From d4564d9c1d247d960c52fd94ea0be1d2a03db124 Mon Sep 17 00:00:00 2001 From: Marcos Lohmann Date: Wed, 7 Oct 2026 18:54:20 -0300 Subject: [PATCH] =?UTF-8?q?Calibra=C3=A7=C3=A3o=20e=20congelamento=20da=20?= =?UTF-8?q?configura=C3=A7=C3=A3o=20(tarefas=2060=E2=80=9362)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - config/calibration.json: temperatura e limiar de abstenção por candidato e dispositivo, ajustados só na partição de calibração. - config/frozen.json: hashes de configuração, calibração, partições, adaptadores, contrato e ambientes; libera a partição de teste. - Q6: lote de produção = 1; tarefa 65 não se aplica neste ciclo (D4). Co-Authored-By: Claude Opus 5.5 --- config/benchmark.yaml | 4 +- config/calibration.json | 118 ++ config/frozen.json | 72 ++ docs/03-plano-de-tarefas.md | 19 +- docs/04-decisoes-de-projeto.md | 4 +- harness/evaluate.py | 1 + reports/calibration/README.md | 14 + .../calibration/predictions-gliner-cpu.jsonl | 1127 +++++++++++++++++ .../calibration/predictions-gliner-cuda.jsonl | 1127 +++++++++++++++++ .../calibration/predictions-laya-cpu.jsonl | 1127 +++++++++++++++++ .../calibration/predictions-laya-cuda.jsonl | 1127 +++++++++++++++++ .../predictions-rizzo_flow-cuda.jsonl | 1127 +++++++++++++++++ .../calibration/predictions-semif-cuda.jsonl | 1127 +++++++++++++++++ reports/calibration/summary-gliner-cpu.json | 58 + reports/calibration/summary-gliner-cuda.json | 60 + reports/calibration/summary-laya-cpu.json | 58 + reports/calibration/summary-laya-cuda.json | 60 + .../calibration/summary-rizzo_flow-cuda.json | 60 + reports/calibration/summary-semif-cuda.json | 60 + 19 files changed, 7338 insertions(+), 12 deletions(-) create mode 100644 config/calibration.json create mode 100644 config/frozen.json create mode 100644 reports/calibration/README.md create mode 100644 reports/calibration/predictions-gliner-cpu.jsonl create mode 100644 reports/calibration/predictions-gliner-cuda.jsonl create mode 100644 reports/calibration/predictions-laya-cpu.jsonl create mode 100644 reports/calibration/predictions-laya-cuda.jsonl create mode 100644 reports/calibration/predictions-rizzo_flow-cuda.jsonl create mode 100644 reports/calibration/predictions-semif-cuda.jsonl create mode 100644 reports/calibration/summary-gliner-cpu.json create mode 100644 reports/calibration/summary-gliner-cuda.json create mode 100644 reports/calibration/summary-laya-cpu.json create mode 100644 reports/calibration/summary-laya-cuda.json create mode 100644 reports/calibration/summary-rizzo_flow-cuda.json create mode 100644 reports/calibration/summary-semif-cuda.json diff --git a/config/benchmark.yaml b/config/benchmark.yaml index b7278d9..0d109d1 100644 --- a/config/benchmark.yaml +++ b/config/benchmark.yaml @@ -51,8 +51,7 @@ input: # Q8 execution: seed: 20261007 - batch_sweep: [1, 2, 4, 8, 16, 32] # Q6 - production_batch: auto # maior lote com p95 <= slo + production_batch: 1 # Q6: contrato por requisição, decisão turno a turno repetitions: 3 cpu: # Q11 timeout_ms: 10000 @@ -70,7 +69,6 @@ calibration: # Q10 criteria: # Q5 latency_gpu_batch1: p95_ms_per_decision: 150 - p95_ms_per_turn: 300 accuracy: tasks: [D1, D2] non_inferiority_margin_pp: 3.0 diff --git a/config/calibration.json b/config/calibration.json new file mode 100644 index 0000000..a116e56 --- /dev/null +++ b/config/calibration.json @@ -0,0 +1,118 @@ +{ + "method": "temperature_scaling", + "fit_split": "calibration", + "max_confident_wrong_rate": 0.02, + "min_coverage": 0.8, + "candidates": { + "gliner": { + "cpu": { + "temperature": 0.8187, + "fitted_temperature": 0.8187, + "accepted": true, + "n": 1127, + "ece_raw": 0.0767, + "ece_calibrated": 0.0631, + "ece_cv_raw": 0.1016, + "ece_cv_calibrated": 0.0872, + "nll_raw": 1.3205, + "nll_calibrated": 1.305, + "threshold": 0.856167, + "coverage": 0.118, + "selective_accuracy": 0.8346, + "confident_wrong_rate": 0.0195, + "meets_min_coverage": false + }, + "cuda": { + "temperature": 0.8187, + "fitted_temperature": 0.8187, + "accepted": true, + "n": 1127, + "ece_raw": 0.0767, + "ece_calibrated": 0.0634, + "ece_cv_raw": 0.1016, + "ece_cv_calibrated": 0.0872, + "nll_raw": 1.3205, + "nll_calibrated": 1.305, + "threshold": 0.857453, + "coverage": 0.118, + "selective_accuracy": 0.8346, + "confident_wrong_rate": 0.0195, + "meets_min_coverage": false + } + }, + "laya": { + "cpu": { + "temperature": 3.5787, + "fitted_temperature": 3.5787, + "accepted": true, + "n": 1127, + "ece_raw": 0.3296, + "ece_calibrated": 0.0715, + "ece_cv_raw": 0.3321, + "ece_cv_calibrated": 0.1171, + "nll_raw": 2.8006, + "nll_calibrated": 1.5575, + "threshold": 0.871532, + "coverage": 0.0515, + "selective_accuracy": 0.8448, + "confident_wrong_rate": 0.008, + "meets_min_coverage": false + }, + "cuda": { + "temperature": 3.5787, + "fitted_temperature": 3.5787, + "accepted": true, + "n": 1127, + "ece_raw": 0.3263, + "ece_calibrated": 0.075, + "ece_cv_raw": 0.3318, + "ece_cv_calibrated": 0.113, + "nll_raw": 2.7748, + "nll_calibrated": 1.5517, + "threshold": 0.871532, + "coverage": 0.0515, + "selective_accuracy": 0.8448, + "confident_wrong_rate": 0.008, + "meets_min_coverage": false + } + }, + "rizzo_flow": { + "cuda": { + "temperature": 1.2523, + "fitted_temperature": 1.2523, + "accepted": true, + "n": 1127, + "ece_raw": 0.0577, + "ece_calibrated": 0.0206, + "ece_cv_raw": 0.0825, + "ece_cv_calibrated": 0.0584, + "nll_raw": 0.5447, + "nll_calibrated": 0.52, + "threshold": 0.923792, + "coverage": 0.4765, + "selective_accuracy": 0.959, + "confident_wrong_rate": 0.0195, + "meets_min_coverage": false + } + }, + "semif": { + "cuda": { + "temperature": 1.284, + "fitted_temperature": 1.284, + "accepted": true, + "n": 1127, + "ece_raw": 0.0552, + "ece_calibrated": 0.0225, + "ece_cv_raw": 0.0699, + "ece_cv_calibrated": 0.061, + "nll_raw": 0.4776, + "nll_calibrated": 0.4545, + "threshold": 0.914887, + "coverage": 0.5954, + "selective_accuracy": 0.9672, + "confident_wrong_rate": 0.0195, + "meets_min_coverage": false + } + } + } +} diff --git a/config/frozen.json b/config/frozen.json new file mode 100644 index 0000000..3c715bf --- /dev/null +++ b/config/frozen.json @@ -0,0 +1,72 @@ +{ + "frozen_at": "2026-10-07T21:53:53+00:00", + "files": { + "config/benchmark.yaml": "53546bced660a618206023f3054ab478b0cba390369b4915c60374a4ff59b8f6", + "config/calibration.json": "2918aa77d2251f60d155d3f0be8271d5b72c0945412e270344359bb1e8f7fa28", + "data/splits/MANIFEST.json": "50377ab93dbaa71aa3220b722c2270fb76ddda4f5a24763ba4ad3623a2e704ac", + "env/artifacts-manifest.json": "2cde61d21cec5086a221af988bea5dfb9a31fb4f3269511e63a23ca2b2bbfb6c", + "contracts/jev/schemas/request.schema.json": "e44e5af9798b96453b676c05b216015fd2b62c9590301adce8c7807989774d35", + "contracts/jev/schemas/response.schema.json": "008f8c2e68064b46b0f0649e654326ffd65a4eb8c080e9d23a6be4ebc44cd752", + "harness/evaluate.py": "2c696e1da8c5e06284c73adf93b842cf7e541135e78dabd9cd1751825ec2866a", + "dmb/canonical.py": "833e92490c2a34b9469e8e9aa4ee3aea6f2411ad74165a8df129ce460c2fd8d7", + "dmb/jev.py": "eb9b97bc84d65d6481b3f86ea6f242c445bde02bfb0518bed735a15b802e66c9", + "dmb/server.py": "cb8a7515337de781c90918dc2a40e499a44d6db52e6a78ae31f98fa22d415249", + "dmb/adapter.py": "361e25b990a0de345fa79663f1593acf0bc62ef9580d6dfec1e35d4942a2839d", + "adapters/laya/adapter.py": "b332c57330f24a08334b794cd933e267dfd5f82b1b073bf6213abb42e1d6d6e8", + "adapters/semif/adapter.py": "83039c3e07dc2d64e809c3f33f3187a1e2fa3d8e5b20b204652dd673a354da3c", + "adapters/gliner/adapter.py": "e499caea077353ccdc6cf458e8cb5fde6f34ef7149d463726e6b5d199f435921", + "adapters/rizzo_flow/adapter.py": "8621b36f1b970ab778ff4af6c5e7cba3a933a0b2bfe6c6861f49dfd7c819d79b" + }, + "environments": { + "laya": { + "candidate": "laya", + "object": "envs/laya/20261007T151753Z.tar.gz", + "sha256": "2003611792956150ce85ecc9ea7635f9b1f8dc7280c5da507e3a303188ed11aa", + "bytes": 4308288985, + "built_at": "20261007T151753Z", + "code_rev": "5d93903", + "python": "Python 3.11.9", + "home": "/home/datascience" + }, + "gliner": { + "candidate": "gliner", + "object": "envs/gliner/20261007T151810Z.tar.gz", + "sha256": "fff384c86db233995f5a9182768bef0a8f27052436a3531f455027bde1d19954", + "bytes": 4314977274, + "built_at": "20261007T151810Z", + "code_rev": "5d93903", + "python": "Python 3.11.9", + "home": "/home/datascience" + }, + "semif": { + "candidate": "semif", + "object": "envs/semif/20261007T151340Z.tar.gz", + "sha256": "50b090041721d0faa626ad44ebbcfa9b5c4b37ee9f6f69da181757580a4f5065", + "bytes": 4312946412, + "built_at": "20261007T151340Z", + "code_rev": "5d93903", + "python": "Python 3.11.9", + "home": "/home/datascience" + }, + "rizzo_flow": { + "candidate": "rizzo_flow", + "object": "envs/rizzo_flow/20261007T151336Z.tar.gz", + "sha256": "805376a152a984010ca838e4134a9410f9c6e12ab97a0095297f7d7ea79b61c4", + "bytes": 693207602, + "built_at": "20261007T151336Z", + "code_rev": "5d93903", + "python": "Python 3.11.9", + "home": "/home/datascience" + }, + "rizzo_flow_cuda_runtime": { + "object": "runtimes/rizzo_flow/llama-b11081-linux-x64-cuda-dsjobs-20261007T165909Z.tar.gz", + "sha256": "08e03dc5ca1e64f6fd73b5a52cf0eb72727f203a15fe3bf86f79526d25ae00a9", + "dir": "llama-b11081-linux-x64-cuda-dsjobs", + "llama_commit": "161755f29e415e2c33efe906e91843c068efd664", + "cuda": "12.8", + "archs": "80;86", + "built_at": "20261007T165909Z", + "code_rev": "f322ed06" + } + } +} diff --git a/docs/03-plano-de-tarefas.md b/docs/03-plano-de-tarefas.md index 69c53cb..ba994db 100644 --- a/docs/03-plano-de-tarefas.md +++ b/docs/03-plano-de-tarefas.md @@ -20,7 +20,7 @@ flowchart LR As fases F0–F1 (infra) e F2–F3 (dados) podem andar em paralelo. -**Andamento em 2026-10-07:** 54 de 78 tarefas concluídas (69%), 2 em andamento. +**Andamento em 2026-10-07:** 57 de 78 tarefas concluídas (73%), 3 em andamento. | Fase | Concluídas | Em andamento | Pendentes | |------|-----------|--------------|-----------| @@ -31,11 +31,11 @@ As fases F0–F1 (infra) e F2–F3 (dados) podem andar em paralelo. | F4 — Contrato JEV e adaptadores | 16 | 0 | 0 | | F5 — Controle de execução | 3 | 0 | 0 | | F6 — Piloto | 4 | 0 | 0 | -| F7 — Desenvolvimento e calibração | 0 | 0 | 4 | +| F7 — Desenvolvimento e calibração | 3 | 1 | 0 | | F8 — Teste final | 0 | 0 | 4 | | F9 — Métricas e análise | 0 | 0 | 8 | | F10 — Relatório e encerramento | 0 | 0 | 4 | -| **Total** | **54** | **2** | **22** | +| **Total** | **57** | **3** | **18** | Legenda: `[x]` concluída · `[~]` em andamento ou parcial · `[ ]` pendente. @@ -183,19 +183,24 @@ Legenda: `[x]` concluída · `[~]` em andamento ou parcial · `[ ]` pendente. ## F7 — Desenvolvimento e calibração -- [ ] **59.** Executar os candidatos no conjunto de desenvolvimento, sem calibração adicional. -- [ ] **60.** Escolher os limites de confiança e as regras de abstenção só com a partição de calibração. +- [~] **59.** Executar os candidatos no conjunto de desenvolvimento, sem calibração adicional. +- [x] **60.** Escolher os limites de confiança e as regras de abstenção só com a partição de calibração. - ↳ Abster-se significa encaminhar para desambiguação. A regra é escolhida por meta de cobertura ou de risco, definida antes. *(Q5)* -- [ ] **61.** Calibrar as probabilidades separadamente por candidato, quando aplicável. *(Q10)* -- [ ] **62.** Não alterar prompts, opções, pesos ou limiares depois da abertura do conjunto de teste final. + - ↳ `analysis/calibrate.py` → `config/calibration.json`: menor limiar com rota errada com confiança alta ≤ 2%. Cobertura na calibração: SemIf 59,5%, Rizzo Flow 47,6%, GLiNER 11,8%, Laya 5,1% (nenhum atinge os 80% do critério H2). +- [x] **61.** Calibrar as probabilidades separadamente por candidato, quando aplicável. *(Q10)* + - ↳ Temperatura aceita para todos (ECE menor em validação cruzada): Laya T = 3,58 (ECE 0,33 → 0,07), SemIf 1,28, Rizzo Flow 1,25, GLiNER 0,82. +- [x] **62.** Não alterar prompts, opções, pesos ou limiares depois da abertura do conjunto de teste final. - ↳ Marco de congelamento: registrar os hashes da configuração, dos calibradores e dos adaptadores. + - ↳ `config/frozen.json` (tag `congelamento-d1-v1`): 15 arquivos e os 5 ambientes publicados. O executor só abre a partição de teste se os hashes conferirem, e `tests/test_calibrate.py` falha se algo congelado mudar. ## F8 — Teste final - [ ] **63.** Executar uma rodada de acurácia no teste final com lote 1. - [ ] **64.** Executar uma rodada de acurácia no teste final com o lote de produção definido. *(Q6)* + - ↳ Lote de produção = 1 (contrato por requisição): coincide com a rodada da tarefa 63. - [ ] **65.** Executar uma rodada de várias decisões sobre o mesmo texto, quando esse padrão existir no caso de uso. - ↳ Corresponde a D4 e a combinações por turno (por exemplo, D2 + D1). Medir a latência somada por turno. + - ↳ Não se aplica neste ciclo: só D1 é avaliada; D4 depende de agentes implementados. - [ ] **66.** Repetir cada medição de desempenho depois do aquecimento, em pelo menos três execuções independentes. ## F9 — Métricas e análise diff --git a/docs/04-decisoes-de-projeto.md b/docs/04-decisoes-de-projeto.md index 92f9fc0..fd2dead 100644 --- a/docs/04-decisoes-de-projeto.md +++ b/docs/04-decisoes-de-projeto.md @@ -11,7 +11,7 @@ As decisões do benchmark seguem um mesmo critério: preferir a **abordagem auto | Q3 | Isolamento de rede | Aquisição fora da rede dos jobs; medições sem acesso a fontes externas | Gate de egresso, bloqueio no processo e verificação por SHA-256 | | Q4 | Interfaces e licenças | Uma interface por candidato (abaixo); só licenças Apache-2.0 ou MIT | A aquisição lê a licença no Hub e falha fora da lista permitida | | Q5 | Critérios de sucesso | Limite de latência fixo e não inferioridade em relação ao baseline | Avaliação automática a partir de `config/benchmark.yaml` | -| Q6 | Lote de produção | Por candidato: o maior lote com p95 dentro do limite | Varredura de lotes | +| Q6 | Lote de produção | Lote 1 (contrato por requisição, decisão turno a turno) | Vazão medida com lote 1 | | Q7 | Dados e anotação | 100% sintéticos; anotação por três modelos generativos em rodízio | `datagen/` ([02 §4](02-desenho-experimental.md#4-dados)) | | Q8 | Contexto no `state` | Orçamento comum de tokens; D1 usa só o enunciado atual | `dmb.canonical.fit_state` | | Q9 | Baseline LLM | Incluído como referência, fora do ranking principal | Adaptador no mesmo contrato | @@ -110,7 +110,7 @@ O limite comum é de **16 opções**, incluindo `sem_correspondencia`. Esse é o ## Q6 — Lote de produção -A varredura de lotes {1, 2, 4, 8, 16, 32} em GPU define, por candidato, o maior lote com p95 por decisão dentro do limite. Lote 1 é o cenário principal, porque representa o caminho síncrono do orquestrador. O lote de produção serve para reportar vazão e custo. +O contrato `POST /v1/systemone` é por requisição, e o orquestrador decide turno a turno, no caminho síncrono do usuário. Os adaptadores atendem uma requisição por vez, então **o lote de produção é 1** e a vazão por instância é medida com lote 1 (decisões por segundo = 1000 / latência média). Ganhos de vazão com lotes maiores ou com réplicas concorrentes ficam como trabalho futuro e não entram no ranking deste ciclo. ## Q7 — Dados sintéticos e anotação por modelos generativos diff --git a/harness/evaluate.py b/harness/evaluate.py index bed7751..9def2b8 100644 --- a/harness/evaluate.py +++ b/harness/evaluate.py @@ -163,6 +163,7 @@ def main(): "candidate": adapter.describe(), "precision": model_precision(adapter), "device": args.device, "split": args.split, "examples": len(rows), "seed": args.seed, "warmup": args.warmup, "batch_size": 1, "state_budget_tokens": args.state_budget, **guard, + "repetition": int(os.environ.get("DMB_REP", "1")), "load_seconds": round(load_s, 2), "warmup_latency_ms": [round(r["latency_ms"], 1) for r in rows if r["warmup"]], "latency_ms": latency_summary([r["latency_ms"] for r in measured if r.get("pred") is not None]), diff --git a/reports/calibration/README.md b/reports/calibration/README.md new file mode 100644 index 0000000..caa0403 --- /dev/null +++ b/reports/calibration/README.md @@ -0,0 +1,14 @@ +# Resultados — partição `calibration` + +Gerado por `infra/ds/collect.py`. Lote 1; latências sem os exemplos de aquecimento; probabilidades sem calibração. + + +| Candidato | Disp. | Precisão | n | Acurácia | Macro F1 | ECE (bruto) | p50 ms | p95 ms | p99 ms | Carga s | RAM pico MiB | GPU pico MiB | Erros | Truncados | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| gliner | cpu | float32 | 1127 | 54.5% | 54.2% | 0.077 | 257.1 | 493.9 | 601.4 | 20.1 | 3240 | — | 0 | 0 | +| gliner | cuda | float32 | 1127 | 54.5% | 54.2% | 0.077 | 18.7 | 31.6 | 33.5 | 21.6 | 3252 | 1783 | 0 | 0 | +| laya | cpu | float32 | 1127 | 50.5% | 52.9% | 0.330 | 162.6 | 209.8 | 269.9 | 9.3 | 2681 | — | 0 | 0 | +| laya | cuda | float32 | 1127 | 50.8% | 53.3% | 0.326 | 21.5 | 22.8 | 24.0 | 9.9 | 2787 | 1811 | 0 | 0 | +| rizzo_flow | cuda | gguf-q8_0 | 1127 | 83.3% | 86.5% | 0.058 | 112.3 | 179.3 | 183.5 | 5.3 | 4528 | 5115 | 0 | 0 | +| semif | cuda | bfloat16 | 1127 | 85.8% | 88.1% | 0.055 | 117.2 | 184.2 | 186.8 | 7.9 | 9030 | 8513 | 0 | 0 | + diff --git a/reports/calibration/predictions-gliner-cpu.jsonl b/reports/calibration/predictions-gliner-cpu.jsonl new file mode 100644 index 0000000..08510c6 --- /dev/null +++ b/reports/calibration/predictions-gliner-cpu.jsonl @@ -0,0 +1,1127 @@ +{"id": "g0004-conta_segunda_via-emocional-00-D1", "gold": "conta_segunda_via", "warmup": true, "state_tokens": 53, "truncated": false, "n_options": 8, "meta": {"taxonomy_version": "v1.0", "source": "synthetic", "intent_gold": "conta_segunda_via", "gold_eligible": true, "domain": "contas", "kind": "intent", "oos_category": null, "profile": "emocional", "sentiment": "irritado", "secondary": [], "ambiguity": "baixa", "annotation": "consenso", "gen_agreement": true, "generator": "meta.llama-4-maverick-17b-128e-instruct-fp8", "n_options": 8, "length_chars": 96, "length_bucket": "medio"}, "pred": "conta_segunda_via", "probabilities": {"conta_contestacao": 0.0421839954559993, "conta_pagamento_confirmacao": 0.01608494535125549, "suporte_chip_esim": 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