabled + REASONING_BODY = "reasoning_body" # OpenRouter: extra_body.reasoning + NONE = "none" # non-reasoning model: send nothing + + +class AudioShape(str, Enum): + FILE_BLOCK = "file_block" # Interfaze: file block, data-uri + AUDIO_URL = "audio_url" # Fireworks: audio_url data-uri + INPUT_AUDIO = "input_audio" # OpenRouter: input_audio {data, format} + GEMINI_PART = "gemini_part" # Gemini: Part.from_bytes(mime_type="audio/...") + NONE = "none" + + +class ImageShape(str, Enum): + IMAGE_URL = "image_url" # OpenAI-family: image_url data-uri + ANTHROPIC_SOURCE = "anthropic_source" # {"type":"image","source":{base64}} + GEMINI_PART = "gemini_part" # types.Part.from_bytes(mime_type="image/...") + NONE = "none" + + +class ResponseShape(str, Enum): + OPENAI_CHAT = "openai_chat" # choices[0].message.content + ANTHROPIC_BLOCKS = "anthropic_blocks" # content[] blocks, join type=="text" + GEMINI_TEXT = "gemini_text" # response.text + + +def deep_merge(base: Mapping[str, Any], override: Mapping[str, Any]) -> dict: + result = copy.deepcopy(dict(base)) + for key, val in override.items(): + existing = result.get(key) + if isinstance(existing, dict) and isinstance(val, Mapping): + result[key] = deep_merge(existing, val) + else: + result[key] = copy.deepcopy(val) + return result + + +@dataclass(frozen=True) +class ReasoningCap: + style: ReasoningStyle + # NB: keys are `off_value`/`on_value`, NOT `off`/`on` — YAML 1.1 parses bare + # `off:`/`on:` as booleans, so `off: low` would become `{False: "low"}`. + off_value: Any = None # concrete value sent for the "off" mode (may be a FLOOR) + on_value: Any = None # concrete value sent for the "high"/"on" mode + true_off: bool = True # is `off` a real disable, or does it still think? + temperature_when_on: bool = True # may temperature accompany active thinking? + extra: dict = field( + default_factory=dict + ) # provider pins, budget/max coupling, etc. + + +@dataclass(frozen=True) +class MediaCap: + audio: AudioShape = AudioShape.NONE + image: ImageShape = ImageShape.NONE + + +@dataclass(frozen=True) +class Capabilities: + reasoning: ReasoningCap + media: MediaCap + response: ResponseShape + max_tokens_param: str = "max_tokens" + + @classmethod + def from_dict(cls, d: Mapping[str, Any]) -> Capabilities: + r = dict(d.get("reasoning") or {}) + try: + style = ReasoningStyle(r.get("style", "none")) + except ValueError as exc: + raise ValueError( + f"unknown reasoning style {r.get('style')!r}; " + f"expected one of {[s.value for s in ReasoningStyle]}" + ) from exc + reasoning = ReasoningCap( + style=style, + off_value=r.get("off_value"), + on_value=r.get("on_value"), + true_off=bool(r.get("true_off", True)), + temperature_when_on=bool(r.get("temperature_when_on", True)), + extra=dict(r.get("extra") or {}), + ) + + m = dict(d.get("media") or {}) + media = MediaCap( + audio=AudioShape(m.get("audio", "none")), + image=ImageShape(m.get("image", "none")), + ) + + return cls( + reasoning=reasoning, + media=media, + response=ResponseShape(d.get("response", "openai_chat")), + max_tokens_param=d.get("max_tokens_param", "max_tokens"), + ) diff --git a/src/cli.py b/src/cli.py new file mode 100644 index 0000000..b3fe5d3 --- /dev/null +++ b/src/cli.py @@ -0,0 +1,181 @@ +from __future__ import annotations + +import argparse +import asyncio +import importlib +import inspect + +from src.config import ( + build_routes, + load_all_targets, + load_target, +) +from src.results import RunStore +from src.runner import run_benchmark + +BENCHMARKS = { + "gpqa": "benchmarks.gpqa.bench", + "asr": "benchmarks.asr.bench", + "mmmlu": "benchmarks.mmmlu.bench", + "mmmu_pro": "benchmarks.mmmu_pro.bench", + "refcoco": "benchmarks.obj_detection.bench", + "ocrbench_v2": "benchmarks.ocrbench_v2.bench", + "olmocr": "benchmarks.olmocr.harness", + "spider2": "benchmarks.spider2_lite.bench", +} + + +def _import_benchmark(name: str): + if name not in BENCHMARKS: + raise SystemExit(f"unknown benchmark {name!r}; known: {sorted(BENCHMARKS)}") + return importlib.import_module(BENCHMARKS[name]) + + +def cmd_run(args) -> None: + target = load_target(args.target) + bench = _import_benchmark(args.benchmark) + mode = args.reasoning or bench.DEFAULTS.get("reasoning", "off") + # primary provider + ordered fallbacks; keep only routes whose key is present + routes = build_routes(target, benchmark=args.benchmark) + live = [] + for rt in routes: + if rt.adapter.resolve_key(): + rt.client = rt.adapter.build_client() + live.append(rt) + else: + print( + f"skipping route {rt.provider} (no API key; looked for {rt.adapter.key_spec})" + ) + if not live: + raise SystemExit(f"no API key for any route of target {target.name!r}") + + variants = getattr(bench, "VARIANTS", None) + if variants: + variant = args.variant or variants[0] + if variant not in variants: + raise SystemExit( + f"--variant must be one of {variants} for {args.benchmark}" + ) + samples = bench.load_samples(args.sample, variant=variant) + result_name = f"{bench.NAME}_{variant}" + elif args.variant: + raise SystemExit(f"{args.benchmark} has no variants") + else: + samples = bench.load_samples(args.sample) + result_name = bench.NAME + + print(f"Loading {result_name} samples...") + + root = "results" if args.sample is None else "results/_smoke" # sampled runs are smokes, so isolated + store = RunStore(result_name, target.name, root=root) + d = bench.DEFAULTS + + result = asyncio.run( + run_benchmark( + routes=live, + samples=samples, + build_request=lambda s: bench.build_request(s, mode), + parse=bench.parse, + store=store, + id_key=bench.ID_KEY, + rate_limit=d.get("rate_limit", 25), + max_in_flight=d.get("max_in_flight", 8), + ) + ) + + responses = store.load_responses() + hosts: dict = {} + for r in responses: + h = r.get("host") + if h: + hosts[h] = hosts.get(h, 0) + 1 + + score_kwargs = {} + if "target" in inspect.signature(bench.score).parameters: + score_kwargs["target"] = target + caps = live[0].caps # primary (first available) route's resolved caps + metrics = bench.score(responses, samples, **score_kwargs) + metrics.update( + { + "benchmark": result_name, + "target": target.name, + "provider": target.provider, + "model_id": target.model_id, + "n": metrics.get("total") or metrics.get("num_samples"), + "reasoning": { + "mode": mode, + "style": caps.reasoning.style.value, + "true_off": caps.reasoning.true_off, + }, + "hosts": hosts, # which provider(s) actually served the rows + "capability_hints": result.hints, + } + ) + store.write_metrics(metrics) + store.write_run( + { + "provider": target.provider, + "model_id": target.model_id, + "routes": [rt.provider for rt in live], # providers available this run + "reasoning_mode": mode, + "n_completed": result.n_completed, + "n_failed": result.n_failed, + "sample_size": args.sample, + } + ) + + primary = getattr(bench, "PRIMARY_METRIC", None) + pv = metrics.get(primary) if primary else None + head = f"{primary}={pv:.4f} " if isinstance(pv, (int, float)) else "" + print( + f"\n{bench.NAME} / {target.name}: {head}" + f"n={metrics.get('n')} failed={result.n_failed}" + ) + print(f"written to {store.metrics_path}") + if result.hints: + print("capability hints (promote to targets.yaml):") + for h in result.hints: + print(" -", h) + + +def cmd_list_targets(args) -> None: + for name, t in sorted(load_all_targets().items()): + print(f"{name:22} {t.provider:11} {t.model_id}") + + +def main(argv=None) -> None: + try: + from dotenv import load_dotenv + + load_dotenv() + except ImportError: + pass + + p = argparse.ArgumentParser(prog="bench") + sub = p.add_subparsers(dest="cmd", required=True) + + r = sub.add_parser("run", help="run a benchmark against a target") + r.add_argument("--target", required=True, help="target name (see list-targets)") + r.add_argument("--benchmark", required=True, choices=sorted(BENCHMARKS)) + r.add_argument( + "--reasoning", default=None, help="off|low|medium|high (default: benchmark's)" + ) + r.add_argument( + "--variant", + default=None, + help="benchmark variant, e.g. mmmu_pro: standard|vision", + ) + r.add_argument( + "--sample", type=int, default=None, help="run only the first N samples" + ) + r.set_defaults(func=cmd_run) + + lt = sub.add_parser("list-targets", help="list configured targets") + lt.set_defaults(func=cmd_list_targets) + + args = p.parse_args(argv) + args.func(args) + + +if __name__ == "__main__": + main() diff --git a/src/commons.py b/src/commons.py deleted file mode 100644 index 284e47b..0000000 --- a/src/commons.py +++ /dev/null @@ -1,54 +0,0 @@ -from pydantic import BaseModel -from openai import OpenAI -import os - -from dotenv import load_dotenv - -load_dotenv() - - -if (INTERFAZE_API_KEY := os.getenv("INTERFAZE_API_KEY", None)) is None: - raise ValueError( - "INTERFAZE_API_KEY is not set in environment variables get it from https://interfaze.ai/dashboard" - ) - -INTERFAZE_BASE_URL = os.getenv("OPENAI_BASE_URL", "https://api.interfaze.ai/v1") - -interfaze_client = OpenAI( - base_url=INTERFAZE_BASE_URL, api_key=os.getenv("INTERFAZE_API_KEY") -) - - -def invoke_interfaze( - messages: list[dict], - model: str = "interfaze-beta", - stream: bool = False, - structured_response: bool = False, - structure_definition: BaseModel | None = None, -) -> dict: - """For invoking interfaze with images you can use - messages = [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "What is in this image?", "image_url": {image_url: {"url": "https://example.com/image.jpg"}}}, - ] - - 2. For structured response you can define a pydantic model and pass it as structure_definition argument. For example: - class ResponseModel(BaseModel): - capital: str - Then you can invoke the function as follows: - response = invoke_interfaze( - messages=[{"role": "user", "content": "What is the capital of France?"}], - structured_response=True, - structure_definition=ResponseModel - ) - """ - try: - response = interfaze_client.chat.completions.create( - model=model, - messages=messages, - stream=stream, - ) - - return response - except Exception as e: - raise RuntimeError(f"Error invoking Interfaze API: {e}") from e diff --git a/src/commons_anthropic.py b/src/commons_anthropic.py deleted file mode 100644 index 7c2bd4a..0000000 --- a/src/commons_anthropic.py +++ /dev/null @@ -1,44 +0,0 @@ -import os - -import anthropic -from dotenv import load_dotenv - -load_dotenv() - - -if (ANTHROPIC_API_KEY := os.getenv("ANTHROPIC_API_KEY", None)) is None: - raise ValueError( - "ANTHROPIC_API_KEY is not set in environment variables get it from https://console.anthropic.com/" - ) - -anthropic_client = anthropic.Anthropic(api_key=ANTHROPIC_API_KEY) - - -def invoke_anthropic( - messages: list[dict], - model: str = "claude-sonnet-4-6", - max_tokens: int = 4096, - system: str | None = None, -): - """Invoke Anthropic Messages API with thinking disabled (omitted). - - Example image message: - messages = [{ - "role": "user", - "content": [ - {"type": "image", "source": {"type": "base64", "media_type": "image/jpeg", "data": b64_str}}, - {"type": "text", "text": "What is in this image?"}, - ], - }] - """ - kwargs: dict = { - "model": model, - "max_tokens": max_tokens, - "messages": messages, - } - if system is not None: - kwargs["system"] = system - try: - return anthropic_client.messages.create(**kwargs) - except Exception as e: - raise RuntimeError(f"Error invoking Anthropic API: {e}") from e diff --git a/src/commons_gemini.py b/src/commons_gemini.py deleted file mode 100644 index 9885b7f..0000000 --- a/src/commons_gemini.py +++ /dev/null @@ -1,44 +0,0 @@ -import os - -from google import genai -from google.genai import types -from dotenv import load_dotenv - -load_dotenv() - - -if (GEMINI_API_KEY := os.getenv("GEMINI_API_KEY", None)) is None: - raise ValueError( - "GEMINI_API_KEY is not set in environment variables get it from https://aistudio.google.com/apikey" - ) - -gemini_client = genai.Client(api_key=GEMINI_API_KEY) - - -def invoke_gemini( - contents, - model: str = "gemini-3.1-pro-preview", - system: str | None = None, -): - """Invoke Gemini generate_content. - - Note: Gemini 3.x Pro models require thinking mode (reject thinking_budget=0). - We let the API use its default thinking level rather than forcing it off. - If you switch to a Flash variant and want thinking disabled, add - `thinking_config=types.ThinkingConfig(thinking_budget=0)` to the config below. - - `contents` can be a list mixing strings and PIL.Image / types.Part objects, e.g.: - contents = [question_text, pil_image] - """ - config = types.GenerateContentConfig( - system_instruction=system, - thinking_config=types.ThinkingConfig(thinking_budget=0), - ) - try: - return gemini_client.models.generate_content( - model=model, - contents=contents, - config=config, - ) - except Exception as e: - raise RuntimeError(f"Error invoking Gemini API: {e}") from e diff --git a/src/commons_openai.py b/src/commons_openai.py deleted file mode 100644 index 1b823f1..0000000 --- a/src/commons_openai.py +++ /dev/null @@ -1,44 +0,0 @@ -import os - -from openai import OpenAI -from dotenv import load_dotenv - -load_dotenv() - - -if (OPENAI_API_KEY := os.getenv("OPENAI_API_KEY", None)) is None: - raise ValueError( - "OPENAI_API_KEY is not set in environment variables get it from https://platform.openai.com/api-keys" - ) - -# Explicit so we don't inherit OPENAI_BASE_URL from commons.py (Interfaze). -# Override with OPENAI_API_BASE_URL for Azure, proxies, or compatible endpoints. -OPENAI_BASE_URL = os.getenv("OPENAI_API_BASE_URL", "https://api.openai.com/v1") - -openai_client = OpenAI(api_key=OPENAI_API_KEY, base_url=OPENAI_BASE_URL) - - -def invoke_openai( - messages: list[dict], - model: str = "gpt-5.4", - stream: bool = False, -): - """Invoke OpenAI Chat Completions API (non-reasoning model, no thinking). - - Example image message: - messages = [{ - "role": "user", - "content": [ - {"type": "text", "text": "What is in this image?"}, - {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}}, - ], - }] - """ - try: - return openai_client.chat.completions.create( - model=model, - messages=messages, - stream=stream, - ) - except Exception as e: - raise RuntimeError(f"Error invoking OpenAI API: {e}") from e diff --git a/src/commons_openrouter.py b/src/commons_openrouter.py deleted file mode 100644 index 934641e..0000000 --- a/src/commons_openrouter.py +++ /dev/null @@ -1,34 +0,0 @@ -import os - -from openai import OpenAI -from dotenv import load_dotenv - -load_dotenv() - - -if (OPENROUTER_API_KEY := os.getenv("OPENROUTER_API_KEY", None)) is None: - raise ValueError( - "OPENROUTER_API_KEY is not set in environment variables get it from https://openrouter.ai/keys" - ) - -OPENROUTER_BASE_URL = "https://openrouter.ai/api/v1" - -openrouter_client = OpenAI(api_key=OPENROUTER_API_KEY, base_url=OPENROUTER_BASE_URL) - - -def invoke_openrouter( - messages: list[dict], - model: str, - temperature: float | None = None, - extra_body: dict | None = None, -): - """Invoke any OpenRouter-hosted model via the OpenAI-compatible chat-completions API.""" - kwargs: dict = {"model": model, "messages": messages} - if temperature is not None: - kwargs["temperature"] = temperature - if extra_body is not None: - kwargs["extra_body"] = extra_body - try: - return openrouter_client.chat.completions.create(**kwargs) - except Exception as e: - raise RuntimeError(f"Error invoking OpenRouter API: {e}") from e diff --git a/src/config.py b/src/config.py new file mode 100644 index 0000000..346c4a5 --- /dev/null +++ b/src/config.py @@ -0,0 +1,246 @@ +from __future__ import annotations + +import os +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any + +import yaml + +from src.capabilities import Capabilities, deep_merge +from src.providers.anthropic import AnthropicAdapter +from src.providers.gemini import GeminiAdapter +from src.providers.openai_compat import OpenAICompatAdapter + + +@dataclass +class ProviderSpec: + adapter_cls: type + base_url: str | None + key_spec: list[str] + capability_defaults: dict + + +_OPENAI_MEDIA = {"image": "image_url"} + +PROVIDERS: dict[str, ProviderSpec] = { + "openai": ProviderSpec( + OpenAICompatAdapter, + "https://api.openai.com/v1", + ["OPENAI_API_KEY"], + { + "reasoning": { + "style": "effort", + "off_value": "none", + "on_value": "high", + "true_off": True, + "temperature_when_on": False, + }, + "media": _OPENAI_MEDIA, + "response": "openai_chat", + }, + ), + "fireworks": ProviderSpec( + OpenAICompatAdapter, + "https://api.fireworks.ai/inference/v1", + ["FIREWORKS_API_KEY", "fireworks_api_key"], + { + # Fireworks "none" is a FLOOR (still thinks); temperature is sent regardless. + "reasoning": { + "style": "effort", + "off_value": "none", + "on_value": "high", + "true_off": False, + "temperature_when_on": True, + }, + "media": {"image": "image_url", "audio": "audio_url"}, + "response": "openai_chat", + }, + ), + "openrouter": ProviderSpec( + OpenAICompatAdapter, + "https://openrouter.ai/api/v1", + ["OPENROUTER_API_KEY", "openrouter_api_key"], + { + "reasoning": { + "style": "reasoning_body", + "off_value": False, + "on_value": True, + "true_off": True, + "extra": {"toggle": "enabled"}, + }, + "media": {"image": "image_url", "audio": "input_audio"}, + "response": "openai_chat", + }, + ), + "interfaze": ProviderSpec( + OpenAICompatAdapter, + None, + ["INTERFAZE_API_KEY"], + { + # Interfaze reasoning vocab is off/minimal/low/medium/high/on/auto (NOT "none"). + "reasoning": { + "style": "effort", + "off_value": "off", + "on_value": "high", + "true_off": True, + "temperature_when_on": True, + }, + "media": {"image": "image_url", "audio": "file_block"}, + "response": "openai_chat", + }, + ), + "anthropic": ProviderSpec( + AnthropicAdapter, + None, + ["ANTHROPIC_API_KEY"], + { + "reasoning": { + "style": "disabled_block", + "on_value": 10000, + "true_off": True, + "temperature_when_on": False, + "extra": {"off_max_tokens": 1024, "on_max_tokens": 16000}, + }, + "media": {"image": "anthropic_source"}, + "response": "anthropic_blocks", + }, + ), + "gemini": ProviderSpec( + GeminiAdapter, + None, + ["GEMINI_API_KEY", "GEMINI_KEY", "GOOGLE_API_KEY"], + { + # 3.x default; 2.5 targets override to thinking_budget. "low" floor is + # safe for 3.7+ flash and Pro (both reject "minimal"). + "reasoning": { + "style": "thinking_level", + "off_value": "low", + "on_value": "high", + "true_off": False, + }, + "media": {"image": "gemini_part", "audio": "gemini_part"}, + "response": "gemini_text", + }, + ), +} + + +# targets + +@dataclass +class Target: + name: str + provider: str + model_id: str + capabilities: dict = field(default_factory=dict) + overrides: dict = field(default_factory=dict) + harness_specific: dict = field(default_factory=dict) + fallbacks: list = field( + default_factory=list + ) # ordered alternate {provider, model_id, capabilities?} + raw: dict = field(default_factory=dict) + + +DEFAULT_TARGETS_FILE = Path(__file__).resolve().parent / "targets.yaml" + + +def load_all_targets(path: str | Path = DEFAULT_TARGETS_FILE) -> dict[str, Target]: + """Parse the single targets file into {name: Target}. The map KEY is the + target name — one file, one entry per model, no code to add a model.""" + data = yaml.safe_load(Path(path).read_text()) or {} + raw_targets = data.get("targets") or {} + out: dict[str, Target] = {} + for name, spec in raw_targets.items(): + spec = spec or {} + provider = spec.get("provider") + if provider not in PROVIDERS: + raise ValueError( + f"target {name!r}: unknown provider {provider!r}; " + f"known: {sorted(PROVIDERS)}" + ) + if not spec.get("model_id"): + raise ValueError(f"target {name!r}: missing model_id") + fallbacks = spec.get("fallbacks") or [] + for fb in fallbacks: + if fb.get("provider") not in PROVIDERS: + raise ValueError( + f"target {name!r}: fallback provider {fb.get('provider')!r} " + f"unknown; known: {sorted(PROVIDERS)}" + ) + if not fb.get("model_id"): + raise ValueError(f"target {name!r}: fallback missing model_id") + out[name] = Target( + name=name, + provider=provider, + model_id=spec["model_id"], + capabilities=spec.get("capabilities") or {}, + overrides=spec.get("overrides") or {}, + harness_specific=spec.get("harness_specific") or {}, + fallbacks=fallbacks, + raw=spec, + ) + return out + + +def load_target(name: str, path: str | Path = DEFAULT_TARGETS_FILE) -> Target: + targets = load_all_targets(path) + if name not in targets: + raise ValueError(f"unknown target {name!r}; available: {sorted(targets)}") + return targets[name] + + +def resolve_capabilities( + target: Target, + benchmark: str | None = None, + cli_overrides: dict | None = None, +) -> Capabilities: + spec = PROVIDERS[target.provider] + merged: dict[str, Any] = deep_merge(spec.capability_defaults, target.capabilities) + if benchmark and benchmark in target.overrides: + merged = deep_merge(merged, target.overrides[benchmark]) + if cli_overrides: + merged = deep_merge(merged, cli_overrides) + return Capabilities.from_dict(merged) + + +def build_adapter(target: Target): + spec = PROVIDERS[target.provider] + base_url = spec.base_url + if target.provider == "interfaze": + base_url = os.getenv("OPENAI_BASE_URL", "https://api.interfaze.ai/v1") + return spec.adapter_cls( + name=target.provider, + base_url=base_url, + key_spec=list(spec.key_spec), + capability_defaults=spec.capability_defaults, + ) + + +def build_routes( + target: Target, benchmark: str | None = None, cli_overrides: dict | None = None +): + from src.runner import Route + + specs = [target] + [ + Target( + name=target.name, + provider=fb["provider"], + model_id=fb["model_id"], + capabilities=fb.get("capabilities") or {}, + overrides=target.overrides, + raw=fb, + ) + for fb in target.fallbacks + ] + return [ + Route( + provider=t.provider, + model_id=t.model_id, + adapter=build_adapter(t), + caps=resolve_capabilities( + t, benchmark=benchmark, cli_overrides=cli_overrides + ), + ) + for t in specs + ] diff --git a/src/datautil.py b/src/datautil.py new file mode 100644 index 0000000..2102b4c --- /dev/null +++ b/src/datautil.py @@ -0,0 +1,18 @@ +# Dataset loading that doesn't download a whole 10k-image split for a smoke. + +from __future__ import annotations + + +def load_rows( + dataset_id: str, + split: str, + sample_size: int | None = None, + config: str | None = None, +): + from datasets import load_dataset + + args = (dataset_id, config) if config else (dataset_id,) + if sample_size: + ds = load_dataset(*args, split=split, streaming=True) + return list(ds.take(sample_size)) + return list(load_dataset(*args, split=split)) diff --git a/src/decode.py b/src/decode.py new file mode 100644 index 0000000..f57a6a2 --- /dev/null +++ b/src/decode.py @@ -0,0 +1,66 @@ +# Normalize a host response into `Response` (unifies the three shapes). + +from __future__ import annotations + +from typing import Any + +from src.capabilities import ResponseShape +from src.response import Response + + +def _get(obj: Any, name: str, default=None): + return getattr(obj, name, default) + + +def _decode_openai(raw: Any) -> Response: + choice = raw.choices[0] + usage = _get(raw, "usage") + reasoning = None + if usage is not None: + details = _get(usage, "completion_tokens_details") + if details is not None: + reasoning = _get(details, "reasoning_tokens") + if reasoning is None: # Fireworks/Inkling report it at the top level + reasoning = _get(usage, "reasoning_tokens") + return Response( + text=_get(choice.message, "content") or "", + reasoning_tokens=reasoning, + input_tokens=_get(usage, "prompt_tokens") if usage else None, + output_tokens=_get(usage, "completion_tokens") if usage else None, + finish_reason=_get(choice, "finish_reason"), + raw_id=_get(raw, "id"), + ) + + +def _decode_anthropic(raw: Any) -> Response: + text = "".join(b.text for b in (raw.content or []) if _get(b, "type") == "text") + usage = _get(raw, "usage") + return Response( + text=text, + reasoning_tokens=None, # Anthropic does not report thinking tokens separately + input_tokens=_get(usage, "input_tokens") if usage else None, + output_tokens=_get(usage, "output_tokens") if usage else None, + raw_id=_get(raw, "id"), + ) + + +def _decode_gemini(raw: Any) -> Response: + meta = _get(raw, "usage_metadata") + return Response( + text=_get(raw, "text") or "", + reasoning_tokens=_get(meta, "thoughts_token_count") if meta else None, + input_tokens=_get(meta, "prompt_token_count") if meta else None, + output_tokens=_get(meta, "candidates_token_count") if meta else None, + raw_id=_get(raw, "response_id"), + ) + + +_DECODERS = { + ResponseShape.OPENAI_CHAT: _decode_openai, + ResponseShape.ANTHROPIC_BLOCKS: _decode_anthropic, + ResponseShape.GEMINI_TEXT: _decode_gemini, +} + + +def decode_response(raw: Any, shape: ResponseShape) -> Response: + return _DECODERS[shape](raw) diff --git a/src/errors.py b/src/errors.py new file mode 100644 index 0000000..066961f --- /dev/null +++ b/src/errors.py @@ -0,0 +1,91 @@ +from __future__ import annotations + +import re +from dataclasses import dataclass +from enum import Enum + + +class ErrorKind(str, Enum): + PARAM_REJECTED = "param_rejected" + RATE_LIMITED = "rate_limited" + TRANSIENT = "transient" # timeout / 5xx / connection -> backoff + retry + EMPTY_CONTENT = "empty_content" + FATAL = "fatal" # auth / missing model / unrecognized 4xx -> fail the sample + + +@dataclass +class Classification: + kind: ErrorKind + action: str | None = ( + None # raise_thinking_floor | force_reasoning_floor | drop_temperature | drop_param + ) + param: str | None = None + message: str = "" + + +# 400s that name a specific, adjustable parameter. Ordered — first match wins. +_PARAM_PATTERNS: list[tuple[re.Pattern, str, str | None]] = [ + ( + re.compile(r"thinking level \w+ is not supported", re.IGNORECASE), + "raise_thinking_floor", + "thinking_level", + ), + ( + re.compile( + r"reasoning is mandatory|reasoning.*cannot be disabled|cannot disable reasoning", + re.IGNORECASE, + ), + "force_reasoning_floor", + "reasoning", + ), + (re.compile(r"temperature", re.IGNORECASE), "drop_temperature", "temperature"), + ( + re.compile( + r"(?:unknown|unsupported|unexpected|invalid) (?:parameter|argument|keyword)[:\s]+['\"]?([\w.]+)", + re.IGNORECASE, + ), + "drop_param", + None, + ), +] + +_TRANSIENT_STATUS = {500, 502, 503, 504} +_TRANSIENT_MSG = re.compile( + r"timed out|timeout|connection|temporarily unavailable|overloaded", re.IGNORECASE +) + + +def _status_of(exc: Exception) -> int | None: + for attr in ("status_code", "code", "http_status"): + val = getattr(exc, attr, None) + if isinstance(val, int): + return val + m = re.match(r"\s*(\d{3})\b", str(exc)) + return int(m.group(1)) if m else None + + +def classify(exc: Exception) -> Classification: + msg = str(exc) + status = _status_of(exc) + + if isinstance(exc, TimeoutError) or _TRANSIENT_MSG.search(msg): + return Classification(ErrorKind.TRANSIENT, message=msg) + if status == 429: + return Classification(ErrorKind.RATE_LIMITED, message=msg) + if status in _TRANSIENT_STATUS: + return Classification(ErrorKind.TRANSIENT, message=msg) + if status in (401, 403, 404): + return Classification(ErrorKind.FATAL, message=msg) + + if status == 400 or status is None: + for pattern, action, param in _PARAM_PATTERNS: + m = pattern.search(msg) + if m: + captured = param or (m.group(1) if m.groups() else None) + return Classification( + ErrorKind.PARAM_REJECTED, action=action, param=captured, message=msg + ) + if status == 400: + return Classification(ErrorKind.FATAL, message=msg) + + return Classification(ErrorKind.FATAL, message=msg) diff --git a/src/execute.py b/src/execute.py new file mode 100644 index 0000000..8421111 --- /dev/null +++ b/src/execute.py @@ -0,0 +1,100 @@ +# The fallback ladder: one request through an adapter, self-healing declared-cap gaps. + +from __future__ import annotations + +import dataclasses + +from src.capabilities import Capabilities, ReasoningStyle +from src.errors import Classification, ErrorKind +from src.request import Request +from src.response import Response + +_THINKING_FLOORS = ["minimal", "low", "medium", "high"] + + +class BenchError(Exception): + def __init__(self, classification: Classification): + super().__init__(classification.message or classification.kind.value) + self.classification = classification + + +def _raise_thinking_floor(caps: Capabilities) -> tuple[Capabilities, str]: + cur = caps.reasoning.off_value + try: + nxt = _THINKING_FLOORS[_THINKING_FLOORS.index(cur) + 1] + except (ValueError, IndexError): + nxt = "low" + reasoning = dataclasses.replace(caps.reasoning, off_value=nxt) + return dataclasses.replace( + caps, reasoning=reasoning + ), f"thinking floor raised to {nxt}" + + +def _force_reasoning_floor(caps: Capabilities) -> tuple[Capabilities, str]: + r = caps.reasoning + if r.style is ReasoningStyle.REASONING_BODY: + extra = dict(r.extra) + extra["toggle"] = "effort" + reasoning = dataclasses.replace( + r, extra=extra, off_value="minimal", true_off=False + ) + elif r.style is ReasoningStyle.EFFORT: + reasoning = dataclasses.replace(r, off_value="low", true_off=False) + else: + reasoning = dataclasses.replace(r, true_off=False) + return dataclasses.replace( + caps, reasoning=reasoning + ), "reasoning cannot be disabled; using floor" + + +def _apply_action( + c: Classification, req: Request, caps: Capabilities +) -> tuple[Request, Capabilities, str] | None: + if c.action == "raise_thinking_floor": + caps, hint = _raise_thinking_floor(caps) + return req, caps, hint + if c.action == "force_reasoning_floor": + caps, hint = _force_reasoning_floor(caps) + return req, caps, hint + if c.action == "drop_temperature": + req = dataclasses.replace(req, temperature=None) + return req, caps, "temperature dropped (rejected with reasoning)" + return None + + +def execute( + adapter, + client, + req: Request, + caps: Capabilities, + model_id: str, + *, + max_param_retries: int = 4, +) -> tuple[Response, list[str]]: + hints: list[str] = [] + work_req, work_caps = req, caps + + for _ in range(max_param_retries + 1): + encoded = adapter.encode(work_req, work_caps, model_id) + try: + raw = adapter.call(client, encoded) + except Exception as exc: + c = adapter.classify_error(exc) + if c.kind is ErrorKind.PARAM_REJECTED: + applied = _apply_action(c, work_req, work_caps) + if applied is not None: + work_req, work_caps, hint = applied + hints.append(hint) + continue + raise BenchError(c) from exc + + resp = adapter.decode(raw, work_caps) + if not resp.text.strip(): + raise BenchError( + Classification(ErrorKind.EMPTY_CONTENT, message="empty content") + ) + return resp, hints + + raise BenchError( + Classification(ErrorKind.FATAL, message="parameter adjustments exhausted") + ) diff --git a/src/media.py b/src/media.py new file mode 100644 index 0000000..99bc7be --- /dev/null +++ b/src/media.py @@ -0,0 +1,102 @@ +# Media block builders — one per host shape (image + audio). + +from __future__ import annotations + +import base64 +import io +from dataclasses import dataclass +from typing import Any + +from src.capabilities import AudioShape, ImageShape +from src.request import AudioPart, ImagePart + + +#Marker for a native `google.genai` part +@dataclass(frozen=True) +class GeminiPart: + data: bytes + mime: str + + +def _b64(data: bytes) -> str: + return base64.b64encode(data).decode("ascii") + + +def _data_uri(data: bytes, mime: str) -> str: + return f"data:{mime};base64,{_b64(data)}" + + +def _subtype(mime: str) -> str: + """ "audio/wav" -> "wav".""" + return mime.split("/", 1)[-1] + + +def audio_block(part: AudioPart, shape: AudioShape) -> Any: + if shape is AudioShape.FILE_BLOCK: + return { + "type": "file", + "file": { + "filename": f"audio.{_subtype(part.mime)}", + "file_data": _data_uri(part.data, part.mime), + }, + } + if shape is AudioShape.AUDIO_URL: + return { + "type": "audio_url", + "audio_url": {"url": _data_uri(part.data, part.mime)}, + } + if shape is AudioShape.INPUT_AUDIO: + # Raw base64 (NOT a data-URI) + explicit format. Sending the audio_url + # data-URI here is accepted but silently drops the audio. + return { + "type": "input_audio", + "input_audio": {"data": _b64(part.data), "format": _subtype(part.mime)}, + } + if shape is AudioShape.GEMINI_PART: + return GeminiPart(data=part.data, mime=part.mime) + raise ValueError(f"cannot send audio to a model with audio shape {shape!r}") + + +def image_block(part: ImagePart, shape: ImageShape) -> Any: + if shape is ImageShape.IMAGE_URL: + return { + "type": "image_url", + "image_url": {"url": _data_uri(part.data, part.mime)}, + } + if shape is ImageShape.ANTHROPIC_SOURCE: + return { + "type": "image", + "source": { + "type": "base64", + "media_type": part.mime, + "data": _b64(part.data), + }, + } + if shape is ImageShape.GEMINI_PART: + return GeminiPart(data=part.data, mime=part.mime) + raise ValueError(f"cannot send image to a model with image shape {shape!r}") + + +def encode_image( + pil_image, mime: str = "image/jpeg", max_side: int | None = None +) -> ImagePart: + from PIL import Image + + img = pil_image + if max_side is not None: + w, h = img.size + longest = max(w, h) + if longest > max_side: + scale = max_side / longest + img = img.resize((round(w * scale), round(h * scale)), Image.Resampling.LANCZOS) + + fmt = _subtype(mime).upper() + if fmt in ("JPG", "JPEG"): + fmt = "JPEG" + if img.mode != "RGB": + img = img.convert("RGB") + + buf = io.BytesIO() + save_kwargs = {"quality": 95} if fmt == "JPEG" else {} + img.save(buf, format=fmt, **save_kwargs) + return ImagePart(data=buf.getvalue(), mime=mime) diff --git a/src/providers/__init__.py b/src/providers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/providers/anthropic.py b/src/providers/anthropic.py new file mode 100644 index 0000000..80c5ea1 --- /dev/null +++ b/src/providers/anthropic.py @@ -0,0 +1,82 @@ +from __future__ import annotations + +from src.capabilities import Capabilities +from src.media import image_block +from src.providers.base import ProviderAdapter +from src.reasoning import build_reasoning +from src.request import ImagePart, Message, Request, TextPart + +_DEFAULT_OFF_MAX = 1024 +_DEFAULT_ON_MAX = 16000 +_BUDGET_MARGIN = 4096 + + +class AnthropicAdapter(ProviderAdapter): + def __init__(self, name, base_url, key_spec, capability_defaults): + self.name = name + self.base_url = base_url + self.key_spec = key_spec + self.capability_defaults = capability_defaults + + def build_client(self, api_key: str | None = None): + from anthropic import Anthropic + + return Anthropic( + api_key=api_key or self.resolve_key(), timeout=180.0, max_retries=2 + ) + + def _content(self, msg: Message, caps: Capabilities): + if all(isinstance(p, TextPart) for p in msg.parts): + text_parts = [p for p in msg.parts if isinstance(p, TextPart)] + return "".join(p.text for p in text_parts) + blocks = [] + for p in msg.parts: + if isinstance(p, TextPart): + blocks.append({"type": "text", "text": p.text}) + elif isinstance(p, ImagePart): + blocks.append(image_block(p, caps.media.image)) + return blocks + + def encode(self, req: Request, caps: Capabilities, model_id: str) -> dict: + system = "".join( + p.text + for m in req.messages + if m.role == "system" + for p in m.parts + if isinstance(p, TextPart) + ) + messages = [ + {"role": m.role, "content": self._content(m, caps)} + for m in req.messages + if m.role != "system" + ] + kwargs: dict = {"model": model_id, "messages": messages} + if system: + kwargs["system"] = system + + inj = build_reasoning(req.reasoning.mode, caps.reasoning) + extra = caps.reasoning.extra + if inj.anthropic_thinking is not None: + kwargs["thinking"] = inj.anthropic_thinking + + thinking_on = (inj.anthropic_thinking or {}).get("type") == "enabled" + if thinking_on: + budget = ( + inj.anthropic_thinking.get("budget_tokens", 0) + if inj.anthropic_thinking + else 0 + ) + max_tokens = req.max_tokens or extra.get("on_max_tokens", _DEFAULT_ON_MAX) + if max_tokens <= budget: + max_tokens = budget + _BUDGET_MARGIN + else: + max_tokens = req.max_tokens or extra.get("off_max_tokens", _DEFAULT_OFF_MAX) + kwargs["max_tokens"] = max_tokens + + if req.temperature is not None and inj.allow_temperature: + kwargs["temperature"] = req.temperature + + return kwargs + + def call(self, client, encoded: dict): + return client.messages.create(**encoded) diff --git a/src/providers/base.py b/src/providers/base.py new file mode 100644 index 0000000..e1e7df8 --- /dev/null +++ b/src/providers/base.py @@ -0,0 +1,43 @@ +from __future__ import annotations + +import os +from typing import Any + +from src.capabilities import Capabilities +from src.decode import decode_response +from src.errors import Classification, classify +from src.request import Request +from src.response import Response + + +class ProviderAdapter: + name: str + base_url: str | None + key_spec: list[str] # env-var precedence, incl. lowercase fallbacks + capability_defaults: dict + + def resolve_key(self) -> str | None: + for env in self.key_spec: + val = os.getenv(env) + if val: + return val + return None + + # --- provider-specific (override) --- + def build_client(self, api_key: str | None = None) -> Any: # pragma: no cover + raise NotImplementedError + + def encode( + self, req: Request, caps: Capabilities, model_id: str + ) -> Any: # pragma: no cover + raise NotImplementedError + + def call(self, client: Any, encoded: Any) -> Any: # pragma: no cover + raise NotImplementedError + + # --- shared --- + def decode(self, raw: Any, caps: Capabilities) -> Response: + return decode_response(raw, caps.response) + + def classify_error(self, exc: Exception) -> Classification: + return classify(exc) diff --git a/src/providers/gemini.py b/src/providers/gemini.py new file mode 100644 index 0000000..f86c0c8 --- /dev/null +++ b/src/providers/gemini.py @@ -0,0 +1,72 @@ +# Reasoning becomes a ThinkingConfig (level for 3.x, budget for 2.5); media parts become genai Parts. +# The client is cached at the factory level to avoid the "client has been closed" GC bug. + +from __future__ import annotations + +from src.capabilities import Capabilities +from src.media import GeminiPart, audio_block, image_block +from src.providers.base import ProviderAdapter +from src.reasoning import build_reasoning +from src.request import AudioPart, ImagePart, Request, TextPart + + +class GeminiAdapter(ProviderAdapter): + def __init__(self, name, base_url, key_spec, capability_defaults): + self.name = name + self.base_url = base_url + self.key_spec = key_spec + self.capability_defaults = capability_defaults + + def build_client(self, api_key: str | None = None): + from google import genai + + return genai.Client(api_key=api_key or self.resolve_key()) + + def _contents(self, req: Request, caps: Capabilities) -> list: + from google.genai import types + + contents: list = [] + for m in req.messages: + for p in m.parts: + if isinstance(p, TextPart): + contents.append(p.text) + elif isinstance(p, ImagePart): + gp = image_block(p, caps.media.image) + contents.append(self._to_part(gp, types)) + elif isinstance(p, AudioPart): + gp = audio_block(p, caps.media.audio) + contents.append(self._to_part(gp, types)) + return contents + + @staticmethod + def _to_part(gp: GeminiPart, types): + return types.Part.from_bytes(data=gp.data, mime_type=gp.mime) + + def encode(self, req: Request, caps: Capabilities, model_id: str) -> dict: + from google.genai import types + + inj = build_reasoning(req.reasoning.mode, caps.reasoning) + thinking = None + if inj.thinking_level is not None: + thinking = types.ThinkingConfig( + thinking_level=types.ThinkingLevel(inj.thinking_level) + ) + elif inj.thinking_budget is not None: + thinking = types.ThinkingConfig(thinking_budget=inj.thinking_budget) + + config_kwargs: dict = {} + if thinking is not None: + config_kwargs["thinking_config"] = thinking + if req.temperature is not None and inj.allow_temperature: + config_kwargs["temperature"] = req.temperature + if req.max_tokens is not None: + config_kwargs["max_output_tokens"] = req.max_tokens + + return { + "model": model_id, + "contents": self._contents(req, caps), + "config": types.GenerateContentConfig(**config_kwargs), + } + + def call(self, client, encoded: dict): + return client.models.generate_content(**encoded) diff --git a/src/providers/openai_compat.py b/src/providers/openai_compat.py new file mode 100644 index 0000000..fee87e6 --- /dev/null +++ b/src/providers/openai_compat.py @@ -0,0 +1,67 @@ +# OpenAI Chat Completions: openai, fireworks, openrouter, interfaze. + +from __future__ import annotations + +from typing import Any + +from src.capabilities import Capabilities +from src.media import audio_block, image_block +from src.providers.base import ProviderAdapter +from src.reasoning import build_reasoning +from src.request import AudioPart, ImagePart, Message, Request, TextPart + + +class OpenAICompatAdapter(ProviderAdapter): + def __init__(self, name, base_url, key_spec, capability_defaults): + self.name = name + self.base_url = base_url + self.key_spec = key_spec + self.capability_defaults = capability_defaults + + def build_client(self, api_key: str | None = None): + from openai import OpenAI + + return OpenAI( + api_key=api_key or self.resolve_key(), + base_url=self.base_url, + timeout=180.0, + max_retries=2, + ) + + def _content(self, msg: Message, caps: Capabilities) -> Any: + if all(isinstance(p, TextPart) for p in msg.parts): + text_parts = [p for p in msg.parts if isinstance(p, TextPart)] + return "".join(p.text for p in text_parts) + blocks = [] + for p in msg.parts: + if isinstance(p, TextPart): + blocks.append({"type": "text", "text": p.text}) + elif isinstance(p, ImagePart): + blocks.append(image_block(p, caps.media.image)) + elif isinstance(p, AudioPart): + blocks.append(audio_block(p, caps.media.audio)) + return blocks + + def encode(self, req: Request, caps: Capabilities, model_id: str) -> dict: + kwargs: dict = { + "model": model_id, + "messages": [ + {"role": m.role, "content": self._content(m, caps)} + for m in req.messages + ], + } + + inj = build_reasoning(req.reasoning.mode, caps.reasoning) + kwargs.update(inj.kwargs) + if inj.extra_body: + kwargs["extra_body"] = inj.extra_body + + if req.temperature is not None and inj.allow_temperature: + kwargs["temperature"] = req.temperature + if req.max_tokens is not None: + kwargs[caps.max_tokens_param] = req.max_tokens + + return kwargs + + def call(self, client, encoded: dict): + return client.chat.completions.create(**encoded) diff --git a/src/commons_reducto.py b/src/providers/reducto.py similarity index 97% rename from src/commons_reducto.py rename to src/providers/reducto.py index 637af9c..9b45797 100644 --- a/src/commons_reducto.py +++ b/src/providers/reducto.py @@ -2,8 +2,9 @@ import json import os import threading +from collections.abc import Iterable from pathlib import Path -from typing import Any, Iterable +from typing import Any import httpx from dotenv import load_dotenv @@ -18,7 +19,9 @@ ) -reducto_client = Reducto(api_key=REDUCTO_API_KEY) +reducto_client: Any = Reducto( + api_key=REDUCTO_API_KEY +) # SDK stubs are strict; used dynamically # ---------- Usage tracking (thread-safe) ---------------------------------- diff --git a/src/reasoning.py b/src/reasoning.py new file mode 100644 index 0000000..f88123e --- /dev/null +++ b/src/reasoning.py @@ -0,0 +1,94 @@ +# Translate an abstract reasoning mode into the host's concrete directive. + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any, assert_never + +from src.capabilities import ReasoningCap, ReasoningStyle + +_HIGH_MODES = {"high", "on", "max"} +_OFF_MODES = {"off", "none", "disabled"} + + +@dataclass +class ReasoningInjection: + kwargs: dict = field( + default_factory=dict + ) # top-level create() kwargs (reasoning_effort) + extra_body: dict = field( + default_factory=dict + ) # OpenRouter reasoning + provider pin + thinking_level: str | None = None # Gemini 3.x + thinking_budget: int | None = None # Gemini 2.5 + anthropic_thinking: dict | None = ( + None # {"type":"disabled"} | {"type":"enabled",...} + ) + allow_temperature: bool = True + + +def _resolve_value(mode: str, cap: ReasoningCap) -> Any: + if mode in _OFF_MODES: + return cap.off_value + if mode in _HIGH_MODES: + return cap.on_value + return mode + + +def _allow_temperature(mode: str, cap: ReasoningCap) -> bool: + if mode in _OFF_MODES and cap.true_off: + return True + return cap.temperature_when_on + + +def build_reasoning(mode: str, cap: ReasoningCap) -> ReasoningInjection: + style = cap.style + allow_temp = _allow_temperature(mode, cap) + + if style is ReasoningStyle.NONE: + return ReasoningInjection(allow_temperature=allow_temp) + + if style is ReasoningStyle.EFFORT: + return ReasoningInjection( + kwargs={"reasoning_effort": _resolve_value(mode, cap)}, + allow_temperature=allow_temp, + ) + + if style is ReasoningStyle.THINKING_LEVEL: + return ReasoningInjection( + thinking_level=_resolve_value(mode, cap), + allow_temperature=allow_temp, + ) + + if style is ReasoningStyle.THINKING_BUDGET: + return ReasoningInjection( + thinking_budget=_resolve_value(mode, cap), + allow_temperature=allow_temp, + ) + + if style is ReasoningStyle.DISABLED_BLOCK: + if mode in _OFF_MODES: + thinking = {"type": "disabled"} + else: + thinking = {"type": "enabled", "budget_tokens": cap.on_value} + return ReasoningInjection( + anthropic_thinking=thinking, + allow_temperature=allow_temp, + ) + + if style is ReasoningStyle.REASONING_BODY: + toggle = cap.extra.get("toggle", "enabled") + value = _resolve_value(mode, cap) + if toggle == "effort": + reasoning = {"effort": value} + else: # "enabled" boolean toggle + reasoning = {"enabled": bool(value)} + extra_body: dict = {"reasoning": reasoning} + pin = cap.extra.get("provider_only") + if pin: + extra_body["provider"] = {"only": list(pin)} + return ReasoningInjection(extra_body=extra_body, allow_temperature=allow_temp) + + assert_never( + style + ) # exhaustive over ReasoningStyle; ty errors if a member is added diff --git a/src/request.py b/src/request.py new file mode 100644 index 0000000..08df66d --- /dev/null +++ b/src/request.py @@ -0,0 +1,45 @@ +# Provider-agnostic request the benchmark layer builds. + +from __future__ import annotations + +from dataclasses import dataclass, field + + +@dataclass +class TextPart: + text: str + + +@dataclass +class ImagePart: + data: bytes + mime: str = "image/jpeg" + + +@dataclass +class AudioPart: + data: bytes + mime: str = "audio/wav" + + +Part = TextPart | ImagePart | AudioPart + + +@dataclass +class Message: + role: str # "system" | "user" | "assistant" + parts: list[Part] = field(default_factory=list) + + +@dataclass +class ReasoningSpec: + mode: str = "off" # off | low | medium | high (host floor applied by the cap) + + +@dataclass +class Request: + messages: list[Message] + reasoning: ReasoningSpec = field(default_factory=ReasoningSpec) + temperature: float | None = 0.0 + max_tokens: int | None = None + schema: dict | None = None # optional structured-output JSON schema diff --git a/src/response.py b/src/response.py new file mode 100644 index 0000000..d2ccd67 --- /dev/null +++ b/src/response.py @@ -0,0 +1,15 @@ +# Normalized response — one shape regardless of host. + +from __future__ import annotations + +from dataclasses import dataclass + + +@dataclass +class Response: + text: str + reasoning_tokens: int | None = None + input_tokens: int | None = None + output_tokens: int | None = None + finish_reason: str | None = None + raw_id: str | None = None diff --git a/src/results.py b/src/results.py new file mode 100644 index 0000000..4dd7a48 --- /dev/null +++ b/src/results.py @@ -0,0 +1,81 @@ +from __future__ import annotations + +import json +import os +import re +from collections.abc import Callable, Iterable +from pathlib import Path + +DEFAULT_ROOT = Path("results") + + +def model_slug(name: str) -> str: + leaf = name.rsplit("/", 1)[-1] + return re.sub(r"[^a-z0-9._-]+", "-", leaf.lower()).strip("-") + + +class RunStore: + def __init__(self, benchmark: str, target: str, root: Path | str = DEFAULT_ROOT): + self.benchmark = benchmark + self.target = target + self.dir = Path(root) / benchmark / model_slug(target) + + @property + def responses_path(self) -> Path: + return self.dir / "responses.jsonl" + + @property + def metrics_path(self) -> Path: + return self.dir / "metrics.json" + + @property + def run_path(self) -> Path: + return self.dir / "run.json" + + def _ensure_dir(self): + self.dir.mkdir(parents=True, exist_ok=True) + + def append_response(self, record: dict) -> None: + """Append + fsync per row so a crash mid-run loses nothing already + billed (the audit found several runners writing only at the end).""" + self._ensure_dir() + with open(self.responses_path, "a", encoding="utf-8") as f: + f.write(json.dumps(record, ensure_ascii=False) + "\n") + f.flush() + os.fsync(f.fileno()) + + def load_responses(self) -> list[dict]: + if not self.responses_path.exists(): + return [] + out = [] + with open(self.responses_path, encoding="utf-8") as f: + for line in f: + line = line.strip() + if line: + out.append(json.loads(line)) + return out + + def completed_ids(self, id_key: str, done: Callable[[dict], bool]) -> set: + return {r[id_key] for r in self.load_responses() if done(r)} + + def write_metrics(self, metrics: dict) -> None: + self._ensure_dir() + self.metrics_path.write_text(json.dumps(metrics, indent=2, ensure_ascii=False)) + + def write_run(self, run: dict) -> None: + self._ensure_dir() + self.run_path.write_text(json.dumps(run, indent=2, ensure_ascii=False)) + + +def discover(root: Path | str = DEFAULT_ROOT) -> list[dict]: + root = Path(root) + out = [] + for path in sorted(root.glob("*/*/metrics.json")): + data = json.loads(path.read_text()) + data["_path"] = str(path) + out.append(data) + return out + + +def load_ids(records: Iterable[dict], id_key: str) -> set: + return {r[id_key] for r in records if id_key in r} diff --git a/src/runner.py b/src/runner.py new file mode 100644 index 0000000..e1af7a6 --- /dev/null +++ b/src/runner.py @@ -0,0 +1,182 @@ +"""The one execution harness: rate-limit, worker pool, provider failover, retry, +resume, checkpoint.""" + +from __future__ import annotations + +import asyncio +from dataclasses import dataclass, field + +from src.capabilities import Capabilities +from src.errors import ErrorKind +from src.execute import BenchError, execute +from src.results import RunStore + +_RETRYABLE = {ErrorKind.RATE_LIMITED, ErrorKind.TRANSIENT, ErrorKind.EMPTY_CONTENT} +# Advance to the next provider route only when the host itself won't serve. +# NOT on EMPTY_CONTENT: the backup runs the same weights and returns the same +# empty output, so failing over there just doubles the bill for a dead sample. +_FAILOVER = {ErrorKind.FATAL, ErrorKind.RATE_LIMITED, ErrorKind.TRANSIENT} + + +@dataclass +class Route: + provider: str + model_id: str + adapter: object + caps: Capabilities + client: object = None + + +@dataclass +class RunResult: + n_completed: int = 0 + n_failed: int = 0 + hints: list[str] = field(default_factory=list) + + +class _RateLimiter: + """Token bucket bounding how many requests *start* per second.""" + + def __init__(self, rate: float): + self.rate = rate + self.tokens = float(rate) + self.last = None + self._lock = asyncio.Lock() + + async def acquire(self): + if self.rate <= 0: + return + while True: + async with self._lock: + now = asyncio.get_running_loop().time() + if self.last is None: + self.last = now + self.tokens = min( + self.rate, self.tokens + (now - self.last) * self.rate + ) + self.last = now + if self.tokens >= 1: + self.tokens -= 1 + return + await asyncio.sleep(1.0 / self.rate) + + +async def run_benchmark( + *, + routes: list[Route], + samples, + build_request, + parse, + store: RunStore, + id_key: str = "id", + done=None, + rate_limit: float = 25.0, + max_in_flight: int = 8, + max_retries: int = 3, + backoff_base: float = 1.0, +) -> RunResult: + if done is None: + + def done(r): + return r.get("response") is not None + + completed = store.completed_ids(id_key, done) + pending = [s for s in samples if s[id_key] not in completed] + + limiter = _RateLimiter(rate_limit) + result = RunResult() + lock = asyncio.Lock() + + async def process(sample): + req = build_request(sample) + last_err = None + for route in routes: + outcome = None + for attempt in range(max_retries + 1): + await limiter.acquire() + try: + resp, hints = await asyncio.to_thread( + execute, + route.adapter, + route.client, + req, + route.caps, + route.model_id, + ) + except BenchError as be: + last_err = be + kind = be.classification.kind + if kind in _RETRYABLE and attempt < max_retries: + if backoff_base > 0: + await asyncio.sleep(backoff_base * (2**attempt)) + continue + outcome = kind + break + await _record_success( + sample, + resp, + hints, + parse, + store, + result, + lock, + id_key, + route.provider, + ) + return + if outcome not in _FAILOVER: + break # a genuine non-answer from this host; a backup won't help + await _record_failure(sample, last_err, store, result, lock, id_key) + + # Bounded worker pool: build_request runs only when a worker pulls a sample, + # so at most max_in_flight requests (and their decoded images) exist at once. + # An eager gather(process(s) for s in pending) builds every request up front + # — for a full image benchmark that decodes all ~10k images and OOMs CI. + queue: asyncio.Queue = asyncio.Queue() + for s in pending: + queue.put_nowait(s) + + async def worker(): + while True: + try: + sample = queue.get_nowait() + except asyncio.QueueEmpty: + return + await process(sample) + + await asyncio.gather(*(worker() for _ in range(min(max_in_flight, len(pending))))) + return result + + +async def _record_success( + sample, resp, hints, parse, store, result, lock, id_key, host +): + prediction = parse(resp, sample) + record = { + id_key: sample[id_key], + "response": resp.text, + "prediction": prediction, + "reasoning_tokens": resp.reasoning_tokens, + "host": host, # which provider actually served this row (failover audit) + } + if hints: + record["capability_hints"] = hints + async with lock: + store.append_response(record) + result.n_completed += 1 + for h in hints: + if h not in result.hints: + result.hints.append(h) + + +async def _record_failure(sample, be, store, result, lock, id_key): + record = { + id_key: sample[id_key], + "response": None, + "prediction": None, + "error": be.classification.kind.value, + "error_message": be.classification.message, + } + async with lock: + store.append_response(record) + result.n_failed += 1 diff --git a/src/targets.yaml b/src/targets.yaml new file mode 100644 index 0000000..df60723 --- /dev/null +++ b/src/targets.yaml @@ -0,0 +1,79 @@ +targets: + # --- Fireworks: reasoning "none" is a FLOOR (still thinks); audio via audio_url --- + inkling: + provider: fireworks + model_id: accounts/fireworks/models/inkling + ci_regression: true + + deepseek-v4-flash: + provider: fireworks + model_id: accounts/fireworks/models/deepseek-v4-flash + + # --- OpenRouter: "none" truly disables; grok/kimi need special toggles --- + inkling-small: + provider: openrouter + model_id: thinkingmachines/inkling-small + capabilities: + reasoning: { extra: { toggle: effort }, off_value: none, on_value: high, true_off: true } + # if OpenRouter can't serve, fall back to the same weights on Fireworks + # (host is recorded per row; proprietary targets declare no fallbacks -> stop) + fallbacks: + - provider: fireworks + model_id: accounts/fireworks/models/inkling-small + + grok-4.3: + provider: openrouter + model_id: x-ai/grok-4.3 + capabilities: + # Grok rejects reasoning.enabled=false ("Reasoning is mandatory"); floor is effort=minimal. + reasoning: { extra: { toggle: effort }, off_value: minimal, on_value: high, true_off: false } + + kimi-k2.6: + provider: openrouter + model_id: moonshotai/kimi-k2.6 + capabilities: + # Pin to Moonshot's own inference; disable via the enabled toggle. + reasoning: + extra: { toggle: enabled, provider_only: [moonshotai] } + off_value: false + on_value: true + true_off: true + + # --- Gemini: 3.x uses thinking_level (floor "low"); 2.5 uses integer budget --- + gemini-3.7-flash: + provider: gemini + model_id: gemini-3.7-flash + ci_regression: true + + gemini-2.5-flash: + provider: gemini + model_id: gemini-2.5-flash + capabilities: + reasoning: { style: thinking_budget, off_value: 0, on_value: -1, true_off: true } + + gemini-2.5-pro: + provider: gemini + model_id: gemini-2.5-pro + capabilities: + reasoning: { style: thinking_budget, off_value: 0, on_value: -1, true_off: true } + + gemini-3.1-pro-preview: + provider: gemini + model_id: gemini-3.1-pro-preview + + # --- OpenAI / Anthropic / Interfaze: provider defaults fit as-is --- + gpt-5.5: + provider: openai + model_id: gpt-5.5 + + gpt-5.4-mini: + provider: openai + model_id: gpt-5.4-mini + + claude-sonnet-4-6: + provider: anthropic + model_id: claude-sonnet-4-6 + + interfaze-beta: + provider: interfaze + model_id: interfaze-beta diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_add_target.py b/tests/test_add_target.py new file mode 100644 index 0000000..2b3bf10 --- /dev/null +++ b/tests/test_add_target.py @@ -0,0 +1,93 @@ +"""Interactive add-target: append a validated entry to targets.yaml, and the +merge-diff helper that decides which targets a push should benchmark. +""" + +import pathlib +import sys + +import pytest + +sys.path.insert(0, str(pathlib.Path(__file__).resolve().parent.parent / "scripts")) + +from add_target import add_target +from ci_matrix import changed_targets + +from src.capabilities import ReasoningStyle +from src.config import load_all_targets, resolve_capabilities + + +def _base(tmp_path): + p = tmp_path / "targets.yaml" + p.write_text( + "targets:\n x:\n provider: fireworks\n model_id: accounts/fireworks/models/x\n" + ) + return p + + +def test_append_and_validate(tmp_path): + p = _base(tmp_path) + add_target("newmodel", "gemini", "gemini-3.7-flash", path=p) + targets = load_all_targets(p) + assert "x" in targets and "newmodel" in targets # existing preserved + assert targets["newmodel"].provider == "gemini" + resolve_capabilities(targets["newmodel"]) # resolves without error + + +def test_append_with_capabilities_json(tmp_path): + p = _base(tmp_path) + add_target( + "g25", + "gemini", + "gemini-2.5-flash", + capabilities_json='{"reasoning":{"style":"thinking_budget","off_value":0,"on_value":-1,"true_off":true}}', + ci_regression=True, + path=p, + ) + t = load_all_targets(p)["g25"] + assert t.raw.get("ci_regression") is True + caps = resolve_capabilities(t) + assert caps.reasoning.style is ReasoningStyle.THINKING_BUDGET + assert caps.reasoning.off_value == 0 + + +def test_rejects_unknown_provider(tmp_path): + with pytest.raises(SystemExit, match="unknown provider"): + add_target("z", "telepathy", "m", path=_base(tmp_path)) + + +def test_rejects_duplicate(tmp_path): + with pytest.raises(SystemExit, match="already exists"): + add_target("x", "fireworks", "y", path=_base(tmp_path)) + + +def test_changed_targets_detects_added_and_modified(tmp_path): + old = tmp_path / "old.yaml" + new = tmp_path / "new.yaml" + old.write_text("targets:\n a:\n provider: fireworks\n model_id: x\n") + new.write_text( + "targets:\n a:\n provider: fireworks\n model_id: x\n" + " b:\n provider: gemini\n model_id: y\n" + ) + assert changed_targets(str(old), str(new)) == ["b"] # only the added one + + new.write_text("targets:\n a:\n provider: openai\n model_id: x\n") + assert changed_targets(str(old), str(new)) == ["a"] # modified entry + + +def test_changed_targets_empty_baseline_runs_nothing(tmp_path): + # first push / force-push / no old file -> don't benchmark the whole registry + new = tmp_path / "new.yaml" + new.write_text("targets:\n a:\n provider: fireworks\n model_id: x\n") + assert changed_targets(None, str(new)) == [] + assert changed_targets(str(tmp_path / "missing.yaml"), str(new)) == [] + + +def test_changed_targets_ignores_ci_regression_flip(tmp_path): + # toggling ci_regression must NOT trigger a (paid) benchmark — model unchanged + old = tmp_path / "old.yaml" + new = tmp_path / "new.yaml" + old.write_text("targets:\n a:\n provider: fireworks\n model_id: x\n") + new.write_text( + "targets:\n a:\n provider: fireworks\n model_id: x\n ci_regression: true\n" + ) + assert changed_targets(str(old), str(new)) == [] diff --git a/tests/test_asr_bench.py b/tests/test_asr_bench.py new file mode 100644 index 0000000..40d5ffe --- /dev/null +++ b/tests/test_asr_bench.py @@ -0,0 +1,76 @@ +"""ASR benchmark: normalize/parse units + offline scoring-parity replay. + +The parity test replays each archived ASR run through the new score and asserts +corpus WER, time-weighted WER, CER, and counts match the archived metrics — +proving the port reproduces the numbers with no audio calls. +""" + +import json +import pathlib +from types import SimpleNamespace as NS + +import pytest + +from benchmarks.asr import bench + +RESULTS = pathlib.Path(__file__).resolve().parent.parent / "results" +ARCHIVED_TAGS = [ + "voxpopuli_aa_fireworks_accounts_fireworks_models_inkling", + "voxpopuli_aa_gemini_gemini-3.7-flash", + "voxpopuli_aa_openrouter_thinkingmachines_inkling-small", + "voxpopuli_aa_openrouter_google_gemini-3.7-flash", +] + + +def test_normalize_text_whisper_style(): + assert bench.normalize_text("Hello, WORLD!") == "hello world" + assert bench.normalize_text("it's fine\n") == "it's fine" + assert bench.normalize_text("") == "" + + +def test_parse_strips_transcript(): + assert bench.parse(NS(text=" hi there \n"), None) == "hi there" + + +def test_score_corpus_wer_basic(): + samples = [ + {"id": "1", "transcript": "the cat sat", "duration": 1.0}, + {"id": "2", "transcript": "hello world", "duration": 1.0}, + ] + records = [ + {"id": "1", "prediction": "the cat sat"}, # 0 errors + {"id": "2", "prediction": "hello there"}, # 1/2 words wrong + ] + m = bench.score(records, samples) + assert m["num_samples"] == 2 + assert m["corpus_wer"] == pytest.approx(1 / 5) # 1 error over 5 ref words + + +@pytest.mark.parametrize("tag", ARCHIVED_TAGS) +def test_scoring_parity_with_archived_run(tag): + resp_path = RESULTS / f"{tag}_responses.jsonl" + metrics_path = RESULTS / f"{tag}_metrics.json" + if not (resp_path.exists() and metrics_path.exists()): + pytest.skip(f"archived run {tag} not present") + + archived = json.loads(metrics_path.read_text()) + samples, records = [], [] + for line in resp_path.read_text().splitlines(): + if not line.strip(): + continue + rec = json.loads(line) + samples.append( + { + "id": rec["id"], + "transcript": rec["transcript"], + "duration": rec.get("duration"), + } + ) + records.append({"id": rec["id"], "prediction": rec["response"]}) + + m = bench.score(records, samples) + assert m["num_samples"] == archived["num_samples"] + assert m["corpus_wer"] == pytest.approx(archived["corpus_wer"]) + assert m["corpus_cer"] == pytest.approx(archived["corpus_cer"]) + assert m["time_weighted_wer"] == pytest.approx(archived["time_weighted_wer"]) + assert m["mean_sample_wer"] == pytest.approx(archived["mean_sample_wer"]) diff --git a/tests/test_capabilities.py b/tests/test_capabilities.py new file mode 100644 index 0000000..a2c822c --- /dev/null +++ b/tests/test_capabilities.py @@ -0,0 +1,74 @@ +"""Capability merge + typed parsing. + +The capability layer is the heart of the redesign: provider adapters ship +default capability dicts, target YAML files override fields, and the CLI can +override again. All three collapse through `deep_merge`, then `Capabilities` +gives the adapters a typed, validated view. +""" + +from src.capabilities import ( + AudioShape, + Capabilities, + ImageShape, + ReasoningStyle, + ResponseShape, + deep_merge, +) + + +def test_deep_merge_overrides_nested_scalar_keeping_siblings(): + base = {"reasoning": {"style": "effort", "off_value": "none", "true_off": True}} + override = {"reasoning": {"off_value": "low"}} + assert deep_merge(base, override) == { + "reasoning": {"style": "effort", "off_value": "low", "true_off": True} + } + + +def test_deep_merge_does_not_mutate_inputs(): + base = {"reasoning": {"off_value": "none"}} + override = {"reasoning": {"off_value": "low"}} + deep_merge(base, override) + assert base == {"reasoning": {"off_value": "none"}} # base untouched + + +def test_deep_merge_adds_new_nested_key(): + base = {"media": {"image": "image_url"}} + override = {"media": {"audio": "input_audio"}} + assert deep_merge(base, override) == { + "media": {"image": "image_url", "audio": "input_audio"} + } + + +def test_capabilities_from_dict_coerces_enums(): + caps = Capabilities.from_dict( + { + "reasoning": { + "style": "thinking_level", + "off_value": "low", + "on_value": "high", + "true_off": False, + "temperature_when_on": True, + }, + "media": {"audio": "gemini_part", "image": "gemini_part"}, + "response": "gemini_text", + "max_tokens_param": "max_tokens", + } + ) + assert caps.reasoning.style is ReasoningStyle.THINKING_LEVEL + assert caps.reasoning.true_off is False + assert caps.media.audio is AudioShape.GEMINI_PART + assert caps.media.image is ImageShape.GEMINI_PART + assert caps.response is ResponseShape.GEMINI_TEXT + + +def test_capabilities_from_dict_rejects_unknown_reasoning_style(): + import pytest + + with pytest.raises(ValueError, match="reasoning style"): + Capabilities.from_dict( + { + "reasoning": {"style": "telepathy"}, + "media": {"audio": "gemini_part", "image": "gemini_part"}, + "response": "gemini_text", + } + ) diff --git a/tests/test_ci_matrix.py b/tests/test_ci_matrix.py new file mode 100644 index 0000000..b021f5f --- /dev/null +++ b/tests/test_ci_matrix.py @@ -0,0 +1,36 @@ +"""CI matrix builder: dispatch (target x chosen benchmarks) and schedule (every +ci_regression target x the CI benchmark set).""" + +import pathlib +import sys + +sys.path.insert(0, str(pathlib.Path(__file__).resolve().parent.parent / "scripts")) + +from ci_matrix import CI_BENCHMARKS, build_matrix + + +def test_dispatch_specific_benchmarks(): + m = build_matrix("workflow_dispatch", "inkling", "gpqa,asr") + assert m["include"] == [ + {"target": "inkling", "benchmark": "gpqa"}, + {"target": "inkling", "benchmark": "asr"}, + ] + + +def test_dispatch_all_expands_to_ci_set(): + m = build_matrix("workflow_dispatch", "gpt-5.5", "all") + assert [i["benchmark"] for i in m["include"]] == CI_BENCHMARKS + assert {i["target"] for i in m["include"]} == {"gpt-5.5"} + + +def test_dispatch_no_target_runs_nothing(): + # no model selected -> empty matrix -> zero jobs (no default model) + assert build_matrix("workflow_dispatch", None, None)["include"] == [] + + +def test_schedule_uses_ci_regression_targets(): + m = build_matrix("schedule") + targets = {i["target"] for i in m["include"]} + # inkling + gemini-3.7-flash are flagged ci_regression in targets.yaml + assert "inkling" in targets and "gemini-3.7-flash" in targets + assert all(i["benchmark"] in CI_BENCHMARKS for i in m["include"]) diff --git a/tests/test_config.py b/tests/test_config.py new file mode 100644 index 0000000..db58421 --- /dev/null +++ b/tests/test_config.py @@ -0,0 +1,213 @@ +"""Target loading + the capability merge chain. Targets live in one file under a +`targets:` map (key = name); a target declares only what differs from its +provider's defaults, and per-benchmark / CLI overrides layer on top. +build_adapter maps the provider to the right adapter class. +""" + +import textwrap + +import pytest + +from src.capabilities import AudioShape, ReasoningStyle +from src.config import ( + build_adapter, + build_routes, + load_all_targets, + load_target, + resolve_capabilities, +) +from src.providers.gemini import GeminiAdapter +from src.providers.openai_compat import OpenAICompatAdapter + + +def _targets(tmp_path, body): + p = tmp_path / "targets.yaml" + p.write_text("targets:\n" + textwrap.indent(textwrap.dedent(body), " ")) + return p + + +def test_load_target_parses_core_fields(tmp_path): + path = _targets( + tmp_path, + """ + inkling: + provider: fireworks + model_id: accounts/fireworks/models/inkling + """, + ) + t = load_target("inkling", path) + assert t.name == "inkling" + assert t.provider == "fireworks" + assert t.model_id == "accounts/fireworks/models/inkling" + + +def test_load_all_targets_returns_every_entry(tmp_path): + path = _targets( + tmp_path, + """ + a: + provider: fireworks + model_id: x + b: + provider: gemini + model_id: y + """, + ) + targets = load_all_targets(path) + assert set(targets) == {"a", "b"} + + +def test_unknown_target_name_raises(tmp_path): + import pytest + + path = _targets( + tmp_path, + """ + a: + provider: fireworks + model_id: x + """, + ) + with pytest.raises(ValueError, match="unknown target"): + load_target("nope", path) + + +def test_target_inherits_provider_defaults(tmp_path): + # A fireworks target declares no media; it should inherit audio_url + the + # "none is a floor" reasoning semantics from the provider defaults. + path = _targets( + tmp_path, + """ + inkling: + provider: fireworks + model_id: x + """, + ) + caps = resolve_capabilities(load_target("inkling", path)) + assert caps.media.audio is AudioShape.AUDIO_URL + assert caps.reasoning.style is ReasoningStyle.EFFORT + assert caps.reasoning.true_off is False # Fireworks "none" still thinks + + +def test_target_overrides_win_over_defaults(tmp_path): + # gemini-2.5-flash uses the budget knob, overriding the thinking_level default. + path = _targets( + tmp_path, + """ + gemini-2.5-flash: + provider: gemini + model_id: gemini-2.5-flash + capabilities: + reasoning: + style: thinking_budget + off_value: 0 + on_value: -1 + true_off: true + """, + ) + caps = resolve_capabilities(load_target("gemini-2.5-flash", path)) + assert caps.reasoning.style is ReasoningStyle.THINKING_BUDGET + assert caps.reasoning.off_value == 0 + + +def test_per_benchmark_override_applies(tmp_path): + path = _targets( + tmp_path, + """ + gemini-3.7-flash: + provider: gemini + model_id: gemini-3.7-flash + capabilities: + reasoning: { style: thinking_level, off_value: low, on_value: high, true_off: false } + overrides: + asr: + reasoning: { off_value: high } + """, + ) + t = load_target("gemini-3.7-flash", path) + assert resolve_capabilities(t, benchmark="gpqa").reasoning.off_value == "low" + assert resolve_capabilities(t, benchmark="asr").reasoning.off_value == "high" + + +def test_cli_override_wins_over_everything(tmp_path): + path = _targets( + tmp_path, + """ + inkling: + provider: fireworks + model_id: x + """, + ) + caps = resolve_capabilities( + load_target("inkling", path), cli_overrides={"reasoning": {"on_value": "max"}} + ) + assert caps.reasoning.on_value == "max" + + +def test_build_adapter_maps_provider_to_class(tmp_path): + path = _targets( + tmp_path, + """ + a: + provider: fireworks + model_id: x + b: + provider: gemini + model_id: y + """, + ) + a = build_adapter(load_target("a", path)) + assert isinstance(a, OpenAICompatAdapter) + assert "fireworks_api_key" in [k.lower() for k in a.key_spec] + assert a.base_url and "fireworks" in a.base_url + assert isinstance(build_adapter(load_target("b", path)), GeminiAdapter) + + +def test_fallbacks_build_ordered_routes_with_own_caps(tmp_path): + path = _targets( + tmp_path, + """ + inkling-small: + provider: openrouter + model_id: thinkingmachines/inkling-small + fallbacks: + - provider: fireworks + model_id: accounts/fireworks/models/inkling-small + """, + ) + routes = build_routes(load_target("inkling-small", path)) + assert [(r.provider, r.model_id) for r in routes] == [ + ("openrouter", "thinkingmachines/inkling-small"), + ("fireworks", "accounts/fireworks/models/inkling-small"), + ] + # each route resolves its OWN provider's caps, not the primary's + assert routes[0].caps.reasoning.style is ReasoningStyle.REASONING_BODY + assert routes[1].caps.reasoning.style is ReasoningStyle.EFFORT + + +def test_no_fallbacks_is_single_route(tmp_path): + path = _targets( + tmp_path, + """ + solo: + provider: interfaze + model_id: interfaze-x + """, + ) + assert len(build_routes(load_target("solo", path))) == 1 + + +def test_fallback_is_validated(tmp_path): + path = _targets( + tmp_path, + """ + bad: + provider: openrouter + model_id: or/x + fallbacks: + - provider: nope + model_id: y + """, + ) + with pytest.raises(ValueError): + load_all_targets(path) diff --git a/tests/test_datautil.py b/tests/test_datautil.py new file mode 100644 index 0000000..04b8af3 --- /dev/null +++ b/tests/test_datautil.py @@ -0,0 +1,48 @@ +import sys +import types + +from src.datautil import load_rows + + +def _install_fake(monkeypatch, capture): + """Replace the `datasets` module so load_rows imports our stub.""" + + class _Stream: + def __init__(self, rows): + self.rows = rows + + def take(self, n): + return self.rows[:n] + + def fake_load_dataset(*args, **kwargs): + capture.append((args, kwargs)) + rows = [{"id": i} for i in range(100)] + return _Stream(rows) if kwargs.get("streaming") else rows + + mod = types.ModuleType("datasets") + mod.load_dataset = fake_load_dataset + monkeypatch.setitem(sys.modules, "datasets", mod) + + +def test_load_rows_streams_only_first_n_when_sampling(monkeypatch): + cap = [] + _install_fake(monkeypatch, cap) + rows = load_rows("ds/x", "test", sample_size=3) + assert rows == [{"id": 0}, {"id": 1}, {"id": 2}] + # the whole point: a smoke must not download the full split + assert cap[0][1]["streaming"] is True + + +def test_load_rows_full_load_when_no_sample(monkeypatch): + cap = [] + _install_fake(monkeypatch, cap) + rows = load_rows("ds/x", "test") + assert len(rows) == 100 + assert "streaming" not in cap[0][1] + + +def test_load_rows_passes_config_positionally(monkeypatch): + cap = [] + _install_fake(monkeypatch, cap) + load_rows("ds/x", "test", sample_size=1, config="cfg") + assert cap[0][0] == ("ds/x", "cfg") diff --git a/tests/test_decode.py b/tests/test_decode.py new file mode 100644 index 0000000..2161913 --- /dev/null +++ b/tests/test_decode.py @@ -0,0 +1,86 @@ +"""Response normalization — unify the three response shapes and the +reasoning-token fallbacks the audit found (Fireworks reports reasoning tokens at +the top level of usage; Gemini as thoughts_token_count). Duck-typed fakes stand +in for the SDK objects so this stays a pure unit test. +""" + +from types import SimpleNamespace as NS + +from src.capabilities import ResponseShape +from src.decode import decode_response + + +def _openai_raw( + content, reasoning_tokens=None, top_level_reasoning=None, finish="stop" +): + details = NS(reasoning_tokens=reasoning_tokens) + usage = NS( + completion_tokens_details=details, + reasoning_tokens=top_level_reasoning, + prompt_tokens=11, + completion_tokens=22, + ) + msg = NS(content=content) + choice = NS(message=msg, finish_reason=finish) + return NS(choices=[choice], usage=usage, id="resp_1") + + +def test_openai_extracts_content_and_finish_reason(): + r = decode_response(_openai_raw("Answer: B"), ResponseShape.OPENAI_CHAT) + assert r.text == "Answer: B" + assert r.finish_reason == "stop" + assert r.raw_id == "resp_1" + + +def test_openai_none_content_becomes_empty_string(): + r = decode_response(_openai_raw(None), ResponseShape.OPENAI_CHAT) + assert r.text == "" + + +def test_openai_reasoning_tokens_prefers_details(): + r = decode_response( + _openai_raw("x", reasoning_tokens=729), ResponseShape.OPENAI_CHAT + ) + assert r.reasoning_tokens == 729 + + +def test_openai_reasoning_tokens_falls_back_to_top_level_for_fireworks(): + r = decode_response( + _openai_raw("x", reasoning_tokens=None, top_level_reasoning=512), + ResponseShape.OPENAI_CHAT, + ) + assert r.reasoning_tokens == 512 + + +def test_anthropic_joins_text_blocks_and_skips_thinking(): + raw = NS( + content=[ + NS(type="thinking", thinking="hmm"), + NS(type="text", text="Hello "), + NS(type="text", text="world"), + ], + usage=NS(input_tokens=5, output_tokens=7), + id="msg_1", + ) + r = decode_response(raw, ResponseShape.ANTHROPIC_BLOCKS) + assert r.text == "Hello world" + assert r.input_tokens == 5 + assert r.output_tokens == 7 + + +def test_gemini_reads_text_and_thoughts_token_count(): + raw = NS( + text="42", + usage_metadata=NS(thoughts_token_count=88, prompt_token_count=3), + response_id="gem_1", + ) + r = decode_response(raw, ResponseShape.GEMINI_TEXT) + assert r.text == "42" + assert r.reasoning_tokens == 88 + assert r.raw_id == "gem_1" + + +def test_gemini_none_text_becomes_empty(): + raw = NS(text=None, usage_metadata=NS(thoughts_token_count=None), response_id="g") + r = decode_response(raw, ResponseShape.GEMINI_TEXT) + assert r.text == "" diff --git a/tests/test_encode_anthropic.py b/tests/test_encode_anthropic.py new file mode 100644 index 0000000..b755085 --- /dev/null +++ b/tests/test_encode_anthropic.py @@ -0,0 +1,71 @@ +"""Anthropic encode: system-message separation, the disabled/enabled thinking +block, temperature omission under thinking, and the max_tokens > budget_tokens +coupling (all declarative via the cap). +""" + +from src.capabilities import Capabilities +from src.providers.anthropic import AnthropicAdapter +from src.request import ImagePart, Message, ReasoningSpec, Request, TextPart + +CAPS = Capabilities.from_dict( + { + "reasoning": { + "style": "disabled_block", + "on_value": 10000, + "true_off": True, + "temperature_when_on": False, + "extra": {"off_max_tokens": 1024, "on_max_tokens": 16000}, + }, + "media": {"image": "anthropic_source"}, + "response": "anthropic_blocks", + } +) +ADAPTER = AnthropicAdapter( + name="anthropic", + base_url=None, + key_spec=["ANTHROPIC_API_KEY"], + capability_defaults={}, +) + + +def test_off_disables_thinking_and_keeps_temperature(): + req = Request( + [Message("user", [TextPart("hi")])], ReasoningSpec("off"), temperature=0.0 + ) + kw = ADAPTER.encode(req, CAPS, "claude-sonnet-4-6") + assert kw["thinking"] == {"type": "disabled"} + assert kw["max_tokens"] == 1024 + assert kw["temperature"] == 0.0 + + +def test_high_enables_budget_bumps_max_tokens_and_omits_temperature(): + req = Request( + [Message("user", [TextPart("hi")])], ReasoningSpec("high"), temperature=0.0 + ) + kw = ADAPTER.encode(req, CAPS, "claude-sonnet-4-6") + assert kw["thinking"] == {"type": "enabled", "budget_tokens": 10000} + assert kw["max_tokens"] > 10000 # must exceed budget + assert "temperature" not in kw + + +def test_system_message_is_separated(): + req = Request( + [ + Message("system", [TextPart("You are terse.")]), + Message("user", [TextPart("hi")]), + ], + ReasoningSpec("off"), + ) + kw = ADAPTER.encode(req, CAPS, "claude-sonnet-4-6") + assert kw["system"] == "You are terse." + assert [m["role"] for m in kw["messages"]] == ["user"] + + +def test_image_uses_anthropic_source_block(): + req = Request( + [Message("user", [TextPart("read"), ImagePart(b"\xff\xd8j", "image/jpeg")])], + ReasoningSpec("off"), + ) + content = ADAPTER.encode(req, CAPS, "claude-sonnet-4-6")["messages"][0]["content"] + assert content[1]["type"] == "image" + assert content[1]["source"]["type"] == "base64" diff --git a/tests/test_encode_gemini.py b/tests/test_encode_gemini.py new file mode 100644 index 0000000..8439b36 --- /dev/null +++ b/tests/test_encode_gemini.py @@ -0,0 +1,64 @@ +"""Gemini encode: native genai config object. Asserts on the config's thinking +field (level vs budget) and that media parts become genai Parts. Uses the real +google-genai types (offline — no network).""" + +from src.capabilities import Capabilities +from src.providers.gemini import GeminiAdapter +from src.request import ImagePart, Message, ReasoningSpec, Request, TextPart + +LEVEL_CAPS = Capabilities.from_dict( + { + "reasoning": { + "style": "thinking_level", + "off_value": "low", + "on_value": "high", + "true_off": False, + }, + "media": {"image": "gemini_part"}, + "response": "gemini_text", + } +) +BUDGET_CAPS = Capabilities.from_dict( + { + "reasoning": { + "style": "thinking_budget", + "off_value": 0, + "on_value": -1, + "true_off": True, + }, + "media": {"image": "gemini_part"}, + "response": "gemini_text", + } +) +ADAPTER = GeminiAdapter( + name="gemini", base_url=None, key_spec=["GEMINI_API_KEY"], capability_defaults={} +) + + +def test_thinking_level_floor_goes_into_config(): + req = Request( + [Message("user", [TextPart("hi")])], ReasoningSpec("off"), temperature=0.0 + ) + enc = ADAPTER.encode(req, LEVEL_CAPS, "gemini-3.7-flash") + assert enc["model"] == "gemini-3.7-flash" + # the SDK coerces "low" -> ThinkingLevel.LOW enum; compare on value, case-insensitive + assert str(enc["config"].thinking_config.thinking_level.value).lower() == "low" + assert enc["config"].temperature == 0.0 + + +def test_thinking_budget_zero_goes_into_config(): + req = Request([Message("user", [TextPart("hi")])], ReasoningSpec("off")) + enc = ADAPTER.encode(req, BUDGET_CAPS, "gemini-2.5-flash") + assert enc["config"].thinking_config.thinking_budget == 0 + + +def test_image_part_becomes_genai_part(): + req = Request( + [Message("user", [TextPart("read"), ImagePart(b"\x89PNGx", "image/png")])], + ReasoningSpec("off"), + ) + contents = ADAPTER.encode(req, LEVEL_CAPS, "gemini-3.7-flash")["contents"] + # text passes through as a string; the image becomes a genai Part with inline bytes + assert "read" in contents + part = contents[-1] + assert getattr(part, "inline_data", None) is not None diff --git a/tests/test_encode_openai_compat.py b/tests/test_encode_openai_compat.py new file mode 100644 index 0000000..29eb9db --- /dev/null +++ b/tests/test_encode_openai_compat.py @@ -0,0 +1,122 @@ +"""OpenAI-compatible encode — the assembly point where reasoning placement, +temperature gating, media blocks and the max-tokens param name all come +together for openai / fireworks / openrouter / interfaze. +""" + +from src.capabilities import Capabilities +from src.providers.openai_compat import OpenAICompatAdapter +from src.request import ImagePart, Message, ReasoningSpec, Request, TextPart + +OPENAI_CAPS = Capabilities.from_dict( + { + "reasoning": { + "style": "effort", + "off_value": "none", + "on_value": "high", + "true_off": True, + "temperature_when_on": False, + }, + "media": {"image": "image_url"}, + "response": "openai_chat", + } +) +FIREWORKS_CAPS = Capabilities.from_dict( + { + "reasoning": { + "style": "effort", + "off_value": "none", + "on_value": "high", + "true_off": False, + "temperature_when_on": True, + }, + "media": {"image": "image_url"}, + "response": "openai_chat", + "max_tokens_param": "max_completion_tokens", + } +) +OPENROUTER_CAPS = Capabilities.from_dict( + { + "reasoning": { + "style": "reasoning_body", + "off_value": False, + "on_value": True, + "true_off": True, + "extra": {"toggle": "enabled"}, + }, + "media": {"image": "image_url"}, + "response": "openai_chat", + } +) + +ADAPTER = OpenAICompatAdapter( + name="test", base_url="http://x", key_spec=["K"], capability_defaults={} +) + + +def _text_req(mode, temperature=0.0): + return Request( + messages=[Message("user", [TextPart("What is 2+2?")])], + reasoning=ReasoningSpec(mode=mode), + temperature=temperature, + ) + + +def test_text_only_content_is_a_plain_string(): + kw = ADAPTER.encode(_text_req("off"), OPENAI_CAPS, "gpt-5.5") + assert kw["model"] == "gpt-5.5" + assert kw["messages"] == [{"role": "user", "content": "What is 2+2?"}] + + +def test_openai_off_sends_effort_and_temperature(): + kw = ADAPTER.encode(_text_req("off"), OPENAI_CAPS, "gpt-5.5") + assert kw["reasoning_effort"] == "none" + assert kw["temperature"] == 0.0 + + +def test_openai_high_omits_temperature(): + kw = ADAPTER.encode(_text_req("high"), OPENAI_CAPS, "gpt-5.5") + assert kw["reasoning_effort"] == "high" + assert "temperature" not in kw + + +def test_fireworks_high_keeps_temperature(): + kw = ADAPTER.encode(_text_req("high"), FIREWORKS_CAPS, "inkling") + assert kw["reasoning_effort"] == "high" + assert kw["temperature"] == 0.0 + + +def test_max_tokens_uses_capability_param_name(): + req = _text_req("off") + req.max_tokens = 4096 + assert ( + ADAPTER.encode(req, FIREWORKS_CAPS, "inkling")["max_completion_tokens"] == 4096 + ) + assert "max_tokens" not in ADAPTER.encode(req, FIREWORKS_CAPS, "inkling") + + +def test_openrouter_reasoning_goes_to_extra_body(): + kw = ADAPTER.encode(_text_req("off"), OPENROUTER_CAPS, "x-ai/grok-4.3") + assert "reasoning_effort" not in kw + assert kw["extra_body"] == {"reasoning": {"enabled": False}} + + +def test_image_message_becomes_block_list(): + req = Request( + messages=[ + Message( + "user", + [TextPart("Read this."), ImagePart(b"\xff\xd8jpg", "image/jpeg")], + ) + ], + reasoning=ReasoningSpec(mode="off"), + ) + content = ADAPTER.encode(req, OPENAI_CAPS, "gpt-5.5")["messages"][0]["content"] + assert isinstance(content, list) + assert content[0] == {"type": "text", "text": "Read this."} + assert content[1]["type"] == "image_url" + assert content[1]["image_url"]["url"].startswith("data:image/jpeg;base64,") + + +def test_none_temperature_is_omitted(): + kw = ADAPTER.encode(_text_req("off", temperature=None), OPENAI_CAPS, "gpt-5.5") + assert "temperature" not in kw diff --git a/tests/test_errors.py b/tests/test_errors.py new file mode 100644 index 0000000..278d7b6 --- /dev/null +++ b/tests/test_errors.py @@ -0,0 +1,75 @@ +"""Error classification — turns a provider exception into the signal the +fallback ladder acts on. Every PARAM_REJECTED case here is a real 4xx the audit +saw a model raise (Gemini "MINIMAL not supported", grok "Reasoning is +mandatory", GPT-5 temperature-under-reasoning). +""" + +from src.errors import ErrorKind, classify + + +class FakeAPIError(Exception): + """Mimics an SDK error carrying a status code + message.""" + + def __init__(self, status_code, message): + super().__init__(message) + self.status_code = status_code + + +def test_429_is_rate_limited(): + assert ( + classify(FakeAPIError(429, "Too Many Requests")).kind is ErrorKind.RATE_LIMITED + ) + + +def test_503_is_transient(): + assert ( + classify(FakeAPIError(503, "Service Unavailable")).kind is ErrorKind.TRANSIENT + ) + + +def test_timeout_is_transient(): + assert classify(TimeoutError("request timed out")).kind is ErrorKind.TRANSIENT + + +def test_401_is_fatal(): + assert classify(FakeAPIError(401, "invalid api key")).kind is ErrorKind.FATAL + + +def test_404_model_not_found_is_fatal(): + assert classify(FakeAPIError(404, "model does not exist")).kind is ErrorKind.FATAL + + +def test_gemini_minimal_not_supported_raises_thinking_floor(): + # Raised with no status_code attr — parsed from the message, as google-genai does. + c = classify( + Exception( + "400 INVALID_ARGUMENT. Thinking level MINIMAL is not supported for this model." + ) + ) + assert c.kind is ErrorKind.PARAM_REJECTED + assert c.action == "raise_thinking_floor" + + +def test_reasoning_mandatory_forces_floor(): + c = classify(FakeAPIError(400, "Reasoning is mandatory and cannot be disabled")) + assert c.kind is ErrorKind.PARAM_REJECTED + assert c.action == "force_reasoning_floor" + + +def test_temperature_rejected_drops_temperature(): + c = classify( + FakeAPIError(400, "temperature is not supported with reasoning enabled") + ) + assert c.kind is ErrorKind.PARAM_REJECTED + assert c.action == "drop_temperature" + + +def test_unknown_parameter_drops_that_param(): + c = classify(FakeAPIError(400, "Unknown parameter: 'top_k'")) + assert c.kind is ErrorKind.PARAM_REJECTED + assert c.action == "drop_param" + assert c.param == "top_k" + + +def test_unrecognized_400_is_fatal(): + assert classify(FakeAPIError(400, "malformed request body")).kind is ErrorKind.FATAL diff --git a/tests/test_execute.py b/tests/test_execute.py new file mode 100644 index 0000000..00a3566 --- /dev/null +++ b/tests/test_execute.py @@ -0,0 +1,162 @@ +"""The fallback ladder. On a PARAM_REJECTED 4xx it mutates the request/caps and +retries in-place (self-heal), recording a capability_hint to promote to YAML. +Transient/rate-limit/empty/fatal are surfaced as BenchError for the runner to +retry-or-fail. A scripted fake adapter drives the error sequence — no network. +""" + +import pytest + +from src.capabilities import Capabilities +from src.errors import ErrorKind +from src.execute import BenchError, execute +from src.request import Message, ReasoningSpec, Request, TextPart +from src.response import Response + + +class FakeAPIError(Exception): + def __init__(self, status_code, message): + super().__init__(message) + self.status_code = status_code + + +class ScriptedAdapter: + """Records the encoded state on each call so tests can assert what the + ladder adjusted; raises a scripted error sequence, then succeeds.""" + + def __init__(self, errors, final_text="ok"): + self.errors = list(errors) + self.history = [] + self.final_text = final_text + + def encode(self, req, caps, model_id): + return { + "mode": req.reasoning.mode, + "off": caps.reasoning.off_value, + "toggle": caps.reasoning.extra.get("toggle"), + "true_off": caps.reasoning.true_off, + "temperature": req.temperature, + } + + def call(self, client, encoded): + self.history.append(encoded) + if self.errors: + raise self.errors.pop(0) + return {"ok": True} + + def decode(self, raw, caps): + return Response(text=self.final_text) + + def classify_error(self, exc): + from src.errors import classify + + return classify(exc) + + +def _req(mode="off", temperature=0.0): + return Request( + [Message("user", [TextPart("q")])], ReasoningSpec(mode), temperature=temperature + ) + + +def _caps(**reasoning): + base = { + "style": "thinking_level", + "off_value": "minimal", + "on_value": "high", + "true_off": False, + } + base.update(reasoning) + return Capabilities.from_dict( + {"reasoning": base, "media": {}, "response": "gemini_text"} + ) + + +def test_success_first_try_returns_response_no_hints(): + adapter = ScriptedAdapter(errors=[]) + resp, hints = execute(adapter, None, _req(), _caps(), "m") + assert resp.text == "ok" + assert hints == [] + assert len(adapter.history) == 1 + + +def test_minimal_not_supported_raises_thinking_floor_then_succeeds(): + adapter = ScriptedAdapter( + errors=[ + Exception( + "400 INVALID_ARGUMENT. Thinking level MINIMAL is not supported for this model." + ) + ] + ) + resp, hints = execute(adapter, None, _req(), _caps(off_value="minimal"), "m") + assert resp.text == "ok" + assert ( + adapter.history[0]["off"] == "minimal" + ) # first attempt used the declared floor + assert adapter.history[1]["off"] == "low" # ladder raised it + assert any("floor" in h.lower() for h in hints) + + +def test_temperature_rejected_drops_temperature_then_succeeds(): + adapter = ScriptedAdapter( + errors=[FakeAPIError(400, "temperature is not supported with reasoning")] + ) + _resp, hints = execute(adapter, None, _req(temperature=0.0), _caps(), "m") + assert adapter.history[0]["temperature"] == 0.0 + assert adapter.history[1]["temperature"] is None + assert any("temperature" in h.lower() for h in hints) + + +def test_reasoning_mandatory_forces_floor_on_reasoning_body(): + caps = Capabilities.from_dict( + { + "reasoning": { + "style": "reasoning_body", + "off_value": False, + "on_value": True, + "true_off": True, + "extra": {"toggle": "enabled"}, + }, + "media": {}, + "response": "openai_chat", + } + ) + adapter = ScriptedAdapter( + errors=[FakeAPIError(400, "Reasoning is mandatory and cannot be disabled")] + ) + _resp, hints = execute(adapter, None, _req(), caps, "m") + assert adapter.history[0]["toggle"] == "enabled" + assert adapter.history[1]["toggle"] == "effort" # switched away from enabled=false + assert any("reasoning" in h.lower() for h in hints) + + +def test_fatal_error_propagates_as_bencherror(): + adapter = ScriptedAdapter(errors=[FakeAPIError(401, "bad key")]) + with pytest.raises(BenchError) as ei: + execute(adapter, None, _req(), _caps(), "m") + assert ei.value.classification.kind is ErrorKind.FATAL + + +def test_empty_content_surfaces_as_retryable_bencherror(): + adapter = ScriptedAdapter(errors=[], final_text=" ") + with pytest.raises(BenchError) as ei: + execute(adapter, None, _req(), _caps(), "m") + assert ei.value.classification.kind is ErrorKind.EMPTY_CONTENT + + +def test_rate_limit_surfaces_as_retryable_bencherror(): + adapter = ScriptedAdapter(errors=[FakeAPIError(429, "slow down")]) + with pytest.raises(BenchError) as ei: + execute(adapter, None, _req(), _caps(), "m") + assert ei.value.classification.kind is ErrorKind.RATE_LIMITED + + +def test_param_retries_are_bounded(): + # A model that rejects MINIMAL forever must not loop indefinitely. + adapter = ScriptedAdapter( + errors=[Exception("Thinking level X is not supported")] * 10 + ) + with pytest.raises(BenchError): + execute( + adapter, None, _req(), _caps(off_value="minimal"), "m", max_param_retries=3 + ) + assert len(adapter.history) <= 4 diff --git a/tests/test_gpqa_bench.py b/tests/test_gpqa_bench.py new file mode 100644 index 0000000..6e6db8a --- /dev/null +++ b/tests/test_gpqa_bench.py @@ -0,0 +1,85 @@ +"""GPQA benchmark: parse unit tests + offline scoring-parity replay. + +The parity test is the real migration gate: replay each archived responses.jsonl +through the NEW parse/score and assert the score is bit-identical to the archived +metrics.json — proving the port preserves scores with zero API spend. +""" + +import json +import pathlib +from types import SimpleNamespace as NS + +import pytest + +from benchmarks.gpqa import bench + +RESULTS = pathlib.Path(__file__).resolve().parent.parent / "results" +ARCHIVED_TAGS = [ + "fireworks_inkling_thinkingoff_gpqa_diamond", + "fireworks_deepseekv4flash_thinkingoff_gpqa_diamond", + "gemini37flash_thinkingdefault_gpqa_diamond", + "thinkingmachinesinklingsmall_thinkingon_gpqa_diamond", +] + + +def _resp(text): + return NS(text=text) + + +def test_parse_single_letter(): + assert bench.parse(_resp("A"), None) == "A" + assert bench.parse(_resp("d"), None) == "D" + + +def test_parse_letter_in_sentence(): + assert bench.parse(_resp("The answer is B."), None) == "B" + assert bench.parse(_resp("Answer: C"), None) == "C" + + +def test_parse_empty_is_none(): + assert bench.parse(_resp(""), None) is None + assert bench.parse(_resp(" "), None) is None + + +def test_score_joins_records_to_samples(): + samples = [ + {"id": "1", "correct_letter": "A", "domain": "Physics"}, + {"id": "2", "correct_letter": "B", "domain": "Chemistry"}, + ] + records = [{"id": "1", "prediction": "A"}, {"id": "2", "prediction": "C"}] + m = bench.score(records, samples) + assert m["correct"] == 1 + assert m["total"] == 2 + assert m["accuracy"] == 0.5 + assert m["per_domain"]["Physics"]["accuracy"] == 1.0 + + +@pytest.mark.parametrize("tag", ARCHIVED_TAGS) +def test_scoring_parity_with_archived_run(tag): + resp_path = RESULTS / f"{tag}_responses.jsonl" + metrics_path = RESULTS / f"{tag}_metrics.json" + if not (resp_path.exists() and metrics_path.exists()): + pytest.skip(f"archived run {tag} not present") + + archived = json.loads(metrics_path.read_text()) + samples, records = [], [] + for line in resp_path.read_text().splitlines(): + if not line.strip(): + continue + rec = json.loads(line) + samples.append( + { + "id": rec["id"], + "correct_letter": rec["correct_letter"], + "domain": rec.get("domain"), + } + ) + records.append( + {"id": rec["id"], "prediction": bench.parse(_resp(rec["response"]), None)} + ) + + m = bench.score(records, samples) + assert m["total"] == archived["total"] + assert m["correct"] == archived["correct"] + assert m["accuracy"] == archived["accuracy"] + assert m["unparseable"] == archived["unparseable"] diff --git a/tests/test_media.py b/tests/test_media.py new file mode 100644 index 0000000..5aa24de --- /dev/null +++ b/tests/test_media.py @@ -0,0 +1,118 @@ +"""Media block shapes — the audit's most dangerous fragmentation. Four audio +shapes and several image shapes look almost interchangeable but are host- +specific and fail *silently* when wrong (an audio_url data-URI sent to +OpenRouter is accepted, the audio dropped, and the model hallucinates a +transcript at WER~1.0). These tests pin each shape exactly. +""" + +import base64 +import io + +from src.capabilities import AudioShape, ImageShape +from src.media import GeminiPart, audio_block, image_block +from src.request import AudioPart, ImagePart + +WAV = b"RIFF....WAVEfake-audio-bytes" +JPEG = b"\xff\xd8\xff\xe0jpeg-bytes" +PNG = b"\x89PNG\r\n\x1a\npng-bytes" + + +def _b64(data: bytes) -> str: + return base64.b64encode(data).decode("ascii") + + +# --- audio --------------------------------------------------------------- + + +def test_audio_file_block_is_interfaze_data_uri(): + block = audio_block(AudioPart(WAV, "audio/wav"), AudioShape.FILE_BLOCK) + assert block["type"] == "file" + assert block["file"]["file_data"] == f"data:audio/wav;base64,{_b64(WAV)}" + + +def test_audio_url_block_is_fireworks_data_uri(): + block = audio_block(AudioPart(WAV, "audio/wav"), AudioShape.AUDIO_URL) + assert block == { + "type": "audio_url", + "audio_url": {"url": f"data:audio/wav;base64,{_b64(WAV)}"}, + } + + +def test_input_audio_block_is_openrouter_raw_b64_with_format(): + # This is the anti-silent-drop shape: raw base64 (NOT a data-URI) + format. + block = audio_block(AudioPart(WAV, "audio/wav"), AudioShape.INPUT_AUDIO) + assert block == { + "type": "input_audio", + "input_audio": {"data": _b64(WAV), "format": "wav"}, + } + assert "data:" not in block["input_audio"]["data"] + + +def test_audio_gemini_part_carries_bytes_and_mime(): + block = audio_block(AudioPart(WAV, "audio/wav"), AudioShape.GEMINI_PART) + assert block == GeminiPart(data=WAV, mime="audio/wav") + + +# --- image --------------------------------------------------------------- + + +def test_image_url_block_jpeg_data_uri(): + block = image_block(ImagePart(JPEG, "image/jpeg"), ImageShape.IMAGE_URL) + assert block == { + "type": "image_url", + "image_url": {"url": f"data:image/jpeg;base64,{_b64(JPEG)}"}, + } + + +def test_image_url_block_preserves_png_mime_for_olmocr(): + block = image_block(ImagePart(PNG, "image/png"), ImageShape.IMAGE_URL) + assert block["image_url"]["url"].startswith("data:image/png;base64,") + + +def test_image_anthropic_source_block(): + block = image_block(ImagePart(JPEG, "image/jpeg"), ImageShape.ANTHROPIC_SOURCE) + assert block == { + "type": "image", + "source": {"type": "base64", "media_type": "image/jpeg", "data": _b64(JPEG)}, + } + + +def test_image_gemini_part_marker(): + block = image_block(ImagePart(JPEG, "image/jpeg"), ImageShape.GEMINI_PART) + assert block == GeminiPart(data=JPEG, mime="image/jpeg") + + +# --- encode_image (central RGB-convert + resize) ------------------------- + + +def test_encode_image_converts_rgba_to_jpeg_without_crashing(): + # PIL raises OSError saving RGBA as JPEG; the convert must happen here so a + # transparent PNG sample doesn't crash the run (it did in 8/9 OCR variants). + from PIL import Image + + from src.media import encode_image + + rgba = Image.new("RGBA", (10, 10), (255, 0, 0, 128)) + part = encode_image(rgba, mime="image/jpeg") + assert part.mime == "image/jpeg" + assert Image.open(io.BytesIO(part.data)).mode == "RGB" + + +def test_encode_image_downscales_to_max_side(): + from PIL import Image + + from src.media import encode_image + + big = Image.new("RGB", (3000, 1500)) + part = encode_image(big, mime="image/jpeg", max_side=1536) + assert max(Image.open(io.BytesIO(part.data)).size) == 1536 + + +def test_encode_image_png_keeps_alpha(): + from PIL import Image + + from src.media import encode_image + + rgba = Image.new("RGBA", (10, 10), (0, 255, 0, 64)) + part = encode_image(rgba, mime="image/png") + assert Image.open(io.BytesIO(part.data)).mode == "RGBA" diff --git a/tests/test_mmmlu_bench.py b/tests/test_mmmlu_bench.py new file mode 100644 index 0000000..d1723c7 --- /dev/null +++ b/tests/test_mmmlu_bench.py @@ -0,0 +1,103 @@ +"""MMMLU: parse units + offline scoring-parity replay of the archived runs.""" + +import json +import pathlib + +import pytest + +from benchmarks.mmmlu import bench + +RESULTS = pathlib.Path(__file__).resolve().parent.parent / "results" +ARCHIVED_TAGS = [ + "mmmlulite_fireworks_accounts-fireworks-models-inkling_reasoningoff", + "mmmlulite_fireworks_accounts-fireworks-models-deepseek-v4-flash_reasoningoff", + "mmmlulite_gemini_gemini-3-7-flash_reasoninghigh", + "mmmlulite_openrouter_thinkingmachines-inkling-small_reasoninghigh", +] + + +def test_parse_answer(): + assert bench.parse_answer("B") == "B" + assert bench.parse_answer("The answer is (C).") == "C" + assert bench.parse_answer("") is None + + +def test_build_sample_lite_and_full_variants(): + # lite (opencompass) schema + lite = bench._build_sample( + { + "subject": "math", + "input": "2+2?", + "A": "3", + "B": "4", + "C": "5", + "D": "6", + "target": "b", + }, + "EN", + 3, + "lite", + ) + assert lite["id"] == "EN:3" and lite["question"] == "2+2?" and lite["answer"] == "B" + # full (openai/MMMLU) schema — id from Unnamed: 0, Question/Subject/Answer columns + full = bench._build_sample( + { + "Unnamed: 0": 7, + "Subject": "math", + "Question": "2+2?", + "A": "3", + "B": "4", + "C": "5", + "D": "6", + "Answer": "b", + }, + "EN", + 0, + "full", + ) + assert full["id"] == "EN:7" and full["subject"] == "math" and full["answer"] == "B" + + +def test_score_macro_averages_languages(): + samples = [ + {"id": "EN:0", "language": "EN", "subject": "math", "answer": "A"}, + {"id": "FR:0", "language": "FR", "subject": "math", "answer": "B"}, + ] + records = [{"id": "EN:0", "prediction": "A"}, {"id": "FR:0", "prediction": "C"}] + m = bench.score(records, samples) + assert m["per_language"]["EN"]["accuracy"] == 1.0 + assert m["per_language"]["FR"]["accuracy"] == 0.0 + assert m["macro_accuracy"] == 0.5 + + +@pytest.mark.parametrize("tag", ARCHIVED_TAGS) +def test_scoring_parity_with_archived_run(tag): + resp_path = RESULTS / f"{tag}_responses.jsonl" + metrics_path = RESULTS / f"{tag}_metrics.json" + if not (resp_path.exists() and metrics_path.exists()): + pytest.skip(f"archived run {tag} not present") + + archived = json.loads(metrics_path.read_text()) + samples, records = [], [] + for line in resp_path.read_text().splitlines(): + if not line.strip(): + continue + rec = json.loads(line) + if rec.get("response") is None: + continue + samples.append( + { + "id": rec["id"], + "language": rec["language"], + "subject": rec["subject"], + "answer": rec["answer"], + } + ) + records.append( + {"id": rec["id"], "prediction": bench.parse_answer(rec["response"])} + ) + + m = bench.score(records, samples) + assert m["num_samples"] == archived["num_samples"] + assert m["macro_accuracy"] == pytest.approx(archived["macro_accuracy"]) + assert m["micro_accuracy"] == pytest.approx(archived["micro_accuracy"]) diff --git a/tests/test_mmmu_pro_bench.py b/tests/test_mmmu_pro_bench.py new file mode 100644 index 0000000..802b1d7 --- /dev/null +++ b/tests/test_mmmu_pro_bench.py @@ -0,0 +1,104 @@ +"""MMMU-Pro: parse units + offline scoring-parity replay (standard + vision).""" + +import json +import pathlib + +import pytest + +from benchmarks.mmmu_pro import bench + +RESULTS = pathlib.Path(__file__).resolve().parent.parent / "results" +ARCHIVED_TAGS = [ + "mmmupro_standard_fireworks_accounts-fireworks-models-inkling_reasoningoff", + "mmmupro_standard_gemini_gemini-3-7-flash_reasoningoff", + "mmmupro_standard_openrouter_thinkingmachines-inkling-small_reasoningoff", + "mmmupro_vision_fireworks_accounts-fireworks-models-inkling_reasoningoff", + "mmmupro_vision_gemini_gemini-3-7-flash_reasoningoff", + "mmmupro_vision_openrouter_thinkingmachines-inkling-small_reasoningoff", +] + + +def test_build_request_embedded_or_lazy_idx(monkeypatch): + """Smokes embed images; full runs carry an index and read images lazily + from _DATASET (so the samples list doesn't hold ~1730 rows of images).""" + from PIL import Image + + img = Image.new("RGB", (16, 16)) + + # standard, embedded (smoke): text + 2 image parts + s = { + "id": "x", + "setting": "standard", + "question": "q?", + "options": ["a", "b"], + "answer": "A", + "images": [img, img], + } + assert len(bench.build_request(s, "off").messages[0].parts) == 3 + + # standard, lazy idx: images read from the _DATASET row (image_2 is None) + monkeypatch.setattr(bench, "_DATASET", {5: {"image_1": img, "image_2": None}}) + s2 = { + "id": "y", + "setting": "standard", + "question": "q?", + "options": ["a", "b"], + "answer": "B", + "idx": 5, + } + assert len(bench.build_request(s2, "off").messages[0].parts) == 2 + + # vision, lazy idx + monkeypatch.setattr(bench, "_DATASET", {7: {"image": img}}) + s3 = { + "id": "z", + "setting": "vision", + "options": ["a", "b"], + "answer": "C", + "idx": 7, + } + assert len(bench.build_request(s3, "off").messages[0].parts) == 2 + + +def test_parse_answer_a_to_j(): + assert bench.parse_answer("H") == "H" + assert bench.parse_answer("(J)") == "J" + assert bench.parse_answer("The answer is E.") == "E" + assert bench.parse_answer("") is None + + +def test_options_block(): + assert bench._options_block(["x", "y", "z"]) == "A. x\nB. y\nC. z" + + +@pytest.mark.parametrize("tag", ARCHIVED_TAGS) +def test_scoring_parity_with_archived_run(tag): + resp_path = RESULTS / f"{tag}_responses.jsonl" + metrics_path = RESULTS / f"{tag}_metrics.json" + if not (resp_path.exists() and metrics_path.exists()): + pytest.skip(f"archived run {tag} not present") + + archived = json.loads(metrics_path.read_text()) + samples, records = [], [] + for line in resp_path.read_text().splitlines(): + if not line.strip(): + continue + rec = json.loads(line) + if rec.get("response") is None: + continue + samples.append( + { + "id": rec["id"], + "answer": rec["answer"], + "subject": rec.get("subject"), + "topic_difficulty": rec.get("topic_difficulty"), + } + ) + records.append( + {"id": rec["id"], "prediction": bench.parse_answer(rec["response"])} + ) + + m = bench.score(records, samples) + assert m["num_samples"] == archived["num_samples"] + assert m["accuracy"] == pytest.approx(archived["accuracy"]) + assert m["unparseable"] == archived["unparseable"] diff --git a/tests/test_ocrbench_bench.py b/tests/test_ocrbench_bench.py new file mode 100644 index 0000000..ba3e327 --- /dev/null +++ b/tests/test_ocrbench_bench.py @@ -0,0 +1,53 @@ +"""OCRBench v2: offline parity of the category aggregation. + +The per-sample scorer is reused verbatim (not re-tested here). This replays each +archived scored.json — which already carries per-sample `score` + `type` — through +the ported `aggregate` and matches en_overall/cn_overall + per-category means. +""" + +import json +import pathlib + +import pytest + +from benchmarks.ocrbench_v2 import bench + +RESULTS = pathlib.Path(__file__).resolve().parent.parent / "results" +ARCHIVED = [ + "ocrbench_v2_fireworks_inkling", + "ocrbench_v2_fireworks_inkling-small", + "ocrbench_v2_gemini", +] + + +def test_aggregate_macro_of_means(): + scored = [ + {"type": "text recognition en", "score": 1.0}, + {"type": "text recognition en", "score": 0.0}, # text_recognition avg = 0.5 + {"type": "math QA en", "score": 1.0}, # mathematical_calculation avg = 1.0 + {"type": "text spotting en", "score": 0.0, "ignore": "True"}, # skipped + ] + agg = bench.aggregate(scored) + assert agg["en_scores"]["text_recognition"] == {"avg": 0.5, "count": 2} + assert agg["en_scores"]["mathematical_calculation"]["avg"] == 1.0 + assert agg["en_scores"]["text_spotting"]["count"] == 0 # ignored one didn't count + # overall = mean of the two non-empty category means + assert agg["en_overall"] == pytest.approx((0.5 + 1.0) / 2) + + +@pytest.mark.parametrize("tag", ARCHIVED) +def test_aggregation_parity_with_archived_run(tag): + scored_path = RESULTS / f"{tag}_scored.json" + metrics_path = RESULTS / f"{tag}_metrics.json" + if not (scored_path.exists() and metrics_path.exists()): + pytest.skip(f"archived run {tag} not present") + + scored = json.loads(scored_path.read_text()) + archived = json.loads(metrics_path.read_text()) + agg = bench.aggregate(scored) + + assert agg["en_overall"] == pytest.approx(archived["en_overall"]) + assert agg["cn_overall"] == pytest.approx(archived["cn_overall"]) + for cat, v in archived["en_scores"].items(): + assert agg["en_scores"][cat]["count"] == v["count"] + assert agg["en_scores"][cat]["avg"] == pytest.approx(v["avg"]) diff --git a/tests/test_olmocr_bench.py b/tests/test_olmocr_bench.py new file mode 100644 index 0000000..2b57acb --- /dev/null +++ b/tests/test_olmocr_bench.py @@ -0,0 +1,39 @@ +"""olmOCR: unit tests for the null-normalization and log parsing. + +olmOCR has no offline score-replay (its scorer needs the full dataset + prints to +a log, no metrics.json). Parity is by-construction: identical PROMPT, PNG render, +and null handling. Here we pin those and the log parser against the archived log. +""" + +import pathlib +from types import SimpleNamespace as NS + +from benchmarks.olmocr import harness as bench + +LOGS = pathlib.Path(__file__).resolve().parent.parent / "logs" + + +def test_parse_null_becomes_empty(): + assert bench.parse(NS(text="null"), None) == "" + assert bench.parse(NS(text=" N/A "), None) == "" + assert bench.parse(NS(text="Real content."), None) == "Real content." + + +def test_prompt_is_stable(): + # the scorer's numbers depend on this prompt; guard against silent drift + assert "Turn tables into markdown format." in bench.PROMPT + assert "Do not hallucinate." in bench.PROMPT + + +def test_parse_scores_from_archived_log(): + log = LOGS / "olmocr_gemini-3-7-flash.log" + if not log.exists(): + import pytest + + pytest.skip("archived olmOCR log not present") + scores = bench._parse_scores(log.read_text(errors="ignore")) + assert scores["overall"] == 77.3 + assert scores["splits"]["arxiv_math"] == 86.7 + assert scores["splits"]["old_scans"] == 44.9 + # matches report_scores.load_olmocr: the first `baseline:` line (summary block) + assert scores["splits"]["baseline"] == 93.4 diff --git a/tests/test_reasoning.py b/tests/test_reasoning.py new file mode 100644 index 0000000..dff175b --- /dev/null +++ b/tests/test_reasoning.py @@ -0,0 +1,174 @@ +"""The reasoning translation — the single place the audit's "6 different ways" +of encoding a thinking floor collapse into. Every case here is a real quirk +that currently lives as a scattered special-case: + +- OpenAI: reasoning_effort="none", and temperature only when reasoning is off +- Fireworks: reasoning_effort="none" is a FLOOR (still thinks), temp always sent +- Gemini 3.7-flash: thinking_level floor "low" (rejects "minimal") +- Gemini 3.x-flash <3.7: thinking_level floor "minimal" +- Gemini 2.5-flash: thinking_budget=0 (true off); 2.5-pro: budget=128 (floor) +- Anthropic: thinking={"type":"disabled"} off / enabled+budget on, temp omitted on +- OpenRouter default: reasoning.enabled bool; grok/google: reasoning.effort (can't disable) +- Moonshot: reasoning.enabled=false + provider pin +""" + +from src.capabilities import ReasoningCap, ReasoningStyle +from src.reasoning import build_reasoning + + +def test_none_style_injects_nothing(): + cap = ReasoningCap(style=ReasoningStyle.NONE) + inj = build_reasoning("high", cap) + assert inj.kwargs == {} + assert inj.extra_body == {} + assert inj.thinking_level is None + assert inj.thinking_budget is None + assert inj.anthropic_thinking is None + assert inj.allow_temperature is True + + +def test_openai_effort_off_sends_none_and_allows_temperature(): + # OpenAI "none" is a true off, so temperature is allowed on the off run. + cap = ReasoningCap( + style=ReasoningStyle.EFFORT, + off_value="none", + on_value="high", + true_off=True, + temperature_when_on=False, + ) + inj = build_reasoning("off", cap) + assert inj.kwargs == {"reasoning_effort": "none"} + assert inj.allow_temperature is True + + +def test_openai_effort_high_omits_temperature(): + # GPT-5.x rejects temperature!=default once reasoning engages. + cap = ReasoningCap( + style=ReasoningStyle.EFFORT, + off_value="none", + on_value="high", + true_off=True, + temperature_when_on=False, + ) + inj = build_reasoning("high", cap) + assert inj.kwargs == {"reasoning_effort": "high"} + assert inj.allow_temperature is False + + +def test_fireworks_effort_floor_still_allows_temperature_on_high(): + # Fireworks/Inkling sends temperature regardless of reasoning. + cap = ReasoningCap( + style=ReasoningStyle.EFFORT, + off_value="none", + on_value="high", + true_off=False, + temperature_when_on=True, + ) + inj = build_reasoning("high", cap) + assert inj.kwargs == {"reasoning_effort": "high"} + assert inj.allow_temperature is True + + +def test_effort_intermediate_mode_passes_through(): + cap = ReasoningCap(style=ReasoningStyle.EFFORT, off_value="none", on_value="high") + assert build_reasoning("low", cap).kwargs == {"reasoning_effort": "low"} + + +def test_gemini_37_flash_thinking_level_floor_is_low(): + cap = ReasoningCap( + style=ReasoningStyle.THINKING_LEVEL, + off_value="low", + on_value="high", + true_off=False, + ) + inj = build_reasoning("off", cap) + assert inj.thinking_level == "low" + assert inj.thinking_budget is None + + +def test_gemini_old_flash_thinking_level_floor_is_minimal(): + cap = ReasoningCap( + style=ReasoningStyle.THINKING_LEVEL, + off_value="minimal", + on_value="high", + true_off=False, + ) + assert build_reasoning("off", cap).thinking_level == "minimal" + + +def test_gemini_25_flash_budget_zero_is_true_off(): + cap = ReasoningCap( + style=ReasoningStyle.THINKING_BUDGET, off_value=0, on_value=-1, true_off=True + ) + inj = build_reasoning("off", cap) + assert inj.thinking_budget == 0 + assert inj.thinking_level is None + + +def test_gemini_25_pro_budget_floor_is_128(): + cap = ReasoningCap( + style=ReasoningStyle.THINKING_BUDGET, off_value=128, on_value=-1, true_off=False + ) + assert build_reasoning("off", cap).thinking_budget == 128 + assert build_reasoning("high", cap).thinking_budget == -1 + + +def test_anthropic_disabled_block_off_allows_temperature(): + cap = ReasoningCap( + style=ReasoningStyle.DISABLED_BLOCK, + on_value=10000, + true_off=True, + temperature_when_on=False, + ) + inj = build_reasoning("off", cap) + assert inj.anthropic_thinking == {"type": "disabled"} + assert inj.allow_temperature is True + + +def test_anthropic_disabled_block_on_enables_budget_and_omits_temperature(): + cap = ReasoningCap( + style=ReasoningStyle.DISABLED_BLOCK, + on_value=10000, + true_off=True, + temperature_when_on=False, + ) + inj = build_reasoning("high", cap) + assert inj.anthropic_thinking == {"type": "enabled", "budget_tokens": 10000} + assert inj.allow_temperature is False + + +def test_openrouter_enabled_toggle_off_disables(): + cap = ReasoningCap( + style=ReasoningStyle.REASONING_BODY, + off_value=False, + on_value=True, + true_off=True, + extra={"toggle": "enabled"}, + ) + inj = build_reasoning("off", cap) + assert inj.extra_body == {"reasoning": {"enabled": False}} + + +def test_openrouter_effort_toggle_cannot_disable_uses_minimal_floor(): + # grok/google via OpenRouter reject enabled=false; floor is effort=minimal. + cap = ReasoningCap( + style=ReasoningStyle.REASONING_BODY, + off_value="minimal", + on_value="high", + true_off=False, + extra={"toggle": "effort"}, + ) + inj = build_reasoning("off", cap) + assert inj.extra_body == {"reasoning": {"effort": "minimal"}} + + +def test_openrouter_provider_pin_is_emitted(): + cap = ReasoningCap( + style=ReasoningStyle.REASONING_BODY, + off_value=False, + on_value=True, + extra={"toggle": "enabled", "provider_only": ["moonshotai"]}, + ) + inj = build_reasoning("off", cap) + assert inj.extra_body["reasoning"] == {"enabled": False} + assert inj.extra_body["provider"] == {"only": ["moonshotai"]} diff --git a/tests/test_refcoco_bench.py b/tests/test_refcoco_bench.py new file mode 100644 index 0000000..971fbce --- /dev/null +++ b/tests/test_refcoco_bench.py @@ -0,0 +1,118 @@ +"""RefCOCO: parse/IoU/oracle units + offline scoring-parity replay. + +Strict replay re-parses each archived response and matches Acc@0.5; the oracle +replay re-runs best-of-interpretation and matches the archived _oracle_ metrics. +""" + +import json +import pathlib + +import pytest + +from benchmarks.obj_detection import bench + +RESULTS = pathlib.Path(__file__).resolve().parent.parent / "results" +STRICT_TAGS = [ + "refcoco_val_fireworks_accounts_fireworks_models_inkling", + "refcoco_val_openrouter_thinkingmachines_inkling-small", + "refcoco_testA_gemini_gemini-3.7-flash", +] +ORACLE_BASE = "refcoco_val_fireworks_accounts_fireworks_models_inkling" + + +def test_parse_variant_restores_plus_and_g_datasets(): + assert bench._parse_variant("val") == ("lmms-lab/RefCOCO", "val") + assert bench._parse_variant("testA") == ("lmms-lab/RefCOCO", "testA") + assert bench._parse_variant("plus-testB") == ("lmms-lab/RefCOCO+", "testB") + assert bench._parse_variant("g-test") == ("lmms-lab/RefCOCOg", "test") + + +def test_build_request_embedded_image_or_lazy_idx(monkeypatch): + """Smokes embed the image; full runs carry an index and read the image + lazily from _DATASET (so the samples list doesn't hold all 8.8k images).""" + from PIL import Image + + img = Image.new("RGB", (16, 16)) + base = { + "id": "x", + "expression": "cat", + "sent_w": 16, + "sent_h": 16, + "gt_bbox_xyxy": [0, 0, 1, 1], + } + + req = bench.build_request({**base, "image": img}, "off") + assert len(req.messages[0].parts) == 2 # text + image + + monkeypatch.setattr(bench, "_DATASET", {5: {"image": img}}) + req2 = bench.build_request({**base, "idx": 5}, "off") + assert len(req2.messages[0].parts) == 2 + + +def test_compute_iou(): + assert bench.compute_iou([0, 0, 10, 10], [0, 0, 10, 10]) == 1.0 + assert bench.compute_iou([0, 0, 10, 10], [20, 20, 30, 30]) == 0.0 + + +def test_parse_box_pixel_and_box2d(): + assert bench.parse_box("[10, 20, 30, 40]", 100, 100) == [10, 20, 30, 40] + # box_2d is yxyx -> swapped to xyxy + assert bench.parse_box('{"box_2d": [20, 10, 40, 30]}', 1000, 1000) == pytest.approx( + [10, 20, 30, 40] + ) + + +def test_best_box_recovers_pixel2x(): + # a box reported in 2x-upscaled space; oracle should halve it to match GT + iou, label = bench._best_box("[100, 100, 200, 200]", 100, 100, [50, 50, 100, 100]) + assert label == "pixel2x-xyxy" + assert iou == 1.0 + + +def _replay(tag): + resp_path = RESULTS / f"{tag}_responses.jsonl" + samples, records = [], [] + for line in resp_path.read_text().splitlines(): + if not line.strip(): + continue + rec = json.loads(line) + w, h = rec["image_width"], rec["image_height"] + samples.append( + { + "id": rec["id"], + "gt_bbox_xyxy": rec["gt_bbox_xyxy"], + "sent_w": w, + "sent_h": h, + } + ) + records.append( + { + "id": rec["id"], + "response": rec["response"], + "prediction": bench.parse_box(rec["response"], w, h), + } + ) + return bench.score(records, samples) + + +@pytest.mark.parametrize("tag", STRICT_TAGS) +def test_strict_scoring_parity(tag): + resp_path = RESULTS / f"{tag}_responses.jsonl" + metrics_path = RESULTS / f"{tag}_metrics.json" + if not (resp_path.exists() and metrics_path.exists()): + pytest.skip(f"archived run {tag} not present") + archived = json.loads(metrics_path.read_text()) + m = _replay(tag) + assert m["total"] == archived["total"] + assert m["accuracy"] == pytest.approx(archived["accuracy"]) + assert m["unparsed"] == archived["unparsed"] + + +def test_oracle_scoring_parity(): + resp_path = RESULTS / f"{ORACLE_BASE}_responses.jsonl" + oracle_metrics = RESULTS / f"{ORACLE_BASE}_oracle_metrics.json" + if not (resp_path.exists() and oracle_metrics.exists()): + pytest.skip("archived oracle run not present") + archived = json.loads(oracle_metrics.read_text()) + m = _replay(ORACLE_BASE) + assert m["oracle"]["accuracy"] == pytest.approx(archived["accuracy"]) diff --git a/tests/test_results.py b/tests/test_results.py new file mode 100644 index 0000000..d395aeb --- /dev/null +++ b/tests/test_results.py @@ -0,0 +1,70 @@ +"""The single result contract: one slug, one layout +(results///{responses.jsonl,metrics.json,run.json}), and ONE +content-based discovery so reporting and re-scoring can't disagree (the audit +found report_scores dropping 5 of 7 RefCOCO runners by prefix mismatch). +""" + +import json + +from src.results import RunStore, discover, model_slug + + +def test_sampled_run_is_isolated_from_full_run(tmp_path): + """A --sample smoke (written under an isolated _smoke root) must neither + clobber a full run's metrics on disk nor appear in the leaderboard.""" + full = RunStore("gpqa", "inkling", root=tmp_path) + full.write_metrics({"n": 198}) + smoke = RunStore("gpqa", "inkling", root=tmp_path / "_smoke") + smoke.write_metrics({"n": 3}) + assert json.loads(full.metrics_path.read_text())["n"] == 198 + assert [d["n"] for d in discover(tmp_path)] == [198] + + +def test_model_slug_is_canonical(): + assert model_slug("accounts/fireworks/models/inkling-small") == "inkling-small" + assert model_slug("google/gemini-3.7-flash") == "gemini-3.7-flash" + assert model_slug("gpt-5.5") == "gpt-5.5" + assert model_slug("x-ai/grok-4.3") == "grok-4.3" + + +def test_append_and_load_responses_round_trip(tmp_path): + store = RunStore("gpqa", "inkling", root=tmp_path) + store.append_response({"id": "a", "response": "A"}) + store.append_response({"id": "b", "response": "B"}) + assert store.load_responses() == [ + {"id": "a", "response": "A"}, + {"id": "b", "response": "B"}, + ] + + +def test_completed_ids_uses_done_predicate(tmp_path): + store = RunStore("gpqa", "inkling", root=tmp_path) + store.append_response({"id": "a", "response": "A"}) + store.append_response({"id": "b", "response": None}) # not done + done = store.completed_ids("id", lambda r: r.get("response") is not None) + assert done == {"a"} + + +def test_write_metrics_and_run(tmp_path): + store = RunStore("gpqa", "inkling", root=tmp_path) + store.write_metrics({"benchmark": "gpqa", "score": 0.87}) + store.write_run({"provider": "fireworks", "model_id": "x"}) + assert store.metrics_path.exists() + assert store.run_path.exists() + + +def test_discover_finds_all_metrics_content_based(tmp_path): + RunStore("gpqa", "inkling", root=tmp_path).write_metrics( + {"benchmark": "gpqa", "target": "inkling", "score": 0.87} + ) + RunStore("refcoco", "gemini-3.7-flash", root=tmp_path).write_metrics( + {"benchmark": "refcoco", "target": "gemini-3.7-flash", "score": 0.3} + ) + found = discover(root=tmp_path) + keys = {(m["benchmark"], m["target"]) for m in found} + assert keys == {("gpqa", "inkling"), ("refcoco", "gemini-3.7-flash")} + + +def test_paths_live_under_benchmark_and_slugged_target(tmp_path): + store = RunStore("mmmu_pro", "accounts/fireworks/models/inkling", root=tmp_path) + assert store.dir == tmp_path / "mmmu_pro" / "inkling" diff --git a/tests/test_runner.py b/tests/test_runner.py new file mode 100644 index 0000000..87d986f --- /dev/null +++ b/tests/test_runner.py @@ -0,0 +1,249 @@ +"""The execution harness: resume, retry-with-backoff on retryable BenchErrors, +fail-fast on fatal, incremental checkpointing, bounded concurrency. Driven by a +scripted fake adapter through the real `execute`, so no network and no sleeps +(backoff_base=0). +""" + +from src.capabilities import Capabilities +from src.request import Message, ReasoningSpec, Request, TextPart +from src.response import Response +from src.results import RunStore +from src.runner import Route, run_benchmark + + +def _route(adapter, provider="test", caps=None): + return Route( + provider=provider, model_id="m", adapter=adapter, caps=caps, client=None + ) + + +class FakeAPIError(Exception): + def __init__(self, status_code, message): + super().__init__(message) + self.status_code = status_code + + +class MapAdapter: + """Answers per-sample from a scripted map of id -> list of outcomes, where + an outcome is either an Exception (raised) or a str (returned as text).""" + + def __init__(self, script): + self.script = {k: list(v) for k, v in script.items()} + self.calls = {} + + def encode(self, req, caps, model_id): + return {"id": req.messages[0].parts[0].text} + + def call(self, client, encoded): + sid = encoded["id"] + self.calls[sid] = self.calls.get(sid, 0) + 1 + outcome = self.script[sid].pop(0) + if isinstance(outcome, Exception): + raise outcome + return {"text": outcome} + + def decode(self, raw, caps): + return Response(text=raw["text"]) + + def classify_error(self, exc): + from src.errors import classify + + return classify(exc) + + +CAPS = Capabilities.from_dict( + {"reasoning": {"style": "none"}, "media": {}, "response": "openai_chat"} +) + + +def _build_request(sample): + return Request([Message("user", [TextPart(sample["id"])])], ReasoningSpec("off")) + + +def _parse(resp, sample): + return resp.text.upper() + + +async def _run(adapter, samples, store, **kw): + return await run_benchmark( + routes=[_route(adapter, caps=CAPS)], + samples=samples, + build_request=_build_request, + parse=_parse, + store=store, + backoff_base=0.0, + **kw, + ) + + +async def test_runs_all_samples_and_records_predictions(tmp_path): + adapter = MapAdapter({"a": ["alpha"], "b": ["beta"]}) + store = RunStore("t", "m", root=tmp_path) + result = await _run(adapter, [{"id": "a"}, {"id": "b"}], store) + assert result.n_completed == 2 + recs = {r["id"]: r for r in store.load_responses()} + assert recs["a"]["prediction"] == "ALPHA" + assert recs["b"]["response"] == "beta" + + +async def test_resume_skips_completed(tmp_path): + store = RunStore("t", "m", root=tmp_path) + store.append_response({"id": "a", "response": "old", "prediction": "OLD"}) + adapter = MapAdapter( + {"b": ["beta"]} + ) # note: no script for "a" — must not be called + result = await _run(adapter, [{"id": "a"}, {"id": "b"}], store) + assert "a" not in adapter.calls + assert result.n_completed == 1 # only the new one + + +async def test_retryable_error_is_retried_then_succeeds(tmp_path): + adapter = MapAdapter({"a": [FakeAPIError(429, "slow"), "ok"]}) + store = RunStore("t", "m", root=tmp_path) + result = await _run(adapter, [{"id": "a"}], store, max_retries=3) + assert adapter.calls["a"] == 2 + assert result.n_completed == 1 + + +async def test_fatal_error_records_failure_without_retry(tmp_path): + adapter = MapAdapter({"a": [FakeAPIError(401, "bad key")]}) + store = RunStore("t", "m", root=tmp_path) + result = await _run(adapter, [{"id": "a"}], store, max_retries=3) + assert adapter.calls["a"] == 1 # not retried + assert result.n_failed == 1 + assert store.load_responses()[0]["response"] is None + + +async def test_build_request_is_bounded_by_max_in_flight(tmp_path): + """Requests must be built lazily as workers pick up samples — not all up + front. Otherwise a full image benchmark decodes/encodes every image at once + and OOMs CI. inflight is incremented in build_request and decremented in + parse; all samples succeed here, so the pairing holds (parse runs only on + success).""" + ids = [str(i) for i in range(10)] + adapter = MapAdapter({i: ["ok"] for i in ids}) + store = RunStore("t", "m", root=tmp_path) + inflight = peak = 0 + + def build_request(sample): + nonlocal inflight, peak + inflight += 1 + peak = max(peak, inflight) + return Request( + [Message("user", [TextPart(sample["id"])])], ReasoningSpec("off") + ) + + def parse(resp, sample): + nonlocal inflight + inflight -= 1 + return resp.text + + await run_benchmark( + routes=[_route(adapter, caps=CAPS)], + samples=[{"id": i} for i in ids], + build_request=build_request, + parse=parse, + store=store, + backoff_base=0.0, + rate_limit=0, + max_in_flight=2, + ) + assert peak <= 2 # not 10: at most max_in_flight requests built at a time + + +async def test_capability_hints_are_aggregated(tmp_path): + adapter = MapAdapter( + {"a": [Exception("Thinking level MINIMAL is not supported"), "ok"]} + ) + store = RunStore("t", "m", root=tmp_path) + # give the reasoning a floor so the ladder has something to raise + caps = Capabilities.from_dict( + { + "reasoning": { + "style": "thinking_level", + "off_value": "minimal", + "on_value": "high", + "true_off": False, + }, + "media": {}, + "response": "openai_chat", + } + ) + result = await run_benchmark( + routes=[_route(adapter, caps=caps)], + samples=[{"id": "a"}], + build_request=_build_request, + parse=_parse, + store=store, + backoff_base=0.0, + ) + assert any("floor" in h.lower() for h in result.hints) + + +async def test_failover_to_next_route_on_fatal(tmp_path): + dead = MapAdapter({"a": [FakeAPIError(404, "no such model")]}) # FATAL + live = MapAdapter({"a": ["ok"]}) + store = RunStore("t", "m", root=tmp_path) + routes = [_route(dead, "openrouter", CAPS), _route(live, "fireworks", CAPS)] + result = await run_benchmark( + routes=routes, + samples=[{"id": "a"}], + build_request=_build_request, + parse=_parse, + store=store, + backoff_base=0.0, + ) + assert result.n_completed == 1 + rec = store.load_responses()[0] + assert rec["host"] == "fireworks" # served by the fallback, recorded + assert live.calls["a"] == 1 + + +async def test_no_failover_on_empty_content(tmp_path): + # EMPTY_CONTENT on every retry: the backup runs the same weights, so failing + # over would just double the bill — it must NOT be tried. + empty = MapAdapter({"a": ["", "", "", ""]}) + backup = MapAdapter({"a": ["ok"]}) + store = RunStore("t", "m", root=tmp_path) + routes = [_route(empty, "openrouter", CAPS), _route(backup, "fireworks", CAPS)] + result = await run_benchmark( + routes=routes, + samples=[{"id": "a"}], + build_request=_build_request, + parse=_parse, + store=store, + backoff_base=0.0, + max_retries=3, + ) + assert result.n_failed == 1 + assert "a" not in backup.calls # backup never called + + +async def test_all_routes_fail_records_failure(tmp_path): + d1 = MapAdapter({"a": [FakeAPIError(404, "x")]}) + d2 = MapAdapter({"a": [FakeAPIError(404, "y")]}) + store = RunStore("t", "m", root=tmp_path) + result = await run_benchmark( + routes=[_route(d1, "openrouter", CAPS), _route(d2, "fireworks", CAPS)], + samples=[{"id": "a"}], + build_request=_build_request, + parse=_parse, + store=store, + backoff_base=0.0, + ) + assert result.n_failed == 1 + assert d2.calls["a"] == 1 # the last route was tried + + +async def test_single_route_records_host(tmp_path): + live = MapAdapter({"a": ["ok"]}) + store = RunStore("t", "m", root=tmp_path) + await run_benchmark( + routes=[_route(live, "interfaze", CAPS)], + samples=[{"id": "a"}], + build_request=_build_request, + parse=_parse, + store=store, + backoff_base=0.0, + ) + assert store.load_responses()[0]["host"] == "interfaze" diff --git a/tests/test_spider2_bench.py b/tests/test_spider2_bench.py new file mode 100644 index 0000000..8f9bf5e --- /dev/null +++ b/tests/test_spider2_bench.py @@ -0,0 +1,72 @@ +"""Spider2-Lite: offline unit tests for the pure logic (SQL extraction, result +comparison). Full scoring re-executes SQL against the ~4GB gitignored data/, so a +score-replay is gated behind data/ presence + BENCH_SPIDER2_PARITY=1. +""" + +import json +import os +import pathlib + +import pytest + +from benchmarks.spider2_lite import bench + +RESULTS = pathlib.Path(__file__).resolve().parent.parent / "results" + + +def test_extract_sql_fenced(): + assert bench.extract_sql("```sql\nSELECT 1\n```") == "SELECT 1" + assert bench.extract_sql("prose\n```\nSELECT 2\n```\nmore") == "SELECT 2" + + +def test_extract_sql_unterminated_fence(): + # a model that opens ```sql, emits SQL, never closes the block + assert bench.extract_sql("Here you go:\n```sql\nSELECT 3;") == "SELECT 3;" + + +def test_extract_sql_no_fence_returns_whole(): + assert bench.extract_sql("SELECT 4") == "SELECT 4" + + +def test_vectors_match_float_tolerance_and_order(): + assert bench._vectors_match([1.0, 2.0], [1.005, 2.0], ignore_order=False) + assert not bench._vectors_match([1.0], [1.5], ignore_order=False) + + +def test_compare_table_column_match_unordered(): + import pandas as pd + + gold = pd.DataFrame({"a": [1, 2, 3]}) + pred = pd.DataFrame({"x": [3, 2, 1]}) + assert bench.compare_table(pred, gold, None, ignore_order=True) == 1 + assert bench.compare_table(pred, gold, None, ignore_order=False) == 0 + + +_PARITY_ON = bench._ALL_EXAMPLES.exists() and os.getenv("BENCH_SPIDER2_PARITY") + + +@pytest.mark.skipif( + not _PARITY_ON, reason="needs data/ + BENCH_SPIDER2_PARITY=1 (re-executes SQL)" +) +def test_full_scoring_parity_when_data_present(): + tag = "spider2_lite_local_fireworks_inkling" + resp_path = RESULTS / f"{tag}_responses.jsonl" + metrics_path = RESULTS / f"{tag}_metrics.json" + archived = json.loads(metrics_path.read_text()) + samples, records = [], [] + for line in resp_path.read_text().splitlines(): + if not line.strip(): + continue + rec = json.loads(line) + samples.append({"instance_id": rec["instance_id"], "db": rec["db"]}) + records.append( + { + "instance_id": rec["instance_id"], + "prediction": bench.extract_sql(rec["response"]), + } + ) + m = bench.score(records, samples) + # NOT exact: Spider2 re-executes SQL with a 120s timeout cap, so a heavy query + # near the boundary can flip between runs (warm cache vs cold). A faithful port + # reproduces the archived score within a small execution-noise tolerance. + assert abs(m["correct"] - archived["correct"]) <= 2 diff --git a/tests/test_targets.py b/tests/test_targets.py new file mode 100644 index 0000000..9b60fbd --- /dev/null +++ b/tests/test_targets.py @@ -0,0 +1,39 @@ +"""Every shipped target must load, resolve to typed capabilities, and build its +adapter — with no network. This is the guard behind the "add a target = add a +YAML entry, no code" promise: a new entry is validated here (and in CI on push) +rather than failing mid-run. +""" + +import pytest + +from src.capabilities import Capabilities +from src.config import build_adapter, load_all_targets, resolve_capabilities + +TARGETS = load_all_targets() +TARGET_NAMES = sorted(TARGETS) + + +def test_targets_exist(): + assert TARGET_NAMES, "no targets defined in src/targets.yaml" + + +@pytest.mark.parametrize("name", TARGET_NAMES) +def test_target_resolves_and_builds_adapter(name): + target = TARGETS[name] + caps = resolve_capabilities(target) + assert isinstance(caps, Capabilities) + resolve_capabilities(target, benchmark="asr") # per-benchmark path must resolve too + adapter = build_adapter(target) + assert adapter.name == target.provider + assert adapter.key_spec + + +@pytest.mark.parametrize("name", TARGET_NAMES) +def test_reasoning_keys_are_not_yaml_booleans_by_accident(name): + # Guards the 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