diff --git a/BENCHMARKS.md b/BENCHMARKS.md new file mode 100644 index 0000000000000000000000000000000000000000..0e9a2385196e2ad5d8ace0def865a6aec162fb85 --- /dev/null +++ b/BENCHMARKS.md @@ -0,0 +1,100 @@ +# Benchmark methodology and complete decision + +These measurements describe this experiment, not standard full-dataset leaderboard submissions. There is deliberately no `model-index` claiming otherwise. + +## Controlled A/B, recorded DEV predictions + +There are 156 new text/premise families per domain, two held-out description styles and all cyclic option rotations: four for AG News, three for SNLI. Keys are opaque. Total: 312 texts, 2,184 requests. Repeated variants of a text are not independent observations. + +Original historical partitions, DEV and reserved FINAL are separated by source/text/premise checks. This does not establish absence of pretraining exposure in BERT or Laya, or of all semantic paraphrases. Description pools are disjoint strings but remain lexically similar. + +Evaluation used the same local CPU, four threads, batches of eight sorted by length, PyTorch FP32 and ONNX Runtime INT8 for both A and B. Confidence intervals use paired percentile bootstrap over families, 10,000 replicates, seed 20260926. They are conditional on these two trained models, not confidence intervals over retraining runs. + +| Domain / precision | A | B | B−A, percentage points | Paired 95% CI | +| --- | ---: | ---: | ---: | ---: | +| AG News FP32 | 57.05% | 89.02% | +31.97 | [26.92, 37.02] | +| AG News INT8 | 54.89% | 88.46% | +33.57 | [28.76, 38.38] | +| SNLI FP32 | 53.42% | 71.05% | +17.63 | [13.78, 21.37] | +| SNLI INT8 | 50.11% | 65.60% | +15.49 | [11.97, 19.02] | + +### Preregistered gates, not reinterpreted after the result + +1. At least +10 accuracy points per domain and precision, with lower paired CI >0: all four pass. +2. B order instability at most half A: AG FP32/INT8 and SNLI FP32 pass; **SNLI INT8 fails**. +3. Fixed-validation lower global accuracy-difference CI ≥−2 points and no task point estimate below −4: both precisions pass. +4. Upper CI for change in FP32/INT8 disagreement ≤+2 points, on each DEV domain and fixed validation overall: all pass. +5. CPU payload 80–90 decimal MB: met. + +**Composite result: FAIL. No checkpoint promotion and no FINAL evaluation.** No threshold, checkpoint or quantization setting was changed after inspecting DEV. + +Order instability is the fraction of text/style pairs whose canonical class changes across rotations, averaged over styles. On SNLI INT8, A has 181 unstable cells out of 312, B has 105. Half of A allows at most 90 whole unstable cells, not 105. Accuracy is checked separately because a consistently wrong model can be stable. + +### SNLI by description style, INT8 + +| Style | A accuracy | B accuracy | Gain | B instability | +| --- | ---: | ---: | ---: | ---: | +| Short | 67.09% | 73.08% | +5.98 points | 21.79% | +| Long | 33.12% | 58.12% | +25.00 points | 45.51% | + +The aggregate +10-point accuracy gate was per domain/precision, not per style. It would be incorrect to claim that every formulation improved by ten points. + +### Quantization is not harmless + +| Domain | A FP32/INT8 disagreement | B disagreement | Change, points | Paired 95% CI | +| --- | ---: | ---: | ---: | ---: | +| AG News | 18.59% | 2.08% | −16.51 | [−19.23, −13.86] | +| SNLI | 22.76% | 11.00% | −11.75 | [−15.17, −8.33] | + +On SNLI's 936 requests, A changes 213 predictions: 95 correct→wrong, 64 wrong→correct, 54 wrong→different-wrong. B changes 103: 73 correct→wrong, 22 wrong→correct, 8 wrong→different-wrong. B loses 51 net correct answers, **5.45 points**, versus A's 31 / 3.31 points. + +Thus B has fewer changes but a larger accuracy loss. B's instability also rises from 16.03% in FP32 to 33.65% in INT8. Do not use the disagreement reduction as a blanket quantization-robustness claim. + +## Historical fixed validation used in A/B + +2,000 requests / 1,980 families, previously consulted during development. Same batch/CPU conditions as DEV. + +| Precision | A | B | Difference, points | Paired 95% CI | +| --- | ---: | ---: | ---: | ---: | +| FP32 | 76.15% | 77.20% | +1.05 | [+0.20, +1.90] | +| INT8 | 76.05% | 75.65% | −0.40 | [−1.31, +0.55] | + +The worst task point regression is −1.00 point. The overall increase in FP32/INT8 changes is +0.15 point, CI [−0.90, +1.20]. Non-regression guards pass on this development set; this is not a blind confirmation or a demonstrated inter-seed improvement. + +## Earlier original-model evaluations — NOT A/B scores + +The original model was selected by historical validation. Earlier test results had already been seen before increasing training data, so the end-to-end development process was not entirely blind. COPA was excluded from training and checkpoint selection. + +| Original INT8, historical test | Student | Laya teacher | +| --- | ---: | ---: | +| AG News, 600 requests | 91.00% | 91.17% | +| BoolQ, 600 | 69.00% | 83.33% | +| Emotion, 600 | 91.17% | 59.00% | +| SNLI, 600 | 78.17% | 76.33% | +| SST5, 600 | 45.00% | 33.33% | +| Balanced total, 3,000 | 74.87% | 68.63% | +| COPA out of domain, 400 | 49.25% | 69.75% | + +These teacher annotations belong to the historical pipeline, not a fresh remeasurement using the A/B FP32 teacher cache. Supervised labels supplement distillation, so in-domain teacher-score comparisons do not establish general student superiority. COPA random-choice reference is 50%. An old descriptive per-answer-vocabulary majority oracle of 100% was not a usable predictor and is omitted from the publication summaries. + +Original historical FP32/INT8 overall test accuracy: 75.00% / 74.87%. Batch size 16, four CPU threads, unlike the newer A/B batch-eight evaluation. Neither test-3000 nor COPA was rerun for A or B. + +## CPU benchmark + +Linux x86-64, Intel Core i7-1260P, ONNX Runtime 1.24.4, two threads, batch one, four options, three warm-up calls per shape and 30 timed repetitions. Synthetic tokens, model already loaded, tokenization/network/loading excluded. Peak RSS is over all four sequence lengths. + +| Variant | 128-token median | p95 | Peak RSS | +| --- | ---: | ---: | ---: | +| Original, current session | 21.36 ms | 33.24 ms | 195.74 MiB | +| Experimental B | 21.55 ms | 28.83 ms | 196.00 MiB | + +Control A's median is 21.43 ms; its full measurement record is in `benchmarks/cpu/control-a.json`. These observations do not establish a speed difference. Raw individual timings/IQR were not retained. Background load, clock and power states were not controlled sufficiently to explain the original's older 10.79 ms figure; that separate-session number must not be used to claim a 2× slowdown or speedup. + +```bash +python benchmark_cpu.py models/experimental-b --threads 2 --repeats 30 --output my-cpu.json +``` + +The benchmark uses Unix `resource`; Windows is not supported by this measurement script as written. The inference runtime itself does not depend on `resource`, but Windows/ARM64 compatibility remains untested. Dynamic quantization kernels and predictions can vary by CPU and batch composition. + +## What the evidence does not establish + +Reliable decisions on new tasks, arbitrary paraphrases, broad out-of-distribution robustness, calibrated confidence, performance on every CPU, bitwise training reproducibility, variance across seeds, or game-playing ability. None of those claims follows from small model files, software test success or a successful ONNX export. diff --git a/LICENSE_NOTES.md b/LICENSE_NOTES.md new file mode 100644 index 0000000000000000000000000000000000000000..10d625e349cb2ea8d7ef80ed375878c0e38c5b9a --- /dev/null +++ b/LICENSE_NOTES.md @@ -0,0 +1,37 @@ +# Source-specific rights and unresolved redistribution questions + +**There is no single permissive license asserted for this mixed-source research release.** The model-card `license: other` and descriptive license name point to this notice; they do not create a new license, clear upstream rights or authorize every commercial use. + +The owner requested a public research release with these reservations explicit. Public accessibility does not resolve copyright, database rights, contract terms or whether particular dataset terms apply to derived weights. This is a source inventory and limitation notice, not legal advice or a legal determination of derivative-work status. + +## Teacher and backbone + +| Component | Pinned revision | Declared model-card license | +| --- | --- | --- | +| [Laya / convaiinnovations](https://huggingface.co/convaiinnovations/laya) | `55cf4c4ebb4ebe31b2550e8bdf3bd21b99753851` | Apache-2.0 | +| [Google compact BERT](https://huggingface.co/google/bert_uncased_L-6_H-512_A-8) | `dd53ec6ca9d05e0a91b309c4e137f31988888071` | Apache-2.0 | + +The Laya Python package used was 0.3.20; its [source repository](https://github.com/NandhaKishorM/laya) should also be reviewed for applicable notices and terms. The Apache-2.0 declarations are not a separate warranty about the teacher's pretraining data, outputs or every downstream distribution scenario. No broader permission for this student is inferred solely from those tags. A copy of the [Apache License 2.0 text](licenses/Apache-2.0.txt) is included for components declaring it; it is not a blanket relicensing of this repository or the mixed data. + +## Training and evaluation datasets + +| Source | Pinned revision | Evidence and limitation | +| --- | --- | --- | +| [AG News / fancyzhx](https://huggingface.co/datasets/fancyzhx/ag_news) | `eb185aade064a813bc0b7f42de02595523103ca4` | Pinned card says `unknown` and describes research / non-commercial activity; no blanket commercial or redistribution clearance asserted. | +| [SST5 / SetFit](https://huggingface.co/datasets/SetFit/sst5) | `e51bdcd8cd3a30da231967c1a249ba59361279a3` | No license declaration in the pinned card; review-text rights not resolved here. | +| [BoolQ / Google](https://huggingface.co/datasets/google/boolq) | `35b264d03638db9f4ce671b711558bf7ff0f80d5` | Card declares [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/); preserve attribution and applicable obligations. | +| [SNLI / Stanford NLP](https://huggingface.co/datasets/stanfordnlp/snli) | `cdb5c3d5eed6ead6e5a341c8e56e669bb666725b` | Card declares [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/); preserve attribution and applicable obligations. | +| [Emotion / DAIR.AI](https://huggingface.co/datasets/dair-ai/emotion) | `cab853a1dbdf4c42c2b3ef2173804746df8825fe` | Card says the dataset should be used for educational and research purposes only. | +| [COPA through SuperGLUE](https://huggingface.co/datasets/aps/super_glue) | `3de24cf8022e94f4ee4b9d55a6f539891524d646` | Historical evaluation only, not fitting; SuperGLUE refers to original task licenses, with COPA-specific rights unresolved in this project. | + +Exact pinned-card links, original homepages, source partitions and provenance are recorded in `research/corpus-manifest.json`. No source corpus or teacher-cache contents are redistributed. Historical reports that contained example-specific answer vocabularies have been reduced to aggregate summaries before publication. + +## Practical restrictions + +- Treat this as experimental research material, not a cleared commercial product or a universally licensed foundation model. +- Verify source-specific rights and obtain any necessary permission before redistribution or commercial deployment. A public download is not itself that permission. +- Retain source attribution and this notice when sharing permitted derivatives. Applicable upstream licenses/terms are not superseded by this notice. +- No license is invented for third-party corpus text or any right the maintainer does not hold. +- Research source files have no newly added blanket license grant in this snapshot. Review applicable upstream code licenses as well as data/model conditions before reuse. + +The scientific claims are similarly limited: all variants have `quality_validated: false`, probabilities are uncalibrated, and the latest composite acceptance criterion failed. Publication must not be represented as certification of safety, generality, legal clearance or endorsement by upstream authors. diff --git a/PUBLICATION_CHECKS.json b/PUBLICATION_CHECKS.json new file mode 100644 index 0000000000000000000000000000000000000000..272115e66ce4a795927613ebcf68b7e2c2d14fba --- /dev/null +++ b/PUBLICATION_CHECKS.json @@ -0,0 +1,55 @@ +{ + "research_snapshot": "2026-09-26", + "package_scope": "Original, control A and experimental B; CPU exports and optional FP32 reference checkpoints.", + "software_unit_tests_passed": 57, + "hugging_face_model_card_validation": "passed", + "recomputed_statistics": { + "dev_rows": 2184, + "fixed_validation_rows": 2000, + "robustness_report_exactly_reproduced": true, + "fixed_validation_report_exactly_reproduced": true, + "new_inference_for_recomputation": false + }, + "runtime": { + "runtime_checks": { + "original": { + "payload_bytes": 87157026, + "CPUExecutionProvider": true, + "known_example_answers": { + "example-canonical-request.json": "sports", + "example-news-request.json": "science" + } + }, + "control-a": { + "payload_bytes": 87155068, + "CPUExecutionProvider": true, + "known_example_answers": { + "example-canonical-request.json": "sports", + "example-news-request.json": "science" + } + }, + "experimental-b": { + "payload_bytes": 87155068, + "CPUExecutionProvider": true, + "known_example_answers": { + "example-canonical-request.json": "sports", + "example-news-request.json": "sports" + } + } + }, + "torch_imported": false, + "note": "Known interface checks, not independent quality evaluations." + }, + "quality_validated": false, + "dev_composite_passed": false, + "final_evaluated": false, + "privacy": { + "raw_dataset_text_in_recorded_decisions": false, + "source_family_ids_replaced_preserving_sort_order": true, + "credentials_included": false, + "private_conversation_transcripts_included": false, + "account_balances_included": false, + "final_input_files_included": false + }, + "limitations": "Passing software checks and model-card validation are not evidence of general decision reliability or legal clearance." +} diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..bd5cc5772a129725339058d41bb6b8db9e989255 --- /dev/null +++ b/README.md @@ -0,0 +1,154 @@ +--- +language: +- en +license: other +license_name: research-use-with-unresolved-upstream-rights +license_link: https://huggingface.co/VynoDePal/laya-mini-cpu-research/blob/main/LICENSE_NOTES.md +library_name: onnx +pipeline_tag: text-classification +base_model: google/bert_uncased_L-6_H-512_A-8 +base_model_relation: finetune +datasets: +- fancyzhx/ag_news +- google/boolq +- dair-ai/emotion +- stanfordnlp/snli +- SetFit/sst5 +tags: +- bert +- onnx +- int8 +- cpu +- knowledge-distillation +- multiple-choice +- experimental +- research +inference: false +--- + +# Laya Mini CPU — research checkpoints and controlled A/B results + +**English decision models, approximately 87.16 MB each. Research artifacts, not a validated general-purpose decision agent.** + +[Français](README_FR.md) · [Benchmarks](BENCHMARKS.md) · [Research notes](RESEARCH.md) · [Reproducibility](REPRODUCIBILITY.md) · [Rights and restrictions](LICENSE_NOTES.md) + +Maintained by [VynoDePal](https://huggingface.co/VynoDePal). Research snapshot: 26 September 2026. This is an independent student-model experiment using [Laya](https://huggingface.co/convaiinnovations/laya) as teacher, not an official Laya release or an affiliation claim. + +## Read this before choosing a checkpoint + +**Experimental B improves accuracy on the tested close reformulations, but fails the preregistered composite acceptance criterion. It is NOT promoted as a replacement for the original.** None of these variants is validated for reliable general decisions. The reserved FINAL set was not evaluated. + +The main experiment covers **312 texts / 2,184 correlated requests, two domains, two description styles and one training seed**. It is not an official full-dataset AG News or SNLI leaderboard evaluation. + +| DEV domain / precision | Control A accuracy | Experimental B accuracy | A order instability | B order instability | Accuracy + stability gate | +| --- | ---: | ---: | ---: | ---: | --- | +| AG News FP32 | 57.05% | 89.02% | 61.54% | 3.53% | Pass | +| AG News INT8 | 54.89% | 88.46% | 65.06% | 6.41% | Pass | +| SNLI FP32 | 53.42% | 71.05% | 48.40% | 16.03% | Pass | +| **SNLI INT8** | **50.11%** | **65.60%** | **58.01%** | **33.65%** | **FAIL: B must be ≤29.01% unstable** | + +- The SNLI INT8 **short-style gain is only 5.98 percentage points**, not the 15.49-point average across styles. +- B loses **5.45 points on SNLI from FP32 to INT8**, despite fewer FP32/INT8 prediction changes than A. Fewer changes do not establish better quantization robustness. +- Confidence intervals resample text/premise families, not individual rotations. They do **not** measure variation across training seeds. +- `experiment-status.json` records `quality_validated: false`, `dev_passed: false`, the failed gate and `final_evaluated: false`. + +## Model variants + +| Directory | Role | CPU model payload | FP32 checkpoint | +| --- | --- | ---: | ---: | +| `models/original/` | Historical reference, before the A/B continuation | 87,157,026 bytes | 152,909,884 bytes | +| `models/control-a/` | Two more epochs using fixed descriptions | 87,155,068 bytes | 152,909,884 bytes | +| `models/experimental-b/` | Same continuation, with controlled description augmentation | 87,155,068 bytes | 152,909,884 bytes | + +Payload means **ONNX INT8 + tokenizer + manifest**; 1 MB = 1,000,000 bytes. Each variant satisfies the 80–90 MB model-file target. The whole repository is larger because it contains multiple variants, optional FP32 reference checkpoints, code and evidence. Python, dependencies and working RAM are extra. + +There is deliberately **no default model at the repository root**. Choose a variant explicitly. Original is a historical reference, not a claim that it is generally reliable. Checkpoints under `checkpoints/` are for inspection, comparison and possible re-export; downloading them is unnecessary for CPU INT8 inference. + +## Download only the CPU variant you need + +Install the Hugging Face CLI if needed (`python -m pip install huggingface_hub`), then: + +```bash +hf download VynoDePal/laya-mini-cpu-research \ + --include 'models/experimental-b/*' runtime.py schema.py requirements-runtime.txt 'examples/*' \ + --local-dir laya-mini +cd laya-mini +python3 -m venv .venv +source .venv/bin/activate +python -m pip install -r requirements-runtime.txt +python runtime.py models/experimental-b examples/example-canonical-request.json --threads 2 +``` + +Use `models/original` or `models/control-a` to select another variant. The custom runtime uses only `CPUExecutionProvider`; it does not require PyTorch, a GPU, paid inference or network access after installation. It is **not** a Transformers `pipeline()` checkpoint, an Ollama/GGUF model or a hosted chat endpoint. + +```python +import json +from pathlib import Path +from runtime import DecisionSession + +session = DecisionSession("models/experimental-b", threads=2) +request = json.loads(Path("examples/example-canonical-request.json").read_text()) +result = session.predict(request["state"], request["questions"]) +print(result["answers"]) +``` + +Keep the session loaded between requests. Inputs, instructions and option descriptions should be in English. The two included tennis examples are previously known interface checks, **not held-out evidence**. B answers `sports` on both in the recorded smoke test; the original fails the long-description version. Do not infer general reliability from either anecdote. + +### Interface and limits + +- `choice`: chooses a key from a dictionary of option descriptions. +- `score`: returns the expectation over an ordered list of levels. +- `noul`: returns a soft boolean score. +- Software supports 2–20 options; training covered only 2–6. +- Maximum total context: 512 WordPiece tokens. Instructions/options have a 192-token budget; oversized question definitions are rejected. Long states may be truncated; check `truncated_state`. +- Probabilities are **not calibrated**. A high `max_probability` is not a factual correctness guarantee. +- No free-form generation, vision, screen reading or automatic action execution. A game-playing system would still need an adapter and game-specific evaluation. + +## CPU performance + +Intel Core i7-1260P, Linux x86-64, ONNX Runtime 1.24.4, two threads, one question / four options, warm model, 30 repetitions. Synthetic inputs; excludes tokenization and loading. + +| B input tokens | Median latency | +| --- | ---: | +| 64 | 11.08 ms | +| 128 | 21.55 ms | +| 256 | 44.83 ms | +| 512 | 111.79 ms | + +B's 128-token p95 is **28.83 ms**, and peak process RSS is **196 MiB** across the benchmark. Original / A / B 128-token medians measured in the same session are **21.36 / 21.43 / 21.55 ms**. No meaningful speed ranking is established. The original's older 10.79 ms result came from a different session and is not a matched comparison; the difference is unexplained. + +Raw per-repetition timings were not retained, so an IQR is unavailable. Windows, ARM64 and old/low-end CPUs were not validated. **“Runs easily on every CPU” is not established.** More settings and p95 values are in [BENCHMARKS.md](BENCHMARKS.md). + +## Architecture and training + +- **38,223,873 parameters**: a pretrained six-layer, 512-wide BERT backbone, an additional decision layer, question-type embeddings and an option-marker scoring head. +- Backbone: `google/bert_uncased_L-6_H-512_A-8`, revision `dd53ec6ca9d05e0a91b309c4e137f31988888071`. +- Teacher: `convaiinnovations/laya`, revision `55cf4c4ebb4ebe31b2550e8bdf3bd21b99753851`, package `laya==0.3.20`. +- Loss: teacher KL at temperature 1 + 2 × cross-entropy against actual labels. This is **distillation plus supervised learning**, not direct quantization of the original Laya weights. +- A/B both continue the same original checkpoint for exactly two epochs / 5,048 steps, seed 42, batch 32, identical schedules, shared padding and last-checkpoint selection. There is no DEV-based checkpoint selection. +- B replaces exactly 12,000 AG News and 12,000 SNLI descriptions, 50% of each training pool. All other fields/tasks remain unchanged. Both arms share **teacher targets on fixed prompts**, not teacher re-annotations of the variants. +- Student: CUDA BF16 with FP32 matmul mode `high` (TF32 allowed; kernels not traced). Shared teacher cache: FP32 CUDA without autocast/TF32. Bitwise training reproducibility is not established. +- Export: ONNX opset 18; dynamic INT8 linear-weight quantization, per channel, `reduce_range=True`; embeddings remain FP32. The reduced range uses seven effective weight bits in INT8 storage. No separate calibration corpus was used. + +The 80,758 training rows comprise 24,000 AG News, 24,000 SNLI, 15,966 Emotion, 8,533 SST5 and 8,259 BoolQ examples. The original model's development history and different historical evaluations are documented separately; **old test/COPA scores must not be attributed to A or B**. + +## Evidence and reproducibility boundary + +- [A/B report](benchmarks/ab/dev-report.json): all acceptance criteria, per-style diagnostics and confidence intervals. +- [Frozen protocol](benchmarks/ab/protocol.json): hyperparameters and cryptographic commitments to inputs, including unevaluated FINAL files. +- [Text-free recorded decisions](benchmarks/ab/decisions-no-text.jsonl): 2,184 DEV + 2,000 fixed-validation rows, allowing aggregate statistics to be recomputed without rerunning inference. +- [CPU reports](benchmarks/cpu): measurements for all three variants in the same session. +- [Historical results](benchmarks/history): clearly scoped to the original or the pre-A/B audit. +- [Research sources](research/src), checkpoint metadata and `SHA256SUMS`: code/provenance and integrity evidence. + +No raw training corpus, teacher-cache contents, FINAL requests/descriptions, credentials, account balances or private conversation transcripts are included. The final-set preparation script is omitted. Published hashes/code are not a cryptographic guarantee that nobody could reconstruct held-out public examples; **the claim is that FINAL was not evaluated in this experiment**. + +Recomputing the recorded statistics is reproducible here. **Training from scratch and rerunning the original DEV inputs are not fully self-contained**, because their data/cache artifacts are not redistributed. FP32 checkpoints do not remove that limitation. See [REPRODUCIBILITY.md](REPRODUCIBILITY.md). + +## Rights, safety and status + +This public research snapshot is released at the owner's request **with unresolved upstream rights explicitly disclosed**. The `other` license tag is a pointer to source-specific restrictions, not a newly granted unified license. The Apache-2.0 model cards of the teacher/backbone do not by themselves clear the mixed training-data rights. Emotion is research/education only, AG News has unknown/non-commercial wording, and the pinned SST5 card declares no license. See [LICENSE_NOTES.md](LICENSE_NOTES.md) before redistribution or commercial use. + +Do not use these experimental models for consequential decisions or irreversible actions without independent validation and safeguards. The size target is met; reliable general decision intelligence is **not**. + +The engineering/review process used Codex and Claude Code assistance. Claude reviewed supplied excerpts and measurements, did not execute these tests, and did not provide independent human peer review. The reported total Vast.ai credit decrease was approximately **US$0.36**, including approximately **US$0.125 for A/B**; these are observations, not final invoices or a reproducible cost quote. Training instances were removed. No new paid job is required to use these CPU models. diff --git a/README_FR.md b/README_FR.md new file mode 100644 index 0000000000000000000000000000000000000000..1d89b488f9130765bd29d99137519e8cb0a23a1e --- /dev/null +++ b/README_FR.md @@ -0,0 +1,61 @@ +# Laya Mini CPU — guide français + +[Carte complète en anglais](README.md) · [Benchmarks](BENCHMARKS.md) · [Recherche](RESEARCH.md) · [Reproductibilité](REPRODUCIBILITY.md) · [Droits](LICENSE_NOTES.md) + +Ce dépôt de recherche contient **trois variantes réellement entraînées**, d'environ **87,16 Mo chacune** pour l'inférence CPU. Il ne s'agit ni d'une simple quantification des poids originaux de Laya, ni d'un chatbot, ni d'un agent de jeu prêt à l'emploi. + +## Quel modèle choisir ? + +- `models/original/` : référence historique avant la dernière expérience, pas une garantie de fiabilité générale. +- `models/control-a/` : contrôle ayant reçu deux epochs supplémentaires avec descriptions fixes. +- `models/experimental-b/` : même continuation avec remplacement contrôlé de descriptions AG News/SNLI. + +**B progresse sur les formulations proches étudiées, mais ne passe pas tous les critères préenregistrés.** Il reste expérimental et n'est pas promu comme remplacement de l'original. Aucun modèle n'est sélectionné par défaut à la racine du dépôt. + +| DEV en INT8 | A | B | +| --- | ---: | ---: | +| AG News, catégories d'actualité | 54,89 % | 88,46 % | +| SNLI, inférence entre phrases | 50,11 % | 65,60 % | + +Ces résultats concernent **312 textes, 2 184 requêtes corrélées, deux domaines et une seule graine**. Ils ne constituent pas les scores officiels sur les jeux de test complets. + +La garde échouée est la stabilité SNLI après quantification : **33,65 %** des couples texte/style changent de réponse selon la rotation des options, contre un maximum admissible de **29,01 %**. Sur le style SNLI court, le gain n'est que de 5,98 points. B perd 5,45 points SNLI entre FP32 et INT8. Le jeu FINAL réservé n'a pas été évalué ; aucun réglage supplémentaire n'a été fait sur ce DEV consommé. + +## Installation minimale + +Installer le CLI Hugging Face si nécessaire (`python -m pip install huggingface_hub`), puis télécharger uniquement la variante souhaitée : + +```bash +hf download VynoDePal/laya-mini-cpu-research \ + --include 'models/experimental-b/*' runtime.py schema.py requirements-runtime.txt 'examples/*' \ + --local-dir laya-mini +cd laya-mini +python3 -m venv .venv +source .venv/bin/activate +python -m pip install -r requirements-runtime.txt +python runtime.py models/experimental-b examples/example-canonical-request.json --threads 2 +``` + +L'inférence est locale, sur CPU, sans PyTorch ni accès réseau après installation. Les entrées doivent être en **anglais**. Le modèle choisit parmi des options ou produit un score ; il ne génère pas de texte libre. Les probabilités ne sont pas calibrées. + +Le logiciel accepte 2 à 20 options, mais l'entraînement n'en couvre que 2 à 6. Le contexte est limité à 512 tokens WordPiece. Vérifier `truncated_state` pour les observations longues. Les exemples de tennis sont déjà connus : leur réussite éventuelle n'est pas un benchmark indépendant. + +## Taille et performances + +- Modèle CPU B : **87 155 068 octets**, tokenizer et manifeste inclus, soit 87,155 Mo décimaux / 83,118 Mio. +- Sur Intel Core i7-1260P, Linux x86-64, deux threads : **21,55 ms médians à 128 tokens**, modèle chaud, hors tokenisation et chargement ; p95 28,83 ms. +- Pic RSS du benchmark : **196 Mio**. La RAM et les dépendances dépassent donc la taille des fichiers. +- A et l'original remesurés dans la même session ont des latences proches. L'ancien chiffre de 10,79 ms provient d'une autre session, non comparable directement. +- Windows, ARM64 et les CPU anciens ne sont pas testés. « Fonctionner aisément sur n'importe quel CPU » n'est pas garanti. + +Les gros checkpoints FP32 de `checkpoints/` sont facultatifs et servent à l'inspection/comparaison/réexport. Le dépôt complet contient plusieurs modèles : sa taille totale dépasse volontairement 90 Mo, mais chaque variante CPU respecte la cible. + +## Recherche et limites de la publication + +Le dépôt conserve les gains **et** le résultat négatif, les statistiques par style, le protocole, les hashes, le code et les mesures CPU. Les résultats historiques du test à 3 000 exemples et de COPA restent attribués uniquement à l'original : ils n'ont pas été réévalués pour A/B. + +Les décisions enregistrées sont publiées sans les textes des corpus, avec identifiants de familles anonymisés conservant l'ordre utile au bootstrap. Elles permettent de recalculer les tableaux et intervalles. En revanche, le réentraînement complet et la nouvelle inférence DEV ne sont pas autonomes sans les données/cache non redistribués. + +Le professeur Laya et le backbone BERT déclarent Apache-2.0, mais les données ont des droits hétérogènes, parfois incomplets ou limités à la recherche. **Une publication publique n'équivaut pas à une autorisation commerciale ni à une clarification juridique de ces droits.** Lire les [réserves de licence](LICENSE_NOTES.md). Aucun corpus brut, contenu FINAL, secret ou solde de compte n'est téléversé. + +Le coût Vast.ai cumulé constaté est d'environ 0,36 USD, dont 0,125 USD pour A/B ; ce n'est pas une facture définitive. Les instances ont été détruites. L'objectif de taille est atteint ; la fiabilité générale recherchée ne l'est pas encore. diff --git a/REPRODUCIBILITY.md b/REPRODUCIBILITY.md new file mode 100644 index 0000000000000000000000000000000000000000..06b88b604dd8e37a40fdc570a3607620f55775fb --- /dev/null +++ b/REPRODUCIBILITY.md @@ -0,0 +1,76 @@ +# Reproducibility and artifact boundaries + +## What is included + +- Three ONNX CPU variants, with their original tokenizer and manifest. +- Three FP32 reference checkpoints and training metadata. These are optional for ordinary inference and are not 80–90 MB files. +- The custom runtime and CPU benchmark code. +- The frozen A/B protocol, checkpoint provenance, historical corpus manifest, aggregate reports and hashes. +- Recorded A/B DEV/fixed-validation decisions with dataset text and private identifiers removed. +- Selected research source snapshots. Training scripts are historical reference material, not a complete one-command reproduction bundle. + +## What is not included + +Raw corpus text, teacher-cache contents, full DEV input prompts, reserved FINAL text/descriptions, the final-set preparation script, GPU orchestration, credentials, account balances, instance IDs and private conversations. Only FINAL file hashes are published as commitments. Generic algorithms and public source datasets do not provide cryptographic secrecy of the holdout; the verifiable experimental status is **FINAL not evaluated**. + +This release can reproduce **statistics from recorded decisions** and run new CPU inference. It cannot independently recreate the whole training experiment or rerun its exact DEV prompts without the omitted data/cache. Public dataset revisions and the historical corpus manifest help describe the source material but do not establish reuse rights. + +## Verify downloaded files + +For a complete snapshot: + +```bash +sha256sum -c SHA256SUMS +``` + +If downloading only one model, filter the checksum list to those files instead of expecting the whole-repository check to pass. Model payloads include only `model.int8.onnx`, `tokenizer.json` and `manifest.json` in one variant directory. Source weights remain unchanged from the measured experiment. + +Checkpoints use the same custom architecture in `research/src/model.py`. They are not standalone `AutoModel.from_pretrained()` directories. A checkpoint must be paired with its architecture, tokenizer and question serialization. The matching tokenizer is under the corresponding `models/` directory. + +## Recompute recorded A/B statistics, without inference + +The local reference used **NumPy 2.5.3**. Install `requirements-stats.txt` in a suitable Python 3.12 environment. No PyTorch or GPU is needed for this operation. + +Run this from the repository root: + +```bash +python - <<'PY' +import json +import sys +from pathlib import Path + +sys.path.insert(0, str(Path("research/src").resolve())) +from ab_evaluate import fixed_summary, robustness_summary + +rows = [json.loads(line) for line in Path("benchmarks/ab/decisions-no-text.jsonl").open()] +dev = [row for row in rows if row["style"] != "fixed"] +fixed = [row for row in rows if row["style"] == "fixed"] +expected = json.loads(Path("benchmarks/ab/dev-report.json").read_text()) +robustness = robustness_summary(dev) +validation = fixed_summary(fixed) +assert robustness == expected["robustness"] +assert validation == expected["fixed_validation"] +assert not (robustness["passed"] and validation["passed"]) +print("Recorded statistics reproduced; the composite criterion fails as reported.") +PY +``` + +The 4,184 records comprise 2,184 DEV and 2,000 historical fixed-validation requests, each with the four A/B × FP32/INT8 predictions/probability vectors. Family identifiers are replaced with fixed-width ordinal IDs preserving the original sorted-family order. Thus both dependence groups and bootstrap index ordering are retained without exposing original identifiers. + +For DEV, `canonical_label` and probability indices are canonical domain classes. For historical fixed validation, they follow each request's original option order; they are suitable for per-request correctness and flip comparisons, **not** a pooled semantic-class confusion matrix without the omitted option definitions. Do not treat every row as independent or interpret these anonymized predictions as a redistributed text dataset. + +The expected original prediction-file SHA256 is `608003d576107260205f758b012ba9a42020c28d1d4d35b682930903dd3b4e7b`. The text-free derivative necessarily has a different hash, `a982420e45e2e16ece356619196cf49bfe36fe8adac2b208e4c9763b3d2b3f99`. The original predictions themselves are not published. + +## Inference and export reproducibility + +Use `requirements-runtime.txt` and the README commands for ONNX CPU inference. Dynamic quantization can produce different results on other CPUs or different batches. The published predictions are the reference observations, not a promise of bitwise equality on every environment. + +Remote export used PyTorch 2.8.0+cu126, Transformers 4.57.6, ONNX 1.20.1 and ONNX Runtime 1.24.4. Local A/B quality evaluation used ONNX Runtime 1.24.4 and NumPy 2.5.3. Dynamic per-channel INT8 used `reduce_range=True` and no separate calibration dataset. FP32 embeddings were retained. + +Re-exporting the same checkpoint can depend on library/platform versions; byte-identical ONNX output is not guaranteed. Synthetic-shape FP32 ONNX parity checks had maximum logit errors of about 2.68×10⁻⁶ for A and 3.76×10⁻⁶ for B; this verifies export mechanics, not task accuracy. + +## Research-source limitations + +`research/src/` contains copies of the actual model, loss, batching, cache-validation, training and evaluation logic. Some historical entry points refer to the original workspace layout or require omitted corpus/configuration files. They are supplied for inspection, not advertised as runnable full training recipes from this snapshot. + +No training, teacher annotation or FINAL inference should be triggered to reproduce the published statistical table. A new experiment requires its own explicit protocol, data access/rights review and compute budget. The old development sets cannot be rebranded as fresh tests. diff --git a/RESEARCH.md b/RESEARCH.md new file mode 100644 index 0000000000000000000000000000000000000000..334a8e4fb4e9c7fdf52f888b50737e665ebf123c --- /dev/null +++ b/RESEARCH.md @@ -0,0 +1,53 @@ +# Research notes: from a size target to an invariance experiment + +## Objective and scope + +The requested target was an English decision model with an **80–90 decimal MB inference payload**, usable on CPU. Reliable general decisions were the aspiration, not an achieved result. Directly shrinking the teacher by ordinary INT8 quantization was not the chosen solution: a smaller pretrained BERT student was trained with teacher distributions and ground-truth labels. + +All reported numbers are project measurements unless explicitly described as claims in a cited source. This document is an engineering research note, not a peer-reviewed paper. + +## Development chronology + +1. **Architecture feasibility.** A 38.2M-parameter student was exported to about 87 MB using INT8 linear matrices and FP32 embeddings. Prototype size checks were not quality evidence. +2. **Historical training.** A first 24,000-example run was followed by an 80,758-example run. The second was selected on validation; its epoch-6 checkpoint is `original`. The historical test improved, but COPA remained 49.25%, close to the 50% random reference. This did not meet the generality objective. +3. **Wording diagnostic.** A known tennis sentence exposed sensitivity to longer option descriptions and option ordering. Correct marker positions, absence of truncation and FP32 export parity were checked; the error also existed before INT8. One sentence and its rotations were not treated as an independent benchmark. +4. **Expanded development audit.** 60 AG News + 60 SNLI texts, four styles, two key families and cyclic rotations produced 3,360 requests per student precision. A bounded teacher audit completed 24 of these texts / 672 requests, not the entire 120-text pool. Teacher results were stronger in some conditions but also showed wording sensitivity. Aggregate evidence is in `benchmarks/history/pre-ab-robustness-audit.json`. +5. **Preregistered A/B continuation.** New DEV/FINAL families and disjoint description pools were reserved. A and B shared start weights, optimizer/loss, teacher targets, batches/padding and selection rule. B alone changed exactly half the AG/SNLI descriptions. The protocol and files were hashed before training. +6. **Result.** Localized reformulation accuracy improved substantially. One preregistered stability guard still failed in SNLI INT8. The experiment ended with B experimental, FINAL unevaluated, original preserved and no post-result tuning. + +## Why the contrast matters + +A also receives the two extra epochs. Therefore B versus A measures the chosen description-augmentation contrast more cleanly than B versus the earlier original checkpoint would. The controls do not eliminate single-seed stochastic training uncertainty. + +Both arms learn from the same Laya distributions on **fixed inputs**. This promotes invariance to semantically matched reformulations; it is not imitation of Laya's outputs on each reformulated prompt. Ground-truth cross-entropy remains present, so distillation is only part of the supervision. + +The cache contains 80,758 annotations. On 64 pilot comparisons, decoded probability argmax agreed and maximum normalized-probability discrepancy was approximately 9.884×10⁻⁵. Native `choice` tie-breaking after rounding was not separately verified. No teacher truncation occurred; its longest encoded input was 383 tokens. A warning about `choice` with 11+ options lies outside this experiment's 2–6-option data. + +Student training used BF16 and allowed TF32 for eligible FP32 matmuls; teacher annotations used strict FP32 CUDA. A seed is not a guarantee of deterministic kernels or bitwise reproducibility. + +## Main finding and unresolved issue + +Augmentation produces positive measured changes on close, held-out formulations within these two domains. It does **not** establish arbitrary-prompt robustness or unseen-task intelligence. The remaining problem is particularly visible in the interaction between SNLI descriptions, option rotations and INT8. + +Fewer FP32/INT8 prediction differences can coexist with a larger net accuracy drop. For B, 73 SNLI decisions change from correct to wrong and only 22 recover; A has 95 losses and 64 recoveries. The directional balance, not only the total disagreement rate, matters. + +The next research direction would investigate this quantization-related loss under a **new, explicit protocol**. DEV is now consumed; it cannot honestly become a fresh test for variants chosen using its errors. The reserved FINAL must not be turned into a tuning set. No such next training run is included here. + +## Literature and primary documentation consulted + +- [GLiClass: Generalist Lightweight Model for Sequence Classification Tasks](https://arxiv.org/html/2508.07662v1), Stepanov et al., 2025: text/label scoring and deliberate training variation motivated possible alternatives. No GLiClass weights were trained or benchmarked in this project. +- [GLiClass small v1.0 card/config](https://huggingface.co/knowledgator/gliclass-small-v1.0): “small” did not imply an 80–90 MB artifact. The consulted configuration's 128,003×768 word embedding table alone has 98,306,304 parameters. This is a size calculation, not an observed quantized-model benchmark. +- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://huggingface.co/papers/2110.08207), Sanh et al., 2021: the consulted abstract motivates prompt diversity and task-held-out evaluation. It does not predict performance for this much smaller encoder. Only metadata/abstract were reviewed for this source. +- [ONNX Runtime quantization](https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html): quantization can reduce accuracy; dynamic quantization is commonly used for transformers; per-channel and reduced-range configurations have hardware-dependent tradeoffs. +- [PyTorch 2.8 reproducibility notes](https://raw.githubusercontent.com/pytorch/pytorch/v2.8.0/docs/source/notes/randomness.rst): reproducibility across versions, platforms and CPU/GPU is not guaranteed, even with matching seeds. +- [Laya](https://huggingface.co/convaiinnovations/laya) and its [source repository](https://github.com/NandhaKishorM/laya): teacher interface, architecture and reported capabilities. The student is not assumed to inherit all teacher capabilities or calibration. + +More precise consulted sections and timestamps are in `research/references.json` and `research/runtime-references-checked.json`. Reading a source is not reproducing its experiments. + +## Review and research integrity + +The owner requested recurring discussion with Claude Code. Actual exchanges covered semantic equivalence, cache/label ordering, experiment controls, quantization and interpretation. Suggestions were checked against local code and saved predictions; the final review supported non-promotion. One timed-out request produced no review and is not counted as approval. + +Claude's conversational connection received excerpts and measurements, not direct workspace/shell access. It did not execute the tests. Two AI reviews are not independent human semantic validation or peer review. Private conversation IDs and transcripts are deliberately not published. + +The trained exports are real, not random probe weights or padded files. The local pipeline had 57 passing unit tests, but software correctness checks are not certification of decision quality. Checkpoint sizes, hashes, mechanical export checks, research conclusions and quality gates are kept separate. diff --git a/SHA256SUMS b/SHA256SUMS new file mode 100644 index 0000000000000000000000000000000000000000..a9ced2ecc71a484ea811246968fb7cfbc26cf33e --- /dev/null +++ b/SHA256SUMS @@ -0,0 +1,58 @@ +fe712bdfec1d7dffc809139de81571f5a3a87847b8907e9a866a92dfca8e3369 BENCHMARKS.md +cc992b2c269c6e24b027c57c2c8ce02121748c9f2e17da8bade9721e4f2016ee LICENSE_NOTES.md +b0cb8a5965b88db04ea8e6ae141e0d880a081a3391ccbf1bf0f933c889b5f3a5 PUBLICATION_CHECKS.json +e2ed8ff3e09f941b917497b145fe45fcb230fb76eeee8b895198b58329d4dbfe README.md +079f860db190333db8dba1954dfad88c60fbda1a05728f38003d94ab9fbe9609 README_FR.md +79852a1ae240ff20c00984c789349fb1fe3180063defee448f1de8cf33b62cf3 REPRODUCIBILITY.md +4425e580d8f75e439c72668c1839f22dad215b495154f7f01ba958efe8c1730b RESEARCH.md +d6eb989eb736559dcac886a47c7be217a11c88f48a62728baff88954f78fec05 benchmark_cpu.py 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+3e059a7b71af78ffd6cb3c2fb99f3ecd5f0a29d9e7c7e278c1328731aa3b44eb runtime.py +9db14f28d71221f7743a6e3602e5d6f07790cf11f4f215da181f050eecde0c10 schema.py diff --git a/benchmark_cpu.py b/benchmark_cpu.py new file mode 100644 index 0000000000000000000000000000000000000000..8650d549996a0582d9d6e2793fe18f3bbadba669 --- /dev/null +++ b/benchmark_cpu.py @@ -0,0 +1,71 @@ +import argparse +import gc +import json +import platform +import resource +import time +from pathlib import Path + +import numpy as np +import onnxruntime as ort + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("model_directory") + parser.add_argument("--threads", type=int, default=2) + parser.add_argument("--repeats", type=int, default=20) + parser.add_argument("--output", required=True) + arguments = parser.parse_args() + if arguments.threads < 1 or arguments.repeats < 5: + parser.error("Use positive threads and at least five repeats") + directory = Path(arguments.model_directory) + manifest = json.loads((directory / "manifest.json").read_text()) + settings = ort.SessionOptions() + settings.intra_op_num_threads = arguments.threads + settings.inter_op_num_threads = 1 + settings.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL + session = ort.InferenceSession(str(directory / "model.int8.onnx"), settings, providers=["CPUExecutionProvider"]) + generator = np.random.default_rng(42) + measurements = [] + for length in [64, 128, 256, 512]: + inputs = { + "input_ids": generator.integers(105, 10000, size=(1, length), dtype=np.int64), + "attention_mask": np.ones((1, length), dtype=np.int64), + "marker_pos": np.array([[4, 8, 12, 16]], dtype=np.int64), + "marker_mask": np.ones((1, 4), dtype=np.bool_), + "qtype": np.zeros(1, dtype=np.int64), + } + for _ in range(3): + session.run(None, inputs) + timings = [] + for _ in range(arguments.repeats): + started = time.perf_counter() + session.run(None, inputs) + timings.append((time.perf_counter() - started) * 1000) + measurements.append({ + "tokens": length, "batch_size": 1, "options": 4, + "p50_ms": float(np.median(timings)), + "p95_ms": float(np.percentile(timings, 95)), + }) + peak_rss = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss + report = { + "architecture": manifest["architecture"], + "trained": manifest["trained"], + "machine": platform.machine(), + "platform": platform.platform(), + "onnxruntime": ort.__version__, + "threads": arguments.threads, + "repeats": arguments.repeats, + "peak_process_RSS_MiB": peak_rss / (1024 * 1024 if platform.system() == "Darwin" else 1024), + "measurements": measurements, + "limitations": "Synthetic inputs; warm model; excludes tokenization, network and loading. Not a quality evaluation or a guarantee for another CPU.", + } + Path(arguments.output).write_text(json.dumps(report, indent=2) + "\n") + print(json.dumps(report, indent=2)) + del session + gc.collect() + + +if __name__ == "__main__": + main() diff --git a/benchmarks/ab/decisions-no-text.jsonl b/benchmarks/ab/decisions-no-text.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a3cef7df407607ee54f26102fe75e9c5172003a7 --- /dev/null +++ b/benchmarks/ab/decisions-no-text.jsonl @@ -0,0 +1,4184 @@ 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"models/experimental-b", + "trained": true, + "quality_validated": false, + "dev_passed": false, + "final_evaluated": false, + "historical_test_3000_evaluated_for_these_weights": false, + "copa_evaluated_for_these_weights": false, + "model_payload_bytes": 87155068, + "protocol_sha256": "143e0890279a6fe339b8a0cc6f394c61e9d806e9b9b1800272418716f3a5b366", + "failed_gate": { + "dataset": "snli", + "precision": "int8", + "metric": "fraction of text/style pairs with changing canonical prediction across cyclic rotations", + "A": 0.5801282051282052, + "B": 0.33653846153846156, + "required_B_max": 0.2900641025641026, + "A_unstable_cells": 181, + "B_unstable_cells": 105, + "total_cells": 312, + "maximum_allowed_integer_count": 90 + }, + "evidence": "benchmarks/ab/dev-report.json", + "limitations": [ + "Positive gains on close reformulations only; general decision reliability not established.", + "Single seed (42); bootstrap intervals do not measure training variance.", + "DEV consumed; no post-result tuning or FINAL evaluation.", + "Fewer FP32/INT8 flips do not prove lower accuracy loss from quantization.", + "CPU compatibility and speed beyond Linux x86-64 i7-1260P not validated." + ] +} diff --git a/licenses/Apache-2.0.txt b/licenses/Apache-2.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..d645695673349e3947e8e5ae42332d0ac3164cd7 --- /dev/null +++ b/licenses/Apache-2.0.txt @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/models/control-a/manifest.json b/models/control-a/manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..c9419f0036647d78e4bb62b88142d1fdc470deda --- /dev/null +++ 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30328, + "##坂": 30329, + "##城": 30330, + "##堂": 30331, + "##場": 30332, + "##士": 30333, + "##夏": 30334, + "##外": 30335, + "##大": 30336, + "##天": 30337, + "##太": 30338, + "##夫": 30339, + "##奈": 30340, + "##女": 30341, + "##子": 30342, + "##学": 30343, + "##宀": 30344, + "##宇": 30345, + "##安": 30346, + "##宗": 30347, + "##定": 30348, + "##宣": 30349, + "##宮": 30350, + "##家": 30351, + "##宿": 30352, + "##寺": 30353, + "##將": 30354, + "##小": 30355, + "##尚": 30356, + "##山": 30357, + "##岡": 30358, + "##島": 30359, + "##崎": 30360, + "##川": 30361, + "##州": 30362, + "##巿": 30363, + "##帝": 30364, + "##平": 30365, + "##年": 30366, + "##幸": 30367, + "##广": 30368, + "##弘": 30369, + "##張": 30370, + "##彳": 30371, + "##後": 30372, + "##御": 30373, + "##德": 30374, + "##心": 30375, + "##忄": 30376, + "##志": 30377, + "##忠": 30378, + "##愛": 30379, + "##成": 30380, + "##我": 30381, + "##戦": 30382, + "##戸": 30383, + "##手": 30384, + "##扌": 30385, + "##政": 30386, + "##文": 30387, + "##新": 30388, + "##方": 30389, + "##日": 30390, + "##明": 30391, + "##星": 30392, + "##春": 30393, + "##昭": 30394, + "##智": 30395, + "##曲": 30396, + "##書": 30397, + "##月": 30398, + "##有": 30399, + "##朝": 30400, + "##木": 30401, + "##本": 30402, + "##李": 30403, + "##村": 30404, + "##東": 30405, + "##松": 30406, + "##林": 30407, + "##森": 30408, + "##楊": 30409, + "##樹": 30410, + "##橋": 30411, + "##歌": 30412, + "##止": 30413, + "##正": 30414, + "##武": 30415, + "##比": 30416, + "##氏": 30417, + "##民": 30418, + "##水": 30419, + "##氵": 30420, + "##氷": 30421, + "##永": 30422, + "##江": 30423, + "##沢": 30424, + "##河": 30425, + "##治": 30426, + "##法": 30427, + "##海": 30428, + "##清": 30429, + "##漢": 30430, + "##瀬": 30431, + "##火": 30432, + "##版": 30433, + "##犬": 30434, + "##王": 30435, + "##生": 30436, + "##田": 30437, + "##男": 30438, + "##疒": 30439, + "##発": 30440, + "##白": 30441, + "##的": 30442, + "##皇": 30443, + "##目": 30444, + "##相": 30445, + "##省": 30446, + "##真": 30447, + "##石": 30448, + "##示": 30449, + "##社": 30450, + "##神": 30451, + "##福": 30452, + "##禾": 30453, + "##秀": 30454, + "##秋": 30455, + "##空": 30456, + "##立": 30457, + "##章": 30458, + "##竹": 30459, + "##糹": 30460, + "##美": 30461, + "##義": 30462, + "##耳": 30463, + "##良": 30464, + "##艹": 30465, + "##花": 30466, + "##英": 30467, + "##華": 30468, + "##葉": 30469, + "##藤": 30470, + "##行": 30471, + "##街": 30472, + "##西": 30473, + "##見": 30474, + "##訁": 30475, + "##語": 30476, + "##谷": 30477, + "##貝": 30478, + "##貴": 30479, + "##車": 30480, + "##軍": 30481, + "##辶": 30482, + "##道": 30483, + "##郎": 30484, + "##郡": 30485, + "##部": 30486, + "##都": 30487, + "##里": 30488, + "##野": 30489, + "##金": 30490, + "##鈴": 30491, + "##镇": 30492, + "##長": 30493, + "##門": 30494, + "##間": 30495, + "##阝": 30496, + "##阿": 30497, + "##陳": 30498, + "##陽": 30499, + "##雄": 30500, + "##青": 30501, + "##面": 30502, + "##風": 30503, + "##食": 30504, + "##香": 30505, + "##馬": 30506, + "##高": 30507, + "##龍": 30508, + "##龸": 30509, + "##fi": 30510, + "##fl": 30511, + "##！": 30512, + "##（": 30513, + "##）": 30514, + "##，": 30515, + "##－": 30516, + "##．": 30517, + "##／": 30518, + "##：": 30519, + "##？": 30520, + "##～": 30521 + } + } +} \ No newline at end of file diff --git a/requirements-runtime.txt b/requirements-runtime.txt new file mode 100644 index 0000000000000000000000000000000000000000..294aa543c2bd1104e539a982e9ec18ebffdeb3c3 --- /dev/null +++ b/requirements-runtime.txt @@ -0,0 +1,3 @@ +onnxruntime==1.24.4 +numpy>=1.26,<3 +tokenizers>=0.22,<0.23 diff --git a/requirements-stats.txt b/requirements-stats.txt new file mode 100644 index 0000000000000000000000000000000000000000..c853fa8fcdedf5d1bd8e4e79d335541549fabe57 --- /dev/null +++ b/requirements-stats.txt @@ -0,0 +1 @@ +numpy==2.5.3 diff --git a/research/corpus-manifest.json b/research/corpus-manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..dab50baec03d14422687d207bef94aa85ea739e6 --- /dev/null +++ b/research/corpus-manifest.json @@ -0,0 +1,88118 @@ +{ + "augmentation": { + "boolean_order": "Fixed schema order false,true; original boolean answer retained", + "choice_keys": [ + [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "I", + "J", + "K", + "L", + "M", + "N", + "O", + "P", + "Q", + "R", + "S", + "T" + ], + [ + "opt1", + "opt2", + "opt3", + "opt4", + "opt5", + "opt6", + "opt7", + "opt8", + "opt9", + "opt10", + "opt11", + "opt12", + "opt13", + "opt14", + "opt15", + "opt16", + "opt17", + "opt18", + "opt19", + "opt20" + ], + [ + "key1", + "key2", + "key3", + "key4", + "key5", + "key6", + "key7", + "key8", + "key9", + "key10", + "key11", + "key12", + "key13", + "key14", + "key15", + "key16", + "key17", + "key18", + "key19", + "key20" + ] + ], + "choice_order": "Independently shuffled semantic alternatives and sampled opaque keys; ground truth remapped", + "instruction_templates": { + "ag_news": { + "test": [ + "A reader wants this report sorted by subject. Where does it belong?", + "Use the report's central theme to select a news desk.", + "Match this item to the section that would normally cover it." + ], + "train": [ + "Choose the topic that best describes this news item.", + "Which news category fits the text?", + "Select the most relevant section for this news report.", + "Read the news item and identify its main subject area.", + "Assign the report to the best matching topic.", + "Pick the category that captures the focus of this news." + ], + "validation": [ + "Under which subject heading should this report be filed?", + "Identify the editorial section most appropriate for the item.", + "Determine which listed subject dominates the report." + ] + }, + "boolq": { + "test": [ + "Taking the passage as evidence, should the question be answered affirmatively?", + "Return true for a yes answer and false for a no answer to the supplied question.", + "Decide from the reading whether the appropriate response is yes." + ], + "train": [ + "Using the passage, decide whether the answer to the question is yes.", + "Answer the included question with yes or no based on the passage.", + "Read the passage and judge whether the question has an affirmative answer.", + "Does the passage support answering yes to the question?", + "Determine the yes-or-no answer to the question from the text.", + "Use the supplied evidence to answer the question as true for yes or false for no." + ], + "validation": [ + "Resolve the question from the passage: true means yes, false means no.", + "Consult the passage and indicate if an affirmative response is correct.", + "Is yes the correct response to the accompanying question?" + ] + }, + "copa_cause": { + "ood": [ + "Which alternative is the more plausible cause of the stated event?", + "Choose the event that most likely explains why the premise occurred.", + "Select the more likely reason for the situation described." + ] + }, + "copa_effect": { + "ood": [ + "Which alternative is the more plausible effect of the stated event?", + "Choose what would most likely happen as a result of the premise.", + "Select the more likely consequence of the situation described." + ] + }, + "emotion": { + "test": [ + "Infer the speaker's predominant feeling from this utterance.", + "What affective state is reflected most clearly in these words?", + "Interpret the message and pick the closest emotional description." + ], + "train": [ + "Choose the emotion expressed in this text.", + "Which feeling best matches the writer's words?", + "Identify the main emotion conveyed by the message.", + "Read the text and select its dominant feeling.", + "Assign the message to the most fitting emotion.", + "Select the emotional category that best describes the text." + ], + "validation": [ + "Which listed feeling is most evident in the writer's expression?", + "Determine the emotional tone communicated by the speaker.", + "Match the passage to the feeling it most strongly conveys." + ] + }, + "snli": { + "test": [ + "Evaluate the hypothesis against the stated premise using the listed relations.", + "Decide whether the premise makes the hypothesis necessary, impossible, or undecided.", + "What evidential relationship connects these two statements?" + ], + "train": [ + "Choose how the hypothesis relates to the premise.", + "Does the premise support, contradict, or leave the hypothesis uncertain?", + "Classify the logical relation between the premise and hypothesis.", + "Given the premise, select the status of the hypothesis.", + "Read both sentences and judge whether the hypothesis follows.", + "Determine the inference relation for the supplied sentence pair." + ], + "validation": [ + "Assuming the premise is true, what can be concluded about the hypothesis?", + "Assess whether the second sentence is implied, ruled out, or unresolved.", + "Use only the premise to categorize the claim made by the hypothesis." + ] + }, + "sst5": { + "test": [ + "Use the ordered levels to express how favorably the reviewer reacts.", + "Translate the review's attitude into one of the five sentiment ratings.", + "Choose the rating that best reflects the author's satisfaction." + ], + "train": [ + "Rate the sentiment of this review on the five ordered levels.", + "How negative or positive is the review? Choose a sentiment level.", + "Select the level that matches the reviewer's overall opinion.", + "Score the expressed sentiment from very negative to very positive.", + "Read the review and choose its sentiment strength.", + "Judge the tone of this review using the ordered sentiment scale." + ], + "validation": [ + "Place the reviewer's attitude on the supplied five-point scale.", + "Which degree of approval does this review convey?", + "Locate the opinion between strong disapproval and strong approval." + ] + } + }, + "questions_per_source_row": 1, + "render_seed": "SHA256(seed, render, source_group, output_split)", + "score_order": "Fixed ordered five-level sentiment scale; original integer labels 0..4 retained" + }, + "budgets": { + "max_prompt_wordpieces_including_markers": 192, + "max_sequence_wordpieces": 512, + "max_state_wordpieces": 320, + "text_truncation": "none; over-budget candidates excluded" + }, + "counts": { + "ood": { + "datasets": { + "copa": 400 + }, + "decisions": 400, + "ground_truth_distribution": { + "copa": { + "A canal was constructed.": 1, + "A cat got stuck in the tree.": 1, + "A debate erupted.": 1, + "A deer appeared on the road.": 1, + "A fly bit the horse.": 1, + "A knock sounded at the door.": 1, + "A screaming ambulance approached.": 1, + "A sound played.": 1, + "A tall person was sitting in front of me.": 1, + "Another car swerved into her lane.": 1, + "Environmentalists started a petition.": 1, + "Everyone in the room was talking.": 1, + "Factories increased their production.": 1, + "He admitted his indiscretion.": 1, + "He approached the building.": 1, + "He asked for directions.": 1, + "He asked his mother for help.": 1, + "He ate a big lunch.": 1, + "He ate a pepper.": 1, + "He bargained with the vendor.": 1, + "He became rich.": 1, + "He began attending school.": 1, + "He blushed.": 1, + "He checked its price tag.": 1, + "He chuckled.": 1, + "He cut back on making frivolous purchases.": 1, + "He discovered that the patient's arm was broken.": 1, + "He entered the house.": 1, + "He felt in awe.": 1, + "He finished reading it.": 1, + "He fought poverty.": 1, + "He had a headache the next day.": 1, + "He insulted her.": 1, + "He left a note.": 1, + "He left his assignment at home.": 1, + "He looked familiar.": 1, + "He made an appointment with the dentist.": 1, + "He noticed a stranded vehicle.": 1, + "He noticed some suspicious activity.": 1, + "He planned to ask the girl out.": 1, + "He promised his friend that he would go.": 1, + "He put fertilizer in the soil.": 1, + "He put out his back.": 1, + "He ran over a nail.": 1, + "He received a perfect score.": 1, + "He received the vaccine for the disease.": 1, + "He refused to talk about it.": 1, + "He reloaded the gun.": 1, + "He scheduled a meeting with an important client.": 1, + "He searched under his car seats for loose change.": 1, + "He shut it off.": 1, + "He talked back to the teacher.": 1, + "He told a racist joke.": 1, + "He trusted the therapist.": 1, + "He used a coupon.": 1, + "He wanted to maintain his privacy.": 1, + "He wanted to relax.": 1, + "He wanted to shut off the alarm clock.": 1, + "He was addicted to nicotine.": 1, + "He was building a sand castle.": 1, + "He was excommunicated from the church.": 1, + "He was fined for littering.": 1, + "He was removed from office.": 1, + "He was speeding.": 1, + "He was sweaty from working out.": 1, + "He was tired of carrying her.": 1, + "He watched a horror movie.": 1, + "He went away to camp.": 1, + "He went blind.": 1, + "He went camping in the woods.": 1, + "He went through a growth spurt.": 1, + "He went underwater.": 1, + "He wore a hat.": 1, + "He yelled whenever a player fumbled the ball.": 1, + "Her alibi checked out.": 1, + "Her brother unwrapped the gift.": 1, + "Her daughter burped at the kitchen table.": 1, + "Her father took away her dessert.": 1, + "Her friend cut off contact with her.": 1, + "Her friend fell over.": 1, + "Her friend forgot to bring his lunch.": 1, + "Her friend stuck an ice cube down her back.": 1, + "Her husband was snoring.": 1, + "Her muscles became fatigued.": 1, + "Her parents came to watch the recital.": 1, + "Her parents gave her a toy.": 1, + "Her parents punished her.": 1, + "Her teammate threw it to her.": 1, + "His brother tickled him.": 1, + "His brother took his toys from him.": 1, + "His family offered him financial support.": 1, + "His friend got sick.": 1, + "His guests were gracious.": 1, + "His hair turned gray.": 1, + "His meal was spicy.": 1, + "His mother passed away.": 1, + "His mother told him to speak up.": 1, + "His obituary appeared in the newspaper.": 1, + "His parents abused him.": 1, + "His pocket was filled with coins.": 1, + "His sister tattled on him.": 1, + "His stubble disappeared.": 1, + "I apologized to him.": 1, + "I asked the professor questions.": 1, + "I ate birthday cake.": 1, + "I bleached the shirt.": 1, + "I blew into it.": 1, + "I blew on the wick.": 1, + "I bought fruits and 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was the top of the hour.": 1, + "It was wrinkled.": 1, + "Its sales were lagging.": 1, + "Leaves fell from the trees.": 1, + "Many citizens took refuge in other territories.": 1, + "My balance wavered.": 1, + "My face got sunburned.": 1, + "My family held a funeral.": 1, + "My finger swelled.": 1, + "My friend awoke.": 1, + "My friend explained the rules to me.": 1, + "My handprint dried in the cement.": 1, + "My itch went away.": 1, + "My phone rang.": 1, + "My yawning ceased.": 1, + "People skated on the pond.": 1, + "Photographers followed her.": 1, + "Schools banned it from its libraries.": 1, + "She adopted a cat.": 1, + "She asked him for his autograph.": 1, + "She believed her superiors were acting unethically.": 1, + "She bought a yacht.": 1, + "She broke her leg.": 1, + "She burned her finger on the toaster.": 1, + "She called the wedding off.": 1, + "She cancelled her appointments.": 1, + "She committed assault.": 1, + "She completed the puzzle.": 1, + "She decided to sue her 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"https://huggingface.co/datasets/SetFit/sst5/resolve/e51bdcd8cd3a30da231967c1a249ba59361279a3/test.jsonl" + }, + "train": { + "rows": 8544, + "url": "https://huggingface.co/datasets/SetFit/sst5/resolve/e51bdcd8cd3a30da231967c1a249ba59361279a3/train.jsonl" + }, + "validation": { + "rows": 1101, + "url": "https://huggingface.co/datasets/SetFit/sst5/resolve/e51bdcd8cd3a30da231967c1a249ba59361279a3/dev.jsonl" + } + }, + "url": "https://huggingface.co/datasets/SetFit/sst5/tree/e51bdcd8cd3a30da231967c1a249ba59361279a3" + }, + { + "configuration": "default", + "dataset": "boolq", + "homepage": "https://github.com/google-research-datasets/boolean-questions", + "label_order": [ + "false", + "true" + ], + "license_evidence_url": "https://huggingface.co/datasets/google/boolq/resolve/35b264d03638db9f4ce671b711558bf7ff0f80d5/README.md", + "license_note": "CC BY-SA 3.0 is declared by the pinned Hub card; retain source attribution and license obligations.", + "license_tags": [ + "cc-by-sa-3.0" + ], + "loader": "datasets.load_dataset('parquet', streaming=True, token=False)", + "metadata_checked_on": "2026-09-25", + "repository": "google/boolq", + "revision": "35b264d03638db9f4ce671b711558bf7ff0f80d5", + "source_splits": { + "train": { + "rows": 9427, + "url": "https://huggingface.co/datasets/google/boolq/resolve/35b264d03638db9f4ce671b711558bf7ff0f80d5/data/train-00000-of-00001.parquet" + }, + "validation": { + "rows": 3270, + "url": "https://huggingface.co/datasets/google/boolq/resolve/35b264d03638db9f4ce671b711558bf7ff0f80d5/data/validation-00000-of-00001.parquet" + } + }, + "url": "https://huggingface.co/datasets/google/boolq/tree/35b264d03638db9f4ce671b711558bf7ff0f80d5" + }, + { + "configuration": "plain_text", + "dataset": "snli", + "homepage": "https://nlp.stanford.edu/projects/snli/", + "label_order": [ + "entailment", + "neutral", + "contradiction" + ], + "license_evidence_url": "https://huggingface.co/datasets/stanfordnlp/snli/resolve/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b/README.md", + "license_note": "CC BY-SA 4.0 is declared by the pinned Hub card; retain source attribution and license obligations.", + "license_tags": [ + "cc-by-sa-4.0" + ], + "loader": "datasets.load_dataset('parquet', streaming=True, token=False)", + "metadata_checked_on": "2026-09-25", + "repository": "stanfordnlp/snli", + "revision": "cdb5c3d5eed6ead6e5a341c8e56e669bb666725b", + "source_splits": { + "test": { + "rows": 10000, + "url": "https://huggingface.co/datasets/stanfordnlp/snli/resolve/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b/plain_text/test-00000-of-00001.parquet" + }, + "train": { + "rows": 550152, + "url": "https://huggingface.co/datasets/stanfordnlp/snli/resolve/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b/plain_text/train-00000-of-00001.parquet" + }, + "validation": { + "rows": 10000, + "url": 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"https://huggingface.co/datasets/dair-ai/emotion/resolve/cab853a1dbdf4c42c2b3ef2173804746df8825fe/split/test-00000-of-00001.parquet" + }, + "train": { + "rows": 16000, + "url": "https://huggingface.co/datasets/dair-ai/emotion/resolve/cab853a1dbdf4c42c2b3ef2173804746df8825fe/split/train-00000-of-00001.parquet" + }, + "validation": { + "rows": 2000, + "url": "https://huggingface.co/datasets/dair-ai/emotion/resolve/cab853a1dbdf4c42c2b3ef2173804746df8825fe/split/validation-00000-of-00001.parquet" + } + }, + "url": "https://huggingface.co/datasets/dair-ai/emotion/tree/cab853a1dbdf4c42c2b3ef2173804746df8825fe" + }, + { + "configuration": "copa", + "dataset": "copa", + "homepage": "https://people.ict.usc.edu/~gordon/copa.html", + "label_order": [ + "choice1", + "choice2" + ], + "license_evidence_url": "https://huggingface.co/datasets/aps/super_glue/resolve/3de24cf8022e94f4ee4b9d55a6f539891524d646/README.md", + "license_note": "SuperGLUE declares other and refers to original task licenses; COPA-specific reuse rights are not verified here.", + "license_tags": [ + "other" + ], + "loader": "datasets.load_dataset('parquet', streaming=True, token=False)", + "metadata_checked_on": "2026-09-25", + "repository": "aps/super_glue", + "revision": "3de24cf8022e94f4ee4b9d55a6f539891524d646", + "source_splits": { + "test": { + "rows": 500, + "url": "https://huggingface.co/datasets/aps/super_glue/resolve/3de24cf8022e94f4ee4b9d55a6f539891524d646/copa/test-00000-of-00001.parquet" + }, + "train": { + "rows": 400, + "url": "https://huggingface.co/datasets/aps/super_glue/resolve/3de24cf8022e94f4ee4b9d55a6f539891524d646/copa/train-00000-of-00001.parquet" + }, + "validation": { + "rows": 100, + "url": "https://huggingface.co/datasets/aps/super_glue/resolve/3de24cf8022e94f4ee4b9d55a6f539891524d646/copa/validation-00000-of-00001.parquet" + } + }, + "url": "https://huggingface.co/datasets/aps/super_glue/tree/3de24cf8022e94f4ee4b9d55a6f539891524d646" + } + ], + "tokenizer": { + "effective_tokenizer_sha256": "c7c7aa366ff8b0b85eae99a2f8cc7ee87e91f4db0e4c316c9104d088099de14e", + "filename": "seed-tokenizer.json", + "kind": "local_tokenizer_json", + "padding_and_truncation": "disabled for exact budget validation", + "sha256": "19a57dcc34c9491ae5049f69d49db9081ecf2556f728b31f24aafadfdc2025de" + } +} diff --git a/research/exports/control-a.json b/research/exports/control-a.json new file mode 100644 index 0000000000000000000000000000000000000000..d7fe12ba6bf9698577e2089f57016fbdf488bd8d --- /dev/null +++ b/research/exports/control-a.json @@ -0,0 +1,46 @@ +{ + "status": "trained_export_requires_quality_evaluation", + "parameters": 38223873, + "fp32_onnx_bytes": 153022850, + "int8_onnx_bytes": 86443598, + "tokenizer_bytes": 710348, + "payload_bytes": 87155068, + "payload_MB_decimal": 87.155068, + "payload_MiB_binary": 83.11754989624023, + "fits_80_to_90_MB": true, + "checks": [ + { + "batch": 1, + "sequence": 24, + "options": 2, + "fp32_max_logit_error": 1.3709068298339844e-06, + "int8_max_probability_difference": 0.03501194715499878 + }, + { + "batch": 3, + "sequence": 96, + "options": 5, + "fp32_max_logit_error": 2.5033950805664062e-06, + "int8_max_probability_difference": 0.030787497758865356 + }, + { + "batch": 1, + "sequence": 256, + "options": 10, + "fp32_max_logit_error": 2.682209014892578e-06, + "int8_max_probability_difference": 0.014649853110313416 + }, + { + "batch": 1, + "sequence": 512, + "options": 20, + "fp32_max_logit_error": 1.862645149230957e-06, + "int8_max_probability_difference": 0.04665376618504524 + } + ], + "limitations": [ + "Checks validate export mechanics, not English decision quality.", + "Payload excludes the runtime, Python, temporary training files and RAM.", + "CPU portability beyond the tested machine requires separate validation." + ] +} diff --git a/research/exports/experimental-b.json b/research/exports/experimental-b.json new file mode 100644 index 0000000000000000000000000000000000000000..dc6bb73734c1d1f444078a0d454832fb5a5f0f30 --- /dev/null +++ b/research/exports/experimental-b.json @@ -0,0 +1,46 @@ +{ + "status": "trained_export_requires_quality_evaluation", + "parameters": 38223873, + "fp32_onnx_bytes": 153022850, + "int8_onnx_bytes": 86443598, + "tokenizer_bytes": 710348, + "payload_bytes": 87155068, + "payload_MB_decimal": 87.155068, + "payload_MiB_binary": 83.11754989624023, + "fits_80_to_90_MB": true, + "checks": [ + { + "batch": 1, + "sequence": 24, + "options": 2, + "fp32_max_logit_error": 4.76837158203125e-07, + "int8_max_probability_difference": 0.006500035524368286 + }, + { + "batch": 3, + "sequence": 96, + "options": 5, + "fp32_max_logit_error": 3.159046173095703e-06, + "int8_max_probability_difference": 0.029584228992462158 + }, + { + "batch": 1, + "sequence": 256, + "options": 10, + "fp32_max_logit_error": 3.7550926208496094e-06, + "int8_max_probability_difference": 0.010657772421836853 + }, + { + "batch": 1, + "sequence": 512, + "options": 20, + "fp32_max_logit_error": 1.9669532775878906e-06, + "int8_max_probability_difference": 0.040227651596069336 + } + ], + "limitations": [ + "Checks validate export mechanics, not English decision quality.", + "Payload excludes the runtime, Python, temporary training files and RAM.", + "CPU portability beyond the tested machine requires separate validation." + ] +} diff --git a/research/original-selection.json b/research/original-selection.json new file mode 100644 index 0000000000000000000000000000000000000000..b3fde4fae9308ce9e2c7c856513db7714a0b94c3 --- /dev/null +++ b/research/original-selection.json @@ -0,0 +1,29 @@ +{ + "selected": "refined", + "criterion": "validation ground-truth accuracy; teacher KL as tiebreaker", + "same_holdout_input_sha256_verified": true, + "holdout_input_sha256": { + "validation": "3e2840cd09a2372e812dbc8f37904abf31dff4c2a74c09f5aec00b9c3bc2e1ce", + "test": "47cc633fd7e1ae38a52c271d2ca36ded1d9002e9a7f84a62b1dc7b3c1adf3cb9", + "ood": "cdad1edf8d687786bfaba10c0d3dfebb8216f94dba26afcb92d4a0699d8eac15" + }, + "candidates": { + "baseline": { + "training_rows": 24000, + "best_epoch": 4, + "validation_accuracy": 0.627, + "teacher_KL": 0.43756801056861877 + }, + "refined": { + "training_rows": 80758, + "best_epoch": 6, + "validation_accuracy": 0.7595, + "teacher_KL": 0.46601298499107363 + } + }, + "development_limitations": [ + "First-pass tests were inspected before increasing the training data; this is not a fully blind end-to-end benchmark.", + "Checkpoint and run selection use validation, never test or OOD accuracy.", + "All COPA data are excluded from fitting and checkpoint selection." + ] +} diff --git a/research/references.json b/research/references.json new file mode 100644 index 0000000000000000000000000000000000000000..849c476108f369e0f64b675066606f701a5d18be --- /dev/null +++ b/research/references.json @@ -0,0 +1,87 @@ +{ + "consulted_on": "2026-09-26", + "scope": "Primary-source reading for a design review, not a reproduction of published model benchmarks.", + "sources": [ + { + "title": "GLiClass: Generalist Lightweight Model for Sequence Classification Tasks", + "authors": "Ihor Stepanov et al.", + "year": 2025, + "urls": [ + "https://arxiv.org/html/2508.07662v1", + "https://huggingface.co/api/papers/2508.07662", + "https://github.com/Knowledgator/GLiClass" + ], + "verified_sections": [ + "Abstract", + "Architecture: scoring text and label representations", + "Training data: logic/NLI and text-length/label-density streams", + "Training framework" + ], + "findings": [ + "The paper describes pooled text and label representations scored using a dot product or a learned scorer.", + "The training description includes logic/NLI examples and deliberate variation of text length and label density.", + "These are possible design directions, not evidence that changing our 38M-parameter model will improve its generalization.", + "Dataset grouping before augmentation remains necessary in our own protocol; do not copy a random split after duplication." + ] + }, + { + "title": "GLiClass small v1.0 model card and configuration", + "urls": [ + "https://huggingface.co/knowledgator/gliclass-small-v1.0", + "https://huggingface.co/knowledgator/gliclass-small-v1.0/raw/main/config.json" + ], + "verified_facts": { + "card_parameter_count": 144000000, + "encoder_type": "deberta-v2", + "hidden_size": 768, + "layers": 6, + "vocabulary": 128003, + "word_embedding_parameters": 98306304 + }, + "findings": [ + "The card reports Apache-2.0 and synthetic training data; this does not resolve the licenses of our current training datasets.", + "The adjective small does not imply an 80–90 MB INT8 artifact: this checkpoint's word embeddings alone require about 98.3 MB at one byte per parameter, before scales and the rest of the network.", + "No GLiClass weights were downloaded, trained, or benchmarked during this review." + ] + }, + { + "title": "Multitask Prompted Training Enables Zero-Shot Task Generalization", + "authors": "Victor Sanh et al.", + "year": 2021, + "urls": [ + "https://huggingface.co/papers/2110.08207", + "https://huggingface.co/api/papers/2110.08207" + ], + "verified_sections": [ + "Title, publication metadata and abstract" + ], + "findings": [ + "The abstract describes explicit multitask learning with diverse prompt formulations and evaluation on fully held-out tasks.", + "This motivates a task-held-out evaluation protocol; it does not predict a score for our compact encoder, whose architecture and scale differ.", + "Only the abstract was inspected for this source, not a full reproduction of its experiments." + ] + }, + { + "title": "ONNX Runtime — Quantize ONNX models", + "urls": [ + "https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html" + ], + "verified_sections": [ + "Quantization debugging", + "Dynamic versus static quantization", + "Data type selection", + "Reduce-range and per-channel" + ], + "findings": [ + "Quantization can harm accuracy; comparing weights and activations can help localize errors and identify tensors to keep unquantized.", + "The documentation recommends dynamic quantization in general for transformers.", + "The current model already uses per-channel quantization and reduce_range=True.", + "Small aggregate accuracy changes do not establish local parity, probability calibration, or portability across every CPU." + ] + } + ], + "failed_research_routes": [ + "Both Hugging Face paper_search connector calls returned 'Tool paper_search not found'. Research continued through primary-source HTTP requests.", + "An initial HTML parsing attempt required an unavailable bs4 module; the successful read used Python standard-library HTML cleanup without installing dependencies." + ] +} diff --git a/research/runtime-references-checked.json b/research/runtime-references-checked.json new file mode 100644 index 0000000000000000000000000000000000000000..31d5d6b2fc80eda50e109bdf7be6789085c2d92e --- /dev/null +++ b/research/runtime-references-checked.json @@ -0,0 +1,18 @@ +[ + { + "url": "https://raw.githubusercontent.com/pytorch/pytorch/v2.8.0/docs/source/notes/randomness.rst", + "checked_at_utc": "2026-09-26T11:33:44.146287+00:00", + "snippets": [ + "_reproducibility: Reproducibility =============== Completely reproducible results are not guaranteed across PyTorch releases, individual commits, or different platforms. Furthermore, results may not be reproducible between CPU and GPU executions, even when using identical seeds. However, there are some steps you can take to limit the number of sources of nondeterministic behavior for a specific platform, device, and PyTorch release. First, you can control sources of randomness", + "e between CPU and GPU executions, even when using identical seeds. However, there are some steps you can take to limit the number of sources of nondeterministic behavior for a specific platform, device, and PyTorch release. First, you can control sources of randomness that can cause multiple executions of your application to behave differently. Second, you can configure PyTorch to avoid using nondeterministic algorithms for some operations, so that multiple cal" + ] + }, + { + "url": "https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html", + "checked_at_utc": "2026-09-26T11:33:45.102559+00:00", + "snippets": [ + "efer to the Method selection section. When to use reduce-range and per-channel quantization? Reduce-range will quantize the weights to 7 bits. It is designed for the U8S8 format on AVX2 and AVX512 (non-VNNI) machines to mitigate saturation issues . This is not needed on machines supporting VNNI. Per-channel quantization can improve the accuracy for models whose weight ranges are large. Try it if the accuracy loss is large. On AVX2 and AVX512 machines, you wi", + " 0 uniquely after quantization, it will result in accuracy errors. ONNX quantization representation format There are two ways to represent quantized ONNX models: Operator-oriented (QOperator) : All the quantized operators have their own ONNX definitions, like QLinearConv, MatMulInteger and etc. Tensor-oriented (QDQ; Quantize and DeQuantize) : This format inserts DeQuantizeLinear(QuantizeLinear(tensor)) between the original operators to simulate the quant" + ] + } +] diff --git a/research/src/ab_evaluate.py b/research/src/ab_evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..95cf74513e888889c28c6376c7ef6cece51d49ce --- /dev/null +++ b/research/src/ab_evaluate.py @@ -0,0 +1,211 @@ +import argparse +import json +import time +from collections import defaultdict +from pathlib import Path + +import numpy as np + +from ab_experiment import checked_feature, family_key, file_hash, write_json, write_rows +from schema import collate, options_for, read_rows + + +def clustered_change(rows, value): + grouped = defaultdict(list) + for row in rows: + grouped[row["family_group"]].append(float(value(row))) + if not grouped: + raise ValueError("Empty paired comparison") + groups = [grouped[key] for key in sorted(grouped)] + sums = np.array([sum(group) for group in groups]) + counts = np.array([len(group) for group in groups]) + generator = np.random.default_rng(20260926) + draws = [] + for offset in range(0, 10000, 500): + indices = generator.integers(0, len(groups), size=(500, len(groups))) + draws.extend((sums[indices].sum(axis=1) / counts[indices].sum(axis=1)).tolist()) + return {"delta": float(sums.sum() / counts.sum()), "ci95": np.quantile(draws, [0.025, 0.975]).tolist(), "clusters": len(groups)} + + +def accuracy(rows, name): + return float(np.mean([row["prediction"][name] == row["canonical_label"] for row in rows])) + + +def confusion(rows, name): + labels = max(max(row["canonical_label"], row["prediction"][name]) for row in rows) + 1 + matrix = [[0] * labels for _ in range(labels)] + for row in rows: + matrix[row["canonical_label"]][row["prediction"][name]] += 1 + return matrix + + +def instability(rows, name): + grouped = defaultdict(set) + for row in rows: + grouped[(row["family_group"], row["style"])].add(row["prediction"][name]) + return float(np.mean([len(predictions) > 1 for predictions in grouped.values()])) + + +def flips(row, arm): + return int(row["prediction"][arm + "_fp32"] != row["prediction"][arm + "_int8"]) + + +def robustness_summary(rows): + summary = {"domains": {}, "passed": True} + for domain in ["ag_news", "snli"]: + subset = [row for row in rows if row["dataset"] == domain] + result = {} + for precision in ["fp32", "int8"]: + left, right = "A_" + precision, "B_" + precision + gain = clustered_change(subset, lambda row: int(row["prediction"][right] == row["canonical_label"]) - int(row["prediction"][left] == row["canonical_label"])) + order_a, order_b = instability(subset, left), instability(subset, right) + passed = gain["delta"] >= 0.10 and gain["ci95"][0] > 0 and order_b <= order_a / 2 + result[precision] = {"A_accuracy": accuracy(subset, left), "B_accuracy": accuracy(subset, right), + "gain": gain, "A_order_instability": order_a, "B_order_instability": order_b, "passed": passed} + summary["passed"] &= passed + change = clustered_change(subset, lambda row: flips(row, "B") - flips(row, "A")) + result["quantization"] = {"A_flip_rate": float(np.mean([flips(row, "A") for row in subset])), + "B_flip_rate": float(np.mean([flips(row, "B") for row in subset])), + "change": change, "passed": change["ci95"][1] <= 0.02} + summary["passed"] &= result["quantization"]["passed"] + result["styles"] = {} + for style in sorted({row["style"] for row in subset}): + cell = [row for row in subset if row["style"] == style] + result["styles"][str(style)] = { + name: {"accuracy": accuracy(cell, name), "order_instability": instability(cell, name), "confusion": confusion(cell, name)} + for name in ["A_fp32", "A_int8", "B_fp32", "B_int8"] + } + summary["domains"][domain] = result + return summary + + +def fixed_summary(rows): + summary = {"passed": True} + for precision in ["fp32", "int8"]: + left, right = "A_" + precision, "B_" + precision + metric = lambda row: int(row["prediction"][right] == row["canonical_label"]) - int(row["prediction"][left] == row["canonical_label"]) + gain = clustered_change(rows, metric) + tasks = {} + for domain in sorted({row["dataset"] for row in rows}): + subset = [row for row in rows if row["dataset"] == domain] + tasks[domain] = {"A_accuracy": accuracy(subset, left), "B_accuracy": accuracy(subset, right), "delta": float(np.mean([metric(row) for row in subset]))} + passed = gain["ci95"][0] >= -0.02 and all(task["delta"] >= -0.04 for task in tasks.values()) + summary[precision] = {"A_accuracy": accuracy(rows, left), "B_accuracy": accuracy(rows, right), "gain": gain, "tasks": tasks, "passed": passed} + summary["passed"] &= passed + change = clustered_change(rows, lambda row: flips(row, "B") - flips(row, "A")) + summary["quantization"] = {"change": change, "passed": change["ci95"][1] <= 0.02} + summary["passed"] &= summary["quantization"]["passed"] + return summary + + +def fixed_requests(rows): + output = [] + for row in rows: + question = row["questions"]["decision"] + labels, _ = options_for(question) + expected = row["expected"]["decision"] + canonical = int(expected) if question["type"] == "noul" else labels.index(str(expected)) + output.append({ + "request_id": row["source_group"] + "|fixed", "source_group": row["source_group"], + "family_group": row["dataset"] + ":" + family_key(row), "dataset": row["dataset"], + "state": row["state"], "question": question, "canonical_label": canonical, + "semantic_order": list(range(len(labels))), "style": "fixed", "rotation": 0, + }) + return output + + +def infer(requests, result_directory): + import onnxruntime as ort + import torch + from safetensors.torch import load_file + from tokenizers import Tokenizer + from model import MiniDecisionModel + + torch.set_num_threads(4) + tokenizer = Tokenizer.from_file(str(result_directory / "A/tokenizer.json")) + features = [checked_feature(row, tokenizer) for row in requests] + ordered = sorted(range(len(requests)), key=lambda index: len(features[index]["input_ids"])) + records = [{**row, "prediction": {}, "probabilities": {}} for row in requests] + timings = {} + for arm in ["A", "B"]: + if file_hash(result_directory / arm / "tokenizer.json") != file_hash(result_directory / "A/tokenizer.json"): + raise ValueError("A and B tokenizers differ") + model = MiniDecisionModel() + model.load_state_dict(load_file(result_directory / arm / "model.safetensors")) + model.eval() + settings = ort.SessionOptions() + settings.intra_op_num_threads = 4 + settings.inter_op_num_threads = 1 + session = ort.InferenceSession(str(result_directory / arm / "model/model.int8.onnx"), settings, providers=["CPUExecutionProvider"]) + started = time.perf_counter() + with torch.inference_mode(): + for offset in range(0, len(ordered), 8): + indices = ordered[offset:offset + 8] + arrays = collate([features[index] for index in indices]) + floating = model(**{name: torch.from_numpy(value) for name, value in arrays.items()}).numpy() + quantized = session.run(None, arrays)[0] + for name, logits in [(arm + "_fp32", floating), (arm + "_int8", quantized)]: + if not np.isfinite(logits).all(): + raise ValueError("Nonfinite logits") + for position, index in enumerate(indices): + order = requests[index]["semantic_order"] + values = torch.tensor(logits[position, :len(order)]).softmax(-1).numpy() + canonical = np.empty(len(order)) + canonical[order] = values + records[index]["probabilities"][name] = canonical.tolist() + records[index]["prediction"][name] = int(canonical.argmax()) + if offset % 800 == 0: + print(json.dumps({"evaluation_arm": arm, "requests": min(offset + 8, len(ordered)), "seconds": time.perf_counter() - started}), flush=True) + timings[arm] = time.perf_counter() - started + del model, session + return records, timings + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--data", type=Path, required=True) + parser.add_argument("--results", type=Path, required=True) + parser.add_argument("--split", choices=["dev", "final"], default="dev") + arguments = parser.parse_args() + directory = arguments.data + protocol = json.loads((directory / "protocol.json").read_text()) + from ab_train import PROTOCOL_SHA256 + if file_hash(directory / "protocol.json") != PROTOCOL_SHA256: + raise ValueError("Frozen evaluation protocol changed") + if file_hash(directory / "validation.jsonl") != protocol["files"]["validation.jsonl"]: + raise ValueError("Fixed validation changed") + provenance = [json.loads((arguments.results / arm / "training.json").read_text()) for arm in ["A", "B"]] + for key in ["start_sha256", "protocol_sha256", "shared_cache_sha256", "shared_schedule_sha256", "seed", "steps", "selection", "bf16"]: + if provenance[0][key] != provenance[1][key]: + raise ValueError(f"A/B training provenance differs: {key}") + if provenance[0]["protocol_sha256"] != PROTOCOL_SHA256 or provenance[0]["steps"] != protocol["steps_per_arm"]: + raise ValueError("Training did not follow this protocol") + if arguments.split == "final" and not json.loads((directory / "dev-report.json").read_text())["passed"]: + raise ValueError("FINAL remains sealed because DEV did not pass") + destination = directory / f"{arguments.split}-predictions.jsonl" + if destination.exists() or (directory / f"{arguments.split}-report.json").exists(): + raise FileExistsError("Evaluation already exists") + request_file = directory / f"{arguments.split}-requests.jsonl" + if file_hash(request_file) != protocol["files"][request_file.name]: + raise ValueError("Evaluation input changed") + requests = read_rows(request_file) + fixed = fixed_requests(read_rows(directory / "validation.jsonl")) if arguments.split == "dev" else [] + predictions, timings = infer(requests + fixed, arguments.results) + write_rows(destination, predictions) + robust = robustness_summary(predictions[:len(requests)]) + regression = fixed_summary(predictions[len(requests):]) if fixed else None + report = { + "protocol_sha256": file_hash(directory / "protocol.json"), "split": arguments.split, + "texts_per_domain": 156, "variant_requests": len(requests), "fixed_requests": len(fixed), + "passed": robust["passed"] and (regression is None or regression["passed"]), + "robustness": robust, "fixed_validation": regression, "cpu_evaluation_seconds": timings, + "prediction_sha256": file_hash(destination), "quality_validated_for_general_decisions": False, + "instability_definition": "Fraction of text/style pairs whose canonical prediction changes across cyclic rotations; average over styles", + "uncertainty": "Paired family-cluster percentile bootstrap, 10000 replicates, seed20260926; not uncertainty over training seeds", + } + write_json(directory / f"{arguments.split}-report.json", report) + print(json.dumps(report, indent=2), flush=True) + + +if __name__ == "__main__": + main() diff --git a/research/src/ab_experiment.py b/research/src/ab_experiment.py new file mode 100644 index 0000000000000000000000000000000000000000..55065531d07a4306098e4bdf29704b2cc8edcdcc --- /dev/null +++ b/research/src/ab_experiment.py @@ -0,0 +1,212 @@ +import copy +import hashlib +import json +import math +import random +import sys +from pathlib import Path + +RELEASE = Path(__file__).resolve().parents[2] / "outputs/laya-mini-cpu" +if RELEASE.is_dir(): + sys.path.insert(0, str(RELEASE)) + +import build_corpus +from schema import encode_question, normalized_probabilities, options_for + +SEED = 42 +TEACHER_REVISION = "55cf4c4ebb4ebe31b2550e8bdf3bd21b99753851" +START_SHA256 = "5f3cfb2292b5ffb24a7e44e0c7dbad6d80e483bce439b02d431124b8572b4008" +DOMAINS = ("ag_news", "snli") + + +def file_hash(path): + with Path(path).open("rb") as handle: + return hashlib.file_digest(handle, "sha256").hexdigest() + + +def write_json(path, value): + with Path(path).open("x", encoding="utf-8") as handle: + json.dump(value, handle, ensure_ascii=False, indent=2) + handle.write("\n") + + +def write_rows(path, rows): + with Path(path).open("x", encoding="utf-8") as handle: + for row in rows: + handle.write(json.dumps(row, ensure_ascii=False, separators=(",", ":")) + "\n") + + +def validate_pools(pools): + if set(pools) != {"train", "dev", "final"}: + raise ValueError("Expected TRAIN/DEV/FINAL pools") + seen = set() + for split, domains in pools.items(): + if set(domains) != set(DOMAINS): + raise ValueError("Missing domain") + for dataset, variants in domains.items(): + if len(variants) < 2: + raise ValueError("At least two variants required per pool") + for descriptions in variants: + if len(descriptions) != len(build_corpus.CHOICES[dataset]): + raise ValueError("Incorrect class count") + for description in descriptions: + normalized = build_corpus.normalize_text(description).casefold() + if normalized in seen: + raise ValueError("Description pools must be disjoint") + seen.add(normalized) + + +def class_order(row, descriptions=None): + descriptions = descriptions or build_corpus.CHOICES[row["dataset"]] + question = row.get("question") or row["questions"]["decision"] + order = [list(descriptions).index(value) for value in question["criteria"].values()] + if sorted(order) != list(range(len(descriptions))): + raise ValueError("Criteria must be a class permutation") + return order + + +def replace_descriptions(row, descriptions): + result = copy.deepcopy(row) + criteria = result["questions"]["decision"]["criteria"] + order = class_order(row) + if len(descriptions) != len(order): + raise ValueError("Description count mismatch") + result["questions"]["decision"]["criteria"] = { + key: descriptions[semantic] for key, semantic in zip(criteria, order) + } + return result + + +def augmentation_groups(rows, seed): + selected = set() + for dataset in DOMAINS: + groups = [row["source_group"] for row in rows if row["dataset"] == dataset] + if len(groups) != len(set(groups)) or len(groups) % 2: + raise ValueError("Need unique even-sized domain pools for exactly 50 percent") + groups.sort(key=lambda group: build_corpus.digest(seed, "augment", group)) + selected.update(groups[:len(groups) // 2]) + return selected + + +def remap_probabilities(values, source_order, target_order): + count = len(source_order) + if sorted(source_order) != list(range(count)) or sorted(target_order) != list(range(count)): + raise ValueError("Class orders must be permutations") + probabilities = normalized_probabilities(values, count) + canonical = dict(zip(source_order, probabilities.tolist())) + return [canonical[index] for index in target_order] + + +def validate_cache(fixed_rows, annotated_rows): + expected = {row["source_group"]: row for row in fixed_rows} + observed = {row["source_group"]: row for row in annotated_rows} + if len(expected) != len(fixed_rows) or len(observed) != len(annotated_rows) or expected.keys() != observed.keys(): + raise ValueError("Teacher cache groups differ from fixed corpus") + for group, fixed in expected.items(): + cached = observed[group] + question = fixed["questions"]["decision"] + if cached["state"] != fixed["state"] or cached["question"] != question: + raise ValueError("Teacher cache does not match the fixed input") + if "label" in cached and cached["label"] != fixed["expected"]["decision"]: + raise ValueError("Teacher cache label differs") + labels, _ = options_for(question) + if options_for(cached["question"])[0] != labels: + raise ValueError("Teacher cache probability order differs from the fixed input") + normalized_probabilities(cached["probabilities"], len(labels)) + return observed + + +def family_key(row): + text = row["state"] + if row["dataset"] == "snli": + if not text.startswith("Premise: ") or "\nHypothesis: " not in text: + raise ValueError("Invalid SNLI state") + text = text[len("Premise: "):].split("\nHypothesis: ", 1)[0] + return build_corpus.state_key(text) + + +def checked_feature(row, tokenizer): + question = row.get("question") or row["questions"]["decision"] + feature = encode_question(tokenizer, row["state"], question) + if feature["truncated_state"]: + raise ValueError("A/B inputs must not be truncated") + return feature + + +def gold_class(row): + question = row["questions"]["decision"] + labels, _ = options_for(question) + return class_order(row)[labels.index(str(row["expected"]["decision"]))] + + +def select_holdout(candidates, count, forbidden_families, split): + if not candidates: + raise ValueError("No candidates") + classes = len(build_corpus.CHOICES[candidates[0]["dataset"]]) + if count < 1 or count % classes: + raise ValueError("Balanced selection requires a multiple of class count") + used = set(forbidden_families) + ordered = sorted(candidates, key=lambda row: build_corpus.digest(SEED, split, row["source_group"])) + selected = [] + for label in range(classes): + chosen = [] + for row in ordered: + family = family_key(row) + if gold_class(row) == label and family not in used: + chosen.append(row) + used.add(family) + if len(chosen) == count // classes: + break + if len(chosen) != count // classes: + raise ValueError("Insufficient disjoint families") + selected.extend(chosen) + return selected + + +def evaluation_requests(records, pool): + requests = [] + for row in records: + order = class_order(row) + criteria = row["questions"]["decision"]["criteria"] + key_by_class = dict(zip(order, criteria)) + for style, descriptions in enumerate(pool[row["dataset"]]): + for rotation in range(len(order)): + rotated = order[rotation:] + order[:rotation] + question = {**row["questions"]["decision"], "criteria": { + key_by_class[label]: descriptions[label] for label in rotated + }} + requests.append({ + "request_id": f"{row['source_group']}|style{style}|{rotation}", + "source_group": row["source_group"], "family_group": family_key(row), + "dataset": row["dataset"], "state": row["state"], "question": question, + "canonical_label": gold_class(row), "label": row["expected"]["decision"], + "semantic_order": rotated, "style": style, "rotation": rotation, + }) + return requests + + +def reserved_cost(hourly_rate, maximum_hours, down_per_gb, up_per_gb): + values = [hourly_rate, maximum_hours, down_per_gb, up_per_gb] + if not all(math.isfinite(value) and value >= 0 for value in values): + raise ValueError("Invalid cost values") + return hourly_rate * (maximum_hours + 2) + 20 * (down_per_gb + up_per_gb) + 0.2 + + +def budget_allowed(prior_spend, reservation): + return (all(math.isfinite(value) and value >= 0 for value in [prior_spend, reservation]) + and reservation <= 3 and prior_spend + reservation <= 10) + + +def batch_indices(lengths, batch_size, seed, epoch): + if batch_size < 1 or not lengths: + raise ValueError("Need nonempty data and positive batch size") + generator = random.Random(seed + epoch) + indices = list(range(len(lengths))) + generator.shuffle(indices) + batches = [] + window = batch_size * 20 + for offset in range(0, len(indices), window): + ordered = sorted(indices[offset:offset + window], key=lambda index: lengths[index]) + batches.extend(ordered[start:start + batch_size] for start in range(0, len(ordered), batch_size)) + generator.shuffle(batches) + return batches diff --git a/research/src/ab_train.py b/research/src/ab_train.py new file mode 100644 index 0000000000000000000000000000000000000000..af336f98a990c8043c3eaf2137a74a60622987da --- /dev/null +++ b/research/src/ab_train.py @@ -0,0 +1,334 @@ +import argparse +import copy +import gc +import json +import math +import random +import shutil +import time +from pathlib import Path + +import numpy as np +import torch +from safetensors.torch import load_file, save_file +from tokenizers import Tokenizer + +from ab_experiment import ( + DOMAINS, START_SHA256, TEACHER_REVISION, batch_indices, checked_feature, + class_order, file_hash, remap_probabilities, validate_cache, write_json, write_rows, +) +from model import MiniDecisionModel +from pipeline import answer_probabilities, distillation_loss, dummy_inputs, export_model, prepare_rows, training_batch +from schema import normalized_probabilities, options_for, read_rows + + +PROTOCOL_SHA256 = "143e0890279a6fe339b8a0cc6f394c61e9d806e9b9b1800272418716f3a5b366" + + +def prepare_pair(fixed, variants, cached, tokenizer, pools): + mapping = validate_cache(fixed, cached) + if len(fixed) != len(variants): + raise ValueError("A and B counts differ") + paired = {"A": [], "B": []} + for original, variant in zip(fixed, variants): + restored = copy.deepcopy(variant) + if original["dataset"] in DOMAINS: + if list(original["questions"]["decision"]["criteria"]) != list(variant["questions"]["decision"]["criteria"]): + raise ValueError("A/B changed key order") + domain = original["dataset"] + for key, semantic in zip(original["questions"]["decision"]["criteria"], class_order(original)): + allowed = {descriptions[semantic] for descriptions in pools["train"][domain]} + allowed.add(original["questions"]["decision"]["criteria"][key]) + if variant["questions"]["decision"]["criteria"][key] not in allowed: + raise ValueError("Variant description is assigned to the wrong class") + restored["questions"]["decision"]["criteria"] = original["questions"]["decision"]["criteria"] + if restored != original: + raise ValueError("A/B differ beyond allowed descriptions") + target = mapping[original["source_group"]] + values = target["probabilities"] + if original["dataset"] in DOMAINS: + order = class_order(original) + values = remap_probabilities(values, order, order) + for arm, row in [("A", original), ("B", variant)]: + feature = checked_feature(row, tokenizer) + annotation = {**target, "question": row["questions"]["decision"], "probabilities": values} + prepared = prepare_rows([annotation], tokenizer)[0] + paired[arm].append((feature, prepared[1], prepared[2])) + return paired + + +def shared_batch(paired, indices, arm, device): + inputs, targets, labels = training_batch([paired[arm][index] for index in indices], device) + length = max(len(paired[version][index][0]["input_ids"]) for version in ["A", "B"] for index in indices) + padding = length - inputs["input_ids"].shape[1] + for name in ["input_ids", "attention_mask"]: + inputs[name] = torch.nn.functional.pad(inputs[name], (0, padding), value=0) + return inputs, targets, labels + + +def encode_teacher(teacher, row): + from laya.common import build_sequence + + question = row["questions"]["decision"] + teacher._check_question("decision", question) + internal = {"decision": teacher._to_internal(question)} + items = teacher._encode_state(row["state"], ["decision"], internal) + full_ids, markers = build_sequence(teacher.tok, row["state"], internal["decision"], 8192, 8192) + if len(items) != 1 or items[0]["ids"] != full_ids or items[0]["markers"] != markers: + raise ValueError(f"Teacher would truncate {row['source_group']}") + return row, items, internal + + +@torch.inference_mode() +def teacher_batch(teacher, entries): + from laya.common import collate_items + + batch = collate_items([entry[1] for entry in entries], teacher.tok.pad_token_id) + logits, actions = teacher._forward(batch) + if (teacher.device.type != "cuda" or teacher.amp_enabled + or torch.backends.cuda.matmul.allow_tf32 or torch.backends.cudnn.allow_tf32 + or torch.get_float32_matmul_precision() != "highest" + or next(teacher.model.parameters()).dtype != torch.float32): + raise RuntimeError("Teacher precision/device changed") + records = [] + for position, (row, items, internal) in enumerate(entries): + answer = teacher._decode_answers(logits, actions, items, ["decision"], internal, position)["decision"] + question = row["questions"]["decision"] + labels, _ = options_for(question) + values = answer_probabilities(answer, question, labels) + records.append({ + "source_group": row["source_group"], "dataset": row["dataset"], + "state": row["state"], "question": question, "label": row["expected"]["decision"], + "probabilities": normalized_probabilities(values, len(labels)).tolist(), + }) + return records + + +def remaining_seconds(deadline): + return deadline - time.time() + + +def make_cache(directory, deadline): + from huggingface_hub import snapshot_download + import laya + + if (directory / "teacher-cache.jsonl").exists(): + raise FileExistsError("Teacher cache already exists; do not regenerate") + rows = read_rows(directory / "train.jsonl") + if len({row["source_group"] for row in rows}) != len(rows): + raise ValueError("Cache needs unique row-level source groups") + location = snapshot_download("convaiinnovations/laya", revision=TEACHER_REVISION, token=False, + allow_patterns=["model.safetensors", "rl_agent_config.json", "encoder/config.json", "tokenizer/*"]) + teacher_hash = file_hash(Path(location) / "model.safetensors") + if teacher_hash != "891102d372688fc2a094dac56a384bc537b87c63f21f9f3dac0be2b7cbc8d86c": + raise ValueError("Pinned teacher weights differ") + teacher = laya.load(location, device="cuda", token=False) + teacher.amp_enabled = False + teacher.dtype = torch.float32 + teacher.model.float().eval() + torch.backends.cuda.matmul.allow_tf32 = False + torch.backends.cudnn.allow_tf32 = False + torch.set_float32_matmul_precision("highest") + started = time.perf_counter() + encoded = [encode_teacher(teacher, row) for row in rows] + encoded.sort(key=lambda entry: len(entry[1][0]["ids"])) + preprocessing = time.perf_counter() - started + pilot = [encoded[index] for index in np.linspace(0, len(encoded) - 1, 64, dtype=int)] + teacher_batch(teacher, pilot[:16]) + torch.cuda.synchronize() + started = time.perf_counter() + results = [record for offset in range(0, len(pilot), 16) for record in teacher_batch(teacher, pilot[offset:offset + 16])] + torch.cuda.synchronize() + elapsed = time.perf_counter() - started + checks = [] + covered = set() + for entry, result in zip(pilot, results): + row = entry[0] + kind = row["questions"]["decision"]["type"] + question = row["questions"]["decision"] + answer = teacher.predict(row["state"], {"decision": question})["answers"]["decision"] + labels, _ = options_for(question) + reference = normalized_probabilities(answer_probabilities(answer, question, labels), len(labels)) + difference = float(np.max(np.abs(reference - result["probabilities"]))) + if difference > 0.001 or int(reference.argmax()) != int(np.argmax(result["probabilities"])): + raise ValueError("Teacher batch/public mismatch") + checks.append({"type": kind, "max_probability_delta": difference}) + covered.add(kind) + estimate = elapsed / len(pilot) * len(encoded) * 2 + if covered != {row["questions"]["decision"]["type"] for row in rows}: + raise ValueError("Teacher pilot misses a question type") + training_estimate = json.loads((directory / "pre-cache-training-pilot.json").read_text())["both_arms_seconds_conservative"] + write_json(directory / "teacher-pilot.json", { + "teacher_revision": TEACHER_REVISION, "teacher_sha256": teacher_hash, + "precision": "FP32 CUDA, no autocast, no TF32", "pilot_rows": 64, + "pilot_seconds": elapsed, "preprocessing_seconds": preprocessing, + "conservative_cache_seconds": estimate, "public_parity": checks, + "truncated_teacher_rows": 0, "maximum_teacher_tokens": max(len(entry[1][0]["ids"]) for entry in encoded), + }) + if remaining_seconds(deadline) < estimate + training_estimate + 900: + raise RuntimeError("Measured annotation throughput leaves insufficient time for both arms and collection") + started = time.perf_counter() + output_path = directory / "teacher-cache.jsonl" + with output_path.open("x") as handle: + for offset in range(0, len(encoded), 16): + if remaining_seconds(deadline) < training_estimate + 900: + raise RuntimeError("Cache time bound reached") + for record in teacher_batch(teacher, encoded[offset:offset + 16]): + handle.write(json.dumps(record, separators=(",", ":"), ensure_ascii=False) + "\n") + if offset % 4000 == 0: + handle.flush() + print(json.dumps({"cache_rows": min(offset + 16, len(encoded)), "elapsed": time.perf_counter() - started}), flush=True) + validate_cache(rows, read_rows(output_path)) + write_json(directory / "teacher-cache-meta.json", { + "rows": len(rows), "fixed_input_sha256": file_hash(directory / "train.jsonl"), + "cache_sha256": file_hash(output_path), "teacher_revision": TEACHER_REVISION, + "precision": "FP32 CUDA, no autocast, no TF32", "elapsed_seconds": time.perf_counter() - started, + }) + + +def fit(directory, checkpoint, deadline): + protocol = json.loads((directory / "protocol.json").read_text()) + if file_hash(checkpoint / "model.safetensors") != START_SHA256: + raise ValueError("Wrong initial checkpoint") + if any((directory / arm).exists() for arm in ["A", "B"]): + raise FileExistsError("A/B output already exists; do not restart the experiment") + cache_meta = json.loads((directory / "teacher-cache-meta.json").read_text()) + if file_hash(directory / "teacher-cache.jsonl") != cache_meta["cache_sha256"]: + raise ValueError("Teacher cache changed since verification") + fixed, variants = read_rows(directory / "train.jsonl"), read_rows(directory / "train-B.jsonl") + tokenizer = Tokenizer.from_file(str(checkpoint / "tokenizer.json")) + pools = json.loads((directory / "descriptions.json").read_text()) + paired = prepare_pair(fixed, variants, read_rows(directory / "teacher-cache.jsonl"), tokenizer, pools) + lengths = [len(row[0]["input_ids"]) for row in paired["A"]] + schedules = [batch_indices(lengths, protocol["batch_size"], protocol["seed"], epoch) for epoch in range(protocol["epochs"])] + total_steps = sum(len(batches) for batches in schedules) + if total_steps != protocol["steps_per_arm"]: + raise ValueError("Actual step count differs from protocol") + write_json(directory / "batch-schedule.json", schedules) + schedule_hash = file_hash(directory / "batch-schedule.json") + use_amp = torch.cuda.is_bf16_supported() + torch.set_float32_matmul_precision("high") + for arm in ["A", "B"]: + random.seed(protocol["seed"]) + np.random.seed(protocol["seed"]) + torch.manual_seed(protocol["seed"]) + torch.cuda.manual_seed_all(protocol["seed"]) + model = MiniDecisionModel() + model.load_state_dict(load_file(checkpoint / "model.safetensors"), strict=True) + model.to("cuda").train() + optimizer = torch.optim.AdamW([ + {"params": model.encoder.parameters(), "lr": protocol["encoder_learning_rate"]}, + {"params": [parameter for name, parameter in model.named_parameters() if not name.startswith("encoder.")], "lr": protocol["head_learning_rate"]}, + ], weight_decay=protocol["weight_decay"]) + warmup = max(1, int(total_steps * protocol["warmup_fraction"])) + def multiplier(step): + return (step + 1) / warmup if step < warmup else max(0.05, (total_steps - step) / (total_steps - warmup)) + scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, multiplier) + started = time.perf_counter() + steps = 0 + for epoch, batches in enumerate(schedules): + for indices in batches: + if remaining_seconds(deadline) < 900: + raise RuntimeError("Training deadline preserves artifact retrieval reserve") + inputs, targets, labels = shared_batch(paired, indices, arm, "cuda") + optimizer.zero_grad(set_to_none=True) + with torch.autocast("cuda", dtype=torch.bfloat16, enabled=use_amp): + loss = distillation_loss(model(**inputs), targets, labels, protocol["label_weight"]) + if not torch.isfinite(loss): + raise RuntimeError("Nonfinite training loss") + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) + optimizer.step() + scheduler.step() + steps += 1 + if steps == 100 and arm == "A": + torch.cuda.synchronize() + elapsed = time.perf_counter() - started + estimated_remaining = elapsed / steps * (2 * total_steps - steps) * 1.5 + 900 + write_json(directory / "training-pilot.json", {"steps": steps, "seconds": elapsed, "both_arms_remaining_seconds_conservative": estimated_remaining}) + if estimated_remaining > remaining_seconds(deadline): + raise RuntimeError("Measured training throughput exceeds the guarded lease") + if steps % 200 == 0: + print(json.dumps({"arm": arm, "epoch": epoch + 1, "step": steps, "total_steps": total_steps, "loss": loss.item(), "seconds": time.perf_counter() - started}), flush=True) + output = directory / arm + output.mkdir() + save_file({name: value.detach().cpu().contiguous() for name, value in model.state_dict().items()}, output / "model.safetensors") + shutil.copyfile(checkpoint / "tokenizer.json", output / "tokenizer.json") + write_json(output / "training.json", { + "trained": True, "quality_validated": False, "arm": arm, "steps": steps, + "selection": protocol["selection"], "start_sha256": START_SHA256, + "protocol_sha256": file_hash(directory / "protocol.json"), + "shared_cache_sha256": file_hash(directory / "teacher-cache.jsonl"), + "shared_schedule_sha256": schedule_hash, "seed": protocol["seed"], + "bf16": use_amp, "elapsed_seconds": time.perf_counter() - started, + }) + del model, optimizer, scheduler + gc.collect() + torch.cuda.empty_cache() + + +def training_pilot(directory, checkpoint): + if file_hash(checkpoint / "model.safetensors") != START_SHA256: + raise ValueError("Wrong pilot checkpoint") + torch.manual_seed(42) + torch.set_float32_matmul_precision("high") + model = MiniDecisionModel() + model.load_state_dict(load_file(checkpoint / "model.safetensors"), strict=True) + model.cuda().train() + optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5) + inputs = {name: value.cuda() for name, value in dummy_inputs(32, 512, 6).items()} + targets = torch.full((32, 6), 1 / 6, device="cuda") + labels = torch.arange(32, device="cuda") % 6 + started = None + for step in range(35): + if step == 5: + torch.cuda.synchronize() + started = time.perf_counter() + optimizer.zero_grad(set_to_none=True) + with torch.autocast("cuda", dtype=torch.bfloat16, enabled=torch.cuda.is_bf16_supported()): + loss = distillation_loss(model(**inputs), targets, labels, 2) + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), 1) + optimizer.step() + torch.cuda.synchronize() + elapsed = time.perf_counter() - started + write_json(directory / "pre-cache-training-pilot.json", { + "steps_timed": 30, "warmup_steps": 5, "batch": 32, "sequence": 512, "options": 6, + "seconds": elapsed, "both_arms_seconds_conservative": elapsed / 30 * 10096 * 1.5, + "targets": "synthetic uniform for throughput only; no pilot weights saved or reused", + }) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("action", choices=["pilot", "cache", "fit", "export"]) + parser.add_argument("--data", type=Path, required=True) + parser.add_argument("--checkpoint", type=Path, required=True) + parser.add_argument("--deadline", type=float, required=True) + arguments = parser.parse_args() + torch.set_num_threads(4) + protocol = json.loads((arguments.data / "protocol.json").read_text()) + if file_hash(arguments.data / "protocol.json") != PROTOCOL_SHA256: + raise ValueError("Frozen protocol changed") + for name in ["train.jsonl", "train-B.jsonl", "descriptions.json"]: + if file_hash(arguments.data / name) != protocol["files"][name]: + raise ValueError(f"Frozen input changed: {name}") + if arguments.action == "pilot": + training_pilot(arguments.data, arguments.checkpoint) + elif arguments.action == "cache": + make_cache(arguments.data, arguments.deadline) + elif arguments.action == "fit": + fit(arguments.data, arguments.checkpoint, arguments.deadline) + else: + for arm in ["A", "B"]: + checkpoint = arguments.data / arm + model = MiniDecisionModel() + model.load_state_dict(load_file(checkpoint / "model.safetensors")) + provenance = json.loads((checkpoint / "training.json").read_text()) + torch.manual_seed(42) + report = export_model(model, checkpoint / "model", provenance, checkpoint / "tokenizer.json") + write_json(checkpoint / "export-report.json", report) + + +if __name__ == "__main__": + main() diff --git a/research/src/audit_robustness.py b/research/src/audit_robustness.py new file mode 100644 index 0000000000000000000000000000000000000000..0590aeea17be4562288379080a13fb239e4b1e58 --- /dev/null +++ b/research/src/audit_robustness.py @@ -0,0 +1,432 @@ +import argparse +import hashlib +import json +import shutil +import sys +import time +import urllib.request +from collections import defaultdict +from pathlib import Path + +import numpy as np + + +ROOT = Path(__file__).resolve().parents[2] +RELEASE = ROOT / "outputs/laya-mini-cpu" +LAB = Path(__file__).resolve().parent +DIRECTORY = LAB / "robustness-audit" +SEED = 20260926 +sys.path.insert(0, str(RELEASE)) + +import build_corpus +from schema import collate, encode_question, normalized_probabilities, read_rows + + +SEMANTIC_KEYS = { + "ag_news": ["world", "sports", "business", "science"], + "snli": ["entailment", "neutral", "contradiction"], +} +SHORT = { + "ag_news": ["world news", "sporting events", "business news", "science or technology"], + "snli": ["entailed by the premise", "not determined by the premise", "inconsistent with the premise"], +} +LONG = { + "ag_news": [ + "International events, politics, diplomacy and public affairs.", + "Competitive sports, athletes, teams, tournaments and match results.", + "Companies, financial markets, trade, industries and the economy.", + "Scientific research, computing, technological products and inventions.", + ], + "snli": [ + "The hypothesis must be true if the premise is true; it is supported by the stated facts.", + "The premise does not establish whether the hypothesis is true or false; either remains possible.", + "The hypothesis cannot be true if the premise is true; the two statements are incompatible.", + ], +} + + +def digest_file(path): + with Path(path).open("rb") as handle: + return hashlib.file_digest(handle, "sha256").hexdigest() + + +def write_json(path, value): + with Path(path).open("x", encoding="utf-8") as handle: + json.dump(value, handle, ensure_ascii=False, indent=2) + handle.write("\n") + + +def write_rows(path, rows): + with Path(path).open("x", encoding="utf-8") as handle: + for row in rows: + handle.write(json.dumps(row, ensure_ascii=False, separators=(",", ":")) + "\n") + + +def build_variants(record): + dataset = record["dataset"] + fixed = list(build_corpus.CHOICES[dataset]) + original = record["questions"]["decision"] + base_order = [fixed.index(value) for value in original["criteria"].values()] + original_keys = {fixed.index(value): key for key, value in original["criteria"].items()} + named = [f"{name}: {description}" for name, description in + zip(build_corpus.SOURCE_BY_NAME[dataset].labels, LONG[dataset])] + styles = {"fixed": fixed, "short": SHORT[dataset], "long": LONG[dataset], "long_named": named} + variants = [] + for keys in ["opaque", "semantic"]: + key_mapping = original_keys if keys == "opaque" else dict(enumerate(SEMANTIC_KEYS[dataset])) + for style, descriptions in styles.items(): + for rotation in range(len(base_order)): + order = base_order[rotation:] + base_order[:rotation] + question = {**original, "criteria": {key_mapping[index]: descriptions[index] for index in order}} + variants.append({ + "request_id": f"{record['source_group']}|{keys}|{style}|{rotation}", + "source_group": record["source_group"], "dataset": dataset, + "state": record["state"], "question": question, + "canonical_label": record["canonical_label"], + "label": key_mapping[record["canonical_label"]], + "semantic_order": order, "keys": keys, "style": style, "rotation": rotation, + }) + return variants + + +def canonical_probabilities(values, semantic_order): + count = len(semantic_order) + if sorted(semantic_order) != list(range(count)): + raise ValueError("Semantic order must be a permutation") + normalized = normalized_probabilities(values, count) + canonical = np.empty(count, dtype=np.float64) + canonical[semantic_order] = normalized + return canonical.tolist() + + +def select_balanced(rows, dataset, count): + label_count = len(SEMANTIC_KEYS[dataset]) + if count <= 0 or count % label_count: + raise ValueError("Count must be positive and divisible by the class count") + selected, families = [], set() + for label in range(label_count): + candidates = [row for row in rows if row["dataset"] == dataset and row["canonical_label"] == label] + candidates.sort(key=lambda row: build_corpus.digest(SEED, "audit", row["source_group"])) + chosen = [] + for row in candidates: + if row["family_group"] not in families: + chosen.append(row) + families.add(row["family_group"]) + if len(chosen) == count // label_count: + break + if len(chosen) != count // label_count: + raise ValueError("Not enough distinct families for balanced sampling") + selected.extend(chosen) + return selected + + +def interval(values): + values = np.asarray(values, dtype=np.float64) + if values.ndim != 1 or not len(values): + raise ValueError("Bootstrap needs nonempty text-level values") + generator = np.random.default_rng(SEED) + means = values[generator.integers(0, len(values), size=(4000, len(values)))].mean(axis=1) + return [float(value) for value in np.quantile(means, [0.025, 0.975])] + + +def grouped_accuracy(rows, model): + grouped = defaultdict(list) + for row in rows: + grouped[row["source_group"]].append(float(row["prediction"][model] == row["canonical_label"])) + return {group: float(np.mean(values)) for group, values in grouped.items()} + + +def paired_interval(reference, variant): + if not reference or set(reference) != set(variant): + raise ValueError("Paired comparisons require identical nonempty source groups") + differences = [variant[group] - reference[group] for group in sorted(reference)] + return {"texts": len(differences), "delta": float(np.mean(differences)), "ci95": interval(differences)} + + +def summarize_cell(rows, model): + groups = defaultdict(list) + for row in rows: + groups[row["source_group"]].append(row) + scores = grouped_accuracy(rows, model) + original = [row for row in rows if row["rotation"] == 0] + return { + "texts": len(groups), "requests": len(rows), + "accuracy": float(np.mean(list(scores.values()))), + "accuracy_ci95_text_bootstrap": interval(list(scores.values())), + "all_rotations_correct": float(np.mean([value == 1.0 for value in scores.values()])), + "order_instability": float(np.mean([ + len({row["prediction"][model] for row in values}) > 1 for values in groups.values() + ])), + "rotation_zero_accuracy": float(np.mean([ + row["prediction"][model] == row["canonical_label"] for row in original + ])), + } + + +def retrieve_source(source, original_split): + import pyarrow.parquet as parquet + + filename = source.files[original_split] + suffix = ".parquet" if source.format == "parquet" else ".jsonl" + path = DIRECTORY / "sources" / f"{source.name}-{original_split}-{source.revision}{suffix}" + path.parent.mkdir(parents=True, exist_ok=True) + if not path.exists(): + if shutil.disk_usage(DIRECTORY).free < 256_000_000: + raise RuntimeError("Keep at least 256 MB disk free; source download not started") + with urllib.request.urlopen(source.url(filename), timeout=120) as response: + payload = response.read(64_000_001) + if len(payload) > 64_000_000: + raise RuntimeError("Source exceeds the audit download bound") + path.write_bytes(payload) + if source.format == "parquet": + return parquet.read_table(path).to_pylist() + return read_rows(path) + + +def prepare(): + if (DIRECTORY / "protocol.json").exists(): + raise FileExistsError("Audit protocol already exists") + DIRECTORY.mkdir(parents=True, exist_ok=True) + manifest = json.loads((RELEASE / "reports/corpus-manifest.json").read_text()) + validation, enriched = [], [] + forbidden_groups = set() + for pool in manifest["pools"]: + for source_split, selection in pool["source_splits"].items(): + if pool["output_split"] != "validation": + forbidden_groups.update(f"{pool['dataset']}:{source_split}:{index}" for index in selection["selected_indices"]) + for pool in manifest["pools"]: + if pool["output_split"] != "validation": + continue + source = build_corpus.SOURCE_BY_NAME[pool["dataset"]] + for source_split, selection in pool["source_splits"].items(): + raw_rows = retrieve_source(source, source_split) + for index in selection["selected_indices"]: + raw = raw_rows[index] + record = build_corpus.render_record(source, source_split, index, raw, "validation", manifest["seed"]) + if record["source_group"] in forbidden_groups: + raise ValueError("Validation source group overlaps another split") + validation.append(record) + if source.name in SEMANTIC_KEYS: + family_text = raw["premise"] if source.name == "snli" else raw["text"] + enriched.append({**record, "canonical_label": raw["label"], + "family_group": build_corpus.state_key(family_text)}) + print(json.dumps({"recovered_validation": source.name, "rows": len(selection["selected_indices"])}), flush=True) + validation.sort(key=lambda row: (build_corpus.digest(manifest["seed"], "output-order", "validation", row["source_group"]), row["source_group"])) + payload = "".join(json.dumps(row, ensure_ascii=False, separators=(",", ":")) + "\n" for row in validation).encode() + checksum = hashlib.sha256(payload).hexdigest() + if checksum != manifest["files"]["validation"]["sha256"]: + raise ValueError(f"Reconstructed validation hash differs: {checksum}") + write_rows(DIRECTORY / "validation.jsonl", validation) + selected = [row for dataset in SEMANTIC_KEYS for row in select_balanced(enriched, dataset, 60)] + requests = [variant for record in selected for variant in build_variants(record)] + tokenizer, _ = build_corpus.load_tokenizer(RELEASE / "model/tokenizer.json") + lengths = [] + for request in requests: + feature = encode_question(tokenizer, request["state"], request["question"]) + if feature["truncated_state"]: + raise ValueError("Audit variant would truncate the student state") + lengths.append(len(feature["input_ids"])) + write_rows(DIRECTORY / "selected.jsonl", selected) + write_rows(DIRECTORY / "requests.jsonl", requests) + write_json(DIRECTORY / "protocol.json", { + "seed": SEED, "purpose": "Exploratory development audit, not a held-out generalization benchmark", + "validation_sha256": checksum, "original_validation_rows": len(validation), + "selection": "60 per task, label-balanced hash sampling; unique normalized premises for SNLI", + "base_texts": len(selected), "requests": len(requests), + "source_groups": [row["source_group"] for row in selected], + "selected_sha256": digest_file(DIRECTORY / "selected.jsonl"), + "requests_sha256": digest_file(DIRECTORY / "requests.jsonl"), + "maximum_student_tokens": max(lengths), "student_truncation": False, + "keys": ["opaque original per-example keys", "semantic keys"], + "styles": {"fixed": build_corpus.CHOICES, "short": SHORT, "long": LONG, + "long_named": "Source class name followed by the identical long description"}, + "order": "All cyclic rotations of original validation order, not all permutations", + "statistics": "Paired source-text bootstrap, 4000 resamples, exploratory marginal 95% intervals; no multiplicity correction", + "primary_contrasts": [ + "AG News: FP32, opaque keys, long without prefix minus fixed descriptions, mean over rotations", + "SNLI: FP32, opaque keys, long without prefix minus fixed descriptions, mean over rotations", + ], + "secondary_analyses": "Other styles, key families, order sensitivity, INT8 and teacher comparisons are exploratory", + "teacher": "convaiinnovations/laya@55cf4c4ebb4ebe31b2550e8bdf3bd21b99753851", + "teacher_runtime": "laya==0.3.20, CPU, verify single/batch parity and token budgets", + "student_int8_batch": 1, + "limitations": ["Development validation already used for checkpoint selection", "Only AG News and SNLI choice tasks", + "One alternative wording set per style", "No score/noul or unseen-task conclusions", + "No weight changes, no GPU rental, no COPA evaluation"], + }) + print(json.dumps({"validation_sha256": checksum, "texts": len(selected), "requests": len(requests), "maximum_tokens": max(lengths)}), flush=True) + + +def inference_record(request, probabilities): + metadata = {key: request[key] for key in ["request_id", "source_group", "dataset", "canonical_label", "keys", "style", "rotation"]} + metadata["probabilities"] = probabilities + metadata["prediction"] = {name: int(np.argmax(values)) for name, values in probabilities.items()} + return metadata + + +def run_student(): + import onnxruntime as ort + import torch + from safetensors.torch import load_file + from model import MiniDecisionModel + + torch.set_num_threads(4) + requests = read_rows(DIRECTORY / "requests.jsonl") + tokenizer, _ = build_corpus.load_tokenizer(RELEASE / "model/tokenizer.json") + prepared = [(request, encode_question(tokenizer, request["state"], request["question"])) for request in requests] + prepared.sort(key=lambda pair: len(pair[1]["input_ids"])) + model = MiniDecisionModel().eval() + model.load_state_dict(load_file(LAB / "collected/checkpoint/model.safetensors"), strict=True) + settings = ort.SessionOptions() + settings.intra_op_num_threads = 2 + settings.inter_op_num_threads = 1 + quantized = ort.InferenceSession(str(RELEASE / "model/model.int8.onnx"), settings, providers=["CPUExecutionProvider"]) + started = time.perf_counter() + with (DIRECTORY / "student.jsonl").open("x", encoding="utf-8") as handle, torch.inference_mode(): + for offset in range(0, len(prepared), 8): + chunk = prepared[offset:offset + 8] + arrays = collate([feature for _, feature in chunk]) + floating = model(**{name: torch.from_numpy(value) for name, value in arrays.items()}).softmax(-1).numpy() + for position, (request, feature) in enumerate(chunk): + logits = quantized.run(None, collate([feature]))[0][0] + exponentials = np.exp(logits - logits.max()) + count = len(feature["labels"]) + values = { + "fp32": canonical_probabilities(floating[position, :count], request["semantic_order"]), + "int8": canonical_probabilities(exponentials / exponentials.sum(), request["semantic_order"]), + } + handle.write(json.dumps(inference_record(request, values)) + "\n") + handle.flush() + if offset % 256 == 0: + print(json.dumps({"student_done": min(offset + 8, len(prepared)), "total": len(prepared), + "elapsed_seconds": time.perf_counter() - started}), flush=True) + write_json(DIRECTORY / "student-meta.json", { + "decisions": len(prepared), "elapsed_seconds": time.perf_counter() - started, + "fp32_threads": 4, "fp32_batch": 8, "int8_threads": 2, "int8_batch": 1, + "checkpoint_sha256": digest_file(LAB / "collected/checkpoint/model.safetensors"), + "int8_sha256": digest_file(RELEASE / "model/model.int8.onnx"), + }) + + +def teacher_coverage(requests, teacher): + expected = {row["request_id"]: row for row in requests} + observed = {row["request_id"]: row for row in teacher} + if len(observed) != len(teacher) or not set(observed) <= set(expected): + raise ValueError("Teacher predictions have duplicate or unknown request IDs") + for identifier, row in observed.items(): + if any(row[field] != expected[identifier][field] + for field in ["source_group", "dataset", "canonical_label", "keys", "style", "rotation"]): + raise ValueError("Teacher prediction metadata differs from the frozen request") + groups = {row["source_group"] for row in teacher} + required = {row["request_id"] for row in requests if row["source_group"] in groups} + if set(observed) != required: + raise ValueError("Teacher coverage must contain all variants for every included text") + return groups + + +def confusion_matrix(rows, model, label_count): + matrix = np.zeros((label_count, label_count), dtype=np.int64) + for row in rows: + matrix[row["canonical_label"], row["prediction"][model]] += 1 + return matrix.tolist() + + +def diagnostics(): + students = read_rows(DIRECTORY / "student.jsonl") + teachers = read_rows(DIRECTORY / "teacher.jsonl") + matrices, margins = [], [] + for dataset, labels in SEMANTIC_KEYS.items(): + for keys in ["opaque", "semantic"]: + for style in ["fixed", "short", "long", "long_named"]: + subset = [row for row in students if row["dataset"] == dataset and row["keys"] == keys and row["style"] == style] + reference = [row for row in teachers if row["dataset"] == dataset and row["keys"] == keys and row["style"] == style] + for model, rows in [("fp32", subset), ("int8", subset), ("teacher", reference)]: + matrices.append({"dataset": dataset, "keys": keys, "style": style, "model": model, + "labels": labels, "texts": len({row["source_group"] for row in rows}), + "matrix_true_rows_predicted_columns": confusion_matrix(rows, model, len(labels))}) + disagreements = [row for row in subset if row["prediction"]["fp32"] != row["prediction"]["int8"]] + values = [float(np.sort(row["probabilities"]["fp32"])[-1] - np.sort(row["probabilities"]["fp32"])[-2]) + for row in disagreements] + margins.append({"dataset": dataset, "keys": keys, "style": style, + "disagreements": len(disagreements), + "fp32_margin_min_median_p90_max": [float(value) for value in np.quantile(values, [0, 0.5, 0.9, 1])] if values else None}) + result = {"purpose": "Post-audit exploratory diagnostics; repeated rotations are not independent examples", + "confusion_matrices": matrices, "quantization_disagreement_margins": margins} + write_json(DIRECTORY / "diagnostics.json", result) + print(json.dumps({"snli_opaque_fp32": [row for row in matrices if row["dataset"] == "snli" and row["keys"] == "opaque" and row["model"] == "fp32"], + "snli_opaque_margins": [row for row in margins if row["dataset"] == "snli" and row["keys"] == "opaque"]}, indent=2)) + + +def report(): + requests = read_rows(DIRECTORY / "requests.jsonl") + expected_ids = {row["request_id"] for row in requests} + rows = read_rows(DIRECTORY / "student.jsonl") + if len(rows) != len(expected_ids) or {row["request_id"] for row in rows} != expected_ids: + raise ValueError("Student predictions do not cover exactly the frozen audit requests") + models = ["fp32", "int8"] + teacher_path = DIRECTORY / "teacher.jsonl" + teacher = read_rows(teacher_path) if teacher_path.exists() else [] + teacher_groups = teacher_coverage(requests, teacher) + teacher_complete = len(teacher) == len(expected_ids) and {row["request_id"] for row in teacher} == expected_ids + if teacher: + by_id = {row["request_id"]: row for row in teacher} + for row in rows: + if row["request_id"] not in by_id: + continue + match = by_id[row["request_id"]] + row["prediction"].update(match["prediction"]) + row["probabilities"].update(match["probabilities"]) + if teacher_complete: + models.append("teacher") + cells, deltas, quantization = [], [], [] + for dataset in SEMANTIC_KEYS: + for keys in ["opaque", "semantic"]: + baseline = [row for row in rows if row["dataset"] == dataset and row["keys"] == keys and row["style"] == "fixed"] + for style in ["fixed", "short", "long", "long_named"]: + subset = [row for row in rows if row["dataset"] == dataset and row["keys"] == keys and row["style"] == style] + for model in models: + cells.append({"dataset": dataset, "keys": keys, "style": style, "model": model, **summarize_cell(subset, model)}) + if style != "fixed": + deltas.append({"dataset": dataset, "keys": keys, "style": style, "model": model, + **paired_interval(grouped_accuracy(baseline, model), grouped_accuracy(subset, model))}) + distances = [float(np.abs(np.asarray(row["probabilities"]["fp32"]) - row["probabilities"]["int8"]).sum() / 2) for row in subset] + quantization.append({"dataset": dataset, "keys": keys, "style": style, + "flip_rate": float(np.mean([row["prediction"]["fp32"] != row["prediction"]["int8"] for row in subset])), + "mean_total_variation": float(np.mean(distances)), "max_total_variation": max(distances)}) + paired_teacher_cells, paired_teacher_deltas = [], [] + for dataset in SEMANTIC_KEYS: + for keys in ["opaque", "semantic"]: + baseline = [row for row in rows if row["source_group"] in teacher_groups and row["dataset"] == dataset and row["keys"] == keys and row["style"] == "fixed"] + for style in ["fixed", "short", "long", "long_named"]: + subset = [row for row in rows if row["source_group"] in teacher_groups and row["dataset"] == dataset and row["keys"] == keys and row["style"] == style] + if not subset: + continue + for model in ["fp32", "int8", "teacher"]: + paired_teacher_cells.append({"dataset": dataset, "keys": keys, "style": style, "model": model, + **summarize_cell(subset, model)}) + if style != "fixed": + paired_teacher_deltas.append({"dataset": dataset, "keys": keys, "style": style, "model": model, + **paired_interval(grouped_accuracy(baseline, model), grouped_accuracy(subset, model))}) + teacher_meta = DIRECTORY / "teacher-meta.json" + teacher_subset_complete = json.loads(teacher_meta.read_text())["complete"] if teacher_meta.exists() else False + result = {"protocol_sha256": digest_file(DIRECTORY / "protocol.json"), "texts": len({row["source_group"] for row in rows}), + "requests": len(rows), "teacher_complete": teacher_complete, "teacher_requests_available": len(teacher), + "teacher_subset_complete": teacher_subset_complete, "teacher_base_texts": len(teacher_groups), + "teacher_comparison_cells_same_texts": paired_teacher_cells, + "teacher_comparison_style_deltas_same_texts": paired_teacher_deltas, + "cells": cells, "paired_style_minus_fixed_accuracy": deltas, "quantization": quantization, + "limitations": json.loads((DIRECTORY / "protocol.json").read_text())["limitations"]} + output = DIRECTORY / ("report.json" if teacher else "student-report.json") + write_json(output, result) + print(json.dumps({"output": str(output), "teacher_complete": teacher_complete, "cells": cells}, indent=2)) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("command", choices=["prepare", "student", "report", "diagnostics"]) + arguments = parser.parse_args() + {"prepare": prepare, "student": run_student, "report": report, "diagnostics": diagnostics}[arguments.command]() + + +if __name__ == "__main__": + main() diff --git a/research/src/audit_teacher.py b/research/src/audit_teacher.py new file mode 100644 index 0000000000000000000000000000000000000000..3eb66ec86d1effc8c5d3b3ec607ee22798e80151 --- /dev/null +++ b/research/src/audit_teacher.py @@ -0,0 +1,196 @@ +import argparse +import importlib.metadata +import json +import os +import resource +import shutil +import time +from collections import defaultdict + +import numpy as np +import torch + +import audit_robustness as audit + + +def encode_checked(teacher, request): + from laya.common import render_options + + question = request["question"] + teacher._check_question("decision", question) + internal = {"decision": teacher._to_internal(question)} + items = teacher._encode_state(request["state"], ["decision"], internal) + tokenizer = teacher.tok + masked = tokenizer.mask_token + instructions = question["instructions"].replace(masked, " ") + prefix = tokenizer(f"choice question: {instructions}", add_special_tokens=False)["input_ids"] + complete = [tokenizer.cls_token_id] + prefix + [tokenizer.sep_token_id] + markers = [] + for option in render_options(internal["decision"]): + markers.append(len(complete)) + tokens = tokenizer(" " + option.replace(masked, " "), add_special_tokens=False)["input_ids"] + complete.extend([tokenizer.mask_token_id] + tokens) + complete.append(tokenizer.sep_token_id) + state = tokenizer(request["state"].replace(masked, " "), add_special_tokens=False)["input_ids"] + complete.extend(state + [tokenizer.sep_token_id]) + if len(items) != 1 or items[0]["ids"] != complete or items[0]["markers"] != markers: + raise ValueError(f"Teacher truncation or marker mismatch: {request['request_id']}") + return {"request": request, "items": items, "internal": internal} + + +def evaluate_batch(teacher, prepared): + from laya.common import collate_items + from pipeline import answer_probabilities + + batch = collate_items([entry["items"] for entry in prepared], teacher.tok.pad_token_id) + logits, actions = teacher._forward(batch) + records = [] + for position, entry in enumerate(prepared): + request = entry["request"] + answers = teacher._decode_answers(logits, actions, entry["items"], ["decision"], entry["internal"], position) + answer = answers["decision"] + labels = list(request["question"]["criteria"]) + values = answer_probabilities(answer, request["question"], labels) + canonical = audit.canonical_probabilities(values, request["semantic_order"]) + record = audit.inference_record(request, {"teacher": canonical}) + record["prediction"]["teacher"] = request["semantic_order"][labels.index(answer["choice"])] + records.append(record) + return records + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--pilot", action="store_true") + parser.add_argument("--max-seconds", type=int, default=1800) + parser.add_argument("--per-dataset", type=int, default=60) + arguments = parser.parse_args() + if arguments.max_seconds <= 0: + raise ValueError("Time bound must be positive") + os.environ["LAYA_CPU_AMP"] = "" + os.environ["HF_HUB_OFFLINE"] = "1" + os.environ["HF_HUB_DISABLE_IMPLICIT_TOKEN"] = "1" + torch.set_num_threads(4) + import laya + + protocol = json.loads((audit.DIRECTORY / "protocol.json").read_text()) + if audit.digest_file(audit.DIRECTORY / "requests.jsonl") != protocol["requests_sha256"]: + raise ValueError("Frozen audit requests have changed") + weight_path = audit.LAB / "teacher/model.safetensors" + checksum = audit.digest_file(weight_path) + if checksum != "891102d372688fc2a094dac56a384bc537b87c63f21f9f3dac0be2b7cbc8d86c": + raise ValueError("Teacher weight hash differs from the pinned Hub object") + requests = audit.read_rows(audit.DIRECTORY / "requests.jsonl") + all_requests = len(requests) + selected = audit.read_rows(audit.DIRECTORY / "selected.jsonl") + subset = [row for dataset in audit.SEMANTIC_KEYS + for row in audit.select_balanced(selected, dataset, arguments.per_dataset)] + subset_groups = {row["source_group"] for row in subset} + requests = [row for row in requests if row["source_group"] in subset_groups] + group_names = sorted({row["source_group"] for row in requests}, + key=lambda group: audit.build_corpus.digest(audit.SEED, "teacher-order", group)) + if not arguments.pilot: + audit.write_json(audit.DIRECTORY / "teacher-execution-plan.json", { + "group_order": group_names, "maximum_seconds_between_groups": arguments.max_seconds, + "partial_coverage_policy": "Stop only between complete text groups in this preselected hash order, not by observed quality", + "batch": 4, "threads": 4, "precision": "CPU FP32, autocast disabled", + "per_dataset": arguments.per_dataset, "requests": len(requests), "full_audit_requests": all_requests, + "selection": "Nested label-balanced hash subset, selected using the measured pilot throughput before inspecting quality", + "minimum_free_disk_bytes": 128_000_000, + }) + print(json.dumps({"teacher_loading": True, "weight_sha256_verified": True}), flush=True) + started_loading = time.perf_counter() + teacher = laya.load(str(audit.LAB / "teacher"), device="cpu", token=False) + if teacher.amp_enabled or str(next(teacher.model.parameters()).dtype) != "torch.float32": + raise ValueError("Expected an FP32 CPU teacher with autocast disabled") + print(json.dumps({"teacher_loaded_seconds": time.perf_counter() - started_loading}), flush=True) + prepared = [encode_checked(teacher, request) for request in requests] + indexed = {entry["request"]["request_id"]: entry for entry in prepared} + for entry in prepared: + request = entry["request"] + if request["keys"] == "opaque": + semantic_id = request["request_id"].replace("|opaque|", "|semantic|") + if entry["items"][0]["ids"] == indexed[semantic_id]["items"][0]["ids"]: + raise ValueError("Teacher did not serialize the changed keys") + if request["rotation"] == 0: + rotated_id = request["request_id"].rsplit("|", 1)[0] + "|1" + if entry["items"][0]["ids"] == indexed[rotated_id]["items"][0]["ids"]: + raise ValueError("Teacher did not serialize the changed option order") + pilot = [] + covered = set() + for entry in prepared: + request = entry["request"] + condition = (request["dataset"], request["keys"], request["style"]) + if condition not in covered: + covered.add(condition) + pilot.append(entry) + with torch.inference_mode(): + evaluate_batch(teacher, pilot[:4]) + started = time.perf_counter() + results = [] + for offset in range(0, len(pilot), 4): + results.extend(evaluate_batch(teacher, pilot[offset:offset + 4])) + elapsed = time.perf_counter() - started + differences = [] + for entry, result in zip(pilot, results): + request = entry["request"] + reference = teacher.predict(request["state"], {"decision": request["question"]})["answers"]["decision"] + labels = list(request["question"]["criteria"]) + expected = audit.canonical_probabilities([reference["probabilities"][label] for label in labels], request["semantic_order"]) + difference = float(np.max(np.abs(np.asarray(expected) - result["probabilities"]["teacher"]))) + differences.append(difference) + if difference > 0.001 or request["semantic_order"][labels.index(reference["choice"])] != result["prediction"]["teacher"]: + raise ValueError(f"Teacher batch/public predict mismatch: {request['request_id']}, {difference}") + checks = { + "weight_sha256": checksum, "laya_version": importlib.metadata.version("laya"), + "runtime_precision": "FP32 CPU, unlike the earlier BF16 CUDA annotations", + "requests_checked_for_truncation": len(prepared), "truncated_requests": 0, + "maximum_tokens": max(len(entry["items"][0]["ids"]) for entry in prepared), + "keys_and_order_present_in_teacher_sequences": True, + "pilot_requests": len(pilot), "pilot_batch_seconds": elapsed, + "pilot_requests_per_second": len(pilot) / elapsed, + "batch_public_predict_max_probability_difference": max(differences), + "batch_public_predict_choice_agreement": len(pilot), + "note": "Local CPU throughput only; pilot size is small, not a GPU cost forecast. Teacher probabilities are rounded by native decode before normalization.", + } + audit.write_json(audit.DIRECTORY / ("teacher-pilot.json" if arguments.pilot else "teacher-checks.json"), checks) + print(json.dumps(checks), flush=True) + if arguments.pilot: + return + grouped = defaultdict(list) + for entry in prepared: + grouped[entry["request"]["source_group"]].append(entry) + started = time.perf_counter() + decisions = 0 + processed_groups = 0 + stopped_for = None + with (audit.DIRECTORY / "teacher.jsonl").open("x", encoding="utf-8") as handle: + for group in group_names: + if time.perf_counter() - started >= arguments.max_seconds: + stopped_for = "time_bound_at_group_boundary" + break + if shutil.disk_usage(audit.DIRECTORY).free < 128_000_000: + stopped_for = "disk_reserve" + break + entries = sorted(grouped[group], key=lambda entry: len(entry["items"][0]["ids"])) + results = [] + for offset in range(0, len(entries), 4): + results.extend(evaluate_batch(teacher, entries[offset:offset + 4])) + for result in results: + handle.write(json.dumps(result) + "\n") + handle.flush() + decisions += len(results) + processed_groups += 1 + print(json.dumps({"teacher_groups": processed_groups, "total_groups": len(group_names), + "teacher_decisions": decisions, "elapsed_seconds": time.perf_counter() - started}), flush=True) + duration = time.perf_counter() - started + audit.write_json(audit.DIRECTORY / "teacher-meta.json", { + "decisions": decisions, "groups": processed_groups, "complete": decisions == len(requests), + "planned_requests": len(requests), "full_audit_requests": all_requests, + "stopped_for": stopped_for, "elapsed_seconds": duration, "requests_per_second": decisions / duration, + "peak_process_rss_mib": resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024, + "model_weight_sha256": checksum, "threads": 4, "batch": 4, + }) + + +if __name__ == "__main__": + main() diff --git a/research/src/build_corpus.py b/research/src/build_corpus.py new file mode 100644 index 0000000000000000000000000000000000000000..ea70afaed4f2ae2cbfe402fdce352e3d0678c01c --- /dev/null +++ b/research/src/build_corpus.py @@ -0,0 +1,721 @@ +"""Build public English decision data; this does not run a teacher or train weights. + +Example: + python build_corpus.py --output-dir corpus --seed 20260925 + +Requires datasets, tokenizers and the numpy dependency of schema.py. Sources are +streamed from immutable public URLs without authentication or model weights. +Only the small BERT vocabulary is fetched when --tokenizer is not supplied. +""" + +import argparse +import hashlib +import heapq +import html +import importlib.metadata +import itertools +import json +import random +import sys +import unicodedata +import urllib.request +from collections import Counter +from dataclasses import dataclass +from pathlib import Path + +from schema import encode_question, options_for + + +DEFAULT_SEED = 20260925 +TOKENIZER_REPO = "google/bert_uncased_L-6_H-512_A-8" +TOKENIZER_REVISION = "dd53ec6ca9d05e0a91b309c4e137f31988888071" +SPLIT_ORDER = ("ood", "test", "validation", "train") +TRAIN_WEIGHTS = {"ag_news": 6, "sst5": 4, "boolq": 4, "snli": 6, "emotion": 4} +KEY_BANKS = ( + tuple("ABCDEFGHIJKLMNOPQRST"), + tuple(f"opt{number}" for number in range(1, 21)), + tuple(f"key{number}" for number in range(1, 21)), +) + + +@dataclass(frozen=True) +class Source: + name: str + repo: str + config: str + revision: str + format: str + files: dict + sizes: dict + labels: tuple + licenses: tuple + license_note: str + homepage: str + + def url(self, filename): + return f"https://huggingface.co/datasets/{self.repo}/resolve/{self.revision}/{filename}" + + def manifest(self): + return { + "dataset": self.name, + "repository": self.repo, + "configuration": self.config, + "revision": self.revision, + "url": f"https://huggingface.co/datasets/{self.repo}/tree/{self.revision}", + "homepage": self.homepage, + "license_tags": list(self.licenses), + "license_note": self.license_note, + "license_evidence_url": self.url("README.md"), + "metadata_checked_on": "2026-09-25", + "source_splits": { + split: {"rows": self.sizes[split], "url": self.url(filename)} + for split, filename in self.files.items() + }, + "label_order": list(self.labels), + "loader": f"datasets.load_dataset({self.format!r}, streaming=True, token=False)", + } + + +SOURCES = ( + Source( + "ag_news", "fancyzhx/ag_news", "default", + "eb185aade064a813bc0b7f42de02595523103ca4", "parquet", + {"train": "data/train-00000-of-00001.parquet", "test": "data/test-00000-of-00001.parquet"}, + {"train": 120000, "test": 7600}, + ("World", "Sports", "Business", "Sci/Tech"), ("unknown",), + "The pinned Hub card declares unknown; public access does not establish reuse rights.", + "https://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html", + ), + Source( + "sst5", "SetFit/sst5", "default", + "e51bdcd8cd3a30da231967c1a249ba59361279a3", "json", + {"train": "train.jsonl", "validation": "dev.jsonl", "test": "test.jsonl"}, + {"train": 8544, "validation": 1101, "test": 2210}, + ("very negative", "negative", "neutral", "positive", "very positive"), (), + "No license is declared in the pinned SetFit card; upstream review-text rights are not verified.", + "https://nlp.stanford.edu/sentiment/", + ), + Source( + "boolq", "google/boolq", "default", + "35b264d03638db9f4ce671b711558bf7ff0f80d5", "parquet", + {"train": "data/train-00000-of-00001.parquet", "validation": "data/validation-00000-of-00001.parquet"}, + {"train": 9427, "validation": 3270}, ("false", "true"), ("cc-by-sa-3.0",), + "CC BY-SA 3.0 is declared by the pinned Hub card; retain source attribution and license obligations.", + "https://github.com/google-research-datasets/boolean-questions", + ), + Source( + "snli", "stanfordnlp/snli", "plain_text", + "cdb5c3d5eed6ead6e5a341c8e56e669bb666725b", "parquet", + {split: f"plain_text/{split}-00000-of-00001.parquet" for split in ("train", "validation", "test")}, + {"train": 550152, "validation": 10000, "test": 10000}, + ("entailment", "neutral", "contradiction"), ("cc-by-sa-4.0",), + "CC BY-SA 4.0 is declared by the pinned Hub card; retain source attribution and license obligations.", + "https://nlp.stanford.edu/projects/snli/", + ), + Source( + "emotion", "dair-ai/emotion", "split", + "cab853a1dbdf4c42c2b3ef2173804746df8825fe", "parquet", + {split: f"split/{split}-00000-of-00001.parquet" for split in ("train", "validation", "test")}, + {"train": 16000, "validation": 2000, "test": 2000}, + ("sadness", "joy", "love", "anger", "fear", "surprise"), ("other",), + "The pinned card restricts use to educational and research purposes.", + "https://github.com/dair-ai/emotion_dataset", + ), + Source( + "copa", "aps/super_glue", "copa", + "3de24cf8022e94f4ee4b9d55a6f539891524d646", "parquet", + {split: f"copa/{split}-00000-of-00001.parquet" for split in ("train", "validation", "test")}, + {"train": 400, "validation": 100, "test": 500}, ("choice1", "choice2"), ("other",), + "SuperGLUE declares other and refers to original task licenses; COPA-specific reuse rights are not verified here.", + "https://people.ict.usc.edu/~gordon/copa.html", + ), +) +SOURCE_BY_NAME = {source.name: source for source in SOURCES} + +INSTRUCTIONS = { + "ag_news": { + "train": ( + "Choose the topic that best describes this news item.", + "Which news category fits the text?", + "Select the most relevant section for this news report.", + "Read the news item and identify its main subject area.", + "Assign the report to the best matching topic.", + "Pick the category that captures the focus of this news.", + ), + "validation": ( + "Under which subject heading should this report be filed?", + "Identify the editorial section most appropriate for the item.", + "Determine which listed subject dominates the report.", + ), + "test": ( + "A reader wants this report sorted by subject. Where does it belong?", + "Use the report's central theme to select a news desk.", + "Match this item to the section that would normally cover it.", + ), + }, + "sst5": { + "train": ( + "Rate the sentiment of this review on the five ordered levels.", + "How negative or positive is the review? Choose a sentiment level.", + "Select the level that matches the reviewer's overall opinion.", + "Score the expressed sentiment from very negative to very positive.", + "Read the review and choose its sentiment strength.", + "Judge the tone of this review using the ordered sentiment scale.", + ), + "validation": ( + "Place the reviewer's attitude on the supplied five-point scale.", + "Which degree of approval does this review convey?", + "Locate the opinion between strong disapproval and strong approval.", + ), + "test": ( + "Use the ordered levels to express how favorably the reviewer reacts.", + "Translate the review's attitude into one of the five sentiment ratings.", + "Choose the rating that best reflects the author's satisfaction.", + ), + }, + "boolq": { + "train": ( + "Using the passage, decide whether the answer to the question is yes.", + "Answer the included question with yes or no based on the passage.", + "Read the passage and judge whether the question has an affirmative answer.", + "Does the passage support answering yes to the question?", + "Determine the yes-or-no answer to the question from the text.", + "Use the supplied evidence to answer the question as true for yes or false for no.", + ), + "validation": ( + "Resolve the question from the passage: true means yes, false means no.", + "Consult the passage and indicate if an affirmative response is correct.", + "Is yes the correct response to the accompanying question?", + ), + "test": ( + "Taking the passage as evidence, should the question be answered affirmatively?", + "Return true for a yes answer and false for a no answer to the supplied question.", + "Decide from the reading whether the appropriate response is yes.", + ), + }, + "snli": { + "train": ( + "Choose how the hypothesis relates to the premise.", + "Does the premise support, contradict, or leave the hypothesis uncertain?", + "Classify the logical relation between the premise and hypothesis.", + "Given the premise, select the status of the hypothesis.", + "Read both sentences and judge whether the hypothesis follows.", + "Determine the inference relation for the supplied sentence pair.", + ), + "validation": ( + "Assuming the premise is true, what can be concluded about the hypothesis?", + "Assess whether the second sentence is implied, ruled out, or unresolved.", + "Use only the premise to categorize the claim made by the hypothesis.", + ), + "test": ( + "Evaluate the hypothesis against the stated premise using the listed relations.", + "Decide whether the premise makes the hypothesis necessary, impossible, or undecided.", + "What evidential relationship connects these two statements?", + ), + }, + "emotion": { + "train": ( + "Choose the emotion expressed in this text.", + "Which feeling best matches the writer's words?", + "Identify the main emotion conveyed by the message.", + "Read the text and select its dominant feeling.", + "Assign the message to the most fitting emotion.", + "Select the emotional category that best describes the text.", + ), + "validation": ( + "Which listed feeling is most evident in the writer's expression?", + "Determine the emotional tone communicated by the speaker.", + "Match the passage to the feeling it most strongly conveys.", + ), + "test": ( + "Infer the speaker's predominant feeling from this utterance.", + "What affective state is reflected most clearly in these words?", + "Interpret the message and pick the closest emotional description.", + ), + }, + "copa_cause": {"ood": ( + "Which alternative is the more plausible cause of the stated event?", + "Choose the event that most likely explains why the premise occurred.", + "Select the more likely reason for the situation described.", + )}, + "copa_effect": {"ood": ( + "Which alternative is the more plausible effect of the stated event?", + "Choose what would most likely happen as a result of the premise.", + "Select the more likely consequence of the situation described.", + )}, +} + +CHOICES = { + "ag_news": ("world affairs", "sports", "business and finance", "science and technology"), + "snli": ("the hypothesis follows from the premise", "the premise leaves the hypothesis uncertain", + "the hypothesis contradicts the premise"), + "emotion": SOURCE_BY_NAME["emotion"].labels, +} + + +class Excluded(ValueError): + pass + + +def digest(*values): + payload = json.dumps(values, ensure_ascii=False, separators=(",", ":")) + return hashlib.sha256(payload.encode("utf-8")).hexdigest() + + +def normalize_text(value): + if not isinstance(value, str): + raise Excluded("missing_or_nontext_field") + text = " ".join(unicodedata.normalize("NFKC", html.unescape(value)).split()) + if not text: + raise Excluded("empty_text") + if any(marker in text.casefold() for marker in ("[mask]", "[cls]", "[sep]", "[pad]", "[unk]")): + raise Excluded("reserved_token_in_source") + return text + + +def state_key(state): + return digest(normalize_text(state).casefold()) + + +def source_group(source, source_split, index): + return f"{source.name}:{source_split}:{index}" + + +def prepare_example(source, row): + if source.name in ("ag_news", "sst5", "emotion"): + state = normalize_text(row.get("text")) + elif source.name == "boolq": + state = f"Passage: {normalize_text(row.get('passage'))}\nQuestion: {normalize_text(row.get('question'))}" + elif source.name == "snli": + state = f"Premise: {normalize_text(row.get('premise'))}\nHypothesis: {normalize_text(row.get('hypothesis'))}" + elif source.name == "copa": + state = normalize_text(row.get("premise")) + if row.get("question") not in ("cause", "effect"): + raise Excluded("invalid_copa_relation") + else: + raise ValueError(f"Unsupported source: {source.name}") + label = row.get("answer") if source.name == "boolq" else row.get("label") + if source.name == "boolq": + if type(label) is not bool: + raise Excluded("missing_or_invalid_label") + elif type(label) is not int or not 0 <= label < len(source.labels): + raise Excluded("missing_or_invalid_label") + if source.name == "copa": + alternatives = (normalize_text(row.get("choice1")), normalize_text(row.get("choice2"))) + if alternatives[0].casefold() == alternatives[1].casefold(): + raise Excluded("identical_choices") + else: + alternatives = CHOICES.get(source.name) + return state, label, alternatives + + +def shuffled_choice(alternatives, correct, generator): + if not 2 <= len(alternatives) <= 20: + raise ValueError("A choice must have between 2 and 20 alternatives") + order = list(range(len(alternatives))) + generator.shuffle(order) + keys = generator.sample(generator.choice(KEY_BANKS), len(order)) + criteria = {key: alternatives[position] for key, position in zip(keys, order)} + return criteria, keys[order.index(correct)] + + +def render_record(source, source_split, index, row, split, seed, prepared=None): + state, label, alternatives = prepared or prepare_example(source, row) + group = source_group(source, source_split, index) + generator = random.Random(int(digest(seed, "render", group, split), 16)) + instruction_key = f"copa_{row['question']}" if source.name == "copa" else source.name + instructions = generator.choice(INSTRUCTIONS[instruction_key][split]) + if source.name == "sst5": + question = {"type": "score", "instructions": instructions, "criteria": list(source.labels)} + expected = label + elif source.name == "boolq": + wording = generator.choice(({"false": "no", "true": "yes"}, {"false": "false", "true": "true"})) + question = { + "type": "noul", "instructions": instructions, "labels": wording, + "criteria": {"false": "the answer to the question is no", "true": "the answer to the question is yes"}, + } + expected = label + else: + criteria, expected = shuffled_choice(alternatives, label, generator) + question = {"type": "choice", "instructions": instructions, "criteria": criteria} + return { + "state": state, "questions": {"decision": question}, "expected": {"decision": expected}, + "source_group": group, "dataset": source.name, "split": split, + } + + +def token_counts(record, tokenizer, max_state_tokens=320, max_prompt_tokens=192, max_length=512): + state_tokens = len(tokenizer.encode(record["state"], add_special_tokens=False).ids) + if state_tokens > max_state_tokens: + raise Excluded("state_token_budget") + question = record["questions"]["decision"] + try: + feature = encode_question(tokenizer, record["state"], question, max_length=max_length) + except ValueError as error: + if "prompt budget" in str(error): + raise Excluded("prompt_token_budget") from error + raise + if feature["truncated_state"]: + raise Excluded("sequence_token_budget") + prompt_tokens = len(feature["input_ids"]) - state_tokens - 1 + if prompt_tokens > max_prompt_tokens: + raise Excluded("prompt_token_budget") + return {"state": state_tokens, "prompt": prompt_tokens, "sequence": len(feature["input_ids"])} + + +def allocate(total, weights): + if total < 0 or not weights or any(weight <= 0 for weight in weights.values()): + raise ValueError("Counts must be nonnegative and allocation weights positive") + denominator = sum(weights.values()) + allocation = {name: total * weight // denominator for name, weight in weights.items()} + priority = sorted(weights, key=lambda name: -(total * weights[name] % denominator)) + for name in priority[:total - sum(allocation.values())]: + allocation[name] += 1 + return allocation + + +def split_plan(source): + if source.name == "copa": + return {"ood": ("train", "validation")} + if "train" not in source.files: + return {"test": ("test" if "test" in source.files else "validation",)} + if "test" not in source.files: + return {"train": ("train",), "validation": ("train",), "test": ("validation",)} + return { + "train": ("train",), + "validation": ("validation" if "validation" in source.files else "train",), + "test": ("test",), + } + + +def in_partition(source, source_split, split, state, seed): + plan = split_plan(source) + if source_split not in plan.get(split, ()): + return False + if split in ("train", "validation") and plan.get("validation") == ("train",): + validation = int(digest(seed, "validation-partition", source.name, state_key(state)), 16) % 10 == 0 + return validation if split == "validation" else not validation + return True + + +def stream_source(source, source_split): + from datasets import load_dataset + + dataset = load_dataset( + source.format, data_files={source_split: source.url(source.files[source_split])}, + split=source_split, streaming=True, token=False, + ) + label_feature = (dataset.features or {}).get("label") + names = getattr(label_feature, "names", None) + if names is not None and tuple(names) != source.labels: + raise RuntimeError(f"Unexpected label order for {source.name}: {names}") + return dataset + + +def select_pool(source, split, target, seed, tokenizer, used_states, used_groups, + loader=stream_source, max_state_tokens=320, max_prompt_tokens=192, + max_length=512, max_source_rows=None): + heap = [] + seen = set() + stats = {} + source_splits = split_plan(source)[split] + for source_split in source_splits: + counts = Counter() + stats[source_split] = counts + if target == 0: + continue + rows = enumerate(loader(source, source_split)) + if max_source_rows is not None: + rows = itertools.islice(rows, max_source_rows) + for index, row in rows: + counts["scanned"] += 1 + group = source_group(source, source_split, index) + try: + prepared = prepare_example(source, row) + state = prepared[0] + if not in_partition(source, source_split, split, state, seed): + counts["excluded_partition"] += 1 + continue + fingerprint = state_key(state) + if group in used_groups or fingerprint in used_states: + counts["excluded_duplicate_previous_pool"] += 1 + continue + if fingerprint in seen: + counts["excluded_duplicate_within_pool"] += 1 + continue + seen.add(fingerprint) + rank = int(digest(seed, "sample", group, split), 16) + if len(heap) == target and rank >= -heap[0][0]: + counts["not_selected_rank"] += 1 + continue + record = render_record(source, source_split, index, row, split, seed, prepared) + lengths = token_counts(record, tokenizer, max_state_tokens, max_prompt_tokens, max_length) + except Excluded as error: + counts[f"excluded_{error}"] += 1 + continue + entry = (-rank, group, record, lengths, source_split, index, row.get("idx")) + if len(heap) < target: + heapq.heappush(heap, entry) + else: + evicted = heapq.heapreplace(heap, entry) + stats[evicted[4]]["not_selected_replaced"] += 1 + counts["scan_limit_reached"] = int(max_source_rows is not None and counts["scanned"] == max_source_rows) + ordered = sorted(heap, key=lambda entry: entry[1]) + for entry in ordered: + used_states.add(state_key(entry[2]["state"])) + used_groups.add(entry[1]) + stats[entry[4]]["selected"] += 1 + return [entry[2] for entry in ordered], { + "dataset": source.name, "output_split": split, "requested": target, "selected": len(heap), + "source_splits": { + source_split: { + "counts": dict(sorted(counts.items())), + "selected_indices": [entry[5] for entry in ordered if entry[4] == source_split], + "selected_native_ids": [entry[6] for entry in ordered if entry[4] == source_split and entry[6] is not None], + } + for source_split, counts in stats.items() + }, + } + + +def audit_corpus(corpus, tokenizer, max_state_tokens=320, max_prompt_tokens=192, max_length=512): + states = set() + groups = set() + summary = {} + for split in SPLIT_ORDER: + rows = corpus[split] + dataset_counts = Counter() + type_counts = Counter() + option_counts = Counter() + label_counts = {} + maxima = {"state": 0, "prompt": 0, "sequence": 0} + for row in rows: + if row["split"] != split or set(row["questions"]) != {"decision"} or set(row["expected"]) != {"decision"}: + raise ValueError("Record split or question IDs are inconsistent") + source = SOURCE_BY_NAME[row["dataset"]] + dataset_name, source_split, index_text = row["source_group"].split(":") + if dataset_name != source.name or int(index_text) < 0 or source_split not in split_plan(source).get(split, ()): + raise ValueError("Record violates original source split semantics") + if not in_partition(source, source_split, split, row["state"], DEFAULT_SEED) and split == "ood": + raise ValueError("OOD source leaked into fitting") + fingerprint = state_key(row["state"]) + if fingerprint in states or row["source_group"] in groups: + raise ValueError("Repeated state or source group in the corpus") + states.add(fingerprint) + groups.add(row["source_group"]) + question = row["questions"]["decision"] + labels, descriptions = options_for(question) + expected = row["expected"]["decision"] + if question["type"] == "noul": + if type(expected) is not bool: + raise ValueError("Boolean ground truth was not retained") + semantic_label = str(expected).lower() + else: + if str(expected) not in labels: + raise ValueError("Ground truth is not one of the presented options") + semantic_label = descriptions[labels.index(str(expected))].split(": ", 1)[-1] + for name, count in token_counts(row, tokenizer, max_state_tokens, max_prompt_tokens, max_length).items(): + maxima[name] = max(maxima[name], count) + dataset_counts[source.name] += 1 + type_counts[question["type"]] += 1 + option_counts[str(len(labels))] += 1 + label_counts.setdefault(source.name, Counter())[semantic_label] += 1 + summary[split] = { + "decisions": len(rows), "datasets": dict(sorted(dataset_counts.items())), + "question_types": dict(sorted(type_counts.items())), "option_counts": dict(sorted(option_counts.items())), + "ground_truth_distribution": {name: dict(sorted(counts.items())) for name, counts in label_counts.items()}, + "maximum_wordpiece_tokens": maxima, + } + return summary + + +def load_tokenizer(path=None): + from tokenizers import BertWordPieceTokenizer, Tokenizer + + if path: + payload = Path(path).read_bytes() + tokenizer = Tokenizer.from_str(payload.decode("utf-8")) + provenance = {"kind": "local_tokenizer_json", "filename": Path(path).name} + else: + url = f"https://huggingface.co/{TOKENIZER_REPO}/resolve/{TOKENIZER_REVISION}/vocab.txt" + request = urllib.request.Request(url, headers={"User-Agent": "laya-mini-corpus/1"}) + with urllib.request.urlopen(request, timeout=60) as response: + payload = response.read(1_000_001) + if len(payload) > 1_000_000: + raise ValueError("Unexpectedly large tokenizer vocabulary") + vocabulary = {token: index for index, token in enumerate(payload.decode("utf-8").splitlines())} + if len(vocabulary) != 30522: + raise ValueError("Unexpected BERT vocabulary") + tokenizer = BertWordPieceTokenizer(vocabulary, lowercase=True) + provenance = {"kind": "pinned_bert_vocabulary", "repository": TOKENIZER_REPO, + "revision": TOKENIZER_REVISION, "url": url, "license": "apache-2.0"} + tokenizer.no_padding() + tokenizer.no_truncation() + configuration = json.loads(tokenizer.to_str()) + if configuration["model"]["type"] != "WordPiece": + raise ValueError("Budget validation requires a WordPiece tokenizer") + provenance["sha256"] = hashlib.sha256(payload).hexdigest() + provenance["effective_tokenizer_sha256"] = hashlib.sha256(tokenizer.to_str().encode()).hexdigest() + provenance["padding_and_truncation"] = "disabled for exact budget validation" + return tokenizer, provenance + + +def build_corpus(arguments, tokenizer, loader=stream_source, progress=None): + requested = {split: getattr(arguments, f"{split}_size") for split in SPLIT_ORDER} + equal_weights = dict.fromkeys(TRAIN_WEIGHTS, 1) + quotas = { + "train": allocate(requested["train"], TRAIN_WEIGHTS), + "validation": allocate(requested["validation"], equal_weights), + "test": allocate(requested["test"], equal_weights), "ood": {"copa": requested["ood"]}, + } + corpus = {split: [] for split in SPLIT_ORDER} + used_states, used_groups = set(), set() + pools = [] + for split in SPLIT_ORDER: + for name, target in quotas[split].items(): + rows, report = select_pool( + SOURCE_BY_NAME[name], split, target, arguments.seed, tokenizer, used_states, used_groups, + loader=loader, max_state_tokens=arguments.max_state_tokens, + max_prompt_tokens=arguments.max_prompt_tokens, max_length=arguments.max_length, + max_source_rows=arguments.max_source_rows, + ) + corpus[split].extend(rows) + pools.append(report) + if progress: + progress({"split": split, "dataset": name, "selected": len(rows), "requested": target}) + corpus[split].sort(key=lambda row: (digest(arguments.seed, "output-order", split, row["source_group"]), row["source_group"])) + shortfalls = [ + {"split": pool["output_split"], "dataset": pool["dataset"], "requested": pool["requested"], "selected": pool["selected"]} + for pool in pools if pool["selected"] < pool["requested"] + ] + if shortfalls and not arguments.allow_shortfall: + raise ValueError(f"Insufficient unique, budget-fitting rows: {json.dumps(shortfalls)}. " + "Reduce requested sizes or explicitly use --allow-shortfall; no corpus was written.") + summary = audit_corpus(corpus, tokenizer, arguments.max_state_tokens, arguments.max_prompt_tokens, arguments.max_length) + return corpus, {"requested": requested, "quotas": quotas, "counts": summary, "pools": pools, "shortfalls": shortfalls} + + +def make_manifest(arguments, report, tokenizer_provenance): + versions = {} + for name in ("datasets", "tokenizers", "numpy", "pyarrow", "fsspec", "huggingface-hub"): + try: + versions[name] = importlib.metadata.version(name) + except importlib.metadata.PackageNotFoundError: + versions[name] = None + return { + "format_version": 1, + "purpose": "Public, ground-truth-preserving English decisions for teacher annotation and distillation", + "seed": arguments.seed, + "implementation_sha256": hashlib.sha256(Path(__file__).read_bytes()).hexdigest(), + "schema_sha256": hashlib.sha256(Path(__file__).with_name("schema.py").read_bytes()).hexdigest(), + "runtime": {"python": sys.version, "packages": versions}, + "sources": [source.manifest() for source in SOURCES], + "tokenizer": tokenizer_provenance, + "budgets": {"max_state_wordpieces": arguments.max_state_tokens, + "max_prompt_wordpieces_including_markers": arguments.max_prompt_tokens, + "max_sequence_wordpieces": arguments.max_length, "text_truncation": "none; over-budget candidates excluded"}, + "selection": { + "algorithm": "Keep the smallest SHA256(seed, sample, source_group, output_split) ranks per quota", + "source_order": "Pinned file order; zero-based row index assigned before sampling or filtering", + "split_priority": list(SPLIT_ORDER), "dataset_priority": [source.name for source in SOURCES], + "deduplication": "NFKC, HTML unescape, whitespace collapse, casefold, SHA256 of rendered state; first occurrence per pool", + "global_deduplication": "Retained states and source groups cannot occur again, including within a split", + "validation_carve": "AG News and BoolQ: state-hash modulo 10 equals zero; other training rows stay in train", + "official_split_policy": {source.name: split_plan(source) for source in SOURCES}, + "ood_policy": "All COPA examples are withheld from fitting and model selection, including its original train split", + "excluded_source_splits": {"copa:test": "Hidden/unavailable ground truth; never read"}, + "max_rows_scanned_per_source_split_per_pool": arguments.max_source_rows, + "rank_pruning": "Token budgets are checked only for candidates competitive for the quota; exclusion counts describe checked rows", + "shortfall_policy": "explicitly allowed" if arguments.allow_shortfall else "fail before writing", + }, + "augmentation": { + "instruction_templates": INSTRUCTIONS, + "render_seed": "SHA256(seed, render, source_group, output_split)", + "choice_keys": [list(bank) for bank in KEY_BANKS], + "choice_order": "Independently shuffled semantic alternatives and sampled opaque keys; ground truth remapped", + "score_order": "Fixed ordered five-level sentiment scale; original integer labels 0..4 retained", + "boolean_order": "Fixed schema order false,true; original boolean answer retained", + "questions_per_source_row": 1, + }, + "limitations": [ + "Public availability is not a blanket license: AG News and SST5 rights are unverified, emotion is research/education only, and COPA requires license review.", + "This builder creates raw labeled data, not teacher outputs, trained weights, or evidence of successful distillation.", + "English short-text benchmarks cover limited tasks; score means sentiment and noul means passage-based yes/no only.", + "Only 2, 3, 4, 5 and 6 alternatives occur; support for up to 20 options in the runtime is not validated by this corpus.", + "COPA is a small causal-commonsense held-out domain; do not use it for training, tuning, early stopping, or template selection.", + "Teacher or base-model pretraining contamination is unknown; held-out here means corpus-level separation only.", + "Exact normalized-state deduplication does not detect paraphrases, shared premises, authors, documents, or cross-benchmark semantic overlap.", + "Length filtering favors short examples; no context is silently truncated. Optional scan limits also favor earlier source rows.", + "Within each dataset selection is not label-stratified; evaluate per dataset, class, question type, and OOD separately.", + "Held-out template wording differs from training, but all templates are human-authored and do not establish arbitrary-instruction generalization.", + "Reproduction requires the pinned public files, this implementation/schema/tokenizer and recorded dependency versions to remain available.", + ], + **report, + } + + +def write_corpus(directory, corpus, manifest): + directory = Path(directory) + directory.mkdir(parents=True, exist_ok=True) + paths = [directory / f"{split}.jsonl" for split in SPLIT_ORDER] + [directory / "corpus-manifest.json"] + if any(path.exists() for path in paths): + raise FileExistsError("Corpus outputs already exist; choose a new output directory") + files = {} + for split in SPLIT_ORDER: + path = directory / f"{split}.jsonl" + checksum = hashlib.sha256() + with path.open("xb") as handle: + for row in corpus[split]: + payload = (json.dumps(row, ensure_ascii=False, separators=(",", ":")) + "\n").encode("utf-8") + handle.write(payload) + checksum.update(payload) + files[split] = {"path": path.name, "sha256": checksum.hexdigest(), "bytes": path.stat().st_size, + "records": len(corpus[split]), "decisions": len(corpus[split])} + manifest["files"] = files + with (directory / "corpus-manifest.json").open("x", encoding="utf-8") as handle: + json.dump(manifest, handle, ensure_ascii=False, indent=2, sort_keys=True) + handle.write("\n") + + +def parse_args(argv=None): + parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--output-dir", "--output", type=Path, required=True) + parser.add_argument("--seed", type=int, default=DEFAULT_SEED) + parser.add_argument("--train-size", type=int, default=24000) + parser.add_argument("--validation-size", type=int, default=2000) + parser.add_argument("--test-size", type=int, default=3000) + parser.add_argument("--ood-size", type=int, default=400, help="COPA domain held out from all fitting (default: 400)") + parser.add_argument("--tokenizer", type=Path, help="Existing WordPiece tokenizer.json; default fetches only the pinned small vocab.txt") + parser.add_argument("--max-state-tokens", type=int, default=320) + parser.add_argument("--max-prompt-tokens", type=int, default=192) + parser.add_argument("--max-length", type=int, default=512) + parser.add_argument("--max-source-rows", type=int, help="Optional per-source-split scan cap for smoke runs; recorded as a sampling limitation") + parser.add_argument("--allow-shortfall", action="store_true", help="Write fewer rows if a requested quota cannot be filled, with explicit manifest shortfalls") + arguments = parser.parse_args(argv) + if any(getattr(arguments, f"{split}_size") < 0 for split in SPLIT_ORDER): + parser.error("Dataset sizes must be nonnegative") + if sum(getattr(arguments, f"{split}_size") for split in SPLIT_ORDER) == 0: + parser.error("At least one requested dataset size must be positive") + if not 32 <= arguments.max_length <= 512 or not 1 <= arguments.max_prompt_tokens <= min(192, arguments.max_length - 16): + parser.error("max-length must be 32..512 and max-prompt-tokens must be 1..min(192, max-length-16)") + if not 1 <= arguments.max_state_tokens < arguments.max_length: + parser.error("max-state-tokens must be positive and smaller than max-length") + if arguments.max_source_rows is not None and arguments.max_source_rows < 1: + parser.error("max-source-rows must be positive") + return arguments + + +def main(argv=None): + arguments = parse_args(argv) + if any((arguments.output_dir / name).exists() for name in ("corpus-manifest.json", *(f"{split}.jsonl" for split in SPLIT_ORDER))): + raise FileExistsError("Corpus outputs already exist; choose a new output directory") + tokenizer, provenance = load_tokenizer(arguments.tokenizer) + corpus, report = build_corpus(arguments, tokenizer, progress=lambda value: print(json.dumps(value), file=sys.stderr, flush=True)) + manifest = make_manifest(arguments, report, provenance) + write_corpus(arguments.output_dir, corpus, manifest) + print(json.dumps({"output_dir": str(arguments.output_dir), "decisions": {split: len(rows) for split, rows in corpus.items()}, + "manifest": str(arguments.output_dir / "corpus-manifest.json")})) + + +if __name__ == "__main__": + main() diff --git a/research/src/evaluate_model.py b/research/src/evaluate_model.py new file mode 100644 index 0000000000000000000000000000000000000000..1a7a175719e56436a43b174890530b494f61c95b --- /dev/null +++ b/research/src/evaluate_model.py @@ -0,0 +1,482 @@ +"""Independent test evaluation; never selects checkpoints or updates manifests.""" + +import argparse +import hashlib +import json +import time +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np + +from schema import QTYPES, collate, encode_question, group_key, options_for + + +def canonical_json(value): + return json.dumps(value, sort_keys=True, ensure_ascii=False, allow_nan=False) + + +def load_jsonl(path): + rows = [] + digest = hashlib.sha256() + with open(path, "rb") as handle: + for line_number, line in enumerate(handle, 1): + digest.update(line) + if not line.strip(): + continue + try: + row = json.loads(line) + canonical_json(row) + if not isinstance(row, dict) or "state" not in row: + raise ValueError("Each row must be an object containing state") + except (ValueError, UnicodeError) as error: + raise ValueError(f"{path}:{line_number}: {error}") from error + rows.append(row) + if not rows: + raise ValueError(f"Empty dataset: {path}") + return rows, {"path": str(Path(path).resolve()), "sha256": digest.hexdigest(), "rows": len(rows)} + + +def source_group(row): + value = row.get("source_group") + return canonical_json(value) if value is not None else None + + +def check_holdout(rows, references): + """References map train/validation to (rows, file metadata).""" + for row in rows: + split = row.get("split") + if split is not None and ( + not isinstance(split, str) + or split.lower().replace("-", "_") not in {"test", "ood", "held_out", "heldout", "held_out_test"} + ): + raise ValueError("--input must be a held-out test, separate from training and selection validation; " + f"found split={split!r}") + states = {group_key(row) for row in rows} + groups = {source_group(row) for row in rows} - {None} + checks = {} + for name, (reference_rows, metadata) in references.items(): + reference_states = {group_key(row) for row in reference_rows} + reference_groups = {source_group(row) for row in reference_rows} - {None} + shared_states = len(states & reference_states) + shared_groups = len(groups & reference_groups) + if shared_states or shared_groups: + raise ValueError(f"Test overlaps {name}: {shared_states} state hashes, {shared_groups} source groups") + checks[name] = { + **metadata, "overlapping_state_hashes": 0, "overlapping_source_groups": 0, + "rows_with_source_group": sum(source_group(row) is not None for row in reference_rows), + } + missing = sorted({"train", "validation"} - references.keys()) + return { + "role": "held_out_test", "status": "unverified" if missing else "no_overlap_detected", + "missing_reference_splits": missing, "checks": checks, + "unique_states": len(states), "unique_source_groups": len(groups), + "rows_with_source_group": sum(source_group(row) is not None for row in rows), + "scope": "Exact state SHA256 and available source_group identifiers; not semantic deduplication. " + "Without both reference files, independence from model selection is unverified.", + } + + +def ground_truth_index(value, kind, labels): + if value is None: + return None + if kind == "noul": + if isinstance(value, (bool, int)) and value in (0, 1): + return int(value) + if isinstance(value, str) and value.lower() in {"false", "true", "0", "1"}: + return int(value.lower() in {"true", "1"}) + raise ValueError("noul ground truth must be false/true or 0/1") + try: + return labels.index(str(value)) + except ValueError as error: + raise ValueError(f"Invalid {kind} ground truth {value!r}; expected one of {labels}") from error + + +def prepare_records(rows): + records = [] + for index, row in enumerate(rows): + try: + canonical_json(row) + labels, _ = options_for(row["question"]) + kind = row["question"]["type"] + dataset = row.get("dataset", "unspecified") + if not isinstance(dataset, str) or not dataset.strip(): + raise ValueError("dataset must be a nonempty string when present") + teacher = np.asarray(row["probabilities"], dtype=np.float64) + if teacher.shape != (len(labels),) or not np.isfinite(teacher).all() or (teacher < 0).any(): + raise ValueError("Teacher probabilities must be finite, nonnegative and match the options") + mass = float(teacher.sum()) + if not np.isfinite(mass) or mass <= 0: + raise ValueError("Teacher probabilities must have finite positive mass") + semantic_labels = list(labels) + if kind == "choice": + criteria = row["question"]["criteria"] + semantic_labels = [ + canonical_json(criteria[label]) if criteria[label] is not None and criteria[label] != "" + else canonical_json(["option_key", label]) for label in labels + ] + if len(set(semantic_labels)) != len(semantic_labels): + semantic_labels = [canonical_json([value, label]) for value, label in zip(semantic_labels, labels)] + label_space = row["question"]["criteria"] if kind == "score" else sorted(semantic_labels) + signature = canonical_json([dataset, kind, label_space]) + records.append({ + "row": row, "dataset": dataset, "type": kind, "labels": labels, + "semantic_labels": semantic_labels, + "target": ground_truth_index(row.get("label"), kind, labels), + "teacher": teacher / mass, "state_sha256": group_key(row), + "task": hashlib.sha256(signature.encode("utf-8")).hexdigest(), + "truncated_state": False, + }) + except (KeyError, TypeError, ValueError) as error: + raise ValueError(f"Evaluation row {index + 1}: {error}") from error + if not records: + raise ValueError("Cannot evaluate an empty dataset") + return records + + +def prediction_from_logits(logits): + logits = np.asarray(logits, dtype=np.float64) + if logits.ndim != 1 or len(logits) < 2 or not np.isfinite(logits).all(): + raise ValueError("Logits must be a finite vector with at least two options") + with np.errstate(over="raise", invalid="raise"): + try: + shifted = logits - logits.max() + log_probabilities = shifted - np.log(np.exp(shifted).sum()) + except FloatingPointError as error: + raise ValueError("Logits exceed the supported finite numeric range") from error + return {"logits": logits, "log_probabilities": log_probabilities, + "probabilities": np.exp(log_probabilities), "index": int(logits.argmax())} + + +def macro_f1(actual, predicted): + if not actual: + return None + classes = set(actual) | set(predicted) + scores = [] + for label in classes: + true_positive = sum(truth == label and guess == label for truth, guess in zip(actual, predicted)) + denominator = actual.count(label) + predicted.count(label) + scores.append(2 * true_positive / denominator) + return float(np.mean(scores)) + + +def expected_calibration_error(probabilities, targets, bins=10): + if not targets: + return None + confidence = np.asarray([values.max() for values in probabilities]) + correct = np.asarray([int(values.argmax()) == target for values, target in zip(probabilities, targets)]) + bucket = np.minimum((confidence * bins).astype(int), bins - 1) + error = 0.0 + for index in range(bins): + selected = bucket == index + if selected.any(): + error += float(selected.mean() * abs(confidence[selected].mean() - correct[selected].mean())) + return error + + +def mean_or_none(values): + return float(np.mean(values)) if values else None + + +def summarize(records, predictions): + if len(records) != len(predictions): + raise ValueError("Each evaluation row must have exactly one prediction") + labelled = [(record, prediction) for record, prediction in zip(records, predictions) + if record["target"] is not None] + actual = [record["semantic_labels"][record["target"]] for record, _ in labelled] + predicted = [record["semantic_labels"][prediction["index"]] for record, prediction in labelled] + task_counts = defaultdict(Counter) + for record, _ in labelled: + task_counts[record["task"]][record["semantic_labels"][record["target"]]] += 1 + f1_reason = None + if not labelled: + f1_reason = "no_ground_truth" + elif len(task_counts) != 1: + f1_reason = "heterogeneous_tasks; see by_task" + elif len(set(actual)) < 2: + f1_reason = "fewer_than_two_observed_ground_truth_classes" + divergences = [] + for record, prediction in zip(records, predictions): + positive = record["teacher"] > 0 + divergence = np.sum(record["teacher"][positive] * ( + np.log(record["teacher"][positive]) - prediction["log_probabilities"][positive] + )) + if not np.isfinite(divergence): + raise ValueError("Nonfinite teacher KL divergence") + divergences.append(max(0.0, float(divergence))) + score_errors = [abs(float(prediction["probabilities"] @ np.arange(len(record["labels"]))) - record["target"]) + for record, prediction in labelled if record["type"] == "score"] + noul_errors = [(float(prediction["probabilities"][1]) - record["target"]) ** 2 + for record, prediction in labelled if record["type"] == "noul"] + return { + "rows": len(records), "labelled_rows": len(labelled), "missing_label_rows": len(records) - len(labelled), + "truncated_rows": sum(record["truncated_state"] for record in records), + "correct": sum(truth == guess for truth, guess in zip(actual, predicted)), + "ground_truth_accuracy": mean_or_none([truth == guess for truth, guess in zip(actual, predicted)]), + "macro_f1": macro_f1(actual, predicted) if f1_reason is None else None, + "macro_f1_unavailable_reason": f1_reason, + "teacher_ground_truth_accuracy": mean_or_none([ + int(record["teacher"].argmax()) == record["target"] for record, _ in labelled]), + "teacher_agreement": mean_or_none([ + prediction["index"] == int(record["teacher"].argmax()) for record, prediction in zip(records, predictions)]), + "teacher_to_student_kl": mean_or_none(divergences), + "score_mae": mean_or_none(score_errors), "score_labelled_rows": len(score_errors), + "noul_brier": mean_or_none(noul_errors), "noul_labelled_rows": len(noul_errors), + "ece_10_bins": expected_calibration_error( + [prediction["probabilities"] for _, prediction in labelled], [record["target"] for record, _ in labelled]), + "uniform_random_baseline_accuracy": mean_or_none([1 / len(record["labels"]) for record, _ in labelled]), + "majority_baseline": { + "accuracy": sum(max(counts.values()) for counts in task_counts.values()) / len(labelled) if labelled else None, + "labelled_rows": len(labelled), "tasks": len(task_counts), + "single_example_tasks": sum(sum(counts.values()) == 1 for counts in task_counts.values()), + "method": "Per-task majority of evaluation ground-truth labels; descriptive oracle baseline, " + "not an independently fitted predictor. Missing labels are excluded.", + }, + } + + +def metric_report(records, predictions): + report = {"overall": summarize(records, predictions)} + for field in ("type", "dataset", "task"): + groups = defaultdict(list) + for record, prediction in zip(records, predictions): + groups[record[field]].append((record, prediction)) + if field == "type": + for kind in QTYPES: + groups.setdefault(kind, []) + report[f"by_{field}"] = {} + for key, pairs in sorted(groups.items()): + metrics = summarize([pair[0] for pair in pairs], [pair[1] for pair in pairs]) + if field == "task": + metrics.update(dataset=pairs[0][0]["dataset"], type=pairs[0][0]["type"], + question_ids=sorted({str(pair[0]["row"].get("question_id", "unspecified")) for pair in pairs})) + report[f"by_{field}"][key] = metrics + return report + + +def compare_predictions(records, quantized, floating): + if not records or len(records) != len(quantized) or len(records) != len(floating): + raise ValueError("Comparison needs matching, nonempty predictions") + labelled = [index for index, record in enumerate(records) if record["target"] is not None] + comparison = { + "rows": len(records), "labelled_rows": len(labelled), + "prediction_agreement": float(np.mean([left["index"] == right["index"] for left, right in zip(quantized, floating)])), + "accuracy_drop": mean_or_none([ + int(floating[index]["index"] == records[index]["target"]) - + int(quantized[index]["index"] == records[index]["target"]) for index in labelled]), + "accuracy_drop_definition": "floating accuracy minus quantized accuracy; positive means quantization is worse", + "floating_correct_quantized_wrong": sum(floating[index]["index"] == records[index]["target"] and + quantized[index]["index"] != records[index]["target"] for index in labelled), + "quantized_correct_floating_wrong": sum(quantized[index]["index"] == records[index]["target"] and + floating[index]["index"] != records[index]["target"] for index in labelled), + } + for field in ("logits", "probabilities"): + differences = np.concatenate([left[field] - right[field] for left, right in zip(quantized, floating)]) + if not np.isfinite(differences).all(): + raise ValueError(f"Nonfinite {field} comparison") + comparison[field] = {"valid_option_values": len(differences), + "mean_absolute_difference": float(np.abs(differences).mean()), + "max_absolute_difference": float(np.abs(differences).max())} + return comparison + + +def runtime_report(elapsed, rows): + total = sum(elapsed) + return { + "batches": len(elapsed), "rows": rows, "inference_seconds": total, + "rows_per_second": rows / total if total else None, + "amortized_ms_per_row": total * 1000 / rows, + "batch_latency_ms": {"mean": float(np.mean(elapsed) * 1000), + "p50": float(np.percentile(elapsed, 50) * 1000), + "p95": float(np.percentile(elapsed, 95) * 1000)}, + "warmup_batches": 0, "includes_first_inference": True, + "scope": "Batched forward pass, excluding model loading, tokenization and collation. " + "PyTorch includes device transfers and synchronization. Amortized time is not single-request latency.", + } + + +def prediction_row(record, quantized, floating, index): + row = record["row"] + result = { + "row_number": index + 1, "state_sha256": record["state_sha256"], "task_sha256": record["task"], + "dataset": record["dataset"], "split": row.get("split"), "question_id": row.get("question_id"), + "source_group": row.get("source_group"), "type": record["type"], "label": row.get("label"), + "teacher": row.get("teacher", "unspecified"), "truncated_state": record["truncated_state"], + "teacher_probabilities": dict(zip(record["labels"], record["teacher"].tolist())), + "teacher_prediction": record["labels"][int(record["teacher"].argmax())], + } + for name, prediction in (("quantized", quantized), ("floating", floating)): + if prediction is None: + continue + output = {"label": record["labels"][prediction["index"]], "calibrated": False, + "logits": dict(zip(record["labels"], prediction["logits"].tolist())), + "probabilities": dict(zip(record["labels"], prediction["probabilities"].tolist()))} + if record["type"] == "score": + output["score"] = float(prediction["probabilities"] @ np.arange(len(record["labels"]))) + elif record["type"] == "noul": + output["noul"] = float(prediction["probabilities"][1]) + result[name] = output + return result + + +def validate_output_paths(arguments): + model_directory = Path(arguments.model).resolve() + protected = {Path(value).resolve() for value in + (arguments.input, arguments.train, arguments.validation, arguments.checkpoint) if value} + targets = [Path(value).resolve() for value in (arguments.output, arguments.predictions) if value] + if len(set(targets)) != len(targets): + raise ValueError("--output and --predictions must be different files") + for target in targets: + if target in protected or target == model_directory or model_directory in target.parents: + raise ValueError(f"Output would overwrite an evaluation input or model artifact: {target}") + if target.exists(): + raise ValueError(f"Output already exists; choose a new path: {target}") + + +def evaluate(arguments): + started = time.perf_counter() + validate_output_paths(arguments) + rows, dataset_metadata = load_jsonl(arguments.input) + references = {name: load_jsonl(path) for name in ("train", "validation") if (path := getattr(arguments, name))} + holdout = check_holdout(rows, references) + records = prepare_records(rows) + from runtime import DecisionSession + + loading_started = time.perf_counter() + session = DecisionSession(arguments.model, threads=arguments.threads) + quantized_load_seconds = time.perf_counter() - loading_started + floating_model = None + checkpoint_path = None + floating_load_seconds = None + if arguments.checkpoint: + import torch + from safetensors.torch import load_file + from model import MiniDecisionModel + + loading_started = time.perf_counter() + torch.set_num_threads(arguments.threads) + if arguments.device == "cuda" and not torch.cuda.is_available(): + raise ValueError("--device cuda requires an available PyTorch CUDA device") + checkpoint_path = Path(arguments.checkpoint) + if checkpoint_path.is_dir(): + checkpoint_path = checkpoint_path / "model.safetensors" + weights = load_file(str(checkpoint_path), device="cpu") + if any(value.is_floating_point() and value.dtype != torch.float32 for value in weights.values()): + raise ValueError("--checkpoint must contain FP32 weights") + floating_model = MiniDecisionModel(pretrained=False) + floating_model.load_state_dict(weights, strict=True) + del weights + floating_model.to(arguments.device).eval() + if arguments.device == "cuda": + torch.cuda.synchronize() + floating_load_seconds = time.perf_counter() - loading_started + preparation_started = time.perf_counter() + features = [encode_question(session.tokenizer, row["state"], row["question"], + max_length=session.manifest.get("max_sequence_length", 512)) for row in rows] + for record, feature in zip(records, features): + record["truncated_state"] = feature["truncated_state"] + preparation_seconds = time.perf_counter() - preparation_started + quantized, floating, quantized_times, floating_times = [], [], [], [] + collation_seconds = 0.0 + for offset in range(0, len(records), arguments.batch_size): + batch_records = records[offset:offset + arguments.batch_size] + collation_started = time.perf_counter() + arrays = collate(features[offset:offset + arguments.batch_size]) + collation_seconds += time.perf_counter() - collation_started + inference_started = time.perf_counter() + quantized_logits = session.session.run(None, arrays)[0] + quantized_times.append(time.perf_counter() - inference_started) + batch_outputs = [(quantized_logits, quantized)] + if floating_model is not None: + if arguments.device == "cuda": + torch.cuda.synchronize() + inference_started = time.perf_counter() + with torch.inference_mode(): + inputs = {name: torch.from_numpy(array).to(arguments.device) for name, array in arrays.items()} + floating_logits = floating_model(**inputs).cpu().numpy() + if arguments.device == "cuda": + torch.cuda.synchronize() + floating_times.append(time.perf_counter() - inference_started) + batch_outputs.append((floating_logits, floating)) + for logits, destination in batch_outputs: + if logits.shape != arrays["marker_mask"].shape or not np.isfinite(logits).all(): + raise ValueError(f"Invalid model logits in batch starting at row {offset + 1}") + destination.extend(prediction_from_logits(logits[index, :len(record["labels"])]) + for index, record in enumerate(batch_records)) + quantized_metrics = metric_report(records, quantized) + labelled = quantized_metrics["overall"]["labelled_rows"] + report = { + "schema_version": 1, "purpose": "independent_held_out_evaluation", "dataset": dataset_metadata, + "ground_truth_status": "missing" if not labelled else "complete" if labelled == len(records) else "partial", + "holdout": holdout, "calibrated": False, + "metric_definitions": { + "accuracy": "Argmax against actual labels, excluding missing/null labels; teacher agreement is separate.", + "macro_f1": "Unweighted F1 over observed true/predicted classes, only for a common label space " + "with at least two true classes. Nonempty choice descriptions identify classes even when keys are randomized; " + "empty or duplicate descriptions retain the option key to avoid merging distinct options.", + "task": "Dataset, question type and option-description vocabulary (ordered level descriptions for score). " + "Question text, question_id, choice keys and option order may vary without changing the label space. " + "Example-specific answer vocabularies form separate tasks; their majority baselines are not evidence of generalization.", + "score_mae": "Absolute error of sum(index * probability), in ordered level units, not argmax error.", + "noul_brier": "Mean (P(true) - boolean ground truth)^2, without the multiclass factor of two.", + "teacher_to_student_kl": "Mean KL(teacher || student), natural logs, including rows without ground truth.", + "ece_10_bins": "Top-label ECE with 10 equal-width bins, labelled rows only; this is measurement, not calibration.", + "ties": "Argmax uses the first option in schema order.", + }, + "quantized": {"model_directory": str(Path(arguments.model).resolve()), + "backend": "onnxruntime", "device": "cpu", "providers": session.session.get_providers(), + "metrics": quantized_metrics, "runtime": runtime_report(quantized_times, len(records))}, + "floating": None, "quantization_comparison": None, + "runtime": {"batch_size": arguments.batch_size, "threads": arguments.threads, + "quantized_load_seconds": quantized_load_seconds, "floating_load_seconds": floating_load_seconds, + "encoding_seconds": preparation_seconds, "collation_seconds": collation_seconds}, + "limitations": ["No confidence calibration is fitted or claimed; the export manifest is never modified.", + "Results describe only the supplied data. Synthetic/smoke fixtures are not evidence of real quality.", + "Missing labels cannot establish accuracy. Missing train/validation references cannot establish independence."], + } + if floating_model is not None: + report["floating"] = {"checkpoint": str(checkpoint_path.resolve()), "backend": "pytorch", + "device": arguments.device, "metrics": metric_report(records, floating), + "runtime": runtime_report(floating_times, len(records))} + report["quantization_comparison"] = compare_predictions(records, quantized, floating) + report["runtime"]["evaluation_wall_seconds_excluding_output_writes"] = time.perf_counter() - started + serialized = json.dumps(report, ensure_ascii=False, indent=2, allow_nan=False) + "\n" + if arguments.predictions: + destination = Path(arguments.predictions) + destination.parent.mkdir(parents=True, exist_ok=True) + with destination.open("x", encoding="utf-8") as handle: + for index, (record, prediction) in enumerate(zip(records, quantized)): + handle.write(canonical_json(prediction_row(record, prediction, floating[index] if floating else None, index)) + "\n") + destination = Path(arguments.output) + destination.parent.mkdir(parents=True, exist_ok=True) + with destination.open("x", encoding="utf-8") as handle: + handle.write(serialized) + return report + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--model", required=True, help="Trained quantized export directory; ONNX Runtime always runs on CPU") + parser.add_argument("--checkpoint", help="Optional matching FP32 .safetensors file, or directory containing model.safetensors") + parser.add_argument("--input", required=True, help="Annotated held-out test JSONL, separate from checkpoint-selection validation") + parser.add_argument("--train", help="Training JSONL for state/source-group overlap checks") + parser.add_argument("--validation", help="Checkpoint-selection validation JSONL for state/source-group overlap checks") + parser.add_argument("--output", required=True, help="New report JSON path, outside the model export") + parser.add_argument("--predictions", help="Optional new per-row prediction JSONL path") + parser.add_argument("--batch-size", type=int, default=8) + parser.add_argument("--threads", type=int, default=2) + parser.add_argument("--device", choices=("cpu", "cuda"), default="cpu", help="PyTorch checkpoint only; does not change ONNX Runtime CPU execution") + arguments = parser.parse_args() + if arguments.batch_size < 1 or arguments.threads < 1: + parser.error("--batch-size and --threads must be positive") + try: + report = evaluate(arguments) + except (ValueError, OSError) as error: + parser.error(str(error)) + print(json.dumps({"report": str(Path(arguments.output).resolve()), + "rows": report["dataset"]["rows"], "ground_truth_status": report["ground_truth_status"], + "holdout_status": report["holdout"]["status"]}, allow_nan=False)) + + +if __name__ == "__main__": + main() diff --git a/research/src/model.py b/research/src/model.py new file mode 100644 index 0000000000000000000000000000000000000000..78da7b4e9b66ddad8c7ba80d8b6f0e1e115e7676 --- /dev/null +++ b/research/src/model.py @@ -0,0 +1,49 @@ +import torch +from torch import nn +from transformers import BertConfig, BertModel +from transformers.models.bert.modeling_bert import BertLayer + + +BASE_MODEL = "google/bert_uncased_L-6_H-512_A-8" +BASE_REVISION = "dd53ec6ca9d05e0a91b309c4e137f31988888071" +INPUT_NAMES = ("input_ids", "attention_mask", "marker_pos", "marker_mask", "qtype") + + +class MiniDecisionModel(nn.Module): + def __init__(self, pretrained=False): + super().__init__() + config = BertConfig( + vocab_size=30522, + hidden_size=512, + num_hidden_layers=6, + num_attention_heads=8, + intermediate_size=2048, + max_position_embeddings=512, + layer_norm_eps=1e-12, + ) + config._attn_implementation = "eager" + self.encoder = ( + BertModel.from_pretrained(BASE_MODEL, revision=BASE_REVISION, config=config, add_pooling_layer=False) + if pretrained + else BertModel(config, add_pooling_layer=False) + ) + self.type_embedding = nn.Embedding(3, config.hidden_size) + self.decision_layer = BertLayer(config) + self.scorer = nn.Sequential( + nn.LayerNorm(config.hidden_size), + nn.Linear(config.hidden_size, config.hidden_size), + nn.GELU(), + nn.Linear(config.hidden_size, 1), + ) + + def forward(self, input_ids, attention_mask, marker_pos, marker_mask, qtype): + hidden = self.encoder( + input_ids=input_ids, attention_mask=attention_mask + ).last_hidden_state + hidden = hidden + self.type_embedding(qtype).unsqueeze(1) + attention_bias = (1.0 - attention_mask[:, None, None, :].to(hidden.dtype)) * -10000.0 + hidden = self.decision_layer(hidden, attention_mask=attention_bias)[0] + positions = marker_pos.unsqueeze(-1).expand(-1, -1, hidden.shape[-1]) + option_hidden = torch.gather(hidden, 1, positions) + logits = self.scorer(option_hidden).squeeze(-1) + return logits.masked_fill(~marker_mask, -10000.0) diff --git a/research/src/pipeline.py b/research/src/pipeline.py new file mode 100644 index 0000000000000000000000000000000000000000..3a58720d6d06cfafdce3e303eb834448d7e46459 --- /dev/null +++ b/research/src/pipeline.py @@ -0,0 +1,467 @@ +import argparse +import hashlib +import json +import math +import random +import shutil +import time +from pathlib import Path + +import numpy as np +import onnx +import onnxruntime as ort +import torch +from huggingface_hub import HfApi, hf_hub_download, snapshot_download +from onnxruntime.quantization import QuantType, quantize_dynamic +from safetensors.torch import load_file, save_file +from tokenizers import BertWordPieceTokenizer, Tokenizer +from torch.nn import functional as functional + +from model import BASE_MODEL, BASE_REVISION, INPUT_NAMES, MiniDecisionModel +from schema import collate, encode_question, group_key, normalized_probabilities, options_for, read_rows, state_text + + +def write_json(path, value): + Path(path).write_text(json.dumps(value, indent=2, ensure_ascii=False) + "\n") + + +def make_tokenizer(directory): + vocabulary = hf_hub_download(BASE_MODEL, "vocab.txt", revision=BASE_REVISION) + tokenizer = BertWordPieceTokenizer(vocabulary, lowercase=True) + tokenizer.save(str(Path(directory) / "tokenizer.json")) + return Tokenizer.from_file(str(Path(directory) / "tokenizer.json")) + + +def dummy_inputs(batch_size, length, options): + positions = torch.linspace(2, length - 2, options).long().repeat(batch_size, 1) + return { + "input_ids": torch.randint(105, 10000, (batch_size, length)), + "attention_mask": torch.ones((batch_size, length), dtype=torch.long), + "marker_pos": positions, + "marker_mask": torch.ones((batch_size, options), dtype=torch.bool), + "qtype": torch.arange(batch_size) % 3, + } + + +def export_model(model, directory, provenance, tokenizer_path=None): + directory = Path(directory) + directory.mkdir(parents=True, exist_ok=True) + manifest_path = directory / "manifest.json" + if manifest_path.exists(): + raise ValueError("Export directory already contains a manifest; choose a new directory") + if tokenizer_path: + shutil.copyfile(tokenizer_path, directory / "tokenizer.json") + else: + make_tokenizer(directory) + model = model.cpu().eval() + floating = directory / "temporary.fp32.onnx" + quantized = directory / "model.int8.onnx" + sample = dummy_inputs(2, 48, 3) + dynamic_axes = { + "input_ids": {0: "batch", 1: "sequence"}, + "attention_mask": {0: "batch", 1: "sequence"}, + "marker_pos": {0: "batch", 1: "options"}, + "marker_mask": {0: "batch", 1: "options"}, + "qtype": {0: "batch"}, + "logits": {0: "batch", 1: "options"}, + } + with torch.no_grad(): + torch.onnx.export( + model, + tuple(sample[name] for name in INPUT_NAMES), + str(floating), + input_names=list(INPUT_NAMES), + output_names=["logits"], + dynamic_axes=dynamic_axes, + opset_version=18, + dynamo=False, + ) + onnx.checker.check_model(str(floating)) + quantize_dynamic( + str(floating), + str(quantized), + op_types_to_quantize=["MatMul", "Gemm"], + weight_type=QuantType.QInt8, + per_channel=True, + reduce_range=True, + extra_options={"MatMulConstBOnly": True}, + ) + onnx.checker.check_model(str(quantized)) + settings = ort.SessionOptions() + settings.intra_op_num_threads = 2 + settings.inter_op_num_threads = 1 + floating_session = ort.InferenceSession(str(floating), settings, providers=["CPUExecutionProvider"]) + quantized_session = ort.InferenceSession(str(quantized), settings, providers=["CPUExecutionProvider"]) + checks = [] + with torch.no_grad(): + for batch_size, length, count in [(1, 24, 2), (3, 96, 5), (1, 256, 10), (1, 512, 20)]: + inputs = dummy_inputs(batch_size, length, count) + if batch_size > 1: + inputs["attention_mask"][0, -12:] = 0 + inputs["marker_mask"][0, -2:] = False + arrays = {name: tensor.numpy() for name, tensor in inputs.items()} + torch_logits = model(**inputs).numpy() + onnx_logits = floating_session.run(None, arrays)[0] + quantized_logits = quantized_session.run(None, arrays)[0] + parity_error = float(np.max(np.abs(torch_logits - onnx_logits))) + torch_probabilities = torch.tensor(torch_logits).softmax(-1).numpy() + quantized_probabilities = torch.tensor(quantized_logits).softmax(-1).numpy() + probability_error = float(np.max(np.abs(torch_probabilities - quantized_probabilities))) + if parity_error > 0.0002 or not np.isfinite(quantized_probabilities).all(): + raise RuntimeError("ONNX numerical validation failed") + checks.append({ + "batch": batch_size, "sequence": length, "options": count, + "fp32_max_logit_error": parity_error, + "int8_max_probability_difference": probability_error, + }) + floating_bytes = floating.stat().st_size + floating.unlink() + manifest = { + "architecture": "BERT 6x512 + 1 decision layer + option-marker scorer", + "parameters": sum(parameter.numel() for parameter in model.parameters()), + "base_model": BASE_MODEL, + "language": "en", + "max_sequence_length": 512, + "max_options": 20, + "question_types": ["choice", "score", "noul"], + "quantization": "dynamic INT8 linear weights, per-channel, reduce_range=True; FP32 embeddings", + "opset": 18, + "trained": provenance.get("trained", False), + "quality_validated": False, + "calibrated": False, + "provenance": provenance, + } + write_json(manifest_path, manifest) + payload_bytes = sum(path.stat().st_size for path in directory.iterdir() if path.is_file()) + report = { + "status": "untrained_architecture_probe" if not manifest["trained"] else "trained_export_requires_quality_evaluation", + "parameters": manifest["parameters"], + "fp32_onnx_bytes": floating_bytes, + "int8_onnx_bytes": quantized.stat().st_size, + "tokenizer_bytes": (directory / "tokenizer.json").stat().st_size, + "payload_bytes": payload_bytes, + "payload_MB_decimal": payload_bytes / 1_000_000, + "payload_MiB_binary": payload_bytes / 1_048_576, + "fits_80_to_90_MB": 80_000_000 <= payload_bytes <= 90_000_000, + "checks": checks, + "limitations": [ + "Checks validate export mechanics, not English decision quality.", + "Payload excludes the runtime, Python, temporary training files and RAM.", + "CPU portability beyond the tested machine requires separate validation.", + ], + } + if payload_bytes > 90_000_000: + raise RuntimeError(f"Payload exceeds 90 MB: {payload_bytes} bytes") + return report + + +@torch.inference_mode() +def annotate(arguments): + import laya + from laya.common import collate_items + + rows = read_rows(arguments.input) + if not rows: + raise ValueError("Annotation input must not be empty") + for row in rows: + for question in row["questions"].values(): + options_for(question) + teacher_location = arguments.teacher + teacher_revision = None + if not Path(arguments.teacher).is_dir(): + teacher_revision = HfApi().model_info(arguments.teacher, token=False).sha + teacher_location = snapshot_download( + arguments.teacher, revision=teacher_revision, token=False, + allow_patterns=["model.safetensors", "rl_agent_config.json", "encoder/config.json", "tokenizer/*"], + ) + teacher = laya.load(teacher_location, device=arguments.device) + rows.sort(key=lambda row: len(state_text(row["state"]))) + started = time.perf_counter() + count = 0 + tested_types = set() + with open(arguments.output, "x", encoding="utf-8") as output: + for offset in range(0, len(rows), arguments.batch_size): + chunk = rows[offset:offset + arguments.batch_size] + encoded = [] + definitions = [] + for row in chunk: + identifiers = list(row["questions"]) + for identifier in identifiers: + teacher._check_question(identifier, row["questions"][identifier]) + internal = {identifier: teacher._to_internal(row["questions"][identifier]) for identifier in identifiers} + encoded.append(teacher._encode_state(row["state"], identifiers, internal)) + definitions.append((identifiers, internal)) + batch = collate_items(encoded, teacher.tok.pad_token_id) + logits, action = teacher._forward(batch) + cursor = 0 + for row, items, (identifiers, internal) in zip(chunk, encoded, definitions): + answers = teacher._decode_answers(logits, action, items, identifiers, internal, cursor) + cursor += len(items) + for identifier, question in row["questions"].items(): + labels, _ = options_for(question) + answer = answers[identifier] + values = answer_probabilities(answer, question, labels) + if question["type"] not in tested_types: + reference = teacher.predict(row["state"], {identifier: question})["answers"][identifier] + difference = np.max(np.abs(np.asarray(values) - answer_probabilities(reference, question, labels))) + if difference > 0.03: + raise RuntimeError("Batched teacher output differs unexpectedly from public predict") + tested_types.add(question["type"]) + record = { + "state": row["state"], "question": question, + "probabilities": normalized_probabilities(values, len(labels)).tolist(), + "teacher": arguments.teacher, + "teacher_revision": teacher_revision or getattr(teacher, "revision", None), + "question_id": identifier, + "source_group": row.get("source_group", group_key(row)), + } + for field in ("dataset", "split", "original_split", "source_index", "canonical_label"): + if field in row: + record[field] = row[field] + if identifier in row.get("expected", {}): + record["label"] = row["expected"][identifier] + output.write(json.dumps(record, ensure_ascii=False) + "\n") + count += 1 + output.flush() + if offset % (arguments.batch_size * 25) == 0: + print(json.dumps({"annotated_decisions": count, "total_states": len(rows), "elapsed_seconds": time.perf_counter() - started}), flush=True) + write_json(str(arguments.output) + ".meta.json", { + "teacher": arguments.teacher, + "teacher_revision": teacher_revision or getattr(teacher, "revision", None), + "teacher_package": "laya==0.3.20", + "input_sha256": file_hash(arguments.input), + "output_sha256": file_hash(arguments.output), + "decisions": count, + "elapsed_seconds": time.perf_counter() - started, + "checked_against_public_predict_types": sorted(tested_types), + }) + + +def answer_probabilities(answer, question, labels): + if question["type"] == "noul": + return [1.0 - answer["noul"], answer["noul"]] + return [answer["probabilities"][label] for label in labels] + + +def file_hash(path): + with open(path, "rb") as source: + return hashlib.file_digest(source, "sha256").hexdigest() + + +def prepare_rows(rows, tokenizer): + prepared = [] + for row in rows: + feature = encode_question(tokenizer, row["state"], row["question"]) + probabilities = normalized_probabilities(row["probabilities"], len(feature["labels"])) + label_index = -1 + if "label" in row: + value = row["label"] + if row["question"]["type"] == "noul": + if value not in (False, True, 0, 1): + raise ValueError("noul ground truth must be boolean") + label_index = int(value) + else: + label_index = feature["labels"].index(str(value)) + prepared.append((feature, probabilities, label_index)) + return prepared + + +def training_batch(rows, device): + inputs = {name: torch.from_numpy(array).to(device) for name, array in collate([row[0] for row in rows]).items()} + targets = torch.zeros(inputs["marker_mask"].shape, device=device) + for index, row in enumerate(rows): + targets[index, :len(row[1])] = torch.tensor(row[1], device=device) + labels = torch.tensor([row[2] for row in rows], device=device) + return inputs, targets, labels + + +def length_batches(rows, batch_size): + shuffled = list(rows) + random.shuffle(shuffled) + batches = [] + window = batch_size * 20 + for offset in range(0, len(shuffled), window): + ordered = sorted(shuffled[offset:offset + window], key=lambda row: len(row[0]["input_ids"])) + batches.extend(ordered[start:start + batch_size] for start in range(0, len(ordered), batch_size)) + random.shuffle(batches) + return batches + + +def distillation_loss(logits, targets, labels, label_weight): + logits = logits.float() + loss = functional.kl_div(logits.log_softmax(-1), targets, reduction="batchmean") + labelled = labels >= 0 + if labelled.any(): + loss = loss + label_weight * functional.cross_entropy(logits[labelled], labels[labelled]) + return loss + + +def selection_score(metrics): + accuracy = metrics["ground_truth_accuracy"] + return (accuracy if accuracy is not None else 0.0, -metrics["teacher_KL"]) + + +def evaluate(model, rows, device, batch_size): + model.eval() + divergence = 0.0 + agreement = 0 + correct = 0 + labelled = 0 + with torch.no_grad(): + for offset in range(0, len(rows), batch_size): + inputs, targets, labels = training_batch(rows[offset:offset + batch_size], device) + logits = model(**inputs) + divergence += functional.kl_div(logits.log_softmax(-1), targets, reduction="sum").item() + predictions = logits.argmax(-1) + agreement += (predictions == targets.argmax(-1)).sum().item() + valid = labels >= 0 + labelled += valid.sum().item() + correct += (predictions[valid] == labels[valid]).sum().item() + return { + "rows": len(rows), "teacher_KL": divergence / len(rows), + "teacher_agreement": agreement / len(rows), + "labelled_rows": labelled, "ground_truth_accuracy": correct / labelled if labelled else None, + } + + +def train(arguments): + train_rows, validation_rows = read_rows(arguments.train), read_rows(arguments.validation) + if not train_rows or not validation_rows: + raise ValueError("Training and validation sets must both be nonempty") + if not arguments.smoke_test and len(train_rows) < 1000: + raise ValueError("Fewer than 1000 examples: use --smoke-test only for pipeline validation") + if set(map(group_key, train_rows)) & set(map(group_key, validation_rows)): + raise ValueError("An identical state appears in both training and validation") + train_groups = {row["source_group"] for row in train_rows if "source_group" in row} + validation_groups = {row["source_group"] for row in validation_rows if "source_group" in row} + if train_groups & validation_groups: + raise ValueError("A source group appears in both training and validation") + output = Path(arguments.output) + output.mkdir(parents=True, exist_ok=True) + if (output / "model.safetensors").exists(): + raise ValueError("Checkpoint already exists; choose a new output directory") + tokenizer = make_tokenizer(output) + training = prepare_rows(train_rows, tokenizer) + validation = prepare_rows(validation_rows, tokenizer) + model = MiniDecisionModel(pretrained=not arguments.smoke_test).to(arguments.device) + optimizer = torch.optim.AdamW([ + {"params": model.encoder.parameters(), "lr": arguments.learning_rate}, + {"params": [parameter for name, parameter in model.named_parameters() if not name.startswith("encoder.")], "lr": arguments.learning_rate * 5}, + ], weight_decay=0.01) + total_steps = math.ceil(len(training) / arguments.batch_size) * arguments.epochs + warmup_steps = max(1, int(total_steps * 0.06)) + + def rate_multiplier(step): + if step < warmup_steps: + return (step + 1) / warmup_steps + return max(0.05, (total_steps - step) / max(1, total_steps - warmup_steps)) + + scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, rate_multiplier) + use_amp = str(arguments.device).startswith("cuda") and torch.cuda.is_bf16_supported() + if str(arguments.device).startswith("cuda"): + torch.set_float32_matmul_precision("high") + history = [] + best_score = (-float("inf"), -float("inf")) + best_epoch = 0 + started = time.perf_counter() + for epoch in range(arguments.epochs): + model.train() + batches = length_batches(training, arguments.batch_size) + for batch_index, rows in enumerate(batches): + inputs, targets, labels = training_batch(rows, arguments.device) + optimizer.zero_grad(set_to_none=True) + with torch.autocast("cuda" if use_amp else "cpu", dtype=torch.bfloat16, enabled=use_amp): + logits = model(**inputs) + loss = distillation_loss(logits, targets, labels, arguments.label_weight) + if not torch.isfinite(loss): + raise RuntimeError("Nonfinite training loss; refusing to save a corrupt model") + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) + optimizer.step() + scheduler.step() + if (batch_index + 1) % 100 == 0: + print(json.dumps({"epoch": epoch + 1, "batch": batch_index + 1, "batches": len(batches), "loss": loss.item(), "elapsed_seconds": time.perf_counter() - started}), flush=True) + metrics = evaluate(model, validation, arguments.device, arguments.batch_size) + metrics["epoch"] = epoch + 1 + history.append(metrics) + print(json.dumps(metrics), flush=True) + if selection_score(metrics) > best_score: + best_score = selection_score(metrics) + best_epoch = epoch + 1 + save_file({name: value.detach().cpu().contiguous() for name, value in model.state_dict().items()}, output / "model.safetensors") + write_json(output / "training.json", { + "trained": not arguments.smoke_test, + "smoke_test": arguments.smoke_test, + "quality_validated": False, + "base_model": BASE_MODEL, + "base_revision": BASE_REVISION, + "seed": 42, + "best_epoch": best_epoch, + "selection": "validation ground-truth accuracy; teacher KL as tiebreaker", + "train_sha256": file_hash(arguments.train), + "validation_sha256": file_hash(arguments.validation), + "hyperparameters": {"epochs": arguments.epochs, "batch_size": arguments.batch_size, "encoder_learning_rate": arguments.learning_rate, "head_learning_rate": arguments.learning_rate * 5, "label_weight": arguments.label_weight, "bf16_training": use_amp}, + "elapsed_seconds": time.perf_counter() - started, + "training_rows": len(training), "validation_rows": len(validation), + "truncated_training_states": sum(row[0]["truncated_state"] for row in training), + "teacher_sources": sorted({row.get("teacher", "unspecified") for row in train_rows}), + "history": history, + }) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--threads", type=int, default=2) + commands = parser.add_subparsers(dest="command", required=True) + probe = commands.add_parser("probe") + probe.add_argument("--output", required=True) + probe.add_argument("--report", required=True) + annotation = commands.add_parser("annotate") + annotation.add_argument("--input", required=True) + annotation.add_argument("--output", required=True) + annotation.add_argument("--teacher", default="convaiinnovations/laya") + annotation.add_argument("--device", default="cuda") + annotation.add_argument("--batch-size", type=int, default=16) + training = commands.add_parser("train") + training.add_argument("--train", required=True) + training.add_argument("--validation", required=True) + training.add_argument("--output", required=True) + training.add_argument("--device", default="cuda") + training.add_argument("--epochs", type=int, default=3) + training.add_argument("--batch-size", type=int, default=8) + training.add_argument("--learning-rate", type=float, default=2e-5) + training.add_argument("--label-weight", type=float, default=0.5) + training.add_argument("--smoke-test", action="store_true") + exporting = commands.add_parser("export") + exporting.add_argument("--checkpoint", required=True) + exporting.add_argument("--output", required=True) + exporting.add_argument("--report", required=True) + arguments = parser.parse_args() + if arguments.threads < 1: + parser.error("--threads must be positive") + torch.set_num_threads(arguments.threads) + torch.manual_seed(42) + random.seed(42) + np.random.seed(42) + if arguments.command == "probe": + report = export_model(MiniDecisionModel(), arguments.output, {"trained": False, "purpose": "size and runtime validation only"}) + write_json(arguments.report, report) + print(json.dumps(report, indent=2)) + elif arguments.command == "annotate": + annotate(arguments) + elif arguments.command == "train": + if arguments.epochs < 1 or arguments.batch_size < 1: + parser.error("epochs and batch size must be positive") + train(arguments) + else: + checkpoint = Path(arguments.checkpoint) + model = MiniDecisionModel() + model.load_state_dict(load_file(checkpoint / "model.safetensors")) + provenance = json.loads((checkpoint / "training.json").read_text()) + report = export_model(model, arguments.output, provenance, checkpoint / "tokenizer.json") + write_json(arguments.report, report) + print(json.dumps(report, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/research/src/requirements-build.txt b/research/src/requirements-build.txt new file mode 100644 index 0000000000000000000000000000000000000000..b8fdb77bf2b6f83c282956c9775a6fbb0b1d1aae --- /dev/null +++ b/research/src/requirements-build.txt @@ -0,0 +1,10 @@ +torch==2.8.0 +transformers==4.57.6 +onnx==1.20.1 +onnxruntime==1.24.4 +numpy>=1.26,<3 +tokenizers>=0.22,<0.23 +safetensors>=0.6,<1 +huggingface-hub>=0.34,<1 +laya==0.3.20 +datasets==3.6.0 diff --git a/research/src/schema.py b/research/src/schema.py new file mode 100644 index 0000000000000000000000000000000000000000..1f04a34312bad7531a3de8eabe0786ee9815524a --- /dev/null +++ b/research/src/schema.py @@ -0,0 +1,119 @@ +import hashlib +import json + +import numpy as np + + +QTYPES = {"choice": 0, "score": 1, "noul": 2} +INPUT_NAMES = ("input_ids", "attention_mask", "marker_pos", "marker_mask", "qtype") + + +def state_text(state): + return state if isinstance(state, str) else json.dumps(state, ensure_ascii=False, sort_keys=True) + + +def group_key(row): + return hashlib.sha256(state_text(row["state"]).encode()).hexdigest() + + +def options_for(question): + kind = question["type"] + criteria = question.get("criteria") + if kind == "choice": + if not isinstance(criteria, dict) or not 2 <= len(criteria) <= 20: + raise ValueError("choice requires a mapping containing 2 to 20 options") + labels = list(criteria) + if any(not isinstance(label, str) or not label for label in labels): + raise ValueError("choice labels must be nonempty strings") + descriptions = [f"{label}: {criteria[label]}" for label in labels] + elif kind == "score": + if not isinstance(criteria, list) or not 2 <= len(criteria) <= 20: + raise ValueError("score requires a list containing 2 to 20 ordered levels") + labels = [str(index) for index in range(len(criteria))] + descriptions = [f"level {index}: {value}" for index, value in enumerate(criteria)] + elif kind == "noul": + criteria = criteria or {} + if not isinstance(criteria, dict) or set(criteria) - {"false", "true"}: + raise ValueError("noul criteria must use false and true") + labels = ["false", "true"] + wording = question.get("labels", {"false": "false", "true": "true"}) + if set(wording) != {"false", "true"}: + raise ValueError("noul labels must contain false and true") + descriptions = [ + f"{wording['false']}: {criteria.get('false', 'no, the statement does not hold')}", + f"{wording['true']}: {criteria.get('true', 'yes, the statement holds')}", + ] + else: + raise ValueError(f"Unsupported question type: {kind}") + return labels, descriptions + + +def encode_question(tokenizer, state, question, max_length=512): + if not 32 <= max_length <= 512: + raise ValueError("max_length must be between 32 and 512") + labels, descriptions = options_for(question) + special = {name: tokenizer.token_to_id(name) for name in ["[CLS]", "[SEP]", "[MASK]"]} + if any(value is None for value in special.values()): + raise ValueError("The tokenizer is missing required BERT special tokens") + + def tokenize(text): + return tokenizer.encode(str(text).replace("[MASK]", " "), add_special_tokens=False).ids + + instructions = question.get("instructions", "") + if not isinstance(instructions, str) or not instructions.strip(): + raise ValueError("A nonempty instructions string is required") + prefix = tokenize(f"{question['type']} question: {instructions}") + identifiers = [special["[CLS]"]] + prefix + [special["[SEP]"]] + markers = [] + for description in descriptions: + markers.append(len(identifiers)) + identifiers.extend([special["[MASK]"]] + tokenize(description)) + identifiers.append(special["[SEP]"]) + if len(identifiers) > min(192, max_length - 16): + raise ValueError("Question/options exceed the prompt budget; shorten them explicitly") + content = tokenize(state_text(state)) + room = max_length - len(identifiers) - 1 + identifiers.extend(content[:room] + [special["[SEP]"]]) + return { + "input_ids": identifiers, + "marker_pos": markers, + "qtype": QTYPES[question["type"]], + "labels": labels, + "truncated_state": len(content) > room, + } + + +def collate(features): + length = max(len(feature["input_ids"]) for feature in features) + option_count = max(len(feature["marker_pos"]) for feature in features) + batch_size = len(features) + arrays = { + "input_ids": np.zeros((batch_size, length), dtype=np.int64), + "attention_mask": np.zeros((batch_size, length), dtype=np.int64), + "marker_pos": np.zeros((batch_size, option_count), dtype=np.int64), + "marker_mask": np.zeros((batch_size, option_count), dtype=np.bool_), + "qtype": np.zeros(batch_size, dtype=np.int64), + } + for index, feature in enumerate(features): + sequence_length = len(feature["input_ids"]) + count = len(feature["marker_pos"]) + arrays["input_ids"][index, :sequence_length] = feature["input_ids"] + arrays["attention_mask"][index, :sequence_length] = 1 + arrays["marker_pos"][index, :count] = feature["marker_pos"] + arrays["marker_mask"][index, :count] = True + arrays["qtype"][index] = feature["qtype"] + return arrays + + +def normalized_probabilities(values, count): + probabilities = np.asarray(values, dtype=np.float64) + if probabilities.shape != (count,) or not np.isfinite(probabilities).all(): + raise ValueError("Teacher probabilities have an invalid shape or nonfinite values") + if (probabilities < 0).any() or probabilities.sum() <= 0: + raise ValueError("Teacher probabilities must be nonnegative and have positive mass") + return probabilities / probabilities.sum() + + +def read_rows(path): + with open(path, encoding="utf-8") as handle: + return [json.loads(line) for line in handle if line.strip()] diff --git a/runtime.py b/runtime.py new file mode 100644 index 0000000000000000000000000000000000000000..bf46d735c727d46ff8a8e789f3c2453df0c89ee4 --- /dev/null +++ b/runtime.py @@ -0,0 +1,76 @@ +import argparse +import json +import time +from pathlib import Path + +import numpy as np +import onnxruntime as ort +from tokenizers import Tokenizer + +from schema import collate, encode_question + + +class DecisionSession: + def __init__(self, directory, threads=2, allow_untrained=False): + directory = Path(directory) + self.manifest = json.loads((directory / "manifest.json").read_text()) + if not self.manifest.get("trained") and not allow_untrained: + raise ValueError("This is an untrained architecture probe, not a usable distilled model") + if threads < 1: + raise ValueError("threads must be positive") + settings = ort.SessionOptions() + settings.intra_op_num_threads = threads + settings.inter_op_num_threads = 1 + settings.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL + self.session = ort.InferenceSession( + str(directory / "model.int8.onnx"), + sess_options=settings, + providers=["CPUExecutionProvider"], + ) + self.tokenizer = Tokenizer.from_file(str(directory / "tokenizer.json")) + + def predict(self, state, questions, max_length=512): + if not questions: + return {"answers": {}, "latency_ms": 0.0} + started = time.perf_counter() + identifiers = list(questions) + features = [encode_question(self.tokenizer, state, questions[key], max_length) for key in identifiers] + logits = self.session.run(None, collate(features))[0] + answers = {} + for index, key in enumerate(identifiers): + labels = features[index]["labels"] + scores = logits[index, :len(labels)].astype(np.float64) + scores -= scores.max() + probabilities = np.exp(scores) + probabilities /= probabilities.sum() + kind = questions[key]["type"] + answer = { + "type": kind, + "probabilities": dict(zip(labels, probabilities.tolist())), + "max_probability": float(probabilities.max()), + "calibrated": False, + "truncated_state": features[index]["truncated_state"], + } + if kind == "choice": + answer["choice"] = labels[int(probabilities.argmax())] + elif kind == "score": + answer["score"] = float(probabilities @ np.arange(len(labels))) + else: + answer["noul"] = float(probabilities[1]) + answers[key] = answer + return {"answers": answers, "latency_ms": (time.perf_counter() - started) * 1000} + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("model_directory") + parser.add_argument("request_json") + parser.add_argument("--threads", type=int, default=2) + arguments = parser.parse_args() + request = json.loads(Path(arguments.request_json).read_text()) + session = DecisionSession(arguments.model_directory, threads=arguments.threads) + print(json.dumps(session.predict(request["state"], request["questions"]), indent=2)) + + +if __name__ == "__main__": + main() diff --git a/schema.py b/schema.py new file mode 100644 index 0000000000000000000000000000000000000000..1f04a34312bad7531a3de8eabe0786ee9815524a --- /dev/null +++ b/schema.py @@ -0,0 +1,119 @@ +import hashlib +import json + +import numpy as np + + +QTYPES = {"choice": 0, "score": 1, "noul": 2} +INPUT_NAMES = ("input_ids", "attention_mask", "marker_pos", "marker_mask", "qtype") + + +def state_text(state): + return state if isinstance(state, str) else json.dumps(state, ensure_ascii=False, sort_keys=True) + + +def group_key(row): + return hashlib.sha256(state_text(row["state"]).encode()).hexdigest() + + +def options_for(question): + kind = question["type"] + criteria = question.get("criteria") + if kind == "choice": + if not isinstance(criteria, dict) or not 2 <= len(criteria) <= 20: + raise ValueError("choice requires a mapping containing 2 to 20 options") + labels = list(criteria) + if any(not isinstance(label, str) or not label for label in labels): + raise ValueError("choice labels must be nonempty strings") + descriptions = [f"{label}: {criteria[label]}" for label in labels] + elif kind == "score": + if not isinstance(criteria, list) or not 2 <= len(criteria) <= 20: + raise ValueError("score requires a list containing 2 to 20 ordered levels") + labels = [str(index) for index in range(len(criteria))] + descriptions = [f"level {index}: {value}" for index, value in enumerate(criteria)] + elif kind == "noul": + criteria = criteria or {} + if not isinstance(criteria, dict) or set(criteria) - {"false", "true"}: + raise ValueError("noul criteria must use false and true") + labels = ["false", "true"] + wording = question.get("labels", {"false": "false", "true": "true"}) + if set(wording) != {"false", "true"}: + raise ValueError("noul labels must contain false and true") + descriptions = [ + f"{wording['false']}: {criteria.get('false', 'no, the statement does not hold')}", + f"{wording['true']}: {criteria.get('true', 'yes, the statement holds')}", + ] + else: + raise ValueError(f"Unsupported question type: {kind}") + return labels, descriptions + + +def encode_question(tokenizer, state, question, max_length=512): + if not 32 <= max_length <= 512: + raise ValueError("max_length must be between 32 and 512") + labels, descriptions = options_for(question) + special = {name: tokenizer.token_to_id(name) for name in ["[CLS]", "[SEP]", "[MASK]"]} + if any(value is None for value in special.values()): + raise ValueError("The tokenizer is missing required BERT special tokens") + + def tokenize(text): + return tokenizer.encode(str(text).replace("[MASK]", " "), add_special_tokens=False).ids + + instructions = question.get("instructions", "") + if not isinstance(instructions, str) or not instructions.strip(): + raise ValueError("A nonempty instructions string is required") + prefix = tokenize(f"{question['type']} question: {instructions}") + identifiers = [special["[CLS]"]] + prefix + [special["[SEP]"]] + markers = [] + for description in descriptions: + markers.append(len(identifiers)) + identifiers.extend([special["[MASK]"]] + tokenize(description)) + identifiers.append(special["[SEP]"]) + if len(identifiers) > min(192, max_length - 16): + raise ValueError("Question/options exceed the prompt budget; shorten them explicitly") + content = tokenize(state_text(state)) + room = max_length - len(identifiers) - 1 + identifiers.extend(content[:room] + [special["[SEP]"]]) + return { + "input_ids": identifiers, + "marker_pos": markers, + "qtype": QTYPES[question["type"]], + "labels": labels, + "truncated_state": len(content) > room, + } + + +def collate(features): + length = max(len(feature["input_ids"]) for feature in features) + option_count = max(len(feature["marker_pos"]) for feature in features) + batch_size = len(features) + arrays = { + "input_ids": np.zeros((batch_size, length), dtype=np.int64), + "attention_mask": np.zeros((batch_size, length), dtype=np.int64), + "marker_pos": np.zeros((batch_size, option_count), dtype=np.int64), + "marker_mask": np.zeros((batch_size, option_count), dtype=np.bool_), + "qtype": np.zeros(batch_size, dtype=np.int64), + } + for index, feature in enumerate(features): + sequence_length = len(feature["input_ids"]) + count = len(feature["marker_pos"]) + arrays["input_ids"][index, :sequence_length] = feature["input_ids"] + arrays["attention_mask"][index, :sequence_length] = 1 + arrays["marker_pos"][index, :count] = feature["marker_pos"] + arrays["marker_mask"][index, :count] = True + arrays["qtype"][index] = feature["qtype"] + return arrays + + +def normalized_probabilities(values, count): + probabilities = np.asarray(values, dtype=np.float64) + if probabilities.shape != (count,) or not np.isfinite(probabilities).all(): + raise ValueError("Teacher probabilities have an invalid shape or nonfinite values") + if (probabilities < 0).any() or probabilities.sum() <= 0: + raise ValueError("Teacher probabilities must be nonnegative and have positive mass") + return probabilities / probabilities.sum() + + +def read_rows(path): + with open(path, encoding="utf-8") as handle: + return [json.loads(line) for line in handle if line.strip()]