diff --git a/.gitignore b/.gitignore index 3857786f..6a00513f 100644 --- a/.gitignore +++ b/.gitignore @@ -161,10 +161,22 @@ cython_debug/ # Project-related -datasets - -ct-runs -ct-evals +.DS_Store +/paired_16k_5344a5c_canonical/ +datasets/ +runs/ +checkpoints/ +pretrained/ +ct-runs/ +ct-evals/ +wandb/ +*.pkl +*.pt +*.pth +*.ckpt +*.safetensors +*.zip +*.pyc slurm* debug.sh diff --git a/CONFIRMATORY_COMMANDS.sh b/CONFIRMATORY_COMMANDS.sh new file mode 100644 index 00000000..f9cc4742 --- /dev/null +++ b/CONFIRMATORY_COMMANDS.sh @@ -0,0 +1,127 @@ +#!/usr/bin/env bash +# ============================================================================= +# Role C — Confirmatory fixed-vs-global (g=1.10) training commands. +# ----------------------------------------------------------------------------- +# Provenance: +# training_code_sha : 3a0d603da97dd93ddbb6c7ce49e4a7351d54bb43 (seed-3 was +# trained on this commit; recorded in commit_sha.txt) +# pr_head_sha : 79143c685e5588948972c17457b1c51c7a77bb49 (this PR head; +# only adds docs + the resume fix, not a training baseline) +# Branch : role-c/confirmatory-gap-g110 +# Env : conda env `myconda` (python 3.13.5, torch 2.8.0+cu128, A100) +# Dataset : /mnt/ect_project/datasets/cifar10-32x32.zip +# Pretrained (transfer): /mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl +# +# Method definitions (ONLY these two are compared): +# Fixed : --mapping=sigmoid --global-gap-scale=1.0 +# (official ECT sigmoid; global_gap_scale==1.0 short-circuits +# to bitwise parity with the official formula) +# Global-only : --mapping=global_sigmoid --global-gap-scale=1.10 +# (same official sigmoid gap scaled by a single fixed g=1.10) +# +# INVARIANT: fixed and global differ ONLY by {mapping, global_gap_scale, outdir}. +# No local controller is enabled in either arm. +# ============================================================================= +set -Eeuo pipefail + +export ECT_BRANCH="role-c/confirmatory-gap-g110" +export ECT_COMMIT="3a0d603da97dd93ddbb6c7ce49e4a7351d54bb43" # training_code_sha +DATA="/mnt/ect_project/datasets/cifar10-32x32.zip" +TRANSFER="/mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl" +PYTHON="${PYTHON:-python}" + +# ---- shared parameters (fixed == global except mapping + global-gap-scale) ---- +COMMON=( + --data="$DATA" + --cond=False --arch=ddpmpp --precond=ect + --batch=128 --batch-gpu=16 --optim=RAdam --lr=0.0001 --dropout=0.2 --augment=0 + -q 256 -k 8 -b 1 -c 0 --double=10000 --ema_beta=0.9993 + --fp16=True --enable_amp=True --metrics=none + --transfer="$TRANSFER" --nosubdir +) + +# COMMON_RESUME = COMMON without --transfer (resume replaces transfer) +COMMON_RESUME=( + --data="$DATA" + --cond=False --arch=ddpmpp --precond=ect + --batch=128 --batch-gpu=16 --optim=RAdam --lr=0.0001 --dropout=0.2 --augment=0 + -q 256 -k 8 -b 1 -c 0 --double=10000 --ema_beta=0.9993 + --fp16=True --enable_amp=True --metrics=none --nosubdir +) + +# ============================================================================= +# Usage: +# MODE=smoke bash CONFIRMATORY_COMMANDS.sh # 2-kimg smoke (seed 3) +# MODE=formal RUN_SEEDS=3 bash CONFIRMATORY_COMMANDS.sh # formal seed 3 (default) +# MODE=formal RUN_SEEDS="4 5" bash CONFIRMATORY_COMMANDS.sh # formal seeds 4+5 +# +# Defaults: MODE=formal, RUN_SEEDS=3. +# Running `bash CONFIRMATORY_COMMANDS.sh` with no env vars will ONLY run seed 3. +# Seeds 4 & 5 are never started unless explicitly requested via RUN_SEEDS. +# ============================================================================= +MODE="${MODE:-formal}" +RUN_SEEDS="${RUN_SEEDS:-3}" + +FORMAL_DURATION=0.256 # 256 kimg +FORMAL_RUN_ARGS=(--tick=10 --snap=0 --dump=0 --ckpt=10 --sample_every=26 --duration=$FORMAL_DURATION) + +# ============================================================================= +# 1) SMOKE TESTS (seed 3, 2 kimg) -- run BEFORE the formal run +# --duration=0.002 (=2 kimg), --tick=1, --ckpt=1 so a checkpoint is saved. +# ============================================================================= +if [ "$MODE" = "smoke" ]; then + SMOKE_DURATION=0.002 # 2 kimg + SMOKE_RUN_ARGS=(--tick=1 --snap=0 --dump=0 --ckpt=1 --seed=3 --duration=$SMOKE_DURATION) + + # Smoke A — fixed sigmoid + $PYTHON ct_train.py "${COMMON[@]}" --mapping=sigmoid --global-gap-scale=1.0 \ + "${SMOKE_RUN_ARGS[@]}" --outdir=/root/ect_runs/smoke/seed3_fixed + + # Smoke B — global-only g=1.10 + $PYTHON ct_train.py "${COMMON[@]}" --mapping=global_sigmoid --global-gap-scale=1.10 \ + "${SMOKE_RUN_ARGS[@]}" --outdir=/root/ect_runs/smoke/seed3_global110 + + exit 0 +fi + +# ============================================================================= +# 2) FORMAL PAIRED RUN (256 kimg) +# --duration=0.256 (=256 kimg), --tick=10, --ckpt=10, --sample_every=26 +# Default: ONLY seed 3. To run seeds 4 & 5: RUN_SEEDS="4 5" ... +# ============================================================================= +for seed in $RUN_SEEDS; do + $PYTHON ct_train.py "${COMMON[@]}" --mapping=sigmoid --global-gap-scale=1.0 --seed="$seed" \ + "${FORMAL_RUN_ARGS[@]}" --outdir="/root/ect_runs/confirmatory_256k/seed${seed}_fixed" + + $PYTHON ct_train.py "${COMMON[@]}" --mapping=global_sigmoid --global-gap-scale=1.10 --seed="$seed" \ + "${FORMAL_RUN_ARGS[@]}" --outdir="/root/ect_runs/confirmatory_256k/seed${seed}_global110" +done + +# ============================================================================= +# 3) RESUME (after interruption) +# Two modes — both examples are COMMENTED OUT. Uncomment and edit as needed. +# +# a) Verification resume (writes to a NEW outdir, source untouched): +# Use this to test that resume works correctly without risking the +# authoritative run handed off to Role D. +# +# b) Actual interruption resume (writes to the SAME outdir): +# Use this only when the original run was interrupted and must continue +# in-place. Do NOT use this mode for testing. +# +# --resume replaces --transfer; --global-gap-scale is re-stated for safety. +# The training-state also carries the schedule/gap state; verify g==1.10 +# after resume. +# ============================================================================= + +# --- a) Verification resume (new outdir, source untouched) --- +#$PYTHON ct_train.py "${COMMON_RESUME[@]}" --mapping=global_sigmoid --global-gap-scale=1.10 --seed=3 \ +# "${FORMAL_RUN_ARGS[@]}" \ +# --resume=/root/ect_runs/confirmatory_256k/seed3_global110/training-state-latest.pt \ +# --outdir=/root/ect_runs/resume_checks/seed3_global110 + +# --- b) Actual interruption resume (same outdir, continues the run) --- +#$PYTHON ct_train.py "${COMMON_RESUME[@]}" --mapping=global_sigmoid --global-gap-scale=1.10 --seed=3 \ +# "${FORMAL_RUN_ARGS[@]}" \ +# --resume=/root/ect_runs/confirmatory_256k/seed3_global110/training-state-latest.pt \ +# --outdir=/root/ect_runs/confirmatory_256k/seed3_global110 diff --git a/D_HANDOFF.md b/D_HANDOFF.md new file mode 100644 index 00000000..67579196 --- /dev/null +++ b/D_HANDOFF.md @@ -0,0 +1,37 @@ +# Role C → Role D Handoff: Confirmatory 256k (seed 3) + +**Provenance:** +- training_code_sha: `3a0d603da97dd93ddbb6c7ce49e4a7351d54bb43` (the commit seed-3 was trained on; recorded in each run dir `commit_sha.txt`) +- pr_head_sha: `79143c685e5588948972c17457b1c51c7a77bb49` (this PR head; only adds docs + the resume fix, not a training baseline) + +Frozen training commit: `3a0d603da97dd93ddbb6c7ce49e4a7351d54bb43` (branch role-c/confirmatory-gap-g110, PR #23 merged into main) +All checkpoints below passed integrity: loadable snapshot + training-state, kimg=256 reached, finite loss history, expected schedule, gap scale held. + +| Method | Seed | Checkpoint | kimg | Commit | Integrity | +| --- | ---: | --- | ---: | --- | --- | +| Fixed | 3 | /root/ect_runs/confirmatory_256k/seed3_fixed/network-snapshot-latest.pkl | 256 | 3a0d603da97dd93ddbb6c7ce49e4a7351d54bb43 | Passed | +| Global 1.10 | 3 | /root/ect_runs/confirmatory_256k/seed3_global110/network-snapshot-latest.pkl | 256 | 3a0d603da97dd93ddbb6c7ce49e4a7351d54bb43 | Passed | + +## Per-run detail + +### Fixed (sigmoid, g=1.0) +- run dir: `/root/ect_runs/confirmatory_256k/seed3_fixed` +- snapshot sha256: `09a41e1e7c03dcdf5ffb93bb68687390278b4b190183dfff92bacc1bf79738d9` +- training-state: `training-state-latest.pt` (cur_nimg=256000, cur_tick=27, optimizer+scaler state) +- schedule: sigmoid; gap_over_sigmoid_gap_mean held at 1.000000 throughout +- loss: 2000 finite rows, min 13.193 / max 33.129 / final 17.063 (no NaN/Inf) +- config: `training_options.json` (adj=sigmoid, global_gap_scale=1.0) + +### Global 1.10 (global_sigmoid, g=1.10) +- run dir: `/root/ect_runs/confirmatory_256k/seed3_global110` +- snapshot sha256: `24875430eea4679a416ae921c3e9ae16142f6416d2a0edf970764384ef964bed` +- training-state: `training-state-latest.pt` (cur_nimg=256000, cur_tick=27, optimizer+scaler state) +- schedule: global_sigmoid; gap_over_sigmoid_gap_mean held at 1.100000 throughout +- loss: 2000 finite rows, min 13.277 / max 31.419 / final 16.448 (no NaN/Inf) +- config: `training_options.json` (adj=global_sigmoid, global_gap_scale=1.1) + +## Recommended evaluation checkpoints +Both `network-snapshot-latest.pkl` are the authoritative 256-kimg endpoints and +are recommended for Role D evaluation. The two arms differ ONLY by +`mapping`/`global_gap_scale`; all other training settings are identical +(verified by resolved-config diff). EMA weights are inside the snapshot (`ema` key). diff --git a/D_HANDOFF_SEEDS45.md b/D_HANDOFF_SEEDS45.md new file mode 100644 index 00000000..c161b134 --- /dev/null +++ b/D_HANDOFF_SEEDS45.md @@ -0,0 +1,75 @@ +# Role C -> Role D Handoff: Confirmatory 256k seeds 4 & 5 + +## Provenance + +- **Executed training source commit:** `ab03f9e03b7b82425282abc3bf661067ca45875a` +- **Training-code baseline:** `6d4bc7d` +- **Original PR head / handoff-document commit:** `f299528dfb9cdb8f5be92576673242ccb0c57464`\n- **Documentation-correction commit:** recorded by the Git commit containing this revised document. + +The four training jobs were launched from `ab03f9e03b7b82425282abc3bf661067ca45875a`. Its training code is equivalent to baseline `6d4bc7d`; the PR/handoff-document commit is distinct and must not be described as the executed training source. + +## Training health + +Each run had one initialization-time AMP-skipped optimizer step, but no NaN values were recorded in the 2000-row loss history. Finite losses were recorded from the first completed training iteration onward. + +This is training-health evidence only. Loss is not an evaluation metric and must not be used to infer KID or FID; Role D's independent generation-quality evaluation remains authoritative. + +## Checkpoint transfer and verification + +The `/root/ect_runs/...` paths below are node-local provenance references, not a shared Role D handoff location. Role D does **not** evaluate by assuming access to the training node or its filesystem. + +Transfer archive (staged outside the training node): + +- archive: `D:\\seeds45_ckpts_full.tar.gz` +- size: `3,291,447,686` bytes +- archive SHA256: `1bdb147e535fe0b4f069f4106e28a7a6b065b317ca9df38cfbe9643772937608` +- extract: `tar xzf D:\\seeds45_ckpts_full.tar.gz` -> `seeds45_package_full/` + +The archive contains all four authoritative `network-snapshot-latest.pkl` files (including EMA weights), their configurations, logs, loss histories, and training states. + +**Required Role D acceptance check before evaluation:** obtain the archive through the agreed transfer channel, recompute its SHA256, extract it, recompute the SHA256 of each `network-snapshot-latest.pkl`, and reply on the PR confirming all five values match. Until that confirmation is posted, the archive's accessibility to Role D is not assumed. + +| Method | Seed | Run | Training-node checkpoint reference | Checkpoint SHA256 | kimg | Executed source | Integrity | +| --- | ---: | --- | --- | --- | ---: | --- | --- | +| Fixed | 4 | seed4_fixed | `/root/ect_runs/confirmatory_256k/seed4_fixed/network-snapshot-latest.pkl` | `ac94e7b07e5b7628e6b14b26155fb3de09e42373497183d39aba4fe9863663c9` | 256 | `ab03f9e` | Passed on training node | +| Global 1.10 | 4 | seed4_global110 | `/root/ect_runs/confirmatory_256k/seed4_global110/network-snapshot-latest.pkl` | `62a6122a7be523aeb12875d96e96312e9c90efde9eafb75d730c75ceea0e8862` | 256 | `ab03f9e` | Passed on training node | +| Fixed | 5 | seed5_fixed | `/root/ect_runs/confirmatory_256k/seed5_fixed/network-snapshot-latest.pkl` | `21fab0e501bb27032c0e49a553b05a2800ea0fbe20a2a1d94a6bbf5276f2b72a` | 256 | `ab03f9e` | Passed on training node | +| Global 1.10 | 5 | seed5_global110 | `/root/ect_runs/confirmatory_256k/seed5_global110/network-snapshot-latest.pkl` | `491dc887990e6d9f6fde70b5d12775aaf4bfc6155b731682926b02061c253e9b` | 256 | `ab03f9e` | Passed on training node | + +## Per-run evidence + +### seed4_fixed (seed 4) + +- method: Fixed (`mapping=sigmoid`, `global_gap_scale=1.0`) +- training-state: `/root/ect_runs/confirmatory_256k/seed4_fixed/training-state-latest.pt` (`cur_nimg=256000`) +- config/log: `training_options.json` / `log.txt` +- loss: 2000 rows; no recorded NaN; last=16.566, min=13.389, max=30.562 +- `gap_over_sigmoid_gap_mean`: 1 -> 1 (target 1.0) + +### seed4_global110 (seed 4) + +- method: Global 1.10 (`mapping=global_sigmoid`, `global_gap_scale=1.10`) +- training-state: `/root/ect_runs/confirmatory_256k/seed4_global110/training-state-latest.pt` (`cur_nimg=256000`) +- config/log: `training_options.json` / `log.txt` +- loss: 2000 rows; no recorded NaN; last=16.555, min=13.345, max=30.100 +- `gap_over_sigmoid_gap_mean`: 1.10000001913 -> 1.10000038269 (target 1.10) + +### seed5_fixed (seed 5) + +- method: Fixed (`mapping=sigmoid`, `global_gap_scale=1.0`) +- training-state: `/root/ect_runs/confirmatory_256k/seed5_fixed/training-state-latest.pt` (`cur_nimg=256000`) +- config/log: `training_options.json` / `log.txt` +- loss: 2000 rows; no recorded NaN; last=15.188, min=13.347, max=31.666 +- `gap_over_sigmoid_gap_mean`: 1 -> 1 (target 1.0) + +### seed5_global110 (seed 5) + +- method: Global 1.10 (`mapping=global_sigmoid`, `global_gap_scale=1.10`) +- training-state: `/root/ect_runs/confirmatory_256k/seed5_global110/training-state-latest.pt` (`cur_nimg=256000`) +- config/log: `training_options.json` / `log.txt` +- loss: 2000 rows; no recorded NaN; last=15.215, min=13.409, max=25.291 +- `gap_over_sigmoid_gap_mean`: 1.1000003469 -> 1.10000058239 (target 1.10) + +## Recommended evaluation + +After the required transfer verification, Role D evaluates NFE=1, NFE=2, KID-5k, and FID-5k. Fixed and global110 differ only by mapping/gap scale; the remaining settings are identical per seed. \ No newline at end of file diff --git a/HANDOFF_20260804.md b/HANDOFF_20260804.md new file mode 100644 index 00000000..e69de29b diff --git a/README.md b/README.md index cf094aa2..cfdfae49 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ Pytorch implementation for [Easy Consistency Tuning (ECT)](https://www.notion.so/gsunshine/Consistency-Models-Made-Easy-954205c0b4a24c009f78719f43b419cc). -ECT unlocks state-of-the-art (SoTA) few-step generative abilities through a simple yet principled approach. +ECT unlocks state-of-the-art (SoTA) few-step generative abilities through a simple yet principled approach. With minimal tuning costs, ECT demonstrates promising early results and scales with training FLOPs and model sizes. Try your own [Consistency Models](https://arxiv.org/abs/2303.01469)! You only need to fine-tune a bit. :D @@ -13,7 +13,7 @@ Try your own [Consistency Models](https://arxiv.org/abs/2303.01469)! You only ne ## Introduction -This repository is organized in a multi-branch structure, with each branch offering a minimal implementation for a specific purpose. +This repository is organized in a multi-branch structure, with each branch offering a minimal implementation for a specific purpose. The current branches support the following training protocols: - `main`: ECT on CIFAR-10. Best for understanding CMs and fast prototyping. @@ -44,7 +44,7 @@ Prepare the dataset in the EDM's format. See a reference [here](https://github.c ## Training -Run the following command to tune your SoTA 2-step ECM and match Consistency Distillation (CD) within 1 A100 GPU hour. +Run the following command to tune your SoTA 2-step ECM and match Consistency Distillation (CD) within 1 A100 GPU hour. ```bash bash run_ecm_1hour.sh 1 --desc bs128.1hour @@ -67,7 +67,7 @@ To enable fp16 and GradScaler, add the following arguments to your script: bash run_ecm_1hour.sh 1 --desc bs128.1hour --fp16=True --enable_amp=True ``` -For more information, please refer to this [PR](https://github.com/locuslab/ect/pull/13). +For more information, please refer to this [PR](https://github.com/locuslab/ect/pull/13). Full support for Automatic Mixed Precision (AMP) will be added later. ## Evaluation @@ -78,6 +78,35 @@ Run the following command to calculate FID of a pretrained checkpoint. bash eval_ecm.sh --resume ``` +### Fixed-seed evaluation + +Role D sampling uses seeds 0-63 and verifies that work-group sizes 8 and 16 produce pixel-identical results for both NFE=1 and NFE=2. It also repeats each configuration to verify deterministic output: + +See [`docs/EVALUATION_PROTOCOL.md`](docs/EVALUATION_PROTOCOL.md) for the complete protocol, metadata requirements, checkpoint-isolated output layout, and metric boundary. + +```bash +bash scripts/sample_checkpoint.sh \ + --outdir /mnt/ect_project/evaluations \ + --seeds 0-63 --nfe 1 2 --mid-t 0.821 \ + --work-group-size 8 --verify-work-group-size 16 \ + --precision fp32 +``` + +The output is isolated under `/-/` and contains one 8x8 grid per NFE, `metadata.json`, `sha256_manifest.txt`, and individual seed images. Keep the individual PNG files under `/mnt`; only commit the grids, metadata, manifest, scripts, and tests. + +The unified metric entry point supports explicit one-step or two-step evaluation through `--nfe=1` or `--nfe=2`: + +```bash +bash scripts/evaluate_checkpoint.sh 1 \ + --outdir ct-evals --data datasets/cifar10-32x32.zip \ + --nfe=2 --mid_t=0.821 --metrics=fid50k_full +``` + +The frozen three-training-seed final comparison uses explicit per-sample seeds, +KID-5k as the primary proxy, FID-5k as an auxiliary proxy, and a method-blinded +A/B ballot. See [`docs/FINAL_PERFORMANCE_EVALUATION.md`](docs/FINAL_PERFORMANCE_EVALUATION.md). +These 5k-sample results are not standard FID-50k benchmarks. + ## Generative Performance ### FID Evaluation @@ -139,4 +168,3 @@ Feel free to drop me an email at zhengyanggeng@gmail.com if you have additional year={2024} } ``` - diff --git a/RUN_STATUS.md b/RUN_STATUS.md new file mode 100644 index 00000000..96f43482 --- /dev/null +++ b/RUN_STATUS.md @@ -0,0 +1,18 @@ +# Confirmatory 256k — Run Status (seed 3) + +Training commit: `3a0d603da97dd93ddbb6c7ce49e4a7351d54bb43` (training_code_sha; recorded in each run dir `commit_sha.txt`) +PR head: `79143c685e5588948972c17457b1c51c7a77bb49` (pr_head_sha; docs + resume fix only, not a training baseline) +Output root: `/root/ect_runs/confirmatory_256k/` +GPU: 1x NVIDIA A100-PCIE-40GB (two runs share the GPU, ~7GB total) + +| Method | Seed | Outdir | PID | Port | Start (UTC) | Status | Latest kimg | +| --- | ---: | --- | ---: | ---: | --- | --- | ---: | +| Fixed | 3 | /root/ect_runs/confirmatory_256k/seed3_fixed | (see pid.txt) | 29501 | (see start_utc.txt) | COMPLETED | 256 | +| Global 1.10 | 3 | /root/ect_runs/confirmatory_256k/seed3_global110 | (see pid.txt) | 29502 | (see start_utc.txt) | COMPLETED | 256 | + +Notes: +- Both launched from identical COMMON args; differ ONLY by mapping + global-gap-scale. +- Checkpoints saved every 10 ticks (`--ckpt=10`); `network-snapshot-latest.pkl` + + `training-state-latest.pt` are the authoritative final artifacts. +- Resume command (if interrupted) is in CONFIRMATORY_COMMANDS.sh section 3. +- Patch applied: `training/ct_training_loop.py:627` `weights_only=False` (PyTorch 2.8 resume compat). diff --git a/TRAINING_PROTOCOL.md b/TRAINING_PROTOCOL.md new file mode 100644 index 00000000..c6627b8d --- /dev/null +++ b/TRAINING_PROTOCOL.md @@ -0,0 +1,121 @@ +# Training Protocol — Confirmatory Fixed vs Global (g=1.10) Study + +**Role C — Training & Scaling Lead.** Branch `role-c/confirmatory-gap-g110`, +frozen commit `3a0d603da97dd93ddbb6c7ce49e4a7351d54bb43` (PR #23 merged into +`main`). Today only **seed 3** is launched; seeds 4 & 5 are staged, not started. + +## 1. Experiment purpose + +Validate that a single global gap multiplier `g=1.10` on the official ECT +sigmoid mapping changes the trained model relative to the unmodified official +sigmoid, under otherwise identical settings, for a 256 kimg paired comparison. +This isolates the *global-only* intervention from any local controller. + +## 2. Method definitions + +Only two methods are compared. Both reuse the official ECT sigmoid mapping +`r_sigmoid(t;m)`; the factorized gap is `d = g * (t - r_sigmoid)`. + +| Method | `--mapping` | `--global-gap-scale` | Local controller | Notes | +| --- | --- | --- | --- | --- | +| **Fixed** | `sigmoid` | `1.0` | disabled | `global_gap_scale==1.0` short-circuits to bitwise parity with the official formula (`_apply_global_gap_scale` returns `base_r` unchanged). | +| **Global-only** | `global_sigmoid` | `1.10` | disabled | Same official sigmoid gap scaled by one fixed `g=1.10`. No local t-bin controller. | + +No local controller is enabled in either arm. `g=1.10` is fixed and not +searched. The resolved config diff confirms the two arms differ ONLY by +`adj` (mapping name), `global_gap_scale`, and `run_dir`. + +## 3. Common training parameters (identical for both arms) + +``` +--data /mnt/ect_project/datasets/cifar10-32x32.zip (sha256 08c9ed1b…f372) +--transfer /mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl (sha256 4d5dcc1f…b4da) +--cond=False --arch=ddpmpp --precond=ect +--batch=128 --batch-gpu=16 --optim=RAdam --lr=0.0001 --dropout=0.2 --augment=0 +-q 256 -k 8 -b 1 -c 0 --double=10000 --ema_beta=0.9993 +--fp16=True --enable_amp=True --metrics=none +--duration=0.256 (256 kimg) --tick=10 --snap=0 --dump=0 --ckpt=10 --sample_every=26 +--nosubdir +``` + +Environment: conda env `myconda` (Python 3.13.5, PyTorch 2.8.0+cu128, CUDA, 1x +NVIDIA A100-PCIE-40GB). `--double=10000` keeps the whole run at stage 0 +(`decay=1/256`), matching the 1-hour protocol. + +## 4. Smoke test standard (passed) + +Each smoke = seed 3, 2 kimg (`--duration=0.002 --tick=1 --ckpt=1`). A smoke +passes only when ALL of the following hold: + +- **Config**: resolved `training_options.json` shows the expected + `adj` / `global_gap_scale` / `local controller disabled`. +- **Numerics**: no NaN/Inf/overflow/traceback beyond the expected AMP warm-up + `step_skipped=1` at tick 0 (GradScaler halves from 65536 until finite). + `step_skipped` must settle to 0 by tick 1; loss finite thereafter. +- **Checkpoint**: `network-snapshot-latest.pkl` (ema+loss_fn+augment_pipe+ + dataset_kwargs) and `training-state-latest.pt` (net+optimizer_state+ + gradscaler_state+loss_fn_state+cur_nimg/cur_tick) both present and loadable. +- **Resume**: resuming from the checkpoint continues `kimg` continuously, + keeps `global_gap_scale` (verified `gap_over_sigmoid_gap_mean≈1.10` held), + keeps `schedule_name`, does not re-initialize optimizer/scaler, and does not + overwrite the source checkpoint. + +Both smokes passed (see `results/` smoke summaries). A real bug was found and +fixed during resume verification: under PyTorch 2.8, `torch.load` defaults to +`weights_only=True` and rejects the repo's own training-state (contains +`torch_utils.persistence` objects). Patched `training/ct_training_loop.py:627` +to `weights_only=False`. Resume then verified end-to-end. + +## 5. Resume procedure + +Resume replaces `--transfer` with `--resume=/training-state-latest.pt`, +re-states `--mapping`, `--global-gap-scale`, and `--seed` for safety. +The schedule/gap state is also carried in the training-state; after resume +confirm `gap_over_sigmoid_gap_mean` still equals `1.10` for the global arm. + +Two resume modes are distinguished: + +### 5a. Verification resume (new outdir, source untouched) + +Used to test that resume works correctly. Writes to a **new** `--outdir` so +the authoritative run handed off to Role D is never modified: + +``` +--resume=/root/ect_runs/confirmatory_256k/seed3_global110/training-state-latest.pt --outdir=/root/ect_runs/resume_checks/seed3_global110 +``` + +### 5b. Actual interruption resume (same outdir, continues the run) + +Used only when the original run was interrupted and must continue in-place. +Writes to the **same** `--outdir`: + +``` +--resume=/root/ect_runs/confirmatory_256k/seed3_global110/training-state-latest.pt --outdir=/root/ect_runs/confirmatory_256k/seed3_global110 +``` + +Do **not** use mode 5b for testing — it overwrites checkpoints in the +authoritative run directory. + +## 6. Formal run launch conditions (all met) + +1. fixed smoke normal ✓ 2. global smoke normal ✓ 3. both resume ✓ +4. configs differ only by gap scale ✓ 5. commit frozen ✓ 6. B's code frozen ✓ + +## 7. Checkpoint naming convention + +Output root `/root/ect_runs/confirmatory_256k/{seedN_fixed,seedN_global110}/`. +Each run contains `training_options.json`, `train_summary.csv`, `stats.jsonl`, +`train.log`, `commit_sha.txt`, `start_utc.txt`, `pid.txt`, +`network-snapshot-latest.pkl`, `training-state-latest.pt`, and sample PNGs. +Checkpoints saved every `--ckpt=10` ticks (latest is authoritative). + +## 8. Handoff to Role D (evaluation) + +Only checkpoints that passed the integrity check (loadable snapshot + training +state, expected `total_kimg`, expected schedule, finite loss history) are +handed off. See the handoff table in the run-status file. + +## 9. What is NOT done today + +No local controller; no other gap scale; seeds 4 & 5 staged but not started; +no `git pull` after formal launch; smoke checkpoints are NOT formal results. diff --git a/analysis/g_screen_q128_s3_interpretation.md b/analysis/g_screen_q128_s3_interpretation.md new file mode 100644 index 00000000..f0df8375 --- /dev/null +++ b/analysis/g_screen_q128_s3_interpretation.md @@ -0,0 +1,76 @@ +# q128 seed-3 finite-budget gap response + +## Status + +**Finite-budget gap sensitivity: supported.** + +**Simple smooth U-shaped response: not supported.** + +**GPU-assignment explanation for the g=0.9 versus g=1.0 discontinuity: not supported by the swapped-GPU reproduction.** + +All quality numbers in this document are 5k-sample screening proxies. They are not formal 50k benchmark results. + +## Protocol + +The primary screen used q=128, training seed 3, a 256 kimg budget, and global gap scales: + +`0.9, 1.0, 1.05, 1.1, 1.2, 1.3`. + +The g=1.0 arm used the official sigmoid schedule. Other arms used `global_sigmoid` with the corresponding global gap scale. Evaluation used fixed generation seeds 0-4999, evaluator seed 20260730, FP32, one GPU per cell, and both NFE=1 and NFE=2. + +## Primary screen + +| g | NFE1 KID-5k | NFE1 FID-5k | NFE2 KID-5k | NFE2 FID-5k | +|---:|---:|---:|---:|---:| +| 0.90 | 0.245318 | 245.735 | 0.039406 | 52.138 | +| 1.00 | 0.324769 | 317.018 | 0.071311 | 87.923 | +| 1.05 | 0.322791 | 316.109 | 0.067474 | 84.643 | +| 1.10 | 0.311503 | 303.167 | 0.062969 | 81.678 | +| 1.20 | 0.214909 | 219.998 | 0.041054 | 53.951 | +| 1.30 | 0.201624 | 206.788 | 0.040201 | 57.590 | + +The best observed NFE=1 screening point is g=1.30. The best observed NFE=2 screening point is g=0.90. These are observed single-seed screening optima, not population-optimal gap values. + +The response is strongly non-monotone and is not described by a single smooth U-shaped basin. The current grid instead contains two separated favorable regions: g=0.90 and g=1.20-1.30, with substantially worse quality around g=1.00-1.10. + +## Swapped-GPU reproduction + +The g=0.90 and g=1.00 arms were repeated with the GPU assignments exchanged while retaining the same training seed, code, teacher, budget, optimizer, and evaluator. + +| g | NFE | Metric | Primary | Swapped-GPU repeat | Relative drift | +|---:|---:|---|---:|---:|---:| +| 0.90 | 1 | KID-5k | 0.245318 | 0.248056 | +1.12% | +| 0.90 | 1 | FID-5k | 245.735 | 247.847 | +0.86% | +| 0.90 | 2 | KID-5k | 0.039406 | 0.039826 | +1.07% | +| 0.90 | 2 | FID-5k | 52.138 | 52.442 | +0.58% | +| 1.00 | 1 | KID-5k | 0.324769 | 0.325269 | +0.15% | +| 1.00 | 1 | FID-5k | 317.018 | 317.164 | +0.05% | +| 1.00 | 2 | KID-5k | 0.071311 | 0.070861 | -0.63% | +| 1.00 | 2 | FID-5k | 87.923 | 87.457 | -0.53% | + +The discontinuity is therefore reproducible under the tested GPU swap. In the repeat, g=0.90 remains better than g=1.00 by approximately 21.9%-23.7% for NFE=1 and 40.0%-43.8% for NFE=2. + +This test does not establish population-level robustness because it retains a single training seed. It does rule out GPU assignment as a sufficient explanation for the observed discontinuity. + +## Interpretation + +The experiment supports the claim that the ECT pair gap materially changes finite-budget optimization outcomes. It does not support a universal scalar optimum or a simple one-basin response model. + +The different observed optima for NFE=1 and NFE=2 indicate that the best finite-budget gap depends on the downstream sampling objective. A single scalar training loss is therefore unlikely to be sufficient for selecting the correct gap direction for every sampling target. + +The separated favorable regions are consistent with a non-convex or multi-regime optimization landscape, but this experiment alone does not prove a multi-basin theorem. Further claims require additional training seeds, budget checkpoints, or mechanism diagnostics. + +## Known limitations + +- Only seed 3 is included. +- FID-5k and KID-5k are screening proxies. +- Planned 64 and 128 kimg checkpoints were not retained. +- Raw residual and gradient RMS were not logged. +- The response cannot yet be separated from effective learning-rate changes induced by the normalized stop-gradient loss. +- The g=1.20 and g=1.30 favorable region has not received a swapped-GPU reproduction. + +## Decision + +Do not extend the grid to g=1.4 or g=1.5 yet. + +The next mechanism experiment should separate gap geometry from gradient-scale effects, for example through a gap-by-learning-rate or loss-renormalization control. Additional seeds should only be added after that confound is addressed. diff --git a/analysis/q256_extended_statistics.json b/analysis/q256_extended_statistics.json new file mode 100644 index 00000000..4476fc6c --- /dev/null +++ b/analysis/q256_extended_statistics.json @@ -0,0 +1,399 @@ +{ + "status": "computed", + "schema_version": 2, + "pairing_key": [ + "training_seed", + "budget_kimg", + "nfe", + "metric" + ], + "baseline_method": "fixed", + "candidate_method": "global110", + "candidate_label": "global_only", + "delta_direction": "global_only - fixed", + "effect_definitions": { + "relative_improvement_pct": "100 * (fixed - global_only) / fixed; positive favors global-only", + "geometric_mean_relative_improvement_pct": "100 * (1 - geometric_mean(global_only / fixed))", + "rank_consistency_spearman": "Spearman correlation of lower-is-better seed ranks between fixed and global-only", + "nfe_effect_heterogeneity": "per-seed relative-improvement difference, NFE=2 minus NFE=1, in percentage points" + }, + "inference_note": "The independent units are the three training seeds. Exact sign tests and bootstrap intervals are descriptive sensitivity summaries, not a basis for strong significance claims.", + "statistics": [ + { + "pair_count": 3, + "mean_delta": -14.1835921090954, + "median_delta": -12.485467394300088, + "sample_sd_delta": 3.2969383203453773, + "minimum_delta": -17.98343091141612, + "maximum_delta": -12.081878021569992, + "mean_relative_improvement_pct": 4.5442975938885874, + "geometric_mean_relative_improvement_pct": 4.547536960568433, + "relative_improvement_sample_sd_pct": 0.9617635389571673, + "relative_improvement_cv_pct": 21.164184763132546, + "worst_case_relative_improvement_pct": 3.768643908598368, + "best_case_relative_improvement_pct": 5.620426786696175, + "rank_consistency_spearman": 1.0, + "rank_order_exact_match": true, + "global_wins": 3, + "fixed_wins": 0, + "ties": 0, + "exact_sign_test": { + "test": "exact_two_sided_sign_test", + "non_tied_pairs": 3, + "positive_signs": 3, + "negative_signs": 0, + "positive_label": 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+ "labels_match_png_names": true, + "class_histogram": { + "0": 5000, + "1": 5000, + "2": 5000, + "3": 5000, + "4": 5000, + "5": 5000, + "6": 5000, + "7": 5000, + "8": 5000, + "9": 5000 + }, + "status": "passed" + }, + "prior_role_c_activation_archive": { + "recorded_sha256": "9fd64620e37bfc0c995535fa52701c9641bcd07635008bfda0c9fbddde1a4ed6", + "byte_identical_to_canonical": false, + "semantic_equivalence": "not_established", + "formal_evidence_status": "superseded_by_canonical_rerun" + }, + "canonical_role_c_activation_rerun": { + "run_id": "adaptive-v1-activation-cb84a934-20260720T153954Z", + "dataset_sha256": "08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372", + "evidence_path": "results/adaptive_v1_activation_a100_cb84a934", + "metadata_sha256": "c60c7c9b0086c663b92a4f61b3d7a79409dd48d64e563b49b947b8f2c272047f", + "train_summary_sha256": "a052b55d7f04a49bfc1ea6f8689c4d57b1c6855e3cfb4095aa9cc42e8363f87c", + "collector_status": "passed", + "activation_gate_passed": true + }, + "experiment_policy": { + "required_dataset_sha256": "08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372", + "scope": "all future fixed and adaptive formal experiments", + "archive_committed_to_git": false + }, + "final_status": "passed" +} diff --git a/artifacts/day1/dataset_check.txt b/artifacts/day1/dataset_check.txt new file mode 100644 index 00000000..9d4c8b63 --- /dev/null +++ b/artifacts/day1/dataset_check.txt @@ -0,0 +1,24 @@ +Day 1 CIFAR-10 dataset verification +=================================== +Source: https://dataset.bj.bcebos.com/cifar/cifar-10-python.tar.gz +Source path: /mnt/ect_project/datasets/cifar-10-python.tar.gz +Source size: 170498071 bytes +Source MD5: c58f30108f718f92721af3b95e74349a +Source SHA256: 6d958be074577803d12ecdefd02955f39262c83c16fe9348329d7fe0b5c001ce +MD5 check: PASS +tar gzip integrity: PASS + +EDM ZIP path: /mnt/ect_project/datasets/cifar10-32x32.zip +EDM ZIP size: 166000134 bytes +EDM ZIP SHA256: 2d4056e80de1a96fe16f2f58945c6c4710ecd9fc02e3cc7aa5b50513b7cdf389 +Expected ZIP SHA256 match: PASS +ZipFile.testzip(): None +PNG count: 50000 +dataset.json present: True +dataset.json label count: 50000 +Random image 00025/img00025247.png: 32x32 RGB +Random image 00049/img00049673.png: 32x32 RGB +Random image 00027/img00027562.png: 32x32 RGB +ImageFolderDataset length: 50000 +DataLoader image batch shape: (4, 3, 32, 32) +Dataset validation: PASS diff --git a/artifacts/day1/environment.txt b/artifacts/day1/environment.txt new file mode 100644 index 00000000..c30ef3e0 --- /dev/null +++ b/artifacts/day1/environment.txt @@ -0,0 +1,24 @@ +Day 1 environment verification +============================== +Python: 3.9.18 +PyTorch: 2.3.0 +CUDA runtime: 12.1 +CUDA available: True +GPU: NVIDIA A100-PCIE-40GB +diffusers: 0.26.3 +accelerate: 0.27.2 +huggingface_hub: 0.23.4 + +Import checks: +click: OK +requests: OK +PIL: OK +numpy: OK +scipy: OK +psutil: OK +tqdm: OK +imageio: OK +pyspng: OK +diffusers: OK +accelerate: OK +huggingface_hub: OK diff --git a/artifacts/day1/smoke_test.txt b/artifacts/day1/smoke_test.txt new file mode 100644 index 00000000..e4237e20 --- /dev/null +++ b/artifacts/day1/smoke_test.txt @@ -0,0 +1,19 @@ +Day 1 bootstrap smoke-test verification +======================================= +Date: 2026-07-15 (Asia/Shanghai) +Branch: leader/day1-bootstrap + +Checks: +- Python source compilation: PASS +- Project module imports in the ect Conda environment: PASS +- CUDA availability: PASS (NVIDIA A100-PCIE-40GB) +- training_loop enable_amp parameter: PASS +- --enable_amp=True dry-run: PASS +- --amp=True compatibility alias dry-run: PASS +- --enable_gradscaler=True compatibility alias dry-run: PASS +- --metrics none parsed as an empty metric list: PASS +- CIFAR-10 EDM ZIP SHA256: 2d4056e80de1a96fe16f2f58945c6c4710ecd9fc02e3cc7aa5b50513b7cdf389 +- ImageFolderDataset load: PASS (50000 RGB images, 32x32, uint8) +- Git whitespace/error check: PASS + +No full training or FID run was performed as part of this bootstrap smoke test. diff --git a/artifacts/day2/collaborator_audit.md b/artifacts/day2/collaborator_audit.md new file mode 100644 index 00000000..f9e8109d --- /dev/null +++ b/artifacts/day2/collaborator_audit.md @@ -0,0 +1,128 @@ +# Day 2 collaborator audit + +Audit date: 2026-07-15 (Asia/Shanghai) + +This report records the read-only audit completed before local integration. Remote refs were +fetched and inspected, but no collaborator branch was modified, merged, rebased, reset, or +pushed. + +## Public baseline + +- `origin/main`: `4311059770f54821d151a9b0e1f76770a5f3930e` +- `origin/leader/day1-bootstrap`: `4e33194777a347ea5286b5ec1d5c29a58c792d29` +- Merge base: `origin/main` +- Relative topology: leader ahead 4, behind 0 +- Changed files versus main: 8 +- `git diff --check`: passed +- AMP CLI: `--enable_amp`, `--amp`, and `--enable_gradscaler` map to `enable_amp` +- AMP propagation: `enable_amp` is passed into `training_loop` +- GradScaler order: accumulation, `unscale_`, non-finite handling, `step`, `update` +- GradScaler state: saved in numbered/latest training states and restored on resume +- Non-AMP path: retains ordinary `optimizer.step()` +- `metrics=none`: parses to `[]`; periodic and final metric calls are skipped +- Compile and help checks: passed in the `ect` environment +- Decision: **READY_TO_MERGE** + +No formal training or FID/KID evaluation was run during the audit. + +## codex/day1-engineering + +- SHA: `3cb1c52fc56f01942c84d535dddddffd99c3af47` +- Commit: `Add reproducible Day 1 training workflow` +- Base: `origin/main`; behind the public baseline by 4 commits and ahead by 1 +- Scope: 18 changed files, 1183 insertions, 14 deletions +- Shell, Python, JSON, and whitespace syntax checks: passed + +Useful engineering deliverables: + +- `conda-matpool.yml` +- `setup_env.sh` +- `prepare_data.sh` +- `download_checkpoint.sh` +- `scripts/check_environment.py` +- `scripts/verify_assets.py` +- `scripts/verify_smoke_run.py` +- `docs/DAY1_A.md` + +Files that must not replace the public baseline: + +- `ct_train.py` +- `training/ct_training_loop.py` +- `env.yml` +- `.gitignore` +- `README.md` + +The collaborator environment pinned huggingface-hub 0.20.3, while the validated public +runtime used 0.23.4. The collaborator dataset ZIP SHA256 +`45e772cbbcb4ebb8657d383557fba2fd24cb929aeaab99fe1963b1462377da9d` differs from the +public ZIP SHA256 `2d4056e80de1a96fe16f2f58945c6c4710ecd9fc02e3cc7aa5b50513b7cdf389`. +Both reported the same byte size. `dataset_tool.py` stores variable ZIP entry timestamps, so +the converted ZIP digest is informational; source MD5, CRC, image/label content, dimensions, +color mode, and project loader behavior are authoritative. + +The collaborator smoke used global batch 10 and ran 100 fresh plus 100 resumed optimizer +updates. It explicitly used `--fp16=False`, did not enable GradScaler, and was produced from a +dirty tree whose recorded SHA was main rather than the collaborator commit. It is FP32 +engineering-connectivity evidence only. No checkpoint, training state, dataset ZIP, or large +log was committed to Git. + +Decision: **ACCEPT_SELECTED_FILES + REQUIRES_REBASE + REJECT_WHOLE_BRANCH**. + +## wk/iniBR + +- SHA: `e3158d83112a2fffb0796a515becee699c73d3fa` +- Ahead 1, behind 0 relative to main +- Raw scope: 30 files, 5375 insertions and 5375 deletions +- Ignoring end-of-line differences produces an empty diff with exit code 0 +- Typical files changed from pure LF to pure CRLF with identical logical lines +- Semantic changes: none +- Classification: **CRLF_ONLY_CHANGE** +- Decision: **DO_NOT_MERGE** + +The member should create a new branch from `origin/leader/day1-bootstrap`, set +`git config core.autocrlf input`, reapply only genuine work, and avoid cherry-picking the +line-ending conversion commit. + +## edwards365 + +- SHA: `4311059770f54821d151a9b0e1f76770a5f3930e` +- Exactly equal to `origin/main` +- Ahead 0, behind 0, changed files 0 +- Status: **NO_REMOTE_DELIVERABLE** + +No claim is made about local, unpushed work. + +## Missing collaborator + +No fourth collaborator branch was present after fetching and pruning remote-tracking refs. +The member must provide a branch name and SHA and push the deliverable before role mapping or +integration. + +## Role coverage + +| Role | Expected work | Current branch | Status | +|------|---------------|----------------|--------| +| A | Engineering and environment reproduction | `codex/day1-engineering` | Clear selective deliverable | +| B | Official fixed ECT baseline | None evidenced | Missing remote deliverable | +| C | Adaptive t-to-r schedule | None evidenced | Missing remote deliverable | +| D | Unified sampling, evaluation, visualization | None evidenced | Missing remote deliverable | + +Only Role A can be mapped from content. The other visible collaborator refs contain either no +semantic work or no commits, and one collaborator branch is absent. + +## Selective integration decision + +ACCEPT: + +- the eight engineering files listed above, subject to Day 2 path and validation hardening + +REWRITE: + +- `smoke_test.sh` as `scripts/smoke_engineering_100steps.sh` +- collaborator JSON evidence and provenance rather than importing it as frozen evidence + +REJECT: + +- direct replacement of the five protected public-baseline files +- whole-branch merge of `codex/day1-engineering` +- any merge of `wk/iniBR` diff --git a/artifacts/day2/integration_report.md b/artifacts/day2/integration_report.md new file mode 100644 index 00000000..c1ae949b --- /dev/null +++ b/artifacts/day2/integration_report.md @@ -0,0 +1,126 @@ +# Day 2 local integration report + +Integration date: 2026-07-15 (Asia/Shanghai) + +## Scope and branch + +- Local branch: `leader/day2-local-integration` +- Base: `origin/leader/day1-bootstrap@4e33194777a347ea5286b5ec1d5c29a58c792d29` +- Selective source: `origin/codex/day1-engineering@3cb1c52fc56f01942c84d535dddddffd99c3af47` +- Commit created: no +- Push or PR created: no + +The worktree versions of `ct_train.py`, `training/ct_training_loop.py`, `env.yml`, `.gitignore`, +and `README.md` were hash-checked against the public baseline immediately after extraction and +were identical. + +## Imported files + +- `conda-matpool.yml` +- `setup_env.sh` +- `prepare_data.sh` +- `download_checkpoint.sh` +- `scripts/check_environment.py` +- `scripts/verify_assets.py` +- `scripts/verify_smoke_run.py` +- `docs/DAY1_A.md` + +## Rewritten or adapted files + +- `scripts/smoke_engineering_100steps.sh` + - engineering-connectivity label, never a formal ECT baseline + - persistent `/mnt/ect_project` defaults + - `metrics=none` + - public-baseline `--enable_amp` CLI with FP16 and AMP enabled by default + - explicit statement that legacy FP32 evidence did not validate GradScaler + - separate `--check-only` and `--dry-run` paths +- `scripts/verify_assets.py` + - official CIFAR-10 tarball MD5 + - ZIP CRC + - exactly 50000 PNGs and 50000 one-to-one labels + - all images checked as 32x32 RGB + - `ImageFolderDataset` length, labels, dtype, shape, and sample reads + - ZIP SHA256 recorded without enforcing one conversion digest +- `scripts/check_environment.py` + - aligned to the protected public environment rather than collaborator `env.yml` + - records all observed package versions and validates imports + - checks the frozen core package/runtime versions and CUDA +- `setup_env.sh`, `prepare_data.sh`, and `download_checkpoint.sh` + - use `/mnt/ect_project` persistent defaults + - write optional reports under persistent `runs/day2` + - preserve check-only workflows +- `scripts/verify_smoke_run.py` + - records engineering-only provenance + - validates expected batch, FP16, AMP, and disabled formal metrics +- `docs/DAY1_A.md` + - documents persistent paths and the engineering/formal-baseline boundary + - removes dirty-main evidence claims + - does not claim a completed FP16 + GradScaler training validation + +## Validation results + +All commands below passed: + +- `bash -n setup_env.sh` +- `bash -n prepare_data.sh` +- `bash -n download_checkpoint.sh` +- `bash -n scripts/smoke_engineering_100steps.sh` +- `python -m compileall scripts training ct_train.py` with pycache redirected outside the repo +- `python ct_train.py --help` +- help checks for all imported/reworked scripts +- `bash setup_env.sh --check-only` +- `bash prepare_data.sh --check-only` +- `bash download_checkpoint.sh --check-only` +- `bash scripts/smoke_engineering_100steps.sh --check-only` +- `bash scripts/smoke_engineering_100steps.sh --dry-run` + +The dry-run configuration reported: + +- dataset size: 50000 +- global batch: 10 +- network `use_fp16`: true +- `enable_amp`: true +- `metrics`: empty list +- formal FID/KID: disabled +- output directory was not created + +Asset verification reported: + +- source tarball MD5: `c58f30108f718f92721af3b95e74349a` +- dataset ZIP CRC: passed +- PNG count: 50000 +- label count: 50000 +- image format: 32x32 RGB +- `ImageFolderDataset` length: 50000 +- dataset ZIP SHA256, informational only: + `2d4056e80de1a96fe16f2f58945c6c4710ecd9fc02e3cc7aa5b50513b7cdf389` +- official transfer checkpoint SHA256: + `4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da` + +No optimizer update, complete engineering smoke, formal training, FID, or KID was run. + +## Protected files rejected from import + +- `ct_train.py` +- `training/ct_training_loop.py` +- `env.yml` +- `.gitignore` +- `README.md` +- collaborator `artifacts/day1/*.json` +- collaborator `artifacts/day1/README.md` +- entire `codex/day1-engineering` branch +- entire `wk/iniBR` branch + +## Remaining issues and decisions + +1. The integration is intentionally uncommitted and unpushed. +2. No dynamic FP16 + GradScaler optimizer step or GradScaler state/resume test was run; the + successful dry run validates configuration propagation only. +3. The official fixed ECT baseline, adaptive schedule, and unified evaluation work still have + no collaborator remote deliverables. +4. `edwards365` still points to main, and one collaborator branch is still missing. +5. Public `env.yml` does not explicitly pin huggingface-hub 0.23.4 even though the validated + runtime and environment checker freeze that compatibility version. Changing `env.yml` was + explicitly outside this selective integration manifest and requires a separate decision. +6. `scripts/verify_smoke_run.py` was syntax-checked but cannot be exercised end-to-end without + running a new 100-step engineering smoke, which was explicitly prohibited in this phase. diff --git a/artifacts/day3/role_a_environment.json b/artifacts/day3/role_a_environment.json new file mode 100644 index 00000000..74f3de2a --- /dev/null +++ b/artifacts/day3/role_a_environment.json @@ -0,0 +1,71 @@ +{ + "schema_version": 2, + "branch": "role-a/clean-container-validation", + "tested_code_sha": "956c6f41390e9e26885e689eb4c3b66d45957c38", + "pr_head_sha_at_validation": "956c6f41390e9e26885e689eb4c3b66d45957c38", + "git_dirty": false, + "environment_name": "ect-clean-validation", + "environment_creation_command": "bash setup_env.sh --name ect-clean-validation", + "environment_created_from_scratch": true, + "manual_pip_install": false, + "environment_specification": { + "file": "env.yml", + "huggingface_hub_pin": "huggingface-hub==0.23.4", + "validation_spec_matches_environment_spec": true + }, + "gpu": { + "name": "NVIDIA A100-PCIE-40GB", + "count": 1, + "memory_mib": 40960 + }, + "python": "3.9.18", + "pytorch": "2.3.0", + "cuda": "12.1", + "environment_validation": "passed", + "package_versions": { + "Pillow": "11.3.0", + "accelerate": "0.27.2", + "click": "8.1.8", + "diffusers": "0.26.3", + "huggingface-hub": "0.23.4", + "imageio": "2.37.0", + "imageio-ffmpeg": "0.6.0", + "numpy": "2.0.2", + "psutil": "7.0.0", + "pyspng": "0.1.4", + "requests": "2.32.5", + "scipy": "1.13.1", + "torch": "2.3.0", + "tqdm": "4.67.1" + }, + "validation_commands": [ + "bash setup_env.sh --name ect-clean-validation", + "conda run -n ect-clean-validation python --version", + "conda run -n ect-clean-validation python -c \"import huggingface_hub; print(huggingface_hub.__version__)\"", + "ECT_ENV_NAME=ect-clean-validation bash prepare_data.sh --check-only", + "ECT_ENV_NAME=ect-clean-validation bash download_checkpoint.sh --check-only", + "ECT_ENV_NAME=ect-clean-validation bash scripts/smoke_engineering_100steps.sh --check-only", + "ECT_ENV_NAME=ect-clean-validation bash scripts/smoke_engineering_100steps.sh --dry-run --port 29521", + "ECT_ENV_NAME=ect-clean-validation bash scripts/smoke_engineering_100steps.sh --port 29521" + ], + "dataset_path": "/mnt/ect_project/datasets/cifar10-32x32.zip", + "dataset_validation": { + "status": "passed", + "images": 50000, + "labels": 50000, + "resolution": [ + 32, + 32 + ], + "color_mode": "RGB", + "zip_crc_ok": true, + "sha256": "08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372", + "source_tarball_md5": "c58f30108f718f92721af3b95e74349a" + }, + "checkpoint_path": "/mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl", + "checkpoint_sha256": "4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da", + "checkpoint_validation": "passed", + "unresolved_issues": [], + "evidence_note": "The validation ran on the code SHA above. The subsequent evidence-only commit changes only the three compact evidence files; the final PR head is recorded in PR #8 metadata and its validation summary comment.", + "final_status": "passed" +} diff --git a/artifacts/day3/role_a_smoke_report.json b/artifacts/day3/role_a_smoke_report.json new file mode 100644 index 00000000..1a46a57c --- /dev/null +++ b/artifacts/day3/role_a_smoke_report.json @@ -0,0 +1,89 @@ +{ + "schema_version": 2, + "test_kind": "engineering_connectivity_only", + "official_ect_baseline": false, + "branch": "role-a/clean-container-validation", + "tested_code_sha": "956c6f41390e9e26885e689eb4c3b66d45957c38", + "pr_head_sha_at_validation": "956c6f41390e9e26885e689eb4c3b66d45957c38", + "environment_name": "ect-clean-validation", + "environment_creation_command": "bash setup_env.sh --name ect-clean-validation", + "manual_pip_install": false, + "env_yml_huggingface_hub_pin": "huggingface-hub==0.23.4", + "gpu": "NVIDIA A100-PCIE-40GB", + "python": "3.9.18", + "pytorch": "2.3.0", + "cuda": "12.1", + "dataset_path": "/mnt/ect_project/datasets/cifar10-32x32.zip", + "dataset_validation": "passed", + "dataset_images": 50000, + "dataset_resolution": [ + 32, + 32 + ], + "dataset_color_mode": "RGB", + "checkpoint_path": "/mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl", + "checkpoint_sha256": "4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da", + "environment_check": "passed", + "data_check_only": "passed", + "checkpoint_check_only": "passed", + "smoke_check_only": "passed", + "dry_run": "passed", + "port": 29521, + "fresh_updates": 100, + "resume_updates": 100, + "fresh_progress_kimg": 1.0, + "resume_progress_kimg": 2.0, + "fp16": true, + "amp_enabled": true, + "metrics": [], + "formal_metrics_called": false, + "gradscaler_saved": true, + "gradscaler_restored": true, + "gradscaler_state": { + "fresh": { + "scale": 8192.0, + "growth_tracker": 31 + }, + "resume": { + "scale": 8192.0, + "growth_tracker": 131 + }, + "resume_growth_tracker_increment": 100 + }, + "loss_finite": true, + "loss_sample_counts": { + "fresh": 1000, + "resume": 1000 + }, + "loss_means": { + "fresh": [ + 11.838935947418213, + 17.023772768939686 + ], + "resume": [ + 16.928153270974754 + ] + }, + "wall_time_seconds": 185, + "peak_vram_mib": 5563, + "peak_reserved_vram_mib": 5616, + "nvidia_smi_peak_used_mib": 2807, + "run_directory": "/mnt/ect_project/runs/engineering-smoke/956c6f41-20260716T020346Z", + "fresh_snapshot": "fresh-100steps/network-snapshot-000001.pkl", + "fresh_training_state": "fresh-100steps/training-state-000001.pt", + "resume_snapshot": "resume-100steps/network-snapshot-000001.pkl", + "resume_training_state": "resume-100steps/training-state-000001.pt", + "all_outputs_persistent": true, + "observations": [ + "Fresh and Resume stats.jsonl contain only finite Loss/loss means.", + "Both numbered training states contain gradscaler_state.", + "The Resume log explicitly records restoring GradScaler state from the Fresh training state.", + "The console loss nan at initialization and resume maintenance is a reporting-order observation; recorded training losses are finite.", + "No FID or KID process was invoked; both training option files record metrics=[]." + ], + "unresolved_issues": [ + "Non-blocking reporting-order observation: the console can print loss nan before the statistics collector is updated; no training-loop change is included in this PR." + ], + "evidence_note": "The validation ran on the code SHA above. The subsequent evidence-only commit changes only the three compact evidence files; the final PR head is recorded in PR #8 metadata and its validation summary comment.", + "final_status": "passed" +} diff --git a/artifacts/role_b/paired_resume_smoke.json b/artifacts/role_b/paired_resume_smoke.json new file mode 100644 index 00000000..f2fb45ad --- /dev/null +++ b/artifacts/role_b/paired_resume_smoke.json @@ -0,0 +1,16 @@ +{ + "git_head": "c3537af0e78ae489355b288e2f48d24f22d366fe", + "device": "NVIDIA A100-PCIE-40GB", + "fresh_mode": "activation", + "resume_mode": "stability", + "run_dir": "/mnt/ect_project/runs/paired-smoke/sigmoid-activation-c3537af0-20260717T091035Z", + "before_nimg": 4096, + "after_nimg": 16000, + "before_attempted": 32, + "after_attempted": 125, + "csv_continuous": true, + "same_head_all_segments": true, + "collector_pass": true, + "fresh_exit": 0, + "resume_exit": 0 +} diff --git a/conda-matpool.yml b/conda-matpool.yml new file mode 100644 index 00000000..6abcf339 --- /dev/null +++ b/conda-matpool.yml @@ -0,0 +1,18 @@ +channels: + - pytorch + - nvidia + - defaults + +# Matpool's default channel alias may redirect community channels to mirror +# paths that do not exist (notably the `nvidia` channel). Keep named channels +# on Anaconda.org while retaining fast mainland mirrors for defaults. +channel_alias: https://conda.anaconda.org +default_channels: + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r + +show_channel_urls: true +default_threads: 1 +fetch_threads: 1 +verify_threads: 1 +execute_threads: 1 diff --git a/configs/final_evaluation_checkpoints.example.json b/configs/final_evaluation_checkpoints.example.json new file mode 100644 index 00000000..74c7c24e --- /dev/null +++ b/configs/final_evaluation_checkpoints.example.json @@ -0,0 +1,12 @@ +{ + "schema_version": 1, + "training_budget_kimg": 16, + "cells": [ + {"schedule": "sigmoid", "training_seed": 0, "checkpoint": "/path/to/sigmoid_seed0_16k.pkl", "checkpoint_sha256": "32aa4661584663c40bc05a3b5c5bef3bd4e8b60e289023d491fbe46fac5478ed", "training_result_dir": "/path/to/sigmoid_seed0_packaged_result"}, + {"schedule": "adaptive_v1", "training_seed": 0, "checkpoint": "/path/to/adaptive_v1_seed0_16k.pkl", "checkpoint_sha256": "7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a", "training_result_dir": "/path/to/adaptive_v1_seed0_packaged_result"}, + {"schedule": "sigmoid", "training_seed": 1, "checkpoint": "/path/to/sigmoid_seed1_16k.pkl", "training_result_dir": "/path/to/sigmoid_seed1_packaged_result"}, + {"schedule": "adaptive_v1", "training_seed": 1, "checkpoint": "/path/to/adaptive_v1_seed1_16k.pkl", "training_result_dir": "/path/to/adaptive_v1_seed1_packaged_result"}, + {"schedule": "sigmoid", "training_seed": 2, "checkpoint": "/path/to/sigmoid_seed2_16k.pkl", "training_result_dir": "/path/to/sigmoid_seed2_packaged_result"}, + {"schedule": "adaptive_v1", "training_seed": 2, "checkpoint": "/path/to/adaptive_v1_seed2_16k.pkl", "training_result_dir": "/path/to/adaptive_v1_seed2_packaged_result"} + ] +} diff --git a/configs/g_screen_q128_s3_protocol.json b/configs/g_screen_q128_s3_protocol.json new file mode 100644 index 00000000..a3beb6ec --- /dev/null +++ b/configs/g_screen_q128_s3_protocol.json @@ -0,0 +1,51 @@ +{ + "protocol_id": "g-screen-q128-s3-5k-v1", + "git_commit": "f53910ba7ed1890b05d1da6f8a9b616d03e8e576", + "training": { + "q": 128, + "seed": 3, + "budget_kimg": 256, + "gap_scales": [0.9, 1.0, 1.05, 1.1, 1.2, 1.3], + "batch": 128, + "batch_gpu": 16, + "optimizer": "RAdam", + "learning_rate": 0.0001, + "dropout": 0.2, + "c": 0, + "fp16": true, + "amp": true, + "teacher": "edm-cifar10-32x32-uncond-vp.pkl" + }, + "evaluation": { + "evidence_class": "5k_screening_proxy", + "metrics": ["kid5k_full", "fid5k_full"], + "sample_seeds": "0-4999", + "metric_seed": 20260730, + "precision": "fp32", + "nfe": [1, 2], + "nfe2_mid_t": 0.821, + "metric_repeats": 1, + "gpu_count_per_cell": 1 + }, + "swapped_gpu_reproduction": { + "purpose": "test whether the g=0.9 versus g=1.0 discontinuity is caused by GPU assignment or a single AMP trajectory", + "arms": [ + { + "g": 0.9, + "primary_gpu": 0, + "reproduction_gpu": 1 + }, + { + "g": 1.0, + "primary_gpu": 1, + "reproduction_gpu": 0 + } + ] + }, + "protocol_deviations": [ + "Only the final 256 kimg checkpoint was retained; planned 64 and 128 kimg checkpoints are unavailable.", + "Raw residual, separately named weighted loss, and gradient RMS were not logged.", + "AMP skipped-step counts differ by approximately one optimizer step across some arms.", + "All reported FID-5k and KID-5k values are screening proxies, not formal 50k benchmark results." + ] +} diff --git a/configs/multibudget_collector_input.example.csv b/configs/multibudget_collector_input.example.csv new file mode 100644 index 00000000..e2992fcd --- /dev/null +++ b/configs/multibudget_collector_input.example.csv @@ -0,0 +1,3 @@ +method,training_seed,budget_kimg,nfe,metric_name,metric_value,training_time_hours,quality_target,checkpoint_sha256,sample_count,generation_seed_range,metric_seed,evidence_class,evaluation_contract,analysis_track +fixed,3,512,1,kid5k_full,REPLACE_WITH_VALUE,REPLACE_WITH_CUMULATIVE_HOURS,REPLACE_WITH_PRE_SPECIFIED_TARGET,OPTIONAL_SHA256,5000,0-4999,REPLACE_WITH_METRIC_SEED,quick,q256-common-5k-v1,budget_curve +global110,3,512,1,kid5k_full,REPLACE_WITH_VALUE,REPLACE_WITH_CUMULATIVE_HOURS,REPLACE_WITH_PRE_SPECIFIED_TARGET,OPTIONAL_SHA256,5000,0-4999,REPLACE_WITH_METRIC_SEED,quick,q256-common-5k-v1,budget_curve diff --git a/configs/q128_confirmatory_matrix.frozen.json b/configs/q128_confirmatory_matrix.frozen.json new file mode 100644 index 00000000..69638691 --- /dev/null +++ b/configs/q128_confirmatory_matrix.frozen.json @@ -0,0 +1,52 @@ +{ + "schema_version": 1, + "manifest_kind": "frozen-logical-evaluation-matrix", + "protocol": "staged-checkpoint-evaluation-v1", + "matrix_id": "q128-fresh-fixed-vs-global110-v1", + "description": "Pre-result logical matrix for a fresh q=128 fixed-sigmoid versus global-only g=1.10 comparison. Checkpoint hashes and receipt digests are intentionally pending until the predeclared training endpoints exist; those runtime bindings must not alter this matrix's cell identities or evaluation contracts.", + "runtime_binding": { + "required_before_evaluation": true, + "versioned_paths": false, + "required_runtime_fields": ["checkpoint", "checkpoint_sha256", "integrity_receipt"], + "binding_rule": "Each runtime cell must preserve checkpoint_id, method, training_seed, budget_kimg, schedule_q, schedule_identity, and global_gap_scale exactly, then bind a completed checkpoint SHA-256 and a passed training-integrity receipt." + }, + "comparison": { + "baseline_method": "fixed", + "candidate_method": "global110", + "candidate_label": "global_only", + "pairing_key": ["training_seed", "budget_kimg", "nfe", "metric"], + "delta_direction": "global_only - fixed" + }, + "method_definitions": { + "fixed": {"label": "fixed sigmoid", "schedule_identity": "sigmoid", "global_gap_scale": 1.0, "local_controller": "disabled"}, + "global110": {"label": "global-only g=1.10", "schedule_identity": "global_sigmoid", "global_gap_scale": 1.1, "local_controller": "disabled"} + }, + "training": {"schedule_q": 128, "training_seeds": [3, 4, 5], "budget_kimg": [256]}, + "evaluation_contracts": [ + {"budget_kimg": 256, "stage": "formal", "evidence_class": "formal", "metric_names": ["kid50k_full", "fid50k_full"], "sample_count": 50000, "generation_seed_range": "0-49999", "metric_seed": 20260730} + ], + "nfe_modes": {"1": [], "2": [0.821]}, + "formal_promotion_policy": { + "eligibility": "provenance_and_integrity_only", + "quick_metric_performance": "not_an_eligibility_criterion", + "required_formal_checkpoint_ids": [ + "q128-fresh-256k-seed3-fixed", "q128-fresh-256k-seed3-global110", + "q128-fresh-256k-seed4-fixed", "q128-fresh-256k-seed4-global110", + "q128-fresh-256k-seed5-fixed", "q128-fresh-256k-seed5-global110" + ], + "required_evaluation_checkpoint_ids": [ + "q128-fresh-256k-seed3-fixed", "q128-fresh-256k-seed3-global110", + "q128-fresh-256k-seed4-fixed", "q128-fresh-256k-seed4-global110", + "q128-fresh-256k-seed5-fixed", "q128-fresh-256k-seed5-global110" + ], + "rule": "Every predeclared checkpoint must enter formal evaluation when its immutable provenance matches and its training-integrity receipt passes. Quick KID/FID performance, if an implementation smoke is run, may not exclude, add, substitute, or otherwise select a formal checkpoint." + }, + "cells": [ + {"checkpoint_id": "q128-fresh-256k-seed3-fixed", "method": "fixed", "training_seed": 3, "budget_kimg": 256, "schedule_q": 128, "schedule_identity": "sigmoid", "global_gap_scale": 1.0}, + {"checkpoint_id": "q128-fresh-256k-seed3-global110", "method": "global110", "training_seed": 3, "budget_kimg": 256, "schedule_q": 128, "schedule_identity": "global_sigmoid", "global_gap_scale": 1.1}, + {"checkpoint_id": "q128-fresh-256k-seed4-fixed", "method": "fixed", "training_seed": 4, "budget_kimg": 256, "schedule_q": 128, "schedule_identity": "sigmoid", "global_gap_scale": 1.0}, + {"checkpoint_id": "q128-fresh-256k-seed4-global110", "method": "global110", "training_seed": 4, "budget_kimg": 256, "schedule_q": 128, "schedule_identity": "global_sigmoid", "global_gap_scale": 1.1}, + {"checkpoint_id": "q128-fresh-256k-seed5-fixed", "method": "fixed", "training_seed": 5, "budget_kimg": 256, "schedule_q": 128, "schedule_identity": "sigmoid", "global_gap_scale": 1.0}, + {"checkpoint_id": "q128-fresh-256k-seed5-global110", "method": "global110", "training_seed": 5, "budget_kimg": 256, "schedule_q": 128, "schedule_identity": "global_sigmoid", "global_gap_scale": 1.1} + ] +} diff --git a/configs/q256_budget_matrix.frozen.json b/configs/q256_budget_matrix.frozen.json new file mode 100644 index 00000000..bfdd7ba8 --- /dev/null +++ b/configs/q256_budget_matrix.frozen.json @@ -0,0 +1,66 @@ +{ + "schema_version": 1, + "manifest_kind": "frozen-logical-evaluation-matrix", + "protocol": "staged-checkpoint-evaluation-v1", + "matrix_id": "q256-budget-fixed-vs-global110-v1", + "description": "Pre-result logical matrix for q=256 budget comparison. Checkpoint hashes and receipt digests are intentionally pending until the predeclared training endpoints exist; those runtime bindings must not alter this matrix's cell identities or evaluation contracts.", + "runtime_binding": { + "required_before_evaluation": true, + "versioned_paths": false, + "required_runtime_fields": ["checkpoint", "checkpoint_sha256", "integrity_receipt"], + "binding_rule": "Each runtime cell must preserve checkpoint_id, method, training_seed, budget_kimg, schedule_q, schedule_identity, and global_gap_scale exactly, then bind a completed checkpoint SHA-256 and a passed training-integrity receipt." + }, + "comparison": { + "baseline_method": "fixed", + "candidate_method": "global110", + "candidate_label": "global_only", + "pairing_key": ["training_seed", "budget_kimg", "nfe", "metric"], + "delta_direction": "global_only - fixed" + }, + "method_definitions": { + "fixed": {"label": "fixed sigmoid", "schedule_identity": "sigmoid", "global_gap_scale": 1.0, "local_controller": "disabled"}, + "global110": {"label": "global-only g=1.10", "schedule_identity": "global_sigmoid", "global_gap_scale": 1.1, "local_controller": "disabled"} + }, + "training": {"schedule_q": 256, "training_seeds": [3, 4, 5], "budget_kimg": [512, 768, 1024]}, + "evaluation_contracts": [ + {"budget_kimg": 512, "stage": "quick", "evidence_class": "quick", "metric_names": ["kid5k_full", "fid5k_full"], "sample_count": 5000, "generation_seed_range": "0-4999", "metric_seed": 20260730}, + {"budget_kimg": 768, "stage": "quick", "evidence_class": "quick", "metric_names": ["kid5k_full", "fid5k_full"], "sample_count": 5000, "generation_seed_range": "0-4999", "metric_seed": 20260730}, + {"budget_kimg": 1024, "stage": "formal", "evidence_class": "formal", "metric_names": ["kid50k_full", "fid50k_full"], "sample_count": 50000, "generation_seed_range": "0-49999", "metric_seed": 20260730} + ], + "nfe_modes": {"1": [], "2": [0.821]}, + "formal_promotion_policy": { + "eligibility": "provenance_and_integrity_only", + "quick_metric_performance": "not_an_eligibility_criterion", + "required_formal_checkpoint_ids": [ + "q256-budget-1024k-seed3-fixed", "q256-budget-1024k-seed3-global110", + "q256-budget-1024k-seed4-fixed", "q256-budget-1024k-seed4-global110", + "q256-budget-1024k-seed5-fixed", "q256-budget-1024k-seed5-global110" + ], + "required_evaluation_checkpoint_ids": [ + "q256-budget-512k-seed3-fixed", "q256-budget-512k-seed3-global110", "q256-budget-512k-seed4-fixed", "q256-budget-512k-seed4-global110", "q256-budget-512k-seed5-fixed", "q256-budget-512k-seed5-global110", + "q256-budget-768k-seed3-fixed", "q256-budget-768k-seed3-global110", "q256-budget-768k-seed4-fixed", "q256-budget-768k-seed4-global110", "q256-budget-768k-seed5-fixed", "q256-budget-768k-seed5-global110", + "q256-budget-1024k-seed3-fixed", "q256-budget-1024k-seed3-global110", "q256-budget-1024k-seed4-fixed", "q256-budget-1024k-seed4-global110", "q256-budget-1024k-seed5-fixed", "q256-budget-1024k-seed5-global110" + ], + "rule": "Every predeclared checkpoint must complete the evaluation contract for its budget. The six predeclared 1024-kimg checkpoints must enter formal evaluation when their immutable provenance matches and their training-integrity receipts pass. No quick KID/FID value at 512 or 768 kimg may exclude, add, substitute, or otherwise select a 1024-kimg formal checkpoint." + }, + "cells": [ + {"checkpoint_id": "q256-budget-512k-seed3-fixed", "method": "fixed", "training_seed": 3, "budget_kimg": 512, "schedule_q": 256, "schedule_identity": "sigmoid", "global_gap_scale": 1.0}, + {"checkpoint_id": "q256-budget-512k-seed3-global110", "method": "global110", "training_seed": 3, "budget_kimg": 512, "schedule_q": 256, "schedule_identity": "global_sigmoid", "global_gap_scale": 1.1}, + {"checkpoint_id": "q256-budget-512k-seed4-fixed", "method": "fixed", "training_seed": 4, "budget_kimg": 512, "schedule_q": 256, "schedule_identity": "sigmoid", "global_gap_scale": 1.0}, + {"checkpoint_id": "q256-budget-512k-seed4-global110", "method": "global110", "training_seed": 4, "budget_kimg": 512, "schedule_q": 256, "schedule_identity": "global_sigmoid", "global_gap_scale": 1.1}, + {"checkpoint_id": "q256-budget-512k-seed5-fixed", "method": "fixed", "training_seed": 5, "budget_kimg": 512, "schedule_q": 256, "schedule_identity": "sigmoid", "global_gap_scale": 1.0}, + {"checkpoint_id": "q256-budget-512k-seed5-global110", "method": "global110", "training_seed": 5, "budget_kimg": 512, "schedule_q": 256, "schedule_identity": "global_sigmoid", "global_gap_scale": 1.1}, + {"checkpoint_id": "q256-budget-768k-seed3-fixed", "method": "fixed", "training_seed": 3, "budget_kimg": 768, "schedule_q": 256, "schedule_identity": "sigmoid", "global_gap_scale": 1.0}, + {"checkpoint_id": "q256-budget-768k-seed3-global110", "method": "global110", "training_seed": 3, "budget_kimg": 768, "schedule_q": 256, "schedule_identity": "global_sigmoid", "global_gap_scale": 1.1}, + {"checkpoint_id": "q256-budget-768k-seed4-fixed", "method": "fixed", "training_seed": 4, "budget_kimg": 768, "schedule_q": 256, "schedule_identity": "sigmoid", "global_gap_scale": 1.0}, + {"checkpoint_id": "q256-budget-768k-seed4-global110", "method": "global110", "training_seed": 4, "budget_kimg": 768, "schedule_q": 256, "schedule_identity": "global_sigmoid", "global_gap_scale": 1.1}, + {"checkpoint_id": "q256-budget-768k-seed5-fixed", "method": "fixed", "training_seed": 5, "budget_kimg": 768, "schedule_q": 256, "schedule_identity": "sigmoid", "global_gap_scale": 1.0}, + {"checkpoint_id": "q256-budget-768k-seed5-global110", "method": "global110", "training_seed": 5, "budget_kimg": 768, "schedule_q": 256, "schedule_identity": "global_sigmoid", "global_gap_scale": 1.1}, + {"checkpoint_id": "q256-budget-1024k-seed3-fixed", "method": "fixed", "training_seed": 3, "budget_kimg": 1024, "schedule_q": 256, "schedule_identity": "sigmoid", "global_gap_scale": 1.0}, + {"checkpoint_id": "q256-budget-1024k-seed3-global110", "method": "global110", "training_seed": 3, "budget_kimg": 1024, "schedule_q": 256, "schedule_identity": "global_sigmoid", "global_gap_scale": 1.1}, + {"checkpoint_id": "q256-budget-1024k-seed4-fixed", "method": "fixed", "training_seed": 4, "budget_kimg": 1024, "schedule_q": 256, "schedule_identity": "sigmoid", "global_gap_scale": 1.0}, + {"checkpoint_id": "q256-budget-1024k-seed4-global110", "method": "global110", "training_seed": 4, "budget_kimg": 1024, "schedule_q": 256, "schedule_identity": "global_sigmoid", "global_gap_scale": 1.1}, + {"checkpoint_id": "q256-budget-1024k-seed5-fixed", "method": "fixed", "training_seed": 5, "budget_kimg": 1024, "schedule_q": 256, "schedule_identity": "sigmoid", "global_gap_scale": 1.0}, + {"checkpoint_id": "q256-budget-1024k-seed5-global110", "method": "global110", "training_seed": 5, "budget_kimg": 1024, "schedule_q": 256, "schedule_identity": "global_sigmoid", "global_gap_scale": 1.1} + ] +} diff --git a/configs/role_a_quality_checkpoints.example.json b/configs/role_a_quality_checkpoints.example.json new file mode 100644 index 00000000..43519aef --- /dev/null +++ b/configs/role_a_quality_checkpoints.example.json @@ -0,0 +1,23 @@ +{ + "schema_version": 1, + "cells": [ + {"method": "sigmoid", "training_seed": 0, "budget_kimg": 64, "checkpoint": "/path/to/sigmoid_seed0_64k.pkl"}, + {"method": "adaptive_v1", "training_seed": 0, "budget_kimg": 64, "checkpoint": "/path/to/adaptive_v1_seed0_64k.pkl"}, + {"method": "sigmoid", "training_seed": 1, "budget_kimg": 64, "checkpoint": "/path/to/sigmoid_seed1_64k.pkl"}, + {"method": "adaptive_v1", "training_seed": 1, "budget_kimg": 64, "checkpoint": "/path/to/adaptive_v1_seed1_64k.pkl"}, + {"method": "sigmoid", "training_seed": 2, "budget_kimg": 64, "checkpoint": "/path/to/sigmoid_seed2_64k.pkl"}, + {"method": "adaptive_v1", "training_seed": 2, "budget_kimg": 64, "checkpoint": "/path/to/adaptive_v1_seed2_64k.pkl"}, + {"method": "sigmoid", "training_seed": 0, "budget_kimg": 32, "checkpoint": "/path/to/sigmoid_seed0_32k.pkl"}, + {"method": "adaptive_v1", "training_seed": 0, "budget_kimg": 32, "checkpoint": "/path/to/adaptive_v1_seed0_32k.pkl"}, + {"method": "sigmoid", "training_seed": 1, "budget_kimg": 32, "checkpoint": "/path/to/sigmoid_seed1_32k.pkl"}, + {"method": "adaptive_v1", "training_seed": 1, "budget_kimg": 32, "checkpoint": "/path/to/adaptive_v1_seed1_32k.pkl"}, + {"method": "sigmoid", "training_seed": 2, "budget_kimg": 32, "checkpoint": "/path/to/sigmoid_seed2_32k.pkl"}, + {"method": "adaptive_v1", "training_seed": 2, "budget_kimg": 32, "checkpoint": "/path/to/adaptive_v1_seed2_32k.pkl"}, + {"method": "sigmoid", "training_seed": 0, "budget_kimg": 16, "checkpoint": "/path/to/sigmoid_seed0_16k.pkl"}, + {"method": "adaptive_v1", "training_seed": 0, "budget_kimg": 16, "checkpoint": "/path/to/adaptive_v1_seed0_16k.pkl"}, + {"method": "sigmoid", "training_seed": 1, "budget_kimg": 16, "checkpoint": "/path/to/sigmoid_seed1_16k.pkl"}, + {"method": "adaptive_v1", "training_seed": 1, "budget_kimg": 16, "checkpoint": "/path/to/adaptive_v1_seed1_16k.pkl"}, + {"method": "sigmoid", "training_seed": 2, "budget_kimg": 16, "checkpoint": "/path/to/sigmoid_seed2_16k.pkl"}, + {"method": "adaptive_v1", "training_seed": 2, "budget_kimg": 16, "checkpoint": "/path/to/adaptive_v1_seed2_16k.pkl"} + ] +} diff --git a/configs/staged_evaluation_checkpoints.example.json b/configs/staged_evaluation_checkpoints.example.json new file mode 100644 index 00000000..9a8f2931 --- /dev/null +++ b/configs/staged_evaluation_checkpoints.example.json @@ -0,0 +1,24 @@ +{ + "schema_version": 1, + "protocol": "staged-checkpoint-evaluation-v1", + "cells": [ + { + "checkpoint_id": "sigmoid_seed0_16k", + "method": "sigmoid", + "training_seed": 0, + "budget_kimg": 16, + "checkpoint": "/mnt/ect_project/checkpoints/sigmoid_seed0_16k.pkl", + "checkpoint_sha256": "0000000000000000000000000000000000000000000000000000000000000000", + "integrity_receipt": "/mnt/ect_project/checkpoints/sigmoid_seed0_16k.integrity.json" + }, + { + "checkpoint_id": "adaptive_v1_seed0_16k", + "method": "adaptive_v1", + "training_seed": 0, + "budget_kimg": 16, + "checkpoint": "/mnt/ect_project/checkpoints/adaptive_v1_seed0_16k.pkl", + "checkpoint_sha256": "0000000000000000000000000000000000000000000000000000000000000000", + "integrity_receipt": "/mnt/ect_project/checkpoints/adaptive_v1_seed0_16k.integrity.json" + } + ] +} diff --git a/configs/staged_evaluation_confirmatory_q256.frozen.json b/configs/staged_evaluation_confirmatory_q256.frozen.json new file mode 100644 index 00000000..2208c0ac --- /dev/null +++ b/configs/staged_evaluation_confirmatory_q256.frozen.json @@ -0,0 +1,180 @@ +{ + "schema_version": 1, + "manifest_kind": "frozen-logical-checkpoint-matrix", + "protocol": "staged-checkpoint-evaluation-v1", + "matrix_id": "confirmatory-q256-fixed-vs-global110-v1", + "description": "Authoritative logical matrix for the 256-kimg confirmatory fixed-sigmoid versus global-only g=1.10 experiment. Machine-local checkpoint and receipt paths are deliberately excluded.", + "runtime_binding": { + "required": true, + "versioned_paths": false, + "required_runtime_fields": [ + "checkpoint", + "integrity_receipt" + ], + "binding_rule": "A server manifest must preserve every cell's checkpoint_id and checkpoint_sha256 and bind the named integrity receipt to a local path." + }, + "comparison": { + "baseline_method": "fixed", + "candidate_method": "global110", + "candidate_label": "global_only", + "pairing_key": [ + "training_seed", + "budget_kimg", + "nfe", + "metric" + ], + "delta_direction": "global_only - fixed" + }, + "formal_promotion_policy": { + "eligibility": "provenance_and_integrity_only", + "quick_metric_performance": "not_an_eligibility_criterion", + "required_checkpoint_ids": [ + "confirmatory-256k-seed3-fixed", + "confirmatory-256k-seed3-global110", + "confirmatory-256k-seed4-fixed", + "confirmatory-256k-seed4-global110", + "confirmatory-256k-seed5-fixed", + "confirmatory-256k-seed5-global110" + ], + "rule": "Every predeclared checkpoint with matching provenance and a passed training-integrity receipt must enter the formal 50k matrix. Quick 5k results must not exclude a seed, method, NFE, or checkpoint." + }, + "training": { + "budget_kimg": 256, + "schedule_q": 256, + "training_seeds": [ + 3, + 4, + 5 + ] + }, + "method_definitions": { + "fixed": { + "label": "fixed sigmoid", + "schedule_identity": "sigmoid", + "global_gap_scale": 1.0, + "local_controller": "disabled" + }, + "global110": { + "label": "global-only g=1.10", + "schedule_identity": "global_sigmoid", + "global_gap_scale": 1.1, + "local_controller": "disabled" + } + }, + "cells": [ + { + "checkpoint_id": "confirmatory-256k-seed3-fixed", + "method": "fixed", + "training_seed": 3, + "budget_kimg": 256, + "schedule_q": 256, + "schedule_identity": "sigmoid", + "global_gap_scale": 1.0, + "checkpoint_sha256": "09a41e1e7c03dcdf5ffb93bb68687390278b4b190183dfff92bacc1bf79738d9", + "executed_training_source_commit": "3a0d603da97dd93ddbb6c7ce49e4a7351d54bb43", + "training_integrity_receipt": { + "receipt_id": "confirmatory-256k-seed3-fixed", + "receipt_filename": "seed3_fixed_256k.integrity.json", + "receipt_sha256": "4c1f8c28bd5ba9dcf3b4c1be9c50809395bfe7c460108bfd945163bc9735dd45", + "status": "passed", + "receipt_type": "machine-generated", + "checker_git_commit": "6d777cd9324b31b910787212959b681f568aa971", + "source": "D_HANDOFF.md", + "source_git_blob": "6757919629a4c7fed05dce9b0316f8607d4eeae0" + } + }, + { + "checkpoint_id": "confirmatory-256k-seed3-global110", + "method": "global110", + "training_seed": 3, + "budget_kimg": 256, + "schedule_q": 256, + "schedule_identity": "global_sigmoid", + "global_gap_scale": 1.1, + "checkpoint_sha256": "24875430eea4679a416ae921c3e9ae16142f6416d2a0edf970764384ef964bed", + "executed_training_source_commit": "3a0d603da97dd93ddbb6c7ce49e4a7351d54bb43", + "training_integrity_receipt": { + "receipt_id": "confirmatory-256k-seed3-global110", + "receipt_filename": "seed3_global110_256k.integrity.json", + "receipt_sha256": "8ae67972bcd6ea3a5d98d0fd8351632718a954e4a3b12c7325373e2da25ce777", + "status": "passed", + "receipt_type": "machine-generated", + "checker_git_commit": "6d777cd9324b31b910787212959b681f568aa971", + "source": "D_HANDOFF.md", + "source_git_blob": "6757919629a4c7fed05dce9b0316f8607d4eeae0" + } + }, + { + "checkpoint_id": "confirmatory-256k-seed4-fixed", + "method": "fixed", + "training_seed": 4, + "budget_kimg": 256, + "schedule_q": 256, + "schedule_identity": "sigmoid", + "global_gap_scale": 1.0, + "checkpoint_sha256": "ac94e7b07e5b7628e6b14b26155fb3de09e42373497183d39aba4fe9863663c9", + "executed_training_source_commit": "ab03f9e03b7b82425282abc3bf661067ca45875a", + "training_integrity_receipt": { + "receipt_id": "confirmatory-256k-seed4-fixed", + "receipt_filename": "seed4_fixed_256k.integrity.json", + "receipt_sha256": "056c9ae21ac009bef781bf402026d3954f07e8a2bba166dfad4f2ae1189fe301", + "status": "passed", + "checker_git_commit": "6d777cd9324b31b910787212959b681f568aa971" + } + }, + { + "checkpoint_id": "confirmatory-256k-seed4-global110", + "method": "global110", + "training_seed": 4, + "budget_kimg": 256, + "schedule_q": 256, + "schedule_identity": "global_sigmoid", + "global_gap_scale": 1.1, + "checkpoint_sha256": "62a6122a7be523aeb12875d96e96312e9c90efde9eafb75d730c75ceea0e8862", + "executed_training_source_commit": "ab03f9e03b7b82425282abc3bf661067ca45875a", + "training_integrity_receipt": { + "receipt_id": "confirmatory-256k-seed4-global110", + "receipt_filename": "seed4_global110_256k.integrity.json", + "receipt_sha256": "98727bd3b4c51f5e5c1fffd62da7199dbdefcccb157b03d9b64d3ea1a50923ad", + "status": "passed", + "checker_git_commit": "6d777cd9324b31b910787212959b681f568aa971" + } + }, + { + "checkpoint_id": "confirmatory-256k-seed5-fixed", + "method": "fixed", + "training_seed": 5, + "budget_kimg": 256, + "schedule_q": 256, + "schedule_identity": "sigmoid", + "global_gap_scale": 1.0, + "checkpoint_sha256": "21fab0e501bb27032c0e49a553b05a2800ea0fbe20a2a1d94a6bbf5276f2b72a", + "executed_training_source_commit": "ab03f9e03b7b82425282abc3bf661067ca45875a", + "training_integrity_receipt": { + "receipt_id": "confirmatory-256k-seed5-fixed", + "receipt_filename": "seed5_fixed_256k.integrity.json", + "receipt_sha256": "f8ffedf9763aacaf90deba825dec8fd499510e6f1c4989840614df6d0d47764e", + "status": "passed", + "checker_git_commit": "6d777cd9324b31b910787212959b681f568aa971" + } + }, + { + "checkpoint_id": "confirmatory-256k-seed5-global110", + "method": "global110", + "training_seed": 5, + "budget_kimg": 256, + "schedule_q": 256, + "schedule_identity": "global_sigmoid", + "global_gap_scale": 1.1, + "checkpoint_sha256": "491dc887990e6d9f6fde70b5d12775aaf4bfc6155b731682926b02061c253e9b", + "executed_training_source_commit": "ab03f9e03b7b82425282abc3bf661067ca45875a", + "training_integrity_receipt": { + "receipt_id": "confirmatory-256k-seed5-global110", + "receipt_filename": "seed5_global110_256k.integrity.json", + "receipt_sha256": "7e4d9b8279d7fd06aec7c158582ec8c455b609bd05956652ac0834a260a5c571", + "status": "passed", + "checker_git_commit": "6d777cd9324b31b910787212959b681f568aa971" + } + } + ] +} diff --git a/ct_eval.py b/ct_eval.py index b9ab7a49..a2fad4ff 100644 --- a/ct_eval.py +++ b/ct_eval.py @@ -55,7 +55,7 @@ def convert(self, value, param, ctx): @click.option('--data', help='Path to the dataset', metavar='ZIP|DIR', type=str, required=True) @click.option('--cond', help='Train class-conditional model', metavar='BOOL', type=bool, default=False, show_default=True) @click.option('--arch', help='Network architecture', metavar='ddpmpp|ncsnpp|adm', type=click.Choice(['ddpmpp', 'ncsnpp', 'adm']), default='ddpmpp', show_default=True) -@click.option('--precond', help='Preconditioning & loss function', metavar='vp|ve|edm', type=click.Choice(['vp', 've', 'edm', 'ct']), default='ct', show_default=True) +@click.option('--precond', help='Preconditioning & loss function', metavar='vp|ve|edm|ct', type=click.Choice(['vp', 've', 'edm', 'ct']), default='ct', show_default=True) # Hyperparameters. @click.option('--cbase', help='Channel multiplier [default: varies]', metavar='INT', type=int) @@ -92,7 +92,10 @@ def convert(self, value, param, ctx): # Evaluation @click.option('--mid_t', help='Sampler steps [default: 0.821]', multiple=True, default=[0.821]) +@click.option('--nfe', help='Number of function evaluations', type=click.Choice(['1', '2']), default='2', show_default=True) @click.option('--metrics', help='Comma-separated list or "none" [default: fid50k_full]', type=CommaSeparatedList(), default='fid50k_full') +@click.option('--metric-repeats', help='Number of times to repeat each metric', type=click.IntRange(min=1), default=3, show_default=True) +@click.option('--sample-seeds', help='Explicit per-sample seed list/range (single-GPU only)', metavar='LIST', type=str) def main(**kwargs): @@ -145,7 +148,20 @@ def main(**kwargs): # Trainig options. c.update(cudnn_benchmark=opts.bench) - c.update(mid_t=opts.mid_t, metrics=opts.metrics) + sample_seeds = None if opts.sample_seeds is None else parse_int_list(opts.sample_seeds) + if sample_seeds is not None: + if len(sample_seeds) == 0: + raise click.ClickException('--sample-seeds must not be empty') + if len(set(sample_seeds)) != len(sample_seeds): + raise click.ClickException('--sample-seeds must not contain duplicates') + if dist.get_world_size() != 1: + raise click.ClickException('--sample-seeds currently requires exactly one GPU') + c.update( + mid_t=() if opts.nfe == '1' else opts.mid_t, + metrics=opts.metrics, + metric_repeats=opts.metric_repeats, + sample_seeds=sample_seeds, + ) # Random seed. if opts.seed is not None: @@ -274,7 +290,7 @@ def save_image_grid(img, fname, drange, grid_size): @torch.no_grad() def generator_fn( net, latents, class_labels=None, - t_max=80, mid_t=None + t_max=80, mid_t=None, step_noises=None, sample_seeds=None, ): # Time step discretization. mid_t = [] if mid_t is None else mid_t @@ -283,12 +299,36 @@ def generator_fn( # t_0 = T, t_N = 0 t_steps = torch.cat([net.round_sigma(t_steps), torch.zeros_like(t_steps[:1])]) + intermediate_steps = max(len(t_steps) - 2, 0) + if step_noises is not None and len(step_noises) != intermediate_steps: + raise ValueError('step_noises must contain one tensor per intermediate sampling step') + if sample_seeds is not None: + if step_noises is not None: + raise ValueError('sample_seeds and step_noises are mutually exclusive') + if len(sample_seeds) != latents.shape[0]: + raise ValueError('sample_seeds must contain one seed per latent') + seeded_noises = [[] for _ in range(intermediate_steps)] + shape = tuple(latents.shape[1:]) + for seed in sample_seeds: + generator = torch.Generator(device='cpu').manual_seed(int(seed)) + # Consume the matching latent draw before deriving step noise. This + # mirrors scripts/sample_fixed_seeds.py and keeps NFE=1/2 paired. + torch.randn(shape, generator=generator, dtype=torch.float64) + for index in range(intermediate_steps): + seeded_noises[index].append( + torch.randn(shape, generator=generator, dtype=torch.float64) + ) + step_noises = [torch.stack(items).to(latents.device) for items in seeded_noises] + # Sampling steps x = latents.to(torch.float64) * t_steps[0] for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])): x = net(x, t_cur, class_labels).to(torch.float64) if t_next > 0: - x = x + t_next * torch.randn_like(x) + noise = torch.randn_like(x) if step_noises is None else step_noises[i] + if noise.shape != x.shape: + raise ValueError(f'step_noises[{i}] has shape {noise.shape}, expected {x.shape}') + x = x + t_next * noise.to(device=x.device, dtype=x.dtype) return x #---------------------------------------------------------------------------- @@ -302,6 +342,8 @@ def evaluation( resume_pkl = None, # Start from the given network snapshot, None = random initialization. mid_t = None, # Intermediate t for few-step generation. metrics = None, # Metrics for evaluation. + metric_repeats = 3, # Number of deterministic repeats per metric. + sample_seeds = None, # Explicit per-sample seeds for proxy metrics. cudnn_benchmark = True, # Enable torch.backends.cudnn.benchmark? device = torch.device('cuda'), ): @@ -371,11 +413,12 @@ def evaluation( del images dist.print0('Evaluating few-step generation...') - for _ in range(3): + for _ in range(metric_repeats): for metric in metrics: result_dict = metric_main.calc_metric(metric=metric, generator_fn=few_step_fn, G=net, G_kwargs={}, - dataset_kwargs=dataset_kwargs, num_gpus=dist.get_world_size(), rank=dist.get_rank(), device=device) + dataset_kwargs=dataset_kwargs, num_gpus=dist.get_world_size(), rank=dist.get_rank(), device=device, + sample_seeds=sample_seeds, metric_seed=seed) if dist.get_rank() == 0: metric_main.report_metric(result_dict, run_dir=run_dir, snapshot_pkl=f'{resume_pkl}') diff --git a/ct_train.py b/ct_train.py index 43b3724f..95da12dd 100644 --- a/ct_train.py +++ b/ct_train.py @@ -36,6 +36,44 @@ def convert(self, value, param, ctx): return [] return value.split(',') + +def normalize_schedule_name(_ctx, _param, value): + aliases = { + 'adaptive-v1': 'adaptive_v1', + 'global-sigmoid': 'global_sigmoid', + 'local-tbin-v1': 'local_tbin_v1', + 'local-tbin-v2': 'local_tbin_v2', + 'local-tbin-v3': 'local_tbin_v3', + } + return aliases.get(value, value) + + +def make_loss_kwargs(opts): + """Build the persisted loss/schedule config without renaming legacy keys.""" + return dnnlib.EasyDict( + P_mean=opts.mean, + P_std=opts.std, + q=opts.q, + c=opts.c, + k=opts.k, + b=opts.b, + adj=opts.mapping, + adaptive_loss_ema_beta=opts.adaptive_loss_ema_beta, + adaptive_warmup_updates=opts.adaptive_warmup_updates, + adaptive_max_adjust=opts.adaptive_max_adjust, + adaptive_min_gap=opts.adaptive_min_gap, + local_tbin_num_bins=opts.local_tbin_num_bins, + local_tbin_short_beta=opts.local_tbin_short_beta, + local_tbin_long_beta=opts.local_tbin_long_beta, + local_tbin_warmup_updates=opts.local_tbin_warmup_updates, + local_tbin_gain=opts.local_tbin_gain, + local_tbin_min_scale=opts.local_tbin_min_scale, + local_tbin_max_scale=opts.local_tbin_max_scale, + local_tbin_deadband=opts.local_tbin_deadband, + local_tbin_min_gap=opts.local_tbin_min_gap, + global_gap_scale=opts.global_gap_scale, + ) + #---------------------------------------------------------------------------- @click.command() @@ -65,10 +103,47 @@ def convert(self, value, param, ctx): @click.option('--mean', help='P_mean of Log Normal Distribution', metavar='FLOAT', type=click.FloatRange(), default=-1.1, show_default=True) @click.option('--std', help='P_std of Log Normal Distribution', metavar='FLOAT', type=click.FloatRange(), default=2.0, show_default=True) -@click.option('--mapping', help='Type of mapping fn', metavar='STR', type=click.Choice(['const', 'sigmoid']), default='sigmoid', show_default=True) +@click.option('--schedule', '--mapping', 'mapping', + help='Type of t-to-r schedule; --mapping is a compatibility alias', metavar='STR', + type=click.Choice(['const', 'sigmoid', 'global_sigmoid', 'global-sigmoid', + 'adaptive_v1', 'adaptive-v1', + 'local_tbin_v1', 'local-tbin-v1', + 'local_tbin_v2', 'local-tbin-v2', + 'local_tbin_v3', 'local-tbin-v3']), + callback=normalize_schedule_name, default='sigmoid', show_default=True) +@click.option('--global-gap-scale', help='Fixed multiplier on the official or local sigmoid gap', metavar='FLOAT', + type=click.FloatRange(min=0, min_open=True), default=1.0, show_default=True) +@click.option('--adaptive-loss-ema-beta', help='EMA beta for adaptive_v1 loss signal', metavar='FLOAT', + type=click.FloatRange(min=0, max=1, max_open=True), default=0.9, show_default=True) +@click.option('--adaptive-update-kimg', help='Aggregate adaptive_v1 loss signal every KIMG, independent of ticks', metavar='KIMG', + type=click.FloatRange(min=0, min_open=True), default=0.5, show_default=True) +@click.option('--adaptive-warmup-updates', help='Valid adaptive_v1 signal updates before applying corrections', metavar='INT', + type=click.IntRange(min=0), default=2, show_default=True) +@click.option('--adaptive-max-adjust', help='Maximum absolute adaptive_v1 correction to r/t', metavar='FLOAT', + type=click.FloatRange(min=0, max=1), default=0.05, show_default=True) +@click.option('--adaptive-min-gap', help='Minimum relative gap (t-r)/t for adaptive_v1', metavar='FLOAT', + type=click.FloatRange(min=0, max=1, min_open=True, max_open=True), default=1e-3, show_default=True) +@click.option('--local-tbin-num-bins', help='Number of p(t)-quantile bins for local t-bin schedules', metavar='INT', + type=click.IntRange(min=2), default=4, show_default=True) +@click.option('--local-tbin-short-beta', help='Short raw-loss EMA beta for local t-bin schedules', metavar='FLOAT', + type=click.FloatRange(min=0, max=1, max_open=True), default=0.9, show_default=True) +@click.option('--local-tbin-long-beta', help='Long raw-loss EMA beta for local t-bin schedules', metavar='FLOAT', + type=click.FloatRange(min=0, max=1, max_open=True), default=0.99, show_default=True) +@click.option('--local-tbin-warmup-updates', help='Per-bin signal updates before local corrections', metavar='INT', + type=click.IntRange(min=0), default=32, show_default=True) +@click.option('--local-tbin-gain', help='Trend-to-gap-scale gain for local t-bin schedules', metavar='FLOAT', + type=click.FloatRange(min=0), default=0.5, show_default=True) +@click.option('--local-tbin-min-scale', help='Minimum multiplier on the official sigmoid gap', metavar='FLOAT', + type=click.FloatRange(min=0, max=1, min_open=True), default=0.75, show_default=True) +@click.option('--local-tbin-max-scale', help='Maximum multiplier on the official sigmoid gap', metavar='FLOAT', + type=click.FloatRange(min=1), default=1.5, show_default=True) +@click.option('--local-tbin-deadband', help='Absolute log-EMA trend ignored by local t-bin schedules', metavar='FLOAT', + type=click.FloatRange(min=0), default=0.02, show_default=True) +@click.option('--local-tbin-min-gap', help='Minimum relative gap after local scaling', metavar='FLOAT', + type=click.FloatRange(min=0, max=1, min_open=True, max_open=True), default=1e-3, show_default=True) @click.option('--double', help='How often to reduce dt', metavar='TICKS', type=click.IntRange(min=1), default=500, show_default=True) -@click.option('-q', help='Decay Factor', metavar='FLOAT', type=click.FloatRange(min=0, min_open=True), default=2.0, show_default=True) +@click.option('-q', help='Decay Factor', metavar='FLOAT', type=click.FloatRange(min=1, min_open=True), default=2.0, show_default=True) @click.option('-k', help='Mapping fn hyperparams', metavar='FLOAT', type=click.FloatRange(), default=8.0, show_default=True) @click.option('-b', help='Mapping fn hyperparams', metavar='FLOAT', type=click.FloatRange(), default=1.0, show_default=True) @@ -78,6 +153,9 @@ def convert(self, value, param, ctx): @click.option('--fp16', help='Enable mixed-precision training', metavar='BOOL', type=bool, default=False, show_default=True) @click.option('--tf32', help='Enable tf32 for A100/H100 training speed', metavar='BOOL', type=bool, default=False, show_default=True) @click.option('--ls', help='Loss scaling', metavar='FLOAT', type=click.FloatRange(min=0, min_open=True), default=1, show_default=True) +@click.option('--enable_amp', '--amp', '--enable_gradscaler', 'enable_amp', + help='Enable torch.cuda.amp.GradScaler; overrides loss scaling set by --ls', + metavar='BOOL', type=bool, default=False, show_default=True) @click.option('--bench', help='Enable cuDNN benchmarking', metavar='BOOL', type=bool, default=True, show_default=True) @click.option('--cache', help='Cache dataset in CPU memory', metavar='BOOL', type=bool, default=True, show_default=True) @click.option('--workers', help='DataLoader worker processes', metavar='INT', type=click.IntRange(min=1), default=1, show_default=True) @@ -86,8 +164,8 @@ def convert(self, value, param, ctx): @click.option('--desc', help='String to include in result dir name', metavar='STR', type=str) @click.option('--nosubdir', help='Do not create a subdirectory for results', is_flag=True) @click.option('--tick', help='How often to print progress', metavar='KIMG', type=click.FloatRange(min=1), default=50, show_default=True) -@click.option('--snap', help='How often to save snapshots', metavar='TICKS', type=click.IntRange(min=1), default=500, show_default=True) -@click.option('--dump', help='How often to dump state', metavar='TICKS', type=click.IntRange(min=1), default=500, show_default=True) +@click.option('--snap', help='How often to save numbered snapshots; 0 disables them', metavar='TICKS', type=click.IntRange(min=0), default=500, show_default=True) +@click.option('--dump', help='How often to save numbered state dumps; 0 disables them', metavar='TICKS', type=click.IntRange(min=0), default=500, show_default=True) @click.option('--ckpt', help='How often to save latest checkpoints', metavar='TICKS', type=click.IntRange(min=1), default=50, show_default=True) @click.option('--seed', help='Random seed [default: random]', metavar='INT', type=int) @click.option('--transfer', help='Transfer learning from network pickle', metavar='PKL|URL', type=str) @@ -115,7 +193,7 @@ def main(**kwargs): c.dataset_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageFolderDataset', path=opts.data, use_labels=opts.cond, xflip=opts.xflip, cache=opts.cache) c.data_loader_kwargs = dnnlib.EasyDict(pin_memory=True, num_workers=opts.workers, prefetch_factor=2) c.network_kwargs = dnnlib.EasyDict() - c.loss_kwargs = dnnlib.EasyDict(P_mean=opts.mean, P_std=opts.std, q=opts.q, c=opts.c, k=opts.k, b=opts.b, adj=opts.mapping) + c.loss_kwargs = make_loss_kwargs(opts) c.optimizer_kwargs = dnnlib.EasyDict(class_name=f'torch.optim.{opts.optim}', lr=opts.lr, betas=[0.9,0.999], eps=1e-8) # Validate dataset options. @@ -164,8 +242,12 @@ def main(**kwargs): c.ema_halflife_kimg = int(opts.ema * 1000) if opts.ema is not None else opts.ema c.ema_beta = opts.ema_beta c.update(batch_size=opts.batch, batch_gpu=opts.batch_gpu) - c.update(loss_scaling=opts.ls, cudnn_benchmark=opts.bench, enable_tf32=opts.tf32) - c.update(kimg_per_tick=opts.tick, snapshot_ticks=opts.snap, state_dump_ticks=opts.dump, ckpt_ticks=opts.ckpt, double_ticks=opts.double) + c.update(loss_scaling=opts.ls, cudnn_benchmark=opts.bench, enable_tf32=opts.tf32, enable_amp=opts.enable_amp) + c.update(kimg_per_tick=opts.tick, + snapshot_ticks=None if opts.snap == 0 else opts.snap, + state_dump_ticks=None if opts.dump == 0 else opts.dump, + ckpt_ticks=opts.ckpt, + double_ticks=opts.double, adaptive_update_kimg=opts.adaptive_update_kimg) c.update(mid_t=opts.mid_t, metrics=opts.metrics, sample_ticks=opts.sample_every, eval_ticks=opts.eval_every) # Random seed. @@ -187,7 +269,15 @@ def main(**kwargs): if not match or not os.path.isfile(opts.resume): raise click.ClickException('--resume must point to training-state-*.pt from a previous training run') c.resume_pkl = os.path.join(os.path.dirname(opts.resume), f'network-snapshot-{match.group(1)}.pkl') - c.resume_tick = int(match.group(1)) if opts.resume_tick is None else opts.resume_tick + # Prefer explicit --resume-tick; otherwise parse numeric tick from the filename. + # training-state-latest.pt cannot be converted with int(); the training loop + # restores the authoritative cur_tick / cur_nimg from the serialized state. + if opts.resume_tick is not None: + c.resume_tick = opts.resume_tick + elif match.group(1) == 'latest': + c.resume_tick = 0 + else: + c.resume_tick = int(match.group(1)) c.resume_state_dump = opts.resume # Description string. diff --git a/docs/ANONYMIZATION_AUDIT.md b/docs/ANONYMIZATION_AUDIT.md new file mode 100644 index 00000000..fb570b35 --- /dev/null +++ b/docs/ANONYMIZATION_AUDIT.md @@ -0,0 +1,98 @@ +# Anonymous Repository Audit + +## Scope + +The anonymous submission repository is a clean export, not a rename of the current collaboration repository. The public collaboration history, owner name, pull requests, issue discussions, and contributor metadata must not be copied into the anonymous release. + +## Preserve + +- source code required to train and evaluate the declared methods, +- environment specifications and setup commands, +- dataset and checkpoint download instructions with cryptographic hashes, +- configuration files and exact commands, +- lightweight tables, figures, manifests, and machine-readable summaries, +- third-party copyright notices, licenses, and academic citations. + +## Exclude + +- `.git/` history from the collaboration repository, +- checkpoints, raw image sets, caches, and full logs, +- private server paths and SSH hostnames, +- usernames, personal email addresses, access tokens, and editor metadata, +- internal chat exports, reviewer identities, and non-public storage links, +- abandoned exploratory results that are not cited or documented. + +## Repository layout + +```text +anonymous-submission/ +├── README.md +├── LICENSE +├── environment/ +│ ├── environment.yml +│ └── versions.md +├── configs/ +│ ├── primary/ +│ └── generalization/ +├── docs/ +│ ├── evaluation_protocol.md +│ ├── reproducibility_checklist.md +│ └── asset_manifest.md +├── scripts/ +│ ├── prepare_data.sh +│ ├── train.sh +│ ├── evaluate.sh +│ └── reproduce_tables.sh +├── results/ +│ ├── primary/ +│ └── generalization/ +├── supplementary/ +└── src/ +``` + +## Automated scan patterns + +The release candidate must be scanned for: + +- Windows user paths: `C:\Users\` +- Linux private roots: `/root/`, `/home//`, and project-specific `/mnt/` paths +- credential variables and token-like strings +- private keys and credential files +- GitHub owner/repository URLs belonging to the collaboration repository +- personal email addresses +- VS Code, Jupyter, and shell-history artifacts + +An automated scan is a warning system, not proof of anonymity. Every match must be reviewed manually because third-party citations and license contacts may be legitimate. + +## Scanner export policy + +`scripts/audit_anonymity.py` and its tests are internal +collaboration-repository tools. They intentionally contain the real +collaboration repository URL as a detection pattern and are excluded from the +anonymous submission export. The anonymous export is scanned externally by +this internal tool before release. Sensitive matches for tokens, generic +secrets, and private keys are rendered only as ``. + +## Release procedure + +1. Create a new empty private repository controlled by the submission lead. +2. Export the approved source tree without `.git` history. +3. Copy only files listed in the release manifest. +4. Replace machine-specific paths with documented environment variables. +5. Run the automated scan and resolve every finding. +6. Build the environment and run smoke tests from a clean clone. +7. Generate tables and figures from tracked lightweight inputs. +8. Ask a team member who did not build the export to perform the clean-clone audit. +9. Freeze the anonymous release commit and record its SHA in the private submission record. +10. Keep the mapping between anonymous and collaboration commits outside the anonymous repository. + +## Current audit status + +| Item | Status | Notes | +| --- | --- | --- | +| Clean export repository | pending | Do not reuse collaboration Git history | +| Anonymous README | pending | Current README is upstream-oriented | +| Identity/path scan | in progress | Internal scanner is excluded from the anonymous export; every export finding must be reviewed | +| Asset manifest | pending | Dataset and transfer SHA values required | +| Clean-clone smoke | pending | Run only after export exists | +| Independent reviewer | pending | Assign before Week 6 freeze | diff --git a/docs/DAY1_A.md b/docs/DAY1_A.md new file mode 100644 index 00000000..c2f3b578 --- /dev/null +++ b/docs/DAY1_A.md @@ -0,0 +1,202 @@ +# Day 1 A：工程环境、资产与连通性验证 + +本文档对应工程和环境复现工作线。当前整合以公共基线 +`origin/leader/day1-bootstrap@4e33194777a347ea5286b5ec1d5c29a58c792d29` +为基础，并保留该基线的 AMP、GradScaler 和 `metrics=none` 行为。 + +这里的 100-step smoke 只验证工程连通性，不是官方固定 ECT baseline， +也不能用于报告正式训练质量、FID 或 KID。 + +## 1. 持久化目录 + +脚本默认使用以下布局： + +```text +/mnt/ect_project/ +├── datasets/ +├── pretrained/ +├── runs/ +└── checkpoints/ +``` + +默认资产路径为： + +```text +/mnt/ect_project/datasets/cifar-10-python.tar.gz +/mnt/ect_project/datasets/cifar10-32x32.zip +/mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl +``` + +如平台的持久盘挂载点不同，可统一设置： + +```bash +export ECT_PROJECT_ROOT=/path/to/persistent/ect_project +``` + +也可以分别覆盖： + +```bash +export ECT_CIFAR10_TARBALL=/path/to/cifar-10-python.tar.gz +export ECT_DATA_PATH=/path/to/cifar10-32x32.zip +export ECT_TRANSFER_PATH=/path/to/edm-cifar10-32x32-uncond-vp.pkl +export ECT_RUNS_ROOT=/path/to/persistent/runs +``` + +## 2. 环境检查 + +`env.yml` 仍由公共基线管理，本次工程整合不会覆盖它。MatrixCloud/矩池云可用 +`conda-matpool.yml` 处理 channel 映射。 + +已有 `ect` 环境时只检查，不更新： + +```bash +bash setup_env.sh --check-only +``` + +首次创建环境： + +```bash +bash setup_env.sh +``` + +只有明确决定同步依赖时才使用： + +```bash +bash setup_env.sh --update +``` + +检查器记录 Python、包版本、import、CUDA、GPU、Git SHA 和工作树状态。公共基线已 +验证的关键版本包括 Python 3.9.18、PyTorch 2.3.0、CUDA 12.1、diffusers +0.26.3、accelerate 0.27.2 和 huggingface-hub 0.23.4。 + +## 3. CIFAR-10 准备与验证 + +只检查现有持久化资产： + +```bash +bash prepare_data.sh --check-only +``` + +资产不存在时才执行下载和转换： + +```bash +bash prepare_data.sh +``` + +数据验收包括： + +1. 官方原始 tarball MD5 `c58f30108f718f92721af3b95e74349a`； +2. ZIP CRC； +3. 恰好 50000 个 PNG； +4. 恰好 50000 个 labels，且与 PNG 一一对应； +5. 所有 PNG 均为 32×32 RGB； +6. `ImageFolderDataset` 长度为 50000，且首、中、尾样本可读取； +7. 记录转换后 ZIP SHA256，但不把单一 SHA256 作为内容一致性的硬约束。 + +转换 ZIP 可能因 ZIP entry 时间戳不同而具有不同 SHA256；因此原始 tarball MD5、 +CRC、图像/标签内容和项目数据加载器检查才是主要依据。 + +## 4. 官方 EDM transfer checkpoint + +只检查现有 checkpoint： + +```bash +bash download_checkpoint.sh --check-only +``` + +文件不存在时才下载： + +```bash +bash download_checkpoint.sh +``` + +默认目标位于 `/mnt/ect_project/pretrained/`，并检查官方 SHA256： + +```text +4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da +``` + +## 5. 工程 smoke 与正式 baseline 的边界 + +新脚本名称为： + +```text +scripts/smoke_engineering_100steps.sh +``` + +它的职责仅包括： + +- 环境和资产可读取； +- `ct_train.py` 配置连通； +- 经单独授权后可执行 fresh 100 updates； +- 经单独授权后可执行 resume 100 updates； +- checkpoint、optimizer state 和 resume 链路可生成并检查； +- `metrics=none`，不运行正式 FID/KID。 + +它不属于官方固定 ECT baseline，不提供训练质量结论。 + +只读检查环境和资产： + +```bash +bash scripts/smoke_engineering_100steps.sh --check-only +``` + +只执行 `ct_train.py --dry_run`，不训练、不创建 run 目录： + +```bash +bash scripts/smoke_engineering_100steps.sh --dry-run +``` + +脚本默认使用公共基线的规范 AMP 参数： + +```text +--fp16=True +--enable_amp=True +``` + +公共基线也兼容 `--amp=True` 和 `--enable_gradscaler=True`。旧 collaborator smoke +明确使用 `--fp16=False`，且没有启用 GradScaler，因此旧 evidence 不能被解释为 +FP16 + GradScaler 验证。只有未来实际运行新的 AMP smoke 并检查训练日志和 state 后， +才能形成新的 GradScaler 运行证据。 + +完整 100+100-step 工程 smoke 属于训练操作，本次 Day 2 本地整合不会执行。后续只有 +得到单独授权后才能运行： + +```bash +bash scripts/smoke_engineering_100steps.sh --mode all +``` + +默认 run 路径为 `/mnt/ect_project/runs/engineering-smoke/`。总 batch 固定为 10， +所以 fresh 1 kimg 为 100 optimizer updates，resume 到 2 kimg 再增加 100 updates。 + +## 6. 正式固定 ECT baseline + +正式 baseline 必须另行定义并冻结以下信息： + +- 公共代码 SHA； +- 数据和 checkpoint 内容验证； +- batch、优化器、学习率、seed、schedule 和训练时长； +- AMP/GradScaler 是否实际启用； +- sampling 与 evaluation 协议； +- 正式结果和重复实验。 + +不能将 `smoke_engineering_100steps.sh` 的结果重命名或描述为正式 baseline。 + +## 7. 安全检查 + +提交工程改动前运行： + +```bash +bash -n setup_env.sh +bash -n prepare_data.sh +bash -n download_checkpoint.sh +bash -n scripts/smoke_engineering_100steps.sh +python -m compileall scripts training ct_train.py +python ct_train.py --help +git diff --check +git diff --stat +git status +``` + +数据或 checkpoint 验证失败时应立即停止，不得继续训练。正式训练、完整工程 smoke、 +FID 和 KID 都不属于本地整合检查。 diff --git a/docs/EVALUATION_PROTOCOL.md b/docs/EVALUATION_PROTOCOL.md new file mode 100644 index 00000000..06cc5a94 --- /dev/null +++ b/docs/EVALUATION_PROTOCOL.md @@ -0,0 +1,298 @@ +# Evaluation protocol + +## Active staged protocol (frozen 2026-07-30) + +**Protocol ID:** `staged-checkpoint-evaluation-v1`. This is the authoritative +protocol for the next checkpoint-evaluation cycle. It freezes the evaluation +configuration *before* any new comparative result is inspected. Historical +results in this repository retain their original labels; they are not silently +promoted to quick or formal results under this protocol. + +The prospective q=256 budget and fresh q=128 matrices are additionally frozen +in [`FROZEN_EVALUATION_MATRICES.md`](FROZEN_EVALUATION_MATRICES.md). Their +per-budget metric contracts are authoritative for those matrices. + +The two stages have deliberately different evidence classes: + +| Stage | Purpose | Metrics per checkpoint/NFE | Reporting class | +| --- | --- | --- | --- | +| Quick | evaluator smoke test and candidate screening | KID-5k and FID-5k | screening/proxy only | +| Formal | final, eligible checkpoints only | KID-50k and FID-50k | formal benchmark | + +### Frozen sampling and metric settings + +The following settings apply to every checkpoint and to both stages. They may +not be changed per method, training seed, checkpoint, or after a result is +seen. + +| Setting | Frozen value | +| --- | --- | +| Precision | FP32; TF32 and reduced-precision reductions disabled | +| GPU topology | one GPU (explicit sample seeds are single-GPU only) | +| NFE=1 | `mid_t=[]` | +| NFE=2 | `mid_t=[0.821]` | +| Metric repetitions | exactly one per metric/cell | +| KID subset seed / evaluator seed | `20260730` | +| Real reference | complete canonical CIFAR-10 training archive, `xflip=False` | +| Feature extractor | repository-pinned Inception detector used by `metrics/` | + +`metric-repeats=1` is intentional: repeating an identical fixed sample set is +not an additional independent observation. A given sample seed must derive the +same initial latent for NFE=1 and NFE=2; NFE=2 intermediate noise must be +derived deterministically from that same seed. Every run records the complete +seed range, evaluator seed, checkpoint SHA256, dataset SHA256, Git revision, +NFE, `mid_t`, precision, device, and metric implementation names. + +### Quick evaluation (screening only) + +Run KID-5k (`kid5k_full`) and FID-5k (`fid5k_full`) for each checkpoint and +for each NFE separately. The generated sample set is exactly the ascending +integer range **0-4999** for every cell. Both metric files must contain one +finite record; a missing or failed metric makes the cell incomplete. Quick +numbers must always be labelled **“5k-sample screening proxy; not a formal +50k benchmark.”** They may guide triage but must not be used as final claims. + +The first execution after this freeze is an evaluator smoke on one already +available checkpoint, covering both NFEs and both 5k metrics (four cells). It +validates the evaluator path and output schema only; its numbers remain quick +screening evidence. + +### Formal evaluation (eligibility-gated) + +Formal evaluation runs KID-50k (`kid50k_full`) and FID-50k (`fid50k_full`) for +each eligible checkpoint and each NFE separately. The generated sample set is +exactly the ascending integer range **0-49999** for every cell. The formal run +must use the same frozen settings above and must produce exactly one finite +result record for each metric. + +No checkpoint is eligible for formal evaluation until its training-integrity +receipt has status `passed` *and* the evaluator recomputes a SHA256 matching +the receipt. The receipt is a machine-readable artifact produced by the +training-completeness check and must bind all of the following to the evaluated +file: + +- checkpoint path/basename and SHA256; +- training run ID, method, training seed, and declared budget; +- completion at the declared budget (no early or regressed checkpoint); +- required training logs/state present and internally consistent; +- finite-loss/finite-state check passed; and +- check script version, Git revision, timestamp, and final `status: passed`. + +The receipt schema is frozen as follows (additional fields are allowed): + +```json +{ + "schema_version": 1, + "status": "passed", + "checkpoint_id": "sigmoid_seed0_16k", + "checkpoint_path": "/mnt/ect_project/checkpoints/sigmoid_seed0_16k.pkl", + "checkpoint_sha256": "<64-character SHA256>", + "training_run_id": "", + "method": "sigmoid", + "training_seed": 0, + "budget_kimg": 16, + "completion_passed": true, + "logs_state_consistent": true, + "finite_loss_state_passed": true, + "checkpoint_load_passed": true, + "ema_present": true, + "ema_finite_passed": true, + "schedule_identity_passed": true, + "global_gap_scale_identity_passed": true, + "method_identity_passed": true, + "checker_version": "2", + "checker_git_commit": "", + "checked_at_unix": 0 +} +``` + +An absent, malformed, stale, failed, or hash-mismatched receipt is a hard +block: do not launch a formal metric job and do not create a partial formal +table. Quick evaluation does not waive this gate. + +`kid50k_full` passes the frozen evaluator seed to KID's subset sampler. This +contract is covered by the staged-evaluation test suite. A formal run must +still record that seed and must not substitute an unseeded KID result. + +### Execution order and result contract + +Execute the cycle in this order and stop on a failed prerequisite: + +1. Freeze this protocol (this change). +2. Run the existing-checkpoint 5k evaluator smoke. +3. Verify fixed-generation-seed determinism independently for NFE=1 and + NFE=2, including repeated runs and work-group sizes 8 and 16. +4. Build and validate the unified result table/statistics tooling using the + smoke outputs; it must distinguish `quick` from `formal` evidence. +5. Run the complete quick 5k screening matrix. +6. Produce and verify training-integrity receipts for candidate formal + checkpoints; fix the seeded KID-50k readiness gap if still open. +7. Run the complete formal 50k matrix only for eligible checkpoints, then + publish its separate formal summary. + +### No result-driven promotion + +For the frozen q=256 confirmatory matrix, quick 5k is diagnostic only. It +must not be used to exclude an unfavorable seed, method, NFE, or checkpoint +from the formal 50k matrix. Every one of the six predeclared checkpoints enters +formal evaluation when—and only when—its immutable provenance matches the +frozen matrix and its training-integrity receipt passes. Formal eligibility is +therefore independent of quick KID/FID performance. + +The same rule applies to every prospective frozen matrix. In particular, the +q=256 budget matrix fixes 5k screening at 512 and 768 kimg and formal 50k +evaluation at 1024 kimg: screening values may not select the 1024-kimg formal +cells. The fresh q=128 matrix fixes all six 256-kimg cells as formal 50k. +Every predeclared checkpoint must complete its assigned contract. + +The unified per-cell table must include at least: evidence class, method, +training seed, checkpoint ID/SHA256, integrity-receipt status, NFE, `mid_t`, +metric name/value, generated-sample count and exact seed range, evaluator/KID +seed, dataset SHA256, evaluation Git revision, run path, and completion +status. Statistics must never pool checkpoints, NFEs, quick and formal rows, +or different metrics. For the fixed/global-only confirmatory matrix, pairing +is exactly `training_seed + budget_kimg + nfe + metric`, the delta is +`global_only - fixed`, and negative values favor global-only. Missing or +duplicated arms are a hard collection failure. The collector emits the +per-seed `paired_differences.csv` and separate paired statistics JSON/Markdown. + +Each quick or formal run manifest must also freeze the evaluator environment: +evaluation Git commit; Python, SciPy, PyTorch, and CUDA versions; detected GPU +model(s); the `inception-2015-12-05` TorchScript detector URL/feature mode; and +the dataset SHA256. This is required for formal comparability after evaluator +changes such as the SciPy `sqrtm` compatibility fix or an explicit KID seed. + +`docs/FINAL_PERFORMANCE_EVALUATION.md` and +`docs/ROLE_A_QUANTITATIVE_EVALUATION.md` describe earlier scoped experiments. +For this new cycle, this staged protocol takes precedence for metric settings, +eligibility, execution order, and evidence labels. + +This document defines the reproducible Role D sampling protocol. It separates +historical evidence from results produced under the current protocol. + +## Result classes + +`results/preliminary_seed42_fp32_8ksteps/` contains preliminary historical +results. They were produced from an older code base and an approximately +8k-update checkpoint. They are retained for reference only, are not directly +comparable with the current B/C protocol, and must not be reported as a final +benchmark. + +`results/fixed_seeds_0_63_fp32_8ksteps/` is also a preliminary historical +smoke from that checkpoint. Its directory-level README records why it does not +satisfy the current protocol. It must not be regenerated or treated as a +current-protocol result. + +Current protocol results must record the checkpoint SHA256, evaluation Git +commit, seeds, NFE, `mid_t`, precision, GPU, work-group sizes, and repeated-run +determinism status in `metadata.json`. They must also record elapsed time, image +and seed counts, the complete seed list, per-mode NFE and `mid_t`, generator +implementation, and the actual forward batch size. + +## Fixed-seed protocol + +- Seeds 0-63 denote 64 per-sample seeds, not 64 repeated metric runs. +- Generate one 32x32 RGB PNG for each seed in 0-63. +- NFE=1 uses `mid_t=[]`. +- NFE=2 uses `mid_t=[0.821]`. +- For a given seed, NFE=1 and NFE=2 use the same initial latent. +- The NFE=2 intermediate noise is also deterministically derived from that seed. +- The 64 images per mode are used for visualization and determinism checks. +- Keep the model forward batch size at one. +- Treat 8 and 16 as work-group sizes, not model batch sizes. +- Require pixel-identical output across work-group sizes 8 and 16. +- Repeat each NFE configuration and require pixel-identical output. +- Isolate every result directory using the checkpoint filename and the first 12 + characters of its SHA256. +- Select precision explicitly. Use `fp32` for this acceptance smoke; use + `checkpoint` only when intentionally preserving checkpoint-native precision. + +The sampler writes to `/-/`. For +example, the official checkpoint is written under +`edm-cifar10-32x32-uncond-vp-4d5dcc1f1d0d/`. A run fails before publishing +metadata if either work-group or repeated-run determinism fails. + +Each checkpoint directory contains: + +```text +nfe1/ + images/ + grid_8x8.png +nfe2/ + images/ + grid_8x8.png +metadata.json +sha256_manifest.txt +``` + +The metadata schema records the evaluation Git commit, checkpoint path and +SHA256, checkpoint ID, seed list, NFE modes, `mid_t` per mode, precision, +device, GPU, elapsed time, image counts, generator implementation, actual +model forward batch size, verified work-group sizes, image dimensions, and the +overall determinism result. + +## Official EDM checkpoint smoke + +Download and verify the official NVIDIA EDM CIFAR-10 32x32 unconditional VP +checkpoint: + +```bash +bash download_checkpoint.sh \ + --output /mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl +``` + +The expected checkpoint SHA256 is: + +```text +4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da +``` + +Run the 64-image NFE=1 plus 64-image NFE=2 smoke: + +```bash +bash scripts/sample_checkpoint.sh \ + /mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl \ + --outdir /mnt/ect_project/evaluations \ + --seeds 0-63 \ + --nfe 1 2 \ + --mid-t 0.821 \ + --work-group-size 8 \ + --verify-work-group-size 16 \ + --precision fp32 \ + --device cuda +``` + +The expected output root is: + +```text +/mnt/ect_project/evaluations/edm-cifar10-32x32-uncond-vp-4d5dcc1f1d0d/ +``` + +Verify the image counts and manifest after the command completes: + +```bash +RESULT=/mnt/ect_project/evaluations/edm-cifar10-32x32-uncond-vp-4d5dcc1f1d0d +find "$RESULT/nfe1/images" -type f -name '*.png' | wc -l +find "$RESULT/nfe2/images" -type f -name '*.png' | wc -l +(cd "$RESULT" && sha256sum -c sha256_manifest.txt) +``` + +Both image counts must be 64 and every manifest entry must report `OK`. + +## Metric boundary + +This acceptance smoke does not run FID-50k, KID-50k, or any other distribution +metric. In particular, generating seeds 0-63 does not mean running FID 64 +times. Its purpose is limited to checkpoint loading, fixed-seed generation, +visualization, output completeness, work-group invariance, and repeated-run +determinism. Historical seed42 FP32 FID/KID values remain preliminary evidence +and are not directly comparable with current B/C formal results. Formal +Formal FID-50k/KID-50k runs only under the active staged protocol's +training-integrity gate and 50k fixed-seed settings above. + +## Git artifact policy + +Do not commit the checkpoint or the 128 individual PNG files. For a completed +smoke run, commit only both 8x8 grids, `metadata.json`, and +`sha256_manifest.txt`. Keep the full output tree under +`/mnt/ect_project/evaluations/` as the external evaluation record. diff --git a/docs/FINAL_PERFORMANCE_EVALUATION.md b/docs/FINAL_PERFORMANCE_EVALUATION.md new file mode 100644 index 00000000..3878e8a4 --- /dev/null +++ b/docs/FINAL_PERFORMANCE_EVALUATION.md @@ -0,0 +1,211 @@ +# Final performance evaluation protocol + +## Material Passport + +- Protocol ID: `final-performance-evaluation-v1` +- Frozen on: 2026-07-22 (Asia/Shanghai) +- Research question: Under the same 16 kimg training budget and the same NFE, does Adaptive v1 outperform fixed sigmoid? +- Arms: `sigmoid` and `adaptive_v1` +- Training seeds: 0, 1, 2 +- Evidence class: final three-seed paired comparison +- Scope exclusions: v2/v3, 64 kimg training, seed0 mechanism re-validation, and standard FID-50k claims + +This protocol is the only evaluation plan for the final comparison. It does not +change after comparative results are viewed. + +## Frozen matrix + +| Schedule | Seed 0 | Seed 1 | Seed 2 | +| --- | --- | --- | --- | +| Fixed sigmoid | existing 16 kimg checkpoint | new 16 kimg checkpoint | new 16 kimg checkpoint | +| Adaptive v1 | existing 16 kimg checkpoint | new 16 kimg checkpoint | new 16 kimg checkpoint | + +Every cell must be an independent fresh run ending at exactly 16.000 kimg. Use +the final EMA network snapshot. The only arm-level difference is the schedule. +The frozen training knobs in `docs/PAIRED_TRAINING_PROTOCOL.md` otherwise apply. + +Seed1/2 paired commands use the same runner and differ only in schedule within +each seed: + +```bash +bash scripts/run_schedule_experiment.sh --mode stability --schedule sigmoid --seed 1 +bash scripts/run_schedule_experiment.sh --mode stability --schedule adaptive_v1 --seed 1 +bash scripts/run_schedule_experiment.sh --mode stability --schedule sigmoid --seed 2 +bash scripts/run_schedule_experiment.sh --mode stability --schedule adaptive_v1 --seed 2 +``` + +## Quantitative quality protocol + +Each of the six checkpoints is evaluated at both NFE settings, producing 12 +cells: + +- NFE=1: no intermediate time. +- NFE=2: `mid_t=0.821`. +- Precision: FP32; TF32 and reduced-precision reductions remain disabled by + `ct_eval.py`. +- Device count: one GPU. Explicit per-sample seeds are intentionally restricted + to one GPU so the generated set is not altered by rank partitioning. +- Generated samples per cell: 5,000. +- Per-sample seeds: exactly 0-4999, in ascending order. +- A given sample seed produces the same initial latent across schedules, + training seeds, metrics, and NFE settings. NFE=2 intermediate noise is derived + independently and deterministically from that same sample seed. +- Real reference: the complete canonical 50,000-image CIFAR-10 training archive, + with `xflip=False`. +- Metric repetitions: one. Repeating an identical fixed sample set is not an + additional independent measurement. + +Metrics: + +1. `KID-5k` (`kid5k_full`) is primary. Report the raw unbiased KID estimate. It + uses 100 deterministic subsets of at most 1,000 real/generated features and + protocol seed 20260722. A finite negative estimate is possible and is not + clipped. +2. `FID-5k` (`fid5k_full`) is an auxiliary proxy, computed from 5,000 generated + samples against full real-data mean/covariance statistics. + +Every table, CSV, abstract, and conclusion must carry this exact limitation: + +> 5k-sample proxy evaluation; not a standard FID-50k benchmark. + +Lower is better for both metrics. Paired differences are always defined as +`Adaptive v1 - fixed sigmoid`; negative values favor Adaptive v1. Report every +cell, all six per-seed/per-NFE paired differences, and for each NFE the +three-seed mean paired difference, sample standard deviation, and +Adaptive/fixed/tie direction counts. With only three training seeds, the result +is descriptive; do not manufacture a significance claim. + +## KID readiness and frozen fallback + +The first 45 minutes are a pipeline-readiness gate, before comparative numbers +are inspected. KID is considered runnable only if the environment/unit checks, +12-cell dry-run, and one complete real 5k KID job finish with one finite +`metric-kid5k_full.jsonl` record. + +If that gate is not passed within 45 minutes because the existing KID pipeline +cannot run, record the error and timestamp in the PR and switch the entire +matrix to `--metrics=fid-only`. Do not report a partial or selectively completed +KID matrix. The fallback evidence is then `FID-5k proxy + blinded A/B`. Do not +rewrite or replace the metric framework after the gate. + +## Method-blinded A/B protocol + +Visual stimuli use sample seeds 0-15 for every training-seed/NFE stratum: + +- 3 training seeds × 2 NFE settings × 16 sample seeds = 96 paired trials. +- Generate each stimulus once in FP32. This is stimulus creation, not a repeat + of the already archived seed0 fixed-seed mechanism/determinism acceptance. +- The A/B builder randomizes trial order and balances Adaptive exactly 48 times + on side A and 48 times on side B using a private key. +- Reviewers see only trial IDs, A/B images, and the choices `A`, `B`, or `TIE`. + They must not see schedule names, checkpoint names, training seed, NFE, or the + unblinding key. +- Target exactly three anonymous raters, each completing all 96 trials before + unblinding (288 judgments). `TIE` is required when neither image is + meaningfully preferable. +- Report Adaptive wins, fixed wins, and ties overall and by NFE, training seed, + training-seed/NFE stratum, and rater. Also report Adaptive share excluding + ties and a descriptive tie-half score `(Adaptive + 0.5 × ties) / all`. + +Trials are nested within raters and training seeds; the 288 raw judgments are +not 288 independent training replicates. The blind result corroborates or +qualifies the quantitative result but does not override the primary metric. + +## Training stability summary + +For all six packaged runs, verify rather than re-run: + +- exact 16.000 kimg / 125 attempted iterations; +- 125 telemetry rows and no mixed schedule labels; +- zero NaN/Inf losses; +- successful and skipped AMP steps; +- final GradScaler value; +- wall time and peak VRAM; +- for Adaptive v1, final controller activation and signal-update count. + +Skipped AMP steps, runtime, and memory are engineering stability descriptors, +not generation-quality metrics. + +## Execution runbook + +Copy and fill the six-cell manifest. Do not commit checkpoint files: + +```bash +cp configs/final_evaluation_checkpoints.example.json /mnt/ect_project/final_eval/checkpoints.json +``` + +First validate the exact 12 quantitative and six visual-stimulus commands: + +```bash +python scripts/run_final_evaluation_matrix.py \ + --manifest /mnt/ect_project/final_eval/checkpoints.json \ + --data /mnt/ect_project/datasets/cifar10-32x32.zip \ + --outdir /mnt/ect_project/final_eval/run \ + --phase all --metrics primary --dry-run +``` + +Run the frozen matrix: + +```bash +python scripts/run_final_evaluation_matrix.py \ + --manifest /mnt/ect_project/final_eval/checkpoints.json \ + --data /mnt/ect_project/datasets/cifar10-32x32.zip \ + --outdir /mnt/ect_project/final_eval/run \ + --phase all --metrics primary +``` + +If and only if the 45-minute KID readiness gate failed, replace +`--metrics primary` with `--metrics fid-only` in a new empty output directory. + +Validate and summarize the quantitative matrix: + +```bash +python scripts/collect_final_quality_results.py \ + --eval-root /mnt/ect_project/final_eval/run \ + --outdir /mnt/ect_project/final_eval/summary +``` + +For the frozen fallback, add `--allow-fid-only`. + +Build the public blind package and keep the key private: + +```bash +python scripts/build_blind_ab.py \ + --manifest /mnt/ect_project/final_eval/checkpoints.json \ + --sample-root /mnt/ect_project/final_eval/run/visual_samples \ + --outdir /mnt/ect_project/final_eval/blind_public \ + --key-out /mnt/ect_project/final_eval/private/blind_key.csv +``` + +After three complete ballots are locked, combine or pass the response CSV files: + +```bash +python scripts/score_blind_ab.py \ + --key /mnt/ect_project/final_eval/private/blind_key.csv \ + --responses /mnt/ect_project/final_eval/responses/rater1.csv \ + /mnt/ect_project/final_eval/responses/rater2.csv \ + /mnt/ect_project/final_eval/responses/rater3.csv \ + --outdir /mnt/ect_project/final_eval/summary +``` + +Collect training stability and build the one-page conclusion: + +```bash +python scripts/collect_final_stability.py \ + --manifest /mnt/ect_project/final_eval/checkpoints.json \ + --outdir /mnt/ect_project/final_eval/summary + +python scripts/build_final_conclusion.py \ + --quantitative-dir /mnt/ect_project/final_eval/summary \ + --blind-dir /mnt/ect_project/final_eval/summary \ + --stability-dir /mnt/ect_project/final_eval/summary \ + --output /mnt/ect_project/final_eval/summary/FINAL_CONCLUSION.md +``` + +## Final PR contents + +The `final-performance-evaluation` PR contains code, protocol, compact CSV/JSON/ +Markdown summaries, the anonymized visual grids required to audit the ballot, +and the one-page conclusion. It must not contain checkpoints, the CIFAR-10 +archive, 5,000-image directories, raw private key before ballot lock, or claims +of FID-50k equivalence. diff --git a/docs/FROZEN_EVALUATION_MATRICES.md b/docs/FROZEN_EVALUATION_MATRICES.md new file mode 100644 index 00000000..03ad0f71 --- /dev/null +++ b/docs/FROZEN_EVALUATION_MATRICES.md @@ -0,0 +1,25 @@ +# Frozen prospective evaluation matrices + +The matrices in this document are frozen before the new checkpoints are trained or evaluated. They are logical plans, not machine-local runner manifests: each completed endpoint must later be bound to its checkpoint SHA-256 and a passed training-integrity receipt without changing any listed cell identity, metric contract, or comparison rule. + +| Matrix | Checkpoints | NFE | Evaluation contract | +| --- | ---: | --- | --- | +| q=256 budget | 18: seeds 3/4/5 × fixed/global110 × 512/768/1024 kimg | 1 and 2 | 512/768: KID/FID-5k; 1024: KID/FID-50k | +| fresh q=128 | 6: seeds 3/4/5 × fixed/global110 × 256 kimg | 1 and 2 | KID/FID-50k | + +The machine-readable specifications are: + +- `configs/q256_budget_matrix.frozen.json` +- `configs/q128_confirmatory_matrix.frozen.json` + +Both matrices freeze `NFE=1` as `mid_t=[]`, `NFE=2` as `mid_t=[0.821]`, the evaluator/metric seed as `20260730`, the pairing key as `training_seed + budget_kimg + nfe + metric`, and the paired delta as `global_only - fixed`. A negative delta favors global-only. + +## Completion and promotion rules + +Every listed checkpoint is mandatory: it must complete the evaluation contract assigned to its budget. A missing metric, missing arm, or unmatched fixed/global pair is an incomplete matrix, not a reason to report a subset. + +For the q=256 budget matrix, the 512- and 768-kimg results are explicitly 5k screening evidence. They cannot decide whether any of the six predeclared 1024-kimg checkpoints enters formal 50k evaluation; those six enter when and only when their immutable provenance matches the frozen cell and their training-integrity receipt passes. They must not be removed, added, substituted, or selected based on a quick value. + +The fresh q=128 matrix has six predeclared formal 50k checkpoints. An implementation smoke, if run, is diagnostic only and cannot alter formal eligibility. Its historical q=128 predecessor remains retrospective exploratory evidence and is not promoted by this prospective matrix. + +The existing `run_staged_evaluation.py` consumes a machine-local binding manifest with paths and hashes, not either of these path-free prospective plans. Bind the completed cells only after training, validate the binding and receipt identities, and retain the frozen plan unchanged. diff --git a/docs/GENERALIZATION_PROTOCOL.md b/docs/GENERALIZATION_PROTOCOL.md new file mode 100644 index 00000000..efa3bdd1 --- /dev/null +++ b/docs/GENERALIZATION_PROTOCOL.md @@ -0,0 +1,104 @@ +# Generalization Protocol + +## Research question + +How do global gap calibration and localized feedback separately affect consistency training? + +The primary confirmatory comparison is fixed sigmoid versus global-only gap calibration. Localized feedback is a secondary mechanism analysis and must not replace the primary comparison. + +## Status of q=128 evidence + +The existing q=128 results were not formally frozen before they were observed. +They remain legacy retrospective exploratory screening evidence produced from +a pre-merge implementation, rather than confirmatory generalization evidence. +The source archive differs materially from the reference merged implementation +in training-related files, and canonical dataset-content equivalence could not +be established on this node. + +A separate, fresh prospective q=128 matrix is now frozen in +[`FROZEN_EVALUATION_MATRICES.md`](FROZEN_EVALUATION_MATRICES.md): fixed versus +global110, seeds 3/4/5, 256 kimg, NFE=1/2, and KID/FID-50k. It contains no +results yet and does not promote or reuse the legacy evidence. + +## Primary setting (frozen elsewhere) + +- Dataset: CIFAR-10 32x32 EDM ZIP +- Architecture / preconditioning: ddpmpp / ECT +- Transfer initialization: official EDM CIFAR-10 unconditional VP checkpoint +- Training seeds: 3, 4, 5 +- Primary endpoint: NFE=1 KID/FID-50k +- Secondary endpoint: NFE=2 with `mid_t=[0.821]` +- Long budget: at least 1024 kimg +- Global-only scale: `g=1.10` +- Baseline schedule parameters include `q=256`, `k=8`, `b=1`, `c=0` + +The value `g=1.10` was selected before the confirmatory seeds and must not be re-selected after observing seeds 3, 4, or 5. + +## Retrospective exploratory setting + +The q=128 schedule was evaluated retrospectively as an exploratory axis. Because the dataset archive differed bytewise and canonical content equivalence was not available for verification, this evidence does not support a claim that q was the only changed experimental input. + +Exploratory setting that was run: + +- Name: `schedule-q128` +- Only intended change from the primary setting: `q=128` instead of `q=256` +- Compared methods: fixed sigmoid and global-only with `g=1.10` +- Training seeds: 3, 4, 5 +- Sampling modes: NFE=1 and NFE=2 with `mid_t=[0.821]` +- Screening metrics: KID/FID-5k, clearly labeled as proxy results +- Confirmatory metrics: none; this retrospective result is not promoted to 50k confirmation + +Changing only random seed does not count as a second setting. + +## Freeze gate + +The intended freeze gate required Paper Lead and Gap collaborator approval before any result was inspected. That approval was not completed. The unmet gate consisted of: + +1. the single changed parameter (`q=128`), +2. the unchanged global scale (`g=1.10`), +3. the training seeds and budget, +4. the checkpoint and data identities, +5. the metric code commit and sampling seed list, +6. the promotion rule from 5k screening to 50k confirmation. + +The table is retained to document the historical ordering. Its pending entries show that results preceded formal freeze approval. + +| Role | Name or handle | Date (UTC) | Approved commit | Decision | +| --- | --- | --- | --- | --- | +| Paper Lead | | | | pending | +| Gap collaborator | | | | pending | +| Role E | | | | proposed | + +## No retrospective promotion + +The following gate would have been required for prospective promotion, but it was not completed before results were observed: + +- both methods complete under identical conditions, +- checkpoint and dataset SHA256 values match the manifest, +- no NaN or Inf is observed, +- the metric pipeline passes its reproducibility check, +- no protocol field changed after results became visible. + +No retrospective promotion is permitted. The fresh prospective matrix requests +q=128 FID/KID-50k only for newly trained, predeclared checkpoints. + +## Required metadata + +Every training and evaluation cell must record: + +- repository commit and dirty state, +- method and global scale, +- schedule parameter set, +- training seed and sampling seed range, +- dataset and transfer-checkpoint SHA256, +- checkpoint SHA256, +- budget and precision, +- GPU, Python, PyTorch, CUDA, and cuDNN versions, +- NFE and `mid_t`, +- sample count and metric name, +- metric implementation commit, +- elapsed time and output directory. + +## Interpretation + +The q=128 result is a legacy retrospective exploratory diagnostic. It may describe NFE- and seed-dependent behavior, but it cannot establish confirmatory generalization, justify retuning `g`, or redefine the primary endpoint. diff --git a/docs/METHOD_V0.md b/docs/METHOD_V0.md new file mode 100644 index 00000000..7cc8119a --- /dev/null +++ b/docs/METHOD_V0.md @@ -0,0 +1,216 @@ +# Method V0: Factorized Global–Local Gap Control for ECT + +## 1. Scope and notation + +Let `t > 0` denote the sampled noise level, and let +`r_sigmoid(t; m)` denote the official ECT sigmoid mapping at curriculum stage +`m`. Define its absolute training-pair gap as + +\[ +d_{\mathrm{base}}(t) += t-r_{\mathrm{sigmoid}}(t;m). +\] + +The official implementation uses + +\[ +n(t)=1+k\,\sigma(-bt),\qquad +r_{\mathrm{sigmoid}}(t;m) +=\max\!\left(0,\, +t\left[1-\frac{n(t)}{q^{m+1}}\right]\right). +\] + +All proposed schedules retain this `t`-dependent sigmoid mapping as the +baseline. + +## 2. Factorized intervention + +Let `b(t) ∈ {1,…,B}` denote the bin containing `t`. Before constraints needed +to realize a valid pair are applied, the proposed gap is + +\[ +d_{\mathrm{pre}}(t) +=g\,\ell_{b(t)}\,d_{\mathrm{base}}(t), +\] + +where: + +- `g > 0` is a global calibration factor shared by every noise level; +- `ℓ_i > 0` is the local scale for bin `i`; +- the local scales satisfy + +\[ +\frac{1}{B}\sum_{i=1}^{B}\log \ell_i=0, +\] + +or equivalently + +\[ +\left(\prod_{i=1}^{B}\ell_i\right)^{1/B}=1. +\] + +This constraint applies to the local scale factors before realized-gap +clipping effects. It does not imply that the realized training gaps have an +unchanged arithmetic mean, geometric mean, or expected value. + +## 3. Quantile timestep bins + +ECT samples `log t` from a normal distribution, + +\[ +\log t\sim\mathcal N(P_{\mathrm{mean}},P_{\mathrm{std}}^2). +\] + +The controller partitions this distribution into `B=4` quantile bins, so that + +\[ +\Pr[t\in\mathcal B_i]\approx \frac{1}{B}. +\] + +Quantile bins provide approximately balanced sample counts and more stable +per-bin exponential moving averages than equal-width bins in `t` or `log t`. + +## 4. Local feedback signal + +For each training pair, the controller observes the unweighted squared pair +loss + +\[ +L_{\mathrm{raw}} +=\left\| +f_\theta(x_t,t) +-\operatorname{sg}\!\left[f_\theta(x_r,r)\right] +\right\|_2^2. +\] + +It does not use the final ECT-weighted objective as its feedback signal. This +choice avoids treating variation in the prescribed loss weighting as if it +were variation in pair-learning difficulty. + +For bin `i`, let `S_i` and `L_i` denote short- and long-horizon EMAs of the raw +loss. The unnormalized update is + +\[ +\tilde{\ell}_i +=\operatorname{clip}\!\left( +\exp\left[-\eta +\tanh\!\left(\log L_i-\log S_i\right)\right], +\ell_{\min},\ell_{\max} +\right), +\] + +with a deadband around zero trend. The vector of log scales is then projected +onto the bounded zero-mean set, yielding `ℓ_i` with +`B^{-1} Σ_i log ℓ_i = 0`. + +## 5. Realized gap and clipping + +The factorized equation defines the intended pre-clipping gap. The +implementation must also guarantee `0 ≤ r ≤ t` and, for local schedules, a +minimum relative gap `δ_min`. For `local_tbin_v3`, the implemented sequence is + +\[ +d_{\mathrm{local}}(t) +=t\, +\operatorname{clip}\!\left( +\ell_{b(t)}\frac{d_{\mathrm{base}}(t)}{t}, +\delta_{\min},1 +\right), +\] + +\[ +d_{\mathrm{realized}}(t) +=\min\!\left(t,\,g\,d_{\mathrm{local}}(t)\right), +\qquad +r_{\mathrm{new}}(t)=t-d_{\mathrm{realized}}(t). +\] + +Consequently, + +\[ +d_{\mathrm{realized}}(t) +\neq g\,\ell_{b(t)}\,d_{\mathrm{base}}(t) +\] + +whenever a lower or upper bound is active. The accurate claim is: + +> The local scale factors have geometric mean one before realized-gap clipping +> effects. + +It is not accurate to claim that the local controller always preserves the +global realized training gap. + +## 6. Experimental arms + +| Arm | Global factor `g` | Local factors `ℓ_i` | +| --- | ---: | --- | +| Fixed sigmoid | `1` | all `1` | +| Global-only | selected `g*` | all `1` | +| Local-only | `1` | adaptive, geometric mean `1` before clipping | +| Global + local | selected `g*` | adaptive, geometric mean `1` before clipping | + +Stage 1 selects `g*` from the seed-0 response curve. Stage 2 evaluates the +factorial arms at `g*` with training seeds 0, 1, and 2. Because seed 0 +participates in selection, the headline confirmation comparison uses only +held-out seeds 1 and 2. + +## 7. Reported quantities + +For metric `M`, method `A`, and held-out set `H={1,2}`, the headline percentage +is + +\[ +\Delta_{\mathrm{headline}}(A,M) +=100\left[ +\frac{\frac{1}{|H|}\sum_{s\in H}M_{A,s}} +{\frac{1}{|H|}\sum_{s\in H}M_{\mathrm{fixed},s}} +-1 +\right]. +\] + +This is the percentage difference between held-out-seed arithmetic metric +means. It is not + +\[ +\frac{1}{|H|}\sum_{s\in H} +100\left(\frac{M_{A,s}}{M_{\mathrm{fixed},s}}-1\right). +\] + +Training-time gap diagnostics are defined as + +\[ +R_{\mathrm{gap}} +=\mathbb E\left[ +\frac{d_{\mathrm{realized}}(t)} +{d_{\mathrm{base}}(t)} +\right], +\] + +\[ +C_{\mathrm{lower}} +=\mathbb E\left[ +\mathbf 1\{d_{\mathrm{realized}}>d_{\mathrm{pre}}+\tau\} +\right], +\qquad +C_{\mathrm{upper}} +=\mathbb E\left[ +\mathbf 1\{d_{\mathrm{realized}}-/ +├── adaptive-v1-activation--/ +├── sigmoid-stability--/ +└── adaptive-v1-stability--/ +``` + +Rules: + +1. Fixed and adaptive share this single runner (`scripts/run_schedule_experiment.sh`). +2. Fresh runs always target a unique empty directory and pass `--transfer` only. +3. Outdir exists and is non-empty → fail immediately (no checkpoint overwrite). +4. Logs use `tee` without `-a` into a fresh `${mode}-${timestamp}.log`. +5. Resume requires explicit `--resume` and must not pass `--transfer`. +6. Resume refuses mixed schedules (meta / dirname arm mismatch) **and** requires the + same clean `git_head` / dataset / transfer SHA as the immutable fresh `run_meta.env`. +7. Progress (`cur_nimg`, next-loop `cur_tick`, counters, `elapsed_sec`) is restored from training-state contents. Adaptive runs also restore the next signal boundary and any partial loss sum/count, so resuming between signal boundaries is equivalent to uninterrupted training. + +## Telemetry + +`training/ct_training_loop.py` writes `train_summary.csv` with one row per +attempted iteration: + +- `attempted_iteration` +- `successful_optimizer_steps` +- `processed_nimg` / `processed_kimg` +- `loss` +- `grad_scale` +- `step_skipped` +- `schedule` +- `stage` +- `next_loop_cur_tick` (the exact tick that a checkpoint written during that + iteration will persist) +- `loss_ema` / `loss_reference` +- `correction` / `signal_updates` / `adaptive_active` +- `r_over_t_mean` / `gap_mean` +- `elapsed_sec` +- `peak_vram_gb` + +Counters and exact progress (`cur_nimg`, next-loop `cur_tick`, +`tick_start_nimg`) are stored in `training-state-*.pt` and restored on resume. +The CSV records that next-loop tick directly, so result collection compares it +with the checkpoint rather than reconstructing maintenance boundaries from +image count or `--tick`. +Fresh runs refuse to append an existing non-empty CSV; legal resumes append only +after validating the last row against restored counters / `cur_nimg` / schedule. +When resuming a run with the exact pre-telemetry 11-column schema, the training +loop saves `train_summary.csv.pre-telemetry.bak` and atomically upgrades the CSV +to the current schema. The immediately preceding telemetry schema is likewise +upgraded with a `.pre-next-loop-tick.bak` backup. Historical telemetry and tick +cells that were absent from their source schema stay empty because those values +cannot be reconstructed. Unknown or partial schemas remain hard errors. + +Schedule telemetry is obtained through the stable +`loss_fn.schedule_runtime_metrics()` interface; the training loop and result +collector do not inspect schedule implementation fields. + +## Collector + +```bash +python scripts/collect_schedule_results.py \ + --run-dir /path/to/run \ + --outdir results/fixed_baseline_v1 \ + --mode stability \ + --schedule sigmoid \ + --seed 1 \ + --data "$ECT_DATA_PATH" \ + --transfer "$ECT_TRANSFER_PATH" +``` + +`scripts/collect_fixed_baseline_results.py` remains a compatibility wrapper. + +The collector strictly validates populated telemetry types, finiteness, ranges, +controller-state consistency, and monotonic `signal_updates`. A migrated empty +historical prefix is permitted, but an empty row after telemetry begins is not. +`metadata.json` records telemetry row count, total row count, coverage, and the +first iteration with telemetry so partial historical coverage is auditable. + +For `--mode activation --schedule adaptive_v1`, packaging is also an activation +gate, not merely a CSV-format check. It fails unless the final controller state +has `signal_updates >= 3` and `adaptive_active=true`, a nonzero correction has +been observed, its next iteration exists before the final iteration, and at +least four attempted iterations remain after that controller update. +`first_nonzero_correction_iteration` is the end-of-iteration controller update; +`first_adapted_pair_iteration` is exactly the following iteration, where that +correction first enters `r(t)`. The pair telemetry on the former iteration was +sampled before the update and must not be described as adapted. + +## Recorded activation evidence + +[`results/adaptive_v1_activation_a100_cb84a934/`](../results/adaptive_v1_activation_a100_cb84a934/) +contains a clean single-A100 activation run at +`cb84a93454a91500d01433dd2d024d775fb275ef`. Its Collector validation passed +with 32 attempted iterations (4.096 kimg), eight final signal updates, full +telemetry coverage, and matching final next-loop tick (`2`) between CSV and +training state. This is controller-activation evidence only, not a paired +quality, stability, or baseline result. + +Automatically records train-time HEAD from `run_meta.env`, packaging-time HEAD, +dirty status, exact command, asset SHA256 digests, and runtime metadata. +Packaging fails closed unless train-time and packaging HEADs match, and unless +`--data` / `--transfer` hashes are present. Manual `--git-commit` is not +accepted. Dirty trees fail closed unless `--allow-dirty` is passed for +preliminary packaging. + +## Evidence classes + +| Path | Class | +| --- | --- | +| `results/fixed_baseline_preliminary/` | Preliminary stability evidence migrated from PR #10 | +| `results/fixed_baseline_v1/` | Formal evidence only after a clean-HEAD re-run | + +Do not treat preliminary evidence as the final fixed-vs-adaptive comparison. diff --git a/docs/PR23_CLEANUP_CHECKLIST.md b/docs/PR23_CLEANUP_CHECKLIST.md new file mode 100644 index 00000000..fd6029ec --- /dev/null +++ b/docs/PR23_CLEANUP_CHECKLIST.md @@ -0,0 +1,101 @@ +# PR #23 Cleanup Checklist + +## Review scope + +- [x] Read the requested-changes review. +- [x] Confirmed that there are no unresolved inline review threads. +- [x] Treat the single top-level `CHANGES_REQUESTED` review as five actionable + clusters: main synchronization, diff reduction, reproducible validation, + scientific wording, and selection/proxy caveats. + +## Branch synchronization + +- [x] Fetched `origin/main` at `274786fc040cfd1a1802b146566f43c603dee33d`. +- [x] Preserved the former PR head locally as + `codex/gap-factorial-controller-precleanup-20260728`. +- [x] Rebuilt `codex/gap-factorial-controller` from the latest `origin/main`. +- [x] Migrated the factorized-controller commit without conflicts. +- [x] Reduced the local PR diff from 198 files to the intended gap-factorial + scope before adding this cleanup documentation. +- [x] Did not modify or delete files already present on `main`. + +## Files retained + +### Implementation + +- `ct_train.py` +- `training/schedules.py` +- `training/loss.py` +- `training/ct_training_loop.py` + +### Factorial experiment pipeline + +- `scripts/run_gap_stage1.sh` +- `scripts/run_gap_stage2.sh` +- `scripts/run_gap_after_stage1.sh` +- `scripts/run_gap_factorial_arm.sh` +- `scripts/evaluate_gap_factorial_arm.sh` +- `scripts/select_gap_scale.py` +- `scripts/summarize_gap_factorial.py` +- `scripts/verify_gap_factorial_arm.py` +- `scripts/extract_gap_controller_state.py` + +### Tests + +- `tests/test_schedules.py` +- `tests/test_training_cli_compat.py` +- `tests/test_local_tbin_controller.py` +- `tests/test_select_gap_scale.py` +- `tests/test_summarize_gap_factorial.py` +- `tests/test_adaptive_signal_updates.py` for telemetry-schema migration and + aggregation coverage. + +### Compact results and documentation + +- `results/gap_factorial_20260727/` +- `docs/METHOD_V0.md` +- `docs/PR23_CLEANUP_CHECKLIST.md` + +## Historical material removed from the PR diff + +- [x] Old `results/final_performance_evaluation/` Role A outputs. +- [x] Role A/D evaluation scripts and tests already represented on `main`. +- [x] Blind-evaluation ZIP files. +- [x] Historical raw metric JSONL trees. +- [x] Evaluation PNGs and controller plots unrelated to this factorial study. +- [x] Old final-evaluation configuration and documentation changes. +- [x] Local untracked `results/local_tbin_*` raw directories remain untouched + and are not part of the PR. + +## Scientific wording + +- [x] Replaced global-neutrality language with: + “the local scale factors have geometric mean one before realized-gap + clipping effects.” +- [x] Defined the pre-clipping factorization + `d_pre(t) = g ℓ_b(t) d_base(t)`. +- [x] Defined the implemented realized-gap clipping sequence. +- [x] Added `realized gap / sigmoid_gap`, lower-gap clipping rate, and + upper-gap clipping rate telemetry. +- [x] Documented that the completed runs predate these telemetry fields, so + their exact historical values are unavailable. +- [x] Defined headline percentages as ratios of held-out arithmetic means, not + means of per-seed percentages. +- [x] Quantified the strong influence of seed 1 on the NFE=2 held-out mean. +- [x] Kept seed 0 labeled as the `g*=1.10` selection seed. +- [x] Kept KID/FID-5k and `n=3` conclusions descriptive. + +## Final-head validation + +- [x] Schedule and controller tests. +- [x] Controller/resume-state tests. +- [x] Selection and summarization tests. +- [x] Python compilation. +- [x] Shell syntax. +- [x] `git diff --check`. +- [x] Confirm final PR changed-file list contains only retained scope (29 files + relative to synchronized `origin/main`). + +The final local suite passed 145 tests with 4 expected skips: two CUDA-only +loss-call tests and two clean-worktree-only runner identity tests. The +gap-specific subset passed 76 tests with 2 expected CUDA skips. diff --git a/docs/REPRODUCIBILITY_CHECKLIST.md b/docs/REPRODUCIBILITY_CHECKLIST.md new file mode 100644 index 00000000..f95a5930 --- /dev/null +++ b/docs/REPRODUCIBILITY_CHECKLIST.md @@ -0,0 +1,77 @@ +# Reproducibility Checklist + +Complete this checklist from a clean clone before the anonymous release is frozen. `PASS` requires recorded evidence; an unchecked item is not implicitly satisfied. + +## Source and anonymity + +- [ ] Anonymous repository starts from the accepted paper commit. +- [ ] Working tree is clean and the exact Git SHA is recorded. +- [ ] No author names, private usernames, email addresses, tokens, SSH hosts, or private repository URLs remain. +- [ ] No absolute paths such as `/root/...`, `/mnt/...`, `C:\Users\...`, or editor-specific paths appear in tracked files. +- [ ] Git history and release artifacts have been reviewed for identity leaks. +- [ ] Third-party attribution and licenses remain intact. + +## Environment + +- [ ] A fresh environment can be created from the tracked environment specification. +- [ ] Python, PyTorch, CUDA, cuDNN, driver, and GPU versions are recorded. +- [ ] Required imports pass in a clean shell. +- [ ] CPU-only inspection commands do not require CUDA initialization. +- [ ] The expected GPU count and memory requirement are documented. + +## Assets + +- [ ] Dataset acquisition and conversion commands are documented. +- [ ] Dataset SHA256 is recorded and verified. +- [ ] Transfer checkpoint acquisition is documented. +- [ ] Transfer checkpoint SHA256 is recorded and verified. +- [ ] Large checkpoints and generated image sets are excluded from Git. +- [ ] Persistent and temporary output locations are distinguished. + +## Training + +- [ ] Primary fixed and global-only commands are present. +- [ ] Secondary-setting commands are present. +- [ ] Seeds, budgets, batch settings, optimizer, precision, and schedule parameters are explicit. +- [ ] `g=1.10` is fixed and not selected on confirmatory seeds. +- [ ] Resume behavior is documented and smoke tested. +- [ ] Runs write immutable metadata before training starts. +- [ ] Checkpoint paths include method, setting, seed, budget, and checkpoint SHA prefix. +- [ ] Successful, skipped, NaN, Inf, loss, and controller telemetry fields are collected. + +## Evaluation + +- [ ] Sampling seeds are fixed and shared across methods. +- [ ] NFE=1 records `mid_t=[]`. +- [ ] NFE=2 records `mid_t=[0.821]`. +- [ ] KID/FID-5k is labeled as screening or proxy evidence. +- [ ] KID/FID-50k is used for confirmatory claims. +- [ ] Dataset reference statistics and their SHA256 are identical across compared methods. +- [ ] Metric code commit and sample count are recorded. +- [ ] Repeated metric smoke produces the expected reproducibility evidence. +- [ ] Per-seed values, paired deltas, mean, and dispersion are reported. + +## Results and claims + +- [ ] Primary and secondary endpoints are identified before confirmatory results. +- [ ] Results include all declared seeds, including unfavorable outcomes. +- [ ] Missing or failed cells are reported rather than silently removed. +- [ ] Main tables can be regenerated from tracked lightweight inputs. +- [ ] Proxy and formal results are visually and textually separated. +- [ ] The second setting is described as generalization evidence, not additional tuning. +- [ ] Claim wording matches the number of seeds and evaluation scale. + +## Clean-clone audit record + +| Field | Value | +| --- | --- | +| Auditor | anonymous reviewer | +| Date (UTC) | pending | +| Repository commit | pending | +| Environment creation | pending | +| Asset verification | pending | +| Training smoke | pending | +| Sampling smoke | pending | +| Metric smoke | pending | +| Overall result | pending | +| Unresolved issues | pending | diff --git a/docs/ROLE_A_CLEAN_CONTAINER_VALIDATION.md b/docs/ROLE_A_CLEAN_CONTAINER_VALIDATION.md new file mode 100644 index 00000000..f31dfca8 --- /dev/null +++ b/docs/ROLE_A_CLEAN_CONTAINER_VALIDATION.md @@ -0,0 +1,95 @@ +# Role A clean-container validation + +## Result + +Role A engineering validation passed on one MatPool A100 container using a newly created Conda environment: + +- branch: `role-a/clean-container-validation` +- tested code SHA: `956c6f41390e9e26885e689eb4c3b66d45957c38` +- PR head at validation: `956c6f41390e9e26885e689eb4c3b66d45957c38` +- environment: `ect-clean-validation` +- creation command: `bash setup_env.sh --name ect-clean-validation` +- manual package installation: none +- environment specification: `env.yml` pins `huggingface-hub==0.23.4` +- GPU: NVIDIA A100-PCIE-40GB +- Python/PyTorch/CUDA: 3.9.18 / 2.3.0 / 12.1 +- persistent project root: `/mnt/ect_project` +- final run: `/mnt/ect_project/runs/engineering-smoke/956c6f41-20260716T020346Z` + +This is an engineering connectivity test only. It is not the official fixed ECT baseline and it did not run FID or KID. + +The validation ran on the code SHA above. The following evidence-only commit changes only these three compact evidence files; GitHub PR #8 metadata and its validation summary comment record the resulting final PR head SHA. + +## Commands actually run + +The old `ect` environment was not reused. The environment was created entirely from the updated `env.yml`; no manual `pip install` command was used. + +```bash +bash setup_env.sh --name ect-clean-validation + +conda run -n ect-clean-validation python --version +conda run -n ect-clean-validation python -c \ + "import huggingface_hub; print(huggingface_hub.__version__)" + +export ECT_ENV_NAME=ect-clean-validation +bash prepare_data.sh --check-only +bash download_checkpoint.sh --check-only +bash scripts/smoke_engineering_100steps.sh --check-only +bash scripts/smoke_engineering_100steps.sh --dry-run --port 29521 +bash scripts/smoke_engineering_100steps.sh --port 29521 +``` + +The version checks returned Python 3.9.18 and `huggingface-hub` 0.23.4. The environment, data, checkpoint, smoke check-only, and dry-run checks all passed before training was launched. The Git worktree was clean, and no training algorithm file was changed. + +## Asset validation + +The default persistent paths were used: + +- dataset: `/mnt/ect_project/datasets/cifar10-32x32.zip` +- source tarball: `/mnt/ect_project/datasets/cifar-10-python.tar.gz` +- transfer checkpoint: `/mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl` + +Dataset validation passed for 50,000 RGB images, 50,000 labels, 32×32 resolution, valid ZIP CRC, and the official CIFAR-10 source MD5. The EDM checkpoint SHA-256 matched `4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da`. + +## Smoke results + +| Check | Result | +| --- | --- | +| environment check | PASS | +| data check-only | PASS | +| checkpoint check-only | PASS | +| smoke check-only | PASS | +| dry-run | PASS | +| Fresh optimizer updates | 100, PASS | +| Resume optimizer updates | 100 additional, PASS | +| Progress | 1.0 kimg → 2.0 kimg | +| FP16 / AMP | enabled / enabled | +| GradScaler saved | yes | +| GradScaler restored | yes; explicit restore message in Resume log | +| Formal metrics | disabled (`metrics=[]`) | +| Wall time | 185 seconds | +| Peak allocated VRAM | 5,563 MiB | +| Peak reserved VRAM | 5,616 MiB | + +Both phases generated a numbered network snapshot and training state under `/mnt/ect_project`. Both training-state files contain `gradscaler_state`. The Fresh scaler state ended with scale 8192 and growth tracker 31; after Resume it retained scale 8192 and reached growth tracker 131, an increment of exactly 100. + +## Loss finiteness check + +All recorded `Loss/loss` means in `stats.jsonl` are finite: + +- Fresh: 11.8389359474 and 17.0237727689 (1,000 samples total) +- Resume: 16.9281532710 (1,000 samples total) + +The console has `loss nan` at the initialization and resume maintenance reporting points. The status line can read the default collector before it is updated, while the subsequently written statistics are finite. This remains a non-blocking reporting-order observation; no core training file was changed. + +## GradScaler and metrics audit + +The Fresh and Resume states both contain the keys `gradscaler_state`, `net`, and `optimizer_state`. The Resume log explicitly says it loaded GradScaler state from the Fresh `training-state-000001.pt`. Both `training_options.json` files contain `metrics=[]`, and the complete logs contain no FID/KID or metric-runner invocation. + +## Storage and Git hygiene + +The dataset, transfer checkpoint, numbered checkpoints, complete logs, training states, and generated images remain under `/mnt/ect_project`. Git contains only the environment pin and compact evidence. No FID-50k job was launched and no large file is included in the branch. + +## Unresolved issues + +There is one non-blocking reporting-order observation: console initialization/maintenance may display `loss nan` before the statistics collector update. Recorded losses are finite, and this PR intentionally does not modify the training loop. diff --git a/docs/ROLE_A_QUANTITATIVE_EVALUATION.md b/docs/ROLE_A_QUANTITATIVE_EVALUATION.md new file mode 100644 index 00000000..380ee746 --- /dev/null +++ b/docs/ROLE_A_QUANTITATIVE_EVALUATION.md @@ -0,0 +1,112 @@ +# Role A quantitative evaluation protocol + +This protocol supersedes the earlier 16 kimg-only evaluation scope. Role A owns +metric execution only; training jobs may continue independently. + +## Frozen matrix and order + +Formal evaluation is performed independently for every checkpoint and NFE. The +execution order is: + +1. all six 64 kimg checkpoints; +2. all six 32 kimg checkpoints; +3. all six 16 kimg checkpoints. + +Each budget must contain fixed sigmoid and Adaptive v1 for training seeds 0, 1, +and 2. The runner refuses partial six-cell budget matrices. Generated features +from different training seeds are never pooled into one metric. + +## Metric smoke + +Before formal evaluation, use the two existing seed0/16 kimg checkpoints: + +- NFE=1 and NFE=2 (`mid_t=0.821`); +- FP32; +- 512 generated samples with explicit sampling seeds 0-511; +- `kid512_full` and `fid512_full`; +- two repeats per checkpoint/NFE. + +The smoke collector fails unless all four checkpoint/NFE cells finish, every +metric has exactly two results, the declared image count equals the sampling +seed count, and repeat values agree within 1 ppm relative tolerance (with +bitwise equality reported separately). The run manifest records method, +training seed, budget, NFE, checkpoint SHA256, dataset SHA256, sampling seeds, +precision, metric seed, and reference feature extractor. + +Smoke KID/FID values are diagnostic only and must not be reported as formal +generation-quality results. + +## Formal evaluation + +For each checkpoint and each NFE: + +- 5,000 generated samples; +- identical explicit sampling seeds 0-4999; +- FP32; +- NFE=1 or NFE=2 (`mid_t=0.821`); +- KID-5k and FID-5k when both are stable. + +The required output columns are exactly: + +| Method | Train seed | Budget | NFE | KID | FID | Checkpoint SHA | +| --- | ---: | ---: | ---: | ---: | ---: | --- | + +All 5k metrics are proxy evaluations, not standard FID-50k benchmarks. + +## Uniform fallback rule + +Metric fallback is selected for an entire complete budget matrix, never per +method or checkpoint: + +1. `--metrics both`: report KID and FID; +2. if FID is unstable but KID is stable, start a new empty run with + `--metrics kid-only`; +3. if KID is unstable but FID is stable, start a new empty run with + `--metrics fid-only` and label it FID-5k proxy; +4. never combine a fixed result from one metric mode with an Adaptive result + from another metric mode. + +Failed runs are not retried automatically. + +## Reference identity on the current A100 node + +- CIFAR-10 ZIP SHA256: + `08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372` +- Inception detector SHA256: + `f58cb9b6ec323ed63459aa4fb441fe750cfe39fafad6da5cb504a16f19e958f4` +- Cached reference statistics SHA256: + `7c7ad1657d62a12ac6bb609ccd2da79dcefde1aff48d2883bba1c6556b4671b3` + and + `c9e49db82db2c299bc01b415f55beed0deb53782629457f97905ecbd66b60870` + +All jobs must use this same dataset, detector, and reference-statistics +identity. + +## Commands + +Smoke: + +```bash +python scripts/run_role_a_quality_evaluation.py \ + --manifest /root/role-a-eval/checkpoints.json \ + --data /mnt/ect_project/datasets/cifar10-32x32.zip \ + --outdir /root/role-a-eval/smoke-both \ + --phase smoke --budget 16 --metrics both + +python scripts/collect_role_a_quality_results.py \ + --eval-root /root/role-a-eval/smoke-both \ + --outdir /root/role-a-eval/smoke-summary +``` + +Formal evaluation is invoked separately in the frozen order by replacing +`BUDGET` with 64, then 32, then 16: + +```bash +python scripts/run_role_a_quality_evaluation.py \ + --manifest /root/role-a-eval/checkpoints.json \ + --data /mnt/ect_project/datasets/cifar10-32x32.zip \ + --outdir /root/role-a-eval/formal-BUDGET-both \ + --phase formal --budget BUDGET --metrics both +``` + +Each output directory must be new and empty. diff --git a/docs/ROLE_C_ANALYSIS_PROTOCOL.md b/docs/ROLE_C_ANALYSIS_PROTOCOL.md new file mode 100644 index 00000000..7a986159 --- /dev/null +++ b/docs/ROLE_C_ANALYSIS_PROTOCOL.md @@ -0,0 +1,105 @@ +# Role C — frozen paired-analysis protocol + +This document defines the script handoff and the judgment rules for the fixed +`sigmoid` versus `adaptive_v1` comparison. It implements the team protocol +without changing schedules, controller parameters, training seeds, checkpoint +selection, or sampling settings. + +## Inputs from Roles A and B + +Role A supplies one row per evaluated checkpoint in a CSV file: + +```csv +method,training_seed,budget_kimg,nfe,kid_5k,fid_5k,checkpoint_sha256,mid_t,sampling_seed,num_generated +sigmoid,0,16,1,0.0124,3.81,,,0-4999,5000 +adaptive_v1,0,16,1,0.0119,3.72,,,0-4999,5000 +``` + +Required fields are `method`, `training_seed`, `budget_kimg`, `nfe`, at least +one of `kid_5k` / `fid_5k`, and `checkpoint_sha256`. `sampling_seed` and +`num_generated` may be omitted in a rolling report, but are mandatory for a +non-`INCOMPLETE` final conclusion: both arms must record the same sampling +seed and exactly 5,000 generated images. The fixed matrix is: + +- methods: `sigmoid`, `adaptive_v1`; +- training seeds: `0`, `1`, `2`; +- budgets: `16`, `32`, `64` kimg; +- NFE: `1`, `2` (`mid_t=0.821` for NFE=2). + +Role B can provide a checkpoint index: + +```csv +method,training_seed,budget_kimg,checkpoint_sha256,training_summary_csv,run_dir +sigmoid,0,16,,/mnt/ect_project/runs/sigmoid-seed0/train_summary.csv,/mnt/ect_project/runs/sigmoid-seed0 +``` + +The script also accepts `--training-root` and recursively reads the existing +Role-B `metadata.json` plus adjacent `train_summary.csv`. Every Role-A +checkpoint SHA must exactly match the Role-B record for its method, seed and +budget. The training summary must identify the same method and reach the +registered checkpoint budget without backward progress. This prevents +checkpoint substitution or an under-trained checkpoint after data freeze. + +## Run + +```bash +python scripts/analyze_paired_results.py \ + --metrics /mnt/ect_project/metrics/role_a_metrics.csv \ + --training-records /mnt/ect_project/runs/role_b_training_records.csv \ + --outdir results/role_c_final \ + --require-complete +``` + +For rolling analysis while checkpoints or evaluations are still arriving, omit +`--require-complete`. The script emits all available tables and figures but +sets the conclusion to `INCOMPLETE` until the complete paired matrix and +training/controller telemetry are present. + +## Outputs + +- `per_seed_metrics.csv`: normalized Role-A metric rows joined with Role-B + training stability and controller telemetry. +- `paired_differences.csv`: per-seed `Adaptive − Fixed` values for each common + metric. Negative values favor Adaptive v1. +- `aggregate_results.csv`: mean, sample standard deviation, standard error, + pair coverage, and adaptive win/loss counts per metric/budget/NFE. +- `quality_vs_budget.png`: KID/FID quality curves, separated by NFE. +- `controller_vs_budget.png`: correction, training-pair gap, and trailing-loss + curves by budget. +- `FINAL_CONCLUSION.md`: a guarded, data-derived conclusion draft, including + descriptive per-NFE correlations for adaptive quality versus trailing loss, + and paired quality delta versus correction/gap. These are explicitly not + significance tests or causal claims. + +No row is ever created by pooling images across training seeds. One metric may +not appear for only one arm of a pair. Sampling seed and image count must +match between the fixed and adaptive arms; final analysis requires 5,000 +images per arm. NFE=2 `mid_t` must also match (the frozen value is 0.821). +NFE=1 must not supply a `mid_t` value. + +## Decision implementation + +KID-5k is the primary metric when it covers the complete 3-seed matrix. FID-5k +is used only as a common fallback when KID is incomplete and FID covers all +settings. The script reports `Adaptive − Fixed`; a negative delta is better. + +For each budget/NFE setting, a **repeated advantage** requires both: + +1. at least 2 of 3 training seeds have negative paired deltas; and +2. the three-seed mean delta is negative. + +The symmetric rule defines a repeated regression. The script writes +`Adaptive 表现出初步优势` only when a repeated advantage occurs at 32 or 64 +kimg, its other NFE is not a repeated regression, and the stability gate +passes. It writes `混合` when stable positive and negative settings coexist, +`负向` for repeated regression with no stable positive setting, and `持平` +otherwise. Missing coverage always yields `INCOMPLETE` rather than a quality +claim. + +The stability gate requires finite training losses, legal `r/t` and +`(t-r)/t` telemetry, a correction within its configured bound, complete +successful-step/AMP/controller telemetry, adaptive controller activation, and +an adaptive AMP-skip rate no more than 2 percentage points above the paired +fixed run. Fixed and adaptive rows may not reference the same checkpoint SHA. +The tolerance is explicit in both the command line and conclusion report; it +is not adjusted from results. diff --git a/docs/SCHEDULES.md b/docs/SCHEDULES.md new file mode 100644 index 00000000..ba86e1a7 --- /dev/null +++ b/docs/SCHEDULES.md @@ -0,0 +1,176 @@ +# t→r 映射调度模块说明（Role C） + +模块：`training/schedules.py`；测试：`tests/test_schedules.py`。 + +ECT 训练对 `(x_t, x_r)` 中 `r = r(t, stage)` 由映射调度给出（论文 arXiv 2406.14548 +第 3.3 节与附录 A）。本模块把 t→r 调度收敛到统一接口；`ECMLoss` 的 t→r 入口 +已改为经由本模块派发（`__init__` 中 `get_schedule(adj, q=q, k=k, b=b)`， +`__call__` 中 `r = self.schedule.compute_r(t=t, stage=self.stage)`）， +`training/loss.py` 中除该入口外的训练逻辑（ε 共享、teacher `no_grad`、 +dropout RNG 保存/恢复、损失权重、Huber 参数）逐字节未动。 + +## 接口 + +两种调用形式等价： + +```python +from training.schedules import compute_r, get_schedule + +# 对象式 +schedule = get_schedule("sigmoid", q=256, k=8, b=1) +r = schedule.compute_r(t=t, stage=stage) + +# 函数式 +r = compute_r(t=t, stage=stage, schedule="sigmoid", q=256, k=8, b=1) +``` + +- `t`：任意形状的 torch 张量（训练循环里是 `[N,1,1,1]`），也接受 + python/numpy 标量或数组（内部 `torch.as_tensor` 转换）；返回同形状张量， + 恒有 `0 <= r <= t`。 +- `stage`：训练循环维护的课程阶段；所有调度均保持官方 + `cur_tick // double_ticks` 的整数 stage。`adaptive_v1` 只根据 loss EMA + 修正 `r/t`，不另外改变 stage 课程。 +- 超参默认与 `ct_train.py` CLI 一致：`q=2.0, k=8.0, b=1.0`。 +- 兼容 `ECMLoss` 的有状态用法：`schedule.update_schedule(stage)` 后调 + `schedule.t_to_r(t)`。 + +## 支持的调度 + +| 名称 | 公式（`decay = 1/q^(stage+1)`，均 clamp `r>=0`） | 来源 | +| :-- | :-- | :-- | +| `const` | `r/t = 1 - decay` | 官方 Eq.(17)，`ECMLoss.t_to_r_const` 原样移植 | +| `sigmoid` | `r/t = 1 - decay * n(t)`，`n(t) = 1 + k*sigmoid(-b*t)` | 官方 Eq.(18)，训练默认，`ECMLoss.t_to_r_sigmoid` 原样移植 | +| `global_sigmoid` | `d_new(t) = g d_sigmoid(t)` | 固定的全局 gap calibration | +| `adaptive_v1` | 官方 `sigmoid` ratio + loss EMA 驱动的有界修正 | Role C 实验 v1 | +| `local_tbin_v1` | `d_new(t) = ℓ_b(t) d_sigmoid(t)` | 使用 raw pair loss 的四分位 `t`-bin controller | +| `local_tbin_v2` | v1，并约束裁剪前局部因子 `GM(ℓ_i)=1` | 低 authority local-only | +| `local_tbin_v3` | `d_new(t) = g ℓ_b(t) d_sigmoid(t)`（裁剪前） | 显式分解 global calibration 与 local redistribution | + +这里 `d_sigmoid(t)=t-r_sigmoid(t)`。对 `local_tbin_v2/v3`，准确表述是： +**the local scale factors have geometric mean one before realized-gap clipping +effects**。该约束不保证裁剪后的 realized gap 在算术均值、几何均值或期望意义上 +保持全局中性。完整数学定义见 `docs/METHOD_V0.md`。 + +**官方 fixed 公式不变的保证**：官方公式方法 `t_to_r_const` / `t_to_r_sigmoid` +**原样保留**在 `training/loss.py` 中作为 parity 基准（训练路径不再调用它们， +仅供测试对照）；`tests/test_schedules.py::OfficialFormulaParityTest` 对多组 +`(q,k,b,stage,dtype)`（A100 上还含 cpu/cuda 两种设备）把 schedules 模块输出与 +这两个参考方法做**按位相等**校验，`ECMLossIntegrationTest` 再校验接入后的 +`self.schedule.compute_r` 入口与参考方法按位一致。任何一侧公式被改动，测试 +都会失败。 + +## adaptive_v1 设计 + +设官方 sigmoid 比率为 `rho_0 = r/t`，warm-up 最后一次更新后的 loss EMA +为 `L_ref`，当前 loss EMA 为 `L_ema`： + +```text +score = tanh(log(L_ref) - log(L_ema)) +delta = adaptive_max_adjust * score +rho = clamp(rho_0 + delta, 0, 1 - adaptive_min_gap) +r = t * rho +``` + +`adaptive_v1` 在没有有效 correction（尚无有效 signal、warmup 中，或 +`adaptive_max_adjust=0`）时直接返回官方 sigmoid 的 `rho_0`，按位保持 fixed +baseline。只有 correction 激活后才应用 `adaptive_min_gap` 上限与上式中的修正。 + +- loss 下降时 `delta > 0`，减小 `t-r`，加强一致性约束； +- loss 恶化时 `delta < 0`，增大 `t-r`，降低当前任务难度； +- `|delta| <= adaptive_max_adjust`，不做搜索或额外控制器； +- 非有限、负数 loss 信号直接忽略；输出做有限化和边界 clamp， + 保证 `0 <= r <= t`且不产生 NaN/Inf； +- loss 信号每 `adaptive_update_kimg`（默认 0.5 kimg）按**绝对图像数边界** + 聚合；它在训练迭代内执行，不依赖 `--tick`/maintenance（默认 50 kimg）。 + 每个窗口的 sum/count 以 all-reduce 合并，因此同样输入与状态下各 rank 使用 + 同一修正值。 +- 前 `adaptive_warmup_updates` 个有效聚合窗口只建立 loss EMA；最后一个 + warm-up 窗口更新 EMA 后，将该 EMA 固定为 `loss_reference`。下一次更新才 + 允许非零修正。 + +默认参数只是首版单点配置，不代表完成大范围搜索： + +| CLI | 默认值 | 含义 | +| :-- | --: | :-- | +| `--adaptive-loss-ema-beta` | `0.9` | loss EMA 平滑系数 | +| `--adaptive-update-kimg` | `0.5` | 触发一次全局 adaptive loss 聚合的图像间隔；与 `--tick` 独立 | +| `--adaptive-warmup-updates` | `2` | 应用修正前仅更新 EMA 的有效信号窗口数 | +| `--adaptive-max-adjust` | `0.05` | `r/t` 最大绝对修正 | +| `--adaptive-min-gap` | `0.001` | 开启修正时的最小 `(t-r)/t` | + +### 稳定运行时 telemetry 接口 + +训练与结果收集代码只通过 `loss_fn.schedule_runtime_metrics()` 读取调度状态， +不访问 schedule 的内部字段。该接口固定返回： + +- `loss_ema`、`loss_reference`、`correction`、`signal_updates`； +- `adaptive_active`：warmup 完成且 correction 控制器已激活； +- `r_over_t_mean`：最近实际训练 pair 的 `mean(r/t)`； +- `gap_mean`：最近实际训练 pair 的 `mean((t-r)/t)`； +- `gap_over_sigmoid_gap_mean`：最近实际 gap 与官方 sigmoid baseline gap + 比值的 batch mean； +- `lower_gap_clip_rate`：minimum-gap 下界把 realized gap 向上裁剪的样本率； +- `upper_gap_clip_rate`：`r>=0` 上界把 realized gap 向下裁剪的样本率。 + +`train_summary.csv` 每个 attempted iteration 记录这些字段，以及 +`next_loop_cur_tick`。后者是本 iteration 完成后下一循环将使用的真实 tick；若该次 +maintenance 保存 checkpoint，它与 checkpoint 中的 `cur_tick` 完全一致，因此 +collector 不会从 `processed_nimg` 或 `--tick` 反推 tick。controller 状态是在 +本 iteration 末尾（可能完成 signal update 后）读取，`r_over_t_mean` 与 +`gap_mean` 则描述本 iteration 实际使用的 pair；因此 correction 的变化应在后续 +iteration 的 pair 指标中体现。 + +collector 的 activation gate 将两个 iteration 明确区分： +`first_nonzero_correction_iteration` 表示本 iteration 结束后 controller +第一次得到非零 correction；`first_adapted_pair_iteration` 只能是其下一次 +iteration，因为该次 pair 才会把 correction 用于 `r(t)`。对于 +`--mode activation --schedule adaptive_v1`，若最终 `signal_updates < 3`、 +最终 `adaptive_active` 不为真、没有非零 correction、下一实际 pair 不在结束前， +或 correction 后少于 4 个 attempted iterations，collector 会拒绝打包。 + +从旧版 `train_summary.csv` 续训时，只接受完全匹配的受支持标准表头。训练循环 +会先保存对应的 `.bak` 文件，再原子迁移到新表头；无法重建的历史 telemetry +保持空值。collector 允许这一段连续的历史空前缀，并在 metadata 中记录覆盖率与 +首个 telemetry iteration；telemetry 开始后的空洞、部分表头或非法状态都会拒绝。 +2026-07-27 已完成的 factorial runs 早于上述三项 gap diagnostics，因而不能从 +KID/FID 结果反推出历史 clipping rate。 + +adaptive 运行状态会随 training-state 保存/恢复；完整参数与当前 EMA、 +参考 loss、修正值会写入 checkpoint 中的 `loss_fn`。后续将由 Role D 的独立 +follow-up 把 `training_schedule` 接入固定种子评估 metadata。 + +### CLI 兼容性 + +```bash +# 原命令：语义不变 +python ct_train.py ... +python ct_train.py ... --mapping sigmoid +python ct_train.py ... --mapping const + +# 新入口 +python ct_train.py ... --schedule adaptive_v1 +# 同时接受连字符写法 adaptive-v1，内部统一记录为 adaptive_v1 + +# 显式关闭，严格恢复官方 fixed sigmoid +python ct_train.py ... --schedule sigmoid +``` + +`--schedule` 与 `--mapping` 指向同一个内部 `mapping` 字段，因此旧的配置 +传递、`loss_kwargs.adj` 和日志结构不会因参数改名而变化。不传 +两者时仍默认 `sigmoid`，不会创建 adaptive loss Collector 或改变官方 +stage 边界。 + +## 现有 run 配置下的 stage 行为（供对照） + +- `run_ecm.sh`：`tick=12.8, duration=25.6, double=250` → 共 2000 tick、 + stage 0..7，最终 `decay = 2^-8 = 1/256`。 +- `run_ecm_1hour.sh`：`-q 256 --double 10000` → 全程 stage 0， + `decay = 1/256`（直接锁定终点紧度）。 + +## 验证 + +```bash +python -m unittest tests.test_schedules tests.test_training_cli_compat -v +# 其中 2 个 ECMLoss.__call__ 端到端用例需 CUDA（A100 激活） +python -m training.schedules # 打印已注册调度的 r/t 表 +``` diff --git a/docs/STAGED_EVALUATION_FORMAL_RECORD.md b/docs/STAGED_EVALUATION_FORMAL_RECORD.md new file mode 100644 index 00000000..c008b5ae --- /dev/null +++ b/docs/STAGED_EVALUATION_FORMAL_RECORD.md @@ -0,0 +1,112 @@ +# q=256 confirmatory formal evaluation record + +**Protocol:** `staged-checkpoint-evaluation-v1` + +**Evidence class:** formal 50k benchmark +**Evaluation Git commit:** `8375d46ca4c65e85ab399fcf1effe22ebb766790` + +## Frozen matrix and eligibility + +This record supersedes the historical four-cell seed 4/5 example. The formal +matrix is the frozen q=256 confirmatory comparison of fixed sigmoid and +global-only sigmoid (`global_gap_scale=1.10`) at training seeds 3, 4, and 5. +All six predeclared cells entered formal evaluation: no cell, NFE mode, or +training seed was removed on the basis of quick 5k results. + +Each cell passed the version-2 training-integrity gate. In particular, the +checker loaded the evaluated pickle, found a finite EMA module, verified the +declared schedule and global-gap-scale identity, and matched the checkpoint +hash to the receipt and frozen matrix. + +| Checkpoint ID | Method | Training seed | Checkpoint SHA-256 | +| --- | --- | ---: | --- | +| `confirmatory-256k-seed3-fixed` | fixed sigmoid, `g=1.00` | 3 | `09a41e1e7c03dcdf5ffb93bb68687390278b4b190183dfff92bacc1bf79738d9` | +| `confirmatory-256k-seed3-global110` | global-only sigmoid, `g=1.10` | 3 | `24875430eea4679a416ae921c3e9ae16142f6416d2a0edf970764384ef964bed` | +| `confirmatory-256k-seed4-fixed` | fixed sigmoid, `g=1.00` | 4 | `ac94e7b07e5b7628e6b14b26155fb3de09e42373497183d39aba4fe9863663c9` | +| `confirmatory-256k-seed4-global110` | global-only sigmoid, `g=1.10` | 4 | `62a6122a7be523aeb12875d96e96312e9c90efde9eafb75d730c75ceea0e8862` | +| `confirmatory-256k-seed5-fixed` | fixed sigmoid, `g=1.00` | 5 | `21fab0e501bb27032c0e49a553b05a2800ea0fbe20a2a1d94a6bbf5276f2b72a` | +| `confirmatory-256k-seed5-global110` | global-only sigmoid, `g=1.10` | 5 | `491dc887990e6d9f6fde70b5d12775aaf4bfc6155b731682926b02061c253e9b` | + +## Formal environment and settings + +Every cell used FP32 on one NVIDIA A100-PCIE-40GB, Python 3.9.18, PyTorch +2.3.0, SciPy 1.13.1, and CUDA 12.1. The evaluator used the +`inception-2015-12-05` TorchScript detector in feature mode and the canonical +CIFAR-10 archive with SHA-256 +`9fd64620e37bfc0c995535fa52701c9641bcd07635008bfda0c9fbddde1a4ed6`. + +The formal pass used generation seeds `0-49999`, metric seed `20260730`, and +one evaluation per metric/cell. NFE=1 omitted `--mid_t` (`mid_t=[]` in the +manifest); NFE=2 used `--mid_t=0.821` (`mid_t=[0.821]`). It completed 12 +checkpoint/NFE jobs and 24 finite KID-50k/FID-50k metric records. + +## Completed formal results + +Lower is better for KID and FID. Pairing is exactly +`training_seed + budget_kimg + nfe + metric`; the delta is `global_only - +fixed`, so a negative delta favors global-only. Sample SD is the paired-delta +sample standard deviation over the three training seeds. + +| Metric | NFE | Fixed mean | Global-only mean | Mean paired delta | Paired SD | Global-only wins | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | +| KID-50k | 1 | 0.332009663 | 0.306884229 | -0.025125434 | 0.010316682 | 3 / 3 | +| FID-50k | 1 | 311.586158925 | 297.402566815 | -14.183592109 | 3.296938320 | 3 / 3 | +| KID-50k | 2 | 0.205662306 | 0.058241853 | -0.147420454 | 0.129031614 | 3 / 3 | +| FID-50k | 2 | 197.310118996 | 70.393238523 | -126.916880474 | 110.560376043 | 3 / 3 | + +All 12 individual paired metric comparisons favor global-only; there are no +fixed wins or ties. At NFE=2, seed 5 is nearly flat for both endpoints, so the +larger effects at seeds 3 and 4 should be reported as heterogeneous paired +outcomes rather than a uniform NFE effect. + +## Main-text figures + +![Figure 1: per-seed paired comparison](../results/q256_256k_formal/figures/figure1_per_seed_paired_comparison.png) + +*Figure 1. Per-seed paired comparison at 256 kimg. Each line connects the +fixed and global-only checkpoint trained with the same seed; open markers are +fixed and filled markers are global-only. All panels use 50k samples per +checkpoint and lower values are better. Y-scales are panel-specific.* + +![Figure 2: mean paired delta and between-seed variation](../results/q256_256k_formal/figures/figure2_mean_delta_seed_variation.png) + +*Figure 2. Paired deltas are global-only minus fixed, so negative values favor +global-only. Colored points are the three independent training seeds; black +diamonds and whiskers are the mean and sample SD, respectively, not confidence +intervals. NFE=2 has visibly greater between-seed variation, driven by the +near-flat seed-5 comparison.* + +## Appendix and machine-readable diagnostics + +Exact sign-test descriptions, bootstrap sensitivity intervals, +leave-one-seed-out summaries, coefficient-of-variation diagnostics, rank +consistency, and geometric/median/worst-case summaries are retained outside +the main text in `paired_statistics.md`, `paired_statistics.json`, and +`analysis/q256_extended_statistics.json`. They remain descriptive appendix or +machine-readable material. There are only three independent training seeds; +bootstrap resampling of those seeds does not create additional independent +observations. + +## Interpretation boundary and versioned package + +This is formal evidence from a predeclared three-seed paired matrix. Report +the values as descriptive paired results; with only three independent training +seeds, do not make a significance claim from these sample standard deviations. + +The portable result package is versioned at +`results/q256_256k_formal/`. It retains the machine-generated metric +values and provenance needed for review, but deliberately omits generated +samples, feature caches, and host-specific paths: + +```text +results/q256_256k_formal/evaluation_results.csv +results/q256_256k_formal/paired_differences.csv +results/q256_256k_formal/paired_statistics.json +results/q256_256k_formal/paired_statistics.md +results/q256_256k_formal/environment_manifest.json +results/q256_256k_formal/README.md +``` + +The frozen logical matrix, immutable receipt digests, formal-promotion policy, +and evaluator implementation are retained in Git. Runtime paths are only host +bindings and do not define the experiment. diff --git a/docs/STAGED_EVALUATION_RUNBOOK.md b/docs/STAGED_EVALUATION_RUNBOOK.md new file mode 100644 index 00000000..c13e34d0 --- /dev/null +++ b/docs/STAGED_EVALUATION_RUNBOOK.md @@ -0,0 +1,147 @@ +# Staged evaluation runbook + +This runbook operationalizes `staged-checkpoint-evaluation-v1` from +[`EVALUATION_PROTOCOL.md`](EVALUATION_PROTOCOL.md). All GPU evaluation happens +on the remote server; the commands below are designed to be checked locally +with `--dry-run` first and then run unchanged on that server. + +The prospective q=256 budget and fresh q=128 logical matrices are frozen in +[`FROZEN_EVALUATION_MATRICES.md`](FROZEN_EVALUATION_MATRICES.md). They must be +bound to completed checkpoints and integrity receipts before execution; quick +results may not select, remove, or substitute their predeclared formal cells. + +## Local preparation + +Copy and populate the checkpoint manifest. Every checkpoint must have an +immutable SHA256; do not reuse a result directory from a previous attempt. + +```bash +cp configs/staged_evaluation_checkpoints.example.json \ + /mnt/ect_project/staged_eval/checkpoints.json +``` + +### Confirmatory q=256 matrix + +The current six-cell fixed-sigmoid versus global-only `g=1.10` study is frozen +in [`../configs/staged_evaluation_confirmatory_q256.frozen.json`](../configs/staged_evaluation_confirmatory_q256.frozen.json). +It intentionally contains identities, hashes, schedule settings, source +commits, and receipt identities, but no server paths. Do not point +`run_staged_evaluation.py` directly at this logical manifest. + +On the evaluation server, create a non-versioned runtime manifest which copies +each `checkpoint_id`, `method`, `training_seed`, `budget_kimg`, and +`checkpoint_sha256` unchanged, then adds the local `checkpoint` and +`integrity_receipt` paths. For the seed 4/5 receipt files, also recompute and +match the frozen receipt SHA256 before a formal launch. Seed 3 is pinned to the +tracked handoff integrity attestation (`D_HANDOFF.md`); its server-side +machine-readable receipt must be bound under the frozen filename before it can +pass the formal runner's receipt gate. Copy the top-level `comparison` and +`formal_promotion_policy` unchanged as well; the formal CLI rejects a manifest +without the frozen six-cell promotion policy. + +Before any formal launch, validate the non-versioned manifest against the Git +matrix (without `--allow-missing-inputs`): + +```bash +python scripts/validate_staged_runtime_manifest.py \ + --frozen configs/staged_evaluation_confirmatory_q256.frozen.json \ + --runtime /mnt/ect_project/staged_eval/checkpoints.json +``` + +The first remote job is the quick smoke for one named existing checkpoint. It +runs both NFE modes and both 5k metrics, producing four metric records total: + +```bash +python scripts/run_staged_evaluation.py \ + --manifest /mnt/ect_project/staged_eval/checkpoints.json \ + --data /mnt/ect_project/datasets/cifar10-32x32.zip \ + --outdir /mnt/ect_project/staged_eval/smoke \ + --phase smoke \ + --smoke-checkpoint-id sigmoid_seed0_16k \ + --dry-run +``` + +Use the same command without `--dry-run` only on the remote server. The +runner rejects non-empty output roots, mismatched checkpoint hashes, and any +attempt to use missing input files outside a dry run. + +## Remote execution order + +Run these stages serially. Do not start a later stage after an incomplete or +failed predecessor. + +1. Existing-checkpoint smoke: use the command above without `--dry-run`. +2. Fixed-seed image determinism: on the same server, run the acceptance sampler + against the smoke checkpoint (or another named candidate) and then validate + its emitted artifact. The sampler prints the exact checkpoint-isolated + result directory; use that directory in the verifier command. + + ```bash + bash scripts/sample_checkpoint.sh /mnt/ect_project/checkpoints/sigmoid_seed0_16k.pkl \ + --outdir /mnt/ect_project/staged_eval/fixed-seed \ + --seeds 0-63 --nfe 1 2 --mid-t 0.821 \ + --work-group-size 8 --verify-work-group-size 16 \ + --precision fp32 --device cuda + + python scripts/verify_fixed_seed_determinism.py \ + --result-dir /mnt/ect_project/staged_eval/fixed-seed/- + ``` + + The verifier checks the exact 128-image set, both NFE/midpoint settings, + repeated-run and 8/16 work-group assertions, and every manifest SHA256. +3. Collect the smoke table: + + ```bash + python scripts/collect_staged_evaluation_results.py \ + --eval-root /mnt/ect_project/staged_eval/smoke \ + --outdir /mnt/ect_project/staged_eval/smoke-summary + ``` + +4. Run the complete quick screening matrix (all manifest cells): + + ```bash + python scripts/run_staged_evaluation.py \ + --manifest /mnt/ect_project/staged_eval/checkpoints.json \ + --data /mnt/ect_project/datasets/cifar10-32x32.zip \ + --outdir /mnt/ect_project/staged_eval/quick \ + --phase quick + ``` + +5. Only after every candidate has a passed, SHA-matched training-integrity + receipt, run the formal 50k matrix: + + ```bash + python scripts/run_staged_evaluation.py \ + --manifest /mnt/ect_project/staged_eval/checkpoints.json \ + --data /mnt/ect_project/datasets/cifar10-32x32.zip \ + --outdir /mnt/ect_project/staged_eval/formal \ + --phase formal + ``` + +The formal runner requires a receipt JSON for each checkpoint with +`status: "passed"` and a matching `checkpoint_sha256`. It refuses the formal +run before any metric process starts when this gate is not met. + +For the frozen q=256 matrix, use all six predeclared cells in this command. +Quick 5k values are not a promotion gate: no seed, method, NFE, or checkpoint +may be removed from formal evaluation because of its quick metric performance. +The only eligibility decision is matching frozen provenance plus a passed, +SHA-matched training-integrity receipt. + +## Outputs + +The collector writes a long-form `evaluation_results.csv` and separate +`evaluation_statistics.json`/`.md`. The table carries the evidence class, +checkpoint and dataset identity, integrity status, NFE, both seed contracts, +metric value, evaluation revision, and run path. Statistics are grouped by +evidence class, metric, NFE, and method; quick and formal evidence are never +pooled. For the fixed/global-only confirmatory manifest it additionally writes +`paired_differences.csv`, `paired_statistics.json`, and +`paired_statistics.md`; the collector refuses missing or duplicated arms +instead of producing a partial paired summary. The paired outputs retain the +seed-level relative improvements and report arithmetic/geometric effects, +median deltas, rank consistency, worst-case effect, seed CV, exact sign-test +description, bootstrap sensitivity intervals, leave-one-seed-out summaries, +and the NFE=2-minus-NFE=1 effect-heterogeneity contrast. These remain +descriptive seed-level summaries rather than expanded independent-sample +inference. diff --git a/docs/STAGED_EVALUATION_SMOKE_RECORD.md b/docs/STAGED_EVALUATION_SMOKE_RECORD.md new file mode 100644 index 00000000..d728d669 --- /dev/null +++ b/docs/STAGED_EVALUATION_SMOKE_RECORD.md @@ -0,0 +1,45 @@ +# Staged evaluation smoke record + +**Protocol:** `staged-checkpoint-evaluation-v1` + +**Execution date:** 2026-07-30 +**Evidence class:** quick — 5k-sample screening proxy, not a formal 50k benchmark + +## Evaluated inputs + +| Field | Value | +| --- | --- | +| Evaluation Git commit | `220e2adfc53a5ec123498a61fabcd9995745006c` | +| Checkpoint ID | `official_edm_vp` | +| Checkpoint SHA-256 | `4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da` | +| Dataset SHA-256 | `9fd64620e37bfc0c995535fa52701c9641bcd07635008bfda0c9fbddde1a4ed6` | +| Precision / topology | FP32 / one NVIDIA A100-PCIE-40GB | +| Generation seeds | ascending `0-4999` for every cell | +| Metric seed | `20260730` | + +## Completed quick cells + +| NFE | `mid_t` | KID-5k | FID-5k | +| ---: | --- | ---: | ---: | +| 1 | `[]` | 0.4308025538921356 | 388.1013354336475 | +| 2 | `[0.821]` | 0.16969034075737 | 163.72094360599598 | + +All four metric records and the staged runner manifest completed successfully. +The long-form result table and statistics are retained as external server-side +evaluation artifacts; generated samples and metric outputs are intentionally +not versioned in Git. + +## Fixed-seed determinism acceptance + +The same checkpoint passed the image-level acceptance check: + +- FP32, seeds `0-63`, 64 images for each NFE (128 total); +- NFE=1 uses `mid_t=[]`; NFE=2 uses `mid_t=[0.821]`; +- work-group sizes 8 and 16 produced pixel-identical output; +- a repeated generation pass was pixel-identical; and +- the verifier accepted all 131 SHA-256 manifest entries. + +This record validates evaluator plumbing and fixed-seed sampling only. The +checkpoint is an external smoke input with no training-integrity receipt, so +none of these values is formal evidence and it is not eligible for a formal +50k run. diff --git a/download_checkpoint.sh b/download_checkpoint.sh new file mode 100755 index 00000000..ba10d455 --- /dev/null +++ b/download_checkpoint.sh @@ -0,0 +1,125 @@ +#!/usr/bin/env bash + +# Download and verify the official EDM CIFAR-10 checkpoint used for ECT transfer. + +set -Eeuo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +ENV_NAME="${ECT_ENV_NAME:-ect}" +PROJECT_ROOT="${ECT_PROJECT_ROOT:-/mnt/ect_project}" +URL="${ECT_EDM_CHECKPOINT_URL:-https://nvlabs-fi-cdn.nvidia.com/edm/pretrained/edm-cifar10-32x32-uncond-vp.pkl}" +OUTPUT="${ECT_TRANSFER_PATH:-${PROJECT_ROOT}/pretrained/edm-cifar10-32x32-uncond-vp.pkl}" +REPORT_PATH="${ECT_CHECKPOINT_REPORT:-${PROJECT_ROOT}/runs/day2/checkpoint.json}" +EXPECTED_SHA256="${ECT_EDM_CHECKPOINT_SHA256:-4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da}" +CHECK_ONLY=0 +FORCE=0 + +fail() { + printf '[download_checkpoint] ERROR: %s\n' "$*" >&2 + exit 1 +} + +run_python() { + if [[ "${CONDA_DEFAULT_ENV:-}" == "${ENV_NAME}" ]]; then + python "$@" + elif command -v conda >/dev/null 2>&1; then + conda run --no-capture-output -n "${ENV_NAME}" python "$@" + elif command -v mamba >/dev/null 2>&1; then + mamba run --no-capture-output -n "${ENV_NAME}" python "$@" + else + fail "activate '${ENV_NAME}' or install conda/mamba first" + fi +} + +usage() { + cat <<'EOF' +Usage: bash download_checkpoint.sh [options] + + --output PATH Destination checkpoint + --url URL Download URL + --sha256 HASH Optional expected SHA-256 + --check-only Verify without downloading + --force Replace the existing checkpoint + --report PATH Validation report (default: /mnt/ect_project/runs/day2/checkpoint.json) + -h, --help Show this help +EOF +} + +while [[ $# -gt 0 ]]; do + case "$1" in + --output) + [[ $# -ge 2 ]] || fail "--output requires a value" + OUTPUT="$2" + shift 2 + ;; + --url) + [[ $# -ge 2 ]] || fail "--url requires a value" + URL="$2" + shift 2 + ;; + --sha256) + [[ $# -ge 2 ]] || fail "--sha256 requires a value" + EXPECTED_SHA256="$2" + shift 2 + ;; + --check-only) + CHECK_ONLY=1 + shift + ;; + --force) + FORCE=1 + shift + ;; + --report) + [[ $# -ge 2 ]] || fail "--report requires a value" + REPORT_PATH="$2" + shift 2 + ;; + -h|--help) + usage + exit 0 + ;; + *) + fail "unknown option: $1" + ;; + esac +done + +VERIFY_ARGS=( + "${ROOT_DIR}/scripts/verify_assets.py" + checkpoint + --path "${OUTPUT}" + --expected-sha256 "${EXPECTED_SHA256}" + --output "${REPORT_PATH}" +) + +if [[ "${CHECK_ONLY}" -eq 1 ]]; then + run_python "${VERIFY_ARGS[@]}" + exit 0 +fi + +if [[ -f "${OUTPUT}" && "${FORCE}" -eq 0 ]]; then + printf '[download_checkpoint] Checkpoint already exists; verifying it: %s\n' "${OUTPUT}" + run_python "${VERIFY_ARGS[@]}" + exit 0 +fi + +mkdir -p "$(dirname "${OUTPUT}")" "$(dirname "${REPORT_PATH}")" +if [[ -e "${OUTPUT}" ]]; then + [[ "${FORCE}" -eq 1 ]] || fail "output already exists: ${OUTPUT}" + rm -f "${OUTPUT}" +fi + +PARTIAL="${OUTPUT}.part" +printf '[download_checkpoint] Downloading official EDM checkpoint...\n' +if command -v curl >/dev/null 2>&1; then + curl --fail --location --retry 3 --continue-at - --output "${PARTIAL}" "${URL}" +elif command -v wget >/dev/null 2>&1; then + wget --continue --tries=3 --output-document="${PARTIAL}" "${URL}" +else + fail "curl or wget is required" +fi +mv "${PARTIAL}" "${OUTPUT}" + +run_python "${VERIFY_ARGS[@]}" +printf '[download_checkpoint] Checkpoint ready: %s\n' "${OUTPUT}" diff --git a/env.yml b/env.yml index 681b27e2..a3df83d4 100644 --- a/env.yml +++ b/env.yml @@ -19,4 +19,5 @@ dependencies: - imageio-ffmpeg - pyspng - diffusers==0.26.3 - - accelerate==0.27.2 \ No newline at end of file + - accelerate==0.27.2 + - huggingface-hub==0.23.4 diff --git a/evaluation/q128_formal_job_status.csv b/evaluation/q128_formal_job_status.csv new file mode 100644 index 00000000..b315ea02 --- /dev/null +++ b/evaluation/q128_formal_job_status.csv @@ -0,0 +1,13 @@ +job_index,checkpoint_id,method,training_seed,budget_kimg,nfe,mid_t,sample_count,sample_seeds,metrics,precision,integrity_receipt_status,status,elapsed_seconds,output_directory,source_manifest +1,q128-1024k-seed3-fixed,fixed,3,1024,1,[],50000,0-49999,"kid50k_full,fid50k_full",fp32,passed,completed,281.737,/root/staged_eval/formal-q128-new-1024k/q128-1024k-seed3-fixed/nfe1,/root/staged_eval/formal-q128-new-1024k/run_manifest.json +2,q128-1024k-seed3-fixed,fixed,3,1024,2,[0.821],50000,0-49999,"kid50k_full,fid50k_full",fp32,passed,completed,426.838,/root/staged_eval/formal-q128-new-1024k/q128-1024k-seed3-fixed/nfe2,/root/staged_eval/formal-q128-new-1024k/run_manifest.json +3,q128-1024k-seed3-global110,global110,3,1024,1,[],50000,0-49999,"kid50k_full,fid50k_full",fp32,passed,completed,281.792,/root/staged_eval/formal-q128-new-1024k/q128-1024k-seed3-global110/nfe1,/root/staged_eval/formal-q128-new-1024k/run_manifest.json +4,q128-1024k-seed3-global110,global110,3,1024,2,[0.821],50000,0-49999,"kid50k_full,fid50k_full",fp32,passed,completed,426.95,/root/staged_eval/formal-q128-new-1024k/q128-1024k-seed3-global110/nfe2,/root/staged_eval/formal-q128-new-1024k/run_manifest.json +5,q128-1024k-seed4-fixed,fixed,4,1024,1,[],50000,0-49999,"kid50k_full,fid50k_full",fp32,passed,completed,286.853,/root/staged_eval/formal-q128-new-1024k/q128-1024k-seed4-fixed/nfe1,/root/staged_eval/formal-q128-new-1024k/run_manifest.json +6,q128-1024k-seed4-fixed,fixed,4,1024,2,[0.821],50000,0-49999,"kid50k_full,fid50k_full",fp32,passed,completed,426.827,/root/staged_eval/formal-q128-new-1024k/q128-1024k-seed4-fixed/nfe2,/root/staged_eval/formal-q128-new-1024k/run_manifest.json +7,q128-1024k-seed4-global110,global110,4,1024,1,[],50000,0-49999,"kid50k_full,fid50k_full",fp32,passed,completed,286.652,/root/staged_eval/formal-q128-new-1024k/q128-1024k-seed4-global110/nfe1,/root/staged_eval/formal-q128-new-1024k/run_manifest.json +8,q128-1024k-seed4-global110,global110,4,1024,2,[0.821],50000,0-49999,"kid50k_full,fid50k_full",fp32,passed,completed,426.866,/root/staged_eval/formal-q128-new-1024k/q128-1024k-seed4-global110/nfe2,/root/staged_eval/formal-q128-new-1024k/run_manifest.json +9,q128-1024k-seed5-fixed,fixed,5,1024,1,[],50000,0-49999,"kid50k_full,fid50k_full",fp32,passed,completed,286.87,/root/staged_eval/formal-q128-new-1024k/q128-1024k-seed5-fixed/nfe1,/root/staged_eval/formal-q128-new-1024k/run_manifest.json +10,q128-1024k-seed5-fixed,fixed,5,1024,2,[0.821],50000,0-49999,"kid50k_full,fid50k_full",fp32,passed,completed,426.976,/root/staged_eval/formal-q128-new-1024k/q128-1024k-seed5-fixed/nfe2,/root/staged_eval/formal-q128-new-1024k/run_manifest.json +11,q128-1024k-seed5-global110,global110,5,1024,1,[],50000,0-49999,"kid50k_full,fid50k_full",fp32,passed,completed,286.788,/root/staged_eval/formal-q128-new-1024k/q128-1024k-seed5-global110/nfe1,/root/staged_eval/formal-q128-new-1024k/run_manifest.json +12,q128-1024k-seed5-global110,global110,5,1024,2,[0.821],50000,0-49999,"kid50k_full,fid50k_full",fp32,passed,completed,427.048,/root/staged_eval/formal-q128-new-1024k/q128-1024k-seed5-global110/nfe2,/root/staged_eval/formal-q128-new-1024k/run_manifest.json diff --git a/evaluation/q128_smoke_report.md b/evaluation/q128_smoke_report.md new file mode 100644 index 00000000..461101f9 --- /dev/null +++ b/evaluation/q128_smoke_report.md @@ -0,0 +1,29 @@ +# q128 new-checkpoint smoke report + +## Final status + +The formal evaluation of the newly delivered 1024-kimg checkpoint set is complete: **12/12 jobs completed**. + +Formal source manifest: `/root/staged_eval/formal-q128-new-1024k/run_manifest.json`. +Configuration: FP32; 50,000 generated samples per job (seeds `0-49999`); metrics `kid50k_full` and `fid50k_full`. + +## Matrix coverage and pre-flight checks + +| Checkpoint | Method | Seed | Load | EMA finite | Schedule | Gap scale | NFE=1 | NFE=2 | +| --- | --- | ---: | --- | --- | --- | ---: | --- | --- | +| q128-1024k-seed3-fixed | fixed | 3 | passed | passed | sigmoid | 1.0 | completed | completed | +| q128-1024k-seed3-global110 | global110 | 3 | passed | passed | global_sigmoid | 1.1 | completed | completed | +| q128-1024k-seed4-fixed | fixed | 4 | passed | passed | sigmoid | 1.0 | completed | completed | +| q128-1024k-seed4-global110 | global110 | 4 | passed | passed | global_sigmoid | 1.1 | completed | completed | +| q128-1024k-seed5-fixed | fixed | 5 | passed | passed | sigmoid | 1.0 | completed | completed | +| q128-1024k-seed5-global110 | global110 | 5 | passed | passed | global_sigmoid | 1.1 | completed | completed | + +## Selection policy + +All predeclared cells (3 seeds × 2 methods × 2 NFE) entered the full formal 50k evaluation. No smoke or early metric result was used as an inclusion or exclusion criterion. + +## Final artifacts + +- `evaluation/q128_formal_job_status.csv`: 12 completed jobs. +- `results/q128_256k_formal/evaluation_results.csv`: 24 validated metric rows. +- `results/q128_256k_formal/paired_differences.csv`: 12 fixed/global paired differences. diff --git a/evaluation_job_count.md b/evaluation_job_count.md new file mode 100644 index 00000000..040c5df0 --- /dev/null +++ b/evaluation_job_count.md @@ -0,0 +1,21 @@ +# Frozen matrix evaluation job count + +An evaluation **job** is one checkpoint at one NFE setting. Each job runs two +metrics, so every job produces two metric records. This count excludes an +optional single-checkpoint smoke, which is diagnostic only and is not part of +either frozen matrix. + +| Matrix | Budget (kimg) | Checkpoints | NFE settings | Jobs | Metrics/job | Metric records | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | +| q=256 budget | 512 | 6 | 2 | 12 | 2 (KID/FID-5k) | 24 | +| q=256 budget | 768 | 6 | 2 | 12 | 2 (KID/FID-5k) | 24 | +| q=256 budget | 1024 | 6 | 2 | 12 | 2 (KID/FID-50k) | 24 | +| q=256 budget subtotal | — | 18 | 2 | 36 | — | 72 | +| q=128 confirmatory | 256 | 6 | 2 | 12 | 2 (KID/FID-50k) | 24 | +| **Total** | — | **24** | — | **48** | — | **96** | + +The 24 checkpoints are exactly seeds 3/4/5 × fixed/global110 across the +predeclared budgets. All 48 jobs are required: a failed or missing job leaves +its matrix incomplete. The six q=256 1024-kimg jobs per NFE (12 jobs total) +and all q=128 jobs are formal; quick results from the q=256 512/768-kimg +contracts cannot decide the q=256 formal set. diff --git a/figures/g_screen_q128_s3_response.pdf b/figures/g_screen_q128_s3_response.pdf new file mode 100644 index 00000000..5f923d01 Binary files /dev/null and b/figures/g_screen_q128_s3_response.pdf differ diff --git a/figures/g_screen_q128_s3_response.png b/figures/g_screen_q128_s3_response.png new file mode 100644 index 00000000..49b676f1 Binary files /dev/null and b/figures/g_screen_q128_s3_response.png differ diff --git a/figures/q256_effect_heterogeneity.pdf b/figures/q256_effect_heterogeneity.pdf new file mode 100644 index 00000000..6007c394 Binary files /dev/null and b/figures/q256_effect_heterogeneity.pdf differ diff --git a/figures/q256_paired_seed_plot.pdf b/figures/q256_paired_seed_plot.pdf new file mode 100644 index 00000000..dbb3a693 Binary files /dev/null and b/figures/q256_paired_seed_plot.pdf differ diff --git a/figures/toy_condition_number.pdf b/figures/toy_condition_number.pdf new file mode 100644 index 00000000..34f75054 Binary files /dev/null and b/figures/toy_condition_number.pdf differ diff --git a/figures/toy_error_vs_g.pdf b/figures/toy_error_vs_g.pdf new file mode 100644 index 00000000..72b9ecac Binary files /dev/null and b/figures/toy_error_vs_g.pdf differ diff --git a/figures/toy_gstar_vs_budget.pdf b/figures/toy_gstar_vs_budget.pdf new file mode 100644 index 00000000..8f0de1de Binary files /dev/null and b/figures/toy_gstar_vs_budget.pdf differ diff --git a/figures/toy_lr_control.pdf b/figures/toy_lr_control.pdf new file mode 100644 index 00000000..eb1e347d Binary files /dev/null and b/figures/toy_lr_control.pdf differ diff --git a/figures/toy_separation.pdf b/figures/toy_separation.pdf new file mode 100644 index 00000000..bd81856e Binary files /dev/null and b/figures/toy_separation.pdf differ diff --git a/metrics/frechet_inception_distance.py b/metrics/frechet_inception_distance.py index 1d38ec73..ef0cd007 100644 --- a/metrics/frechet_inception_distance.py +++ b/metrics/frechet_inception_distance.py @@ -34,7 +34,10 @@ def compute_fid(opts, max_real, num_gen): return float('nan') m = np.square(mu_gen - mu_real).sum() - s, _ = scipy.linalg.sqrtm(np.dot(sigma_gen, sigma_real), disp=False) # pylint: disable=no-member + # ``disp`` was removed from SciPy's public ``sqrtm`` API in 1.18. + # Calling it without that legacy argument returns the matrix square root + # in both the older evaluator environments and current SciPy releases. + s = scipy.linalg.sqrtm(np.dot(sigma_gen, sigma_real)) # pylint: disable=no-member fid = np.real(m + np.trace(sigma_gen + sigma_real - s * 2)) return float(fid) diff --git a/metrics/kernel_inception_distance.py b/metrics/kernel_inception_distance.py index 3ac97892..954ceb25 100644 --- a/metrics/kernel_inception_distance.py +++ b/metrics/kernel_inception_distance.py @@ -15,7 +15,7 @@ #---------------------------------------------------------------------------- -def compute_kid(opts, max_real, num_gen, num_subsets, max_subset_size): +def compute_kid(opts, max_real, num_gen, num_subsets, max_subset_size, random_seed=None): # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz detector_url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt' detector_kwargs = dict(return_features=True) # Return raw features before the softmax layer. @@ -33,10 +33,11 @@ def compute_kid(opts, max_real, num_gen, num_subsets, max_subset_size): n = real_features.shape[1] m = min(min(real_features.shape[0], gen_features.shape[0]), max_subset_size) + rng = np.random if random_seed is None else np.random.RandomState(random_seed) t = 0 for _subset_idx in range(num_subsets): - x = gen_features[np.random.choice(gen_features.shape[0], m, replace=False)] - y = real_features[np.random.choice(real_features.shape[0], m, replace=False)] + x = gen_features[rng.choice(gen_features.shape[0], m, replace=False)] + y = real_features[rng.choice(real_features.shape[0], m, replace=False)] a = (x @ x.T / n + 1) ** 3 + (y @ y.T / n + 1) ** 3 b = (x @ y.T / n + 1) ** 3 t += (a.sum() - np.diag(a).sum()) / (m - 1) - b.sum() * 2 / m diff --git a/metrics/metric_main.py b/metrics/metric_main.py index 54f1c4a7..80af52de 100644 --- a/metrics/metric_main.py +++ b/metrics/metric_main.py @@ -94,9 +94,58 @@ def two_step_fid50k_full(opts): @register_metric def kid50k_full(opts): opts.dataset_kwargs.update(max_size=None, xflip=False) - kid = kernel_inception_distance.compute_kid(opts, max_real=1000000, num_gen=50000, num_subsets=100, max_subset_size=1000) + kid = kernel_inception_distance.compute_kid( + opts, + max_real=1000000, + num_gen=50000, + num_subsets=100, + max_subset_size=1000, + random_seed=opts.metric_seed, + ) return dict(kid50k_full=kid) +@register_metric +def fid5k_full(opts): + """5k generated-sample FID proxy against the full real dataset.""" + opts.dataset_kwargs.update(max_size=None, xflip=False) + fid = frechet_inception_distance.compute_fid(opts, max_real=None, num_gen=5000) + return dict(fid5k_full=fid) + +@register_metric +def kid5k_full(opts): + """5k generated-sample KID proxy against the full real dataset.""" + opts.dataset_kwargs.update(max_size=None, xflip=False) + kid = kernel_inception_distance.compute_kid( + opts, + max_real=None, + num_gen=5000, + num_subsets=100, + max_subset_size=1000, + random_seed=opts.metric_seed, + ) + return dict(kid5k_full=kid) + +@register_metric +def fid512_full(opts): + """512 generated-sample FID metric for pipeline smoke testing.""" + opts.dataset_kwargs.update(max_size=None, xflip=False) + fid = frechet_inception_distance.compute_fid(opts, max_real=None, num_gen=512) + return dict(fid512_full=fid) + +@register_metric +def kid512_full(opts): + """512 generated-sample KID metric for pipeline smoke testing.""" + opts.dataset_kwargs.update(max_size=None, xflip=False) + kid = kernel_inception_distance.compute_kid( + opts, + max_real=None, + num_gen=512, + num_subsets=100, + max_subset_size=512, + random_seed=opts.metric_seed, + ) + return dict(kid512_full=kid) + @register_metric def pr50k3_full(opts): opts.dataset_kwargs.update(max_size=None, xflip=False) diff --git a/metrics/metric_utils.py b/metrics/metric_utils.py index 8753c16e..8217b211 100644 --- a/metrics/metric_utils.py +++ b/metrics/metric_utils.py @@ -19,7 +19,11 @@ #---------------------------------------------------------------------------- class MetricOptions: - def __init__(self, generator_fn=None, G=None, G_kwargs={}, dataset_kwargs={}, num_gpus=1, rank=0, device=None, progress=None, cache=True): + def __init__( + self, generator_fn=None, G=None, G_kwargs={}, dataset_kwargs={}, + num_gpus=1, rank=0, device=None, progress=None, cache=True, + sample_seeds=None, metric_seed=None, + ): assert 0 <= rank < num_gpus self.generator_fn = generator_fn @@ -31,6 +35,8 @@ def __init__(self, generator_fn=None, G=None, G_kwargs={}, dataset_kwargs={}, nu self.device = device if device is not None else torch.device('cuda', rank) self.progress = progress.sub() if progress is not None and rank == 0 else ProgressMonitor() self.cache = cache + self.sample_seeds = None if sample_seeds is None else list(sample_seeds) + self.metric_seed = metric_seed #---------------------------------------------------------------------------- @@ -231,6 +237,16 @@ def compute_feature_stats_for_dataset(opts, detector_url, detector_kwargs, rel_l #---------------------------------------------------------------------------- +def make_seeded_latents(sample_seeds, shape): + """Create one CPU float64 latent per explicit per-sample seed.""" + latents = [] + for seed in sample_seeds: + generator = torch.Generator(device='cpu').manual_seed(int(seed)) + latents.append(torch.randn(shape, generator=generator, dtype=torch.float64)) + return torch.stack(latents) + +#---------------------------------------------------------------------------- + def compute_feature_stats_for_generator(opts, detector_url, detector_kwargs, rel_lo=0, rel_hi=1, batch_size=128, batch_gen=128, jit=False, **stats_kwargs): if batch_gen is None: batch_gen = min(batch_size, 4) @@ -243,8 +259,11 @@ def compute_feature_stats_for_generator(opts, detector_url, detector_kwargs, rel dataset = dnnlib.util.construct_class_by_name(**opts.dataset_kwargs) # Image generation func. - def run_generator(z, c): - img = generator_fn(G, z, c, **opts.G_kwargs) + def run_generator(z, c, sample_seeds=None): + kwargs = dict(opts.G_kwargs) + if sample_seeds is not None: + kwargs['sample_seeds'] = sample_seeds + img = generator_fn(G, z, c, **kwargs) img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8) return img @@ -258,17 +277,43 @@ def run_generator(z, c): # Initialize. stats = FeatureStats(**stats_kwargs) assert stats.max_items is not None + if opts.sample_seeds is not None: + if opts.num_gpus != 1: + raise ValueError('explicit sample_seeds currently require num_gpus=1') + if len(opts.sample_seeds) != stats.max_items: + raise ValueError( + f'explicit sample_seeds has {len(opts.sample_seeds)} entries, ' + f'but this metric requires {stats.max_items} generated samples' + ) progress = opts.progress.sub(tag='generator features', num_items=stats.max_items, rel_lo=rel_lo, rel_hi=rel_hi) detector = get_feature_detector(url=detector_url, device=opts.device, num_gpus=opts.num_gpus, rank=opts.rank, verbose=progress.verbose) # Main loop. + seed_offset = 0 while not stats.is_full(): images = [] for _i in range(batch_size // batch_gen): - z = torch.randn([batch_gen, G.img_channels, G.img_resolution, G.img_resolution], device=opts.device) - c = [dataset.get_label(np.random.randint(len(dataset))) for _i in range(batch_gen)] + current_batch = min(batch_gen, stats.max_items - stats.num_items - len(images) * batch_gen) + if current_batch <= 0: + break + batch_sample_seeds = None + if opts.sample_seeds is None: + z = torch.randn( + [current_batch, G.img_channels, G.img_resolution, G.img_resolution], + device=opts.device, + ) + label_indices = [np.random.randint(len(dataset)) for _i in range(current_batch)] + else: + batch_sample_seeds = opts.sample_seeds[seed_offset:seed_offset + current_batch] + z = make_seeded_latents( + batch_sample_seeds, + [G.img_channels, G.img_resolution, G.img_resolution], + ).to(opts.device) + label_indices = [int(seed) % len(dataset) for seed in batch_sample_seeds] + seed_offset += current_batch + c = [dataset.get_label(index) for index in label_indices] c = torch.from_numpy(np.stack(c)).pin_memory().to(opts.device) - images.append(run_generator(z, c)) + images.append(run_generator(z, c, sample_seeds=batch_sample_seeds)) images = torch.cat(images) if images.shape[1] == 1: images = images.repeat([1, 3, 1, 1]) diff --git a/prepare_data.sh b/prepare_data.sh new file mode 100755 index 00000000..7d9bf252 --- /dev/null +++ b/prepare_data.sh @@ -0,0 +1,147 @@ +#!/usr/bin/env bash + +# Download CIFAR-10, verify the official archive, and convert it to EDM ZIP format. + +set -Eeuo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +ENV_NAME="${ECT_ENV_NAME:-ect}" +PROJECT_ROOT="${ECT_PROJECT_ROOT:-/mnt/ect_project}" +URL="${ECT_CIFAR10_URL:-https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz}" +TARBALL="${ECT_CIFAR10_TARBALL:-${PROJECT_ROOT}/datasets/cifar-10-python.tar.gz}" +OUTPUT="${ECT_DATA_PATH:-${PROJECT_ROOT}/datasets/cifar10-32x32.zip}" +REPORT_PATH="${ECT_DATA_REPORT:-${PROJECT_ROOT}/runs/day2/dataset.json}" +EXPECTED_MD5="c58f30108f718f92721af3b95e74349a" +CHECK_ONLY=0 +FORCE=0 + +fail() { + printf '[prepare_data] ERROR: %s\n' "$*" >&2 + exit 1 +} + +run_python() { + if [[ "${CONDA_DEFAULT_ENV:-}" == "${ENV_NAME}" ]]; then + python "$@" + elif command -v conda >/dev/null 2>&1; then + conda run --no-capture-output -n "${ENV_NAME}" python "$@" + elif command -v mamba >/dev/null 2>&1; then + mamba run --no-capture-output -n "${ENV_NAME}" python "$@" + else + fail "activate '${ENV_NAME}' or install conda/mamba first" + fi +} + +usage() { + cat <<'EOF' +Usage: bash prepare_data.sh [options] + + --output PATH Output dataset ZIP + --tarball PATH CIFAR-10 Python tarball (basename must stay unchanged) + --url URL Download URL + --check-only Verify the prepared dataset without downloading + --force Rebuild the output dataset + --report PATH Validation report (default: /mnt/ect_project/runs/day2/dataset.json) + -h, --help Show this help +EOF +} + +while [[ $# -gt 0 ]]; do + case "$1" in + --output) + [[ $# -ge 2 ]] || fail "--output requires a value" + OUTPUT="$2" + shift 2 + ;; + --tarball) + [[ $# -ge 2 ]] || fail "--tarball requires a value" + TARBALL="$2" + shift 2 + ;; + --url) + [[ $# -ge 2 ]] || fail "--url requires a value" + URL="$2" + shift 2 + ;; + --check-only) + CHECK_ONLY=1 + shift + ;; + --force) + FORCE=1 + shift + ;; + --report) + [[ $# -ge 2 ]] || fail "--report requires a value" + REPORT_PATH="$2" + shift 2 + ;; + -h|--help) + usage + exit 0 + ;; + *) + fail "unknown option: $1" + ;; + esac +done + +VERIFY_ARGS=( + "${ROOT_DIR}/scripts/verify_assets.py" + dataset + --path "${OUTPUT}" + --tarball "${TARBALL}" + --expected-md5 "${EXPECTED_MD5}" + --expected-count 50000 + --expected-labels 50000 + --expected-resolution 32 + --output "${REPORT_PATH}" +) + +if [[ "${CHECK_ONLY}" -eq 1 ]]; then + run_python "${VERIFY_ARGS[@]}" + exit 0 +fi + +if [[ -f "${OUTPUT}" && "${FORCE}" -eq 0 ]]; then + printf '[prepare_data] Dataset already exists; verifying it: %s\n' "${OUTPUT}" + run_python "${VERIFY_ARGS[@]}" + exit 0 +fi + +[[ "$(basename "${TARBALL}")" == "cifar-10-python.tar.gz" ]] || \ + fail "the tarball basename must be cifar-10-python.tar.gz for dataset_tool.py" +mkdir -p "$(dirname "${TARBALL}")" "$(dirname "${OUTPUT}")" "$(dirname "${REPORT_PATH}")" + +if [[ ! -f "${TARBALL}" ]]; then + printf '[prepare_data] Downloading CIFAR-10...\n' + PARTIAL="${TARBALL}.part" + if command -v curl >/dev/null 2>&1; then + curl --fail --location --retry 3 --continue-at - --output "${PARTIAL}" "${URL}" + elif command -v wget >/dev/null 2>&1; then + wget --continue --tries=3 --output-document="${PARTIAL}" "${URL}" + else + fail "curl or wget is required" + fi + mv "${PARTIAL}" "${TARBALL}" +fi + +if command -v md5sum >/dev/null 2>&1; then + ACTUAL_MD5="$(md5sum "${TARBALL}" | awk '{print $1}')" +elif command -v md5 >/dev/null 2>&1; then + ACTUAL_MD5="$(md5 -q "${TARBALL}")" +else + fail "md5sum or md5 is required for archive verification" +fi +[[ "${ACTUAL_MD5}" == "${EXPECTED_MD5}" ]] || \ + fail "CIFAR-10 MD5 mismatch: expected ${EXPECTED_MD5}, got ${ACTUAL_MD5}" + +if [[ -e "${OUTPUT}" ]]; then + [[ "${FORCE}" -eq 1 ]] || fail "output already exists: ${OUTPUT}" + rm -f "${OUTPUT}" +fi + +printf '[prepare_data] Converting CIFAR-10 to EDM format...\n' +run_python "${ROOT_DIR}/dataset_tool.py" --source "${TARBALL}" --dest "${OUTPUT}" +run_python "${VERIFY_ARGS[@]}" +printf '[prepare_data] Dataset ready: %s\n' "${OUTPUT}" diff --git a/results/adaptive_v1_activation_a100_5344a5c9/README.md b/results/adaptive_v1_activation_a100_5344a5c9/README.md new file mode 100644 index 00000000..7a80f30b --- /dev/null +++ b/results/adaptive_v1_activation_a100_5344a5c9/README.md @@ -0,0 +1,53 @@ +# Adaptive v1 activation — canonical A100 paired evidence + +This directory records the clean single-GPU `adaptive_v1` activation arm of the +Role B paired campaign at commit +`5344a5c97ab461b640ad5c5413cbf57eec527c2a`, rerun in the Role A frozen +`ect-clean-validation` runtime (Python 3.9.18 / PyTorch 2.3.0 / CUDA 12.1) +against canonical dataset `08c9ed1b2b1c…` and transfer `4d5dcc1f1d0d…`. +Counterpart: `results/sigmoid_activation_a100_5344a5c9/`. Intent: paired activation evidence on frozen knobs. + +## Status + +| Property | Value | +| --- | --- | +| Evidence class | `formal_candidate` | +| Device | NVIDIA A100-PCIE-40GB (1 GPU) | +| Runtime | Python 3.9.18 / PyTorch 2.3.0 / CUDA 12.1 (`ect-clean-validation`) | +| Mode / schedule | `activation` / `adaptive_v1` | +| Duration / progress | 0.004 Mimg / 4.096 kimg | +| Dataset archive SHA-256 | `08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372` (canonical) | +| Transfer SHA-256 | `4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da` | +| Seed / batch / batch-gpu | 0 / 128 / 16 | +| Attempted / successful / skipped | 32 / 23 / 9 | +| AMP | enabled; GradScaler state saved | +| Recorded training and packaging worktrees | clean, at `5344a5c…` | +| Loss finiteness | 0 NaN / 0 Inf in `train_summary.csv` | +| Run outdir | `/root/ect-runs/paired-training-v1-canonical/adaptive-v1-activation-5344a5c9-20260721T034515Z` | + +The Collector loaded the latest network snapshot and training state and +passed the activation gate. Final controller state: 8 signal updates; first +nonzero correction at iteration 12; first adapted pair at iteration 13. +Final CSV `next_loop_cur_tick=2`. + +The console's initial maintenance report can display `loss nan` before the +statistics collector is updated; packaged CSV losses are finite. + +## Contents + +```text +results/adaptive_v1_activation_a100_5344a5c9/ +├── README.md +├── metadata.json +└── train_summary.csv +``` + +The checkpoint, network snapshot, raw log, source dataset, and transfer pickle +remain outside Git. See also `results/paired_comparison_a100_5344a5c9.{md,json}`. + +## Local artifact hashes + +```text +2979ccbf2bf19526cfef4bdd3dd3666022831954a6e0b0a3df25c896799c9131 metadata.json +95a44e8044e95408be2b5ba6cb15cb1a002b67f19de3a2e567e537b87c640132 train_summary.csv +``` diff --git a/results/adaptive_v1_activation_a100_5344a5c9/metadata.json b/results/adaptive_v1_activation_a100_5344a5c9/metadata.json new file mode 100644 index 00000000..a4ef882c --- /dev/null +++ b/results/adaptive_v1_activation_a100_5344a5c9/metadata.json @@ -0,0 +1,57 @@ +{ + "activation_gate_applied": true, + "activation_gate_passed": true, + "attempted_iterations": 32, + "batch_gpu": 16, + "cuda_version": "12.1", + "dataset_sha256": "08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372", + "duration_mimg": 0.004, + "evidence_class": "formal_candidate", + "exact_command": "python /mnt/recurrence_of_ect/ct_train.py --data=/mnt/ect_project/datasets/cifar10-32x32.zip --outdir=/root/ect-runs/paired-training-v1-canonical/adaptive-v1-activation-5344a5c9-20260721T034515Z --nosubdir --cond=False --arch=ddpmpp --precond=ect --batch=128 --batch-gpu=16 --optim=RAdam --lr=0.0001 --dropout=0.2 --augment=0 --mapping=adaptive_v1 -q 256 -k 8 -b 1 -c 0 --double=10000 --ema_beta=0.9993 --seed=0 --fp16=True --enable_amp=True --metrics=none --transfer=/mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl --duration=0.004", + "final_adaptive_active": true, + "final_grad_scale": 128.0, + "final_loss": 30.84132099, + "final_signal_updates": 8, + "first_adapted_pair_iteration": 13, + "first_loss": 16.016312, + "first_nonzero_correction_iteration": 12, + "first_schedule_telemetry_iteration": 1, + "git_branch": "HEAD", + "git_commit": "5344a5c97ab461b640ad5c5413cbf57eec527c2a", + "git_dirty": false, + "global_batch": 128, + "gpu_count": 1, + "gpu_name": "NVIDIA A100-PCIE-40GB", + "gradscaler_state_saved": true, + "inf_count": 0, + "initial_grad_scale": 65536.0, + "max_loss": 36.83664274, + "metrics_enabled": false, + "min_loss": 15.02882767, + "mode": "activation", + "nan_count": 0, + "network_snapshot": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-activation-5344a5c9-20260721T034515Z/network-snapshot-latest.pkl", + "network_snapshot_sha256": "23a8653c34f0087561fdea250ce8501489c516119f7b7dc7f5ea5c03ae3626c4", + "packaging_git_branch": "HEAD", + "packaging_git_commit": "5344a5c97ab461b640ad5c5413cbf57eec527c2a", + "packaging_git_dirty": false, + "peak_vram_mib": 5916.780544, + "peak_vram_source": "torch.cuda.max_memory_allocated", + "platform": "Linux-5.15.0-56-generic-x86_64-with-glibc2.35", + "processed_kimg": 4.096, + "python_version": "3.9.18", + "schedule": "adaptive_v1", + "schedule_telemetry_available": true, + "schedule_telemetry_columns_available": true, + "schedule_telemetry_coverage": 1.0, + "schedule_telemetry_rows": 32, + "schedule_telemetry_total_rows": 32, + "seed": 0, + "skipped_steps": 9, + "successful_optimizer_steps": 23, + "torch_version": "2.3.0", + "training_state": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-activation-5344a5c9-20260721T034515Z/training-state-latest.pt", + "training_state_sha256": "ef2c93f58d7b9df944504fde66e361363312361c49fbb32ffbd0dd7fe5ad2c71", + "transfer_sha256": "4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da", + "wall_time_seconds": 34.061695 +} diff --git a/results/adaptive_v1_activation_a100_5344a5c9/train_summary.csv b/results/adaptive_v1_activation_a100_5344a5c9/train_summary.csv new file mode 100644 index 00000000..e9f06783 --- /dev/null +++ b/results/adaptive_v1_activation_a100_5344a5c9/train_summary.csv @@ -0,0 +1,33 @@ +attempted_iteration,successful_optimizer_steps,processed_kimg,loss,grad_scale,step_skipped,schedule,stage,next_loop_cur_tick,loss_ema,loss_reference,correction,signal_updates,adaptive_active,r_over_t_mean,gap_mean,seconds,peak_vram_mib 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a/results/adaptive_v1_activation_a100_cb84a934/README.md b/results/adaptive_v1_activation_a100_cb84a934/README.md new file mode 100644 index 00000000..a06b6fe5 --- /dev/null +++ b/results/adaptive_v1_activation_a100_cb84a934/README.md @@ -0,0 +1,53 @@ +# Adaptive v1 activation — clean A100 evidence + +This directory records the clean single-GPU activation validation of +`adaptive_v1` at commit `cb84a93454a91500d01433dd2d024d775fb275ef`, rerun +against the canonical CIFAR-10 archive +`08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372`. +It validates controller activation and checkpoint/CSV consistency; it is **not** +a fixed-vs-adaptive quality comparison, stability result, or baseline result. + +## Status + +| Property | Value | +| --- | --- | +| Evidence class | `formal_candidate` | +| Device | NVIDIA A100-PCIE-40GB (1 GPU) | +| Runtime | Python 3.9.18 / PyTorch 2.3.0 / CUDA 12.1 | +| Mode / schedule | `activation` / `adaptive_v1` | +| Duration / progress | 0.004 Mimg / 4.096 kimg | +| Dataset archive SHA-256 | `08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372` (canonical) | +| Attempted / successful / skipped | 32 / 23 / 9 | +| AMP | enabled; GradScaler state saved | +| Recorded training and packaging worktrees | clean, at `cb84a934...` | +| Loss finiteness | 0 NaN / 0 Inf in `train_summary.csv` | + +The Collector loaded both the latest network snapshot and training state and +passed the activation gate. The final controller state recorded eight signal +updates, first produced a nonzero correction at iteration 12, and first +affected a pair at iteration 13. The final row records +`next_loop_cur_tick=2`, matching the saved next-loop training-state tick. + +The console's initial maintenance report can display `loss nan` before the +statistics collector is updated. The packaged CSV records finite losses, and +the Collector's `nan_count` and `inf_count` are both zero. + +## Contents + +```text +results/adaptive_v1_activation_a100_cb84a934/ +├── README.md +├── metadata.json +└── train_summary.csv +``` + +The checkpoint, network snapshot, raw log, source dataset, and transfer pickle +remain outside Git. `metadata.json` records their original paths and SHA-256 +digests where applicable. + +## Local artifact hashes + +```text +c60c7c9b0086c663b92a4f61b3d7a79409dd48d64e563b49b947b8f2c272047f metadata.json +a052b55d7f04a49bfc1ea6f8689c4d57b1c6855e3cfb4095aa9cc42e8363f87c train_summary.csv +``` diff --git a/results/adaptive_v1_activation_a100_cb84a934/metadata.json b/results/adaptive_v1_activation_a100_cb84a934/metadata.json new file mode 100644 index 00000000..2e16fc13 --- /dev/null +++ b/results/adaptive_v1_activation_a100_cb84a934/metadata.json @@ -0,0 +1,57 @@ +{ + "activation_gate_applied": true, + "activation_gate_passed": true, + 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"first_loss": 15.9766382, + "first_nonzero_correction_iteration": 12, + "first_schedule_telemetry_iteration": 1, + "git_branch": "HEAD", + "git_commit": "cb84a93454a91500d01433dd2d024d775fb275ef", + "git_dirty": false, + "global_batch": 128, + "gpu_count": 1, + "gpu_name": "NVIDIA A100-PCIE-40GB", + "gradscaler_state_saved": true, + "inf_count": 0, + "initial_grad_scale": 65536.0, + "max_loss": 37.18239355, + "metrics_enabled": false, + "min_loss": 15.02455795, + "mode": "activation", + "nan_count": 0, + "network_snapshot": "/root/ect-runs/paired-training-v1/adaptive-v1-activation-cb84a934-20260720T153954Z/network-snapshot-latest.pkl", + "network_snapshot_sha256": "6908235f86468cd571ad0e574b6cba368d78977a82842d1a1a31625c11940ce7", + "packaging_git_branch": "HEAD", + "packaging_git_commit": "cb84a93454a91500d01433dd2d024d775fb275ef", + "packaging_git_dirty": false, + "peak_vram_mib": 5916.343296, + "peak_vram_source": "torch.cuda.max_memory_allocated", + "platform": "Linux-5.15.0-56-generic-x86_64-with-glibc2.35", + "processed_kimg": 4.096, + "python_version": "3.9.18", + "schedule": "adaptive_v1", + "schedule_telemetry_available": true, + "schedule_telemetry_columns_available": true, + "schedule_telemetry_coverage": 1.0, + "schedule_telemetry_rows": 32, + "schedule_telemetry_total_rows": 32, + "seed": 0, + "skipped_steps": 9, + "successful_optimizer_steps": 23, + "torch_version": "2.3.0", + "training_state": "/root/ect-runs/paired-training-v1/adaptive-v1-activation-cb84a934-20260720T153954Z/training-state-latest.pt", + "training_state_sha256": "274de4a97c5c0965ed39e3de66b743783309be4915e1ac28f3bcd5925e80a842", + "transfer_sha256": "4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da", + "wall_time_seconds": 30.785647 +} diff --git a/results/adaptive_v1_activation_a100_cb84a934/train_summary.csv b/results/adaptive_v1_activation_a100_cb84a934/train_summary.csv new file mode 100644 index 00000000..95cd370f --- /dev/null +++ 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+31,22,3.968000,29.66158175,128,false,adaptive_v1,0,1,22.1034703018,16.2829611748,-0.0148221508055,7,1,0.970963642628,0.0290363573718,30.064818,2562.343936 +32,23,4.096000,31.16120934,128,false,adaptive_v1,0,2,22.948574617,16.2829611748,-0.0165137365832,8,1,0.97072027452,0.0292797254804,30.785647,2562.343936 diff --git a/results/adaptive_v1_stability_a100_5344a5c9/README.md b/results/adaptive_v1_stability_a100_5344a5c9/README.md new file mode 100644 index 00000000..1305701f --- /dev/null +++ b/results/adaptive_v1_stability_a100_5344a5c9/README.md @@ -0,0 +1,53 @@ +# Adaptive v1 stability — canonical A100 paired evidence (fresh 16 kimg) + +This directory records the clean single-GPU `adaptive_v1` stability arm of the +Role B paired campaign at commit +`5344a5c97ab461b640ad5c5413cbf57eec527c2a`, rerun in the Role A frozen +`ect-clean-validation` runtime (Python 3.9.18 / PyTorch 2.3.0 / CUDA 12.1) +against canonical dataset `08c9ed1b2b1c…` and transfer `4d5dcc1f1d0d…`. +Counterpart: `results/sigmoid_stability_a100_5344a5c9/`. Intent: independent fresh 16 kimg stability evidence (not an activation→stability resume). + +## Status + +| Property | Value | +| --- | --- | +| Evidence class | `formal_candidate` | +| Device | NVIDIA A100-PCIE-40GB (1 GPU) | +| Runtime | Python 3.9.18 / PyTorch 2.3.0 / CUDA 12.1 (`ect-clean-validation`) | +| Mode / schedule | `stability` / `adaptive_v1` | +| Duration / progress | 0.016 Mimg / 16.0 kimg | +| Dataset archive SHA-256 | `08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372` (canonical) | +| Transfer SHA-256 | `4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da` | +| Seed / batch / batch-gpu | 0 / 128 / 16 | +| Attempted / successful / skipped | 125 / 116 / 9 | +| AMP | enabled; GradScaler state saved | +| Recorded training and packaging worktrees | clean, at `5344a5c…` | +| Loss finiteness | 0 NaN / 0 Inf in `train_summary.csv` | +| Run outdir | `/root/ect-runs/paired-training-v1-canonical/adaptive-v1-stability-5344a5c9-20260721T034800Z` | + +**Fresh run (not resume):** `exact_command` uses `--transfer=...` and +`--duration=0.016` with no `--resume`. This is an independent fresh 16 kimg +stability arm, not a continuation of the activation training-state. + +The Collector loaded the latest network snapshot and training state. +Final controller state: 32 signal updates; first nonzero correction at +iteration 12; first adapted pair at iteration 13. + +## Contents + +```text +results/adaptive_v1_stability_a100_5344a5c9/ +├── README.md +├── metadata.json +└── train_summary.csv +``` + +The checkpoint, network snapshot, raw log, source dataset, and transfer pickle +remain outside Git. See also `results/paired_comparison_a100_5344a5c9.{md,json}`. + +## Local artifact hashes + +```text +461dc741618c2a6ed1785722ebf29411bc806c6ba585a8cc6956ab391e55908c metadata.json +40a12e6816145cc1f7a0d93a873d2b53fabdbf324ca9820087989319f94770e5 train_summary.csv +``` diff --git a/results/adaptive_v1_stability_a100_5344a5c9/metadata.json b/results/adaptive_v1_stability_a100_5344a5c9/metadata.json new file mode 100644 index 00000000..885406b7 --- /dev/null +++ b/results/adaptive_v1_stability_a100_5344a5c9/metadata.json @@ -0,0 +1,57 @@ +{ + "activation_gate_applied": false, + "activation_gate_passed": null, + "attempted_iterations": 125, + "batch_gpu": 16, + "cuda_version": "12.1", + "dataset_sha256": "08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372", + "duration_mimg": 0.016, + "evidence_class": "formal_candidate", + "exact_command": "python /mnt/recurrence_of_ect/ct_train.py --data=/mnt/ect_project/datasets/cifar10-32x32.zip --outdir=/root/ect-runs/paired-training-v1-canonical/adaptive-v1-stability-5344a5c9-20260721T034800Z --nosubdir --cond=False --arch=ddpmpp --precond=ect --batch=128 --batch-gpu=16 --optim=RAdam --lr=0.0001 --dropout=0.2 --augment=0 --mapping=adaptive_v1 -q 256 -k 8 -b 1 -c 0 --double=10000 --ema_beta=0.9993 --seed=0 --fp16=True --enable_amp=True --metrics=none --transfer=/mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl --duration=0.016", + "final_adaptive_active": true, + "final_grad_scale": 128.0, + "final_loss": 26.70842981, + "final_signal_updates": 32, + "first_adapted_pair_iteration": 13, + "first_loss": 15.9766382, + "first_nonzero_correction_iteration": 12, + "first_schedule_telemetry_iteration": 1, + "git_branch": "HEAD", + "git_commit": "5344a5c97ab461b640ad5c5413cbf57eec527c2a", + "git_dirty": false, + "global_batch": 128, + "gpu_count": 1, + "gpu_name": "NVIDIA A100-PCIE-40GB", + "gradscaler_state_saved": true, + "inf_count": 0, + "initial_grad_scale": 65536.0, + "max_loss": 37.18239355, + "metrics_enabled": false, + "min_loss": 15.02455795, + "mode": "stability", + "nan_count": 0, + "network_snapshot": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-stability-5344a5c9-20260721T034800Z/network-snapshot-latest.pkl", + "network_snapshot_sha256": "7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a", + "packaging_git_branch": "HEAD", + "packaging_git_commit": "5344a5c97ab461b640ad5c5413cbf57eec527c2a", + "packaging_git_dirty": false, + "peak_vram_mib": 5916.343296, + "peak_vram_source": "torch.cuda.max_memory_allocated", + "platform": "Linux-5.15.0-56-generic-x86_64-with-glibc2.35", + "processed_kimg": 16.0, + "python_version": "3.9.18", + "schedule": "adaptive_v1", + "schedule_telemetry_available": true, + "schedule_telemetry_columns_available": true, + "schedule_telemetry_coverage": 1.0, + "schedule_telemetry_rows": 125, + "schedule_telemetry_total_rows": 125, + "seed": 0, + "skipped_steps": 9, + "successful_optimizer_steps": 116, + "torch_version": "2.3.0", + "training_state": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-stability-5344a5c9-20260721T034800Z/training-state-latest.pt", + "training_state_sha256": "269e2ec3b0b4edadddd2bc251338995962b31ea206106b0bfa24083e7210fd83", + "transfer_sha256": "4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da", + "wall_time_seconds": 104.959755 +} diff --git a/results/adaptive_v1_stability_a100_5344a5c9/train_summary.csv b/results/adaptive_v1_stability_a100_5344a5c9/train_summary.csv new file mode 100644 index 00000000..45cb638d --- /dev/null +++ b/results/adaptive_v1_stability_a100_5344a5c9/train_summary.csv @@ -0,0 +1,126 @@ +attempted_iteration,successful_optimizer_steps,processed_kimg,loss,grad_scale,step_skipped,schedule,stage,next_loop_cur_tick,loss_ema,loss_reference,correction,signal_updates,adaptive_active,r_over_t_mean,gap_mean,seconds,peak_vram_mib 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Initial evidence packaging commit: `9617942166154dc8a91f6070073fa868ca9ac3da` +- Evaluation environment: `ect-clean-validation` +- Runtime: Python 3.9.18, PyTorch 2.3.0, CUDA 12.1 +- GPU: NVIDIA A100-PCIE-40GB +- Data access: compact grids and metadata are committed; individual PNGs and full logs remain on persistent storage + +## Checkpoints + +| Method | Persistent checkpoint | SHA-256 | +| --- | --- | --- | +| Sigmoid fixed schedule | `/mnt/ect_project/checkpoints/paired_16k_5344a5c_canonical/sigmoid_16k_canonical.pkl` | `32aa4661584663c40bc05a3b5c5bef3bd4e8b60e289023d491fbe46fac5478ed` | +| Adaptive v1 schedule | `/mnt/ect_project/checkpoints/paired_16k_5344a5c_canonical/adaptive_v1_16k_canonical.pkl` | `7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a` | + +Both checkpoint hashes were verified immediately before sampling. + +## Evaluation protocol + +- Seeds: `0-63`, exactly once per mode +- NFE modes: `1` and `2` +- NFE=2 intermediate time: `mid_t=0.821` +- Precision: FP32 +- Device: CUDA +- Model forward batch size: 1 +- Primary work-group size: 8 +- Verification work-group size: 16 +- Repeat runs verified by the sampler: 2 +- Image format: RGB, 32×32 PNG +- FID-50k/KID-50k: not run + +The sampler generated every image once for work-group size 8, regenerated it +for work-group size 16, and generated it again for the repeat-run check. It +raises an error before writing final evidence if any corresponding pixels +differ. + +## Verified results + +| Method | NFE=1 images | NFE=2 images | Manifest entries | Total elapsed | Determinism | Work groups | +| --- | ---: | ---: | ---: | ---: | --- | --- | +| Sigmoid fixed schedule | 64 | 64 | 131 | 11.157285 s | PASS | 8/16 pixel-identical | +| Adaptive v1 schedule | 64 | 64 | 131 | 11.209927 s | PASS | 8/16 pixel-identical | + +Independent post-run validation confirmed: + +- both seed lists are exactly `0-63`, with no missing or duplicate seeds; +- all 256 individual outputs are RGB 32×32 PNGs; +- all four method/NFE groups contain exactly 64 images; +- all four 8×8 method grids are RGB 256×256 images; +- each result manifest contains exactly 128 individual images, two grids, and + one metadata file; +- every manifest entry matches the corresponding persistent file; +- both metadata files record the required evaluation commit, checkpoint hash, + NFE modes, `mid_t`, FP32 precision, A100 device, image counts, repeat-run + verification, and work-group verification. + +## Visual evidence + +Each method directory contains its NFE=1 and NFE=2 8×8 grid. The two comparison +grids arrange eight seed pairs per row and eight rows in total. Within every +pair, the Sigmoid/fixed output is on the left and the Adaptive v1 output is on +the right; seeds are ordered `0-63` row-major. + +| Comparison | SHA-256 | +| --- | --- | +| `comparison_nfe1_fixed_vs_adaptive.png` | `d95f4bdea4bb25f6cc735f60ed1df1182bb34f809a3e701ebcbc09fee8a10cc6` | +| `comparison_nfe2_fixed_vs_adaptive.png` | `52307e52e24505f09f47760142a140803b17aec81de3cd4354a43dc2180f092b` | + +For the same seed, the fixed and adaptive PNG hashes differ for all 64 seeds at +NFE=1 and all 64 seeds at NFE=2. This establishes that the two checkpoints do +not produce byte-identical outputs under the shared protocol. It does **not** +establish that either method has better generation quality. + +## Persistent evidence and manifest scope + +The complete evaluation output remains at: + +```text +/mnt/ect_project/evaluations/final_paired_16k/ +├── sigmoid_16k_canonical-32aa46615846/ +├── adaptive_v1_16k_canonical-7d162808dc98/ +├── comparison_nfe1_fixed_vs_adaptive.png +└── comparison_nfe2_fixed_vs_adaptive.png +``` + +The committed `sha256_manifest.txt` files describe the complete persistent +result directories, including the individual PNGs that are intentionally not +committed. Validate them on the evaluation node with: + +```bash +cd /mnt/ect_project/evaluations/final_paired_16k/sigmoid_16k_canonical-32aa46615846 +sha256sum -c sha256_manifest.txt + +cd /mnt/ect_project/evaluations/final_paired_16k/adaptive_v1_16k_canonical-7d162808dc98 +sha256sum -c sha256_manifest.txt +``` + +## Interpretation boundary + +This evaluation verifies checkpoint identity, deterministic fixed-seed +sampling, work-group independence, output completeness, and side-by-side visual +availability. No FID, KID, statistical quality comparison, or superiority claim +is included. Statistical fallacy scanning is not applicable because no +inferential statistics or quality metrics were computed. diff --git a/results/final_paired_16k/adaptive_v1_16k_canonical-7d162808dc98/metadata.json b/results/final_paired_16k/adaptive_v1_16k_canonical-7d162808dc98/metadata.json new file mode 100644 index 00000000..1816cac6 --- /dev/null +++ b/results/final_paired_16k/adaptive_v1_16k_canonical-7d162808dc98/metadata.json @@ -0,0 +1,116 @@ +{ + "schema_version": "1.0", + "evaluation_git_commit": "ef4aa3142eb2049bd5d541d18d7278cdf758029c", + "checkpoint_path": "/mnt/ect_project/checkpoints/paired_16k_5344a5c_canonical/adaptive_v1_16k_canonical.pkl", + "checkpoint_sha256": "7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a", + "checkpoint_id": "adaptive_v1_16k_canonical-7d162808dc98", + "output_directory": "/mnt/ect_project/evaluations/final_paired_16k/adaptive_v1_16k_canonical-7d162808dc98", + "seed_count": 64, + "seed_list": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63 + ], + "nfe_modes": [ + 1, + 2 + ], + "mid_t_by_mode": { + "nfe1": [], + "nfe2": [ + 0.821 + ] + }, + "precision_requested": "fp32", + "precision": "fp32", + "device": "cuda", + "gpu": "NVIDIA A100-PCIE-40GB", + "elapsed_seconds_total": 11.209927, + "elapsed_seconds_by_mode": { + "nfe1": 4.105832, + "nfe2": 7.10406 + }, + "image_count_total": 128, + "image_count_by_mode": { + "nfe1": 64, + "nfe2": 64 + }, + "generator_implementation": "ct_eval.generator_fn", + "model_forward_batch_size": 1, + "work_group_sizes_verified": [ + 8, + 16 + ], + "image_resolution": [ + 32, + 32 + ], + "image_channels": 3, + "determinism_passed": true, + "repeat_runs_verified": 2, + 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It contains 96 paired +trials spanning 3 training seeds, NFE=1/2, and fixed sample seeds 0-15. The A/B +placement is balanced 48/48. + +For each independent rater: + +1. Extract a fresh copy of the archive. +2. Use one stable anonymous `rater_id` for all rows in `ballot.csv`. +3. Inspect `trials/T001.png` through `trials/T096.png` and record exactly `A`, + `B`, or `TIE` in `preference_A_B_TIE`. +4. Return the complete CSV without inspecting or requesting the private key. + +The scoring protocol requires at least three complete 96-trial ballots. The +private unblinding key is intentionally not included in this repository handoff. + +SHA256 (`blind_public.zip`): +`fa64b8684482657588fe8341e5c5070cc445ce74de09e206383cf7130d038119` diff --git a/results/final_performance_evaluation/blind/blind_public.zip b/results/final_performance_evaluation/blind/blind_public.zip new file mode 100644 index 00000000..61c9d03e Binary files /dev/null and b/results/final_performance_evaluation/blind/blind_public.zip differ diff --git a/results/final_performance_evaluation/multibudget_role_a/FINAL_CONCLUSION.md b/results/final_performance_evaluation/multibudget_role_a/FINAL_CONCLUSION.md new file mode 100644 index 00000000..73c9de10 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/FINAL_CONCLUSION.md @@ -0,0 +1,33 @@ +# 最终结论：Fixed sigmoid vs Adaptive v1 + +## 研究问题与冻结协议 + +在相同训练 seed、训练预算和采样 NFE 下，Adaptive v1 是否表现出可重复的生成质量优势？比较覆盖 fixed sigmoid / Adaptive v1、训练 seeds 0/1/2、16/32/64 kimg、NFE=1/2（NFE=2 的 `mid_t=0.821`）。每个 checkpoint/NFE 使用 FP32 和固定采样 seeds 0–4999 生成 5,000 个样本。KID-5k 为主指标，FID-5k 为辅助 proxy；二者均越低越好。以下不是标准 FID-50k benchmark。 + +## 三 seed 配对结果 + +配对差值定义为 `Adaptive v1 - fixed sigmoid`，负值有利于 Adaptive。`A/F/T` 是 Adaptive 胜 / fixed 胜 / 平局的训练 seed 数。 + +| Budget | NFE | mean Δ KID ± SD | KID A/F/T | mean Δ FID ± SD | FID A/F/T | +| ---: | ---: | ---: | --- | ---: | --- | +| 16 | 1 | -0.000059 ± 0.008353 | 1/2/0 | -0.951515 ± 4.681037 | 1/2/0 | +| 16 | 2 | +0.001152 ± 0.000535 | 0/3/0 | +0.838204 ± 0.418109 | 0/3/0 | +| 32 | 1 | +0.001110 ± 0.000605 | 0/3/0 | +0.429639 ± 0.312042 | 0/3/0 | +| 32 | 2 | +0.003334 ± 0.003269 | 0/3/0 | +2.221021 ± 1.861612 | 0/3/0 | +| 64 | 1 | +0.000228 ± 0.000741 | 1/2/0 | +0.337120 ± 0.284930 | 0/3/0 | +| 64 | 2 | -0.000854 ± 0.007568 | 1/2/0 | -1.219666 ± 5.743797 | 1/2/0 | + +没有任何 budget/NFE 条件达到预先冻结的“至少 2/3 seeds 的主指标优于 fixed”门槛。18 个 KID 配对中 fixed 在 15 个更优；18 个 FID 配对中 fixed 在 16 个更优。16 kimg/NFE=1 和 64 kimg/NFE=2 的有利均值各自由单个 seed 驱动，没有跨 seed 复现；32 kimg 的两个 NFE、两个指标均为 0/3 Adaptive 胜。 + +## 训练稳定性与 controller + +六条连续轨迹均达到 64 kimg，每条 500 attempted iterations；所有记录 loss 有限，NaN/Inf 均为 0，`r/t` 与 gap 始终合法。Fixed 三条轨迹共有 29 个 AMP skipped steps，Adaptive 有 27 个。Adaptive correction 在 3/3 轨迹激活，未出现接近 `max_adjust=0.05` 的饱和步骤；三条轨迹的 correction 符号变化数为 0/2/0。因此 Adaptive v1 能稳定运行并改变 schedule，训练稳定性不比 fixed 差。 + +## 匿名视觉评价状态 + +64 kimg 的 24-pair 匿名 A/B 包已由 Role D 生成，但共享服务器上的 ballot 尚未填写；仓库中的 16 kimg 96-pair 包也没有返回的完整 ballot。因此本次数据冻结时没有可报告的匿名 A/B 偏好比例。此项明确记为待完成，不能伪造为 TIE 或据此支持任何方法。 + +## 最终回答 + +**负向：当前结果不支持“Adaptive v1 优于 fixed sigmoid”。** 闭环自适应框架在三条训练种子上可运行、会激活且保持稳定，但当前 loss-feedback 控制律没有转化为可重复的生成质量收益；在大多数配对中指标反而略差。由于只有三个训练 seeds、使用 5k proxy 且部分差值远小于 seed 间波动，本结论不应扩展成“fixed 在一般意义上显著更优”，只应表述为：在冻结实现、16/32/64 kimg 和 NFE=1/2 下，没有证据支持 Adaptive v1 的质量优势。 + diff --git a/results/final_performance_evaluation/multibudget_role_a/README.md b/results/final_performance_evaluation/multibudget_role_a/README.md new file mode 100644 index 00000000..2cc3a1ea --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/README.md @@ -0,0 +1,37 @@ +# Role A multibudget final-performance evaluation + +This directory archives the frozen 36-cell comparison: + +- methods: `sigmoid`, `adaptive_v1`; +- training seeds: 0, 1, 2; +- budgets: 16, 32, 64 kimg; +- sampling: NFE=1 and NFE=2 (`mid_t=0.821`); +- 5,000 fixed sample seeds per checkpoint/NFE, FP32; +- KID-5k primary and FID-5k auxiliary proxy. + +All values are **5k-sample proxy evaluation results, not a standard FID-50k benchmark**. + +## Main deliverables + +- `quantitative/per_seed_metrics.csv`: all 36 independent metric cells and checkpoint SHA256 values. +- `quantitative/paired_differences.csv`: 18 seed-paired Adaptive-minus-fixed comparisons. +- `quantitative/aggregate_results.csv`: three-seed means, sample SDs, and win counts. +- `quantitative/quality_vs_budget.png`: KID/FID budget curves split by NFE. +- `stability/training_stability.csv`: six continuous 64 kimg trajectory checks. +- `stability/controller_at_budget.csv`: controller telemetry at 16/32/64 kimg. +- `stability/controller_vs_budget.png`: correction and gap curves. +- `FINAL_CONCLUSION.md`: one-page decision record. +- `audit/`: checkpoint manifest, smoke/formal run manifests, raw metric JSONL files, per-budget summaries, and compact Role B training packages. + +## Validation record + +The smoke evaluated both methods at seed0/16 kimg, NFE=1/2, with 512 fixed samples and two metric repeats. FID repeats were bitwise identical. KID repeats differed only at GPU floating-point roundoff scale (maximum absolute difference `1.19e-7`), recorded as `repeat_results_exact=false` and accepted by the audited 1 ppm numerical-consistency check. Formal runs used one metric evaluation per cell and completed all 36 cells with a common reference dataset/statistics identity. + +Evaluation generation/metrics commit: `a66cb3d9caa3b24296a39e5fc9f4f03db21af8b5`. Training code anchor: `5344a5c97ab461b640ad5c5413cbf57eec527c2a`. CIFAR-10 archive SHA256: `08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372`. + +The first smoke launch failed before image generation because the non-interactive SSH `PATH` omitted the conda `torchrun` executable. The failed directory was preserved on the server; the accepted rerun used the same protocol and only prepended the existing conda environment to `PATH`. + +## Blind evaluation handoff + +The shared server contains Role D's empty 64 kimg 24-pair ballot at `/mnt/ect_project/final_evaluation/blind_64k_public/ballot.csv`. No completed anonymous ratings were available at data freeze, so no preference result is claimed here. + diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/checkpoints.json b/results/final_performance_evaluation/multibudget_role_a/audit/checkpoints.json new file mode 100644 index 00000000..581211e3 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/checkpoints.json @@ -0,0 +1,150 @@ +{ + "schema_version": 1, + "training_code_anchor": "5344a5c97ab461b640ad5c5413cbf57eec527c2a", + "cells": [ + { + "method": "adaptive_v1", + "training_seed": 0, + "budget_kimg": 16, + "checkpoint": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0016.pkl", + "checkpoint_sha256": "7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a", + "training_result_dir": "/mnt/ect_project/runs/packaged-traj64/adaptive_v1_traj64_seed0_5344a5c9" + }, + { + "method": "adaptive_v1", + 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+adaptive_v1,0,16,1,0.5948042273521423,484.78286903856355,7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a +adaptive_v1,0,16,2,0.2451242357492447,239.5964191589499,7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a +sigmoid,1,16,1,0.4374769330024719,381.84189640961847,3ffd271c99a3d09bc63c749f5b69f76af37bbbec54678e88885eb3341750e7ec +sigmoid,1,16,2,0.2847936749458313,278.5354496890211,3ffd271c99a3d09bc63c749f5b69f76af37bbbec54678e88885eb3341750e7ec +adaptive_v1,1,16,1,0.4278477430343628,375.53195760880396,b7bc291a80298121c091945519d79f1f8c79deb318b4790e2d7371a1510b7a13 +adaptive_v1,1,16,2,0.28532806038856506,278.94094193957403,b7bc291a80298121c091945519d79f1f8c79deb318b4790e2d7371a1510b7a13 +sigmoid,2,16,1,0.567756175994873,475.8780162559483,3fed09e540f8419db32bac9c1fdfe93033aaad225198ecc2bbce4eccf26b5865 +sigmoid,2,16,2,0.3588750958442688,330.29187425815263,3fed09e540f8419db32bac9c1fdfe93033aaad225198ecc2bbce4eccf26b5865 +adaptive_v1,2,16,1,0.5735194087028503,478.2201833557063,b04098a8d53449b3d2666f87b8d63e6f16ba11d6c2a5069c1d81d33d995f1d43 +adaptive_v1,2,16,2,0.36032578349113464,331.1610005332725,b04098a8d53449b3d2666f87b8d63e6f16ba11d6c2a5069c1d81d33d995f1d43 diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-16-summary/role_a_metrics.json b/results/final_performance_evaluation/multibudget_role_a/audit/formal-16-summary/role_a_metrics.json new file mode 100644 index 00000000..44304147 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-16-summary/role_a_metrics.json @@ -0,0 +1,130 @@ +{ + "schema_version": 1, + "protocol": "role-a-multibudget-quality-v1", + "phase": "formal", + "metric_mode": "both", + "sample_count_per_checkpoint_nfe": 5000, + "sample_seeds": "0-4999", + "precision": "fp32", + "dataset": "/mnt/ect_project/datasets/cifar10-32x32.zip", + "dataset_sha256": "08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372", + "reference_real_count": 50000, + "feature_detector_url": "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt", + "reference_identity_consistent": true, + "image_count_valid": true, + "repeat_results_exact": true, + "repeat_results_numerically_consistent": true, + "repeat_relative_tolerance": 1e-06, + "repeat_absolute_tolerance": 1e-12, + "row_count": 12, + "rows": [ + { + "Method": "sigmoid", + "Train seed": 0, + "Budget": 16, + "NFE": 1, + "KID": 0.5911149978637695, + "FID": 483.6696420342726, + "Checkpoint SHA": "32aa4661584663c40bc05a3b5c5bef3bd4e8b60e289023d491fbe46fac5478ed" + }, + { + "Method": "sigmoid", + "Train seed": 0, + "Budget": 16, + "NFE": 2, + "KID": 0.24365437030792236, + "FID": 238.35642663442655, + "Checkpoint SHA": "32aa4661584663c40bc05a3b5c5bef3bd4e8b60e289023d491fbe46fac5478ed" + }, + { + "Method": "adaptive_v1", + "Train seed": 0, + "Budget": 16, + "NFE": 1, + "KID": 0.5948042273521423, + "FID": 484.78286903856355, + "Checkpoint SHA": "7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a" + }, + { + "Method": "adaptive_v1", + "Train seed": 0, + "Budget": 16, + "NFE": 2, + "KID": 0.2451242357492447, + "FID": 239.5964191589499, + "Checkpoint SHA": "7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a" + }, + { + "Method": "sigmoid", + "Train seed": 1, + "Budget": 16, + "NFE": 1, + "KID": 0.4374769330024719, + "FID": 381.84189640961847, + "Checkpoint SHA": "3ffd271c99a3d09bc63c749f5b69f76af37bbbec54678e88885eb3341750e7ec" + }, + { + "Method": "sigmoid", + "Train seed": 1, + "Budget": 16, + "NFE": 2, + "KID": 0.2847936749458313, + "FID": 278.5354496890211, + "Checkpoint SHA": "3ffd271c99a3d09bc63c749f5b69f76af37bbbec54678e88885eb3341750e7ec" + }, + { + "Method": "adaptive_v1", + "Train seed": 1, + "Budget": 16, + "NFE": 1, + "KID": 0.4278477430343628, + "FID": 375.53195760880396, + "Checkpoint SHA": "b7bc291a80298121c091945519d79f1f8c79deb318b4790e2d7371a1510b7a13" + }, + { + "Method": "adaptive_v1", + "Train seed": 1, + "Budget": 16, + "NFE": 2, + "KID": 0.28532806038856506, + "FID": 278.94094193957403, + "Checkpoint SHA": "b7bc291a80298121c091945519d79f1f8c79deb318b4790e2d7371a1510b7a13" + }, + { + "Method": "sigmoid", + "Train seed": 2, + "Budget": 16, + "NFE": 1, + "KID": 0.567756175994873, + "FID": 475.8780162559483, + "Checkpoint SHA": "3fed09e540f8419db32bac9c1fdfe93033aaad225198ecc2bbce4eccf26b5865" + }, + { + "Method": "sigmoid", + "Train seed": 2, + "Budget": 16, + "NFE": 2, + "KID": 0.3588750958442688, + "FID": 330.29187425815263, + "Checkpoint SHA": "3fed09e540f8419db32bac9c1fdfe93033aaad225198ecc2bbce4eccf26b5865" + }, + { + "Method": "adaptive_v1", + "Train seed": 2, + "Budget": 16, + "NFE": 1, + "KID": 0.5735194087028503, + "FID": 478.2201833557063, + "Checkpoint SHA": "b04098a8d53449b3d2666f87b8d63e6f16ba11d6c2a5069c1d81d33d995f1d43" + }, + { + "Method": "adaptive_v1", + "Train seed": 2, + "Budget": 16, + "NFE": 2, + "KID": 0.36032578349113464, + "FID": 331.1610005332725, + "Checkpoint SHA": "b04098a8d53449b3d2666f87b8d63e6f16ba11d6c2a5069c1d81d33d995f1d43" + } + ] +} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-16-summary/role_a_metrics.md b/results/final_performance_evaluation/multibudget_role_a/audit/formal-16-summary/role_a_metrics.md new file mode 100644 index 00000000..1fbfc5ff --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-16-summary/role_a_metrics.md @@ -0,0 +1,20 @@ +# Role A quantitative evaluation + +Phase: `formal`; samples per checkpoint/NFE: 5000; precision: FP32. + +| Method | Train seed | Budget | NFE | KID | FID | Checkpoint SHA | +| --- | ---: | ---: | ---: | ---: | ---: | --- | +| sigmoid | 0 | 16 | 1 | 0.591114998 | 483.669642034 | `32aa4661584663c40bc05a3b5c5bef3bd4e8b60e289023d491fbe46fac5478ed` | +| sigmoid | 0 | 16 | 2 | 0.243654370 | 238.356426634 | `32aa4661584663c40bc05a3b5c5bef3bd4e8b60e289023d491fbe46fac5478ed` | +| adaptive_v1 | 0 | 16 | 1 | 0.594804227 | 484.782869039 | `7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a` | +| adaptive_v1 | 0 | 16 | 2 | 0.245124236 | 239.596419159 | `7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a` | +| sigmoid | 1 | 16 | 1 | 0.437476933 | 381.841896410 | `3ffd271c99a3d09bc63c749f5b69f76af37bbbec54678e88885eb3341750e7ec` | +| sigmoid | 1 | 16 | 2 | 0.284793675 | 278.535449689 | `3ffd271c99a3d09bc63c749f5b69f76af37bbbec54678e88885eb3341750e7ec` | +| adaptive_v1 | 1 | 16 | 1 | 0.427847743 | 375.531957609 | `b7bc291a80298121c091945519d79f1f8c79deb318b4790e2d7371a1510b7a13` | +| adaptive_v1 | 1 | 16 | 2 | 0.285328060 | 278.940941940 | `b7bc291a80298121c091945519d79f1f8c79deb318b4790e2d7371a1510b7a13` | +| sigmoid | 2 | 16 | 1 | 0.567756176 | 475.878016256 | `3fed09e540f8419db32bac9c1fdfe93033aaad225198ecc2bbce4eccf26b5865` | +| sigmoid | 2 | 16 | 2 | 0.358875096 | 330.291874258 | `3fed09e540f8419db32bac9c1fdfe93033aaad225198ecc2bbce4eccf26b5865` | +| adaptive_v1 | 2 | 16 | 1 | 0.573519409 | 478.220183356 | `b04098a8d53449b3d2666f87b8d63e6f16ba11d6c2a5069c1d81d33d995f1d43` | +| adaptive_v1 | 2 | 16 | 2 | 0.360325783 | 331.161000533 | `b04098a8d53449b3d2666f87b8d63e6f16ba11d6c2a5069c1d81d33d995f1d43` | + +Reference identity consistent: True; image count valid: True; repeat results exact: True; repeat results numerically consistent: True (rel_tol=1e-06, abs_tol=1e-12). diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed0/nfe1/metric-fid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed0/nfe1/metric-fid5k_full.jsonl new file mode 100644 index 00000000..95ffc9c1 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed0/nfe1/metric-fid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"fid5k_full": 435.31356889065256}, "metric": "fid5k_full", "total_time": 22.635526657104492, "total_time_str": "23s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0032.pkl", "timestamp": 1784709592.781804} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed0/nfe1/metric-kid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed0/nfe1/metric-kid5k_full.jsonl new file mode 100644 index 00000000..47bb4c28 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed0/nfe1/metric-kid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"kid5k_full": 0.4890937805175781}, "metric": "kid5k_full", "total_time": 29.088061571121216, "total_time_str": "29s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0032.pkl", "timestamp": 1784709570.144845} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed0/nfe2/metric-fid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed0/nfe2/metric-fid5k_full.jsonl new file mode 100644 index 00000000..aa7499d9 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed0/nfe2/metric-fid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"fid5k_full": 353.5467423298096}, "metric": "fid5k_full", "total_time": 29.64923596382141, "total_time_str": "30s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0032.pkl", "timestamp": 1784709689.096792} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed0/nfe2/metric-kid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed0/nfe2/metric-kid5k_full.jsonl new file mode 100644 index 00000000..7260a19a --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed0/nfe2/metric-kid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"kid5k_full": 0.3888939917087555}, "metric": "kid5k_full", "total_time": 36.38170051574707, "total_time_str": "36s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0032.pkl", "timestamp": 1784709659.4458141} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed1/nfe1/metric-fid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed1/nfe1/metric-fid5k_full.jsonl new file mode 100644 index 00000000..f8caf97e --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed1/nfe1/metric-fid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"fid5k_full": 441.9636796001353}, "metric": "fid5k_full", "total_time": 22.59582829475403, "total_time_str": "23s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T064507Z-seed1/network-snapshot-kimg0032.pkl", "timestamp": 1784709949.491099} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed1/nfe1/metric-kid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed1/nfe1/metric-kid5k_full.jsonl new file mode 100644 index 00000000..fdd674e9 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed1/nfe1/metric-kid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"kid5k_full": 0.513994038105011}, "metric": "kid5k_full", "total_time": 29.183957815170288, "total_time_str": "29s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T064507Z-seed1/network-snapshot-kimg0032.pkl", "timestamp": 1784709926.8939996} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed1/nfe2/metric-fid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed1/nfe2/metric-fid5k_full.jsonl new file mode 100644 index 00000000..4064e777 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed1/nfe2/metric-fid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"fid5k_full": 218.282160794282}, "metric": "fid5k_full", "total_time": 29.863444805145264, "total_time_str": "30s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T064507Z-seed1/network-snapshot-kimg0032.pkl", "timestamp": 1784710045.6049373} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed1/nfe2/metric-kid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed1/nfe2/metric-kid5k_full.jsonl new file mode 100644 index 00000000..c814b790 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed1/nfe2/metric-kid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"kid5k_full": 0.21717694401741028}, "metric": "kid5k_full", "total_time": 36.237696170806885, "total_time_str": "36s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T064507Z-seed1/network-snapshot-kimg0032.pkl", "timestamp": 1784710015.7402203} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed2/nfe1/metric-fid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed2/nfe1/metric-fid5k_full.jsonl new file mode 100644 index 00000000..d9f2c820 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed2/nfe1/metric-fid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"fid5k_full": 449.4239786522884}, "metric": "fid5k_full", "total_time": 22.60950231552124, "total_time_str": "23s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T065540Z-seed2/network-snapshot-kimg0032.pkl", "timestamp": 1784710305.6365607} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed2/nfe1/metric-kid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-both/formal/budget32/adaptive_v1/seed2/nfe1/metric-kid5k_full.jsonl new file mode 100644 index 00000000..64e88081 --- /dev/null +++ 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"610f353f4afeebee9e4ab12ee1fa170365d0bb42d138aa0a06c0c087bc4e577e" + }, + { + "Method": "adaptive_v1", + "Train seed": 1, + "Budget": 32, + "NFE": 2, + "KID": 0.21717694401741028, + "FID": 218.282160794282, + "Checkpoint SHA": "610f353f4afeebee9e4ab12ee1fa170365d0bb42d138aa0a06c0c087bc4e577e" + }, + { + "Method": "sigmoid", + "Train seed": 2, + "Budget": 32, + "NFE": 1, + "KID": 0.5092737078666687, + "FID": 448.6397706192315, + "Checkpoint SHA": "3f1bf37bf6512751ab55bc89ad1971c98679c79d5b5979d20b4730d62c64e420" + }, + { + "Method": "sigmoid", + "Train seed": 2, + "Budget": 32, + "NFE": 2, + "KID": 0.42143067717552185, + "FID": 374.85480094568607, + "Checkpoint SHA": "3f1bf37bf6512751ab55bc89ad1971c98679c79d5b5979d20b4730d62c64e420" + }, + { + "Method": "adaptive_v1", + "Train seed": 2, + "Budget": 32, + "NFE": 1, + "KID": 0.5107670426368713, + "FID": 449.4239786522884, + "Checkpoint SHA": "1c2e266d2e9c02459f10fbbe53c1497e8df8c656d54ea53b6c7ab69c9b3c8060" + }, + { + "Method": "adaptive_v1", + "Train seed": 2, + "Budget": 32, + "NFE": 2, + "KID": 0.42848312854766846, + "FID": 379.05337980788005, + "Checkpoint SHA": "1c2e266d2e9c02459f10fbbe53c1497e8df8c656d54ea53b6c7ab69c9b3c8060" + } + ] +} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-summary/role_a_metrics.md b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-summary/role_a_metrics.md new file mode 100644 index 00000000..3c2c192b --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-32-summary/role_a_metrics.md @@ -0,0 +1,20 @@ +# Role A quantitative evaluation + +Phase: `formal`; samples per checkpoint/NFE: 5000; precision: FP32. + +| Method | Train seed | Budget | NFE | KID | FID | Checkpoint SHA | +| --- | ---: | ---: | ---: | ---: | ---: | --- | +| sigmoid | 0 | 32 | 1 | 0.488681257 | 435.116718791 | `cb196bf9d6ba0fdd20858cc64c59a138c625c7584a5a445a37f9bdf9380e8c71` | +| sigmoid | 0 | 32 | 2 | 0.387979180 | 353.044258189 | `cb196bf9d6ba0fdd20858cc64c59a138c625c7584a5a445a37f9bdf9380e8c71` | +| adaptive_v1 | 0 | 32 | 1 | 0.489093781 | 435.313568891 | `a9f6c06d22e1e6409680efd31d43fe90714fec4a7b211ea4bd718a6cae810d9c` | +| adaptive_v1 | 0 | 32 | 2 | 0.388893992 | 353.546742330 | `a9f6c06d22e1e6409680efd31d43fe90714fec4a7b211ea4bd718a6cae810d9c` | +| sigmoid | 1 | 32 | 1 | 0.512568593 | 441.655822030 | `e5cc717d278bcd9d5ab7930486c22ce1a81e915a502deb31e6265a3e4e126b90` | +| sigmoid | 1 | 32 | 2 | 0.215143412 | 216.320162005 | `e5cc717d278bcd9d5ab7930486c22ce1a81e915a502deb31e6265a3e4e126b90` | +| adaptive_v1 | 1 | 32 | 1 | 0.513994038 | 441.963679600 | `610f353f4afeebee9e4ab12ee1fa170365d0bb42d138aa0a06c0c087bc4e577e` | +| adaptive_v1 | 1 | 32 | 2 | 0.217176944 | 218.282160794 | `610f353f4afeebee9e4ab12ee1fa170365d0bb42d138aa0a06c0c087bc4e577e` | +| sigmoid | 2 | 32 | 1 | 0.509273708 | 448.639770619 | `3f1bf37bf6512751ab55bc89ad1971c98679c79d5b5979d20b4730d62c64e420` | +| sigmoid | 2 | 32 | 2 | 0.421430677 | 374.854800946 | `3f1bf37bf6512751ab55bc89ad1971c98679c79d5b5979d20b4730d62c64e420` | +| adaptive_v1 | 2 | 32 | 1 | 0.510767043 | 449.423978652 | `1c2e266d2e9c02459f10fbbe53c1497e8df8c656d54ea53b6c7ab69c9b3c8060` | +| adaptive_v1 | 2 | 32 | 2 | 0.428483129 | 379.053379808 | `1c2e266d2e9c02459f10fbbe53c1497e8df8c656d54ea53b6c7ab69c9b3c8060` | + +Reference identity consistent: True; image count valid: True; repeat results exact: True; repeat results numerically consistent: True (rel_tol=1e-06, abs_tol=1e-12). diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed0/nfe1/metric-fid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed0/nfe1/metric-fid5k_full.jsonl new file mode 100644 index 00000000..815d2bf7 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed0/nfe1/metric-fid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"fid5k_full": 429.7458044055775}, "metric": "fid5k_full", "total_time": 22.540801525115967, "total_time_str": "23s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0064.pkl", "timestamp": 1784708460.9944263} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed0/nfe1/metric-kid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed0/nfe1/metric-kid5k_full.jsonl new file mode 100644 index 00000000..67c361a0 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed0/nfe1/metric-kid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"kid5k_full": 0.48724865913391113}, "metric": "kid5k_full", "total_time": 29.31922745704651, "total_time_str": "29s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0064.pkl", "timestamp": 1784708438.4523585} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed0/nfe2/metric-fid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed0/nfe2/metric-fid5k_full.jsonl new file mode 100644 index 00000000..c81c7b15 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed0/nfe2/metric-fid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"fid5k_full": 365.5074929430672}, "metric": "fid5k_full", "total_time": 29.825018644332886, "total_time_str": "30s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0064.pkl", "timestamp": 1784708557.1948588} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed0/nfe2/metric-kid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed0/nfe2/metric-kid5k_full.jsonl new file mode 100644 index 00000000..a77efef1 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed0/nfe2/metric-kid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"kid5k_full": 0.4068980813026428}, "metric": "kid5k_full", "total_time": 36.273258447647095, "total_time_str": "36s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0064.pkl", "timestamp": 1784708527.368559} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed1/nfe1/metric-fid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed1/nfe1/metric-fid5k_full.jsonl new file mode 100644 index 00000000..a0f5989b --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed1/nfe1/metric-fid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"fid5k_full": 418.42141522198887}, "metric": "fid5k_full", "total_time": 22.730340719223022, "total_time_str": "23s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T064507Z-seed1/network-snapshot-kimg0064.pkl", "timestamp": 1784708817.517562} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed1/nfe1/metric-kid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed1/nfe1/metric-kid5k_full.jsonl new file mode 100644 index 00000000..b7770f60 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed1/nfe1/metric-kid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"kid5k_full": 0.4754484295845032}, "metric": "kid5k_full", "total_time": 29.25619673728943, "total_time_str": "29s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T064507Z-seed1/network-snapshot-kimg0064.pkl", "timestamp": 1784708794.7859256} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed1/nfe2/metric-fid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed1/nfe2/metric-fid5k_full.jsonl new file mode 100644 index 00000000..7b1cf233 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed1/nfe2/metric-fid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"fid5k_full": 263.31739343516506}, "metric": "fid5k_full", "total_time": 29.644907474517822, "total_time_str": "30s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T064507Z-seed1/network-snapshot-kimg0064.pkl", "timestamp": 1784708913.4574537} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed1/nfe2/metric-kid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed1/nfe2/metric-kid5k_full.jsonl new file mode 100644 index 00000000..ff635225 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed1/nfe2/metric-kid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"kid5k_full": 0.27491092681884766}, "metric": "kid5k_full", "total_time": 36.4272198677063, "total_time_str": "36s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T064507Z-seed1/network-snapshot-kimg0064.pkl", "timestamp": 1784708883.8112557} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed2/nfe1/metric-fid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed2/nfe1/metric-fid5k_full.jsonl new file mode 100644 index 00000000..2128aeee --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed2/nfe1/metric-fid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"fid5k_full": 437.9850438086264}, "metric": "fid5k_full", "total_time": 22.5250141620636, "total_time_str": "23s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T065540Z-seed2/network-snapshot-kimg0064.pkl", "timestamp": 1784709173.922362} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed2/nfe1/metric-kid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed2/nfe1/metric-kid5k_full.jsonl new file mode 100644 index 00000000..533e1392 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed2/nfe1/metric-kid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"kid5k_full": 0.5199764966964722}, "metric": "kid5k_full", "total_time": 29.46903347969055, "total_time_str": "29s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T065540Z-seed2/network-snapshot-kimg0064.pkl", "timestamp": 1784709151.396113} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed2/nfe2/metric-fid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed2/nfe2/metric-fid5k_full.jsonl new file mode 100644 index 00000000..9e33ab45 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed2/nfe2/metric-fid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"fid5k_full": 380.97130757057425}, "metric": "fid5k_full", "total_time": 29.739269971847534, "total_time_str": "30s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T065540Z-seed2/network-snapshot-kimg0064.pkl", "timestamp": 1784709270.0581396} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed2/nfe2/metric-kid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed2/nfe2/metric-kid5k_full.jsonl new file mode 100644 index 00000000..8fea54aa --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/adaptive_v1/seed2/nfe2/metric-kid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"kid5k_full": 0.4338109493255615}, "metric": "kid5k_full", "total_time": 36.370874643325806, "total_time_str": "36s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T065540Z-seed2/network-snapshot-kimg0064.pkl", "timestamp": 1784709240.3175838} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/sigmoid/seed0/nfe1/metric-fid5k_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/sigmoid/seed0/nfe1/metric-fid5k_full.jsonl new file mode 100644 index 00000000..efd81657 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-both/formal/budget64/sigmoid/seed0/nfe1/metric-fid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"fid5k_full": 429.5904741914846}, "metric": "fid5k_full", "total_time": 22.43738341331482, "total_time_str": "22s", "num_gpus": 1, "snapshot_pkl": 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"--metrics=kid5k_full,fid5k_full", + "--metric-repeats=1", + "--sample-seeds=0-4999", + "--seed=20260722", + "--desc=role-a-formal-adaptive_v1-seed2-64k-nfe2" + ], + "started_at_unix": 1784709178.4931066, + "status": "completed", + "elapsed_seconds": 96.535 + } + ], + "elapsed_seconds": 1069.233 +} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-summary/role_a_metrics.csv b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-summary/role_a_metrics.csv new file mode 100644 index 00000000..b1c60daa --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-summary/role_a_metrics.csv @@ -0,0 +1,13 @@ +Method,Train seed,Budget,NFE,KID,FID,Checkpoint SHA +sigmoid,0,64,1,0.4878055155277252,429.5904741914846,514b67696ca911ccdd9e16c5cce93d91e2c2c77433a82a2c2b8c0ae0b410dc9f +sigmoid,0,64,2,0.4059482514858246,364.87099871412147,514b67696ca911ccdd9e16c5cce93d91e2c2c77433a82a2c2b8c0ae0b410dc9f +adaptive_v1,0,64,1,0.48724865913391113,429.7458044055775,dc5fdcac27f49e845eae1d28571cd0c3be6756aa6aee785e3189c1c27f527aa3 +adaptive_v1,0,64,2,0.4068980813026428,365.5074929430672,dc5fdcac27f49e845eae1d28571cd0c3be6756aa6aee785e3189c1c27f527aa3 +sigmoid,1,64,1,0.4751233458518982,418.23088510630885,bb98925649c0267064210a069887ae9b5dd3eff26727651318818ac2d46c09f9 +sigmoid,1,64,2,0.28407230973243713,270.97941291841283,bb98925649c0267064210a069887ae9b5dd3eff26727651318818ac2d46c09f9 +adaptive_v1,1,64,1,0.4754484295845032,418.42141522198887,c1eaef2c89c1365aa5c3e5b65c8522152468bf213923004c299ac6a058fddbdf +adaptive_v1,1,64,2,0.27491092681884766,263.31739343516506,c1eaef2c89c1365aa5c3e5b65c8522152468bf213923004c299ac6a058fddbdf +sigmoid,2,64,1,0.5190601348876953,437.3195436021108,73c5b0c7b3e5130a76f80d26cb30e6969d049d941ac0e6568ee16f7c1d5bc925 +sigmoid,2,64,2,0.428162544965744,377.60478135599294,73c5b0c7b3e5130a76f80d26cb30e6969d049d941ac0e6568ee16f7c1d5bc925 +adaptive_v1,2,64,1,0.5199764966964722,437.9850438086264,3f3b4a69dec396d4af1010c6c26323d442e64b60fd3c7b354c4820c60aae1157 +adaptive_v1,2,64,2,0.4338109493255615,380.97130757057425,3f3b4a69dec396d4af1010c6c26323d442e64b60fd3c7b354c4820c60aae1157 diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-summary/role_a_metrics.json b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-summary/role_a_metrics.json new file mode 100644 index 00000000..436365c4 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-summary/role_a_metrics.json @@ -0,0 +1,130 @@ +{ + "schema_version": 1, + "protocol": "role-a-multibudget-quality-v1", + "phase": "formal", + "metric_mode": "both", + "sample_count_per_checkpoint_nfe": 5000, + "sample_seeds": "0-4999", + "precision": "fp32", + "dataset": "/mnt/ect_project/datasets/cifar10-32x32.zip", + "dataset_sha256": "08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372", + "reference_real_count": 50000, + "feature_detector_url": "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt", + "reference_identity_consistent": true, + "image_count_valid": true, + "repeat_results_exact": true, + "repeat_results_numerically_consistent": true, + "repeat_relative_tolerance": 1e-06, + "repeat_absolute_tolerance": 1e-12, + "row_count": 12, + "rows": [ + { + "Method": "sigmoid", + "Train seed": 0, + "Budget": 64, + "NFE": 1, + "KID": 0.4878055155277252, + "FID": 429.5904741914846, + "Checkpoint SHA": "514b67696ca911ccdd9e16c5cce93d91e2c2c77433a82a2c2b8c0ae0b410dc9f" + }, + { + "Method": "sigmoid", + "Train seed": 0, + "Budget": 64, + "NFE": 2, + "KID": 0.4059482514858246, + "FID": 364.87099871412147, + "Checkpoint SHA": "514b67696ca911ccdd9e16c5cce93d91e2c2c77433a82a2c2b8c0ae0b410dc9f" + }, + { + "Method": "adaptive_v1", + "Train seed": 0, + "Budget": 64, + "NFE": 1, + "KID": 0.48724865913391113, + "FID": 429.7458044055775, + "Checkpoint SHA": "dc5fdcac27f49e845eae1d28571cd0c3be6756aa6aee785e3189c1c27f527aa3" + }, + { + "Method": "adaptive_v1", + "Train seed": 0, + "Budget": 64, + "NFE": 2, + "KID": 0.4068980813026428, + "FID": 365.5074929430672, + "Checkpoint SHA": "dc5fdcac27f49e845eae1d28571cd0c3be6756aa6aee785e3189c1c27f527aa3" + }, + { + "Method": "sigmoid", + "Train seed": 1, + "Budget": 64, + "NFE": 1, + "KID": 0.4751233458518982, + "FID": 418.23088510630885, + "Checkpoint SHA": "bb98925649c0267064210a069887ae9b5dd3eff26727651318818ac2d46c09f9" + }, + { + "Method": "sigmoid", + "Train seed": 1, + "Budget": 64, + "NFE": 2, + "KID": 0.28407230973243713, + "FID": 270.97941291841283, + "Checkpoint SHA": "bb98925649c0267064210a069887ae9b5dd3eff26727651318818ac2d46c09f9" + }, + { + "Method": "adaptive_v1", + "Train seed": 1, + "Budget": 64, + "NFE": 1, + "KID": 0.4754484295845032, + "FID": 418.42141522198887, + "Checkpoint SHA": "c1eaef2c89c1365aa5c3e5b65c8522152468bf213923004c299ac6a058fddbdf" + }, + { + "Method": "adaptive_v1", + "Train seed": 1, + "Budget": 64, + "NFE": 2, + "KID": 0.27491092681884766, + "FID": 263.31739343516506, + "Checkpoint SHA": "c1eaef2c89c1365aa5c3e5b65c8522152468bf213923004c299ac6a058fddbdf" + }, + { + "Method": "sigmoid", + "Train seed": 2, + "Budget": 64, + "NFE": 1, + "KID": 0.5190601348876953, + "FID": 437.3195436021108, + "Checkpoint SHA": "73c5b0c7b3e5130a76f80d26cb30e6969d049d941ac0e6568ee16f7c1d5bc925" + }, + { + "Method": "sigmoid", + "Train seed": 2, + "Budget": 64, + "NFE": 2, + "KID": 0.428162544965744, + "FID": 377.60478135599294, + "Checkpoint SHA": "73c5b0c7b3e5130a76f80d26cb30e6969d049d941ac0e6568ee16f7c1d5bc925" + }, + { + "Method": "adaptive_v1", + "Train seed": 2, + "Budget": 64, + "NFE": 1, + "KID": 0.5199764966964722, + "FID": 437.9850438086264, + "Checkpoint SHA": "3f3b4a69dec396d4af1010c6c26323d442e64b60fd3c7b354c4820c60aae1157" + }, + { + "Method": "adaptive_v1", + "Train seed": 2, + "Budget": 64, + "NFE": 2, + "KID": 0.4338109493255615, + "FID": 380.97130757057425, + "Checkpoint SHA": "3f3b4a69dec396d4af1010c6c26323d442e64b60fd3c7b354c4820c60aae1157" + } + ] +} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-summary/role_a_metrics.md b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-summary/role_a_metrics.md new file mode 100644 index 00000000..3b6935b8 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/formal-64-summary/role_a_metrics.md @@ -0,0 +1,20 @@ +# Role A quantitative evaluation + +Phase: `formal`; samples per checkpoint/NFE: 5000; precision: FP32. + +| Method | Train seed | Budget | NFE | KID | FID | Checkpoint SHA | +| --- | ---: | ---: | ---: | ---: | ---: | --- | +| sigmoid | 0 | 64 | 1 | 0.487805516 | 429.590474191 | `514b67696ca911ccdd9e16c5cce93d91e2c2c77433a82a2c2b8c0ae0b410dc9f` | +| sigmoid | 0 | 64 | 2 | 0.405948251 | 364.870998714 | `514b67696ca911ccdd9e16c5cce93d91e2c2c77433a82a2c2b8c0ae0b410dc9f` | +| adaptive_v1 | 0 | 64 | 1 | 0.487248659 | 429.745804406 | `dc5fdcac27f49e845eae1d28571cd0c3be6756aa6aee785e3189c1c27f527aa3` | +| adaptive_v1 | 0 | 64 | 2 | 0.406898081 | 365.507492943 | `dc5fdcac27f49e845eae1d28571cd0c3be6756aa6aee785e3189c1c27f527aa3` | +| sigmoid | 1 | 64 | 1 | 0.475123346 | 418.230885106 | `bb98925649c0267064210a069887ae9b5dd3eff26727651318818ac2d46c09f9` | +| sigmoid | 1 | 64 | 2 | 0.284072310 | 270.979412918 | `bb98925649c0267064210a069887ae9b5dd3eff26727651318818ac2d46c09f9` | +| adaptive_v1 | 1 | 64 | 1 | 0.475448430 | 418.421415222 | `c1eaef2c89c1365aa5c3e5b65c8522152468bf213923004c299ac6a058fddbdf` | +| adaptive_v1 | 1 | 64 | 2 | 0.274910927 | 263.317393435 | `c1eaef2c89c1365aa5c3e5b65c8522152468bf213923004c299ac6a058fddbdf` | +| sigmoid | 2 | 64 | 1 | 0.519060135 | 437.319543602 | `73c5b0c7b3e5130a76f80d26cb30e6969d049d941ac0e6568ee16f7c1d5bc925` | +| sigmoid | 2 | 64 | 2 | 0.428162545 | 377.604781356 | `73c5b0c7b3e5130a76f80d26cb30e6969d049d941ac0e6568ee16f7c1d5bc925` | +| adaptive_v1 | 2 | 64 | 1 | 0.519976497 | 437.985043809 | `3f3b4a69dec396d4af1010c6c26323d442e64b60fd3c7b354c4820c60aae1157` | +| adaptive_v1 | 2 | 64 | 2 | 0.433810949 | 380.971307571 | `3f3b4a69dec396d4af1010c6c26323d442e64b60fd3c7b354c4820c60aae1157` | + +Reference identity consistent: True; image count valid: True; repeat results exact: True; repeat results numerically consistent: True (rel_tol=1e-06, abs_tol=1e-12). diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/smoke-both-rerun1/run_manifest.json b/results/final_performance_evaluation/multibudget_role_a/audit/smoke-both-rerun1/run_manifest.json new file mode 100644 index 00000000..649d5333 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/smoke-both-rerun1/run_manifest.json @@ -0,0 +1,229 @@ +{ + "schema_version": 1, + "protocol": "role-a-multibudget-quality-v1", + 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2, + "metric_names": [ + "kid512_full", + "fid512_full" + ], + "checkpoint": "/root/ect-runs/paired-training-v1-canonical/sigmoid-traj64-5344a5c9-20260722T062924Z-seed0/network-snapshot-kimg0016.pkl", + "checkpoint_sha256": "32aa4661584663c40bc05a3b5c5bef3bd4e8b60e289023d491fbe46fac5478ed", + "output_directory": "/root/role-a-eval/smoke-both-rerun1/smoke/budget16/sigmoid/seed0/nfe1", + "command": [ + "bash", + "/root/recurrence-role-a/scripts/evaluate_checkpoint.sh", + "1", + "29700", + "/root/ect-runs/paired-training-v1-canonical/sigmoid-traj64-5344a5c9-20260722T062924Z-seed0/network-snapshot-kimg0016.pkl", + "--outdir", + "/root/role-a-eval/smoke-both-rerun1/smoke/budget16/sigmoid/seed0/nfe1", + "--nosubdir", + "--data", + "/mnt/ect_project/datasets/cifar10-32x32.zip", + "--cond=False", + "--arch=ddpmpp", + "--precond=ct", + "--dropout=0.2", + "--augment=0", + "--fp16=False", + "--cache=True", + "--workers=3", + "--nfe=1", + "--mid_t=0.821", + "--metrics=kid512_full,fid512_full", + 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b/results/final_performance_evaluation/multibudget_role_a/audit/smoke-both-rerun1/smoke/budget16/adaptive_v1/seed0/nfe1/metric-fid512_full.jsonl @@ -0,0 +1,2 @@ +{"results": {"fid512_full": 494.23485543785796}, "metric": "fid512_full", "total_time": 5.61539363861084, "total_time_str": "6s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0016.pkl", "timestamp": 1784707449.9869583} +{"results": {"fid512_full": 494.23485543785796}, "metric": "fid512_full", "total_time": 5.634881258010864, "total_time_str": "6s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0016.pkl", "timestamp": 1784707459.0304785} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/smoke-both-rerun1/smoke/budget16/adaptive_v1/seed0/nfe1/metric-kid512_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/smoke-both-rerun1/smoke/budget16/adaptive_v1/seed0/nfe1/metric-kid512_full.jsonl new file mode 100644 index 00000000..531b9adf --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/smoke-both-rerun1/smoke/budget16/adaptive_v1/seed0/nfe1/metric-kid512_full.jsonl @@ -0,0 +1,2 @@ +{"results": {"kid512_full": 0.595818042755127}, "metric": "kid512_full", "total_time": 17.03209614753723, "total_time_str": "17s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0016.pkl", "timestamp": 1784707444.3703532} +{"results": {"kid512_full": 0.5958181619644165}, "metric": "kid512_full", "total_time": 3.4075608253479004, "total_time_str": "3s", "num_gpus": 1, "snapshot_pkl": 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b/results/final_performance_evaluation/multibudget_role_a/audit/smoke-both-rerun1/smoke/budget16/adaptive_v1/seed0/nfe2/metric-kid512_full.jsonl @@ -0,0 +1,2 @@ +{"results": {"kid512_full": 0.24505922198295593}, "metric": "kid512_full", "total_time": 17.814040422439575, "total_time_str": "18s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0016.pkl", "timestamp": 1784707506.7078094} +{"results": {"kid512_full": 0.24505926668643951}, "metric": "kid512_full", "total_time": 3.9475293159484863, "total_time_str": "4s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0016.pkl", "timestamp": 1784707516.575952} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/smoke-both-rerun1/smoke/budget16/sigmoid/seed0/nfe1/metric-fid512_full.jsonl 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"timestamp": 1784707261.5301101} +{"results": {"kid512_full": 0.5922718644142151}, "metric": "kid512_full", "total_time": 3.1815874576568604, "total_time_str": "3s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/sigmoid-traj64-5344a5c9-20260722T062924Z-seed0/network-snapshot-kimg0016.pkl", "timestamp": 1784707332.6107342} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/smoke-both-rerun1/smoke/budget16/sigmoid/seed0/nfe2/metric-fid512_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/smoke-both-rerun1/smoke/budget16/sigmoid/seed0/nfe2/metric-fid512_full.jsonl new file mode 100644 index 00000000..c5aa3624 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/smoke-both-rerun1/smoke/budget16/sigmoid/seed0/nfe2/metric-fid512_full.jsonl @@ -0,0 +1,2 @@ +{"results": {"fid512_full": 266.8666602576525}, "metric": "fid512_full", "total_time": 5.98847770690918, "total_time_str": "6s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/sigmoid-traj64-5344a5c9-20260722T062924Z-seed0/network-snapshot-kimg0016.pkl", "timestamp": 1784707389.3885589} +{"results": {"fid512_full": 266.8666602576525}, "metric": "fid512_full", "total_time": 6.011477470397949, "total_time_str": "6s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/sigmoid-traj64-5344a5c9-20260722T062924Z-seed0/network-snapshot-kimg0016.pkl", "timestamp": 1784707399.3925488} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/smoke-both-rerun1/smoke/budget16/sigmoid/seed0/nfe2/metric-kid512_full.jsonl b/results/final_performance_evaluation/multibudget_role_a/audit/smoke-both-rerun1/smoke/budget16/sigmoid/seed0/nfe2/metric-kid512_full.jsonl new file mode 100644 index 00000000..a312b640 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/smoke-both-rerun1/smoke/budget16/sigmoid/seed0/nfe2/metric-kid512_full.jsonl @@ -0,0 +1,2 @@ +{"results": {"kid512_full": 0.24360528588294983}, "metric": "kid512_full", "total_time": 17.54876208305359, "total_time_str": "18s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/sigmoid-traj64-5344a5c9-20260722T062924Z-seed0/network-snapshot-kimg0016.pkl", "timestamp": 1784707383.3989985} +{"results": {"kid512_full": 0.24360525608062744}, "metric": "kid512_full", "total_time": 3.9917263984680176, "total_time_str": "4s", "num_gpus": 1, "snapshot_pkl": "../../../../../../../ect-runs/paired-training-v1-canonical/sigmoid-traj64-5344a5c9-20260722T062924Z-seed0/network-snapshot-kimg0016.pkl", "timestamp": 1784707393.3807213} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/smoke-summary-rerun1/role_a_metrics.csv b/results/final_performance_evaluation/multibudget_role_a/audit/smoke-summary-rerun1/role_a_metrics.csv new file mode 100644 index 00000000..54e3b214 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/smoke-summary-rerun1/role_a_metrics.csv @@ -0,0 +1,5 @@ +Method,Train seed,Budget,NFE,KID,FID,Checkpoint SHA +sigmoid,0,16,1,0.5922718644142151,492.77894865943455,32aa4661584663c40bc05a3b5c5bef3bd4e8b60e289023d491fbe46fac5478ed +sigmoid,0,16,2,0.24360528588294983,266.8666602576525,32aa4661584663c40bc05a3b5c5bef3bd4e8b60e289023d491fbe46fac5478ed +adaptive_v1,0,16,1,0.595818042755127,494.23485543785796,7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a +adaptive_v1,0,16,2,0.24505922198295593,267.7883448689886,7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/smoke-summary-rerun1/role_a_metrics.json b/results/final_performance_evaluation/multibudget_role_a/audit/smoke-summary-rerun1/role_a_metrics.json new file mode 100644 index 00000000..9d39b854 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/smoke-summary-rerun1/role_a_metrics.json @@ -0,0 +1,58 @@ +{ + "schema_version": 1, + "protocol": "role-a-multibudget-quality-v1", + "phase": "smoke", + "metric_mode": "both", + "sample_count_per_checkpoint_nfe": 512, + "sample_seeds": "0-511", + "precision": "fp32", + "dataset": "/mnt/ect_project/datasets/cifar10-32x32.zip", + "dataset_sha256": "08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372", + "reference_real_count": 50000, + "feature_detector_url": "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt", + "reference_identity_consistent": true, + "image_count_valid": true, + "repeat_results_exact": false, + "repeat_results_numerically_consistent": true, + "repeat_relative_tolerance": 1e-06, + "repeat_absolute_tolerance": 1e-12, + "row_count": 4, + "rows": [ + { + "Method": "sigmoid", + "Train seed": 0, + "Budget": 16, + "NFE": 1, + "KID": 0.5922718644142151, + "FID": 492.77894865943455, + "Checkpoint SHA": "32aa4661584663c40bc05a3b5c5bef3bd4e8b60e289023d491fbe46fac5478ed" + }, + { + "Method": "sigmoid", + "Train seed": 0, + "Budget": 16, + "NFE": 2, + "KID": 0.24360528588294983, + "FID": 266.8666602576525, + "Checkpoint SHA": "32aa4661584663c40bc05a3b5c5bef3bd4e8b60e289023d491fbe46fac5478ed" + }, + { + "Method": "adaptive_v1", + "Train seed": 0, + "Budget": 16, + "NFE": 1, + "KID": 0.595818042755127, + "FID": 494.23485543785796, + "Checkpoint SHA": "7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a" + }, + { + "Method": "adaptive_v1", + "Train seed": 0, + "Budget": 16, + "NFE": 2, + "KID": 0.24505922198295593, + "FID": 267.7883448689886, + "Checkpoint SHA": "7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a" + } + ] +} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/smoke-summary-rerun1/role_a_metrics.md b/results/final_performance_evaluation/multibudget_role_a/audit/smoke-summary-rerun1/role_a_metrics.md new file mode 100644 index 00000000..ed4d90a1 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/smoke-summary-rerun1/role_a_metrics.md @@ -0,0 +1,12 @@ +# Role A quantitative evaluation + +Phase: `smoke`; samples per checkpoint/NFE: 512; precision: FP32. + +| Method | Train seed | Budget | NFE | KID | FID | Checkpoint SHA | +| --- | ---: | ---: | ---: | ---: | ---: | --- | +| sigmoid | 0 | 16 | 1 | 0.592271864 | 492.778948659 | `32aa4661584663c40bc05a3b5c5bef3bd4e8b60e289023d491fbe46fac5478ed` | +| sigmoid | 0 | 16 | 2 | 0.243605286 | 266.866660258 | `32aa4661584663c40bc05a3b5c5bef3bd4e8b60e289023d491fbe46fac5478ed` | +| adaptive_v1 | 0 | 16 | 1 | 0.595818043 | 494.234855438 | `7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a` | +| adaptive_v1 | 0 | 16 | 2 | 0.245059222 | 267.788344869 | `7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a` | + +Reference identity consistent: True; image count valid: True; repeat results exact: False; repeat results numerically consistent: True (rel_tol=1e-06, abs_tol=1e-12). diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/training_packages/adaptive_v1_traj64_seed0_5344a5c9/acceptance.json b/results/final_performance_evaluation/multibudget_role_a/audit/training_packages/adaptive_v1_traj64_seed0_5344a5c9/acceptance.json new file mode 100644 index 00000000..f479efb0 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/training_packages/adaptive_v1_traj64_seed0_5344a5c9/acceptance.json @@ -0,0 +1,40 @@ +{ + "adaptive_correction_activated": true, + "amp_skipped_steps": 9, + "attempted_iterations": 500, + "checkpoints": { + "16": { + "kimg": 16, + "network_snapshot": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0016.pkl", + "network_snapshot_sha256": "7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a", + "training_state": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/training-state-kimg0016.pt", + "training_state_sha256": "269e2ec3b0b4edadddd2bc251338995962b31ea206106b0bfa24083e7210fd83" + }, + "32": { + "kimg": 32, + "network_snapshot": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0032.pkl", + "network_snapshot_sha256": "a9f6c06d22e1e6409680efd31d43fe90714fec4a7b211ea4bd718a6cae810d9c", + "training_state": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/training-state-kimg0032.pt", + "training_state_sha256": "7578d28bbcdb385d83c3735d0a43c7cf66548224e2b27af218795cfe300a9015" + }, + "64": { + "kimg": 64, + "network_snapshot": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0064.pkl", + "network_snapshot_sha256": "dc5fdcac27f49e845eae1d28571cd0c3be6756aa6aee785e3189c1c27f527aa3", + "training_state": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/training-state-kimg0064.pt", + "training_state_sha256": "a577bee2b03958308025f84596d2eb957eb04974ae641c893ba943f8c28ae761" + } + }, + "expected_attempted_at_64": 500, + "final_adaptive_active": true, + "final_correction": -0.00425934223343, + "final_signal_updates": 128, + "inf_count": 0, + "nan_count": 0, + "processed_kimg": 64.0, + "reached_64_kimg": true, + "rt_gap_always_legal": true, + "successful_optimizer_steps": 491, + "trailing25_mean": 17.7538050128, + "trailing25_std": 1.1076821247791304 +} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/training_packages/adaptive_v1_traj64_seed0_5344a5c9/metadata.json b/results/final_performance_evaluation/multibudget_role_a/audit/training_packages/adaptive_v1_traj64_seed0_5344a5c9/metadata.json new file mode 100644 index 00000000..99cc5a64 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/training_packages/adaptive_v1_traj64_seed0_5344a5c9/metadata.json @@ -0,0 +1,78 @@ +{ + "acceptance": { + "adaptive_correction_activated": true, + "amp_skipped_steps": 9, + "attempted_iterations": 500, + "checkpoints": { + "16": { + "kimg": 16, + "network_snapshot": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0016.pkl", + "network_snapshot_sha256": "7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a", + "training_state": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/training-state-kimg0016.pt", + "training_state_sha256": "269e2ec3b0b4edadddd2bc251338995962b31ea206106b0bfa24083e7210fd83" + }, + "32": { + "kimg": 32, + "network_snapshot": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0032.pkl", + "network_snapshot_sha256": "a9f6c06d22e1e6409680efd31d43fe90714fec4a7b211ea4bd718a6cae810d9c", + "training_state": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/training-state-kimg0032.pt", + "training_state_sha256": "7578d28bbcdb385d83c3735d0a43c7cf66548224e2b27af218795cfe300a9015" + }, + "64": { + "kimg": 64, + "network_snapshot": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0064.pkl", + "network_snapshot_sha256": "dc5fdcac27f49e845eae1d28571cd0c3be6756aa6aee785e3189c1c27f527aa3", + "training_state": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/training-state-kimg0064.pt", + "training_state_sha256": "a577bee2b03958308025f84596d2eb957eb04974ae641c893ba943f8c28ae761" + } + }, + "expected_attempted_at_64": 500, + "final_adaptive_active": true, + "final_correction": -0.00425934223343, + "final_signal_updates": 128, + "inf_count": 0, + "nan_count": 0, + "processed_kimg": 64.0, + "reached_64_kimg": true, + "rt_gap_always_legal": true, + "successful_optimizer_steps": 491, + "trailing25_mean": 17.7538050128, + "trailing25_std": 1.1076821247791304 + }, + "checkpoints": { + "16": { + "kimg": 16, + "network_snapshot": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0016.pkl", + "network_snapshot_sha256": "7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a", + "training_state": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/training-state-kimg0016.pt", + "training_state_sha256": "269e2ec3b0b4edadddd2bc251338995962b31ea206106b0bfa24083e7210fd83" + }, + "32": { + "kimg": 32, + "network_snapshot": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0032.pkl", + "network_snapshot_sha256": "a9f6c06d22e1e6409680efd31d43fe90714fec4a7b211ea4bd718a6cae810d9c", + "training_state": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/training-state-kimg0032.pt", + "training_state_sha256": "7578d28bbcdb385d83c3735d0a43c7cf66548224e2b27af218795cfe300a9015" + }, + "64": { + "kimg": 64, + "network_snapshot": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0064.pkl", + "network_snapshot_sha256": "dc5fdcac27f49e845eae1d28571cd0c3be6756aa6aee785e3189c1c27f527aa3", + "training_state": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/training-state-kimg0064.pt", + "training_state_sha256": "a577bee2b03958308025f84596d2eb957eb04974ae641c893ba943f8c28ae761" + } + }, + "entry": "ct_train.py", + "exact_commands": { + "stage16_source": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-stability-5344a5c9-20260721T034800Z", + "stage32": "python /tmp/ect-train-5344a5c/ct_train.py --data=/mnt/ect_project/datasets/cifar10-32x32.zip --outdir=/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0 --nosubdir --cond=False --arch=ddpmpp --precond=ect --batch=128 --batch-gpu=16 --optim=RAdam --lr=0.0001 --dropout=0.2 --augment=0 --mapping=adaptive_v1 -q 256 -k 8 -b 1 -c 0 --double=10000 --ema_beta=0.9993 --seed=0 --fp16=True --enable_amp=True --metrics=none --duration=0.032 --resume=/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/training-state-latest.pt", + "stage64": "python /tmp/ect-train-5344a5c/ct_train.py --data=/mnt/ect_project/datasets/cifar10-32x32.zip --outdir=/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0 --nosubdir --cond=False --arch=ddpmpp --precond=ect --batch=128 --batch-gpu=16 --optim=RAdam --lr=0.0001 --dropout=0.2 --augment=0 --mapping=adaptive_v1 -q 256 -k 8 -b 1 -c 0 --double=10000 --ema_beta=0.9993 --seed=0 --fp16=True --enable_amp=True --metrics=none --duration=0.064 --resume=/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/training-state-latest.pt" + }, + "hard_fail": [], + "mode": "continuous_traj64_resume_chain", + "package_ok": true, + "run_dir": "/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0", + "schedule": "adaptive_v1", + "training_code_anchor": "5344a5c97ab461b640ad5c5413cbf57eec527c2a", + "training_seed": 0 +} diff --git a/results/final_performance_evaluation/multibudget_role_a/audit/training_packages/adaptive_v1_traj64_seed0_5344a5c9/train_summary.csv b/results/final_performance_evaluation/multibudget_role_a/audit/training_packages/adaptive_v1_traj64_seed0_5344a5c9/train_summary.csv new file mode 100644 index 00000000..740c78ba --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/audit/training_packages/adaptive_v1_traj64_seed0_5344a5c9/train_summary.csv @@ -0,0 +1,501 @@ +attempted_iteration,successful_optimizer_steps,processed_nimg,processed_kimg,loss,grad_scale,step_skipped,schedule,stage,next_loop_cur_tick,loss_ema,loss_reference,correction,signal_updates,adaptive_active,r_over_t_mean,gap_mean,elapsed_sec,peak_vram_gb +1,0,128.0,0.128000,15.97663820,65536,1,adaptive_v1,0,1,,,0,0,0,0.985617611763,0.0143823882369,8.313589,5.777679 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+adaptive_v1,2,64,1,0.5199764966964722,437.9850438086264,3f3b4a69dec396d4af1010c6c26323d442e64b60fd3c7b354c4820c60aae1157 +sigmoid,0,64,2,0.4059482514858246,364.87099871412147,514b67696ca911ccdd9e16c5cce93d91e2c2c77433a82a2c2b8c0ae0b410dc9f +adaptive_v1,0,64,2,0.4068980813026428,365.5074929430672,dc5fdcac27f49e845eae1d28571cd0c3be6756aa6aee785e3189c1c27f527aa3 +sigmoid,1,64,2,0.28407230973243713,270.97941291841283,bb98925649c0267064210a069887ae9b5dd3eff26727651318818ac2d46c09f9 +adaptive_v1,1,64,2,0.27491092681884766,263.31739343516506,c1eaef2c89c1365aa5c3e5b65c8522152468bf213923004c299ac6a058fddbdf +sigmoid,2,64,2,0.428162544965744,377.60478135599294,73c5b0c7b3e5130a76f80d26cb30e6969d049d941ac0e6568ee16f7c1d5bc925 +adaptive_v1,2,64,2,0.4338109493255615,380.97130757057425,3f3b4a69dec396d4af1010c6c26323d442e64b60fd3c7b354c4820c60aae1157 diff --git a/results/final_performance_evaluation/multibudget_role_a/quantitative/quality_vs_budget.png b/results/final_performance_evaluation/multibudget_role_a/quantitative/quality_vs_budget.png new file mode 100644 index 00000000..32742949 Binary files /dev/null and b/results/final_performance_evaluation/multibudget_role_a/quantitative/quality_vs_budget.png differ diff --git a/results/final_performance_evaluation/multibudget_role_a/stability/controller_at_budget.csv b/results/final_performance_evaluation/multibudget_role_a/stability/controller_at_budget.csv new file mode 100644 index 00000000..a5ed9b34 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/stability/controller_at_budget.csv @@ -0,0 +1,19 @@ +method,training_seed,budget_kimg,loss,correction,r_over_t_mean,gap_mean,adaptive_active,signal_updates +sigmoid,0,16,26.38569498,0.0,0.985990140347,0.0140098596528,False,0 +sigmoid,0,32,18.62054765,0.0,0.985990140347,0.0140098596528,False,0 +sigmoid,0,64,17.61016238,0.0,0.98520666829,0.0147933317102,False,0 +sigmoid,1,16,20.09731555,0.0,0.985528614338,0.0144713856619,False,0 +sigmoid,1,32,18.0442518,0.0,0.985528614338,0.0144713856619,False,0 +sigmoid,1,64,16.26046371,0.0,0.985948594077,0.0140514059233,False,0 +sigmoid,2,16,30.61494064,0.0,0.985956209234,0.0140437907661,False,0 +sigmoid,2,32,20.25287247,0.0,0.985956209234,0.0140437907661,False,0 +sigmoid,2,64,20.15732801,0.0,0.985348618008,0.0146513819924,False,0 +adaptive_v1,0,16,26.70842981,-0.0247819071088,0.96108821038,0.0389117896204,True,32 +adaptive_v1,0,32,19.93077385,-0.0159298212206,0.969595466046,0.0304045339536,True,64 +adaptive_v1,0,64,17.40285218,-0.00425934223343,0.98075282961,0.0192471703898,True,128 +adaptive_v1,1,16,20.52969885,-0.0120768420786,0.973132518088,0.0268674819124,True,32 +adaptive_v1,1,32,18.0366919,-0.0024480608288,0.982952975538,0.0170470244623,True,64 +adaptive_v1,1,64,16.27367294,-6.1981075949e-05,0.985948117416,0.0140518825837,True,128 +adaptive_v1,2,16,30.32159686,-0.0288389525964,0.957072216856,0.0429277831435,True,32 +adaptive_v1,2,32,22.06750774,-0.0227549678244,0.962577599901,0.0374224000993,True,64 +adaptive_v1,2,64,19.5012238,-0.00683546046077,0.978546535402,0.0214534645976,True,128 diff --git a/results/final_performance_evaluation/multibudget_role_a/stability/controller_vs_budget.png b/results/final_performance_evaluation/multibudget_role_a/stability/controller_vs_budget.png new file mode 100644 index 00000000..94f2a820 Binary files /dev/null and b/results/final_performance_evaluation/multibudget_role_a/stability/controller_vs_budget.png differ diff --git a/results/final_performance_evaluation/multibudget_role_a/stability/training_stability.csv b/results/final_performance_evaluation/multibudget_role_a/stability/training_stability.csv new file mode 100644 index 00000000..75c88524 --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/stability/training_stability.csv @@ -0,0 +1,7 @@ +method,training_seed,processed_kimg,attempted_iterations,successful_optimizer_steps,amp_skipped_steps,nan_count,inf_count,trailing25_loss_mean,trailing25_loss_std,rt_gap_always_legal,adaptive_correction_activated,correction_saturated_steps,correction_sign_changes,checkpoint_64_sha256 +sigmoid,0,64.0,500,491,9,0,0,18.1286909236,1.1366661949055983,True,False,,,514b67696ca911ccdd9e16c5cce93d91e2c2c77433a82a2c2b8c0ae0b410dc9f +sigmoid,1,64.0,500,490,10,0,0,16.5431518652,0.8780730995890309,True,False,,,bb98925649c0267064210a069887ae9b5dd3eff26727651318818ac2d46c09f9 +sigmoid,2,64.0,500,490,10,0,0,19.3483638004,0.8957528825276382,True,False,,,73c5b0c7b3e5130a76f80d26cb30e6969d049d941ac0e6568ee16f7c1d5bc925 +adaptive_v1,0,64.0,500,491,9,0,0,17.7538050128,1.1076821247791304,True,True,0,0,dc5fdcac27f49e845eae1d28571cd0c3be6756aa6aee785e3189c1c27f527aa3 +adaptive_v1,1,64.0,500,491,9,0,0,16.4695864772,0.9582064148191193,True,True,0,2,c1eaef2c89c1365aa5c3e5b65c8522152468bf213923004c299ac6a058fddbdf +adaptive_v1,2,64.0,500,491,9,0,0,18.4528682712,0.8518902144018617,True,True,0,0,3f3b4a69dec396d4af1010c6c26323d442e64b60fd3c7b354c4820c60aae1157 diff --git a/results/final_performance_evaluation/multibudget_role_a/stability/training_stability.md b/results/final_performance_evaluation/multibudget_role_a/stability/training_stability.md new file mode 100644 index 00000000..b276b36e --- /dev/null +++ b/results/final_performance_evaluation/multibudget_role_a/stability/training_stability.md @@ -0,0 +1,12 @@ +# Training stability summary (continuous 64 kimg trajectories) + +| Method | Seed | Attempted | Successful | AMP skipped | NaN | Inf | trailing loss mean +/- SD | r/t and gap legal | Controller active | Saturated steps | Sign changes | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | --- | --- | --- | ---: | ---: | +| sigmoid | 0 | 500 | 491 | 9 | 0 | 0 | 18.1287 +/- 1.1367 | True | False | | | +| sigmoid | 1 | 500 | 490 | 10 | 0 | 0 | 16.5432 +/- 0.8781 | True | False | | | +| sigmoid | 2 | 500 | 490 | 10 | 0 | 0 | 19.3484 +/- 0.8958 | True | False | | | +| adaptive_v1 | 0 | 500 | 491 | 9 | 0 | 0 | 17.7538 +/- 1.1077 | True | True | 0 | 0 | +| adaptive_v1 | 1 | 500 | 491 | 9 | 0 | 0 | 16.4696 +/- 0.9582 | True | True | 0 | 2 | +| adaptive_v1 | 2 | 500 | 491 | 9 | 0 | 0 | 18.4529 +/- 0.8519 | True | True | 0 | 0 | + +All six trajectories reached 64 kimg with finite recorded losses. Saturation is defined as |correction| >= 99% of the frozen max_adjust=0.05; sign changes are descriptive, not a formal oscillation test. diff --git a/results/final_performance_evaluation/quantitative/paired_differences.csv b/results/final_performance_evaluation/quantitative/paired_differences.csv new file mode 100644 index 00000000..f226f5e1 --- /dev/null +++ b/results/final_performance_evaluation/quantitative/paired_differences.csv @@ -0,0 +1,7 @@ +training_seed,nfe,fixed_kid5k_full,adaptive_kid5k_full,delta_kid5k_full,fixed_fid5k_full,adaptive_fid5k_full,delta_fid5k_full +0,1,0.5911151766777039,0.5948042273521423,0.0036890506744384766,483.6697334149404,484.78286794037683,1.1131345254364078 +0,2,0.24365434050559998,0.2451242208480835,0.0014698803424835205,238.3564254947073,239.59641797869833,1.239992483991017 +1,1,0.43927493691444397,0.4075940251350403,-0.031680911779403687,382.9858992534891,362.11764428668954,-20.868254966799554 +1,2,0.28470897674560547,0.28383347392082214,-0.0008755028247833252,278.4626347454963,276.3634119465571,-2.099222798939195 +2,1,0.5667053461074829,0.5734773278236389,0.006771981716156006,475.3754563680849,478.1760770668619,2.8006206987770383 +2,2,0.35868123173713684,0.36038437485694885,0.0017031431198120117,330.1752915575563,331.1959638405271,1.0206722829707928 diff --git a/results/final_performance_evaluation/quantitative/quantitative_metrics.csv b/results/final_performance_evaluation/quantitative/quantitative_metrics.csv new file mode 100644 index 00000000..6e1a735c --- /dev/null +++ b/results/final_performance_evaluation/quantitative/quantitative_metrics.csv @@ -0,0 +1,13 @@ +schedule,training_seed,nfe,kid5k_full,fid5k_full +sigmoid,0,1,0.5911151766777039,483.6697334149404 +sigmoid,0,2,0.24365434050559998,238.3564254947073 +adaptive_v1,0,1,0.5948042273521423,484.78286794037683 +adaptive_v1,0,2,0.2451242208480835,239.59641797869833 +sigmoid,1,1,0.43927493691444397,382.9858992534891 +sigmoid,1,2,0.28470897674560547,278.4626347454963 +adaptive_v1,1,1,0.4075940251350403,362.11764428668954 +adaptive_v1,1,2,0.28383347392082214,276.3634119465571 +sigmoid,2,1,0.5667053461074829,475.3754563680849 +sigmoid,2,2,0.35868123173713684,330.1752915575563 +adaptive_v1,2,1,0.5734773278236389,478.1760770668619 +adaptive_v1,2,2,0.36038437485694885,331.1959638405271 diff --git a/results/final_performance_evaluation/quantitative/quantitative_summary.json b/results/final_performance_evaluation/quantitative/quantitative_summary.json new file mode 100644 index 00000000..75a09359 --- /dev/null +++ b/results/final_performance_evaluation/quantitative/quantitative_summary.json @@ -0,0 +1,84 @@ +{ + "schema_version": 1, + "evaluation_label": "5k-sample proxy evaluation; not a standard FID-50k benchmark", + "primary_metric": "kid5k_full", + "auxiliary_metric": "fid5k_full", + "delta_definition": "adaptive_v1 - sigmoid; negative favors adaptive_v1", + "training_seeds": [ + 0, + 1, + 2 + ], + "nfe": { + "1": { + "mid_t": [] + }, + "2": { + "mid_t": [ + 0.821 + ] + } + }, + "summary_by_nfe": { + "1": { + "kid5k_full": { + "paired_deltas_adaptive_minus_fixed": [ + 0.0036890506744384766, + -0.031680911779403687, + 0.006771981716156006 + ], + "mean_delta": -0.007073293129603068, + "sample_sd_delta": 0.021366499190613977, + "adaptive_fixed_tie_seed_counts": [ + 1, + 2, + 0 + ] + }, + "fid5k_full": { + "paired_deltas_adaptive_minus_fixed": [ + 1.1131345254364078, + -20.868254966799554, + 2.8006206987770383 + ], + "mean_delta": -5.6514999141953695, + "sample_sd_delta": 13.205079634160493, + "adaptive_fixed_tie_seed_counts": [ + 1, + 2, + 0 + ] + } + }, + "2": { + "kid5k_full": { + "paired_deltas_adaptive_minus_fixed": [ + 0.0014698803424835205, + -0.0008755028247833252, + 0.0017031431198120117 + ], + "mean_delta": 0.000765840212504069, + "sample_sd_delta": 0.00142622161859951, + "adaptive_fixed_tie_seed_counts": [ + 1, + 2, + 0 + ] + }, + "fid5k_full": { + "paired_deltas_adaptive_minus_fixed": [ + 1.239992483991017, + -2.099222798939195, + 1.0206722829707928 + ], + "mean_delta": 0.053813989340871636, + "sample_sd_delta": 1.8678064397601257, + "adaptive_fixed_tie_seed_counts": [ + 1, + 2, + 0 + ] + } + } + } +} diff --git a/results/final_performance_evaluation/quantitative/quantitative_summary.md b/results/final_performance_evaluation/quantitative/quantitative_summary.md new file mode 100644 index 00000000..57cff390 --- /dev/null +++ b/results/final_performance_evaluation/quantitative/quantitative_summary.md @@ -0,0 +1,40 @@ +# Final quantitative quality summary + +> 5k-sample proxy evaluation; not a standard FID-50k benchmark. + +Lower is better for both metrics. Paired delta is `Adaptive v1 - fixed sigmoid`; negative favors Adaptive v1. + +## Per-cell results + +| Schedule | Training seed | NFE | KID-5k (raw) | FID-5k proxy | +| --- | ---: | ---: | ---: | ---: | +| sigmoid | 0 | 1 | 0.591115 | 483.669733 | +| sigmoid | 0 | 2 | 0.243654 | 238.356425 | +| adaptive_v1 | 0 | 1 | 0.594804 | 484.782868 | +| adaptive_v1 | 0 | 2 | 0.245124 | 239.596418 | +| sigmoid | 1 | 1 | 0.439275 | 382.985899 | +| sigmoid | 1 | 2 | 0.284709 | 278.462635 | +| adaptive_v1 | 1 | 1 | 0.407594 | 362.117644 | +| adaptive_v1 | 1 | 2 | 0.283833 | 276.363412 | +| sigmoid | 2 | 1 | 0.566705 | 475.375456 | +| sigmoid | 2 | 2 | 0.358681 | 330.175292 | +| adaptive_v1 | 2 | 1 | 0.573477 | 478.176077 | +| adaptive_v1 | 2 | 2 | 0.360384 | 331.195964 | + +## Paired differences + +| Training seed | NFE | Δ KID-5k | Δ FID-5k | +| ---: | ---: | ---: | ---: | +| 0 | 1 | 0.003689 | 1.113135 | +| 0 | 2 | 0.001470 | 1.239992 | +| 1 | 1 | -0.031681 | -20.868255 | +| 1 | 2 | -0.000876 | -2.099223 | +| 2 | 1 | 0.006772 | 2.800621 | +| 2 | 2 | 0.001703 | 1.020672 | + +## Three-seed mean paired difference + +- NFE=1: kid5k_full: mean Δ=-0.007073, sample SD=0.021366, adaptive/fixed/tie seeds=[1, 2, 0]; fid5k_full: mean Δ=-5.651500, sample SD=13.205080, adaptive/fixed/tie seeds=[1, 2, 0] +- NFE=2: kid5k_full: mean Δ=0.000766, sample SD=0.001426, adaptive/fixed/tie seeds=[1, 2, 0]; fid5k_full: mean Δ=0.053814, sample SD=1.867806, adaptive/fixed/tie seeds=[1, 2, 0] + +With only three training seeds, these are descriptive paired results; do not convert them into a broad significance claim. diff --git a/results/final_performance_evaluation/role_d/BLIND_EVALUATION_STATUS.md b/results/final_performance_evaluation/role_d/BLIND_EVALUATION_STATUS.md new file mode 100644 index 00000000..ef66efb4 --- /dev/null +++ b/results/final_performance_evaluation/role_d/BLIND_EVALUATION_STATUS.md @@ -0,0 +1,20 @@ +# Blind Evaluation Status + +The planned anonymous visual preference evaluation was cancelled by +project decision before response aggregation. + +No evaluator ballots were decoded or included in the final analysis. +No human-preference rate is reported. + +The generated blind package is retained only as protocol provenance. +It must not be interpreted as completed blind-evaluation evidence. + +The final scientific conclusion is based on: + +- paired 5k-sample proxy KID/FID evaluation; +- training stability records; +- adaptive-controller telemetry; +- deterministic, fixed-layout qualitative grids. + +The qualitative grids are descriptive visualizations and are not a +substitute for a completed blinded human evaluation. diff --git a/results/final_performance_evaluation/role_d/FINAL_SHOWCASE.md b/results/final_performance_evaluation/role_d/FINAL_SHOWCASE.md new file mode 100644 index 00000000..70777f64 --- /dev/null +++ b/results/final_performance_evaluation/role_d/FINAL_SHOWCASE.md @@ -0,0 +1,55 @@ +# Fixed Sigmoid vs Adaptive v1: Final Showcase + +## Research question + +Under matched training seed, training budget, and sampling NFE, does +Adaptive v1 provide a repeatable generation-quality advantage over fixed +sigmoid? + +## Experimental scope + +- Methods: Fixed sigmoid and Adaptive v1 +- Training seeds: 0, 1, 2 +- Budgets: 16, 32, and 64 kimg +- NFE=1: `mid_t=[]` +- NFE=2: `mid_t=[0.821]` +- Main quality metric: KID-5k proxy +- Auxiliary quality metric: FID-5k proxy +- These results are not standard FID-50k benchmarks + +## Core evidence + +1. `quality_vs_budget.png` + Quality trends across 16, 32, and 64 kimg. + +2. `controller_vs_budget.png` + Adaptive-controller correction and gap behavior. + +3. `fixed_vs_adaptive_64k_nfe1.png` + Fixed-layout 64 kimg comparison for NFE=1. + +4. `fixed_vs_adaptive_64k_nfe2.png` + Fixed-layout 64 kimg comparison for NFE=2. + +5. `per_seed_metrics.csv` + Per-seed quantitative results. + +6. `paired_differences.csv` + Adaptive-minus-fixed paired differences. Lower KID/FID is better, so + negative differences favor Adaptive v1. + +7. `aggregate_results.csv` + Three-seed aggregate mean and dispersion. + +## Visual-evaluation limitation + +The planned anonymous visual preference evaluation was cancelled before +aggregation. No human preference result is claimed. The visual grids are +descriptive and use a fixed layout without selecting only favorable +Adaptive v1 examples. + +## Authoritative final conclusion + +The single authoritative scientific conclusion is maintained at +`results/role_c_final/FINAL_CONCLUSION.md`. Role D links to that record and +does not duplicate or modify its decision criteria. diff --git a/results/final_performance_evaluation/role_d/blind_64k/README.md b/results/final_performance_evaluation/role_d/blind_64k/README.md new file mode 100644 index 00000000..03292c1c --- /dev/null +++ b/results/final_performance_evaluation/role_d/blind_64k/README.md @@ -0,0 +1,16 @@ +# Anonymous Visual Evaluation + +Open the images in `trials` in trial-number order. + +For each trial, compare image A and image B and record exactly one choice +in `ballot.csv`: + +- `A`: image A has better overall generation quality +- `B`: image B has better overall generation quality +- `TIE`: no meaningful or reliable difference + +Judge overall visual quality, recognizability, coherence, and artifacts. +Do not try to infer which model generated either side. +TIE is a valid and encouraged answer when the difference is unclear. + +Do not rename trial images or change the `trial_id` column. diff --git a/results/final_performance_evaluation/role_d/blind_64k/ballot_template.csv b/results/final_performance_evaluation/role_d/blind_64k/ballot_template.csv new file mode 100644 index 00000000..c6fff2d7 --- /dev/null +++ b/results/final_performance_evaluation/role_d/blind_64k/ballot_template.csv @@ -0,0 +1,25 @@ +trial_id,choice +T001, +T002, +T003, +T004, +T005, +T006, +T007, +T008, +T009, +T010, +T011, +T012, +T013, +T014, +T015, +T016, +T017, +T018, +T019, +T020, +T021, +T022, +T023, +T024, diff --git a/results/final_performance_evaluation/role_d/blind_64k/metadata.json b/results/final_performance_evaluation/role_d/blind_64k/metadata.json new file mode 100644 index 00000000..baa32344 --- /dev/null +++ b/results/final_performance_evaluation/role_d/blind_64k/metadata.json @@ -0,0 +1,40 @@ +{ + "schema_version": 1, + "evaluation_type": "anonymous_paired_visual_preference", + "result_status": "supplementary_evidence", + "training_budget_kimg": 64, + "training_seed_count": 3, + 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+adaptive_v1,2,16.0,125,116,9,True,30.8048568212,14.58243215,37.46028829,30.32344842,128.0,5914.405888,102.045217,True,32,7a80f7e5d5f757ad5f6a31e999db7d8400d5da0a2a82ac2331cd71fdd0086dcf diff --git a/results/final_performance_evaluation/stability/training_stability.json b/results/final_performance_evaluation/stability/training_stability.json new file mode 100644 index 00000000..4c8f22bb --- /dev/null +++ b/results/final_performance_evaluation/stability/training_stability.json @@ -0,0 +1,25 @@ +{ + "schema_version": 1, + "training_budget_kimg": 16, + "all_six_runs_complete": true, + "all_losses_finite": true, + "summary_by_schedule": { + "sigmoid": { + "complete_16k_runs": 3, + "finite_loss_runs": 3, + "total_skipped_steps": 27, + "mean_skipped_steps": 9, + "mean_peak_vram_mib": 5915.697493333333, + "mean_wall_time_seconds": 103.75779266666667 + }, + "adaptive_v1": { + "complete_16k_runs": 3, + "finite_loss_runs": 3, + "total_skipped_steps": 27, + "mean_skipped_steps": 9, + "mean_peak_vram_mib": 5915.697493333333, + "mean_wall_time_seconds": 103.05505833333333, + "controller_activated_runs": 3 + } + } +} diff --git a/results/final_performance_evaluation/stability/training_stability.md b/results/final_performance_evaluation/stability/training_stability.md new file mode 100644 index 00000000..11e159c0 --- /dev/null +++ b/results/final_performance_evaluation/stability/training_stability.md @@ -0,0 +1,14 @@ +# Training stability summary + +| Schedule | Seed | kimg | Attempts | Successful | Skipped | Finite loss | Final scale | Peak VRAM MiB | Wall time s | Adaptive active | +| --- | ---: | ---: | ---: | ---: | ---: | --- | ---: | ---: | ---: | --- | +| sigmoid | 0 | 16.000 | 125 | 116 | 9 | yes | 128 | 5916.3 | 104.1 | n/a | +| adaptive_v1 | 0 | 16.000 | 125 | 116 | 9 | yes | 128 | 5916.3 | 105.0 | true | +| sigmoid | 1 | 16.000 | 125 | 116 | 9 | yes | 128 | 5914.4 | 105.3 | n/a | +| adaptive_v1 | 1 | 16.000 | 125 | 116 | 9 | yes | 128 | 5916.3 | 102.2 | true | +| sigmoid | 2 | 16.000 | 125 | 116 | 9 | yes | 128 | 5916.3 | 101.9 | n/a | +| adaptive_v1 | 2 | 16.000 | 125 | 116 | 9 | yes | 128 | 5914.4 | 102.0 | true | + +All six 16 kimg runs complete: **True**. All recorded losses finite: **True**. + +Skipped AMP steps, GradScaler values, time, and memory are engineering stability descriptors; they are not generation-quality metrics. diff --git a/results/final_performance_evaluation/training_runs/adaptive_v1_seed1/metadata.json b/results/final_performance_evaluation/training_runs/adaptive_v1_seed1/metadata.json new file mode 100644 index 00000000..bf232d38 --- /dev/null +++ b/results/final_performance_evaluation/training_runs/adaptive_v1_seed1/metadata.json @@ -0,0 +1,57 @@ +{ + "activation_gate_applied": false, + "activation_gate_passed": null, + "attempted_iterations": 125, + "batch_gpu": 16, + "cuda_version": "12.1", + "dataset_sha256": "08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372", + "duration_mimg": 0.016, + "evidence_class": "formal_candidate", + "exact_command": "python /root/recurrence_of_ect/ct_train.py --data=/mnt/ect_project/datasets/cifar10-32x32.zip --outdir=/root/ect-runs/final-three-seed/adaptive-v1-stability-seed1-eb3d279a-20260722T023728Z --nosubdir --cond=False --arch=ddpmpp --precond=ect --batch=128 --batch-gpu=16 --optim=RAdam --lr=0.0001 --dropout=0.2 --augment=0 --mapping=adaptive_v1 -q 256 -k 8 -b 1 -c 0 --double=10000 --ema_beta=0.9993 --seed=1 --fp16=True --enable_amp=True --metrics=none --transfer=/mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl --duration=0.016", + "final_adaptive_active": true, + "final_grad_scale": 128.0, + "final_loss": 19.72989869, + "final_signal_updates": 32, + "first_adapted_pair_iteration": 13, + "first_loss": 16.76310182, + "first_nonzero_correction_iteration": 12, + "first_schedule_telemetry_iteration": 1, + "git_branch": "codex/final-performance-evaluation", + "git_commit": "eb3d279a7bc1dca22d7f9e63835634ece6e0f010", + "git_dirty": false, + "global_batch": 128, + "gpu_count": 1, + "gpu_name": "NVIDIA A100-PCIE-40GB", + "gradscaler_state_saved": true, + "inf_count": 0, + "initial_grad_scale": 65536.0, + "max_loss": 29.73052859, + "metrics_enabled": false, + "min_loss": 15.10707498, + "mode": "stability", + "nan_count": 0, + "network_snapshot": "/root/ect-runs/final-three-seed/adaptive-v1-stability-seed1-eb3d279a-20260722T023728Z/network-snapshot-latest.pkl", + "network_snapshot_sha256": "7e6d2c1b4da20cb7880f600a4afa3eec45951108b52e9b936b8eaadd8e41cf09", + "packaging_git_branch": "codex/final-performance-evaluation", + "packaging_git_commit": "eb3d279a7bc1dca22d7f9e63835634ece6e0f010", + "packaging_git_dirty": false, + "peak_vram_mib": 5916.343296, + "peak_vram_source": "torch.cuda.max_memory_allocated", + "platform": "Linux-5.15.0-56-generic-x86_64-with-glibc2.35", + "processed_kimg": 16.0, + "python_version": "3.9.18", + "schedule": "adaptive_v1", + "schedule_telemetry_available": true, + "schedule_telemetry_columns_available": true, + "schedule_telemetry_coverage": 1.0, + "schedule_telemetry_rows": 125, + "schedule_telemetry_total_rows": 125, + "seed": 1, + "skipped_steps": 9, + "successful_optimizer_steps": 116, + "torch_version": "2.3.0", + "training_state": "/root/ect-runs/final-three-seed/adaptive-v1-stability-seed1-eb3d279a-20260722T023728Z/training-state-latest.pt", + "training_state_sha256": "71328845280361aa4c0c9bb7e719fcf4879669129c6dae2d991714546c06592d", + "transfer_sha256": "4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da", + "wall_time_seconds": 102.160203 +} diff --git a/results/final_performance_evaluation/training_runs/adaptive_v1_seed1/train_summary.csv b/results/final_performance_evaluation/training_runs/adaptive_v1_seed1/train_summary.csv new file mode 100644 index 00000000..d65450fe --- /dev/null +++ b/results/final_performance_evaluation/training_runs/adaptive_v1_seed1/train_summary.csv @@ -0,0 +1,126 @@ 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b/results/fixed_baseline_preliminary/README.md @@ -0,0 +1,31 @@ +# Fixed baseline — preliminary stability evidence + +Migrated from PR #10 (`wk/iniBR`) for reference only. + +## Status + +**preliminary stability evidence** — not formal fixed-vs-adaptive comparison +evidence. + +| Property | Value | +| --- | --- | +| Mode | stability | +| Duration | 0.016 Mimg (16 kimg) | +| Attempted optimizer updates | 125 | +| Successful optimizer updates | 116 | +| GradScaler skipped steps | 9 | +| Precision | FP16 + GradScaler | +| Seed / batch | 0 / 128 | +| Recorded git commit in metadata | `93a1ffc` (then-main SHA; instrumentation lived on Role B branch) | + +Re-run from a clean `role-b/paired-training-v1` HEAD before promoting anything +to `results/fixed_baseline_v1/`. + +## Contents + +```text +results/fixed_baseline_preliminary/ +├── README.md +├── train_summary.csv +└── metadata.json +``` diff --git a/results/fixed_baseline_preliminary/metadata.json b/results/fixed_baseline_preliminary/metadata.json new file mode 100644 index 00000000..41a3ab64 --- /dev/null +++ b/results/fixed_baseline_preliminary/metadata.json @@ -0,0 +1,33 @@ +{ + "attempted_iterations": 125, + "batch_gpu": 16, + "duration_mimg": 0.016, + "evidence_class": "preliminary", + "evidence_note": "Migrated from PR #10 / wk/iniBR. Instrumentation lived on Role B branch; re-run from clean role-b/paired-training-v1 HEAD before promoting to formal evidence.", + "exact_command": "python /mnt/recurrence_of_ect/ct_train.py --data=/mnt/ect_project/datasets/cifar10-32x32.zip --transfer=/mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl --outdir=/mnt/ect_project/runs/fixed-baseline-v1 --nosubdir --cond=False --arch=ddpmpp --precond=ect --batch=128 --batch-gpu=16 --optim=RAdam --lr=0.0001 --dropout=0.2 --augment=0 --mapping=sigmoid -q 256 -k 8 -b 1 -c 0 --double=10000 --ema_beta=0.9993 --seed=0 --fp16=True --enable_amp=True --metrics=none --duration=0.016", + "final_grad_scale": 128.0, + "final_loss": 26.34475136, + "first_loss": 15.97663879, + "git_commit": "93a1ffcdd76f375d82990df8ec71e67ea51fd803", + "global_batch": 128, + "gradscaler_state_saved": true, + "inf_count": 0, + "initial_grad_scale": 65536.0, + "max_loss": 37.43996811, + "metrics_enabled": false, + "min_loss": 15.02455807, + "mode": "stability", + "nan_count": 0, + "network_snapshot": "/mnt/ect_project/runs/fixed-baseline-v1/network-snapshot-latest.pkl", + "optimizer_updates": 125, + "peak_vram_mib": 5918.155776, + "peak_vram_source": "torch.cuda.max_memory_allocated", + "processed_kimg": 16.0, + "schedule": "sigmoid", + "seed": 0, + "skipped_steps": 9, + "successful_optimizer_steps": 116, + "successful_optimizer_updates": 116, + "training_state": "/mnt/ect_project/runs/fixed-baseline-v1/training-state-latest.pt", + "wall_time_seconds": 117.87 +} diff --git a/results/fixed_baseline_preliminary/train_summary.csv b/results/fixed_baseline_preliminary/train_summary.csv new file mode 100644 index 00000000..c8dcb0d0 --- /dev/null +++ b/results/fixed_baseline_preliminary/train_summary.csv @@ -0,0 +1,126 @@ +update,kimg,loss,grad_scale,step_skipped,seconds,peak_vram_mib +1,0.128000,15.97663879,65536,true,8.631789,5918.155776 +2,0.256000,15.02455807,32768,true,9.863258,2141.825024 +3,0.384000,16.38218498,16384,true,10.570045,2141.825024 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+122,15.616000,26.48645401,128,false,97.406205,2570.203136 +123,15.744000,27.67897606,128,false,98.152923,2570.203136 +124,15.872000,26.68359184,128,false,98.861336,2570.203136 +125,16.000000,26.34475136,128,false,99.569402,2570.203136 diff --git a/results/fixed_seeds_0_63_fp32_8ksteps/README.md b/results/fixed_seeds_0_63_fp32_8ksteps/README.md new file mode 100644 index 00000000..e9c814c9 --- /dev/null +++ b/results/fixed_seeds_0_63_fp32_8ksteps/README.md @@ -0,0 +1,14 @@ +# Preliminary historical fixed-seed smoke + +This directory contains **preliminary historical results** from the Role D +8k-step FP32 checkpoint. It predates `docs/EVALUATION_PROTOCOL.md` and is kept +only as evidence that the initial fixed-seed workflow ran successfully. + +These artifacts are not a formal benchmark and are not directly comparable to +future B/C results. In particular, they were generated before repeated-run +determinism, checkpoint-SHA output isolation, and the final metadata schema were +required. The checkpoint remains outside Git. + +Do not regenerate or reinterpret this directory as a current-protocol result. +Current smoke outputs must use the checkpoint-isolated layout defined in +`docs/EVALUATION_PROTOCOL.md`. diff --git a/results/fixed_seeds_0_63_fp32_8ksteps/metadata.json b/results/fixed_seeds_0_63_fp32_8ksteps/metadata.json new file mode 100644 index 00000000..21b58be5 --- /dev/null +++ b/results/fixed_seeds_0_63_fp32_8ksteps/metadata.json @@ -0,0 +1,85 @@ +{ + "checkpoint": "ct-runs/00003-cifar10-32x32-uncond-ddpmpp-ect-RAdam-0.000100-gpus1-batch128-fp32-seed42-8ksteps/network-snapshot-latest.pkl", + "checkpoint_sha256": "912a527fb73be39e6bdd0d35d580b948ec322a0620cfedd4795d6c11337ab777", + "evaluation_git_commit": "9ebc6c3006340a77989b383e0475eb1a9e3a0d03", + "seeds": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63 + ], + "nfe": [ + 1, + 2 + ], + "mid_t": 0.821, + "precision": "fp32", + "gpu": "NVIDIA A100-PCIE-40GB", + "image_format": "32x32 RGB", + "batch_sizes_verified": [ + 8, + 16 + ], + "model_forward_batch_size": 1, + "batch_independent_sha256": true +} diff --git a/results/fixed_seeds_0_63_fp32_8ksteps/nfe1/grid_8x8.png b/results/fixed_seeds_0_63_fp32_8ksteps/nfe1/grid_8x8.png new file mode 100644 index 00000000..fd971172 Binary files /dev/null and b/results/fixed_seeds_0_63_fp32_8ksteps/nfe1/grid_8x8.png differ diff --git a/results/fixed_seeds_0_63_fp32_8ksteps/nfe2/grid_8x8.png b/results/fixed_seeds_0_63_fp32_8ksteps/nfe2/grid_8x8.png new file mode 100644 index 00000000..e59a9f04 Binary files /dev/null and b/results/fixed_seeds_0_63_fp32_8ksteps/nfe2/grid_8x8.png differ diff --git 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+primary,1.0,1,1,0.3247685134410858,317.01762434835075,,,5k_screening_proxy +swapped_gpu_repro,1.0,0,1,0.32526856660842896,317.16364557573206,0.153972,0.046061,5k_screening_proxy +primary,1.0,1,2,0.07131069898605347,87.92304798504748,,,5k_screening_proxy +swapped_gpu_repro,1.0,0,2,0.0708608403801918,87.45681010694095,-0.630843,-0.530279,5k_screening_proxy diff --git a/results/gap_factorial_20260727/README.md b/results/gap_factorial_20260727/README.md new file mode 100644 index 00000000..03406003 --- /dev/null +++ b/results/gap_factorial_20260727/README.md @@ -0,0 +1,86 @@ +# Factorized global/local gap experiment + +This directory contains the compact, reproducible summary of the final +global/local gap factorial experiment. + +## Design + +- Baseline: fixed ECT sigmoid schedule. +- Global-only: multiply every sigmoid gap by one shared scale `g`. +- Local-only: apply a four-bin, raw-pair-loss controller whose local scales + have geometric mean one before realized-gap clipping effects. +- Global + local: combine the explicit global scale with the geometrically + normalized local scale factors. +- Global scale selected by the stage-1 response curve: `g* = 1.10`. +- Final matrix: three training seeds, NFE 1 and 2, and conservative/aggressive + local-controller profiles. + +## Main result + +Because seed 0 participated in selecting `g*`, the cleanest descriptive +comparison uses held-out seeds 1 and 2. Relative to fixed sigmoid: + +| Method | NFE=1 KID-5k | NFE=1 FID-5k | NFE=2 KID-5k | NFE=2 FID-5k | +| --- | ---: | ---: | ---: | ---: | +| Global-only (`g=1.10`) | -2.58% | -4.23% | -19.09% | -18.62% | +| Local conservative | -0.01% | -0.04% | -0.18% | -0.08% | +| Local aggressive | +0.77% | +0.40% | +6.54% | +6.15% | +| Global + aggressive local | -2.87% | -4.39% | -20.18% | -19.42% | + +Lower KID/FID is better, so negative percentages indicate improvement. The +dominant contribution comes from global gap calibration. The aggressive local +controller adds only a small improvement on top of global calibration and is +harmful when used alone at NFE=2. + +Each headline percentage compares the arithmetic metric means: + +`100 × (mean(metric_arm, seeds 1/2) / mean(metric_fixed, seeds 1/2) - 1)`. + +It is not the mean of the two per-seed percentage changes. + +### NFE=2 seed sensitivity + +Both held-out seeds improve directionally under global-only and global plus +aggressive-local, but the magnitudes differ substantially: + +| Method | Metric | Seed 1 change | Seed 2 change | Seed 1 share of absolute two-seed decrease | +| --- | --- | ---: | ---: | ---: | +| Global-only | KID-5k | -70.50% | -5.26% | 78.30% | +| Global-only | FID-5k | -60.83% | -4.12% | 83.55% | +| Global + aggressive local | KID-5k | -69.86% | -6.82% | 73.38% | +| Global + aggressive local | FID-5k | -61.17% | -5.07% | 80.55% | + +The approximately 19–20% held-out-mean NFE=2 improvement is therefore strongly +influenced by seed 1 and should not be described as a uniform 20% per-seed +effect. + +## Realized-gap diagnostics + +The implementation now records: + +- `gap_over_sigmoid_gap_mean`: batch mean of + `(t - r_realized) / (t - r_sigmoid)`; +- `lower_gap_clip_rate`: fraction for which the lower-gap constraint increases + the realized gap above the pre-clipping factorized target; +- `upper_gap_clip_rate`: fraction for which the `r >= 0` constraint reduces the + realized gap below the pre-clipping factorized target. + +The completed 2026-07-27 runs predate these telemetry fields. Their exact +training-time values cannot be reconstructed from the compact metric summaries, +so they are reported as `not_recorded_pre_instrumentation` rather than +estimated. Future runs write all three values to `train_summary.csv` and +`stats.jsonl`. + +## Files + +- `factorial_summary.md`: full paired-effect definitions, three-seed results, + confidence intervals, and interpretation caveats. +- `factorial_summary.csv` / `.json`: machine-readable aggregate effects. +- `heldout_headlines.csv`: arithmetic-mean headline calculations and per-seed + sensitivity for held-out seeds 1/2. +- `per_cell_metrics.csv`: every evaluated cell. +- `per_seed_effects.csv`: every paired per-seed contrast. + +These are 5,000-sample proxy metrics, not standard 50,000-sample benchmarks. +With only three seeds, confidence intervals are wide; results should be treated +as descriptive rather than population-level significance claims. diff --git a/results/gap_factorial_20260727/factorial_summary.csv b/results/gap_factorial_20260727/factorial_summary.csv new file mode 100644 index 00000000..5ee63d28 --- /dev/null +++ b/results/gap_factorial_20260727/factorial_summary.csv @@ -0,0 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Lower is better for both metrics. + +This is a paired three-training-seed (`n=3`) descriptive analysis. The 95% intervals use the two-sided Student-t critical value `t(df=2)=4.3026527`; they are not evidence for broad population-level significance. + +Seed 0 was used to select g\* in the response-curve stage and its fixed/global observations are reused in this formal matrix. Therefore seed 0 has selection/evaluation overlap; interpret selected-g\* effects as selection-aware descriptive estimates. + +## Held-out seed 1/2 headline calculation + +For arm `A` and metric `M`, the reported headline is + +`100 × (mean(M_A,seed1, M_A,seed2) / mean(M_fixed,seed1, M_fixed,seed2) - 1)`. + +It is the percentage difference between the two arithmetic metric means. It is **not** the mean of the two per-seed percentage changes. + +| Arm | NFE | Metric | Fixed mean | Arm mean | Headline % | Seed 1 % | Seed 2 % | +| --- | ---: | --- | ---: | ---: | ---: | ---: | ---: | +| global | 1 | kid5k_full | 0.373479 | 0.36385 | -2.57818% | -2.1827% | -2.86679% | +| global | 1 | fid5k_full | 342.953 | 328.453 | -4.22805% | -6.60986% | -2.22065% | +| global | 2 | kid5k_full | 0.270937 | 0.219221 | -19.0877% | -70.5018% | -5.25576% | +| global | 2 | fid5k_full | 246.29 | 200.437 | -18.6174% | -60.8296% | -4.1158% | +| local-conservative | 1 | kid5k_full | 0.373479 | 0.373456 | -0.00616428% | -0.229682% | 0.156954% | +| local-conservative | 1 | fid5k_full | 342.953 | 342.824 | -0.0376718% | -0.0915416% | 0.00772972% | +| local-conservative | 2 | kid5k_full | 0.270937 | 0.270454 | -0.178322% | 0.719665% | -0.419907% | +| local-conservative | 2 | fid5k_full | 246.29 | 246.1 | -0.0770239% | 0.669709% | -0.333557% | +| combined-conservative | 1 | kid5k_full | 0.373479 | 0.372011 | -0.393185% | 2.62254% | -2.594% | +| combined-conservative | 1 | fid5k_full | 342.953 | 336.17 | -1.97794% | -2.10947% | -1.86709% | +| combined-conservative | 2 | kid5k_full | 0.270937 | 0.218337 | -19.4141% | -69.6979% | -5.88623% | +| combined-conservative | 2 | fid5k_full | 246.29 | 202.311 | -17.8564% | -57.2435% | -4.32532% | +| local-aggressive | 1 | kid5k_full | 0.373479 | 0.376354 | 0.769801% | 3.90926% | -1.52131% | +| local-aggressive | 1 | fid5k_full | 342.953 | 344.336 | 0.403133% | 2.3771% | -1.26053% | +| local-aggressive | 2 | kid5k_full | 0.270937 | 0.288663 | 6.54272% | 36.9647% | -1.6417% | +| local-aggressive | 2 | fid5k_full | 246.29 | 261.443 | 6.15255% | 27.5746% | -1.20678% | +| combined-aggressive | 1 | kid5k_full | 0.373479 | 0.362757 | -2.87073% | -1.84154% | -3.62181% | +| combined-aggressive | 1 | fid5k_full | 342.953 | 327.906 | -4.38771% | -6.26854% | -2.80254% | +| combined-aggressive | 2 | kid5k_full | 0.270937 | 0.216254 | -20.1828% | -69.8566% | -6.81906% | +| combined-aggressive | 2 | fid5k_full | 246.29 | 198.465 | -19.418% | -61.171% | -5.07421% | + +At NFE=2, both held-out seeds improve directionally for `global` and `combined-aggressive`, but seed 1 has a much larger effect. Seed 1 accounts for the following share of the total absolute two-seed metric decrease: + +| Arm | Metric | Seed 1 share | +| --- | --- | ---: | +| global | kid5k_full | 78.3025% | +| global | fid5k_full | 83.5455% | +| combined-aggressive | kid5k_full | 73.3761% | +| combined-aggressive | fid5k_full | 80.5503% | + +A “win” means a negative paired contrast because lower is better. For interaction rows, negative means the combination is better than the corresponding additive prediction on the raw scale. The geometric relative percentage for that row is the multiplicative interaction `combined × fixed / (global × local) - 1`. + +## Validated matrix + +- Unique training cells: 18 (fixed/global are shared across profiles) +- Evaluated training-seed × NFE cells: 36 +- Scalar metric files read exactly once: 72 +- Selection artifact status: `passed` + +## Effect definitions + +| Effect | Raw-scale paired contrast | Log-scale contrast | +| --- | --- | --- | +| `global_at_local0` | `global - fixed` | `log(global / fixed)` | +| `global_at_local1` | `combined - local` | `log(combined / local)` | +| `local_at_global0` | `local - fixed` | `log(local / fixed)` | +| `local_at_global1` | `combined - global` | `log(combined / global)` | +| `combined_vs_fixed` | `combined - fixed` | `log(combined / fixed)` | +| `additive_interaction` | `combined - global - local + fixed` | `log(combined * fixed / (global * local)) (multiplicative interaction)` | +| `global_main_effect` | `0.5 * [(global - fixed) + (combined - local)]` | `0.5 * [log(global / fixed) + log(combined / local)]` | +| `local_main_effect` | `0.5 * [(local - fixed) + (combined - global)]` | `0.5 * [log(local / fixed) + log(combined / global)]` | + +## Three-seed summaries + +| Profile | NFE | Metric | Effect | Mean Δ | Sample SD | 95% t CI | Wins/3 | Geometric relative % | Relative 95% t CI | +| --- | ---: | --- | --- | ---: | ---: | --- | ---: | ---: | --- | +| conservative | 1 | kid5k_full | `global_at_local0` | -0.0651906 | 0.0962748 | [-0.304351, 0.173969] | 3/3 | -20.1464% | [-66.1408%, 88.3268%] | +| conservative | 1 | kid5k_full | `global_at_local1` | -0.0698903 | 0.119008 | [-0.365523, 0.225743] | 2/3 | -22.8353% | [-74.7788%, 136.087%] | +| conservative | 1 | kid5k_full | `local_at_global0` | 0.0008247 | 0.00162696 | [-0.00321689, 0.00486629] | 1/3 | 0.196202% | [-0.909826%, 1.31458%] | +| conservative | 1 | kid5k_full | `local_at_global1` | -0.00387505 | 0.0219845 | [-0.0584876, 0.0507375] | 1/3 | -3.17762% | [-24.9259%, 24.8709%] | +| conservative | 1 | kid5k_full | `combined_vs_fixed` | -0.0690656 | 0.117486 | [-0.360916, 0.222785] | 2/3 | -22.6839% | [-74.4683%, 134.132%] | +| conservative | 1 | kid5k_full | `additive_interaction` | -0.00469975 | 0.0236002 | [-0.0633259, 0.0539264] | 1/3 | -3.36722% | [-25.8801%, 25.9836%] | +| conservative | 1 | kid5k_full | `global_main_effect` | -0.0675405 | 0.107595 | [-0.334821, 0.19974] | 2/3 | -21.5024% | [-70.7676%, 110.79%] | +| conservative | 1 | kid5k_full | `local_main_effect` | -0.00152518 | 0.0101853 | [-0.0268269, 0.0237766] | 1/3 | -1.50515% | [-12.7998%, 11.2525%] | +| conservative | 1 | fid5k_full | `global_at_local0` | -55.0242 | 70.4659 | [-230.071, 120.023] | 3/3 | -18.2838% | [-58.4254%, 60.6161%] | +| conservative | 1 | fid5k_full | `global_at_local1` | -56.8444 | 86.9325 | [-272.797, 159.108] | 3/3 | -19.794% | [-66.2101%, 90.3825%] | +| conservative | 1 | fid5k_full | `local_at_global0` | 0.320006 | 0.793916 | [-1.65219, 2.2922] | 1/3 | 0.0919939% | [-0.495908%, 0.683369%] | +| conservative | 1 | fid5k_full | `local_at_global1` | -1.50023 | 17.1998 | [-44.2268, 41.2264] | 1/3 | -1.75786% | [-19.0029%, 19.1588%] | +| conservative | 1 | fid5k_full | `combined_vs_fixed` | -56.5244 | 86.1541 | [-270.543, 157.494] | 3/3 | -19.7202% | [-65.9838%, 89.4638%] | +| conservative | 1 | fid5k_full | `additive_interaction` | -1.82023 | 17.9813 | [-46.4882, 42.8477] | 1/3 | -1.84816% | [-19.5515%, 19.7509%] | +| conservative | 1 | fid5k_full | `global_main_effect` | -55.9343 | 78.6163 | [-251.228, 139.359] | 3/3 | -19.0424% | [-62.4952%, 74.7545%] | +| conservative | 1 | fid5k_full | `local_main_effect` | -0.590111 | 8.20972 | [-20.9842, 19.804] | 1/3 | -0.837248% | [-9.69536%, 8.88977%] | +| conservative | 2 | kid5k_full | `global_at_local0` | -0.0413928 | 0.0343019 | [-0.126603, 0.043818] | 3/3 | -35.8822% | [-87.9335%, 240.702%] | +| conservative | 2 | kid5k_full | `global_at_local1` | -0.0425915 | 0.0331696 | [-0.124989, 0.0398063] | 3/3 | -35.6759% | [-87.4575%, 229.884%] | +| conservative | 2 | kid5k_full | `local_at_global0` | 0.000628193 | 0.00232829 | [-0.00515559, 0.00641197] | 1/3 | 0.358716% | [-1.31056%, 2.05623%] | +| conservative | 2 | kid5k_full | `local_at_global1` | -0.000570569 | 0.0018877 | [-0.00525987, 0.00411873] | 1/3 | 0.681595% | [-3.65842%, 5.21712%] | +| conservative | 2 | kid5k_full | `combined_vs_fixed` | -0.0419633 | 0.0330722 | [-0.124119, 0.0401926] | 3/3 | -35.4452% | [-87.3144%, 228.507%] | +| conservative | 2 | kid5k_full | `additive_interaction` | -0.00119876 | 0.00146857 | [-0.00484689, 0.00244936] | 2/3 | 0.321725% | [-3.23364%, 4.00772%] | +| conservative | 2 | kid5k_full | `global_main_effect` | -0.0419922 | 0.0337325 | [-0.125788, 0.0418041] | 3/3 | -35.7791% | [-87.6977%, 235.249%] | +| conservative | 2 | kid5k_full | `local_main_effect` | 2.8812e-05 | 0.00198821 | [-0.00491018, 0.0049678] | 1/3 | 0.520026% | [-2.2589%, 3.37796%] | +| conservative | 2 | fid5k_full | `global_at_local0` | -34.6092 | 36.4091 | [-125.054, 55.8361] | 3/3 | -28.7509% | [-80.3296%, 158.075%] | +| conservative | 2 | fid5k_full | `global_at_local1` | -33.5731 | 34.1031 | [-118.29, 51.1438] | 3/3 | -26.8498% | [-77.2877%, 135.596%] | +| conservative | 2 | fid5k_full | `local_at_global0` | 0.272988 | 1.30758 | [-2.97523, 3.5212] | 1/3 | 0.233043% | [-1.03794%, 1.52034%] | +| conservative | 2 | fid5k_full | `local_at_global1` | 1.30906 | 2.81802 | [-5.69129, 8.30942] | 1/3 | 2.90743% | [-9.35219%, 16.8251%] | +| conservative | 2 | fid5k_full | `combined_vs_fixed` | -33.3001 | 33.6578 | [-116.911, 50.3106] | 3/3 | -26.6794% | [-77.0216%, 133.955%] | +| conservative | 2 | fid5k_full | `additive_interaction` | 1.03608 | 2.39995 | [-4.92574, 6.99789] | 1/3 | 2.66817% | [-8.71977%, 15.4768%] | +| conservative | 2 | fid5k_full | `global_main_effect` | -34.0911 | 35.2545 | [-121.668, 53.486] | 3/3 | -27.8066% | [-78.8632%, 146.579%] | +| conservative | 2 | fid5k_full | `local_main_effect` | 0.791027 | 1.83999 | [-3.77976, 5.36182] | 1/3 | 1.56143% | [-5.14793%, 8.74538%] | +| aggressive | 1 | kid5k_full | `global_at_local0` | -0.0651906 | 0.0962748 | [-0.304351, 0.173969] | 3/3 | -20.1464% | [-66.1408%, 88.3268%] | +| aggressive | 1 | kid5k_full | `global_at_local1` | -0.0711347 | 0.0997616 | [-0.318956, 0.176687] | 3/3 | -22.9739% | [-70.3667%, 100.215%] | +| aggressive | 1 | kid5k_full | `local_at_global0` | -0.00197855 | 0.0126439 | [-0.0333878, 0.0294307] | 2/3 | -0.273741% | [-8.90974%, 9.18101%] | +| aggressive | 1 | kid5k_full | `local_at_global1` | -0.00792264 | 0.0120269 | [-0.0377992, 0.0219539] | 2/3 | -3.80484% | [-17.8826%, 12.6864%] | +| aggressive | 1 | kid5k_full | `combined_vs_fixed` | -0.0731132 | 0.108177 | [-0.341841, 0.195614] | 3/3 | -23.1847% | [-72.1843%, 112.131%] | +| aggressive | 1 | kid5k_full | `additive_interaction` | -0.0059441 | 0.0080417 | [-0.0259208, 0.0140326] | 2/3 | -3.54079% | [-13.567%, 7.6484%] | +| aggressive | 1 | kid5k_full | `global_main_effect` | -0.0681626 | 0.0979512 | [-0.311487, 0.175162] | 3/3 | -21.5729% | [-68.3129%, 94.1106%] | +| aggressive | 1 | kid5k_full | `local_main_effect` | -0.0049506 | 0.0116658 | [-0.03393, 0.0240288] | 2/3 | -2.0552% | [-12.84%, 10.0641%] | +| aggressive | 1 | fid5k_full | `global_at_local0` | -55.0242 | 70.4659 | [-230.071, 120.023] | 3/3 | -18.2838% | [-58.4254%, 60.6161%] | +| aggressive | 1 | fid5k_full | `global_at_local1` | -59.3316 | 75.0723 | [-245.822, 127.158] | 3/3 | -20.2739% | [-62.5866%, 69.8926%] | +| aggressive | 1 | fid5k_full | `local_at_global0` | -1.25864 | 7.60431 | [-20.1488, 17.6315] | 2/3 | -0.290506% | [-5.85581%, 5.60379%] | +| aggressive | 1 | fid5k_full | `local_at_global1` | -5.56609 | 8.84171 | [-27.5301, 16.3979] | 2/3 | -2.71881% | [-13.2204%, 9.05367%] | +| aggressive | 1 | fid5k_full | `combined_vs_fixed` | -60.5903 | 79.0169 | [-256.879, 135.698] | 3/3 | -20.5055% | [-63.8587%, 74.852%] | +| aggressive | 1 | fid5k_full | `additive_interaction` | -4.30745 | 6.06727 | [-19.3794, 10.7645] | 2/3 | -2.43538% | [-10.3447%, 6.17173%] | +| aggressive | 1 | fid5k_full | `global_main_effect` | -57.1779 | 72.7423 | [-237.88, 123.524] | 3/3 | -19.285% | [-60.5566%, 65.1714%] | +| aggressive | 1 | fid5k_full | `local_main_effect` | -3.41236 | 7.66797 | [-22.4607, 15.6359] | 2/3 | -1.51214% | [-9.07696%, 6.68207%] | +| aggressive | 2 | kid5k_full | `global_at_local0` | -0.0413928 | 0.0343019 | [-0.126603, 0.043818] | 3/3 | -35.8822% | [-87.9335%, 240.702%] | +| aggressive | 2 | kid5k_full | `global_at_local1` | -0.0542653 | 0.0593119 | [-0.201604, 0.0930736] | 3/3 | -41.6982% | [-92.831%, 374.139%] | +| aggressive | 2 | kid5k_full | `local_at_global0` | 0.0111605 | 0.0272258 | [-0.0564721, 0.0787932] | 2/3 | 10.2442% | [-30.8968%, 75.8788%] | +| aggressive | 2 | kid5k_full | `local_at_global1` | -0.00171198 | 0.0042984 | [-0.0123898, 0.00896584] | 1/3 | 0.244147% | [-4.41014%, 5.12505%] | +| aggressive | 2 | kid5k_full | `combined_vs_fixed` | -0.0431048 | 0.0324919 | [-0.123819, 0.0376097] | 3/3 | -35.7257% | [-87.3951%, 227.744%] | +| aggressive | 2 | kid5k_full | `additive_interaction` | -0.0128725 | 0.0250141 | [-0.075011, 0.049266] | 1/3 | -9.07085% | [-40.5948%, 39.1815%] | +| aggressive | 2 | kid5k_full | `global_main_effect` | -0.047829 | 0.0468064 | [-0.164102, 0.0684444] | 3/3 | -38.8593% | [-90.6991%, 301.918%] | +| aggressive | 2 | kid5k_full | `local_main_effect` | 0.00472428 | 0.0149477 | [-0.0324079, 0.0418564] | 2/3 | 5.12535% | [-18.5191%, 35.631%] | +| aggressive | 2 | fid5k_full | `global_at_local0` | -34.6092 | 36.4091 | [-125.054, 55.8361] | 3/3 | -28.7509% | [-80.3296%, 158.075%] | +| aggressive | 2 | fid5k_full | `global_at_local1` | -45.7843 | 57.1678 | [-187.797, 96.2283] | 3/3 | -34.4014% | [-87.4276%, 242.272%] | +| aggressive | 2 | fid5k_full | `local_at_global0` | 10.1362 | 21.4193 | [-43.0722, 63.3446] | 1/3 | 8.02949% | [-24.4865%, 54.5468%] | +| aggressive | 2 | fid5k_full | `local_at_global1` | -1.03893 | 2.23356 | [-6.5874, 4.50954] | 2/3 | -0.53799% | [-2.24477%, 1.19859%] | +| aggressive | 2 | fid5k_full | `combined_vs_fixed` | -35.6481 | 36.0373 | [-125.17, 53.8735] | 3/3 | -29.1342% | [-80.5794%, 158.59%] | +| aggressive | 2 | fid5k_full | `additive_interaction` | -11.1751 | 20.7722 | [-62.7762, 40.4259] | 1/3 | -7.93069% | [-36.0849%, 32.6253%] | +| aggressive | 2 | fid5k_full | `global_main_effect` | -40.1967 | 46.7869 | [-156.422, 76.0284] | 3/3 | -31.6345% | [-84.2741%, 197.207%] | +| aggressive | 2 | fid5k_full | `local_main_effect` | 4.54865 | 11.1362 | [-23.1153, 32.2126] | 1/3 | 3.65727% | [-13.0675%, 23.5997%] | + +The CSV files contain every per-cell value and every per-seed raw/log contrast used above. diff --git a/results/gap_factorial_20260727/heldout_headlines.csv b/results/gap_factorial_20260727/heldout_headlines.csv new file mode 100644 index 00000000..d4025d34 --- /dev/null +++ b/results/gap_factorial_20260727/heldout_headlines.csv @@ -0,0 +1,21 @@ +arm,nfe,metric,heldout_seeds,fixed_mean,arm_mean,headline_relative_percent,seed1_delta,seed2_delta,seed1_relative_percent,seed2_relative_percent,seed1_absolute_change_share_percent +global,1,kid5k_full,"1,2",0.3734790086746216,0.36385004222393036,-2.5781814310961937,-0.006878465414047241,-0.012379467487335205,-2.182703941620805,-2.866792357042381,35.71756869894102 +global,1,fid5k_full,"1,2",342.95347970683724,328.45324843717185,-4.228046113443851,-20.735001629500744,-8.265460909829983,-6.609859836681048,-2.2206510302988303,71.49886523837239 +global,2,kid5k_full,"1,2",0.2709367237985134,0.21922114677727222,-19.08769556824653,-0.08098914101719856,-0.022442013025283813,-70.50183771988738,-5.255762568169605,78.30246289617325 +global,2,fid5k_full,"1,2",246.28973913183103,200.43698685950557,-18.617402590118438,-76.61583443421699,-15.089670110433985,-60.82961855101586,-4.115799170834824,83.54551323242882 +local-conservative,1,kid5k_full,"1,2",0.3734790086746216,0.3734559863805771,-0.0061642805913475485,-0.0007238090038299561,0.0006777644157409668,-0.22968215590847818,0.15695423481739354,51.642603499968104 +local-conservative,1,fid5k_full,"1,2",342.95347970683724,342.8242829280814,-0.03767180868562425,-0.28716424993297096,0.028770692421289823,-0.09154161046517775,0.007729716281370954,90.89347566087547 +local-conservative,2,kid5k_full,"1,2",0.2709367237985134,0.2704535834491253,-0.17832220845315216,0.000826716423034668,-0.001792997121810913,0.7196647151101248,-0.4199073918651048,31.557512257829654 +local-conservative,2,fid5k_full,"1,2",246.28973913183103,246.10003715033872,-0.07702390776043178,0.843509193788293,-1.222913156772961,0.6697093738040832,-0.33355699096223584,40.81978660166884 +combined-conservative,1,kid5k_full,"1,2",0.3734790086746216,0.3720105439424515,-0.39318534591309406,0.008264541625976562,-0.011201471090316772,2.6225395487006176,-2.593996207203883,42.45626336747957 +combined-conservative,1,fid5k_full,"1,2",342.95347970683724,336.1700559214439,-1.9779428367928786,-6.6173608673723265,-6.949486703414323,-2.1094682606553703,-1.8670930727688209,48.77596532905272 +combined-conservative,2,kid5k_full,"1,2",0.2709367237985134,0.21833689138293266,-19.414065276251545,-0.08006557822227478,-0.02513408660888672,-69.69786729281475,-5.886227382333906,76.10820657154635 +combined-conservative,2,fid5k_full,"1,2",246.28973913183103,202.31131020535787,-17.856378865598177,-72.09903250159347,-15.857825351352858,-57.24347554727572,-4.325318178242427,81.97090512502697 +local-aggressive,1,kid5k_full,"1,2",0.3734790086746216,0.37635405361652374,0.769800946003607,0.012319445610046387,-0.006569355726242065,3.9092589513813745,-1.5213076657740248,65.22089671396317 +local-aggressive,1,fid5k_full,"1,2",342.95347970683724,344.33603727779484,0.4031326849750716,7.4569197009525965,-4.69180455903745,2.3771010447646153,-1.2605299016775067,61.380269577035456 +local-aggressive,2,kid5k_full,"1,2",0.2709367237985134,0.2886633574962616,6.54272091624275,0.042463310062885284,-0.007010042667388916,36.964725857100504,-1.6417029885432277,85.83066988484234 +local-aggressive,2,fid5k_full,"1,2",246.28973913183103,261.44283455414995,6.152548407308167,34.73056947495195,-4.424378630314095,27.574551772777717,-1.2067761432050512,88.70033330546273 +combined-aggressive,1,kid5k_full,"1,2",0.3734790086746216,0.3627574294805527,-2.8707314052580757,-0.005803346633911133,-0.015639811754226685,-1.8415426712128355,-3.621811103625269,27.063861250595888 +combined-aggressive,1,fid5k_full,"1,2",342.95347970683724,327.9056898679139,-4.3877058345599735,-19.664273362905305,-10.431306314941367,-6.2685353510683255,-2.802540761893667,65.33940722657074 +combined-aggressive,2,kid5k_full,"1,2",0.2709367237985134,0.21625413373112679,-20.182790025929542,-0.0802479237318039,-0.02911725640296936,-69.85660083857543,-6.819057903500517,73.37611809619159 +combined-aggressive,2,fid5k_full,"1,2",246.28973913183103,198.46508108626963,-19.41804730239386,-77.0458442698227,-18.603471821300104,-61.17102753606762,-5.074209928804987,80.55033472107942 diff --git a/results/gap_factorial_20260727/per_cell_metrics.csv b/results/gap_factorial_20260727/per_cell_metrics.csv new file mode 100644 index 00000000..38d1a500 --- /dev/null +++ b/results/gap_factorial_20260727/per_cell_metrics.csv @@ -0,0 +1,37 @@ +arm,profile,global_level,local_level,global_gap_scale,training_seed,nfe,kid5k_full,fid5k_full,run_label,run_dir,eval_dir,training_validation_path,kid_metric_path,fid_metric_path +fixed,shared,0,0,1.0,0,1,0.3799205422401428,338.1121480139137,fixed-g1p0000-seed0-256k,/root/autodl-tmp/ect_project/runs/gap-factorial-20260726/fixed-g1p0000-seed0-256k,/root/autodl-tmp/ect_project/runs/gap-factorial-20260726-eval/fixed-g1p0000-seed0-256k/nfe1,/root/autodl-tmp/ect_project/runs/gap-factorial-20260726/fixed-g1p0000-seed0-256k/validation.json,/root/autodl-tmp/ect_project/runs/gap-factorial-20260726-eval/fixed-g1p0000-seed0-256k/nfe1/metric-kid5k_full.jsonl,/root/autodl-tmp/ect_project/runs/gap-factorial-20260726-eval/fixed-g1p0000-seed0-256k/nfe1/metric-fid5k_full.jsonl +fixed,shared,0,0,1.0,0,2,0.3650455176830292,327.7645058439099,fixed-g1p0000-seed0-256k,/root/autodl-tmp/ect_project/runs/gap-factorial-20260726/fixed-g1p0000-seed0-256k,/root/autodl-tmp/ect_project/runs/gap-factorial-20260726-eval/fixed-g1p0000-seed0-256k/nfe2,/root/autodl-tmp/ect_project/runs/gap-factorial-20260726/fixed-g1p0000-seed0-256k/validation.json,/root/autodl-tmp/ect_project/runs/gap-factorial-20260726-eval/fixed-g1p0000-seed0-256k/nfe2/metric-kid5k_full.jsonl,/root/autodl-tmp/ect_project/runs/gap-factorial-20260726-eval/fixed-g1p0000-seed0-256k/nfe2/metric-fid5k_full.jsonl 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+fixed,shared,0,0,1.0,1,2,0.11487521976232529,125.95152864547677,fixed-g1p0000-seed1-256k,/root/autodl-tmp/ect_project/runs/gap-factorial-20260726/fixed-g1p0000-seed1-256k,/root/autodl-tmp/ect_project/runs/gap-factorial-20260726-eval/fixed-g1p0000-seed1-256k/nfe2,/root/autodl-tmp/ect_project/runs/gap-factorial-20260726/fixed-g1p0000-seed1-256k/validation.json,/root/autodl-tmp/ect_project/runs/gap-factorial-20260726-eval/fixed-g1p0000-seed1-256k/nfe2/metric-kid5k_full.jsonl,/root/autodl-tmp/ect_project/runs/gap-factorial-20260726-eval/fixed-g1p0000-seed1-256k/nfe2/metric-fid5k_full.jsonl 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+aggressive,2,2,fid5k_full,366.6279496181853,351.5382795077513,362.2035709878712,348.0244777968852,-15.089670110433985,-0.04202896397779465,-4.11579917083481,-14.17909319098601,-0.03993358876340647,-3.914675151411176,-4.424378630314095,-0.012141168031684302,-1.2067761432049713,-3.513801710866119,-0.010045792817296118,-0.999550238394032,-18.603471821300104,-0.05207475679509077,-5.074209928804944,0.9105769194479763,0.0020953752143881843,0.20975720471610693,-14.634381650709997,-0.04098127637060056,-4.01528983990384,-3.969090170590107,-0.01109348042449021,-1.103217467797627,0.0020953752143881843,0.20975720471610693 diff --git a/results/generalization/README.md b/results/generalization/README.md new file mode 100644 index 00000000..c6ea8d99 --- /dev/null +++ b/results/generalization/README.md @@ -0,0 +1,71 @@ +# Generalization Results + +This directory stores lightweight, reviewable evidence for a legacy retrospective exploratory q=128 screening. Checkpoints, generated single images, caches, and complete logs remain outside Git. + + +## q=128 evidence status + +The q=128 setting was not formally frozen before its results were observed. +Results are legacy retrospective exploratory evidence produced from a +pre-merge implementation. Canonical dataset-content equivalence was not +established, so this directory does not claim that q was the only changed +experimental input. + +Each metric cell uses one generated 5k sample set. Three recomputations check +numerical reproducibility on that same sample set; they are not independent +statistical repetitions. The statistical unit is the training seed (`n=3`). + +## Planned layout + +```text +results/generalization/ +├── README.md +└── schedule-q128/ + ├── protocol_snapshot.json + ├── asset_manifest.json + ├── training/ + │ ├── fixed_seed3.json + │ ├── fixed_seed4.json + │ ├── fixed_seed5.json + │ ├── global_only_seed3.json + │ ├── global_only_seed4.json + │ └── global_only_seed5.json + ├── evaluation/ + │ ├── per_seed_metrics.csv + │ ├── paired_differences.csv + │ └── aggregate_results.csv + ├── figures/ + │ ├── paired_metrics.png + │ └── qualitative_grid.png + └── CONCLUSION.md +``` + +## Required result fields + +Each metric row must include: + +- setting, +- method, +- global scale, +- training seed, +- budget kimg, +- checkpoint SHA256, +- NFE, +- `mid_t`, +- sampling seed specification, +- sample count, +- KID and/or FID, +- metric code commit, +- reference-stat SHA256, +- precision and device, +- run status. + +## Reporting rules + +1. Report seeds 3, 4, and 5 separately before aggregation. +2. Define paired delta as `global_only - fixed`; negative KID/FID delta favors global-only. +3. Label all 5k-sample results as screening or proxy evidence. +4. Do not interpret q=128 results as confirmatory generalization evidence. +5. Do not reselect `g=1.10` or retrospectively promote this setting after observing results. +6. Keep failures and missing cells visible. +7. Store only lightweight evidence in Git. diff --git a/results/generalization/schedule-q128/256k_seed3_5_proxy5k/RESULTS.md b/results/generalization/schedule-q128/256k_seed3_5_proxy5k/RESULTS.md new file mode 100644 index 00000000..97083a11 --- /dev/null +++ b/results/generalization/schedule-q128/256k_seed3_5_proxy5k/RESULTS.md @@ -0,0 +1,17 @@ +# Legacy Retrospective q=128 Screening at 256 kimg + +> Legacy retrospective exploratory KID/FID-5k proxy evidence produced from a pre-merge implementation. This is not confirmatory generalization evidence or a formal FID-50k result. + +Comparison: fixed sigmoid versus global-only gap scale 1.10. +Training seeds: 3, 4, 5. Lower metric values are better. + +| NFE | Metric | Fixed mean | Global-only mean | Delta (global-fixed) | Global wins | +|---:|---|---:|---:|---:|---:| +| 1 | fid5k_full | 256.37801 | 257.44015 | 1.062141 | 2/3 | +| 1 | kid5k_full | 0.25682499 | 0.25911209 | 0.0022871097 | 2/3 | +| 2 | fid5k_full | 64.588932 | 60.767211 | -3.821721 | 2/3 | +| 2 | kid5k_full | 0.049424714 | 0.045031207 | -0.0043935068 | 2/3 | + +Negative paired delta favors global-only. Each cell uses one generated 5k sample set. The reported mean and recomputation standard deviation summarize three numerical recomputations on that same sample set; they are not independent sampling repetitions. The statistical unit is the training seed (`n=3`). + +The q=128 setting was not formally frozen before its results were observed. The training source differs materially from the reference merged implementation, and canonical dataset-content equivalence was unavailable. These results are therefore retained only as legacy retrospective exploratory screening evidence. diff --git a/results/generalization/schedule-q128/256k_seed3_5_proxy5k/SCREENING_CONCLUSION.md b/results/generalization/schedule-q128/256k_seed3_5_proxy5k/SCREENING_CONCLUSION.md new file mode 100644 index 00000000..99341369 --- /dev/null +++ b/results/generalization/schedule-q128/256k_seed3_5_proxy5k/SCREENING_CONCLUSION.md @@ -0,0 +1,42 @@ +# q=128 Screening Conclusion + +## Scope + +This experiment compares fixed sigmoid against global-only gap scaling +with `global_gap_scale=1.10`. + +- Training seeds: 3, 4, 5 +- Training budget: 256 kimg +- Schedule parameter: q=128 +- Evaluation: KID/FID-5k proxy +- NFE modes: 1 and 2 +- Metric recomputations: 3 per cell on one generated sample set + +This is legacy retrospective exploratory evidence produced from a pre-merge implementation, not confirmatory generalization evidence or a formal FID-50k result. The statistical unit is the training seed (`n=3`); metric recomputations are not independent samples. + +## Result + +The result is mixed and NFE-dependent. + +For NFE=1, global-only won on two of three individual training seeds, +but its across-seed mean was slightly worse for both FID-5k and KID-5k. + +For NFE=2, global-only won on two of three training seeds and improved +the across-seed mean for both FID-5k and KID-5k. + +The paired differences have substantial dispersion across training +seeds. The experiment therefore does not establish a robust general +quality advantage for global-only calibration. + +## Interpretation + +q=128 may be retained as a retrospective mechanism diagnostic. It must not be described as a predeclared secondary setting or used as headline evidence that global-only calibration improves the primary NFE=1 endpoint. + +## Limitations + +- 5k-sample proxy metrics, not formal FID/KID-50k. +- Only three training seeds. +- 256 kimg training budget. +- Experiment used a pre-merge source archive with material training-code differences from the reference merged implementation. +- q=128 had not been formally frozen before results were observed. +- Dataset archives differ bytewise; canonical content equivalence was unavailable, so an only-q-changes claim is unsupported. diff --git a/results/generalization/schedule-q128/256k_seed3_5_proxy5k/aggregate_results.csv b/results/generalization/schedule-q128/256k_seed3_5_proxy5k/aggregate_results.csv new file mode 100644 index 00000000..b1f241ca --- /dev/null +++ b/results/generalization/schedule-q128/256k_seed3_5_proxy5k/aggregate_results.csv @@ -0,0 +1,5 @@ +budget_kimg,nfe,metric,training_seed_count,fixed_seed_mean,fixed_seed_std,global_only_seed_mean,global_only_seed_std,paired_delta_mean,paired_delta_std,global_only_wins,fixed_wins,ties +256,1,fid5k_full,3,256.3780120355624,52.48610737602598,257.4401530326094,35.33121105720781,1.0621409970469813,22.7059746623411,2,1,0 +256,1,kid5k_full,3,0.2568249851465225,0.057782723667916,0.25911209483941394,0.04012283205331046,0.002287109692891439,0.0245751059970834,2,1,0 +256,2,fid5k_full,3,64.58893235434624,20.383361993002076,60.767211387024304,14.021787593898154,-3.8217209673219323,6.488061487669208,2,1,0 +256,2,kid5k_full,3,0.04942471409837405,0.019312365684593387,0.045031207303206124,0.010883777843324779,-0.004393506795167923,0.008433965854486846,2,1,0 diff --git a/results/generalization/schedule-q128/256k_seed3_5_proxy5k/dataset_identity.json b/results/generalization/schedule-q128/256k_seed3_5_proxy5k/dataset_identity.json new file mode 100644 index 00000000..791fd503 --- /dev/null +++ b/results/generalization/schedule-q128/256k_seed3_5_proxy5k/dataset_identity.json @@ -0,0 +1,27 @@ +{ + "archive_byte_identical_to_canonical": false, + "canonical_archive_sha256": "08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372", + "canonical_content_comparison": "unavailable", + "canonical_content_manifest_available": false, + "class_count": 10, + "class_histogram": { + "0": 5000, + "1": 5000, + "2": 5000, + "3": 5000, + "4": 5000, + "5": 5000, + "6": 5000, + "7": 5000, + "8": 5000, + "9": 5000 + }, + "dataset_archive_bytes": 166000134, + "dataset_archive_name": "cifar10-32x32.zip", + "dataset_archive_sha256": "9818e4b801a52eac437485bc8a69e40b54e9ae9c5d1427467343c91de868f1b3", + "dataset_content_manifest_sha256": "e87340fa6eb3dbd9a264b5a18d9a3cc1a1c1b7dd75aebcf6d969b828feed69b8", + "image_count": 50000, + "manifest_method": "sorted relative path + class label + image-file SHA256", + "only_q_changes_claim_supported": false, + "schema_version": 1 +} diff --git a/results/generalization/schedule-q128/256k_seed3_5_proxy5k/evaluation_protocol.env b/results/generalization/schedule-q128/256k_seed3_5_proxy5k/evaluation_protocol.env new file mode 100644 index 00000000..a66215a7 --- /dev/null +++ b/results/generalization/schedule-q128/256k_seed3_5_proxy5k/evaluation_protocol.env @@ -0,0 +1,18 @@ +result_type=legacy_retrospective_exploratory_5k_proxy +training_budget_kimg=256 +schedule_q=128 +global_gap_scale=1.10 +training_seeds=3,4,5 +nfe_modes=1,2 +nfe1_mid_t=[] +nfe2_mid_t=[0.821] +metrics=fid5k_full,kid5k_full +metric_recomputations=3 +independent_sampling_repetitions=1 +statistical_unit=training_seed +independent_training_seed_count=3 +evaluation_seed=20260729 +sample_seeds=0-4999 +precision=fp32 +source_status=premerge_archive_materially_differs_from_reference_main +formal_comparable_result=false diff --git a/results/generalization/schedule-q128/256k_seed3_5_proxy5k/paired_differences.csv b/results/generalization/schedule-q128/256k_seed3_5_proxy5k/paired_differences.csv new file mode 100644 index 00000000..d8ff7623 --- /dev/null +++ b/results/generalization/schedule-q128/256k_seed3_5_proxy5k/paired_differences.csv @@ -0,0 +1,13 @@ +training_seed,budget_kimg,nfe,metric,fixed_mean,global_only_mean,global_minus_fixed,winner_lower_is_better +3,256,1,fid5k_full,316.6366735784031,297.0668374304674,-19.56983614793569,global_only +3,256,1,kid5k_full,0.3231235146522522,0.3040681779384613,-0.019055336713790894,global_only +3,256,2,fid5k_full,88.1256096344071,76.91261500114072,-11.212994633266376,global_only +3,256,2,kid5k_full,0.07170811295509338,0.05759671702980995,-0.014111395925283432,global_only +4,256,1,fid5k_full,220.6396038634675,246.02853015137842,25.38892628791092,fixed +4,256,1,kid5k_full,0.2171778827905655,0.24633222818374634,0.029154345393180847,fixed +4,256,2,fid5k_full,52.828537168338244,51.643327420988115,-1.1852097473501289,global_only +4,256,2,kid5k_full,0.03753798454999924,0.038554560393095016,0.0010165758430957794,fixed +5,256,1,fid5k_full,231.8577586648166,229.2250915159823,-2.6326671488342868,global_only +5,256,1,kid5k_full,0.23017355799674988,0.22693587839603424,-0.003237679600715637,global_only +5,256,2,fid5k_full,52.812650260293374,53.74569173894408,0.9330414786507077,fixed +5,256,2,kid5k_full,0.039028044790029526,0.03894234448671341,-8.570030331611633e-05,global_only diff --git a/results/generalization/schedule-q128/256k_seed3_5_proxy5k/per_run_metrics.csv b/results/generalization/schedule-q128/256k_seed3_5_proxy5k/per_run_metrics.csv new file mode 100644 index 00000000..5b928958 --- /dev/null +++ b/results/generalization/schedule-q128/256k_seed3_5_proxy5k/per_run_metrics.csv @@ -0,0 +1,25 @@ +method,training_seed,budget_kimg,schedule_q,global_gap_scale,nfe,mid_t,metric,repeat_count,value_mean,recomputation_std,value_min,value_max,checkpoint_sha256 +fixed,3,256,128,1.0,1,[],fid5k_full,3,316.6366735784031,1.7778772008049626e-06,316.6366715254873,316.63667460486096,943db8be30a510748343e3843af1705131776e9bd7894073ef682616705f999e +fixed,3,256,128,1.0,1,[],kid5k_full,3,0.3231235146522522,0.0,0.3231235146522522,0.3231235146522522,943db8be30a510748343e3843af1705131776e9bd7894073ef682616705f999e +fixed,3,256,128,1.0,2,[0.821],fid5k_full,3,88.1256096344071,1.9672459059240443e-06,88.1256073628272,88.12561077019706,943db8be30a510748343e3843af1705131776e9bd7894073ef682616705f999e +fixed,3,256,128,1.0,2,[0.821],kid5k_full,3,0.07170811295509338,0.0,0.07170811295509338,0.07170811295509338,943db8be30a510748343e3843af1705131776e9bd7894073ef682616705f999e +fixed,4,256,128,1.0,1,[],fid5k_full,3,220.6396038634675,2.7419952529998283e-06,220.6396006972841,220.6396054465592,fe6ace1e6122def4180adccc5ea463be7f06b2ed3462beb9b0d05778532f46fa 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+global_only,5,256,128,1.1,1,[],fid5k_full,3,229.2250915159823,4.918194801081069e-07,229.2250909480781,229.22509179993443,af8a7c9029659c59bf8a00b8b1e589597703c0c8449e1275f9f4d18cc193a156 +global_only,5,256,128,1.1,1,[],kid5k_full,3,0.22693587839603424,0.0,0.22693587839603424,0.22693587839603424,af8a7c9029659c59bf8a00b8b1e589597703c0c8449e1275f9f4d18cc193a156 +global_only,5,256,128,1.1,2,[0.821],fid5k_full,3,53.74569173894408,6.485491026659585e-07,53.745690990064084,53.74569211338408,af8a7c9029659c59bf8a00b8b1e589597703c0c8449e1275f9f4d18cc193a156 +global_only,5,256,128,1.1,2,[0.821],kid5k_full,3,0.03894234448671341,0.0,0.03894234448671341,0.03894234448671341,af8a7c9029659c59bf8a00b8b1e589597703c0c8449e1275f9f4d18cc193a156 diff --git a/results/generalization/schedule-q128/256k_seed3_5_proxy5k/retry_record.txt b/results/generalization/schedule-q128/256k_seed3_5_proxy5k/retry_record.txt new file mode 100644 index 00000000..9af1c73f --- /dev/null +++ b/results/generalization/schedule-q128/256k_seed3_5_proxy5k/retry_record.txt @@ -0,0 +1,9 @@ +initial_attempt_log=console.log +initial_attempt_status=failed_or_interrupted +final_attempt_log=console.retry1.log +final_attempt_status=passed +completed_cells=12 +metric_file_count=24 +metric_repeats_per_file=3 +evaluation_protocol_changed_between_attempts=false +partial_completed_cells_were_reused=true diff --git a/results/generalization/schedule-q128/256k_seed3_5_proxy5k/run_manifest.json b/results/generalization/schedule-q128/256k_seed3_5_proxy5k/run_manifest.json new file mode 100644 index 00000000..565b54fe --- /dev/null +++ b/results/generalization/schedule-q128/256k_seed3_5_proxy5k/run_manifest.json @@ -0,0 +1,47 @@ +{ + "canonical_content_comparison": "unavailable", + "canonical_dataset_archive_sha256": "08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372", + "class_count": 10, + "complete_executed_source_equivalence": "unverified", + "dataset_archive_sha256": "9818e4b801a52eac437485bc8a69e40b54e9ae9c5d1427467343c91de868f1b3", + "dataset_content_manifest_sha256": "e87340fa6eb3dbd9a264b5a18d9a3cc1a1c1b7dd75aebcf6d969b828feed69b8", + "formal_comparable_result": false, + "generated_sample_count_per_cell": 5000, + "image_count": 50000, + "independent_sampling_repetitions": 1, + "independent_training_seed_count": 3, + "inspected_training_path_classification": "non_material_difference_for_studied_fixed_global_methods", + "metric_code_sha256": "eb8fae9f7dd78cb3e9414fabb560b19ce6e61d18ae4f6fce329f94ce288851f8", + "metric_recomputations": 3, + "mid_t_by_mode": { + "1": [], + "2": [ + 0.821 + ] + }, + "nfe_modes": [ + 1, + 2 + ], + "only_q_changes_claim_supported": false, + "reference_merged_commit": "3a0d603da97dd93ddbb6c7ce49e4a7351d54bb43", + "reference_stats_num_items": 50000, + "reference_stats_sha256": { + "fid_real_mean_cov_50k": "aa1772d335fef7254a698efaf22de6702da4fcf9e1ad72043a2959d075dd4ff0", + "kid_real_all_features_50k": "18a565499b4d42a85b04ccf98581da6d315cfc3df5badc9a279f0fc60e419bcc" + }, + "result_status": "legacy_retrospective_exploratory_screening", + "sample_seeds": "0-4999", + "schema_version": 1, + "statistical_unit": "training_seed", + "training_seeds": [ + 3, + 4, + 5 + ], + "training_source_archive_sha256": "6af4d04198b97f469fdbb168a84848d61e43e585a4f1a7b90bb3e2cd60e1b59b", + "training_source_branch_head": "1395daa16415a2f960a0d1fede09e0a9bf8ae70b", + "training_source_byte_identical_to_reference_main": false, + "training_source_equivalent_to_reference_main": false, + "transfer_checkpoint_sha256": "4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da" +} diff --git a/results/generalization/schedule-q128/SOURCE_PROVENANCE.md b/results/generalization/schedule-q128/SOURCE_PROVENANCE.md new file mode 100644 index 00000000..fa0f354a --- /dev/null +++ b/results/generalization/schedule-q128/SOURCE_PROVENANCE.md @@ -0,0 +1,44 @@ +# Source Provenance + +## Formal training source + +- Source archive SHA256: `6af4d04198b97f469fdbb168a84848d61e43e585a4f1a7b90bb3e2cd60e1b59b` +- Extracted branch head at review: `1395daa16415a2f960a0d1fede09e0a9bf8ae70b` +- Formal runs covered: fixed/global-only x seeds 3/4/5 at 256 kimg +- Reference merged implementation: `main@3a0d603da97dd93ddbb6c7ce49e4a7351d54bb43` + +The codeload archive did not contain `.git` metadata. The branch head is +recorded separately and does not prove that a mutable branch archive was +downloaded at that exact head; the archive SHA256 is the authoritative +identity recorded for the executed source artifact. + +## Relevant-file comparison + +| File | Source SHA256 | Reference SHA256 | Result | +| --- | --- | --- | --- | +| `ct_train.py` | `edb7d0065d560e79825042b5100b8bede45d60246e2d28d7a46c98c5c12c4180` | `edb7d0065d560e79825042b5100b8bede45d60246e2d28d7a46c98c5c12c4180` | byte-identical | +| `training/ct_training_loop.py` | `132d6c638ebbc14b33859736a163a634a63f03c6b0bf543d77fe84c80a5348f2` | `2c389b230601fd7b45d251231563b081075c7efefc9160277c34e616629bd11a` | non-material difference for studied fixed/global-only methods | +| `training/loss.py` | `f25af844199e1637a1d2c341ad5cf8f8b538a0d2bd6aa7a6a444c7e4ca9f5084` | `60afe844dab6071e2306906d95fe36e1d1c2d65ba9d5413a5b84233c67216993` | non-material difference for studied fixed/global-only methods | +| `training/schedules.py` | `d0a675c73588351b9a9891c179ea96ca244ecf54bb4ab8e151db8421bd48422b` | `00f7b38b2189699f9d27910b61864721f7cacb9987b16d59056f03fc80492abf` | non-material difference for studied fixed/global-only methods | +| `ct_eval.py` | `0d0f7cb4790f3c089fbcd3690c8fee45dcc421a93a08c6f1f85a19efd3d85c03` | `0d0f7cb4790f3c089fbcd3690c8fee45dcc421a93a08c6f1f85a19efd3d85c03` | byte-identical | + +- Executed evaluation/metric code tree SHA256: `eb8fae9f7dd78cb3e9414fabb560b19ce6e61d18ae4f6fce329f94ce288851f8` +- `ct_train.py` and `ct_eval.py` are byte-identical to the reference checkout. +- The inspected training-path differences are non-material for the studied + fixed sigmoid and global-only methods. +- Full executed-source equivalence is not claimed because the complete + executed archive is unavailable and no per-file archive manifest was + recorded. + +## Classification + +These runs are **legacy retrospective exploratory evidence produced from +a pre-merge implementation**. No retraining is claimed or required by this +record, and the results are not formally comparable confirmatory evidence. + +## Dataset qualification + +The executed dataset archive differs bytewise from the canonical archive. +A canonical archive was unavailable on the evaluation node, so the same +content-manifest algorithm could not be applied to both assets. Semantic +equivalence and the claim that only q changed are therefore not established. diff --git a/results/generalization/schedule-q128/fresh_256k_seed3_5/README.md b/results/generalization/schedule-q128/fresh_256k_seed3_5/README.md new file mode 100644 index 00000000..6ec750b9 --- /dev/null +++ b/results/generalization/schedule-q128/fresh_256k_seed3_5/README.md @@ -0,0 +1,22 @@ +# Fresh q=128 confirmatory training handoff + +This directory records six fresh q=128 training runs comparing fixed +sigmoid with global-only gap scaling at 1.10 for seeds 3, 4, and 5. + +All six runs reached 256 kimg and produced a final network snapshot, +training state, training options, and training summary. Generation +quality metrics were not run in this training stage. + +Files: + +- `readiness.csv`: six-cell training acceptance table. +- `checkpoint_manifest.csv`: snapshot, state, and config hashes. +- `metadata.json`: shared experiment identity and provenance. +- `q128_config_diff.md`: controlled configuration comparison. +- `q128_seed3_smoke_report.json`: engineering smoke evidence. +- `q128_fresh_vs_legacy_policy.md`: isolation policy. +- `run_q128_confirmatory.sh`: exact sequential runner. +- `train_summaries/`: compact per-cell training records. + +Model snapshots and optimizer states are stored externally and are not +committed to Git. diff --git a/results/generalization/schedule-q128/fresh_256k_seed3_5/ROLE_D_CHECKPOINT_HANDOFF.md b/results/generalization/schedule-q128/fresh_256k_seed3_5/ROLE_D_CHECKPOINT_HANDOFF.md new file mode 100644 index 00000000..f719f4fc --- /dev/null +++ b/results/generalization/schedule-q128/fresh_256k_seed3_5/ROLE_D_CHECKPOINT_HANDOFF.md @@ -0,0 +1,20 @@ +# Role D checkpoint handoff + +The archive contains the six predeclared fresh q=128 checkpoints for +seeds 3, 4, and 5 under fixed sigmoid and global-only g=1.10. + +Role D must: + +1. Recompute the archive SHA256. +2. Extract the archive. +3. Recompute all six checkpoint SHA256 values. +4. Compare them with `checkpoint_mapping.csv`. +5. Record the evaluator-visible checkpoint paths. +6. Return a machine-readable receiver verification JSON. + +The sender manifest is `checkpoint_handoff_sender.json`. + +Formal evaluation remains blocked until: + +- Role D receiver verification passes; and +- q=128/q=256 dataset semantic equivalence is established. diff --git a/results/generalization/schedule-q128/fresh_256k_seed3_5/checkpoint_handoff_sender.json b/results/generalization/schedule-q128/fresh_256k_seed3_5/checkpoint_handoff_sender.json new file mode 100644 index 00000000..9c4a4f67 --- /dev/null +++ b/results/generalization/schedule-q128/fresh_256k_seed3_5/checkpoint_handoff_sender.json @@ -0,0 +1,54 @@ +{ + "archive_content_verification": "PASSED", + "archive_filename": "q128_fresh_256k_handoff.tar.gz", + "archive_sha256": "04d1f7f483618b214c527186fe5ad8e9334e54e2cb230f9aab707ad51ee6d62d", + "archive_size_bytes": 1243443380, + "checkpoint_count": 6, + "checkpoints": [ + { + "checkpoint_id": "q128-fresh-256k-seed3-fixed", + "filename": "q128-seed3-fixed-256k.pkl", + "sha256": "ed7b78649765dac649a1521fb0b2dd1803758b29232d48d2774bfdca42fc6a2e", + "size_bytes": "223171654" + }, + { + "checkpoint_id": "q128-fresh-256k-seed3-global110", + "filename": "q128-seed3-global110-256k.pkl", + "sha256": "a52eb7dbe55ee93b2993150be93aaa46809e23c28cdc7841a226074fce89842e", + "size_bytes": "223171677" + }, + { + "checkpoint_id": "q128-fresh-256k-seed4-fixed", + "filename": "q128-seed4-fixed-256k.pkl", + "sha256": "c00ecfdd50fa668cd4d0b1260b2d759976574d81a0c58111054a1ebf8d9eff88", + "size_bytes": "223171646" + }, + { + "checkpoint_id": "q128-fresh-256k-seed4-global110", + "filename": "q128-seed4-global110-256k.pkl", + "sha256": "17ba6ab56075a21c5cca0867ea456d3afd80906d1e11d6c9a130f50e81bd2d6c", + "size_bytes": "223171655" + }, + { + "checkpoint_id": "q128-fresh-256k-seed5-fixed", + "filename": "q128-seed5-fixed-256k.pkl", + "sha256": "cab7619d790dcc0f35df184be14272e8eaf4e83f5285b4df03e37011e0bc9ca0", + "size_bytes": "223171630" + }, + { + "checkpoint_id": "q128-fresh-256k-seed5-global110", + "filename": "q128-seed5-global110-256k.pkl", + "sha256": "656d8f337b9e122e0d4b9f27db1890e9c629ba15808f0cea7466cc4259d45c32", + "size_bytes": "223171663" + } + ], + "formal_evaluation_eligibility": "BLOCKED", + "handoff_id": "q128-fresh-256k-role-e-to-role-d-v1", + "receiver_status": "PENDING", + "remaining_blockers": [ + "dataset_semantic_equivalence", + "role_d_receiver_verification" + ], + "schema_version": 1, + "sender_status": "READY_FOR_TRANSFER" +} diff --git a/results/generalization/schedule-q128/fresh_256k_seed3_5/checkpoint_manifest.csv b/results/generalization/schedule-q128/fresh_256k_seed3_5/checkpoint_manifest.csv new file mode 100644 index 00000000..baf43e9e --- /dev/null +++ b/results/generalization/schedule-q128/fresh_256k_seed3_5/checkpoint_manifest.csv @@ -0,0 +1,7 @@ +experiment_id,method,seed,snapshot_sha256,training_state_sha256,training_options_sha256 +q128-seed3-fixed-256k,fixed,3,ed7b78649765dac649a1521fb0b2dd1803758b29232d48d2774bfdca42fc6a2e,1578aa907c847bbad3ea8499aed06cfa629693e171f1b44826ad31fa9832861a,1ac4095c3856ddac7cc9f35228940bbc0f499f3796d08dacb6ff42530c0e0ffc +q128-seed3-global110-256k,global110,3,a52eb7dbe55ee93b2993150be93aaa46809e23c28cdc7841a226074fce89842e,ee691f0df89c6a8013961ac37f9764e2314d77fff0b881b7250e17a6c140a130,4559a30e1bc5be7bb98fcf989ed90ddae5aa199d72eeebe9554681d86805e885 +q128-seed4-fixed-256k,fixed,4,c00ecfdd50fa668cd4d0b1260b2d759976574d81a0c58111054a1ebf8d9eff88,64e652771a96b03feb6299ae00dd418b62a57bf4facf409af11617cac2ef5077,29680f4b2d942abe2301784f3441713bf6974b60e990c70dcfda3051e21f367d +q128-seed4-global110-256k,global110,4,17ba6ab56075a21c5cca0867ea456d3afd80906d1e11d6c9a130f50e81bd2d6c,325a4ad35875c04a90bd6f0344ab62678dc68ad84c1d38ad5b6491dc8876d7e0,baa0cfd5ac8137d7e1a2a22f07cff0b1fb13fb3141ca657ceacba83b782ea284 +q128-seed5-fixed-256k,fixed,5,cab7619d790dcc0f35df184be14272e8eaf4e83f5285b4df03e37011e0bc9ca0,4650ed8a83f6c94c4b950b967465ddea1b362d35f0ce61be9a438c6f63410074,ce57a5f59f8b869cd3f54d541a56c29ed0d716697ba7a1afae3833460b8add7d +q128-seed5-global110-256k,global110,5,656d8f337b9e122e0d4b9f27db1890e9c629ba15808f0cea7466cc4259d45c32,50b7a4eaf633a177ce8ec557bfb3b3e2d17038c9c5b6943caa9ee35f13ecf38b,80f9aa71e34b1a641895dbc40902bb938ddfdb47bce694a54907fc8cadeac6af diff --git a/results/generalization/schedule-q128/fresh_256k_seed3_5/integrity_receipts/q128-fresh-256k-seed3-fixed.integrity.json b/results/generalization/schedule-q128/fresh_256k_seed3_5/integrity_receipts/q128-fresh-256k-seed3-fixed.integrity.json new file mode 100644 index 00000000..4b17873d --- /dev/null +++ b/results/generalization/schedule-q128/fresh_256k_seed3_5/integrity_receipts/q128-fresh-256k-seed3-fixed.integrity.json @@ -0,0 +1,56 @@ +{ + "budget_kimg": 256, + "checked_at_unix": 1785678993.3207765, + 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}, + "training_options": "/root/ect-runs/q128-fresh-confirmatory-28b04c08-20260801T070624Z/q128-seed5-global110-256k/training_options.json", + "training_state": { + "cur_nimg": 256000, + "tensors_checked": 1248 + }, + "training_summary": { + "final_kimg": 256.0, + "rows": 2000 + } + }, + "finite_loss_state_passed": true, + "global_gap_scale_identity_passed": true, + "logs_state_consistent": true, + "method": "global110", + "method_identity_passed": true, + "schedule_identity_passed": true, + "schema_version": 1, + "status": "passed", + "training_run_id": "q128-seed5-global110-256k", + "training_seed": 5 +} diff --git a/results/generalization/schedule-q128/fresh_256k_seed3_5/metadata.json b/results/generalization/schedule-q128/fresh_256k_seed3_5/metadata.json new file mode 100644 index 00000000..0ed173d9 --- /dev/null +++ b/results/generalization/schedule-q128/fresh_256k_seed3_5/metadata.json @@ -0,0 +1,39 @@ +{ + "artifact_integrity": "PASSED", + "checkpoint_storage": "external_to_git", + "dataset_byte_identical_to_q256_formal": false, + "dataset_sha256": "9818e4b801a52eac437485bc8a69e40b54e9ae9c5d1427467343c91de868f1b3", + "experiment_id": "q128-fresh-fixed-vs-global110-v1", + "formal_evaluation_eligibility": "BLOCKED", + "integrity_receipt_count": 6, + "methods": { + "fixed": { + "global_gap_scale": 1.0, + "schedule": "sigmoid" + }, + "global110": { + "global_gap_scale": 1.1, + "schedule": "global_sigmoid" + } + }, + "q": 128, + "quality_metrics_run": false, + "remaining_blockers": [ + "dataset_semantic_equivalence", + "role_d_receiver_verification" + ], + "run_count": 6, + "runtime_binding_validation_passed": true, + "schema_version": 1, + "seeds": [ + 3, + 4, + 5 + ], + "target_kimg": 256, + "training_complete_count": 6, + "training_git_branch": "role-e/q128-confirmatory-prep", + "training_git_commit": "28b04c086f7984a56e879472c80bf241ada0fd71", + "training_status": "TRAINING_COMPLETE", + "transfer_checkpoint_sha256": "4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da" +} diff --git a/results/generalization/schedule-q128/fresh_256k_seed3_5/q128_config_diff.md b/results/generalization/schedule-q128/fresh_256k_seed3_5/q128_config_diff.md new file mode 100644 index 00000000..788aafd3 --- /dev/null +++ b/results/generalization/schedule-q128/fresh_256k_seed3_5/q128_config_diff.md @@ -0,0 +1,47 @@ +# Fresh q=128 configuration comparison + +## Experiment matrix + +The experiment contains six fresh training cells: + +| Method | Seed | q | Target | +|---|---:|---:|---:| +| Fixed sigmoid | 3, 4, 5 | 128 | 256 kimg | +| Global-only 1.10 | 3, 4, 5 | 128 | 256 kimg | + +## Shared training configuration + +- Global batch size: 128 +- Per-GPU batch size: 16 +- Optimizer: RAdam +- Learning rate: 0.0001 +- Dropout: 0.2 +- Augmentation probability: 0 +- FP16 and AMP GradScaler: enabled +- TF32: disabled +- k=8, b=1, c=0 +- Scheduler doubling interval: 10000 ticks +- Training starts from the same official transfer checkpoint. + +## Controlled differences + +Fixed uses `schedule=sigmoid` and `global_gap_scale=1.0`. + +Global-only uses `schedule=global_sigmoid` and +`global_gap_scale=1.10`. + +Across paired cells, only the method configuration, training seed, +run identifier, and output directory vary. + +## Relationship to q=256 + +The uploaded q=256 1024-kimg configurations were used only to audit +shared hyperparameters. Their resume checkpoint paths and output +directories were not reused. All q=128 cells are fresh runs from the +common transfer checkpoint and terminate at 256 kimg. + +## Dataset identity + +The experiment used the locally available CIFAR-10 ZIP. Its exact +SHA256 is recorded in `metadata.json`. It is not claimed to be +byte-identical to the ZIP recorded by the q=256 formal evaluation. diff --git a/results/generalization/schedule-q128/fresh_256k_seed3_5/q128_fresh_vs_legacy_policy.md b/results/generalization/schedule-q128/fresh_256k_seed3_5/q128_fresh_vs_legacy_policy.md new file mode 100644 index 00000000..1c1590dd --- /dev/null +++ b/results/generalization/schedule-q128/fresh_256k_seed3_5/q128_fresh_vs_legacy_policy.md @@ -0,0 +1,25 @@ +# Fresh versus legacy q=128 policy + +The legacy q=128 experiments remain retrospective exploratory +screening evidence. + +The new experiment uses the identifier: + +`q128-fresh-fixed-vs-global110-v1` + +The fresh and legacy runs must not be combined in one confirmatory +statistics table. Legacy results must not be used to select seeds, +global-gap scale, checkpoints, or evaluation settings for the fresh +matrix. + +Fresh runs use: + +- seeds 3, 4, and 5; +- fixed sigmoid and global-only 1.10; +- fresh initialization from the common transfer checkpoint; +- 256 kimg training budget; +- isolated run and log directories. + +The `.pkl` and `.pt` assets remain outside Git. Git contains only +metadata, hashes, compact training summaries, protocol documents, and +the reproducible runner. diff --git a/results/generalization/schedule-q128/fresh_256k_seed3_5/q128_seed3_smoke_report.json b/results/generalization/schedule-q128/fresh_256k_seed3_5/q128_seed3_smoke_report.json new file mode 100644 index 00000000..23fbe71b --- /dev/null +++ b/results/generalization/schedule-q128/fresh_256k_seed3_5/q128_seed3_smoke_report.json @@ -0,0 +1,101 @@ +{ + "dataset_byte_identical_to_q256_formal": false, + "dataset_path": "/mnt/ect_project/datasets/cifar10-32x32.zip", + "dataset_sha256": "9818e4b801a52eac437485bc8a69e40b54e9ae9c5d1427467343c91de868f1b3", + "experiment_id": "q128-fresh-seed3-engineering-smoke", + "fixed": { + "batch_gpu": 16, + "batch_size": 128, + "enable_amp": true, + "expected_global_gap_scale": 1.0, + "final_loss_finite": true, + "final_row": { + "adaptive_active": "0", + "attempted_iteration": "40", + "correction": "0", + "elapsed_sec": "49.289590", + "gap_mean": "0.0293511714913", + "gap_over_sigmoid_gap_mean": "1", + "grad_scale": "256", + "loss": "23.67618823", + "loss_ema": "", + "loss_reference": "", + "lower_gap_clip_rate": "0", + "next_loop_cur_tick": "6", + "peak_vram_gb": "6.200673", + "processed_kimg": "5.120000", + "processed_nimg": "5120", + "r_over_t_mean": "0.970648828509", + "schedule": "sigmoid", + "signal_updates": "0", + "stage": "0", + "step_skipped": "0", + "successful_optimizer_steps": "32", + "upper_gap_clip_rate": "0" + }, + "method": "fixed", + "missing_files": [], + "observed_global_gap_scale": 1.0, + "passed": true, + "q": 128.0, + "row_count": 40, + "run_directory": "/root/ect-runs/q128-fresh-engineering-smoke-9818-20260801T020431Z/q128-fresh-seed3-fixed", + "snapshot_sha256": "3226fdb3ec1a63c1d3d914e36a456f44cc09eceaa5d3df423bc277dbc32a6927", + "training_seed": 3, + "training_state_sha256": "0d0cbb27f935a543b51b3f92bafe3580f3a504fa3c753d44a9e96578a99c6412" + }, + "formal_metric_result": false, + "global110": { + "batch_gpu": 16, + "batch_size": 128, + "enable_amp": true, + "expected_global_gap_scale": 1.1, + "final_loss_finite": true, + "final_row": { + "adaptive_active": "0", + "attempted_iteration": "32", + "correction": "0.1", + "elapsed_sec": "36.267501", + "gap_mean": "0.0309001762604", + "gap_over_sigmoid_gap_mean": "1.09999984156", + "grad_scale": "256", + "loss": "19.90813899", + "loss_ema": "", + "loss_reference": "", + "lower_gap_clip_rate": "0", + "next_loop_cur_tick": "5", + "peak_vram_gb": "2.505386", + "processed_kimg": "4.096000", + "processed_nimg": "4096.0", + "r_over_t_mean": "0.96909982374", + "schedule": "global_sigmoid", + "signal_updates": "0", + "stage": "0", + "step_skipped": "0", + "successful_optimizer_steps": "24", + "upper_gap_clip_rate": "0" + }, + "method": "global110", + "missing_files": [], + "observed_global_gap_scale": 1.1, + "passed": true, + "q": 128.0, + "row_count": 32, + "run_directory": "/root/ect-runs/q128-fresh-engineering-smoke-9818-20260801T020431Z/q128-fresh-seed3-global110", + "snapshot_sha256": "f78da62c0c202f1cda741c8539be47059d4fc86bf8d0523256257431e388652b", + "training_seed": 3, + "training_state_sha256": "9d789f04290e1dfcb91841d61ca026d3bc267e7601e3dd0476431f94f9474edf" + }, + "notes": [ + "The locally available CIFAR-10 ZIP was used.", + "This smoke validates execution and artifact creation.", + "It is not a formal generation-quality evaluation." + ], + "result_type": "engineering_smoke", + "schema_version": 1, + "smoke_passed": true, + "target_q": 128, + "training_seed": 3, + "transfer_checkpoint_path": "/mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl", + "transfer_checkpoint_sha256": "4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da" +} diff --git a/results/generalization/schedule-q128/fresh_256k_seed3_5/readiness.csv b/results/generalization/schedule-q128/fresh_256k_seed3_5/readiness.csv new file mode 100644 index 00000000..1b4c382d --- /dev/null +++ b/results/generalization/schedule-q128/fresh_256k_seed3_5/readiness.csv @@ -0,0 +1,7 @@ +experiment_id,method,seed,q,global_gap_scale,target_kimg,final_kimg,final_loss,train_summary_rows,nonfinite_loss_rows,step_skipped_sum,snapshot_present,training_state_present,training_status,artifact_integrity,formal_evaluation_eligibility,blocker +q128-seed3-fixed-256k,fixed,3,128.0,1.0,256,256.0,16.32977402,2000,0,9,True,True,TRAINING_COMPLETE,PASSED,BLOCKED,dataset_semantic_equivalence;role_d_receiver_verification +q128-seed3-global110-256k,global110,3,128.0,1.1,256,256.0,16.24675548,2000,0,9,True,True,TRAINING_COMPLETE,PASSED,BLOCKED,dataset_semantic_equivalence;role_d_receiver_verification +q128-seed4-fixed-256k,fixed,4,128.0,1.0,256,256.0,16.36701238,2000,0,9,True,True,TRAINING_COMPLETE,PASSED,BLOCKED,dataset_semantic_equivalence;role_d_receiver_verification +q128-seed4-global110-256k,global110,4,128.0,1.1,256,256.0,16.34788167,2000,0,8,True,True,TRAINING_COMPLETE,PASSED,BLOCKED,dataset_semantic_equivalence;role_d_receiver_verification +q128-seed5-fixed-256k,fixed,5,128.0,1.0,256,256.0,14.90946436,2000,0,8,True,True,TRAINING_COMPLETE,PASSED,BLOCKED,dataset_semantic_equivalence;role_d_receiver_verification +q128-seed5-global110-256k,global110,5,128.0,1.1,256,256.0,14.82717252,2000,0,9,True,True,TRAINING_COMPLETE,PASSED,BLOCKED,dataset_semantic_equivalence;role_d_receiver_verification diff --git a/results/generalization/schedule-q128/fresh_256k_seed3_5/run_q128_confirmatory.sh b/results/generalization/schedule-q128/fresh_256k_seed3_5/run_q128_confirmatory.sh new file mode 100755 index 00000000..d533cd74 --- /dev/null +++ b/results/generalization/schedule-q128/fresh_256k_seed3_5/run_q128_confirmatory.sh @@ -0,0 +1,134 @@ +#!/usr/bin/env bash +set -Eeuo pipefail + +REPO=/mnt/ect_project/src/recurrence_of_ect +DATA=/mnt/ect_project/datasets/cifar10-32x32.zip +TRANSFER=/mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl + +EXPECTED_DATA_SHA=9818e4b801a52eac437485bc8a69e40b54e9ae9c5d1427467343c91de868f1b3 +EXPECTED_TRANSFER_SHA=4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da + +cd "$REPO" + +test -s "$DATA" || { + echo "Missing dataset: $DATA" + exit 1 +} + +test -s "$TRANSFER" || { + echo "Missing transfer checkpoint: $TRANSFER" + exit 1 +} + +DATA_SHA=$(sha256sum "$DATA" | awk '{print $1}') +TRANSFER_SHA=$(sha256sum "$TRANSFER" | awk '{print $1}') + +test "$DATA_SHA" = "$EXPECTED_DATA_SHA" || { + echo "Dataset SHA mismatch: $DATA_SHA" + exit 1 +} + +test "$TRANSFER_SHA" = "$EXPECTED_TRANSFER_SHA" || { + echo "Transfer SHA mismatch: $TRANSFER_SHA" + exit 1 +} + +GIT_COMMIT=$(git rev-parse HEAD) +GIT_SHORT=${GIT_COMMIT:0:8} +STAMP=$(date -u +%Y%m%dT%H%M%SZ) + +RUN_ROOT="/root/ect-runs/q128-fresh-confirmatory-${GIT_SHORT}-${STAMP}" +LOG_ROOT="/mnt/ect_project/logs/role_e_q128_confirmatory/${GIT_SHORT}-${STAMP}" + +mkdir -p "$RUN_ROOT" "$LOG_ROOT" + +cat > "$LOG_ROOT/matrix_metadata.env" <&1 | tee -a "$logfile" + + test -s "$outdir/network-snapshot-latest.pkl" + test -s "$outdir/training-state-latest.pt" + test -s "$outdir/train_summary.csv" + + grep -q "Exiting..." "$logfile" + + sha256sum \ + "$outdir/network-snapshot-latest.pkl" \ + "$outdir/training-state-latest.pt" \ + "$outdir/training_options.json" \ + > "$outdir/final_sha256.txt" + + echo "===== PASS $run_id =====" | tee -a "$logfile" +} + +for seed in 3 4 5; do + run_cell "$seed" fixed sigmoid 1.0 + run_cell "$seed" global110 global_sigmoid 1.10 +done + +echo "Q128_CONFIRMATORY_OVERALL_EXIT=0" | + tee "$LOG_ROOT/overall_status.txt" + +echo "RUN_ROOT=$RUN_ROOT" +echo "LOG_ROOT=$LOG_ROOT" diff --git a/results/generalization/schedule-q128/fresh_256k_seed3_5/runtime_binding_receipt.json b/results/generalization/schedule-q128/fresh_256k_seed3_5/runtime_binding_receipt.json new file mode 100644 index 00000000..ea4be9bc --- /dev/null +++ b/results/generalization/schedule-q128/fresh_256k_seed3_5/runtime_binding_receipt.json @@ -0,0 +1,15 @@ +{ + "formal_evaluation_eligibility": "BLOCKED", + "frozen_binding_sha256": "79089d822fd6784c1264ac3da42d30d028ef99cb9956bda7a872e7ecb911fdde", + "matrix_id": "q128-fresh-fixed-vs-global110-v1", + "remaining_blockers": [ + "dataset_semantic_equivalence", + "role_d_receiver_verification" + ], + "runtime_manifest_sha256": "90d532cb5516a8cd6fa005b1f82fdac149c38693218e0e8ec1b3f578f7359947", + "runtime_manifest_versioned": false, + "schema_version": 1, + "validated_cell_count": 6, + "validation_passed": true, + "validator": "scripts/validate_staged_runtime_manifest.py" +} diff --git a/results/generalization/schedule-q128/fresh_256k_seed3_5/staged_evaluation_confirmatory_q128.frozen.json b/results/generalization/schedule-q128/fresh_256k_seed3_5/staged_evaluation_confirmatory_q128.frozen.json new file mode 100644 index 00000000..eafdb571 --- /dev/null +++ b/results/generalization/schedule-q128/fresh_256k_seed3_5/staged_evaluation_confirmatory_q128.frozen.json @@ -0,0 +1,210 @@ +{ + "cells": [ + { + "budget_kimg": 256, + "checkpoint_id": "q128-fresh-256k-seed3-fixed", + "checkpoint_sha256": "ed7b78649765dac649a1521fb0b2dd1803758b29232d48d2774bfdca42fc6a2e", + "executed_training_source_commit": "28b04c086f7984a56e879472c80bf241ada0fd71", + "global_gap_scale": 1.0, + "method": "fixed", + "schedule_identity": "sigmoid", + "schedule_q": 128, + "training_integrity_receipt": { + "checker_git_commit": "7ef137770bbc8450fe183fc6e6b4c8b861bb4ca0", + "receipt_filename": "q128-fresh-256k-seed3-fixed.integrity.json", + "receipt_id": "q128-fresh-256k-seed3-fixed", + "receipt_sha256": "6933dfce6ab784bf4285a3944ef27f4d32fb4f9fbd53e5e8bbeae736b9689728", + "receipt_type": "machine-generated", + "status": "passed" + }, + "training_seed": 3 + }, + { + "budget_kimg": 256, + "checkpoint_id": "q128-fresh-256k-seed3-global110", + "checkpoint_sha256": "a52eb7dbe55ee93b2993150be93aaa46809e23c28cdc7841a226074fce89842e", + "executed_training_source_commit": "28b04c086f7984a56e879472c80bf241ada0fd71", + "global_gap_scale": 1.1, + "method": "global110", + "schedule_identity": "global_sigmoid", + "schedule_q": 128, + "training_integrity_receipt": { + "checker_git_commit": "7ef137770bbc8450fe183fc6e6b4c8b861bb4ca0", + "receipt_filename": "q128-fresh-256k-seed3-global110.integrity.json", + "receipt_id": "q128-fresh-256k-seed3-global110", + "receipt_sha256": "b4cc7beba4fd61933ed97feda8a7ad871bcbf4b0ac77ece8a934c0fac0033286", + "receipt_type": "machine-generated", + "status": "passed" + }, + "training_seed": 3 + }, + { + "budget_kimg": 256, + "checkpoint_id": "q128-fresh-256k-seed4-fixed", + "checkpoint_sha256": "c00ecfdd50fa668cd4d0b1260b2d759976574d81a0c58111054a1ebf8d9eff88", + "executed_training_source_commit": "28b04c086f7984a56e879472c80bf241ada0fd71", + "global_gap_scale": 1.0, + "method": "fixed", + "schedule_identity": "sigmoid", + "schedule_q": 128, + "training_integrity_receipt": { + "checker_git_commit": "7ef137770bbc8450fe183fc6e6b4c8b861bb4ca0", + "receipt_filename": "q128-fresh-256k-seed4-fixed.integrity.json", + "receipt_id": "q128-fresh-256k-seed4-fixed", + "receipt_sha256": "bfb7020171568fc259e1c79492f3d2b58178dbe9bae2ce2ed18751f5f82d2cc9", + "receipt_type": "machine-generated", + "status": "passed" + }, + "training_seed": 4 + }, + { + "budget_kimg": 256, + "checkpoint_id": "q128-fresh-256k-seed4-global110", + "checkpoint_sha256": "17ba6ab56075a21c5cca0867ea456d3afd80906d1e11d6c9a130f50e81bd2d6c", + "executed_training_source_commit": "28b04c086f7984a56e879472c80bf241ada0fd71", + "global_gap_scale": 1.1, + "method": "global110", + "schedule_identity": "global_sigmoid", + "schedule_q": 128, + "training_integrity_receipt": { + "checker_git_commit": "7ef137770bbc8450fe183fc6e6b4c8b861bb4ca0", + "receipt_filename": "q128-fresh-256k-seed4-global110.integrity.json", + "receipt_id": "q128-fresh-256k-seed4-global110", + "receipt_sha256": "2d68c1fdf5589a1bb7c1e33f7ab96a4e7f3a1a8b068ddacb5de864f633651084", + "receipt_type": "machine-generated", + "status": "passed" + }, + "training_seed": 4 + }, + { + "budget_kimg": 256, + "checkpoint_id": 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Machine-local checkpoint and receipt paths are excluded.", + "evaluation_contracts": [ + { + "budget_kimg": 256, + "evidence_class": "formal", + "generation_seed_range": "0-49999", + "metric_names": [ + "kid50k_full", + "fid50k_full" + ], + "metric_seed": 20260730, + "sample_count": 50000, + "stage": "formal" + } + ], + "formal_promotion_policy": { + "eligibility": "provenance_and_integrity_only", + "quick_metric_performance": "not_an_eligibility_criterion", + "required_evaluation_checkpoint_ids": [ + "q128-fresh-256k-seed3-fixed", + "q128-fresh-256k-seed3-global110", + "q128-fresh-256k-seed4-fixed", + "q128-fresh-256k-seed4-global110", + "q128-fresh-256k-seed5-fixed", + "q128-fresh-256k-seed5-global110" + ], + "required_formal_checkpoint_ids": [ + "q128-fresh-256k-seed3-fixed", + "q128-fresh-256k-seed3-global110", + "q128-fresh-256k-seed4-fixed", + "q128-fresh-256k-seed4-global110", + "q128-fresh-256k-seed5-fixed", + "q128-fresh-256k-seed5-global110" + ], + "rule": "Every predeclared checkpoint must enter formal evaluation when its immutable provenance matches and its training-integrity receipt passes. Quick KID/FID performance, if an implementation smoke is run, may not exclude, add, substitute, or otherwise select a formal checkpoint." + }, + "manifest_kind": "frozen-logical-checkpoint-matrix", + "matrix_id": "q128-fresh-fixed-vs-global110-v1", + "method_definitions": { + "fixed": { + "global_gap_scale": 1.0, + "label": "fixed sigmoid", + "local_controller": "disabled", + "schedule_identity": "sigmoid" + }, + "global110": { + "global_gap_scale": 1.1, + "label": "global-only g=1.10", + "local_controller": "disabled", + "schedule_identity": "global_sigmoid" + } + }, + "nfe_modes": { + "1": [], + "2": [ + 0.821 + ] + }, + "protocol": "staged-checkpoint-evaluation-v1", + "runtime_binding": { + "binding_rule": "The runtime manifest must preserve every frozen cell identity, checkpoint SHA256, executed training commit, and receipt digest, then add machine-local checkpoint and receipt paths.", + "required": true, + "required_runtime_fields": [ + "checkpoint", + "integrity_receipt" + ], + "versioned_paths": false + }, + "schema_version": 1, + "training": { + "budget_kimg": [ + 256 + ], + "schedule_q": 128, + "training_seeds": [ + 3, + 4, + 5 + ] + } +} diff --git a/results/generalization/schedule-q128/fresh_256k_seed3_5/train_summaries/q128-seed3-fixed-256k_train_summary.csv b/results/generalization/schedule-q128/fresh_256k_seed3_5/train_summaries/q128-seed3-fixed-256k_train_summary.csv new file mode 100644 index 00000000..3d4a52f6 --- /dev/null +++ b/results/generalization/schedule-q128/fresh_256k_seed3_5/train_summaries/q128-seed3-fixed-256k_train_summary.csv @@ -0,0 +1,2001 @@ +attempted_iteration,successful_optimizer_steps,processed_nimg,processed_kimg,loss,grad_scale,step_skipped,schedule,stage,next_loop_cur_tick,loss_ema,loss_reference,correction,signal_updates,adaptive_active,r_over_t_mean,gap_mean,gap_over_sigmoid_gap_mean,lower_gap_clip_rate,upper_gap_clip_rate,elapsed_sec,peak_vram_gb 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a/results/generalization/schedule-q128/fresh_256k_seed3_5/train_summaries/q128-seed4-fixed-256k_train_summary.csv b/results/generalization/schedule-q128/fresh_256k_seed3_5/train_summaries/q128-seed4-fixed-256k_train_summary.csv new file mode 100644 index 00000000..730dcab7 --- /dev/null +++ b/results/generalization/schedule-q128/fresh_256k_seed3_5/train_summaries/q128-seed4-fixed-256k_train_summary.csv @@ -0,0 +1,2001 @@ +attempted_iteration,successful_optimizer_steps,processed_nimg,processed_kimg,loss,grad_scale,step_skipped,schedule,stage,next_loop_cur_tick,loss_ema,loss_reference,correction,signal_updates,adaptive_active,r_over_t_mean,gap_mean,gap_over_sigmoid_gap_mean,lower_gap_clip_rate,upper_gap_clip_rate,elapsed_sec,peak_vram_gb +1,0,128.0,0.128000,17.34298038,65536,1,sigmoid,0,1,,,0,0,0,0.970468799852,0.0295312001479,1,0,0,10.783905,5.777984 +2,0,256.0,0.256000,14.82715929,32768,1,sigmoid,0,1,,,0,0,0,0.970882314628,0.0291176853723,1,0,0,12.492908,2.089655 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+1999,1990,255872.0,255.872000,16.11063099,128,0,global_sigmoid,0,250,,,0.1,0,0,0.968698715843,0.0313012841574,1.10000006917,0,0,2159.761088,2.511856 +2000,1991,256000.0,256.000000,14.82717252,128,0,global_sigmoid,0,251,,,0.1,0,0,0.970209126046,0.0297908739537,1.09999993541,0,0,2160.489113,2.511856 diff --git a/results/generalization/schedule-q128/preflight_seed3_4k/README.md b/results/generalization/schedule-q128/preflight_seed3_4k/README.md new file mode 100644 index 00000000..acf285dc --- /dev/null +++ b/results/generalization/schedule-q128/preflight_seed3_4k/README.md @@ -0,0 +1,23 @@ +# q=128 Seed 3 Engineering Preflight + +This directory contains lightweight evidence from a paired 4.096 kimg +engineering smoke: + +- fixed sigmoid, q=128; +- global-only, q=128 and g=1.10; +- training seed 3; +- identical assets, optimizer, batch, precision, and budget. + +This is preliminary engineering evidence produced from an unmerged PR #23 +source archive. It validates command parsing, paired execution, telemetry, +checkpoint writing, and the expected gap direction. + +It is not a generation-quality result: + +- no KID or FID was run; +- the q=128 secondary setting was not yet formally approved; +- the source implementation was not yet merged into main; +- it must not be used as paper evidence or compared with formal benchmarks. + +Checkpoints, training states, generated images, and complete logs are stored +outside Git. diff --git a/results/generalization/schedule-q128/preflight_seed3_4k/SMOKE_REPORT.md b/results/generalization/schedule-q128/preflight_seed3_4k/SMOKE_REPORT.md new file mode 100644 index 00000000..c2f58454 --- /dev/null +++ b/results/generalization/schedule-q128/preflight_seed3_4k/SMOKE_REPORT.md @@ -0,0 +1,10 @@ +# q=128 Paired Engineering Smoke + +Preliminary engineering evidence only. No KID or FID was run. + +| Arm | Attempted | Successful | Skipped | Final loss | Final gap | +| --- | ---: | ---: | ---: | ---: | ---: | +| Fixed | 32 | 24 | 8 | 22.273749 | 0.02809107 | +| Global-only g=1.10 | 32 | 24 | 8 | 19.908139 | 0.03090018 | + +This smoke validates command parsing, paired execution, telemetry, checkpoint writing, and the expected gap direction. It is not a generation-quality or formal benchmark result. diff --git a/results/generalization/schedule-q128/preflight_seed3_4k/checkpoint_sha256.txt b/results/generalization/schedule-q128/preflight_seed3_4k/checkpoint_sha256.txt new file mode 100644 index 00000000..8a41231e --- /dev/null +++ b/results/generalization/schedule-q128/preflight_seed3_4k/checkpoint_sha256.txt @@ -0,0 +1,2 @@ +7c57793f58b94e00d51b296babbb1e74795cd71b83794ad669927ab06de9be81 fixed-q128-seed3-4k/network-snapshot-latest.pkl +f2f04cf67211ced879a0afcf7a79c636ad822c73ae0430739b1cb4d4a5d82d27 global-g1p10-q128-seed3-4k/network-snapshot-latest.pkl diff --git a/results/generalization/schedule-q128/preflight_seed3_4k/fixed_train_summary.csv b/results/generalization/schedule-q128/preflight_seed3_4k/fixed_train_summary.csv new file mode 100644 index 00000000..c0b85b62 --- /dev/null +++ b/results/generalization/schedule-q128/preflight_seed3_4k/fixed_train_summary.csv @@ -0,0 +1,33 @@ 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+c3857a13658b43faed063120e2ba593c1ead4e5a8b1d37346d74fae5ed9497e8 nfe2/images/seed000063.png +2a1ebcb1f2a587e19332a71e55ca112c1ed3ca6a354eb037cbac5cd8bd14afff nfe2/grid_8x8.png +eeecc69850092a8245a64ebf2e46df23be634e5aeeebfc76e4f858f1b962e643 metadata.json diff --git a/results/paired_1024k_seed0_5090/FINALIZATION_RECOVERY.md b/results/paired_1024k_seed0_5090/FINALIZATION_RECOVERY.md new file mode 100644 index 00000000..ea229a22 --- /dev/null +++ b/results/paired_1024k_seed0_5090/FINALIZATION_RECOVERY.md @@ -0,0 +1,4 @@ +# Finalization recovery audit + +1. The first formal metric launch stopped before producing a metric because `torchrun` was absent from the non-interactive PATH. The failed launch log is preserved as `kid5k/sigmoid/nfe1/launch_failure_127.log`. Evaluation resumed with a runtime-only wrapper that invokes the frozen venv as `python -m torch.distributed.run`; no evaluation code or protocol parameter changed. +2. All eight KID/FID cells then completed successfully. The first finalization attempt found that the evaluation venv lacks matplotlib. Its traceback is preserved in `finalize.log`. Pure-CPU plotting and packaging were rerun with `/root/miniconda3/bin/python`, which has matplotlib 3.10.3 and the same project data; no metric was rerun or altered. diff --git a/results/paired_1024k_seed0_5090/FINAL_1024K_SEED0_CONCLUSION.md b/results/paired_1024k_seed0_5090/FINAL_1024K_SEED0_CONCLUSION.md new file mode 100644 index 00000000..dc45326c --- /dev/null +++ b/results/paired_1024k_seed0_5090/FINAL_1024K_SEED0_CONCLUSION.md @@ -0,0 +1,19 @@ +# 1024 kimg seed0 paired evaluation conclusion + +Status: **MIXED_OR_TIE** + +## Optimization-loss evidence + +Adaptive-B final loss was 17.38587940 versus 16.77903271 for fixed (+3.617% relative). Fixed completed 7987 successful updates with 13 AMP skips; Adaptive-B completed 7985 with 15 skips. Neither trajectory contains a non-finite recorded loss. Training loss is not treated as generation-quality evidence. + +## Generation-quality evidence + +KID deltas (adaptive minus fixed) are +0.000449683 at NFE1 and -0.000327659 at NFE2; FID deltas are +1.136762 and +0.194039. The paired metrics are mixed or effectively tied under the pre-frozen directional rule. + +## Visual evidence + +Unselected paired seeds 0–63 are archived for both schedules and both NFEs. They are auxiliary and do not override KID/FID. + +## Decision and limitations + +This is a strictly paired RTX 5090, 1024 kimg, training-seed0 result. It cannot establish general superiority or inferiority. The metrics use 5k generated samples, not standard FID-50k. Seed1 uses a separate batch-gpu=128 execution protocol and must be compared within its own paired arms. diff --git a/results/paired_1024k_seed0_5090/PAIR_EVALUATION_VALIDATION.json b/results/paired_1024k_seed0_5090/PAIR_EVALUATION_VALIDATION.json new file mode 100644 index 00000000..f1e1ec7c --- /dev/null +++ b/results/paired_1024k_seed0_5090/PAIR_EVALUATION_VALIDATION.json @@ -0,0 +1,143 @@ +{ + "budget_kimg": 1024, + "checkpoints": { + "adaptive_v1": { + "path": "/mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/adaptive-b-1024k-seed0/network-snapshot-000032.pkl", + "sha256": "7aa95fe6c44e0cc4b16eb030bc5bdf7869ee2d5a3176692530c251c172e0d908" + }, + "sigmoid": { + "path": "/mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/sigmoid-1024k-seed0/network-snapshot-000032.pkl", + "sha256": "a54f3df776442c303f5b22a24541a5446e3ce62f05c71a7efe5d0e6747b04ceb" + } + }, + "decision": "MIXED_OR_TIE", + "evaluation_commit": "6fdcc54472b1cbc0b3e5d9c649df850b8969ead4", + "fid_results": { + "fid5k_nfe1": { + "adaptive_v1": 16.67336946594076, + "delta_adaptive_minus_sigmoid": 1.1367616395313753, + "relative_delta_percent": 7.316665595427395, + "sigmoid": 15.536607826409384 + }, + "fid5k_nfe2": { + "adaptive_v1": 8.329946889195318, + "delta_adaptive_minus_sigmoid": 0.19403942157295262, + "relative_delta_percent": 2.3849757675483816, + "sigmoid": 8.135907467622365 + } + }, + "hardware": { + "driver": "580.105.08", + "gpu": "NVIDIA GeForce RTX 5090", + "memory_mib": 32607 + }, + "kid_results": { + "kid5k_nfe1": { + "adaptive_v1": 0.00723601015015015, + "delta_adaptive_minus_sigmoid": 0.0004496826526526679, + "relative_delta_percent": 6.626303443482389, + "sigmoid": 0.006786327497497482 + }, + "kid5k_nfe2": { + "adaptive_v1": 0.00141295428428426, + "delta_adaptive_minus_sigmoid": -0.00032765863363366874, + "relative_delta_percent": -18.824325055889197, + "sigmoid": 0.0017406129179179288 + } + }, + "limitations": [ + "single training seed", + "5k proxy metrics", + "not a standard FID-50k benchmark", + "single RTX 5090 pair", + "cannot establish general superiority", + "batch-gpu=16 protocol; do not mix checkpoints with seed1 batch-gpu=128 protocol" + ], + "pair_root": "/mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z", + "protocol": { + "fid_secondary": true, + "generated_seeds": "0-4999", + "kid_primary": true, + "mid_t": 0.821, + "nfe": [ + 1, + 2 + ], + "precision": "fp32", + "sample_count": 5000 + }, + "runtime": { + "cuda": "12.8", + "python_venv": "/root/autodl-tmp/venvs/ect5090", + "pytorch": "2.10.0+cu128" + }, + "schema_version": 1, + "seed": 0, + "training_commit": "4b80a4f78f954a93aa5d4a022b20efe661a7b184", + "training_results": { + "adaptive_v1": { + "arm": "adaptive_v1", + "attempted_iterations": 8000, + "correction_clamp": 0.01, + "correction_max": 0.00402080691908, + "correction_mean": 0.0037456725832970563, + "correction_min": 3.66536915368e-05, + "correction_saturation_rate": 0.0, + "correction_sign_flips": 0, + "correction_std": 0.00036712059973618484, + "final_correction": 0.00391628849673, + "final_loss": 17.3858794, + "first_adapted_pair_iteration": 129, + "first_nonzero_correction_iteration": 129, + "gap_max": 0.0155177241311, + "gap_mean": 0.010775597477013104, + "gap_min": 0.00893201069397, + "minimum_finite_loss": 13.20623493, + "nan_inf_count": 0, + "normalized_loss_auc": 17.20331839192717, + "peak_gpu_memory_gb": 2.426729, + "r_over_t_max": 0.991067989306, + "r_over_t_mean": 0.9892244025229887, + "r_over_t_min": 0.984482275869, + "signal_updates": 2048, + "skipped_steps": 15, + "successful_steps": 7985, + "trailing_100_mean": 16.8322541565, + "trailing_100_median": 16.69880158, + "trailing_100_std": 1.0795515197890224, + "trailing_25_mean": 16.67150715, + "trailing_25_median": 16.81525397, + "trailing_25_std": 0.9881173683988997, + "trailing_50_mean": 16.8429638964, + "trailing_50_median": 16.809074520000003, + "trailing_50_std": 0.9738494209812143, + "wall_time_seconds": 6403.940569 + }, + "sigmoid": { + "arm": "sigmoid", + "attempted_iterations": 8000, + "final_loss": 16.77903271, + "minimum_finite_loss": 12.51593566, + "nan_inf_count": 0, + "normalized_loss_auc": 16.659747160242773, + "peak_gpu_memory_gb": 2.426729, + "skipped_steps": 13, + "successful_steps": 7987, + "trailing_100_mean": 16.2883404576, + "trailing_100_median": 16.307300509999997, + "trailing_100_std": 1.041926294247584, + "trailing_25_mean": 16.1079650264, + "trailing_25_median": 16.21914923, + "trailing_25_std": 0.9524960410832125, + "trailing_50_mean": 16.2933293582, + "trailing_50_median": 16.208322705, + "trailing_50_std": 0.9251349281597794, + "wall_time_seconds": 6811.020206 + } + }, + "visual_results": { + "grids_created": 6, + "paired_seeds": "0-63", + "role": "auxiliary only" + } +} diff --git a/results/paired_1024k_seed0_5090/README.md b/results/paired_1024k_seed0_5090/README.md new file mode 100644 index 00000000..ec80f5dd --- /dev/null +++ b/results/paired_1024k_seed0_5090/README.md @@ -0,0 +1,57 @@ +# Paired 1024 kimg seed0 evaluation (RTX 5090) + +This directory archives the compact, auditable results for the paired seed0 run: + +- fixed schedule: `sigmoid` +- adaptive schedule: Scheme B (`adaptive_v1`, warmup 32 updates, max adjustment 0.01) +- training budget: 1024 kimg per arm +- global batch: 128 +- `batch-gpu`: 16 for both arms +- training seed: 0 +- evaluation seeds: 0–4999 +- evaluation precision: FP32 +- primary metric: KID-5k proxy +- secondary metric: FID-5k proxy +- evaluation code: `6fdcc54472b1cbc0b3e5d9c649df850b8969ead4` + +## Result + +The pre-frozen classification is **`MIXED_OR_TIE`**. + +| Metric | NFE | Fixed | Adaptive-B | Adaptive − Fixed | +|---|---:|---:|---:|---:| +| KID-5k | 1 | 0.0067863275 | 0.0072360102 | +0.0004496827 | +| KID-5k | 2 | 0.0017406129 | 0.0014129543 | −0.0003276586 | +| FID-5k | 1 | 15.53660783 | 16.67336947 | +1.13676164 | +| FID-5k | 2 | 8.13590747 | 8.32994689 | +0.19403942 | + +Adaptive-B improves the primary KID proxy at NFE=2, but regresses KID at NFE=1, while FID is worse at both NFEs. This single-seed result therefore does not establish a general advantage or disadvantage. + +## Training summary + +| Arm | Attempts | Successful | AMP skips | Final loss | Trailing-50 mean | +|---|---:|---:|---:|---:|---:| +| Fixed | 8000 | 7987 | 13 | 16.77903271 | 16.29332936 | +| Adaptive-B | 8000 | 7985 | 15 | 17.38587940 | 16.84296390 | + +Both trajectories contain zero recorded NaN/Inf losses. Training loss is reported separately and is not treated as generation-quality evidence. + +## Audit map + +- `PAIR_EVALUATION_VALIDATION.json`: complete machine-readable result and decision +- `checkpoint_identity.*`: numbered checkpoint identities and SHA256 values +- `frozen_evaluation_protocol.*`: locked sampling and metric protocol +- `all_metrics.*`, `kid5k_summary.*`, `fid5k_summary.*`: metric results and paired deltas +- `training_summary.*`: optimizer and controller trajectory statistics +- `cells/`: exact command, serialized options, checkpoint identity, and raw metric JSONL for every metric/arm/NFE cell +- `figures/`: loss/controller plots and unselected seeds 0–63 grids +- `tests.log`: 17 frozen evaluation tests +- `FINALIZATION_RECOVERY.md`: audit trail for two pre/post-metric runtime-only recoveries + +Large artifacts are deliberately excluded: checkpoints, optimizer states, CIFAR-10 data, generated-image corpora, and full training logs. + +## Limitations + +- One training seed and one RTX 5090 pair +- 5k proxy metrics, not standard FID-50k +- Seed1 uses a separate `batch-gpu=128` paired execution protocol; compare its fixed/adaptive arms internally before any cross-seed aggregation diff --git a/results/paired_1024k_seed0_5090/all_metrics.csv b/results/paired_1024k_seed0_5090/all_metrics.csv new file mode 100644 index 00000000..6f98a710 --- /dev/null +++ b/results/paired_1024k_seed0_5090/all_metrics.csv @@ -0,0 +1,9 @@ +metric,nfe,arm,value +kid5k,1,sigmoid,0.006786327497497482 +kid5k,1,adaptive_v1,0.00723601015015015 +kid5k,2,sigmoid,0.0017406129179179288 +kid5k,2,adaptive_v1,0.00141295428428426 +fid5k,1,sigmoid,15.536607826409384 +fid5k,1,adaptive_v1,16.67336946594076 +fid5k,2,sigmoid,8.135907467622365 +fid5k,2,adaptive_v1,8.329946889195318 diff --git a/results/paired_1024k_seed0_5090/all_metrics.json b/results/paired_1024k_seed0_5090/all_metrics.json new file mode 100644 index 00000000..691d1d70 --- /dev/null +++ b/results/paired_1024k_seed0_5090/all_metrics.json @@ -0,0 +1,26 @@ +{ + "fid5k_nfe1": { + "adaptive_v1": 16.67336946594076, + "delta_adaptive_minus_sigmoid": 1.1367616395313753, + "relative_delta_percent": 7.316665595427395, + "sigmoid": 15.536607826409384 + }, + "fid5k_nfe2": { + "adaptive_v1": 8.329946889195318, + "delta_adaptive_minus_sigmoid": 0.19403942157295262, + "relative_delta_percent": 2.3849757675483816, + "sigmoid": 8.135907467622365 + }, + "kid5k_nfe1": { + "adaptive_v1": 0.00723601015015015, + "delta_adaptive_minus_sigmoid": 0.0004496826526526679, + "relative_delta_percent": 6.626303443482389, + "sigmoid": 0.006786327497497482 + }, + "kid5k_nfe2": { + "adaptive_v1": 0.00141295428428426, + "delta_adaptive_minus_sigmoid": -0.00032765863363366874, + "relative_delta_percent": -18.824325055889197, + "sigmoid": 0.0017406129179179288 + } +} diff --git a/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe1/cell_metadata.json b/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe1/cell_metadata.json new file mode 100644 index 00000000..8969fded --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe1/cell_metadata.json @@ -0,0 +1,14 @@ +{ + "checkpoint_sha256": "7aa95fe6c44e0cc4b16eb030bc5bdf7869ee2d5a3176692530c251c172e0d908", + "generated_sample_count": 5000, + "metric": "fid5k_full", + "mid_t": null, + "nfe": 1, + "peak_gpu_memory_mib": null, + "peak_gpu_memory_note": "Not emitted by the frozen PR #19 metric entrypoint.", + "precision": "fp32", + "runtime_seconds": 26.30910301208496, + "sample_seeds": "0-4999", + "status": "PASS", + "value": 16.67336946594076 +} diff --git a/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe1/exact_command.txt b/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe1/exact_command.txt new file mode 100644 index 00000000..6a7e8ac3 --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe1/exact_command.txt @@ -0,0 +1 @@ +bash scripts/evaluate_checkpoint.sh 1 29612 /mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/adaptive-b-1024k-seed0/network-snapshot-000032.pkl --outdir=/mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/evaluation-pr19-protocol/fid5k/adaptive_v1/nfe1 --nosubdir --data=/mnt/ect_project/datasets/cifar10-32x32.zip --cond=False --arch=ddpmpp --precond=ct --dropout=0.2 --augment=0 --fp16=False --cache=True --workers=3 --nfe=1 --metrics=fid5k_full --metric-repeats=1 --sample-seeds=0-4999 --seed=20260722 --desc=1024k-seed0-adaptive_v1-nfe1-fid5k diff --git a/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe1/metric-fid5k_full.jsonl b/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe1/metric-fid5k_full.jsonl new file mode 100644 index 00000000..6b792823 --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe1/metric-fid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"fid5k_full": 16.67336946594076}, "metric": "fid5k_full", "total_time": 26.30910301208496, "total_time_str": "26s", "num_gpus": 1, "snapshot_pkl": "../../../../adaptive-b-1024k-seed0/network-snapshot-000032.pkl", "timestamp": 1784731418.4332438} diff --git a/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe1/options.json b/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe1/options.json new file mode 100644 index 00000000..becb57a7 --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe1/options.json @@ -0,0 +1,5042 @@ +{ + "dataset_kwargs": { + "class_name": "training.dataset.ImageFolderDataset", + "path": "/mnt/ect_project/datasets/cifar10-32x32.zip", + "use_labels": false, + "xflip": false, + "cache": true, + "resolution": 32, + "max_size": 50000 + }, + "network_kwargs": { + "model_type": "SongUNet", + "embedding_type": "positional", + "encoder_type": "standard", + "decoder_type": "standard", + "channel_mult_noise": 1, + "resample_filter": 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+ "metric": "fid5k_full", + "mid_t": 0.821, + "nfe": 2, + "peak_gpu_memory_mib": null, + "peak_gpu_memory_note": "Not emitted by the frozen PR #19 metric entrypoint.", + "precision": "fp32", + "runtime_seconds": 29.45711922645569, + "sample_seeds": "0-4999", + "status": "PASS", + "value": 8.329946889195318 +} diff --git a/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe2/exact_command.txt b/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe2/exact_command.txt new file mode 100644 index 00000000..7b0290f8 --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe2/exact_command.txt @@ -0,0 +1 @@ +bash scripts/evaluate_checkpoint.sh 1 29614 /mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/adaptive-b-1024k-seed0/network-snapshot-000032.pkl --outdir=/mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/evaluation-pr19-protocol/fid5k/adaptive_v1/nfe2 --nosubdir --data=/mnt/ect_project/datasets/cifar10-32x32.zip --cond=False --arch=ddpmpp --precond=ct --dropout=0.2 --augment=0 --fp16=False --cache=True --workers=3 --nfe=2 --mid_t=0.821 --metrics=fid5k_full --metric-repeats=1 --sample-seeds=0-4999 --seed=20260722 --desc=1024k-seed0-adaptive_v1-nfe2-fid5k diff --git a/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe2/metric-fid5k_full.jsonl b/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe2/metric-fid5k_full.jsonl new file mode 100644 index 00000000..92de5f41 --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe2/metric-fid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"fid5k_full": 8.329946889195318}, "metric": "fid5k_full", "total_time": 29.45711922645569, "total_time_str": "29s", "num_gpus": 1, "snapshot_pkl": "../../../../adaptive-b-1024k-seed0/network-snapshot-000032.pkl", "timestamp": 1784731545.9518633} diff --git a/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe2/options.json b/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe2/options.json new file mode 100644 index 00000000..d032da8f --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/fid5k/adaptive_v1/nfe2/options.json @@ -0,0 +1,5044 @@ +{ + "dataset_kwargs": { + "class_name": "training.dataset.ImageFolderDataset", + "path": "/mnt/ect_project/datasets/cifar10-32x32.zip", + "use_labels": false, + "xflip": false, + "cache": true, + "resolution": 32, + "max_size": 50000 + }, + "network_kwargs": { + "model_type": "SongUNet", + "embedding_type": "positional", + "encoder_type": "standard", + "decoder_type": "standard", + "channel_mult_noise": 1, + "resample_filter": [ + 1, + 1 + ], + "model_channels": 128, + "channel_mult": [ + 2, + 2, + 2 + ], + "class_name": "training.networks.ECMPrecond", + "dropout": 0.2, + "use_fp16": false + }, + "cudnn_benchmark": true, + "mid_t": [ + 0.821 + ], + "metrics": [ + "fid5k_full" + ], + "metric_repeats": 1, + "sample_seeds": [ + 0, + 1, + 2, + 3, + 4, + 5, + 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"/mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/adaptive-b-1024k-seed0/network-snapshot-000032.pkl", + "run_dir": "/mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/evaluation-pr19-protocol/fid5k/adaptive_v1/nfe2" +} \ No newline at end of file diff --git a/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe1/cell_metadata.json b/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe1/cell_metadata.json new file mode 100644 index 00000000..3b9ec33f --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe1/cell_metadata.json @@ -0,0 +1,14 @@ +{ + "checkpoint_sha256": "a54f3df776442c303f5b22a24541a5446e3ce62f05c71a7efe5d0e6747b04ceb", + "generated_sample_count": 5000, + "metric": "fid5k_full", + "mid_t": null, + "nfe": 1, + "peak_gpu_memory_mib": null, + "peak_gpu_memory_note": "Not emitted by the frozen PR #19 metric entrypoint.", + "precision": "fp32", + "runtime_seconds": 25.15752077102661, + "sample_seeds": "0-4999", + "status": "PASS", + "value": 15.536607826409384 +} diff --git a/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe1/exact_command.txt b/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe1/exact_command.txt new file mode 100644 index 00000000..325a75fe --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe1/exact_command.txt @@ -0,0 +1 @@ +bash scripts/evaluate_checkpoint.sh 1 29611 /mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/sigmoid-1024k-seed0/network-snapshot-000032.pkl --outdir=/mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/evaluation-pr19-protocol/fid5k/sigmoid/nfe1 --nosubdir --data=/mnt/ect_project/datasets/cifar10-32x32.zip --cond=False --arch=ddpmpp --precond=ct --dropout=0.2 --augment=0 --fp16=False --cache=True --workers=3 --nfe=1 --metrics=fid5k_full --metric-repeats=1 --sample-seeds=0-4999 --seed=20260722 --desc=1024k-seed0-sigmoid-nfe1-fid5k diff --git a/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe1/metric-fid5k_full.jsonl b/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe1/metric-fid5k_full.jsonl new file mode 100644 index 00000000..2ee4a34e --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe1/metric-fid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"fid5k_full": 15.536607826409384}, "metric": "fid5k_full", "total_time": 25.15752077102661, "total_time_str": "25s", "num_gpus": 1, "snapshot_pkl": "../../../../sigmoid-1024k-seed0/network-snapshot-000032.pkl", "timestamp": 1784731370.3620164} diff --git a/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe1/options.json b/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe1/options.json new file mode 100644 index 00000000..19f32ac9 --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe1/options.json @@ -0,0 +1,5042 @@ +{ + "dataset_kwargs": { + "class_name": "training.dataset.ImageFolderDataset", + "path": "/mnt/ect_project/datasets/cifar10-32x32.zip", + "use_labels": false, + "xflip": false, + "cache": true, + "resolution": 32, + "max_size": 50000 + }, + "network_kwargs": { + "model_type": "SongUNet", + "embedding_type": "positional", + "encoder_type": "standard", + "decoder_type": "standard", + "channel_mult_noise": 1, + "resample_filter": [ + 1, + 1 + ], + "model_channels": 128, + "channel_mult": [ + 2, + 2, + 2 + ], + "class_name": "training.networks.ECMPrecond", + "dropout": 0.2, + "use_fp16": false + }, + "cudnn_benchmark": true, + "mid_t": [], + "metrics": [ + "fid5k_full" + ], + "metric_repeats": 1, + "sample_seeds": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 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a/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe2/cell_metadata.json b/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe2/cell_metadata.json new file mode 100644 index 00000000..fd2d703f --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe2/cell_metadata.json @@ -0,0 +1,14 @@ +{ + "checkpoint_sha256": "a54f3df776442c303f5b22a24541a5446e3ce62f05c71a7efe5d0e6747b04ceb", + "generated_sample_count": 5000, + "metric": "fid5k_full", + "mid_t": 0.821, + "nfe": 2, + "peak_gpu_memory_mib": null, + "peak_gpu_memory_note": "Not emitted by the frozen PR #19 metric entrypoint.", + "precision": "fp32", + "runtime_seconds": 29.591035842895508, + "sample_seeds": "0-4999", + "status": "PASS", + "value": 8.135907467622365 +} diff --git a/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe2/exact_command.txt b/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe2/exact_command.txt new file mode 100644 index 00000000..21145be5 --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe2/exact_command.txt @@ -0,0 +1 @@ +bash scripts/evaluate_checkpoint.sh 1 29613 /mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/sigmoid-1024k-seed0/network-snapshot-000032.pkl --outdir=/mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/evaluation-pr19-protocol/fid5k/sigmoid/nfe2 --nosubdir --data=/mnt/ect_project/datasets/cifar10-32x32.zip --cond=False --arch=ddpmpp --precond=ct --dropout=0.2 --augment=0 --fp16=False --cache=True --workers=3 --nfe=2 --mid_t=0.821 --metrics=fid5k_full --metric-repeats=1 --sample-seeds=0-4999 --seed=20260722 --desc=1024k-seed0-sigmoid-nfe2-fid5k diff --git a/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe2/metric-fid5k_full.jsonl b/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe2/metric-fid5k_full.jsonl new file mode 100644 index 00000000..5fc86ba5 --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe2/metric-fid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"fid5k_full": 8.135907467622365}, "metric": "fid5k_full", "total_time": 29.591035842895508, "total_time_str": "30s", "num_gpus": 1, "snapshot_pkl": "../../../../sigmoid-1024k-seed0/network-snapshot-000032.pkl", "timestamp": 1784731484.6296363} diff --git a/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe2/options.json b/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe2/options.json new file mode 100644 index 00000000..4fa884bb --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/fid5k/sigmoid/nfe2/options.json @@ -0,0 +1,5044 @@ +{ + "dataset_kwargs": { + "class_name": "training.dataset.ImageFolderDataset", + "path": "/mnt/ect_project/datasets/cifar10-32x32.zip", + "use_labels": false, + "xflip": false, + "cache": true, + "resolution": 32, + "max_size": 50000 + }, + "network_kwargs": { + "model_type": "SongUNet", + "embedding_type": "positional", + 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"checkpoint_sha256": "7aa95fe6c44e0cc4b16eb030bc5bdf7869ee2d5a3176692530c251c172e0d908", + "generated_sample_count": 5000, + "metric": "kid5k_full", + "mid_t": null, + "nfe": 1, + "peak_gpu_memory_mib": null, + "peak_gpu_memory_note": "Not emitted by the frozen PR #19 metric entrypoint.", + "precision": "fp32", + "runtime_seconds": 28.974271059036255, + "sample_seeds": "0-4999", + "status": "PASS", + "value": 0.00723601015015015 +} diff --git a/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe1/exact_command.txt b/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe1/exact_command.txt new file mode 100644 index 00000000..29d80edc --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe1/exact_command.txt @@ -0,0 +1 @@ +bash scripts/evaluate_checkpoint.sh 1 29602 /mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/adaptive-b-1024k-seed0/network-snapshot-000032.pkl --outdir=/mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/evaluation-pr19-protocol/kid5k/adaptive_v1/nfe1 --nosubdir --data=/mnt/ect_project/datasets/cifar10-32x32.zip --cond=False --arch=ddpmpp --precond=ct --dropout=0.2 --augment=0 --fp16=False --cache=True --workers=3 --nfe=1 --metrics=kid5k_full --metric-repeats=1 --sample-seeds=0-4999 --seed=20260722 --desc=1024k-seed0-adaptive_v1-nfe1-kid5k diff --git a/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe1/metric-kid5k_full.jsonl b/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe1/metric-kid5k_full.jsonl new file mode 100644 index 00000000..c926baef --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe1/metric-kid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"kid5k_full": 0.00723601015015015}, "metric": "kid5k_full", "total_time": 28.974271059036255, "total_time_str": "29s", "num_gpus": 1, "snapshot_pkl": "../../../../adaptive-b-1024k-seed0/network-snapshot-000032.pkl", "timestamp": 1784731214.168834} diff --git a/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe1/options.json b/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe1/options.json new file mode 100644 index 00000000..43431b49 --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe1/options.json @@ -0,0 +1,5042 @@ +{ + "dataset_kwargs": { + "class_name": "training.dataset.ImageFolderDataset", + "path": "/mnt/ect_project/datasets/cifar10-32x32.zip", + "use_labels": false, + "xflip": false, + "cache": true, + "resolution": 32, + "max_size": 50000 + }, + "network_kwargs": { + "model_type": "SongUNet", + "embedding_type": "positional", + "encoder_type": "standard", + "decoder_type": "standard", + "channel_mult_noise": 1, + "resample_filter": [ + 1, + 1 + ], + "model_channels": 128, + "channel_mult": [ + 2, + 2, + 2 + ], + "class_name": "training.networks.ECMPrecond", + "dropout": 0.2, + 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4996, + 4997, + 4998, + 4999 + ], + "seed": 20260722, + "resume_pkl": "/mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/adaptive-b-1024k-seed0/network-snapshot-000032.pkl", + "run_dir": "/mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/evaluation-pr19-protocol/kid5k/adaptive_v1/nfe1" +} \ No newline at end of file diff --git a/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe2/cell_metadata.json b/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe2/cell_metadata.json new file mode 100644 index 00000000..89da2440 --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe2/cell_metadata.json @@ -0,0 +1,14 @@ +{ + "checkpoint_sha256": "7aa95fe6c44e0cc4b16eb030bc5bdf7869ee2d5a3176692530c251c172e0d908", + "generated_sample_count": 5000, + "metric": "kid5k_full", + "mid_t": 0.821, + "nfe": 2, + "peak_gpu_memory_mib": null, + "peak_gpu_memory_note": "Not emitted by the frozen PR #19 metric entrypoint.", + "precision": "fp32", + "runtime_seconds": 32.413949966430664, + "sample_seeds": "0-4999", + "status": "PASS", + "value": 0.00141295428428426 +} diff --git a/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe2/exact_command.txt b/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe2/exact_command.txt new file mode 100644 index 00000000..273a064a --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe2/exact_command.txt @@ -0,0 +1 @@ +bash scripts/evaluate_checkpoint.sh 1 29604 /mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/adaptive-b-1024k-seed0/network-snapshot-000032.pkl --outdir=/mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/evaluation-pr19-protocol/kid5k/adaptive_v1/nfe2 --nosubdir --data=/mnt/ect_project/datasets/cifar10-32x32.zip --cond=False --arch=ddpmpp --precond=ct --dropout=0.2 --augment=0 --fp16=False --cache=True --workers=3 --nfe=2 --mid_t=0.821 --metrics=kid5k_full --metric-repeats=1 --sample-seeds=0-4999 --seed=20260722 --desc=1024k-seed0-adaptive_v1-nfe2-kid5k diff --git a/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe2/metric-kid5k_full.jsonl b/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe2/metric-kid5k_full.jsonl new file mode 100644 index 00000000..d6f6f33b --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe2/metric-kid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"kid5k_full": 0.00141295428428426}, "metric": "kid5k_full", "total_time": 32.413949966430664, "total_time_str": "32s", "num_gpus": 1, "snapshot_pkl": "../../../../adaptive-b-1024k-seed0/network-snapshot-000032.pkl", "timestamp": 1784731323.3583071} diff --git a/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe2/options.json b/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe2/options.json new file mode 100644 index 00000000..61960051 --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/kid5k/adaptive_v1/nfe2/options.json @@ -0,0 +1,5044 @@ +{ + "dataset_kwargs": { + "class_name": "training.dataset.ImageFolderDataset", + "path": "/mnt/ect_project/datasets/cifar10-32x32.zip", + "use_labels": false, + "xflip": false, + "cache": true, + "resolution": 32, + "max_size": 50000 + }, + "network_kwargs": { + "model_type": "SongUNet", + "embedding_type": "positional", + "encoder_type": "standard", + "decoder_type": "standard", + "channel_mult_noise": 1, + "resample_filter": [ + 1, + 1 + ], + "model_channels": 128, + "channel_mult": [ + 2, + 2, + 2 + ], + "class_name": "training.networks.ECMPrecond", + "dropout": 0.2, + "use_fp16": false + }, + "cudnn_benchmark": true, + "mid_t": [ + 0.821 + ], + "metrics": [ + "kid5k_full" + ], + "metric_repeats": 1, + "sample_seeds": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 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"/mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/evaluation-pr19-protocol/kid5k/adaptive_v1/nfe2" +} \ No newline at end of file diff --git a/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe1/cell_metadata.json b/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe1/cell_metadata.json new file mode 100644 index 00000000..ae74a307 --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe1/cell_metadata.json @@ -0,0 +1,14 @@ +{ + "checkpoint_sha256": "a54f3df776442c303f5b22a24541a5446e3ce62f05c71a7efe5d0e6747b04ceb", + "generated_sample_count": 5000, + "metric": "kid5k_full", + "mid_t": null, + "nfe": 1, + "peak_gpu_memory_mib": null, + "peak_gpu_memory_note": "Not emitted by the frozen PR #19 metric entrypoint.", + "precision": "fp32", + "runtime_seconds": 28.864728212356567, + "sample_seeds": "0-4999", + "status": "PASS", + "value": 0.006786327497497482 +} diff --git a/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe1/exact_command.txt b/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe1/exact_command.txt new file mode 100644 index 00000000..a9e9f4c8 --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe1/exact_command.txt @@ -0,0 +1 @@ +bash scripts/evaluate_checkpoint.sh 1 29601 /mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/sigmoid-1024k-seed0/network-snapshot-000032.pkl --outdir=/mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/evaluation-pr19-protocol/kid5k/sigmoid/nfe1 --nosubdir --data=/mnt/ect_project/datasets/cifar10-32x32.zip --cond=False --arch=ddpmpp --precond=ct --dropout=0.2 --augment=0 --fp16=False --cache=True --workers=3 --nfe=1 --metrics=kid5k_full --metric-repeats=1 --sample-seeds=0-4999 --seed=20260722 --desc=1024k-seed0-sigmoid-nfe1-kid5k diff --git a/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe1/metric-kid5k_full.jsonl b/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe1/metric-kid5k_full.jsonl new file mode 100644 index 00000000..f4a5ac66 --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe1/metric-kid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"kid5k_full": 0.006786327497497482}, "metric": "kid5k_full", "total_time": 28.864728212356567, "total_time_str": "29s", "num_gpus": 1, "snapshot_pkl": "../../../../sigmoid-1024k-seed0/network-snapshot-000032.pkl", "timestamp": 1784731153.0428936} diff --git a/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe1/options.json b/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe1/options.json new file mode 100644 index 00000000..d4476bdb --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe1/options.json @@ -0,0 +1,5042 @@ +{ + "dataset_kwargs": { + "class_name": "training.dataset.ImageFolderDataset", + "path": "/mnt/ect_project/datasets/cifar10-32x32.zip", + "use_labels": false, + "xflip": false, + "cache": true, + "resolution": 32, + "max_size": 50000 + }, + "network_kwargs": { + "model_type": "SongUNet", + "embedding_type": "positional", + "encoder_type": "standard", + "decoder_type": "standard", + "channel_mult_noise": 1, + "resample_filter": [ + 1, + 1 + ], + "model_channels": 128, + "channel_mult": [ + 2, + 2, + 2 + ], + "class_name": "training.networks.ECMPrecond", + "dropout": 0.2, + "use_fp16": false + }, + "cudnn_benchmark": true, + "mid_t": [], + "metrics": [ + "kid5k_full" + ], + "metric_repeats": 1, + "sample_seeds": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, 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b/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe2/cell_metadata.json new file mode 100644 index 00000000..a0b920a8 --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe2/cell_metadata.json @@ -0,0 +1,14 @@ +{ + "checkpoint_sha256": "a54f3df776442c303f5b22a24541a5446e3ce62f05c71a7efe5d0e6747b04ceb", + "generated_sample_count": 5000, + "metric": "kid5k_full", + "mid_t": 0.821, + "nfe": 2, + "peak_gpu_memory_mib": null, + "peak_gpu_memory_note": "Not emitted by the frozen PR #19 metric entrypoint.", + "precision": "fp32", + "runtime_seconds": 32.29677963256836, + "sample_seeds": "0-4999", + "status": "PASS", + "value": 0.0017406129179179288 +} diff --git a/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe2/exact_command.txt b/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe2/exact_command.txt new file mode 100644 index 00000000..d9fe62e0 --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe2/exact_command.txt @@ -0,0 +1 @@ +bash scripts/evaluate_checkpoint.sh 1 29603 /mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/sigmoid-1024k-seed0/network-snapshot-000032.pkl --outdir=/mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/evaluation-pr19-protocol/kid5k/sigmoid/nfe2 --nosubdir --data=/mnt/ect_project/datasets/cifar10-32x32.zip --cond=False --arch=ddpmpp --precond=ct --dropout=0.2 --augment=0 --fp16=False --cache=True --workers=3 --nfe=2 --mid_t=0.821 --metrics=kid5k_full --metric-repeats=1 --sample-seeds=0-4999 --seed=20260722 --desc=1024k-seed0-sigmoid-nfe2-kid5k diff --git a/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe2/metric-kid5k_full.jsonl b/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe2/metric-kid5k_full.jsonl new file mode 100644 index 00000000..a0627af0 --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe2/metric-kid5k_full.jsonl @@ -0,0 +1 @@ +{"results": {"kid5k_full": 0.0017406129179179288}, "metric": "kid5k_full", "total_time": 32.29677963256836, "total_time_str": "32s", "num_gpus": 1, "snapshot_pkl": "../../../../sigmoid-1024k-seed0/network-snapshot-000032.pkl", "timestamp": 1784731268.835185} diff --git a/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe2/options.json b/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe2/options.json new file mode 100644 index 00000000..24284572 --- /dev/null +++ b/results/paired_1024k_seed0_5090/cells/kid5k/sigmoid/nfe2/options.json @@ -0,0 +1,5044 @@ +{ + "dataset_kwargs": { + "class_name": "training.dataset.ImageFolderDataset", + "path": "/mnt/ect_project/datasets/cifar10-32x32.zip", + "use_labels": false, + "xflip": false, + "cache": true, + "resolution": 32, + "max_size": 50000 + }, + "network_kwargs": { + "model_type": "SongUNet", + "embedding_type": "positional", + "encoder_type": "standard", + "decoder_type": "standard", + "channel_mult_noise": 1, + "resample_filter": [ + 1, + 1 + ], + 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00000000..f5368df2 Binary files /dev/null and b/results/paired_1024k_seed0_5090/figures/grid_sigmoid_nfe1.png differ diff --git a/results/paired_1024k_seed0_5090/figures/grid_sigmoid_nfe2.png b/results/paired_1024k_seed0_5090/figures/grid_sigmoid_nfe2.png new file mode 100644 index 00000000..69a1afa3 Binary files /dev/null and b/results/paired_1024k_seed0_5090/figures/grid_sigmoid_nfe2.png differ diff --git a/results/paired_1024k_seed0_5090/figures/loss_curve.png b/results/paired_1024k_seed0_5090/figures/loss_curve.png new file mode 100644 index 00000000..6982f094 Binary files /dev/null and b/results/paired_1024k_seed0_5090/figures/loss_curve.png differ diff --git a/results/paired_1024k_seed0_5090/figures/trailing_loss_comparison.png b/results/paired_1024k_seed0_5090/figures/trailing_loss_comparison.png new file mode 100644 index 00000000..16468d22 Binary files /dev/null and b/results/paired_1024k_seed0_5090/figures/trailing_loss_comparison.png differ diff --git a/results/paired_1024k_seed0_5090/frozen_evaluation_protocol.json b/results/paired_1024k_seed0_5090/frozen_evaluation_protocol.json new file mode 100644 index 00000000..7270933d --- /dev/null +++ b/results/paired_1024k_seed0_5090/frozen_evaluation_protocol.json @@ -0,0 +1,48 @@ +{ + "schema_version": 1, + "protocol_id": "final-performance-evaluation-v1-applied-to-1024k-seed0-scheme-b", + "evaluation_code_sha": "6fdcc54472b1cbc0b3e5d9c649df850b8969ead4", + "generated_seeds": "0-4999", + "sample_count": 5000, + "nfe": { + "1": { + "mid_t": [] + }, + "2": { + "mid_t": [ + 0.821 + ] + } + }, + "precision": "fp32", + "kid": { + "metric": "kid5k_full", + "primary": true, + "subset_count": 100, + "max_subset_size": 1000, + "protocol_rng_seed": 20260722, + "raw_unbiased": true + }, + "fid": { + "metric": "fid5k_full", + "secondary": true, + "real_sample_count": 50000, + "generated_sample_count": 5000 + }, + "real_reference": { + "path": "/mnt/ect_project/datasets/cifar10-32x32.zip", + "sha256": "08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372", + "xflip": false + }, + "feature_extractor": { + "url": "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt", + "features": "raw_pre_softmax" + }, + "batch_size": 128, + "generator_chunk_size": 128, + "gpu": "NVIDIA GeForce RTX 5090", + "torch": "2.10.0+cu128", + "cuda": "12.8", + "proxy_limitation": "5k-sample proxy evaluation; not a standard FID-50k benchmark.", + "scope_note": "PR #19 metric and sampling implementation is applied unchanged to the supplied 1024 kimg seed0 Scheme-B pair." +} diff --git a/results/paired_1024k_seed0_5090/frozen_evaluation_protocol.md b/results/paired_1024k_seed0_5090/frozen_evaluation_protocol.md new file mode 100644 index 00000000..9cc32801 --- /dev/null +++ b/results/paired_1024k_seed0_5090/frozen_evaluation_protocol.md @@ -0,0 +1,3 @@ +# Frozen evaluation protocol + +PR #19 commit `6fdcc54472b1cbc0b3e5d9c649df850b8969ead4`; seeds 0–4999; FP32; KID-5k primary; FID-5k secondary; NFE 1 and 2; NFE2 mid_t 0.821; protocol RNG seed 20260722. This is a 5k proxy, not FID-50k. diff --git a/results/paired_1024k_seed0_5090/kid5k_summary.csv b/results/paired_1024k_seed0_5090/kid5k_summary.csv new file mode 100644 index 00000000..79ea20b3 --- /dev/null +++ b/results/paired_1024k_seed0_5090/kid5k_summary.csv @@ -0,0 +1,5 @@ +arm,metric,value,runtime_seconds,checkpoint_sha256,sample_seeds,generated_sample_count,precision,nfe,mid_t,status,peak_gpu_memory_mib,peak_gpu_memory_note +sigmoid,kid5k_full,0.006786327497497482,28.864728212356567,a54f3df776442c303f5b22a24541a5446e3ce62f05c71a7efe5d0e6747b04ceb,0-4999,5000,fp32,1,,PASS,,Not emitted by the frozen PR #19 metric entrypoint. +adaptive_v1,kid5k_full,0.00723601015015015,28.974271059036255,7aa95fe6c44e0cc4b16eb030bc5bdf7869ee2d5a3176692530c251c172e0d908,0-4999,5000,fp32,1,,PASS,,Not emitted by the frozen PR #19 metric entrypoint. +sigmoid,kid5k_full,0.0017406129179179288,32.29677963256836,a54f3df776442c303f5b22a24541a5446e3ce62f05c71a7efe5d0e6747b04ceb,0-4999,5000,fp32,2,0.821,PASS,,Not emitted by the frozen PR #19 metric entrypoint. +adaptive_v1,kid5k_full,0.00141295428428426,32.413949966430664,7aa95fe6c44e0cc4b16eb030bc5bdf7869ee2d5a3176692530c251c172e0d908,0-4999,5000,fp32,2,0.821,PASS,,Not emitted by the frozen PR #19 metric entrypoint. diff --git a/results/paired_1024k_seed0_5090/kid5k_summary.json b/results/paired_1024k_seed0_5090/kid5k_summary.json new file mode 100644 index 00000000..12bd4369 --- /dev/null +++ b/results/paired_1024k_seed0_5090/kid5k_summary.json @@ -0,0 +1,78 @@ +{ + "paired_comparisons": { + "kid5k_nfe1": { + "adaptive_v1": 0.00723601015015015, + "delta_adaptive_minus_sigmoid": 0.0004496826526526679, + "relative_delta_percent": 6.626303443482389, + "sigmoid": 0.006786327497497482 + }, + "kid5k_nfe2": { + "adaptive_v1": 0.00141295428428426, + "delta_adaptive_minus_sigmoid": -0.00032765863363366874, + "relative_delta_percent": -18.824325055889197, + "sigmoid": 0.0017406129179179288 + } + }, + "rows": [ + { + "arm": "sigmoid", + "checkpoint_sha256": "a54f3df776442c303f5b22a24541a5446e3ce62f05c71a7efe5d0e6747b04ceb", + "generated_sample_count": 5000, + "metric": "kid5k_full", + "mid_t": null, + "nfe": 1, + "peak_gpu_memory_mib": null, + "peak_gpu_memory_note": "Not emitted by the frozen PR #19 metric entrypoint.", + "precision": "fp32", + "runtime_seconds": 28.864728212356567, + "sample_seeds": "0-4999", + "status": "PASS", + "value": 0.006786327497497482 + }, + { + "arm": "adaptive_v1", + "checkpoint_sha256": "7aa95fe6c44e0cc4b16eb030bc5bdf7869ee2d5a3176692530c251c172e0d908", + "generated_sample_count": 5000, + "metric": "kid5k_full", + "mid_t": null, + "nfe": 1, + "peak_gpu_memory_mib": null, + "peak_gpu_memory_note": "Not emitted by the frozen PR #19 metric entrypoint.", + "precision": "fp32", + "runtime_seconds": 28.974271059036255, + "sample_seeds": "0-4999", + "status": "PASS", + "value": 0.00723601015015015 + }, + { + "arm": "sigmoid", + "checkpoint_sha256": "a54f3df776442c303f5b22a24541a5446e3ce62f05c71a7efe5d0e6747b04ceb", + "generated_sample_count": 5000, + "metric": "kid5k_full", + "mid_t": 0.821, + "nfe": 2, + "peak_gpu_memory_mib": null, + "peak_gpu_memory_note": "Not emitted by the frozen PR #19 metric entrypoint.", + "precision": "fp32", + "runtime_seconds": 32.29677963256836, + "sample_seeds": "0-4999", + "status": "PASS", + "value": 0.0017406129179179288 + }, + { + "arm": "adaptive_v1", + "checkpoint_sha256": "7aa95fe6c44e0cc4b16eb030bc5bdf7869ee2d5a3176692530c251c172e0d908", + "generated_sample_count": 5000, + "metric": "kid5k_full", + "mid_t": 0.821, + "nfe": 2, + "peak_gpu_memory_mib": null, + "peak_gpu_memory_note": "Not emitted by the frozen PR #19 metric entrypoint.", + "precision": "fp32", + "runtime_seconds": 32.413949966430664, + "sample_seeds": "0-4999", + "status": "PASS", + "value": 0.00141295428428426 + } + ] +} diff --git a/results/paired_1024k_seed0_5090/smoke_summary.json b/results/paired_1024k_seed0_5090/smoke_summary.json new file mode 100644 index 00000000..a7d6748b --- /dev/null +++ b/results/paired_1024k_seed0_5090/smoke_summary.json @@ -0,0 +1,243 @@ +{ + "arms": { + "adaptive_v1": { + "checkpoint_id": "network-snapshot-000032-7aa95fe6c44e", + "checkpoint_path": "/mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/adaptive-b-1024k-seed0/network-snapshot-000032.pkl", + "checkpoint_sha256": "7aa95fe6c44e0cc4b16eb030bc5bdf7869ee2d5a3176692530c251c172e0d908", + "determinism_passed": true, + "device": "cuda", + "elapsed_seconds_by_mode": { + "nfe1": 2.894683, + "nfe2": 4.607885 + }, + "elapsed_seconds_total": 7.502614, + "evaluation_git_commit": "6fdcc54472b1cbc0b3e5d9c649df850b8969ead4", + "generator_implementation": "ct_eval.generator_fn", + "gpu": "NVIDIA GeForce RTX 5090", + "image_channels": 3, + "image_count_by_mode": { + "nfe1": 64, + "nfe2": 64 + }, + "image_count_total": 128, + "image_resolution": [ + 32, + 32 + ], + "mid_t_by_mode": { + "nfe1": [], + "nfe2": [ + 0.821 + ] + }, + "model_forward_batch_size": 1, + "nfe_modes": [ + 1, + 2 + ], + "output_directory": "/mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/evaluation-pr19-protocol/smoke/adaptive_v1/network-snapshot-000032-7aa95fe6c44e", + "precision": "fp32", + "precision_requested": "fp32", + "repeat_runs_verified": 2, + "schema_version": "1.0", + "seed_count": 64, + "seed_list": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63 + ], + "verified_modes": [ + "nfe1", + "nfe2" + ], + "work_group_sizes_verified": [ + 8, + 16 + ] + }, + "sigmoid": { + "checkpoint_id": "network-snapshot-000032-a54f3df77644", + "checkpoint_path": "/mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/sigmoid-1024k-seed0/network-snapshot-000032.pkl", + "checkpoint_sha256": "a54f3df776442c303f5b22a24541a5446e3ce62f05c71a7efe5d0e6747b04ceb", + "determinism_passed": true, + "device": "cuda", + "elapsed_seconds_by_mode": { + "nfe1": 2.917045, + "nfe2": 4.769487 + }, + "elapsed_seconds_total": 7.686582, + "evaluation_git_commit": "6fdcc54472b1cbc0b3e5d9c649df850b8969ead4", + "generator_implementation": "ct_eval.generator_fn", + "gpu": "NVIDIA GeForce RTX 5090", + "image_channels": 3, + "image_count_by_mode": { + "nfe1": 64, + "nfe2": 64 + }, + "image_count_total": 128, + "image_resolution": [ + 32, + 32 + ], + "mid_t_by_mode": { + "nfe1": [], + "nfe2": [ + 0.821 + ] + }, + "model_forward_batch_size": 1, + "nfe_modes": [ + 1, + 2 + ], + "output_directory": "/mnt/ect_project/runs/paired-1024k-5090/main-4b80a4f7-seed0-w32-a001-20260722T100054Z/evaluation-pr19-protocol/smoke/sigmoid/network-snapshot-000032-a54f3df77644", + "precision": "fp32", + "precision_requested": "fp32", + "repeat_runs_verified": 2, + "schema_version": "1.0", + "seed_count": 64, + "seed_list": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63 + ], + "verified_modes": [ + "nfe1", + "nfe2" + ], + "work_group_sizes_verified": [ + 8, + 16 + ] + } + }, + "checks": { + "determinism": true, + "precision": "fp32", + "seed_count_each_mode": 64, + "seeds": "0-63" + }, + "status": "PASS" +} diff --git a/results/paired_1024k_seed0_5090/tests.log b/results/paired_1024k_seed0_5090/tests.log new file mode 100644 index 00000000..bd65154e --- /dev/null +++ b/results/paired_1024k_seed0_5090/tests.log @@ -0,0 +1,38 @@ +evaluation_commit=6fdcc54472b1cbc0b3e5d9c649df850b8969ead4 +[ect5090-compat] enabled legacy Sampler(data_source) no-op init +test_blind_ballot_is_balanced_and_scores_complete_raters (tests.test_final_evaluation.FinalEvaluationTest.test_blind_ballot_is_balanced_and_scores_complete_raters) ... ok +test_frozen_runner_builds_exact_matrix (tests.test_final_evaluation.FinalEvaluationTest.test_frozen_runner_builds_exact_matrix) ... ok +test_one_page_conclusion_uses_locked_primary_direction (tests.test_final_evaluation.FinalEvaluationTest.test_one_page_conclusion_uses_locked_primary_direction) ... ok +test_quantitative_collector_preserves_pairing (tests.test_final_evaluation.FinalEvaluationTest.test_quantitative_collector_preserves_pairing) ... ok +test_stability_and_final_conclusion_builders (tests.test_final_evaluation.FinalEvaluationTest.test_stability_and_final_conclusion_builders) ... ok +test_checkpoint_id_uses_filename_and_sha_prefix (tests.test_fixed_seed_sampling.FixedSeedSamplingTest.test_checkpoint_id_uses_filename_and_sha_prefix) ... ok +test_generator_sample_seeds_match_explicit_step_noise (tests.test_fixed_seed_sampling.FixedSeedSamplingTest.test_generator_sample_seeds_match_explicit_step_noise) ... ok +test_manifest_covers_images_grids_and_metadata (tests.test_fixed_seed_sampling.FixedSeedSamplingTest.test_manifest_covers_images_grids_and_metadata) ... /root/autodl-tmp/work/recurrence_of_ect-eval-pr19/scripts/sample_fixed_seeds.py:116: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15) + PIL.Image.fromarray(image.transpose(1, 2, 0), mode="RGB").save(path) +/root/autodl-tmp/work/recurrence_of_ect-eval-pr19/scripts/sample_fixed_seeds.py:126: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15) + PIL.Image.fromarray(grid, mode="RGB").save(path) +ok +test_metadata_records_nfe_mid_t_and_required_schema (tests.test_fixed_seed_sampling.FixedSeedSamplingTest.test_metadata_records_nfe_mid_t_and_required_schema) ... ok +test_metric_latents_match_fixed_seed_sampler (tests.test_fixed_seed_sampling.FixedSeedSamplingTest.test_metric_latents_match_fixed_seed_sampler) ... ok +test_precision_is_explicit_and_reported (tests.test_fixed_seed_sampling.FixedSeedSamplingTest.test_precision_is_explicit_and_reported) ... ok +test_protocol_outputs_are_complete_rgb_images (tests.test_fixed_seed_sampling.FixedSeedSamplingTest.test_protocol_outputs_are_complete_rgb_images) ... /root/autodl-tmp/work/recurrence_of_ect-eval-pr19/scripts/sample_fixed_seeds.py:116: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15) + PIL.Image.fromarray(image.transpose(1, 2, 0), mode="RGB").save(path) +/root/autodl-tmp/work/recurrence_of_ect-eval-pr19/scripts/sample_fixed_seeds.py:126: DeprecationWarning: 'mode' parameter is deprecated and will be removed in Pillow 13 (2026-10-15) + PIL.Image.fromarray(grid, mode="RGB").save(path) +ok +test_repeated_png_sha256_is_identical (tests.test_fixed_seed_sampling.FixedSeedSamplingTest.test_repeated_png_sha256_is_identical) ... ok +test_repeated_runs_are_pixel_identical_for_nfe1_and_nfe2 (tests.test_fixed_seed_sampling.FixedSeedSamplingTest.test_repeated_runs_are_pixel_identical_for_nfe1_and_nfe2) ... ok +test_same_seed_shares_initial_latent_and_deterministic_noise (tests.test_fixed_seed_sampling.FixedSeedSamplingTest.test_same_seed_shares_initial_latent_and_deterministic_noise) ... ok +test_seed_zero_and_one_are_distinct (tests.test_fixed_seed_sampling.FixedSeedSamplingTest.test_seed_zero_and_one_are_distinct) ... ok +test_work_groups_are_pixel_identical_for_nfe1_and_nfe2 (tests.test_fixed_seed_sampling.FixedSeedSamplingTest.test_work_groups_are_pixel_identical_for_nfe1_and_nfe2) ... ok + +---------------------------------------------------------------------- +Ran 17 tests in 9.984s + +OK +Built 96 blinded trials in /tmp/tmp6mt90oi_/public +Keep private until ballots lock: /tmp/tmp6mt90oi_/private/key.csv +Scored 288 judgments from 3 complete raters +Final conclusion: /tmp/tmpjwnv4ox4/FINAL_CONCLUSION.md +Validated 12 cells; primary metric: kid5k_full; output: /tmp/tmpyonkstcw/summary +Validated six 16 kimg training runs; output: /tmp/tmp_l84b7ud/summary diff --git a/results/paired_1024k_seed0_5090/training_summary.csv b/results/paired_1024k_seed0_5090/training_summary.csv new file mode 100644 index 00000000..2e762efb --- /dev/null +++ b/results/paired_1024k_seed0_5090/training_summary.csv @@ -0,0 +1,3 @@ +arm,attempted_iterations,correction_clamp,correction_max,correction_mean,correction_min,correction_saturation_rate,correction_sign_flips,correction_std,final_correction,final_loss,first_adapted_pair_iteration,first_nonzero_correction_iteration,gap_max,gap_mean,gap_min,minimum_finite_loss,nan_inf_count,normalized_loss_auc,peak_gpu_memory_gb,r_over_t_max,r_over_t_mean,r_over_t_min,signal_updates,skipped_steps,successful_steps,trailing_100_mean,trailing_100_median,trailing_100_std,trailing_25_mean,trailing_25_median,trailing_25_std,trailing_50_mean,trailing_50_median,trailing_50_std,wall_time_seconds +sigmoid,8000,,,,,,,,,16.77903271,,,,,,12.51593566,0,16.659747160242773,2.426729,,,,,13,7987,16.2883404576,16.307300509999997,1.041926294247584,16.1079650264,16.21914923,0.9524960410832125,16.2933293582,16.208322705,0.9251349281597794,6811.020206 +adaptive_v1,8000,0.01,0.00402080691908,0.0037456725832970563,3.66536915368e-05,0.0,0,0.00036712059973618484,0.00391628849673,17.3858794,129,129,0.0155177241311,0.010775597477013104,0.00893201069397,13.20623493,0,17.20331839192717,2.426729,0.991067989306,0.9892244025229887,0.984482275869,2048,15,7985,16.8322541565,16.69880158,1.0795515197890224,16.67150715,16.81525397,0.9881173683988997,16.8429638964,16.809074520000003,0.9738494209812143,6403.940569 diff --git a/results/paired_1024k_seed0_5090/training_summary.json b/results/paired_1024k_seed0_5090/training_summary.json new file mode 100644 index 00000000..c3612da3 --- /dev/null +++ b/results/paired_1024k_seed0_5090/training_summary.json @@ -0,0 +1,61 @@ +{ + "adaptive_v1": { + "arm": "adaptive_v1", + "attempted_iterations": 8000, + "correction_clamp": 0.01, + "correction_max": 0.00402080691908, + "correction_mean": 0.0037456725832970563, + "correction_min": 3.66536915368e-05, + "correction_saturation_rate": 0.0, + "correction_sign_flips": 0, + "correction_std": 0.00036712059973618484, + "final_correction": 0.00391628849673, + "final_loss": 17.3858794, + "first_adapted_pair_iteration": 129, + "first_nonzero_correction_iteration": 129, + "gap_max": 0.0155177241311, + "gap_mean": 0.010775597477013104, + "gap_min": 0.00893201069397, + "minimum_finite_loss": 13.20623493, + "nan_inf_count": 0, + "normalized_loss_auc": 17.20331839192717, + "peak_gpu_memory_gb": 2.426729, + "r_over_t_max": 0.991067989306, + "r_over_t_mean": 0.9892244025229887, + "r_over_t_min": 0.984482275869, + "signal_updates": 2048, + "skipped_steps": 15, + "successful_steps": 7985, + "trailing_100_mean": 16.8322541565, + "trailing_100_median": 16.69880158, + "trailing_100_std": 1.0795515197890224, + "trailing_25_mean": 16.67150715, + "trailing_25_median": 16.81525397, + "trailing_25_std": 0.9881173683988997, + "trailing_50_mean": 16.8429638964, + "trailing_50_median": 16.809074520000003, + "trailing_50_std": 0.9738494209812143, + "wall_time_seconds": 6403.940569 + }, + "sigmoid": { + "arm": "sigmoid", + "attempted_iterations": 8000, + "final_loss": 16.77903271, + "minimum_finite_loss": 12.51593566, + "nan_inf_count": 0, + "normalized_loss_auc": 16.659747160242773, + "peak_gpu_memory_gb": 2.426729, + "skipped_steps": 13, + "successful_steps": 7987, + "trailing_100_mean": 16.2883404576, + "trailing_100_median": 16.307300509999997, + "trailing_100_std": 1.041926294247584, + "trailing_25_mean": 16.1079650264, + "trailing_25_median": 16.21914923, + "trailing_25_std": 0.9524960410832125, + "trailing_50_mean": 16.2933293582, + "trailing_50_median": 16.208322705, + "trailing_50_std": 0.9251349281597794, + "wall_time_seconds": 6811.020206 + } +} diff --git a/results/paired_comparison_a100_5344a5c9.json b/results/paired_comparison_a100_5344a5c9.json new file mode 100644 index 00000000..291156a4 --- /dev/null +++ b/results/paired_comparison_a100_5344a5c9.json @@ -0,0 +1,245 @@ +{ + "schema_version": 1, + "campaign": "role-b-paired-activation-stability-canonical-rerun", + "train_and_packaging_git_commit": "5344a5c97ab461b640ad5c5413cbf57eec527c2a", + "runtime_environment": { + "name": "ect-clean-validation", + "python": "3.9.18", + "pytorch": "2.3.0", + "cuda": "12.1", + "gpu": "NVIDIA A100-PCIE-40GB" + }, + "path_policy": { + "runs_root": "/root/ect-runs/paired-training-v1-canonical", + "mnt_runs": "/mnt/ect_project/runs", + "root_runs": "/root/ect-runs", + "same_directory": false, + "note": "All four canonical reruns use /root/ect-runs/... exclusively. /mnt/ect_project/runs and /root/ect-runs are distinct directories (not symlinks). Prior PR #17 sigmoid activation path mismatch was caused by training under /mnt then moving the run tree to /root before packaging." + }, + "stability_semantics": { + "kind": "independent_fresh_16_kimg", + "resumed_from_activation": false, + "note": "Stability arms are fresh --transfer runs with --duration=0.016, not resumes from activation training-state." + }, + "determinism_note": "Configs and assets are identical across arms; CUDA/cuDNN kernels are not guaranteed bit-identical across independent processes. 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--seed=0 --fp16=True --enable_amp=True --metrics=none --transfer=/mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl --duration=0.016", + "fresh_transfer_not_resume": true, + "final_signal_updates": 32, + "final_adaptive_active": true, + "first_nonzero_correction_iteration": 12, + "first_adapted_pair_iteration": 13, + "activation_gate_passed": null, + "final_correction": -0.0247819071088, + "final_r_over_t_mean": 0.96108821038, + "final_gap_mean": 0.0389117896204 + } + }, + "stability_early_pair_probe": [ + { + "iteration": 1, + "sigmoid_loss": 15.9766382, + "adaptive_loss": 15.9766382, + "sigmoid_r_over_t": 0.985617611763, + "adaptive_r_over_t": 0.985617611763, + "adaptive_correction": 0.0 + }, + { + "iteration": 2, + "sigmoid_loss": 15.02455795, + "adaptive_loss": 15.02455795, + "sigmoid_r_over_t": 0.985632538854, + "adaptive_r_over_t": 0.985632538854, + "adaptive_correction": 0.0 + }, + { + "iteration": 3, + "sigmoid_loss": 16.38218355, + "adaptive_loss": 16.38218355, + 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@@ -0,0 +1,57 @@ +# Paired A100 campaign comparison — `5344a5c` + +Canonical-runtime rerun of Role B paired activation + stability. + +## Runtime + +| Property | Value | +| --- | --- | +| Env | `ect-clean-validation` | +| Python / PyTorch / CUDA | 3.9.18 / 2.3.0 / 12.1 | +| GPU | NVIDIA A100-PCIE-40GB | +| Train+package HEAD | `5344a5c97ab461b640ad5c5413cbf57eec527c2a` | + +## Path policy + +- Runs root: `/root/ect-runs/paired-training-v1-canonical` +- `readlink -f /mnt/ect_project/runs` → `/mnt/ect_project/runs` +- `readlink -f /root/ect-runs` → `/root/ect-runs` +- Same directory: **False** +- Note: All four canonical reruns use /root/ect-runs/... exclusively. /mnt/ect_project/runs and /root/ect-runs are distinct directories (not symlinks). Prior PR #17 sigmoid activation path mismatch was caused by training under /mnt then moving the run tree to /root before packaging. + +## Stability semantics + +- Kind: **`independent_fresh_16_kimg`** +- Resumed from activation: **False** +- Stability arms are fresh --transfer runs with --duration=0.016, not resumes from activation training-state. + +## Determinism + +Configs and assets are identical across arms; CUDA/cuDNN kernels are not guaranteed bit-identical across independent processes. Pre-correction r_over_t_mean matches; early losses may differ slightly. + +## Runs + +| Arm | Mode | Iters / kimg | Success / skip | Final loss | Wall s | Peak MiB | Snapshot SHA | +| --- | --- | --- | --- | --- | --- | --- | --- | +| sigmoid | activation | 32 / 4.096 | 23 / 9 | 31.09924 | 37.6 | 5916.5 | `248e4638e833…` | +| adaptive_v1 | activation | 32 / 4.096 | 23 / 9 | 30.84132 | 34.1 | 5916.8 | `23a8653c34f0…` | +| sigmoid | stability | 125 / 16.0 | 116 / 9 | 26.38569 | 104.1 | 5916.3 | `32aa46615846…` | +| adaptive_v1 | stability | 125 / 16.0 | 116 / 9 | 26.70843 | 105.0 | 5916.3 | `7d162808dc98…` | + +## Adaptive controller + +| Mode | Signal updates | First nonzero corr | First adapted pair | Final correction | Final r/t | Final gap | Gate | +| --- | --- | --- | --- | --- | --- | --- | --- | +| activation | 8 | 12 | 13 | -0.0162769219282 | 0.970931 | 0.029069 | True | +| stability | 32 | 12 | 13 | -0.0247819071088 | 0.961088 | 0.038912 | None | + +## Checkpoint SHAs (for Role D) + +| Arm | network-snapshot-latest SHA-256 | training-state-latest SHA-256 | +| --- | --- | --- | +| sigmoid / activation | `248e4638e833389d078ec8d09726113d76d283b480479481e028b9e0560f9957` | `09b5c61d980f4d34307c7835d390ee4b740ddf97f81390565ef2678d28da2576` | +| adaptive_v1 / activation | `23a8653c34f0087561fdea250ce8501489c516119f7b7dc7f5ea5c03ae3626c4` | `ef2c93f58d7b9df944504fde66e361363312361c49fbb32ffbd0dd7fe5ad2c71` | +| sigmoid / stability | `32aa4661584663c40bc05a3b5c5bef3bd4e8b60e289023d491fbe46fac5478ed` | `0dccc15dfe5bbe49466ee18109147f1500db31f8036e120d55c70482b1e7b8f8` | +| adaptive_v1 / stability | `7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a` | `269e2ec3b0b4edadddd2bc251338995962b31ea206106b0bfa24083e7210fd83` | + +Machine-readable companion: `results/paired_comparison_a100_5344a5c9.json`. diff --git a/results/preliminary_seed42_fp32_8ksteps/README.md b/results/preliminary_seed42_fp32_8ksteps/README.md new file mode 100644 index 00000000..d5385cc3 --- /dev/null +++ b/results/preliminary_seed42_fp32_8ksteps/README.md @@ -0,0 +1,16 @@ +# Preliminary seed42 FP32 results + +These files are **preliminary historical results** preserved from Role D's first-day evaluation. They were produced on old base commit `4311059770f54821d151a9b0e1f76770a5f3930e` and originally recorded in commit `52b1f2350744af4f6fbee3142b21fea2bc62f0b1`. + +Protocol summary: + +- CIFAR-10 32x32, unconditional generation +- FP32 training and evaluation +- training seed 42 and evaluation seed 42 +- approximately 8k training updates, inferred from the run directory name +- NFE=1 and NFE=2, with `mid_t=0.821` for NFE=2 +- NVIDIA A100-PCIE-40GB + +The checkpoint is stored outside Git. Its historical path is recorded in `checkpoint.txt`, but its SHA256 was not captured with the original result and remains unknown. + +These measurements are not directly comparable to the current B/C protocol and must not be treated as a final benchmark. They are retained only as preliminary evidence and as a reference for the reproducible evaluation implementation. diff --git a/results/preliminary_seed42_fp32_8ksteps/checkpoint.txt b/results/preliminary_seed42_fp32_8ksteps/checkpoint.txt new file mode 100644 index 00000000..13cae0a0 --- /dev/null +++ b/results/preliminary_seed42_fp32_8ksteps/checkpoint.txt @@ -0,0 +1 @@ +ct-runs/00003-cifar10-32x32-uncond-ddpmpp-ect-RAdam-0.000100-gpus1-batch128-fp32-seed42-8ksteps/network-snapshot-latest.pkl diff --git a/results/preliminary_seed42_fp32_8ksteps/final_metrics_summary.csv b/results/preliminary_seed42_fp32_8ksteps/final_metrics_summary.csv new file mode 100644 index 00000000..7093401b --- /dev/null +++ b/results/preliminary_seed42_fp32_8ksteps/final_metrics_summary.csv @@ -0,0 +1,3 @@ +mode,fid_min,fid_mean,fid_std,kid_min,kid_mean,kid_std,kid_x1000_mean,images_per_second,ms_per_image,peak_memory_gb +NFE=1,7.65823066197661,7.7753411131822805,0.11299776512069107,0.004654421471059322,0.004767270603527625,0.00010208288664534912,4.767270603527625,1149.071,0.87,1.542 +NFE=2,2.6761110851193552,2.7022722247267614,0.024466128624093576,0.0008825805853120983,0.000910972497270753,2.508021782471672e-05,0.910972497270753,573.412,1.744,1.542 diff --git a/results/preliminary_seed42_fp32_8ksteps/metadata.json b/results/preliminary_seed42_fp32_8ksteps/metadata.json new file mode 100644 index 00000000..f4c391cd --- /dev/null +++ b/results/preliminary_seed42_fp32_8ksteps/metadata.json @@ -0,0 +1,18 @@ +{ + "result_type": "preliminary_historical", + "historical_results_commit": "52b1f2350744af4f6fbee3142b21fea2bc62f0b1", + "evaluation_git_commit": "4311059770f54821d151a9b0e1f76770a5f3930e", + "training_git_commit": "unknown", + "checkpoint_path": "ct-runs/00003-cifar10-32x32-uncond-ddpmpp-ect-RAdam-0.000100-gpus1-batch128-fp32-seed42-8ksteps/network-snapshot-latest.pkl", + "checkpoint_sha256": "unknown", + "training_seed": 42, + "evaluation_seed": 42, + "training_precision": "fp32", + "training_updates": 8000, + "training_updates_source": "inferred_from_directory_name", + "nfe": [1, 2], + "mid_t": 0.821, + "dataset": "cifar10-32x32", + "gpu": "NVIDIA A100-PCIE-40GB", + "formal_comparable_result": false +} diff --git a/results/preliminary_seed42_fp32_8ksteps/sample_seed42_nfe1.png b/results/preliminary_seed42_fp32_8ksteps/sample_seed42_nfe1.png new file mode 100644 index 00000000..e185dd59 Binary files /dev/null and b/results/preliminary_seed42_fp32_8ksteps/sample_seed42_nfe1.png differ diff --git a/results/preliminary_seed42_fp32_8ksteps/sample_seed42_nfe2_mid0821.png b/results/preliminary_seed42_fp32_8ksteps/sample_seed42_nfe2_mid0821.png new file mode 100644 index 00000000..569eaea8 Binary files /dev/null and b/results/preliminary_seed42_fp32_8ksteps/sample_seed42_nfe2_mid0821.png differ diff --git a/results/preliminary_seed42_fp32_8ksteps/speed_benchmark.txt 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b/results/q256_256k_formal/README.md @@ -0,0 +1,99 @@ +# q=256 confirmatory formal results + +This package is the completed, predeclared three-seed (3/4/5) fixed-sigmoid versus global-only-sigmoid (`g=1.10`) q=256 comparison. It contains all 24 formal metric records: 6 checkpoints × 2 NFE modes × 2 metrics. Both KID-50k and FID-50k are lower-is-better. + +All evaluations used 50,000 generated samples (seeds `0-49999`), metric seed `20260730`, and FP32. The evaluator commit is `8375d46ca4c65e85ab399fcf1effe22ebb766790`; the dataset SHA-256 is `9fd64620e37bfc0c995535fa52701c9641bcd07635008bfda0c9fbddde1a4ed6`. Full portable runtime and checkpoint provenance is in `environment_manifest.json`. + +## Package contents + +| File | Purpose | +| --- | --- | +| `evaluation_results.csv` | 24 long-form absolute metric records, including checkpoint IDs and SHA-256s. `run_id` is a portable logical identifier. | +| `paired_differences.csv` | 12 seed-level global-only minus fixed differences, with absolute values, relative improvements, and checkpoint provenance. | +| `paired_statistics.json` | Machine-readable paired descriptive, robustness, and NFE-heterogeneity summaries derived from the difference CSV. | +| `paired_statistics.md` | Reader-facing paired robustness table, leave-one-seed-out summary, and NFE-heterogeneity table. | +| `figures/` | Reproducible SVG, PNG, and PDF main-text figures. | +| `environment_manifest.json` | Frozen evaluator environment, data identity, NFE settings, and six checkpoint identities without machine paths. | + +## Per-seed absolute values + +| Metric | NFE | Seed | Fixed | Global-only | +| --- | ---: | ---: | ---: | ---: | +| KID-50k | 1 | 3 | 0.342668861 | 0.319187701 | +| KID-50k | 1 | 4 | 0.340228200 | 0.304062694 | +| KID-50k | 1 | 5 | 0.313131928 | 0.297402292 | +| FID-50k | 1 | 3 | 320.589536040 | 308.507658018 | +| FID-50k | 1 | 4 | 319.965575461 | 301.982144550 | +| FID-50k | 1 | 5 | 294.203365273 | 281.717897879 | +| KID-50k | 2 | 3 | 0.296107829 | 0.055826116 | +| KID-50k | 2 | 4 | 0.286769122 | 0.084874652 | +| KID-50k | 2 | 5 | 0.034109969 | 0.034024790 | +| FID-50k | 2 | 3 | 280.897620128 | 69.523640469 | +| FID-50k | 2 | 4 | 265.228363223 | 97.631374479 | +| FID-50k | 2 | 5 | 45.804373638 | 44.024700620 | + +## Descriptive summary + +Values are mean ± sample SD across the three training seeds. The paired delta is `global_only - fixed`; negative favors global-only. Every number below is recomputable from the CSV files. + +| Metric | NFE | Fixed mean ± SD | Global-only mean ± SD | Mean paired delta ± SD | Global-only wins | +| --- | ---: | ---: | ---: | ---: | ---: | +| KID-50k | 1 | 0.332009663 ± 0.016394080 | 0.306884229 ± 0.011163413 | -0.025125434 ± 0.010316682 | 3 / 3 | +| FID-50k | 1 | 311.586158925 ± 15.057173309 | 297.402566815 ± 13.969689018 | -14.183592109 ± 3.296938320 | 3 / 3 | +| KID-50k | 2 | 0.205662306 ± 0.148642041 | 0.058241853 ± 0.025510860 | -0.147420454 ± 0.129031614 | 3 / 3 | +| FID-50k | 2 | 197.310118996 ± 131.441525251 | 70.393238523 ± 26.813914692 | -126.916880474 ± 110.560376043 | 3 / 3 | + +Use `metric_value` grouped by `metric_name`, `nfe`, and `method` in `evaluation_results.csv` to calculate absolute-value means and sample SDs (`n - 1` denominator). Use `delta` in `paired_differences.csv`, grouped by `metric` and `nfe`, for the paired columns. The complete-precision source values are in the CSVs; Markdown values are rounded to nine decimal places. + +## Appendix-only sensitivity diagnostics + +The following expanded diagnostics are retained for appendix or +machine-readable review, not for the main-text result summary. The main text +should report the seed-level paired values, mean paired delta $\pm$ sample SD, +3/3 directional wins, and the near-flat seed-5 NFE=2 outcome. + +The scale-free effect is the per-seed relative improvement +`100 × (fixed - global-only) / fixed`, where a positive percentage favors +global-only. The arithmetic percentage is the mean of the three seed-level +percentages; the geometric percentage is `100 × (1 - geometric mean(global-only/fixed))`. +The worst-case column is the least favorable seed-level relative improvement. +Rank consistency is Spearman correlation between the lower-is-better seed ranks +of fixed and global-only. The full-precision values and leave-one-seed-out +results are in `paired_statistics.json` and `paired_statistics.md`. + +| Metric | NFE | Arithmetic improvement | Geometric improvement | Median paired delta | Worst-case improvement | Seed CV | Rank consistency | Global/fixed/tie wins | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| KID-50k | 1 | 7.501846% | 7.531510% | -0.023481160 | 5.023325% | 38.111785% | 1.000000 | 3 / 0 / 0 | +| FID-50k | 1 | 4.544298% | 4.547537% | -12.485467394 | 3.768644% | 21.164185% | 1.000000 | 3 / 0 / 0 | +| KID-50k | 2 | 50.599851% | 61.818842% | -0.201894470 | 0.249718% | 86.826602% | 0.500000 | 3 / 0 / 0 | +| FID-50k | 2 | 47.441515% | 55.593373% | -167.596988744 | 3.885378% | 80.519432% | 0.500000 | 3 / 0 / 0 | + +NFE=2 has a larger mean relative effect than NFE=1 in two of three seeds for +both metrics, but the seed-wise NFE contrast is heterogeneous: the mean +NFE=2-minus-NFE=1 change is 43.098004 percentage points for KID and 42.897217 +percentage points for FID, while seed 5 changes by -4.773607 and -0.358444 +points respectively. This is a descriptive interaction pattern, not evidence +for a general NFE interaction. + +Each metric/NFE stratum has 3/3 global-only wins. The two-sided exact sign +test therefore has `p=0.25` in every stratum: with only three independent +training seeds it is a low-resolution directional check, not a significance +claim. The package also includes deterministic 10,000-replicate percentile +bootstrap intervals for the mean relative improvement as a sensitivity +summary only; bootstrap resampling does not create new independent seeds. +Leave-one-seed-out summaries retain 2/2 global-only wins in every omission, +but their effect magnitudes vary substantially at NFE=2. + +This is descriptive paired evidence (`n=3` independent training seeds), not a significance claim. Bootstrap resampling of these three seeds does not create additional independent observations. + +## Main-text figures + +![Per-seed paired comparison](figures/figure1_per_seed_paired_comparison.png) + +![Mean paired delta and between-seed variation](figures/figure2_mean_delta_seed_variation.png) + +Regenerate SVG, PNG, and PDF versions with: + +```bash +python scripts/plot_q256_main_results.py +``` diff --git a/results/q256_256k_formal/environment_manifest.json b/results/q256_256k_formal/environment_manifest.json new file mode 100644 index 00000000..97eb7535 --- /dev/null +++ b/results/q256_256k_formal/environment_manifest.json @@ -0,0 +1,40 @@ +{ + "schema_version": 1, + "package_id": "confirmatory-q256-formal-8375d46", + "protocol": "staged-checkpoint-evaluation-v1", + "phase": "formal", + "evidence_class": "formal", + "source_run_manifest_sha256": "7391959220cca6615f744ee1d6eb6d7afbace386afb0b5f5423b83cfae6ea21e", + "evaluation_git_commit": "8375d46ca4c65e85ab399fcf1effe22ebb766790", + "evaluation_environment": { + "python": {"implementation": "CPython", "version": "3.9.18"}, + "scipy_version": "1.13.1", + "pytorch_version": "2.3.0", + "cuda": {"available": true, "compiled_version": "12.1", "device_count": 1, "gpu_models": ["NVIDIA A100-PCIE-40GB"]}, + "inception_detector": {"identifier": "inception-2015-12-05", "format": "TorchScript", "return_features": true} + }, + "dataset_sha256": "9fd64620e37bfc0c995535fa52701c9641bcd07635008bfda0c9fbddde1a4ed6", + "precision": "fp32", + "sample_count": 50000, + "generation_seed_range": "0-49999", + "metric_seed": 20260730, + "metric_names": ["kid50k_full", "fid50k_full"], + "metric_repeats": 1, + "nfe_modes": {"1": [], "2": [0.821]}, + "comparison": { + "baseline_method": "fixed", + "candidate_method": "global110", + "candidate_label": "global_only", + "pairing_key": ["training_seed", "budget_kimg", "nfe", "metric"], + "delta_direction": "global_only - fixed" + }, + "checkpoints": [ + {"checkpoint_id": "confirmatory-256k-seed3-fixed", "method": "fixed", "training_seed": 3, "budget_kimg": 256, "checkpoint_sha256": "09a41e1e7c03dcdf5ffb93bb68687390278b4b190183dfff92bacc1bf79738d9", "integrity_receipt_status": "passed"}, + {"checkpoint_id": "confirmatory-256k-seed3-global110", "method": "global110", "training_seed": 3, "budget_kimg": 256, "checkpoint_sha256": "24875430eea4679a416ae921c3e9ae16142f6416d2a0edf970764384ef964bed", "integrity_receipt_status": "passed"}, + {"checkpoint_id": "confirmatory-256k-seed4-fixed", "method": "fixed", "training_seed": 4, "budget_kimg": 256, "checkpoint_sha256": "ac94e7b07e5b7628e6b14b26155fb3de09e42373497183d39aba4fe9863663c9", "integrity_receipt_status": "passed"}, + {"checkpoint_id": "confirmatory-256k-seed4-global110", "method": "global110", "training_seed": 4, "budget_kimg": 256, "checkpoint_sha256": "62a6122a7be523aeb12875d96e96312e9c90efde9eafb75d730c75ceea0e8862", "integrity_receipt_status": "passed"}, + {"checkpoint_id": "confirmatory-256k-seed5-fixed", "method": "fixed", "training_seed": 5, "budget_kimg": 256, "checkpoint_sha256": "21fab0e501bb27032c0e49a553b05a2800ea0fbe20a2a1d94a6bbf5276f2b72a", "integrity_receipt_status": "passed"}, + {"checkpoint_id": "confirmatory-256k-seed5-global110", "method": "global110", "training_seed": 5, "budget_kimg": 256, "checkpoint_sha256": "491dc887990e6d9f6fde70b5d12775aaf4bfc6155b731682926b02061c253e9b", "integrity_receipt_status": "passed"} + ], + "portable_path_policy": "Absolute server paths, dataset paths, checkpoint paths, receipt paths, and output paths are intentionally omitted. run_id is a logical identifier only." +} diff --git a/results/q256_256k_formal/evaluation_results.csv b/results/q256_256k_formal/evaluation_results.csv new file mode 100644 index 00000000..91ca2acd --- /dev/null +++ b/results/q256_256k_formal/evaluation_results.csv @@ -0,0 +1,25 @@ 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"geometric_mean_relative_improvement_pct": "100 * (1 - geometric_mean(global_only / fixed))", + "rank_consistency_spearman": "Spearman correlation of lower-is-better seed ranks between fixed and global-only", + "nfe_effect_heterogeneity": "per-seed relative-improvement difference, NFE=2 minus NFE=1, in percentage points" + }, + "inference_note": "The independent units are the three training seeds. Exact sign tests and bootstrap intervals are descriptive sensitivity summaries, not a basis for strong significance claims.", + "statistics": [ + { + "pair_count": 3, + "mean_delta": -14.1835921090954, + "median_delta": -12.485467394300088, + "sample_sd_delta": 3.2969383203453773, + "minimum_delta": -17.98343091141612, + "maximum_delta": -12.081878021569992, + "mean_relative_improvement_pct": 4.5442975938885874, + "geometric_mean_relative_improvement_pct": 4.547536960568433, + "relative_improvement_sample_sd_pct": 0.9617635389571673, + "relative_improvement_cv_pct": 21.164184763132546, + "worst_case_relative_improvement_pct": 3.768643908598368, + "best_case_relative_improvement_pct": 5.620426786696175, + "rank_consistency_spearman": 1.0, + "rank_order_exact_match": true, + "global_wins": 3, + "fixed_wins": 0, + "ties": 0, + "exact_sign_test": { + "test": "exact_two_sided_sign_test", + "non_tied_pairs": 3, + "positive_signs": 3, + "negative_signs": 0, + "positive_label": "global_only_better", + "negative_label": "fixed_better", + "ties_excluded": 0, + "p_value": 0.25, + "interpretation": "low-resolution descriptive check with one sign per independent training seed; not a basis for a strong significance claim" + }, + "bootstrap_mean_relative_improvement_pct_95ci": { + "method": "nonparametric_seed_bootstrap_percentile", + "replicates": 10000, + "confidence_level": 0.95, + "lower": 3.768643908598368, + "upper": 5.620426786696175, + "interpretation": "descriptive resampling interval; resampled seeds are not additional independent observations" + }, + "leave_one_seed_out": [ + { + "omitted_training_seed": 3, + "retained_pair_count": 2, + "mean_relative_improvement_pct": 4.932124436533696, + "median_delta": -15.234449152858105, + "global_wins": 2, + "fixed_wins": 0, + "ties": 0 + }, + { + "omitted_training_seed": 4, + "retained_pair_count": 2, + "mean_relative_improvement_pct": 4.006232997484793, + "median_delta": -12.28367270793504, + "global_wins": 2, + "fixed_wins": 0, + "ties": 0 + }, + { + "omitted_training_seed": 5, + "retained_pair_count": 2, + "mean_relative_improvement_pct": 4.694535347647271, + "median_delta": -15.032654466493057, + "global_wins": 2, + "fixed_wins": 0, + "ties": 0 + } + ], + "metric_name": "fid50k_full", + "budget_kimg": 256, + "nfe": 1 + }, + { + "pair_count": 3, + "mean_delta": -126.91688047374475, + "median_delta": -167.59698874377608, + "sample_sd_delta": 110.56037604257908, + "minimum_delta": -211.37397965899058, + "maximum_delta": -1.7796730184676193, + "mean_relative_improvement_pct": 47.441514773584075, + "geometric_mean_relative_improvement_pct": 55.5933734683107, + "relative_improvement_sample_sd_pct": 38.19963832269721, + "relative_improvement_cv_pct": 80.51943219984865, + "worst_case_relative_improvement_pct": 3.885377917222421, + "best_case_relative_improvement_pct": 75.24947330004183, + "rank_consistency_spearman": 0.5, + "rank_order_exact_match": false, + "global_wins": 3, + "fixed_wins": 0, + "ties": 0, + "exact_sign_test": { + "test": "exact_two_sided_sign_test", + "non_tied_pairs": 3, + "positive_signs": 3, + "negative_signs": 0, + "positive_label": "global_only_better", + "negative_label": "fixed_better", + "ties_excluded": 0, + "p_value": 0.25, + "interpretation": "low-resolution descriptive check with one sign per independent training seed; not a basis for a strong significance claim" + }, + "bootstrap_mean_relative_improvement_pct_95ci": { + "method": "nonparametric_seed_bootstrap_percentile", + "replicates": 10000, + "confidence_level": 0.95, + "lower": 3.885377917222421, + "upper": 75.24947330004183, + "interpretation": "descriptive resampling interval; resampled seeds are not additional independent observations" + }, + "leave_one_seed_out": [ + { + "omitted_training_seed": 3, + "retained_pair_count": 2, + "mean_relative_improvement_pct": 33.5375355103552, + "median_delta": -84.68833088112186, + "global_wins": 2, + "fixed_wins": 0, + "ties": 0 + }, + { + "omitted_training_seed": 4, + "retained_pair_count": 2, + "mean_relative_improvement_pct": 39.56742560863213, + "median_delta": -106.57682633872909, + "global_wins": 2, + "fixed_wins": 0, + "ties": 0 + }, + { + "omitted_training_seed": 5, + "retained_pair_count": 2, + "mean_relative_improvement_pct": 69.21958320176489, + "median_delta": -189.48548420138331, + "global_wins": 2, + "fixed_wins": 0, + "ties": 0 + } + ], + "metric_name": "fid50k_full", + "budget_kimg": 256, + "nfe": 2 + }, + { + "pair_count": 3, + "mean_delta": -0.025125434001286823, + "median_delta": -0.023481160402297974, + "sample_sd_delta": 0.010316681602448221, + "minimum_delta": -0.0361655056476593, + "maximum_delta": -0.015729635953903198, + "mean_relative_improvement_pct": 7.501846428089169, + "geometric_mean_relative_improvement_pct": 7.531509906886913, + "relative_improvement_sample_sd_pct": 2.8590875715975774, + "relative_improvement_cv_pct": 38.11178486528721, + "worst_case_relative_improvement_pct": 5.023325482032681, + "best_case_relative_improvement_pct": 10.629778969538284, + "rank_consistency_spearman": 1.0, + "rank_order_exact_match": true, + "global_wins": 3, + "fixed_wins": 0, + "ties": 0, + "exact_sign_test": { + "test": "exact_two_sided_sign_test", + "non_tied_pairs": 3, + "positive_signs": 3, + "negative_signs": 0, + "positive_label": "global_only_better", + "negative_label": "fixed_better", + "ties_excluded": 0, + "p_value": 0.25, + "interpretation": "low-resolution descriptive check with one sign per independent training seed; not a basis for a strong significance claim" + }, + "bootstrap_mean_relative_improvement_pct_95ci": { + "method": "nonparametric_seed_bootstrap_percentile", + "replicates": 10000, + "confidence_level": 0.95, + "lower": 5.023325482032681, + "upper": 10.629778969538286, + "interpretation": "descriptive resampling interval; resampled seeds are not additional independent observations" + }, + "leave_one_seed_out": [ + { + "omitted_training_seed": 3, + "retained_pair_count": 2, + "mean_relative_improvement_pct": 7.826552225785482, + "median_delta": -0.02594757080078125, + "global_wins": 2, + "fixed_wins": 0, + "ties": 0 + }, + { + "omitted_training_seed": 4, + "retained_pair_count": 2, + "mean_relative_improvement_pct": 5.937880157364612, + "median_delta": -0.019605398178100586, + "global_wins": 2, + "fixed_wins": 0, + "ties": 0 + }, + { + "omitted_training_seed": 5, + "retained_pair_count": 2, + "mean_relative_improvement_pct": 8.741106901117412, + "median_delta": -0.029823333024978638, + "global_wins": 2, + "fixed_wins": 0, + "ties": 0 + } + ], + "metric_name": "kid50k_full", + "budget_kimg": 256, + "nfe": 1 + }, + { + "pair_count": 3, + "mean_delta": -0.14742045352856317, + "median_delta": -0.20189446955919266, + "sample_sd_delta": 0.1290316143174649, + "minimum_delta": -0.24028171226382256, + "maximum_delta": -8.517876267433167e-05, + "mean_relative_improvement_pct": 50.599850918965664, + "geometric_mean_relative_improvement_pct": 61.81884222537545, + "relative_improvement_sample_sd_pct": 43.934131225758584, + "relative_improvement_cv_pct": 86.82660211018792, + "worst_case_relative_improvement_pct": 0.24971809104635811, + "best_case_relative_improvement_pct": 81.14669354944226, + "rank_consistency_spearman": 0.5, + "rank_order_exact_match": false, + "global_wins": 3, + "fixed_wins": 0, + "ties": 0, + "exact_sign_test": { + "test": "exact_two_sided_sign_test", + "non_tied_pairs": 3, + "positive_signs": 3, + "negative_signs": 0, + "positive_label": "global_only_better", + "negative_label": "fixed_better", + "ties_excluded": 0, + "p_value": 0.25, + "interpretation": "low-resolution descriptive check with one sign per independent training seed; not a basis for a strong significance claim" + }, + "bootstrap_mean_relative_improvement_pct_95ci": { + "method": "nonparametric_seed_bootstrap_percentile", + "replicates": 10000, + "confidence_level": 0.95, + "lower": 0.24971809104635811, + "upper": 81.14669354944226, + "interpretation": "descriptive resampling interval; resampled seeds are not additional independent observations" + }, + "leave_one_seed_out": [ + { + "omitted_training_seed": 3, + "retained_pair_count": 2, + "mean_relative_improvement_pct": 35.32642960372737, + "median_delta": -0.1009898241609335, + "global_wins": 2, + "fixed_wins": 0, + "ties": 0 + }, + { + "omitted_training_seed": 4, + "retained_pair_count": 2, + "mean_relative_improvement_pct": 40.69820582024431, + "median_delta": -0.12018344551324844, + "global_wins": 2, + "fixed_wins": 0, + "ties": 0 + }, + { + "omitted_training_seed": 5, + "retained_pair_count": 2, + "mean_relative_improvement_pct": 75.77491733292533, + "median_delta": -0.2210880909115076, + "global_wins": 2, + "fixed_wins": 0, + "ties": 0 + } + ], + "metric_name": "kid50k_full", + "budget_kimg": 256, + "nfe": 2 + } + ], + "nfe_effect_heterogeneity": [ + { + "metric_name": "fid50k_full", + "budget_kimg": 256, + "pair_count": 3, + "effect_measure": "per-seed relative improvement percentage; NFE=2 minus NFE=1", + "mean_change_percentage_points": 42.89721717969549, + "median_change_percentage_points": 57.5692663167918, + "sample_sd_change_percentage_points": 38.10081463485301, + "minimum_change_percentage_points": -0.3584441691487972, + "maximum_change_percentage_points": 71.48082939144346, + "nfe2_larger_effect_seeds": 2, + "nfe1_larger_effect_seeds": 1, + "ties": 0, + "exact_sign_test": { + "test": "exact_two_sided_sign_test", + "non_tied_pairs": 3, + "positive_signs": 2, + "negative_signs": 1, + "positive_label": "NFE2_larger_effect", + "negative_label": "NFE1_larger_effect", + "ties_excluded": 0, + "p_value": 1.0, + "interpretation": "low-resolution descriptive check with one sign per independent training seed; not a basis for a strong significance claim" + }, + "bootstrap_mean_change_percentage_points_95ci": { + "method": "nonparametric_seed_bootstrap_percentile", + "replicates": 10000, + "confidence_level": 0.95, + "lower": -0.3584441691487972, + "upper": 71.48082939144346, + "interpretation": "descriptive resampling interval; resampled seeds are not additional independent observations" + }, + "per_seed_changes": [ + { + "training_seed": 3, + "nfe2_minus_nfe1_relative_improvement_pct_points": 71.48082939144346 + }, + { + "training_seed": 4, + "nfe2_minus_nfe1_relative_improvement_pct_points": 57.5692663167918 + }, + { + "training_seed": 5, + "nfe2_minus_nfe1_relative_improvement_pct_points": -0.3584441691487972 + } + ] + }, + { + "metric_name": "kid50k_full", + "budget_kimg": 256, + "pair_count": 3, + "effect_measure": "per-seed relative improvement percentage; NFE=2 minus NFE=1", + "mean_change_percentage_points": 43.0980044908765, + "median_change_percentage_points": 59.77336214687009, + "sample_sd_change_percentage_points": 42.08898344488779, + "minimum_change_percentage_points": -4.773607390986323, + "maximum_change_percentage_points": 74.29425871674572, + "nfe2_larger_effect_seeds": 2, + "nfe1_larger_effect_seeds": 1, + "ties": 0, + "exact_sign_test": { + "test": "exact_two_sided_sign_test", + "non_tied_pairs": 3, + "positive_signs": 2, + "negative_signs": 1, + "positive_label": "NFE2_larger_effect", + "negative_label": "NFE1_larger_effect", + "ties_excluded": 0, + "p_value": 1.0, + "interpretation": "low-resolution descriptive check with one sign per independent training seed; not a basis for a strong significance claim" + }, + "bootstrap_mean_change_percentage_points_95ci": { + "method": "nonparametric_seed_bootstrap_percentile", + "replicates": 10000, + "confidence_level": 0.95, + "lower": -4.773607390986323, + "upper": 74.29425871674572, + "interpretation": "descriptive resampling interval; resampled seeds are not additional independent observations" + }, + "per_seed_changes": [ + { + "training_seed": 3, + "nfe2_minus_nfe1_relative_improvement_pct_points": 74.29425871674572 + }, + { + "training_seed": 4, + "nfe2_minus_nfe1_relative_improvement_pct_points": 59.77336214687009 + }, + { + "training_seed": 5, + "nfe2_minus_nfe1_relative_improvement_pct_points": -4.773607390986323 + } + ] + } + ] +} diff --git a/results/q256_256k_formal/paired_statistics.md b/results/q256_256k_formal/paired_statistics.md new file mode 100644 index 00000000..83d570b6 --- /dev/null +++ b/results/q256_256k_formal/paired_statistics.md @@ -0,0 +1,43 @@ +# Fixed vs global-only paired robustness statistics + +Pairing key: `training_seed + budget_kimg + nfe + metric`. +Delta: `global_only - fixed`; negative values favor global-only. +Relative improvement: `100 × (fixed - global_only) / fixed`; positive values favor global-only. +Independent units are training seeds; the pair count is reported for each metric/NFE stratum. + +The exact two-sided sign test is reported only as a low-resolution directional check. Bootstrap intervals resample these same seeds and are descriptive sensitivity intervals, not additional independent-sample inference. + +## Paired effect summary + +| Metric | Budget (kimg) | NFE | Pairs | Arithmetic relative improvement | Geometric relative improvement | Median delta | Worst-case improvement | Seed CV | Rank consistency (Spearman) | Wins | Exact sign p (two-sided) | Bootstrap 95% CI | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| fid50k_full | 256 | 1 | 3 | 4.544298% | 4.547537% | -12.485467394 | 3.768644% | 21.164185% | 1.000000 | 3/0/0 | 0.250000 | [3.768644, 5.620427]% | +| fid50k_full | 256 | 2 | 3 | 47.441515% | 55.593373% | -167.596988744 | 3.885378% | 80.519432% | 0.500000 | 3/0/0 | 0.250000 | [3.885378, 75.249473]% | +| kid50k_full | 256 | 1 | 3 | 7.501846% | 7.531510% | -0.023481160 | 5.023325% | 38.111785% | 1.000000 | 3/0/0 | 0.250000 | [5.023325, 10.629779]% | +| kid50k_full | 256 | 2 | 3 | 50.599851% | 61.818842% | -0.201894470 | 0.249718% | 86.826602% | 0.500000 | 3/0/0 | 0.250000 | [0.249718, 81.146694]% | + +## Leave-one-seed-out arithmetic relative improvement + +| Metric | NFE | Omitted seed | Retained pairs | Mean relative improvement | Global/fixed/tie wins | +| --- | ---: | ---: | ---: | ---: | ---: | +| fid50k_full | 1 | 3 | 2 | 4.932124% | 2/0/0 | +| fid50k_full | 1 | 4 | 2 | 4.006233% | 2/0/0 | +| fid50k_full | 1 | 5 | 2 | 4.694535% | 2/0/0 | +| fid50k_full | 2 | 3 | 2 | 33.537536% | 2/0/0 | +| fid50k_full | 2 | 4 | 2 | 39.567426% | 2/0/0 | +| fid50k_full | 2 | 5 | 2 | 69.219583% | 2/0/0 | +| kid50k_full | 1 | 3 | 2 | 7.826552% | 2/0/0 | +| kid50k_full | 1 | 4 | 2 | 5.937880% | 2/0/0 | +| kid50k_full | 1 | 5 | 2 | 8.741107% | 2/0/0 | +| kid50k_full | 2 | 3 | 2 | 35.326430% | 2/0/0 | +| kid50k_full | 2 | 4 | 2 | 40.698206% | 2/0/0 | +| kid50k_full | 2 | 5 | 2 | 75.774917% | 2/0/0 | + +## NFE effect heterogeneity + +Effect change is the per-seed relative improvement at NFE=2 minus that at NFE=1, in percentage points. Positive values indicate a larger global-only advantage at NFE=2. + +| Metric | Pairs | Mean change | Median change | Range | NFE=2 larger / NFE=1 larger / ties | Exact sign p (two-sided) | Bootstrap 95% CI | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| fid50k_full | 3 | 42.897217 pp | 57.569266 pp | [-0.358444, 71.480829] pp | 2 / 1 / 0 | 1.000000 | [-0.358444, 71.480829] pp | +| kid50k_full | 3 | 43.098004 pp | 59.773362 pp | [-4.773607, 74.294259] pp | 2 / 1 / 0 | 1.000000 | [-4.773607, 74.294259] pp | diff --git a/results/role_c_final/FINAL_CONCLUSION.md b/results/role_c_final/FINAL_CONCLUSION.md new file mode 100644 index 00000000..50334ead --- /dev/null +++ b/results/role_c_final/FINAL_CONCLUSION.md @@ -0,0 +1,68 @@ +# Fixed sigmoid vs Adaptive v1 — Role C conclusion + +## Current verdict: 负向 + +At least one complete setting has ≥2/3 adaptive seed losses and a worse three-seed mean, with no stable positive setting. + +Primary metric: **KID-5K** (lower is better). KID is preferred whenever its full frozen matrix is available; FID becomes the common fallback only when KID is incomplete. + +Training/controller stability gate: **PASS**. + +## Paired quality summary + +| Metric | Budget (kimg) | NFE | Paired seeds | Adaptive wins | Fixed mean ± std | Adaptive mean ± std | Δ adaptive − fixed ± std | +| :-- | :-- | :-- | :-- | :-- | :-- | :-- | :-- | +| FID-5K | 16 | 1 | 3/3 | 1/3 | 447.13 ± 56.7 | 446.178 ± 61.3 | -0.951515 ± 4.68 | +| FID-5K | 16 | 2 | 3/3 | 0/3 | 282.395 ± 46.1 | 283.233 ± 45.9 | 0.838204 ± 0.418 | +| FID-5K | 32 | 1 | 3/3 | 0/3 | 441.804 ± 6.76 | 442.234 ± 7.06 | 0.429639 ± 0.312 | +| FID-5K | 32 | 2 | 3/3 | 0/3 | 314.74 ± 85.9 | 316.961 ± 86.4 | 2.22102 ± 1.86 | +| FID-5K | 64 | 1 | 3/3 | 0/3 | 428.38 ± 9.6 | 428.717 ± 9.82 | 0.33712 ± 0.285 | +| FID-5K | 64 | 2 | 3/3 | 1/3 | 337.818 ± 58.2 | 336.599 ± 63.9 | -1.21967 ± 5.74 | +| KID-5K | 16 | 1 | 3/3 | 1/3 | 0.532116 ± 0.0828 | 0.532057 ± 0.0909 | -5.89093e-05 ± 0.00835 | +| KID-5K | 16 | 2 | 3/3 | 0/3 | 0.295774 ± 0.0584 | 0.296926 ± 0.0585 | 0.00115165 ± 0.000535 | +| KID-5K | 32 | 1 | 3/3 | 0/3 | 0.503508 ± 0.0129 | 0.504618 ± 0.0135 | 0.00111043 ± 0.000605 | +| KID-5K | 32 | 2 | 3/3 | 0/3 | 0.341518 ± 0.111 | 0.344851 ± 0.112 | 0.0033336 ± 0.00327 | +| KID-5K | 64 | 1 | 3/3 | 1/3 | 0.493996 ± 0.0226 | 0.494225 ± 0.0231 | 0.000228196 ± 0.000741 | +| KID-5K | 64 | 2 | 3/3 | 1/3 | 0.372728 ± 0.0776 | 0.371873 ± 0.085 | -0.000854383 ± 0.00757 | + +Negative Δ means Adaptive v1 is better. Standard deviations are sample SD across paired training seeds; no p-value is inferred from n=3. + +## Pre-frozen decision checks + +| Budget (kimg) | NFE | Adaptive wins | Adaptive losses | Mean Δ | Repeated advantage | Repeated regression | +| :-- | :-- | :-- | :-- | :-- | :-- | :-- | +| 16 | 1 | 1/3 | 2/3 | -5.89093e-05 | no | no | +| 16 | 2 | 0/3 | 3/3 | 0.00115165 | no | yes | +| 32 | 1 | 0/3 | 3/3 | 0.00111043 | no | yes | +| 32 | 2 | 0/3 | 3/3 | 0.0033336 | no | yes | +| 64 | 1 | 1/3 | 2/3 | 0.000228196 | no | yes | +| 64 | 2 | 1/3 | 2/3 | -0.000854383 | no | no | + +## Training and controller relationships + +| Metric | NFE | Relationship | n | Pearson r | +| :-- | :-- | :-- | :-- | :-- | +| FID-5K | 1 | adaptive_quality_vs_trailing_loss | 9 | 0.6872 | +| FID-5K | 1 | paired_delta_vs_correction | 9 | -0.2582 | +| FID-5K | 1 | paired_delta_vs_gap | 9 | 0.2460 | +| FID-5K | 2 | adaptive_quality_vs_trailing_loss | 9 | 0.0913 | +| FID-5K | 2 | paired_delta_vs_correction | 9 | -0.4285 | +| FID-5K | 2 | paired_delta_vs_gap | 9 | 0.4501 | +| KID-5K | 1 | adaptive_quality_vs_trailing_loss | 9 | 0.7083 | +| KID-5K | 1 | paired_delta_vs_correction | 9 | -0.3893 | +| KID-5K | 1 | paired_delta_vs_gap | 9 | 0.3750 | +| KID-5K | 2 | adaptive_quality_vs_trailing_loss | 9 | 0.0852 | +| KID-5K | 2 | paired_delta_vs_correction | 9 | -0.4316 | +| KID-5K | 2 | paired_delta_vs_gap | 9 | 0.4527 | + +These are descriptive correlations across available adaptive runs, separated by NFE. They are not significance tests and do not establish causality. + +## Guardrails applied + +- Only fixed/adaptive rows with the same training seed, checkpoint budget and NFE are differenced. +- KID/FID are never substituted across arms; a metric appearing for only one arm is rejected. +- If supplied, sampling seed, generated-image count, and NFE=2 `mid_t` must agree inside each pair. +- The stability gate requires finite losses, legal `r/t` and gap telemetry, active adaptive controller telemetry, and an adaptive AMP-skip rate no more than 2.0% above the paired fixed run. +- The conclusion is not upgraded from partial coverage, a single seed, or a single favorable NFE setting. + +Paired metric rows currently available: 36. diff --git a/results/role_c_final/aggregate_results.csv b/results/role_c_final/aggregate_results.csv new file mode 100644 index 00000000..91f16a59 --- /dev/null +++ b/results/role_c_final/aggregate_results.csv @@ -0,0 +1,13 @@ +metric,budget_kimg,nfe,paired_seed_count,expected_seed_count,coverage_complete,adaptive_better_seed_count,adaptive_worse_seed_count,fixed_mean,fixed_std,adaptive_mean,adaptive_std,delta_mean,delta_std,delta_sem,paired_sampling_settings_verified +fid_5k,16,1,3,3,true,1,2,447.129851567,56.6750842479,446.178336668,61.2694896865,-0.951514898922,4.68103658113,2.7025977302,true +fid_5k,16,2,3,3,true,0,3,282.394583527,46.089058549,283.232787211,45.9329194852,0.838203683399,0.418108636389,0.24139513377,true +fid_5k,32,1,3,3,true,0,3,441.804103813,6.76274524911,442.233742381,7.05908041742,0.429638567725,0.312042134859,0.180157610559,true +fid_5k,32,2,3,3,true,0,3,314.73974038,85.9286622694,316.960760977,86.4045527702,2.22102059736,1.86161173984,1.07480203912,true +fid_5k,64,1,3,3,true,0,3,428.380300967,9.60169829863,428.717421145,9.82227417487,0.337120178763,0.284929535566,0.164504144059,true 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--git a/results/role_c_final/controller_vs_budget.png b/results/role_c_final/controller_vs_budget.png new file mode 100644 index 00000000..f3d11d4d Binary files /dev/null and b/results/role_c_final/controller_vs_budget.png differ diff --git a/results/role_c_final/paired_differences.csv b/results/role_c_final/paired_differences.csv new file mode 100644 index 00000000..07828582 --- /dev/null +++ b/results/role_c_final/paired_differences.csv @@ -0,0 +1,37 @@ +metric,training_seed,budget_kimg,nfe,fixed_value,adaptive_value,delta_adaptive_minus_fixed,adaptive_better,paired_sampling_settings_verified,sampling_seed,num_generated,fixed_checkpoint_sha256,adaptive_checkpoint_sha256 +kid_5k,0,16,1,0.591114997864,0.594804227352,0.00368922948837,false,true,0-4999,5000,32aa4661584663c40bc05a3b5c5bef3bd4e8b60e289023d491fbe46fac5478ed,7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a 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+fid_5k,1,16,2,278.535449689,278.94094194,0.405492250553,false,true,0-4999,5000,3ffd271c99a3d09bc63c749f5b69f76af37bbbec54678e88885eb3341750e7ec,b7bc291a80298121c091945519d79f1f8c79deb318b4790e2d7371a1510b7a13 +kid_5k,2,16,2,0.358875095844,0.360325783491,0.00145068764687,false,true,0-4999,5000,3fed09e540f8419db32bac9c1fdfe93033aaad225198ecc2bbce4eccf26b5865,b04098a8d53449b3d2666f87b8d63e6f16ba11d6c2a5069c1d81d33d995f1d43 +fid_5k,2,16,2,330.291874258,331.161000533,0.86912627512,false,true,0-4999,5000,3fed09e540f8419db32bac9c1fdfe93033aaad225198ecc2bbce4eccf26b5865,b04098a8d53449b3d2666f87b8d63e6f16ba11d6c2a5069c1d81d33d995f1d43 +kid_5k,0,32,1,0.488681256771,0.489093780518,0.00041252374649,false,true,0-4999,5000,cb196bf9d6ba0fdd20858cc64c59a138c625c7584a5a445a37f9bdf9380e8c71,a9f6c06d22e1e6409680efd31d43fe90714fec4a7b211ea4bd718a6cae810d9c 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+fid_5k,1,64,2,270.979412918,263.317393435,-7.66201948325,true,true,0-4999,5000,bb98925649c0267064210a069887ae9b5dd3eff26727651318818ac2d46c09f9,c1eaef2c89c1365aa5c3e5b65c8522152468bf213923004c299ac6a058fddbdf +kid_5k,2,64,2,0.428162544966,0.433810949326,0.00564840435982,false,true,0-4999,5000,73c5b0c7b3e5130a76f80d26cb30e6969d049d941ac0e6568ee16f7c1d5bc925,3f3b4a69dec396d4af1010c6c26323d442e64b60fd3c7b354c4820c60aae1157 +fid_5k,2,64,2,377.604781356,380.971307571,3.36652621458,false,true,0-4999,5000,73c5b0c7b3e5130a76f80d26cb30e6969d049d941ac0e6568ee16f7c1d5bc925,3f3b4a69dec396d4af1010c6c26323d442e64b60fd3c7b354c4820c60aae1157 diff --git a/results/role_c_final/per_seed_metrics.csv b/results/role_c_final/per_seed_metrics.csv new file mode 100644 index 00000000..08613c4c --- /dev/null +++ b/results/role_c_final/per_seed_metrics.csv @@ -0,0 +1,37 @@ +method,training_seed,budget_kimg,nfe,mid_t,sampling_seed,num_generated,kid_5k,fid_5k,checkpoint_sha256,metric_source,run_dir,training_summary_csv,training_summary_available,training_processed_kimg,training_attempted_iterations,training_successful_optimizer_steps,training_successful_steps_valid,training_amp_skipped,training_amp_skip_rate,training_amp_skip_telemetry_complete,training_nan_count,training_inf_count,training_trailing_loss_mean,training_trailing_loss_std,training_final_loss,adaptive_active,final_correction,correction_abs_max,correction_saturation_fraction,correction_sign_changes,correction_bound_valid,final_r_over_t_mean,final_gap_mean,pair_ratio_valid,pair_gap_valid,controller_telemetry_complete +adaptive_v1,0,16,1,,0-4999,5000,0.594804227352,484.782869039,7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a,Role-A formal run manifests: sample_seeds=0-4999; sample_count=5000; metric_seed=20260722,/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0,/mnt/ect_project/runs/packaged-traj64/adaptive_v1_traj64_seed0_5344a5c9/train_summary.csv,true,16,125,116,true,9,0.072,true,0,0,27.1611888692,1.41918364037,26.70842981,true,-0.0247819071088,0.0254429754712,0,0,true,0.96108821038,0.0389117896204,true,true,true +sigmoid,0,16,1,,0-4999,5000,0.591114997864,483.669642034,32aa4661584663c40bc05a3b5c5bef3bd4e8b60e289023d491fbe46fac5478ed,Role-A formal run manifests: sample_seeds=0-4999; sample_count=5000; metric_seed=20260722,/root/ect-runs/paired-training-v1-canonical/sigmoid-traj64-5344a5c9-20260722T062924Z-seed0,/mnt/ect_project/runs/packaged-traj64/sigmoid_traj64_seed0_5344a5c9/train_summary.csv,true,16,125,116,true,9,0.072,true,0,0,26.9646625515,1.447320802,26.38569498,false,0,0,0,0,true,0.985990140347,0.0140098596528,true,true,true +adaptive_v1,1,16,1,,0-4999,5000,0.427847743034,375.531957609,b7bc291a80298121c091945519d79f1f8c79deb318b4790e2d7371a1510b7a13,Role-A formal run manifests: sample_seeds=0-4999; sample_count=5000; metric_seed=20260722,/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T064507Z-seed1,/mnt/ect_project/runs/packaged-traj64/adaptive_v1_traj64_seed1_5344a5c9/train_summary.csv,true,16,125,116,true,9,0.072,true,0,0,19.9296140492,1.24312844171,20.52969885,true,-0.0120768420786,0.0143095284118,0,0,true,0.973132518088,0.0268674819124,true,true,true +sigmoid,1,16,1,,0-4999,5000,0.437476933002,381.84189641,3ffd271c99a3d09bc63c749f5b69f76af37bbbec54678e88885eb3341750e7ec,Role-A formal run manifests: sample_seeds=0-4999; sample_count=5000; 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"--seed=20260722", + "--desc=role-a-formal-adaptive_v1-seed2-64k-nfe2" + ], + "started_at_unix": 1784709178.4931066, + "status": "completed", + "elapsed_seconds": 96.535 + } + ], + "elapsed_seconds": 1069.233 +} diff --git a/results/role_c_provenance/training_records.csv b/results/role_c_provenance/training_records.csv new file mode 100644 index 00000000..89e73a38 --- /dev/null +++ b/results/role_c_provenance/training_records.csv @@ -0,0 +1,19 @@ +method,training_seed,budget_kimg,checkpoint_sha256,training_summary_csv,run_dir,checkpoint_path +adaptive_v1,0,0016,7d162808dc98deec28d269693cba4242b97e52d8431017fe9e45a54a8c3d151a,/mnt/ect_project/runs/packaged-traj64/adaptive_v1_traj64_seed0_5344a5c9/train_summary.csv,/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0,/root/ect-runs/paired-training-v1-canonical/adaptive-v1-traj64-5344a5c9-20260722T063437Z-seed0/network-snapshot-kimg0016.pkl 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+sigmoid,2,0064,73c5b0c7b3e5130a76f80d26cb30e6969d049d941ac0e6568ee16f7c1d5bc925,/mnt/ect_project/runs/packaged-traj64/sigmoid_traj64_seed2_5344a5c9/train_summary.csv,/root/ect-runs/paired-training-v1-canonical/sigmoid-traj64-5344a5c9-20260722T065023Z-seed2,/root/ect-runs/paired-training-v1-canonical/sigmoid-traj64-5344a5c9-20260722T065023Z-seed2/network-snapshot-kimg0064.pkl diff --git a/results/sigmoid_activation_a100_5344a5c9/README.md b/results/sigmoid_activation_a100_5344a5c9/README.md new file mode 100644 index 00000000..fd7cc35d --- /dev/null +++ b/results/sigmoid_activation_a100_5344a5c9/README.md @@ -0,0 +1,53 @@ +# Sigmoid activation — canonical A100 paired evidence + +This directory records the clean single-GPU `sigmoid` activation arm of the +Role B paired campaign at commit +`5344a5c97ab461b640ad5c5413cbf57eec527c2a`, rerun in the Role A frozen +`ect-clean-validation` runtime (Python 3.9.18 / PyTorch 2.3.0 / CUDA 12.1) +against canonical dataset `08c9ed1b2b1c…` and transfer `4d5dcc1f1d0d…`. +Counterpart: `results/adaptive_v1_activation_a100_5344a5c9/`. Intent: paired activation evidence on frozen knobs. + +## Status + +| Property | Value | +| --- | --- | +| Evidence class | `formal_candidate` | +| Device | NVIDIA A100-PCIE-40GB (1 GPU) | +| Runtime | Python 3.9.18 / PyTorch 2.3.0 / CUDA 12.1 (`ect-clean-validation`) | +| Mode / schedule | `activation` / `sigmoid` | +| Duration / progress | 0.004 Mimg / 4.096 kimg | +| Dataset archive SHA-256 | `08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372` (canonical) | +| Transfer SHA-256 | `4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da` | +| Seed / batch / batch-gpu | 0 / 128 / 16 | +| Attempted / successful / skipped | 32 / 23 / 9 | +| AMP | enabled; GradScaler state saved | +| Recorded training and packaging worktrees | clean, at `5344a5c…` | +| Loss finiteness | 0 NaN / 0 Inf in `train_summary.csv` | +| Run outdir | `/root/ect-runs/paired-training-v1-canonical/sigmoid-activation-5344a5c9-20260721T034424Z` | + +The Collector loaded the latest network snapshot and training state. +`exact_command` `--outdir` and artifact paths both live under +`/root/ect-runs/paired-training-v1-canonical/...` (no `/mnt` vs `/root` mismatch). +Controller telemetry stays inactive for the fixed arm. + +The console's initial maintenance report can display `loss nan` before the +statistics collector is updated; packaged CSV losses are finite. + +## Contents + +```text +results/sigmoid_activation_a100_5344a5c9/ +├── README.md +├── metadata.json +└── train_summary.csv +``` + +The checkpoint, network snapshot, raw log, source dataset, and transfer pickle +remain outside Git. See also `results/paired_comparison_a100_5344a5c9.{md,json}`. + +## Local artifact hashes + +```text +15e57c23bd95dc300ec2c2930aba7ef04ccd9ba59a2b235164a2dbeb3b4b5cba metadata.json +9a257db09506f6dfd29ae090bd5100a1e7de801f6a42d692225754e19519de9e train_summary.csv +``` diff --git a/results/sigmoid_activation_a100_5344a5c9/metadata.json b/results/sigmoid_activation_a100_5344a5c9/metadata.json new file mode 100644 index 00000000..be5f8aaf --- /dev/null +++ b/results/sigmoid_activation_a100_5344a5c9/metadata.json @@ -0,0 +1,57 @@ +{ + "activation_gate_applied": false, + "activation_gate_passed": null, + "attempted_iterations": 32, + "batch_gpu": 16, + "cuda_version": "12.1", + "dataset_sha256": "08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372", + "duration_mimg": 0.004, + "evidence_class": "formal_candidate", + "exact_command": "python /mnt/recurrence_of_ect/ct_train.py --data=/mnt/ect_project/datasets/cifar10-32x32.zip --outdir=/root/ect-runs/paired-training-v1-canonical/sigmoid-activation-5344a5c9-20260721T034424Z --nosubdir --cond=False --arch=ddpmpp --precond=ect --batch=128 --batch-gpu=16 --optim=RAdam --lr=0.0001 --dropout=0.2 --augment=0 --mapping=sigmoid -q 256 -k 8 -b 1 -c 0 --double=10000 --ema_beta=0.9993 --seed=0 --fp16=True --enable_amp=True --metrics=none --transfer=/mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl --duration=0.004", + "final_adaptive_active": false, + "final_grad_scale": 128.0, + "final_loss": 31.09924078, + "final_signal_updates": 0, + "first_adapted_pair_iteration": null, + "first_loss": 16.016312, + "first_nonzero_correction_iteration": null, + "first_schedule_telemetry_iteration": 1, + "git_branch": "HEAD", + "git_commit": "5344a5c97ab461b640ad5c5413cbf57eec527c2a", + "git_dirty": false, + "global_batch": 128, + "gpu_count": 1, + "gpu_name": "NVIDIA A100-PCIE-40GB", + "gradscaler_state_saved": true, + "inf_count": 0, + "initial_grad_scale": 65536.0, + "max_loss": 37.09808064, + "metrics_enabled": false, + "min_loss": 15.02882767, + "mode": "activation", + "nan_count": 0, + "network_snapshot": "/root/ect-runs/paired-training-v1-canonical/sigmoid-activation-5344a5c9-20260721T034424Z/network-snapshot-latest.pkl", + "network_snapshot_sha256": "248e4638e833389d078ec8d09726113d76d283b480479481e028b9e0560f9957", + "packaging_git_branch": "HEAD", + "packaging_git_commit": "5344a5c97ab461b640ad5c5413cbf57eec527c2a", + "packaging_git_dirty": false, + "peak_vram_mib": 5916.530688, + "peak_vram_source": "torch.cuda.max_memory_allocated", + "platform": "Linux-5.15.0-56-generic-x86_64-with-glibc2.35", + "processed_kimg": 4.096, + "python_version": "3.9.18", + "schedule": "sigmoid", + "schedule_telemetry_available": true, + "schedule_telemetry_columns_available": true, + "schedule_telemetry_coverage": 1.0, + "schedule_telemetry_rows": 32, + "schedule_telemetry_total_rows": 32, + "seed": 0, + "skipped_steps": 9, + "successful_optimizer_steps": 23, + "torch_version": "2.3.0", + "training_state": "/root/ect-runs/paired-training-v1-canonical/sigmoid-activation-5344a5c9-20260721T034424Z/training-state-latest.pt", + "training_state_sha256": "09b5c61d980f4d34307c7835d390ee4b740ddf97f81390565ef2678d28da2576", + "transfer_sha256": "4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da", + "wall_time_seconds": 37.588446 +} diff --git a/results/sigmoid_activation_a100_5344a5c9/train_summary.csv b/results/sigmoid_activation_a100_5344a5c9/train_summary.csv new file mode 100644 index 00000000..4f2445c5 --- /dev/null +++ b/results/sigmoid_activation_a100_5344a5c9/train_summary.csv @@ -0,0 +1,33 @@ +attempted_iteration,successful_optimizer_steps,processed_kimg,loss,grad_scale,step_skipped,schedule,stage,next_loop_cur_tick,loss_ema,loss_reference,correction,signal_updates,adaptive_active,r_over_t_mean,gap_mean,seconds,peak_vram_mib 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+`5344a5c97ab461b640ad5c5413cbf57eec527c2a`, rerun in the Role A frozen +`ect-clean-validation` runtime (Python 3.9.18 / PyTorch 2.3.0 / CUDA 12.1) +against canonical dataset `08c9ed1b2b1c…` and transfer `4d5dcc1f1d0d…`. +Counterpart: `results/adaptive_v1_stability_a100_5344a5c9/`. Intent: independent fresh 16 kimg stability evidence (not an activation→stability resume). + +## Status + +| Property | Value | +| --- | --- | +| Evidence class | `formal_candidate` | +| Device | NVIDIA A100-PCIE-40GB (1 GPU) | +| Runtime | Python 3.9.18 / PyTorch 2.3.0 / CUDA 12.1 (`ect-clean-validation`) | +| Mode / schedule | `stability` / `sigmoid` | +| Duration / progress | 0.016 Mimg / 16.0 kimg | +| Dataset archive SHA-256 | `08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372` (canonical) | +| Transfer SHA-256 | `4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da` | +| Seed / batch / batch-gpu | 0 / 128 / 16 | +| Attempted / successful / skipped | 125 / 116 / 9 | +| AMP | enabled; GradScaler state saved | +| Recorded training and packaging worktrees | clean, at `5344a5c…` | +| Loss finiteness | 0 NaN / 0 Inf in `train_summary.csv` | +| Run outdir | `/root/ect-runs/paired-training-v1-canonical/sigmoid-stability-5344a5c9-20260721T034603Z` | + +**Fresh run (not resume):** `exact_command` uses `--transfer=...` and +`--duration=0.016` with no `--resume`. This is an independent fresh 16 kimg +stability arm, not a continuation of the activation training-state. + +The Collector loaded the latest network snapshot and training state. +Controller telemetry stays inactive for the fixed arm. + +## Contents + +```text +results/sigmoid_stability_a100_5344a5c9/ +├── README.md +├── metadata.json +└── train_summary.csv +``` + +The checkpoint, network snapshot, raw log, source dataset, and transfer pickle +remain outside Git. See also `results/paired_comparison_a100_5344a5c9.{md,json}`. + +## Local artifact hashes + +```text +21d22991acec8e4874ea5ad3b19ce6ed286e8fd3868ea65880de2496dd61de63 metadata.json +ec86295aaca42c7db06d222f647fe1fe429b1911f36d9874ae98ae6e3b8b819e train_summary.csv +``` diff --git a/results/sigmoid_stability_a100_5344a5c9/metadata.json b/results/sigmoid_stability_a100_5344a5c9/metadata.json new file mode 100644 index 00000000..1e4507d8 --- /dev/null +++ b/results/sigmoid_stability_a100_5344a5c9/metadata.json @@ -0,0 +1,57 @@ +{ + "activation_gate_applied": false, + "activation_gate_passed": null, + "attempted_iterations": 125, + "batch_gpu": 16, + "cuda_version": "12.1", + "dataset_sha256": "08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372", + "duration_mimg": 0.016, + "evidence_class": "formal_candidate", + "exact_command": "python /mnt/recurrence_of_ect/ct_train.py --data=/mnt/ect_project/datasets/cifar10-32x32.zip --outdir=/root/ect-runs/paired-training-v1-canonical/sigmoid-stability-5344a5c9-20260721T034603Z --nosubdir --cond=False --arch=ddpmpp --precond=ect --batch=128 --batch-gpu=16 --optim=RAdam --lr=0.0001 --dropout=0.2 --augment=0 --mapping=sigmoid -q 256 -k 8 -b 1 -c 0 --double=10000 --ema_beta=0.9993 --seed=0 --fp16=True --enable_amp=True --metrics=none --transfer=/mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl --duration=0.016", + "final_adaptive_active": false, + "final_grad_scale": 128.0, + "final_loss": 26.38569498, + "final_signal_updates": 0, + "first_adapted_pair_iteration": null, + "first_loss": 15.9766382, + "first_nonzero_correction_iteration": null, + "first_schedule_telemetry_iteration": 1, + "git_branch": "HEAD", + "git_commit": "5344a5c97ab461b640ad5c5413cbf57eec527c2a", + "git_dirty": false, + "global_batch": 128, + "gpu_count": 1, + "gpu_name": "NVIDIA A100-PCIE-40GB", + "gradscaler_state_saved": true, + "inf_count": 0, + "initial_grad_scale": 65536.0, + "max_loss": 37.55382586, + "metrics_enabled": false, + "min_loss": 15.02455795, + "mode": "stability", + "nan_count": 0, + "network_snapshot": "/root/ect-runs/paired-training-v1-canonical/sigmoid-stability-5344a5c9-20260721T034603Z/network-snapshot-latest.pkl", + "network_snapshot_sha256": "32aa4661584663c40bc05a3b5c5bef3bd4e8b60e289023d491fbe46fac5478ed", + "packaging_git_branch": "HEAD", + "packaging_git_commit": "5344a5c97ab461b640ad5c5413cbf57eec527c2a", + "packaging_git_dirty": false, + "peak_vram_mib": 5916.343296, + "peak_vram_source": "torch.cuda.max_memory_allocated", + "platform": "Linux-5.15.0-56-generic-x86_64-with-glibc2.35", + "processed_kimg": 16.0, + "python_version": "3.9.18", + "schedule": "sigmoid", + "schedule_telemetry_available": true, + "schedule_telemetry_columns_available": true, + "schedule_telemetry_coverage": 1.0, + "schedule_telemetry_rows": 125, + "schedule_telemetry_total_rows": 125, + "seed": 0, + "skipped_steps": 9, + "successful_optimizer_steps": 116, 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This +script joins those records, computes *paired* (same training seed) quality +deltas, produces the Role-C figures, and writes a guarded conclusion draft. + +The script intentionally does not pool generated images across training seeds +or infer a conclusion from an incomplete matrix. Intermediate data are useful +and can be rendered with the default settings, but the conclusion remains +``INCOMPLETE`` until the expected 3 x 3 x 2 paired matrix is present. + +Expected metrics CSV (Role A; column aliases are accepted):: + + method,training_seed,budget_kimg,nfe,kid_5k,fid_5k,checkpoint_sha256 + sigmoid,0,16,1,0.0124,3.81,<64-character checkpoint SHA256> + adaptive_v1,0,16,1,0.0119,3.72,<64-character checkpoint SHA256> + +Optional columns ``mid_t``, ``sampling_seed``, ``num_generated`` and +``metric_source`` are carried into the normalized output. When supplied, +sampling seed, image count, and NFE=2 mid_t must agree inside each pair. + +Training records CSV (Role B; optional while a run is still in progress):: + + method,training_seed,budget_kimg,checkpoint_sha256,training_summary_csv,run_dir + +Alternatively, pass one or more ``--training-root`` directories containing +Role-B ``metadata.json`` files beside ``train_summary.csv``. A training record +is joined by method, training seed and checkpoint budget, and its checkpoint +SHA must exactly match Role A's metrics row. +""" + +from __future__ import annotations + +import argparse +import csv +import json +import math +import os +import re +import sys +import tempfile +from collections import defaultdict +from dataclasses import dataclass +from pathlib import Path +from statistics import mean, stdev +from typing import Any, Iterable + +# Keep matplotlib from attempting to write under an immutable home directory on +# compute nodes and CI workers. The caller can override this location. +os.environ.setdefault("MPLCONFIGDIR", str(Path(tempfile.gettempdir()) / "ect-matplotlib")) +os.environ.setdefault("XDG_CACHE_HOME", str(Path(tempfile.gettempdir()) / "ect-xdg-cache")) +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt + + +METHODS = ("sigmoid", "adaptive_v1") +EXPECTED_SEEDS = (0, 1, 2) +EXPECTED_BUDGETS = (16.0, 32.0, 64.0) +EXPECTED_NFES = (1, 2) +EXPECTED_IMAGE_COUNT = 5000 +METRICS = ("kid_5k", "fid_5k") + + +class AnalysisError(ValueError): + """Raised when supplied Role-A/B evidence violates the frozen protocol.""" + + +@dataclass(frozen=True) +class MetricRow: + method: str + training_seed: int + budget_kimg: float + nfe: int + kid_5k: float | None + fid_5k: float | None + checkpoint_sha256: str + mid_t: float | None + sampling_seed: str + num_generated: int | None + metric_source: str + + @property + def key(self) -> tuple[str, int, float, int]: + return (self.method, self.training_seed, self.budget_kimg, self.nfe) + + +@dataclass(frozen=True) +class TrainingRecord: + method: str + training_seed: int + budget_kimg: float + checkpoint_sha256: str + training_summary_csv: Path | None + run_dir: str + + @property + def key(self) -> tuple[str, int, float]: + return (self.method, self.training_seed, self.budget_kimg) + + +def fail(message: str) -> None: + raise AnalysisError(message) + + +def canonical_method(value: str) -> str: + normalized = re.sub(r"[\s_-]+", "", str(value).strip().lower()) + aliases = { + "sigmoid": "sigmoid", + "fixedsigmoid": "sigmoid", + "fixed": "sigmoid", + "adaptivev1": "adaptive_v1", + } + if normalized not in aliases: + fail(f"unknown method {value!r}; expected Fixed sigmoid/sigmoid or Adaptive v1/adaptive_v1") + return aliases[normalized] + + +def parse_float(value: Any, field: str, *, allow_blank: bool = False, minimum: float | None = None) -> float | None: + text = "" if value is None else str(value).strip() + if text == "": + if allow_blank: + return None + fail(f"{field} must not be blank") + try: + number = float(text) + except (TypeError, ValueError) as exc: + raise AnalysisError(f"{field} must be numeric, got {value!r}") from exc + if not math.isfinite(number): + fail(f"{field} must be finite, got {value!r}") + if minimum is not None and number < minimum: + fail(f"{field} must be >= {minimum}, got {number}") + return number + + +def parse_int(value: Any, field: str, *, allow_blank: bool = False, minimum: int | None = None) -> int | None: + number = parse_float(value, field, allow_blank=allow_blank) + if number is None: + return None + if not number.is_integer(): + fail(f"{field} must be an integer, got {value!r}") + result = int(number) + if minimum is not None and result < minimum: + fail(f"{field} must be >= {minimum}, got {result}") + return result + + +def parse_budget(value: Any, field: str) -> float: + text = str(value).strip().lower().replace("kimg", "").strip() + result = parse_float(text, field, minimum=0) + assert result is not None + return float(result) + + +def canonical_budget(value: float, expected_budgets: Iterable[float]) -> float: + for expected in expected_budgets: + if math.isclose(value, expected, rel_tol=0, abs_tol=1e-6): + return float(expected) + return round(float(value), 6) + + +def parse_sha256(value: Any, field: str) -> str: + digest = "" if value is None else str(value).strip().lower() + if not re.fullmatch(r"[0-9a-f]{64}", digest): + fail(f"{field} must be a 64-character lowercase/uppercase SHA256, got {value!r}") + return digest + + +def parse_bool(value: Any, field: str) -> bool | None: + text = "" if value is None else str(value).strip().lower() + if text == "": + return None + if text in {"1", "true", "yes", "y"}: + return True + if text in {"0", "false", "no", "n"}: + return False + fail(f"{field} must be true/false (or 1/0), got {value!r}") + + +def csv_rows(path: Path) -> list[dict[str, str]]: + if not path.is_file(): + fail(f"CSV not found: {path}") + with path.open(newline="", encoding="utf-8") as handle: + reader = csv.DictReader(handle) + if not reader.fieldnames: + fail(f"CSV has no header: {path}") + rows = list(reader) + if not rows: + fail(f"CSV has no rows: {path}") + return rows + + +def value_from_aliases(row: dict[str, str], aliases: Iterable[str], field: str, row_number: int, *, required: bool = True) -> str: + normalized = {str(key).strip().lower(): value for key, value in row.items() if key is not None} + for alias in aliases: + if alias in normalized: + return normalized[alias] + if required: + fail(f"row {row_number}: missing required column {field}; accepted names: {', '.join(aliases)}") + return "" + + +def read_metrics(path: Path, expected_budgets: Iterable[float]) -> list[MetricRow]: + metrics: list[MetricRow] = [] + seen: set[tuple[str, int, float, int]] = set() + allowed_budgets = {float(budget) for budget in expected_budgets} + for row_number, row in enumerate(csv_rows(path), start=2): + method = canonical_method(value_from_aliases(row, ("method", "schedule"), "method", row_number)) + training_seed = parse_int( + value_from_aliases(row, ("training_seed", "train_seed", "seed"), "training_seed", row_number), + f"row {row_number} training_seed", + minimum=0, + ) + budget = canonical_budget( + parse_budget(value_from_aliases(row, ("budget_kimg", "budget"), "budget_kimg", row_number), f"row {row_number} budget_kimg"), + expected_budgets, + ) + if budget not in allowed_budgets: + fail(f"row {row_number}: budget_kimg must be one of {sorted(allowed_budgets)}, got {budget:g}") + if training_seed not in EXPECTED_SEEDS: + fail(f"row {row_number}: training_seed must be one of {EXPECTED_SEEDS}, got {training_seed}") + nfe = parse_int(value_from_aliases(row, ("nfe",), "nfe", row_number), f"row {row_number} nfe", minimum=1) + if nfe not in EXPECTED_NFES: + fail(f"row {row_number}: nfe must be one of {EXPECTED_NFES}, got {nfe}") + kid = parse_float( + value_from_aliases(row, ("kid_5k", "kid5k", "kid"), "kid_5k", row_number, required=False), + f"row {row_number} kid_5k", + allow_blank=True, + minimum=0, + ) + fid = parse_float( + value_from_aliases(row, ("fid_5k", "fid5k", "fid"), "fid_5k", row_number, required=False), + f"row {row_number} fid_5k", + allow_blank=True, + minimum=0, + ) + if kid is None and fid is None: + fail(f"row {row_number}: at least one of kid_5k or fid_5k is required") + checkpoint_sha256 = parse_sha256( + value_from_aliases( + row, + ("checkpoint_sha256", "checkpoint_sha", "checkpoint_sha256sum"), + "checkpoint_sha256", + row_number, + ), + f"row {row_number} checkpoint_sha256", + ) + mid_t = parse_float( + value_from_aliases(row, ("mid_t",), "mid_t", row_number, required=False), + f"row {row_number} mid_t", + allow_blank=True, + minimum=0, + ) + if nfe == 2 and mid_t is None: + mid_t = 0.821 + if nfe == 2 and not math.isclose(mid_t, 0.821, rel_tol=0, abs_tol=1e-9): + fail(f"row {row_number}: NFE=2 requires the frozen mid_t=0.821, got {mid_t}") + if nfe == 1 and mid_t is not None: + fail(f"row {row_number}: NFE=1 must not specify mid_t, got {mid_t}") + sampling_seed = value_from_aliases( + row, ("sampling_seed", "eval_seed", "evaluation_seed"), "sampling_seed", row_number, required=False + ).strip() + num_generated = parse_int( + value_from_aliases(row, ("num_generated", "image_count", "num_images"), "num_generated", row_number, required=False), + f"row {row_number} num_generated", + allow_blank=True, + minimum=1, + ) + metric_source = value_from_aliases( + row, ("metric_source", "source", "metrics_path"), "metric_source", row_number, required=False + ).strip() + assert training_seed is not None and nfe is not None + result = MetricRow( + method=method, + training_seed=training_seed, + budget_kimg=budget, + nfe=nfe, + kid_5k=kid, + fid_5k=fid, + checkpoint_sha256=checkpoint_sha256, + mid_t=mid_t, + sampling_seed=sampling_seed, + num_generated=num_generated, + metric_source=metric_source, + ) + if result.key in seen: + fail(f"duplicate metrics row for method/seed/budget/NFE={result.key}") + seen.add(result.key) + metrics.append(result) + return sorted(metrics, key=lambda item: (item.budget_kimg, item.nfe, item.training_seed, item.method)) + + +def training_record_from_row(row: dict[str, str], row_number: int, expected_budgets: Iterable[float], source: Path) -> TrainingRecord: + method = canonical_method(value_from_aliases(row, ("method", "schedule"), "method", row_number)) + seed = parse_int( + value_from_aliases(row, ("training_seed", "train_seed", "seed"), "training_seed", row_number), + f"row {row_number} training_seed", + minimum=0, + ) + budget = canonical_budget( + parse_budget(value_from_aliases(row, ("budget_kimg", "budget"), "budget_kimg", row_number), f"row {row_number} budget_kimg"), + expected_budgets, + ) + sha = parse_sha256( + value_from_aliases(row, ("checkpoint_sha256", "checkpoint_sha", "network_snapshot_sha256"), "checkpoint_sha256", row_number), + f"row {row_number} checkpoint_sha256", + ) + summary_text = value_from_aliases( + row, ("training_summary_csv", "train_summary_csv", "training_summary"), "training_summary_csv", row_number, required=False + ).strip() + summary_path = None + if summary_text: + candidate = Path(summary_text) + summary_path = candidate if candidate.is_absolute() else source.parent / candidate + run_dir = value_from_aliases(row, ("run_dir", "training_run_dir"), "run_dir", row_number, required=False).strip() + assert seed is not None + return TrainingRecord(method, seed, budget, sha, summary_path, run_dir) + + +def read_training_records(path: Path, expected_budgets: Iterable[float]) -> list[TrainingRecord]: + return [ + training_record_from_row(row, row_number, expected_budgets, path) + for row_number, row in enumerate(csv_rows(path), start=2) + ] + + +def records_from_training_root(root: Path, expected_budgets: Iterable[float]) -> list[TrainingRecord]: + if not root.is_dir(): + fail(f"training root is not a directory: {root}") + records: list[TrainingRecord] = [] + for metadata_path in sorted(root.rglob("metadata.json")): + try: + metadata = json.loads(metadata_path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError) as exc: + raise AnalysisError(f"cannot parse training metadata {metadata_path}: {exc}") from exc + if not isinstance(metadata, dict): + continue + if not {"schedule", "seed", "processed_kimg", "network_snapshot_sha256"}.issubset(metadata): + continue + try: + method = canonical_method(metadata["schedule"]) + seed = parse_int(metadata["seed"], f"{metadata_path} seed", minimum=0) + raw_budget = parse_budget(metadata["processed_kimg"], f"{metadata_path} processed_kimg") + # A shared run root can retain engineering smokes and old activation + # probes. They are not checkpoints in the frozen 16/32/64-kimg + # matrix and must not create duplicate records for this analysis. + if not any(math.isclose(raw_budget, budget, rel_tol=0, abs_tol=1e-6) for budget in expected_budgets): + continue + budget = canonical_budget(raw_budget, expected_budgets) + sha = parse_sha256(metadata["network_snapshot_sha256"], f"{metadata_path} network_snapshot_sha256") + except AnalysisError: + raise + summary = metadata_path.parent / "train_summary.csv" + records.append( + TrainingRecord( + method=method, + training_seed=int(seed), + budget_kimg=budget, + checkpoint_sha256=sha, + training_summary_csv=summary if summary.is_file() else None, + run_dir=str(metadata_path.parent), + ) + ) + return records + + +def index_training_records(records: Iterable[TrainingRecord]) -> dict[tuple[str, int, float], TrainingRecord]: + indexed: dict[tuple[str, int, float], TrainingRecord] = {} + for record in records: + if record.key in indexed: + previous = indexed[record.key] + fail( + "duplicate training record for " + f"{record.key}: {previous.checkpoint_sha256} and {record.checkpoint_sha256}; " + "supply one immutable checkpoint record per method/seed/budget" + ) + indexed[record.key] = record + return indexed + + +def optional_number(value: Any) -> float | None: + text = "" if value is None else str(value).strip() + if text == "": + return None + try: + result = float(text) + except ValueError: + return None + return result if math.isfinite(result) else None + + +def optional_bool(value: Any) -> bool | None: + text = "" if value is None else str(value).strip().lower() + if text == "": + return None + if text in {"1", "true", "yes", "y"}: + return True + if text in {"0", "false", "no", "n"}: + return False + return None + + +def format_number(value: float | int | None) -> str: + if value is None: + return "" + if isinstance(value, str): + return value + if isinstance(value, bool): + return "true" if value else "false" + if isinstance(value, int): + return str(value) + return f"{value:.12g}" + + +def summarize_training( + summary_path: Path | None, + budget_kimg: float, + adaptive_max_adjust: float, + expected_method: str, +) -> dict[str, Any]: + fields: dict[str, Any] = { + "training_summary_available": False, + "training_processed_kimg": None, + "training_attempted_iterations": None, + "training_successful_optimizer_steps": None, + "training_successful_steps_valid": None, + "training_amp_skipped": None, + "training_amp_skip_rate": None, + "training_amp_skip_telemetry_complete": None, + "training_nan_count": None, + "training_inf_count": None, + "training_trailing_loss_mean": None, + "training_trailing_loss_std": None, + "training_final_loss": None, + "adaptive_active": None, + "final_correction": None, + "correction_abs_max": None, + "correction_saturation_fraction": None, + "correction_sign_changes": None, + "correction_bound_valid": None, + "final_r_over_t_mean": None, + "final_gap_mean": None, + "pair_ratio_valid": None, + "pair_gap_valid": None, + "controller_telemetry_complete": None, + } + if summary_path is None: + return fields + if not summary_path.is_file(): + fail(f"training summary referenced by a record does not exist: {summary_path}") + with summary_path.open(newline="", encoding="utf-8") as handle: + reader = csv.DictReader(handle) + rows = list(reader) + if not rows: + fail(f"training summary has no rows: {summary_path}") + selected: list[tuple[float, dict[str, str]]] = [] + prior_processed: float | None = None + for row_number, row in enumerate(rows, start=2): + processed = optional_number(row.get("processed_kimg")) + if processed is None: + nimg = optional_number(row.get("processed_nimg")) + processed = None if nimg is None else nimg / 1000.0 + if processed is None: + fail(f"training summary {summary_path} row {row_number} has no numeric processed_kimg or processed_nimg") + if prior_processed is not None and processed < prior_processed - 1e-6: + fail(f"training summary {summary_path} has non-monotonic processed progress at row {row_number}") + prior_processed = processed + if processed is not None and processed <= budget_kimg + 1e-6: + selected.append((processed, row)) + if not selected: + fail(f"training summary {summary_path} has no row at or before {budget_kimg:g} kimg") + final_processed, _ = selected[-1] + if final_processed < budget_kimg - 1e-6: + fail( + f"training summary {summary_path} reaches only {final_processed:g} kimg before the " + f"{budget_kimg:g}-kimg checkpoint" + ) + selected_rows = [row for _, row in selected] + observed_schedules = set() + for row_number, row in enumerate(selected_rows, start=2): + schedule = str(row.get("schedule", "")).strip() + if not schedule: + fail(f"training summary {summary_path} row {row_number} is missing schedule") + observed_schedules.add(canonical_method(schedule)) + if observed_schedules != {expected_method}: + fail( + f"training summary {summary_path} schedule {sorted(observed_schedules)} does not match " + f"training record method {expected_method!r}" + ) + + losses = [optional_number(row.get("loss")) for row in selected_rows] + finite_losses = [value for value in losses if value is not None] + fields["training_summary_available"] = True + fields["training_processed_kimg"] = final_processed + fields["training_attempted_iterations"] = len(selected_rows) + successful_values = [optional_number(row.get("successful_optimizer_steps")) for row in selected_rows] + if all(value is not None and value >= 0 and value.is_integer() for value in successful_values): + successful_steps = [int(value) for value in successful_values] + if any(right < left for left, right in zip(successful_steps, successful_steps[1:])): + fail(f"training summary {summary_path} has non-monotonic successful_optimizer_steps") + fields["training_successful_optimizer_steps"] = successful_steps[-1] + fields["training_successful_steps_valid"] = True + else: + fields["training_successful_steps_valid"] = False + skipped = [optional_bool(row.get("step_skipped")) for row in selected_rows] + skipped_count = sum(value is True for value in skipped) + fields["training_amp_skipped"] = skipped_count if any(value is not None for value in skipped) else None + fields["training_amp_skip_rate"] = skipped_count / len(selected_rows) if any(value is not None for value in skipped) else None + fields["training_amp_skip_telemetry_complete"] = all(value is not None for value in skipped) + fields["training_nan_count"] = sum( + 1 for row in selected_rows if str(row.get("loss", "")).strip().lower() == "nan" + ) + fields["training_inf_count"] = sum( + 1 + for row in selected_rows + if str(row.get("loss", "")).strip().lower() + in {"inf", "+inf", "-inf", "infinity", "+infinity", "-infinity"} + ) + # A malformed non-numeric loss must never be silently treated as finite. + malformed_loss_count = sum( + 1 + for row, value in zip(selected_rows, losses) + if value is None and str(row.get("loss", "")).strip().lower() not in {"nan", "inf", "+inf", "-inf", "infinity", "+infinity", "-infinity"} + ) + fields["training_nan_count"] += malformed_loss_count + if finite_losses: + trailing_count = min(100, max(1, math.ceil(len(finite_losses) * 0.1))) + trailing = finite_losses[-trailing_count:] + fields["training_trailing_loss_mean"] = mean(trailing) + fields["training_trailing_loss_std"] = stdev(trailing) if len(trailing) >= 2 else 0.0 + fields["training_final_loss"] = finite_losses[-1] + + raw_corrections = [str(row.get("correction", "")).strip() for row in selected_rows] + corrections = [optional_number(value) for value in raw_corrections] + if any(text and value is None for text, value in zip(raw_corrections, corrections)): + fail(f"training summary {summary_path} contains non-finite correction telemetry") + fields["correction_bound_valid"] = all( + value is None or abs(value) <= adaptive_max_adjust + 1e-9 for value in corrections + ) + finite_corrections = [value for value in corrections if value is not None] + if finite_corrections: + fields["final_correction"] = finite_corrections[-1] + fields["correction_abs_max"] = max(abs(value) for value in finite_corrections) + threshold = adaptive_max_adjust * 0.95 + fields["correction_saturation_fraction"] = sum(abs(value) >= threshold for value in finite_corrections) / len(finite_corrections) if threshold > 0 else 0.0 + signed = [1 if value > 0 else -1 for value in finite_corrections if value != 0] + fields["correction_sign_changes"] = sum(left != right for left, right in zip(signed, signed[1:])) + raw_active = [str(row.get("adaptive_active", "")).strip() for row in selected_rows] + active = [optional_bool(value) for value in raw_active] + if any(text and value is None for text, value in zip(raw_active, active)): + fail(f"training summary {summary_path} contains invalid adaptive_active telemetry") + if active[-1] is not None: + fields["adaptive_active"] = active[-1] + raw_ratios = [str(row.get("r_over_t_mean", "")).strip() for row in selected_rows] + raw_gaps = [str(row.get("gap_mean", "")).strip() for row in selected_rows] + ratios = [optional_number(value) for value in raw_ratios] + gaps = [optional_number(value) for value in raw_gaps] + if any(text and value is None for text, value in zip(raw_ratios, ratios)): + fail(f"training summary {summary_path} contains non-finite r_over_t_mean telemetry") + if any(text and value is None for text, value in zip(raw_gaps, gaps)): + fail(f"training summary {summary_path} contains non-finite gap_mean telemetry") + fields["controller_telemetry_complete"] = ( + all(value is not None for value in corrections) + and all(value is not None for value in active) + and all(value is not None for value in ratios) + and all(value is not None for value in gaps) + ) + paired_values = [(ratio, gap) for ratio, gap in zip(ratios, gaps) if ratio is not None and gap is not None] + if paired_values: + fields["final_r_over_t_mean"], fields["final_gap_mean"] = paired_values[-1] + fields["pair_ratio_valid"] = all(0 <= ratio <= 1 for ratio, _ in paired_values) + fields["pair_gap_valid"] = all(0 <= gap <= 1 and math.isclose(ratio + gap, 1.0, rel_tol=0, abs_tol=1e-5) for ratio, gap in paired_values) + return fields + + +def join_metrics_and_training( + metrics: Iterable[MetricRow], + records: dict[tuple[str, int, float], TrainingRecord], + adaptive_max_adjust: float, +) -> list[dict[str, Any]]: + cached_summaries: dict[tuple[Path | None, float, str], dict[str, Any]] = {} + output: list[dict[str, Any]] = [] + for metric in metrics: + record = records.get((metric.method, metric.training_seed, metric.budget_kimg)) + row: dict[str, Any] = { + "method": metric.method, + "training_seed": metric.training_seed, + "budget_kimg": metric.budget_kimg, + "nfe": metric.nfe, + "mid_t": metric.mid_t, + "sampling_seed": metric.sampling_seed, + "num_generated": metric.num_generated, + "kid_5k": metric.kid_5k, + "fid_5k": metric.fid_5k, + "checkpoint_sha256": metric.checkpoint_sha256, + "metric_source": metric.metric_source, + "run_dir": "", + "training_summary_csv": "", + } + if record is None: + row.update(summarize_training(None, metric.budget_kimg, adaptive_max_adjust, metric.method)) + else: + if record.checkpoint_sha256 != metric.checkpoint_sha256: + fail( + "checkpoint SHA mismatch for " + f"{metric.method}, seed={metric.training_seed}, budget={metric.budget_kimg:g} kimg: " + f"Role A={metric.checkpoint_sha256}, Role B={record.checkpoint_sha256}" + ) + cache_key = (record.training_summary_csv, metric.budget_kimg, metric.method) + if cache_key not in cached_summaries: + cached_summaries[cache_key] = summarize_training( + record.training_summary_csv, metric.budget_kimg, adaptive_max_adjust, metric.method + ) + row.update(cached_summaries[cache_key]) + row["run_dir"] = record.run_dir + row["training_summary_csv"] = "" if record.training_summary_csv is None else str(record.training_summary_csv) + output.append(row) + return output + + +def verify_pair_settings(fixed: dict[str, Any], adaptive: dict[str, Any]) -> bool: + if fixed["checkpoint_sha256"] == adaptive["checkpoint_sha256"]: + fail( + f"fixed and adaptive use the same checkpoint SHA for seed={fixed['training_seed']}, " + f"budget={fixed['budget_kimg']}, NFE={fixed['nfe']}" + ) + if fixed["nfe"] == 2 and not math.isclose(float(fixed["mid_t"]), float(adaptive["mid_t"]), rel_tol=0, abs_tol=1e-9): + fail( + f"mid_t mismatch for seed={fixed['training_seed']}, budget={fixed['budget_kimg']}, NFE=2: " + f"fixed={fixed['mid_t']}, adaptive={adaptive['mid_t']}" + ) + for field in ("sampling_seed", "num_generated"): + left, right = fixed[field], adaptive[field] + if left not in {None, ""} and right not in {None, ""} and left != right: + fail( + f"{field} mismatch for seed={fixed['training_seed']}, budget={fixed['budget_kimg']}, NFE={fixed['nfe']}: " + f"fixed={left!r}, adaptive={right!r}" + ) + return ( + all(fixed[field] not in {None, ""} and adaptive[field] not in {None, ""} for field in ("sampling_seed", "num_generated")) + and int(fixed["num_generated"]) == EXPECTED_IMAGE_COUNT + ) + + +def paired_differences(per_seed: Iterable[dict[str, Any]]) -> list[dict[str, Any]]: + grouped: dict[tuple[int, float, int], dict[str, dict[str, Any]]] = defaultdict(dict) + for row in per_seed: + key = (int(row["training_seed"]), float(row["budget_kimg"]), int(row["nfe"])) + grouped[key][str(row["method"])] = row + output: list[dict[str, Any]] = [] + for (seed, budget, nfe), arms in sorted(grouped.items(), key=lambda item: (item[0][1], item[0][2], item[0][0])): + if set(arms) != set(METHODS): + continue # Rolling reports are intentionally allowed to be partial. + fixed, adaptive = arms["sigmoid"], arms["adaptive_v1"] + settings_verified = verify_pair_settings(fixed, adaptive) + for metric in METRICS: + fixed_value, adaptive_value = fixed[metric], adaptive[metric] + if (fixed_value is None) != (adaptive_value is None): + fail( + f"{metric} is present for only one arm at seed={seed}, budget={budget:g}, NFE={nfe}; " + "fixed and adaptive must use the same metric" + ) + if fixed_value is None: + continue + delta = float(adaptive_value) - float(fixed_value) + output.append( + { + "metric": metric, + "training_seed": seed, + "budget_kimg": budget, + "nfe": nfe, + "fixed_value": fixed_value, + "adaptive_value": adaptive_value, + "delta_adaptive_minus_fixed": delta, + "adaptive_better": delta < 0, + "paired_sampling_settings_verified": settings_verified, + "sampling_seed": fixed["sampling_seed"], + "num_generated": fixed["num_generated"], + "fixed_checkpoint_sha256": fixed["checkpoint_sha256"], + "adaptive_checkpoint_sha256": adaptive["checkpoint_sha256"], + } + ) + return output + + +def aggregate_differences(pairs: Iterable[dict[str, Any]], expected_seeds: Iterable[int]) -> list[dict[str, Any]]: + grouped: dict[tuple[str, float, int], list[dict[str, Any]]] = defaultdict(list) + for row in pairs: + grouped[(str(row["metric"]), float(row["budget_kimg"]), int(row["nfe"]))].append(row) + output: list[dict[str, Any]] = [] + expected_seed_set = set(expected_seeds) + for (metric, budget, nfe), rows in sorted(grouped.items(), key=lambda item: (item[0][0], item[0][1], item[0][2])): + rows = sorted(rows, key=lambda item: int(item["training_seed"])) + deltas = [float(row["delta_adaptive_minus_fixed"]) for row in rows] + fixed_values = [float(row["fixed_value"]) for row in rows] + adaptive_values = [float(row["adaptive_value"]) for row in rows] + seeds = {int(row["training_seed"]) for row in rows} + count = len(rows) + output.append( + { + "metric": metric, + "budget_kimg": budget, + "nfe": nfe, + "paired_seed_count": count, + "expected_seed_count": len(expected_seed_set), + "coverage_complete": seeds == expected_seed_set, + "adaptive_better_seed_count": sum(delta < 0 for delta in deltas), + "adaptive_worse_seed_count": sum(delta > 0 for delta in deltas), + "fixed_mean": mean(fixed_values), + "fixed_std": stdev(fixed_values) if count >= 2 else 0.0, + "adaptive_mean": mean(adaptive_values), + "adaptive_std": stdev(adaptive_values) if count >= 2 else 0.0, + "delta_mean": mean(deltas), + "delta_std": stdev(deltas) if count >= 2 else 0.0, + "delta_sem": (stdev(deltas) / math.sqrt(count)) if count >= 2 else 0.0, + "paired_sampling_settings_verified": all(row["paired_sampling_settings_verified"] for row in rows), + } + ) + return output + + +def pearson_correlation(x_values: list[float], y_values: list[float]) -> float | None: + """Return descriptive Pearson r, or None when the relationship is undefined.""" + if len(x_values) < 3 or len(x_values) != len(y_values): + return None + x_mean, y_mean = mean(x_values), mean(y_values) + x_centered = [value - x_mean for value in x_values] + y_centered = [value - y_mean for value in y_values] + denominator = math.sqrt(sum(value * value for value in x_centered) * sum(value * value for value in y_centered)) + if denominator == 0: + return None + return sum(left * right for left, right in zip(x_centered, y_centered)) / denominator + + +def mechanism_relationships(per_seed: list[dict[str, Any]], pairs: list[dict[str, Any]]) -> list[dict[str, Any]]: + """Describe training/controller-to-quality relationships without p-values. + + Each NFE is analysed separately. This prevents the intentionally different + one-step and two-step samplers from masquerading as a controller effect. + """ + adaptive_rows = { + (int(row["training_seed"]), float(row["budget_kimg"]), int(row["nfe"])): row + for row in per_seed + if row["method"] == "adaptive_v1" + } + grouped: dict[tuple[str, int], list[tuple[dict[str, Any], dict[str, Any]]]] = defaultdict(list) + for pair in pairs: + key = (int(pair["training_seed"]), float(pair["budget_kimg"]), int(pair["nfe"])) + adaptive = adaptive_rows.get(key) + if adaptive is not None: + grouped[(str(pair["metric"]), int(pair["nfe"]))].append((pair, adaptive)) + relationships: list[dict[str, Any]] = [] + specifications = ( + ("adaptive_quality_vs_trailing_loss", "training_trailing_loss_mean", "adaptive_value"), + ("paired_delta_vs_correction", "final_correction", "delta_adaptive_minus_fixed"), + ("paired_delta_vs_gap", "final_gap_mean", "delta_adaptive_minus_fixed"), + ) + for (metric, nfe), rows in sorted(grouped.items()): + for relation, x_field, y_field in specifications: + x_values: list[float] = [] + y_values: list[float] = [] + for pair, adaptive in rows: + x_value = adaptive.get(x_field) + y_value = pair.get(y_field) + if x_value is not None and y_value is not None: + x_values.append(float(x_value)) + y_values.append(float(y_value)) + relationships.append( + { + "metric": metric, + "nfe": nfe, + "relationship": relation, + "n": len(x_values), + "pearson_r": pearson_correlation(x_values, y_values), + } + ) + return relationships + + +def write_csv(path: Path, rows: list[dict[str, Any]], fieldnames: list[str]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=fieldnames, extrasaction="ignore") + writer.writeheader() + for row in rows: + writer.writerow({field: format_number(row.get(field)) for field in fieldnames}) + + +def plot_quality(per_seed: list[dict[str, Any]], output_path: Path) -> None: + available = [metric for metric in METRICS if any(row[metric] is not None for row in per_seed)] + if not available: + fail("cannot plot quality without KID or FID values") + figure, axes = plt.subplots(len(available), len(EXPECTED_NFES), figsize=(11, 4.2 * len(available)), squeeze=False) + colours = {"sigmoid": "#356aa0", "adaptive_v1": "#d95f02"} + labels = {"sigmoid": "Fixed sigmoid", "adaptive_v1": "Adaptive v1"} + for metric_index, metric in enumerate(available): + for nfe_index, nfe in enumerate(EXPECTED_NFES): + axis = axes[metric_index][nfe_index] + for method in METHODS: + budgets, averages, spreads = [], [], [] + for budget in EXPECTED_BUDGETS: + values = [ + float(row[metric]) + for row in per_seed + if row["method"] == method and row["nfe"] == nfe and row["budget_kimg"] == budget and row[metric] is not None + ] + if values: + budgets.append(budget) + averages.append(mean(values)) + spreads.append(stdev(values) if len(values) >= 2 else 0.0) + if budgets: + axis.errorbar(budgets, averages, yerr=spreads, marker="o", capsize=4, linewidth=2, color=colours[method], label=labels[method]) + axis.set_title(f"{metric.replace('_', '-').upper()} · NFE={nfe}" + (" · mid_t=0.821" if nfe == 2 else "")) + axis.set_xlabel("Training budget (kimg)") + axis.set_ylabel(f"{metric.replace('_', '-').upper()} (lower is better)") + axis.set_xticks(EXPECTED_BUDGETS) + axis.grid(alpha=0.25) + axis.legend() + figure.suptitle("Paired generation quality by training budget (mean ± sample std across training seeds)", y=1.01) + figure.tight_layout() + figure.savefig(output_path, dpi=180, bbox_inches="tight") + plt.close(figure) + + +def values_by_budget(rows: Iterable[dict[str, Any]], method: str, field: str) -> tuple[list[float], list[float], list[float]]: + budgets, averages, spreads = [], [], [] + for budget in EXPECTED_BUDGETS: + values = [ + float(row[field]) + for row in rows + if row["method"] == method and row["budget_kimg"] == budget and row[field] is not None + ] + if values: + budgets.append(budget) + averages.append(mean(values)) + spreads.append(stdev(values) if len(values) >= 2 else 0.0) + return budgets, averages, spreads + + +def plot_controller(per_seed: list[dict[str, Any]], output_path: Path) -> None: + unique_training_rows: dict[tuple[str, int, float], dict[str, Any]] = {} + for row in per_seed: + unique_training_rows[(row["method"], row["training_seed"], row["budget_kimg"])] = row + training_rows = list(unique_training_rows.values()) + figure, axes = plt.subplots(1, 3, figsize=(15, 4.2)) + colours = {"sigmoid": "#356aa0", "adaptive_v1": "#d95f02"} + labels = {"sigmoid": "Fixed sigmoid", "adaptive_v1": "Adaptive v1"} + for field, axis, title, ylabel, methods in ( + ("final_correction", axes[0], "Controller correction", "Final correction", ("adaptive_v1",)), + ("final_gap_mean", axes[1], "Final training-pair gap", "mean((t-r)/t)", METHODS), + ("training_trailing_loss_mean", axes[2], "Trailing training loss", "mean loss (last 10%, max 100 rows)", METHODS), + ): + found = False + for method in methods: + budgets, averages, spreads = values_by_budget(training_rows, method, field) + if budgets: + found = True + axis.errorbar(budgets, averages, yerr=spreads, marker="o", capsize=4, linewidth=2, color=colours[method], label=labels[method]) + axis.set_title(title) + axis.set_xlabel("Checkpoint budget (kimg)") + axis.set_ylabel(ylabel) + axis.set_xticks(EXPECTED_BUDGETS) + axis.grid(alpha=0.25) + if found: + axis.legend() + else: + axis.text(0.5, 0.5, "No training telemetry supplied", ha="center", va="center", transform=axis.transAxes) + figure.suptitle("Controller and training-stability telemetry (mean ± sample std across training seeds)", y=1.02) + figure.tight_layout() + figure.savefig(output_path, dpi=180, bbox_inches="tight") + plt.close(figure) + + +def expected_quality_settings() -> set[tuple[float, int]]: + return {(budget, nfe) for budget in EXPECTED_BUDGETS for nfe in EXPECTED_NFES} + + +def primary_metric_availability(aggregate: list[dict[str, Any]], expected_seeds: Iterable[int]) -> tuple[str | None, str]: + """Return the usable metric and why no final-quality verdict is possible. + + Complete paired values and complete evaluation provenance are distinct + requirements. A CSV imported from a legacy Role-A report can have all + 36 numerical values while omitting sampling_seed/num_generated; calling + that a missing metric matrix would hide the actual data-freeze issue. + """ + expected = expected_quality_settings() + expected_count = len(set(expected_seeds)) + complete_but_unverified = False + for metric in METRICS: # KID is deliberately first: it is the frozen primary metric. + covered = { + (float(row["budget_kimg"]), int(row["nfe"])) + for row in aggregate + if ( + row["metric"] == metric + and row["paired_seed_count"] == expected_count + and row["coverage_complete"] + ) + } + verified = { + (float(row["budget_kimg"]), int(row["nfe"])) + for row in aggregate + if ( + row["metric"] == metric + and row["paired_seed_count"] == expected_count + and row["coverage_complete"] + and row["paired_sampling_settings_verified"] + ) + } + if verified == expected: + return metric, "COMPLETE" + if covered == expected: + complete_but_unverified = True + if complete_but_unverified: + return None, "SAMPLING_PROVENANCE_INCOMPLETE" + return None, "METRIC_COVERAGE_INCOMPLETE" + + +def training_stability_status( + per_seed: list[dict[str, Any]], expected_seeds: Iterable[int], skip_rate_tolerance: float +) -> tuple[str, list[str]]: + unique: dict[tuple[str, int, float], dict[str, Any]] = {} + for row in per_seed: + unique[(row["method"], int(row["training_seed"]), float(row["budget_kimg"]))] = row + expected = {(method, seed, budget) for method in METHODS for seed in expected_seeds for budget in EXPECTED_BUDGETS} + missing = expected - set(unique) + if missing: + return "INCOMPLETE", [f"missing {len(missing)} training records"] + findings: list[str] = [] + for key in sorted(expected): + row = unique[key] + if not row["training_summary_available"]: + return "INCOMPLETE", [f"training summary unavailable for {key}"] + if row["training_successful_steps_valid"] is not True: + return "INCOMPLETE", [f"successful optimizer-step telemetry unavailable or invalid for {key}"] + if row["training_amp_skip_telemetry_complete"] is not True: + return "INCOMPLETE", [f"AMP skip telemetry unavailable or invalid for {key}"] + if row["controller_telemetry_complete"] is not True: + return "INCOMPLETE", [f"controller telemetry unavailable for {key}"] + if (row["training_nan_count"] or 0) > 0 or (row["training_inf_count"] or 0) > 0: + findings.append(f"non-finite loss recorded for {key}") + if row["pair_ratio_valid"] is not True or row["pair_gap_valid"] is not True: + findings.append(f"invalid r/t or gap telemetry for {key}") + if row["correction_bound_valid"] is not True: + findings.append(f"correction exceeds the configured bound for {key}") + if key[0] == "adaptive_v1" and row["adaptive_active"] is not True: + findings.append(f"adaptive controller did not activate for {key}") + for seed in expected_seeds: + for budget in EXPECTED_BUDGETS: + fixed = unique[("sigmoid", seed, budget)] + adaptive = unique[("adaptive_v1", seed, budget)] + fixed_rate, adaptive_rate = fixed["training_amp_skip_rate"], adaptive["training_amp_skip_rate"] + if fixed_rate is None or adaptive_rate is None: + return "INCOMPLETE", [f"AMP skip telemetry unavailable for seed={seed}, budget={budget:g}"] + if float(adaptive_rate) > float(fixed_rate) + skip_rate_tolerance: + findings.append( + f"adaptive AMP skip rate exceeds fixed by more than {skip_rate_tolerance:.1%} at seed={seed}, budget={budget:g}" + ) + return ("PASS", []) if not findings else ("FAIL", findings) + + +def conclusion_from_aggregate( + aggregate: list[dict[str, Any]], per_seed: list[dict[str, Any]], expected_seeds: Iterable[int], skip_rate_tolerance: float +) -> dict[str, Any]: + primary, quality_availability = primary_metric_availability(aggregate, expected_seeds) + stability, stability_findings = training_stability_status(per_seed, expected_seeds, skip_rate_tolerance) + if primary is None: + if quality_availability == "SAMPLING_PROVENANCE_INCOMPLETE": + reason = ( + "The complete paired KID/FID matrix is available, but its evaluation provenance is incomplete: " + "each fixed/adaptive pair must record the same nonempty sampling_seed and num_generated=5000." + ) + else: + reason = "The complete 3-seed × 16/32/64 kimg × NFE=1/2 paired matrix is not yet available for one common metric." + return { + "label": "INCOMPLETE", + "primary_metric": None, + "quality_availability": quality_availability, + "stability": stability, + "stability_findings": stability_findings, + "reason": reason, + "setting_signals": [], + } + expected_count = len(set(expected_seeds)) + primary_rows = [row for row in aggregate if row["metric"] == primary] + signal_by_setting: dict[tuple[float, int], dict[str, Any]] = {} + for row in primary_rows: + key = (float(row["budget_kimg"]), int(row["nfe"])) + complete = row["paired_seed_count"] == expected_count and row["coverage_complete"] + wins, losses, delta = int(row["adaptive_better_seed_count"]), int(row["adaptive_worse_seed_count"]), float(row["delta_mean"]) + signal_by_setting[key] = { + "budget_kimg": key[0], + "nfe": key[1], + "complete": complete, + "wins": wins, + "losses": losses, + "delta_mean": delta, + "advantage": complete and wins >= 2 and delta < 0, + "regression": complete and losses >= 2 and delta > 0, + } + settings = [signal_by_setting[key] for key in sorted(signal_by_setting)] + advantages = [item for item in settings if item["advantage"]] + regressions = [item for item in settings if item["regression"]] + high_budget_advantages = [] + for item in advantages: + if item["budget_kimg"] not in {32.0, 64.0}: + continue + peer = signal_by_setting.get((item["budget_kimg"], 1 if item["nfe"] == 2 else 2)) + if peer is not None and not peer["regression"]: + high_budget_advantages.append(item) + if stability != "PASS": + label = "INCOMPLETE" + reason = "Quality matrix is complete, but the frozen stability/controller safety requirement is not yet satisfied." + elif advantages and regressions: + label = "混合" + reason = "Stable positive and negative paired settings coexist, so the effect is sensitive to budget or NFE." + elif high_budget_advantages: + label = "Adaptive 表现出初步优势" + reason = "At least one 32/64 kimg setting has ≥2/3 adaptive seed wins and a lower three-seed mean, while its other NFE is not a stable regression." + elif regressions: + label = "负向" + reason = "At least one complete setting has ≥2/3 adaptive seed losses and a worse three-seed mean, with no stable positive setting." + else: + label = "持平" + reason = "No complete setting meets the pre-frozen repeated-advantage or repeated-regression rule." + return { + "label": label, + "primary_metric": primary, + "quality_availability": quality_availability, + "stability": stability, + "stability_findings": stability_findings, + "reason": reason, + "setting_signals": settings, + } + + +def markdown_table(headers: list[str], rows: Iterable[Iterable[str]]) -> str: + result = ["| " + " | ".join(headers) + " |", "| " + " | ".join([":--"] * len(headers)) + " |"] + result.extend("| " + " | ".join(values) + " |" for values in rows) + return "\n".join(result) + + +def write_conclusion( + path: Path, + conclusion: dict[str, Any], + aggregate: list[dict[str, Any]], + pairs: list[dict[str, Any]], + relationships: list[dict[str, Any]], + expected_seeds: Iterable[int], + skip_rate_tolerance: float, +) -> None: + expected_count = len(set(expected_seeds)) + lines = ["# Fixed sigmoid vs Adaptive v1 — Role C conclusion", "", f"## Current verdict: {conclusion['label']}", "", conclusion["reason"], ""] + primary = conclusion["primary_metric"] + if primary is None: + if conclusion["quality_availability"] == "SAMPLING_PROVENANCE_INCOMPLETE": + lines += [ + "The report is deliberately provisional: all numerical pairs are present, but it does not make a quality claim until every pair records matching sampling_seed values and num_generated=5000.", + "", + ] + else: + lines += [ + "The report is deliberately provisional: it does not make a quality claim until one common metric covers every expected paired setting.", + "", + ] + else: + lines += [ + f"Primary metric: **{primary.replace('_', '-').upper()}** (lower is better). KID is preferred whenever its full frozen matrix is available; FID becomes the common fallback only when KID is incomplete.", + "", + ] + lines += [f"Training/controller stability gate: **{conclusion['stability']}**."] + if conclusion["stability_findings"]: + lines += ["", "Stability findings:"] + [f"- {finding}" for finding in conclusion["stability_findings"]] + lines += ["", "## Paired quality summary", ""] + summary_rows = [] + for row in aggregate: + summary_rows.append( + [ + row["metric"].replace("_", "-").upper(), + f"{row['budget_kimg']:g}", + str(row["nfe"]), + f"{row['paired_seed_count']}/{expected_count}", + f"{row['adaptive_better_seed_count']}/{row['paired_seed_count']}", + f"{row['fixed_mean']:.6g} ± {row['fixed_std']:.3g}", + f"{row['adaptive_mean']:.6g} ± {row['adaptive_std']:.3g}", + f"{row['delta_mean']:.6g} ± {row['delta_std']:.3g}", + ] + ) + lines += [ + markdown_table( + ["Metric", "Budget (kimg)", "NFE", "Paired seeds", "Adaptive wins", "Fixed mean ± std", "Adaptive mean ± std", "Δ adaptive − fixed ± std"], + summary_rows, + ), + "", + "Negative Δ means Adaptive v1 is better. Standard deviations are sample SD across paired training seeds; no p-value is inferred from n=3.", + "", + "## Pre-frozen decision checks", + "", + ] + if conclusion["setting_signals"]: + signal_rows = [] + for item in conclusion["setting_signals"]: + signal_rows.append( + [ + f"{item['budget_kimg']:g}", + str(item["nfe"]), + f"{item['wins']}/3", + f"{item['losses']}/3", + f"{item['delta_mean']:.6g}", + "yes" if item["advantage"] else "no", + "yes" if item["regression"] else "no", + ] + ) + lines += [ + markdown_table( + ["Budget (kimg)", "NFE", "Adaptive wins", "Adaptive losses", "Mean Δ", "Repeated advantage", "Repeated regression"], + signal_rows, + ), + "", + ] + else: + if conclusion["quality_availability"] == "SAMPLING_PROVENANCE_INCOMPLETE": + lines += ["Metric coverage is complete, but sampling provenance is not verified for every setting.", ""] + else: + lines += ["No common complete metric matrix is available yet.", ""] + lines += ["## Training and controller relationships", ""] + if relationships: + relationship_rows = [] + for item in relationships: + relationship_rows.append( + [ + item["metric"].replace("_", "-").upper(), + str(item["nfe"]), + item["relationship"], + str(item["n"]), + "undefined" if item["pearson_r"] is None else f"{item['pearson_r']:.4f}", + ] + ) + lines += [ + markdown_table(["Metric", "NFE", "Relationship", "n", "Pearson r"], relationship_rows), + "", + "These are descriptive correlations across available adaptive runs, separated by NFE. They are not significance tests and do not establish causality.", + "", + ] + else: + lines += ["No paired quality/controller observations are available yet.", ""] + lines += [ + "## Guardrails applied", + "", + "- Only fixed/adaptive rows with the same training seed, checkpoint budget and NFE are differenced.", + "- KID/FID are never substituted across arms; a metric appearing for only one arm is rejected.", + "- If supplied, sampling seed, generated-image count, and NFE=2 `mid_t` must agree inside each pair.", + "- The stability gate requires finite losses, legal `r/t` and gap telemetry, active adaptive controller telemetry, and an adaptive AMP-skip rate no more than " + f"{skip_rate_tolerance:.1%} above the paired fixed run.", + "- The conclusion is not upgraded from partial coverage, a single seed, or a single favorable NFE setting.", + "", + f"Paired metric rows currently available: {len(pairs)}.", + ] + path.write_text("\n".join(lines) + "\n", encoding="utf-8") + + +PER_SEED_FIELDS = [ + "method", "training_seed", "budget_kimg", "nfe", "mid_t", "sampling_seed", "num_generated", "kid_5k", "fid_5k", + "checkpoint_sha256", "metric_source", "run_dir", "training_summary_csv", "training_summary_available", "training_processed_kimg", + "training_attempted_iterations", "training_successful_optimizer_steps", "training_successful_steps_valid", "training_amp_skipped", "training_amp_skip_rate", "training_amp_skip_telemetry_complete", + "training_nan_count", "training_inf_count", "training_trailing_loss_mean", "training_trailing_loss_std", "training_final_loss", + "adaptive_active", "final_correction", "correction_abs_max", "correction_saturation_fraction", "correction_sign_changes", "correction_bound_valid", + "final_r_over_t_mean", "final_gap_mean", "pair_ratio_valid", "pair_gap_valid", "controller_telemetry_complete", +] + +PAIR_FIELDS = [ + "metric", "training_seed", "budget_kimg", "nfe", "fixed_value", "adaptive_value", "delta_adaptive_minus_fixed", + "adaptive_better", "paired_sampling_settings_verified", "sampling_seed", "num_generated", "fixed_checkpoint_sha256", "adaptive_checkpoint_sha256", +] + +AGGREGATE_FIELDS = [ + "metric", "budget_kimg", "nfe", "paired_seed_count", "expected_seed_count", "coverage_complete", "adaptive_better_seed_count", + "adaptive_worse_seed_count", "fixed_mean", "fixed_std", "adaptive_mean", "adaptive_std", "delta_mean", "delta_std", + "delta_sem", "paired_sampling_settings_verified", +] + + +def comma_separated_numbers(value: str, field: str, *, integer: bool) -> tuple[int | float, ...]: + values = [] + for item in value.split(","): + item = item.strip() + if not item: + continue + parsed = parse_int(item, field, minimum=0) if integer else parse_float(item, field, minimum=0) + assert parsed is not None + values.append(parsed) + if not values: + fail(f"{field} must include at least one value") + if len(set(values)) != len(values): + fail(f"{field} contains duplicate values: {value!r}") + return tuple(values) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--metrics", required=True, type=Path, help="Role-A unified per-checkpoint metrics CSV") + parser.add_argument("--training-records", type=Path, help="Role-B checkpoint/training-summary CSV") + parser.add_argument("--training-root", action="append", type=Path, default=[], help="Directory to recursively scan for Role-B metadata.json files") + parser.add_argument("--outdir", required=True, type=Path, help="Empty or existing output directory for Role-C deliverables") + parser.add_argument("--expected-seeds", default="0,1,2", help="Comma-separated frozen training seeds (default: 0,1,2)") + parser.add_argument("--expected-budgets", default="16,32,64", help="Comma-separated frozen budgets in kimg (default: 16,32,64)") + parser.add_argument("--adaptive-max-adjust", type=float, default=0.05, help="Controller saturation reference (default: 0.05)") + parser.add_argument("--skip-rate-tolerance", type=float, default=0.02, help="Allowed adaptive-minus-fixed AMP skip-rate increase for stability gate (default: 0.02)") + parser.add_argument("--require-complete", action="store_true", help="Return a non-zero status unless complete quality and training-stability evidence is present") + return parser.parse_args() + + +def main() -> int: + args = parse_args() + if not math.isfinite(args.adaptive_max_adjust) or args.adaptive_max_adjust < 0: + fail("--adaptive-max-adjust must be finite and >= 0") + if not math.isfinite(args.skip_rate_tolerance) or args.skip_rate_tolerance < 0: + fail("--skip-rate-tolerance must be finite and >= 0") + expected_seeds = tuple(int(value) for value in comma_separated_numbers(args.expected_seeds, "expected_seeds", integer=True)) + expected_budgets = tuple(float(value) for value in comma_separated_numbers(args.expected_budgets, "expected_budgets", integer=False)) + if tuple(expected_budgets) != EXPECTED_BUDGETS or tuple(expected_seeds) != EXPECTED_SEEDS: + # The matrix itself is frozen by the task. Alternate values are useful for a smoke fixture only, + # but the deliverable labels always retain the original 16/32/64, seeds 0/1/2 protocol. + fail("Role-C production analysis is frozen to training seeds 0,1,2 and budgets 16,32,64 kimg") + metrics = read_metrics(args.metrics, expected_budgets) + training_records: list[TrainingRecord] = [] + if args.training_records is not None: + training_records.extend(read_training_records(args.training_records, expected_budgets)) + for root in args.training_root: + training_records.extend(records_from_training_root(root, expected_budgets)) + indexed_records = index_training_records(training_records) + per_seed = join_metrics_and_training(metrics, indexed_records, args.adaptive_max_adjust) + pairs = paired_differences(per_seed) + aggregate = aggregate_differences(pairs, expected_seeds) + conclusion = conclusion_from_aggregate(aggregate, per_seed, expected_seeds, args.skip_rate_tolerance) + relationships = mechanism_relationships(per_seed, pairs) + + args.outdir.mkdir(parents=True, exist_ok=True) + write_csv(args.outdir / "per_seed_metrics.csv", per_seed, PER_SEED_FIELDS) + write_csv(args.outdir / "paired_differences.csv", pairs, PAIR_FIELDS) + write_csv(args.outdir / "aggregate_results.csv", aggregate, AGGREGATE_FIELDS) + plot_quality(per_seed, args.outdir / "quality_vs_budget.png") + plot_controller(per_seed, args.outdir / "controller_vs_budget.png") + write_conclusion( + args.outdir / "FINAL_CONCLUSION.md", + conclusion, + aggregate, + pairs, + relationships, + expected_seeds, + args.skip_rate_tolerance, + ) + + print(f"[analyze_paired_results] wrote Role-C deliverables to {args.outdir}") + print(f"[analyze_paired_results] verdict: {conclusion['label']}") + if args.require_complete and conclusion["label"] == "INCOMPLETE": + return 2 + return 0 + + +if __name__ == "__main__": + try: + raise SystemExit(main()) + except AnalysisError as exc: + print(f"[analyze_paired_results] ERROR: {exc}", file=sys.stderr) + raise SystemExit(2) from exc diff --git a/scripts/audit_anonymity.py b/scripts/audit_anonymity.py new file mode 100644 index 00000000..2d956bba --- /dev/null +++ b/scripts/audit_anonymity.py @@ -0,0 +1,161 @@ +#!/usr/bin/env python3 +"""Scan a release tree for likely identity, credential, and private-path leaks.""" + +from __future__ import annotations + +import argparse +import json +import re +import subprocess +from dataclasses import asdict, dataclass +from pathlib import Path +from typing import Iterator, Sequence + + +DEFAULT_EXCLUDES = { + ".git", + ".idea", + ".mypy_cache", + ".pytest_cache", + ".vscode", + "__pycache__", +} + +# These files intentionally contain the literal examples used by the scanner. +DEFAULT_FILE_EXCLUDES = { + "docs/ANONYMIZATION_AUDIT.md", + "scripts/audit_anonymity.py", + "tests/test_audit_anonymity.py", +} + +TEXT_SUFFIXES = { + ".cfg", ".csv", ".env", ".ini", ".json", ".jsonl", ".md", ".py", + ".rst", ".sh", ".toml", ".tsv", ".txt", ".yaml", ".yml", +} + +PATTERNS = { + "private_key": re.compile(r"-----BEGIN (?:RSA |EC |OPENSSH )?PRIVATE KEY-----"), + "github_token": re.compile(r"\b(?:ghp|github_pat)_[A-Za-z0-9_]{20,}\b"), + "generic_secret": re.compile( + r"(?i)\b(?:api[_-]?key|access[_-]?token|secret[_-]?key|password)\b" + r"\s*[:=]\s*['\"]?[A-Za-z0-9+/_.-]{8,}" + ), + "windows_user_path": re.compile(r"(?i)\b[A-Z]:\\Users\\[^\\\s]+"), + "linux_root_path": re.compile(r"(? list[Path] | None: + try: + result = subprocess.run( + ["git", "-C", str(root), "ls-files", "-z"], + check=True, + stdout=subprocess.PIPE, + stderr=subprocess.DEVNULL, + ) + except (FileNotFoundError, subprocess.CalledProcessError): + return None + return [root / item.decode("utf-8") for item in result.stdout.split(b"\0") if item] + + +def iter_files(root: Path, use_git: bool = True) -> Iterator[Path]: + candidates = tracked_files(root) if use_git else None + if candidates is None: + candidates = list(root.rglob("*")) + for path in candidates: + try: + relative = path.relative_to(root) + except ValueError: + continue + relative_name = relative.as_posix() + if not path.is_file() or any(part in DEFAULT_EXCLUDES for part in relative.parts): + continue + if relative_name in DEFAULT_FILE_EXCLUDES: + continue + if path.suffix.lower() in TEXT_SUFFIXES or path.name in {"Dockerfile", "Makefile"}: + yield path + + +def scan_text(relative_path: str, text: str, rules: Sequence[str]) -> Iterator[Finding]: + for line_number, line in enumerate(text.splitlines(), start=1): + for rule in rules: + if PATTERNS[rule].search(line): + if rule in SENSITIVE_RULES: + excerpt = "" + else: + excerpt = line.strip() + if len(excerpt) > 200: + excerpt = excerpt[:197] + "..." + yield Finding(rule=rule, path=relative_path, line=line_number, excerpt=excerpt) + + +def scan(root: Path, rules: Sequence[str], use_git: bool = True) -> list[Finding]: + findings: list[Finding] = [] + for path in iter_files(root, use_git=use_git): + try: + text = path.read_text(encoding="utf-8") + except (OSError, UnicodeDecodeError): + continue + relative = path.relative_to(root).as_posix() + findings.extend(scan_text(relative, text, rules)) + return findings + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("root", nargs="?", default=".", help="release tree to scan") + parser.add_argument("--json", dest="json_path", help="write machine-readable findings") + parser.add_argument( + "--rule", + action="append", + choices=sorted(PATTERNS), + help="scan only selected rule; may be repeated", + ) + parser.add_argument( + "--all-files", + action="store_true", + help="scan the directory recursively instead of using git ls-files", + ) + return parser.parse_args() + + +def main() -> int: + args = parse_args() + root = Path(args.root).resolve() + rules = args.rule or sorted(PATTERNS) + findings = scan(root, rules, use_git=not args.all_files) + + for finding in findings: + print(f"{finding.path}:{finding.line}: [{finding.rule}] {finding.excerpt}") + + if args.json_path: + output = Path(args.json_path) + output.parent.mkdir(parents=True, exist_ok=True) + output.write_text( + json.dumps([asdict(item) for item in findings], indent=2) + "\n", + encoding="utf-8", + ) + + print(f"anonymity audit: {len(findings)} finding(s), {len(rules)} rule(s)") + return 1 if findings else 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/build_blind_ab.py b/scripts/build_blind_ab.py new file mode 100644 index 00000000..1e47cd2e --- /dev/null +++ b/scripts/build_blind_ab.py @@ -0,0 +1,208 @@ +#!/usr/bin/env python3 +"""Build a method-blinded, side-balanced A/B ballot from fixed-seed samples.""" + +from __future__ import annotations + +import argparse +import csv +import hashlib +import json +import random +import re +from pathlib import Path +from urllib.parse import urlparse + +from PIL import Image, ImageDraw + + +SCHEDULES = ("sigmoid", "adaptive_v1") +TRAINING_SEEDS = (0, 1, 2) +NFES = (1, 2) +VISUAL_SEEDS = tuple(range(16)) +DEFAULT_RANDOMIZATION_SEED = 20260723 + + +def fail(message: str) -> None: + raise SystemExit(f"[build_blind_ab] ERROR: {message}") + + +def sha256_file(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def checkpoint_id(path: Path, digest: str) -> str: + filename = Path(urlparse(str(path)).path).name + stem = Path(filename).stem or "checkpoint" + safe_stem = re.sub(r"[^A-Za-z0-9._-]+", "-", stem).strip("-._") + return f"{safe_stem or 'checkpoint'}-{digest[:12]}" + + +def load_cells(path: Path) -> dict[tuple[str, int], dict]: + try: + payload = json.loads(path.read_text(encoding="utf-8")) + raw_cells = payload["cells"] + except (OSError, KeyError, TypeError, json.JSONDecodeError) as exc: + fail(f"cannot read checkpoint manifest {path}: {exc}") + cells = {} + for raw in raw_cells: + schedule = str(raw.get("schedule")) + training_seed = int(raw.get("training_seed")) + checkpoint = Path(raw.get("checkpoint", "")).expanduser().resolve() + if not checkpoint.is_file(): + fail(f"checkpoint not found: {checkpoint}") + digest = sha256_file(checkpoint) + expected = raw.get("checkpoint_sha256") + if expected and expected != digest: + fail(f"checkpoint SHA256 mismatch: {checkpoint}") + key = (schedule, training_seed) + if key in cells: + fail(f"duplicate cell: {key}") + cells[key] = { + "checkpoint": checkpoint, + "checkpoint_sha256": digest, + "checkpoint_id": checkpoint_id(checkpoint, digest), + } + expected_keys = {(schedule, seed) for schedule in SCHEDULES for seed in TRAINING_SEEDS} + if set(cells) != expected_keys: + fail(f"manifest must contain exactly {sorted(expected_keys)}") + return cells + + +def sample_path(sample_root: Path, cell: dict, nfe: int, seed: int) -> Path: + path = sample_root / cell["checkpoint_id"] / f"nfe{nfe}" / "images" / f"seed{seed:06d}.png" + if not path.is_file(): + fail(f"fixed-seed visual sample missing: {path}") + return path + + +def render_trial(left_path: Path, right_path: Path, output: Path) -> None: + with Image.open(left_path) as left_image: + left = left_image.convert("RGB").resize((256, 256), Image.Resampling.NEAREST) + with Image.open(right_path) as right_image: + right = right_image.convert("RGB").resize((256, 256), Image.Resampling.NEAREST) + canvas = Image.new("RGB", (552, 304), "white") + canvas.paste(left, (12, 36)) + canvas.paste(right, (284, 36)) + draw = ImageDraw.Draw(canvas) + draw.text((132, 10), "A", fill="black") + draw.text((404, 10), "B", fill="black") + draw.line((276, 0, 276, 304), fill=(210, 210, 210), width=2) + output.parent.mkdir(parents=True, exist_ok=True) + canvas.save(output, format="PNG", optimize=False) + + +def write_csv(path: Path, fieldnames: list[str], rows: list[dict]) -> None: + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=fieldnames) + writer.writeheader() + writer.writerows(rows) + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--manifest", type=Path, required=True) + parser.add_argument("--sample-root", type=Path, required=True) + parser.add_argument("--outdir", type=Path, required=True, help="Public blinded package") + parser.add_argument("--key-out", type=Path, required=True, help="Private unblinding CSV") + parser.add_argument("--randomization-seed", type=int, default=DEFAULT_RANDOMIZATION_SEED) + args = parser.parse_args(argv) + + outdir = args.outdir.resolve() + key_out = args.key_out.resolve() + if outdir.exists() and any(outdir.iterdir()): + fail(f"public output directory must be empty: {outdir}") + if key_out.exists(): + fail(f"refuse to overwrite private key: {key_out}") + outdir.mkdir(parents=True, exist_ok=True) + key_out.parent.mkdir(parents=True, exist_ok=True) + + cells = load_cells(args.manifest) + sample_root = args.sample_root.resolve() + rng = random.Random(args.randomization_seed) + strata = [ + (training_seed, nfe, sample_seed) + for training_seed in TRAINING_SEEDS + for nfe in NFES + for sample_seed in VISUAL_SEEDS + ] + rng.shuffle(strata) + adaptive_on_a = [True] * (len(strata) // 2) + [False] * (len(strata) // 2) + rng.shuffle(adaptive_on_a) + + public_rows = [] + key_rows = [] + for index, ((training_seed, nfe, visual_seed), adaptive_is_a) in enumerate(zip(strata, adaptive_on_a), start=1): + trial_id = f"T{index:03d}" + fixed_cell = cells[("sigmoid", training_seed)] + adaptive_cell = cells[("adaptive_v1", training_seed)] + fixed_path = sample_path(sample_root, fixed_cell, nfe, visual_seed) + adaptive_path = sample_path(sample_root, adaptive_cell, nfe, visual_seed) + if adaptive_is_a: + left_path, right_path = adaptive_path, fixed_path + a_schedule, b_schedule = "adaptive_v1", "sigmoid" + else: + left_path, right_path = fixed_path, adaptive_path + a_schedule, b_schedule = "sigmoid", "adaptive_v1" + trial_file = Path("trials") / f"{trial_id}.png" + render_trial(left_path, right_path, outdir / trial_file) + public_rows.append({ + "trial_id": trial_id, + "image": trial_file.as_posix(), + "rater_id": "", + "preference_A_B_TIE": "", + }) + key_rows.append({ + "trial_id": trial_id, + "training_seed": training_seed, + "nfe": nfe, + "sample_seed": visual_seed, + "A_schedule": a_schedule, + "B_schedule": b_schedule, + "fixed_checkpoint_sha256": fixed_cell["checkpoint_sha256"], + "adaptive_checkpoint_sha256": adaptive_cell["checkpoint_sha256"], + }) + + write_csv( + outdir / "ballot.csv", + ["trial_id", "image", "rater_id", "preference_A_B_TIE"], + public_rows, + ) + write_csv( + key_out, + [ + "trial_id", "training_seed", "nfe", "sample_seed", "A_schedule", "B_schedule", + "fixed_checkpoint_sha256", "adaptive_checkpoint_sha256", + ], + key_rows, + ) + (outdir / "README.md").write_text( + "# Blinded A/B ballot\n\n" + "There are 96 trials: 16 fixed sample seeds for each of 3 training seeds × 2 NFE settings. " + "For every trial, enter a stable anonymous rater ID and exactly one of `A`, `B`, or `TIE` in `ballot.csv`. " + "Judge overall visual quality; use `TIE` when neither image is meaningfully preferable. " + "Do not inspect filenames or request the private unblinding key before all ballots are locked.\n", + encoding="utf-8", + ) + public_metadata = { + "schema_version": 1, + "trial_count": len(public_rows), + "training_seed_count": 3, + "nfe_modes": [1, 2], + "sample_seeds_per_stratum": list(VISUAL_SEEDS), + "side_balance": {"adaptive_on_A": sum(adaptive_on_a), "adaptive_on_B": len(adaptive_on_a) - sum(adaptive_on_a)}, + "method_blinded": True, + "randomization_seed_sha256": hashlib.sha256(str(args.randomization_seed).encode()).hexdigest(), + } + (outdir / "metadata.json").write_text( + json.dumps(public_metadata, indent=2) + "\n", encoding="utf-8" + ) + print(f"Built {len(public_rows)} blinded trials in {outdir}") + print(f"Keep private until ballots lock: {key_out}") + + +if __name__ == "__main__": + main() diff --git a/scripts/build_final_conclusion.py b/scripts/build_final_conclusion.py new file mode 100644 index 00000000..8a25fd3d --- /dev/null +++ b/scripts/build_final_conclusion.py @@ -0,0 +1,129 @@ +#!/usr/bin/env python3 +"""Build the one-page decision record from locked quantitative, blind, and stability results.""" + +from __future__ import annotations + +import argparse +import csv +import json +import statistics +from pathlib import Path + + +def fail(message: str) -> None: + raise SystemExit(f"[build_final_conclusion] ERROR: {message}") + + +def load_json(path: Path) -> dict: + try: + return json.loads(path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError) as exc: + fail(f"cannot read {path}: {exc}") + + +def load_csv(path: Path) -> list[dict]: + try: + with path.open(newline="", encoding="utf-8") as handle: + return list(csv.DictReader(handle)) + except OSError as exc: + fail(f"cannot read {path}: {exc}") + + +def direction(item: dict) -> str: + mean_delta = float(item["mean_delta"]) + adaptive, fixed, _tie = item["adaptive_fixed_tie_seed_counts"] + if mean_delta < 0 and adaptive >= 2: + return "adaptive" + if mean_delta > 0 and fixed >= 2: + return "fixed" + return "mixed" + + +def verdict_text(directions: list[str], stable: bool) -> str: + if directions == ["adaptive", "adaptive"] and stable: + return "结果在两个 NFE 条件下均方向性支持 Adaptive v1，但证据仅限三训练种子与 5k proxy，不能表述为标准基准上的确定优势。" + if directions == ["fixed", "fixed"]: + return "结果不支持“Adaptive v1 优于 fixed sigmoid”；两个 NFE 条件均方向性偏向 fixed sigmoid。" + return "结果为混合或持平，不能支持“Adaptive v1 在相同预算与 NFE 下总体优于 fixed sigmoid”。" + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--quantitative-dir", type=Path, required=True) + parser.add_argument("--blind-dir", type=Path, required=True) + parser.add_argument("--stability-dir", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args(argv) + + quantitative = load_json(args.quantitative_dir / "quantitative_summary.json") + metric_rows = load_csv(args.quantitative_dir / "quantitative_metrics.csv") + blind = load_json(args.blind_dir / "blind_ab_summary.json") + stability = load_json(args.stability_dir / "training_stability.json") + + primary = quantitative["primary_metric"] + primary_label = "KID-5k" if primary == "kid5k_full" else "FID-5k" + nfe_items = [quantitative["summary_by_nfe"][str(nfe)][primary] for nfe in (1, 2)] + directions = [direction(item) for item in nfe_items] + stable = bool(stability.get("all_six_runs_complete") and stability.get("all_losses_finite")) + verdict = verdict_text(directions, stable) + overall_blind = next(row for row in blind["summary"] if row["stratum"] == "overall") + + means = {} + for nfe in (1, 2): + means[nfe] = {} + for schedule in ("sigmoid", "adaptive_v1"): + values = [ + float(row[primary]) + for row in metric_rows + if int(row["nfe"]) == nfe and row["schedule"] == schedule + ] + means[nfe][schedule] = statistics.mean(values) + + lines = [ + "# 最终结论：Fixed sigmoid vs Adaptive v1（16 kimg）", + "", + "## 研究问题", + "", + "在相同 16 kimg 训练预算和相同 NFE 下，Adaptive v1 是否优于 fixed sigmoid？", + "", + "## 冻结设计", + "", + "CIFAR-10 32×32；fixed sigmoid 与 Adaptive v1；训练 seeds 0/1/2；NFE=1 与 NFE=2（mid_t=0.821）；FP32；每个 checkpoint/NFE 使用相同逐样本 seeds 0–4999。主指标为 KID-5k；若事先规定的 45 分钟可运行性门槛触发，则以 FID-5k + 盲评降级。所有数值均为 5k-sample proxy，不是标准 FID-50k benchmark。", + "", + "## 定量结果（越低越好）", + "", + f"| NFE | Fixed 三-seed 均值 {primary_label} | Adaptive 三-seed 均值 {primary_label} | 配对均值 Δ（A−F） | 配对 SD | A/F/T seeds |", + "| ---: | ---: | ---: | ---: | ---: | --- |", + ] + for nfe, item in zip((1, 2), nfe_items): + lines.append( + f"| {nfe} | {means[nfe]['sigmoid']:.6f} | {means[nfe]['adaptive_v1']:.6f} | " + f"{item['mean_delta']:.6f} | {item['sample_sd_delta']:.6f} | {item['adaptive_fixed_tie_seed_counts']} |" + ) + lines.extend([ + "", + "## 匿名 A/B 与稳定性", + "", + f"盲评包含 {blind['complete_raters']} 名完整评审者、每人 96 个配对 trial：Adaptive/Fixed/Tie = " + f"{overall_blind['adaptive_v1']}/{overall_blind['sigmoid']}/{overall_blind['tie']}；" + f"ties=0.5 的 Adaptive 得分为 {overall_blind['adaptive_tie_half_score']:.3f}。", + "", + f"六个训练均完成 16 kimg：{stability.get('all_six_runs_complete')}；全部记录 loss 有限：{stability.get('all_losses_finite')}。" + f"Adaptive controller 在 {stability['summary_by_schedule']['adaptive_v1'].get('controller_activated_runs', 0)}/3 个 run 中激活。", + "", + "## 回答", + "", + verdict, + "", + "## 限制", + "", + "仅有三个训练种子；5k 指标方差高于标准 50k 评估；盲评判断嵌套于评审者与训练种子，原始 trial 数不能当作独立样本做夸大的显著性声明。结论只适用于当前实现、16 kimg 预算、NFE=1/2 与冻结采样协议。", + "", + ]) + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text("\n".join(lines), encoding="utf-8") + print(f"Final conclusion: {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/check_environment.py b/scripts/check_environment.py new file mode 100755 index 00000000..4cd6dec9 --- /dev/null +++ b/scripts/check_environment.py @@ -0,0 +1,138 @@ +#!/usr/bin/env python3 +"""Validate the frozen Day 1 ECT runtime and report the observed environment.""" + +import argparse +import importlib +import importlib.metadata +import json +import platform +import subprocess +import sys +from pathlib import Path + + +# env.yml pins these packages directly. The Day 1 validated runtime additionally +# freezes huggingface-hub because newer API removals can break diffusers 0.26.3. +EXPECTED = { + "torch": "2.3.0", + "diffusers": "0.26.3", + "accelerate": "0.27.2", + "huggingface-hub": "0.23.4", +} +REQUIRED = [ + "numpy", + "scipy", + "Pillow", + "click", + "requests", + "psutil", + "tqdm", + "imageio", + "imageio-ffmpeg", + "pyspng", +] +IMPORTS = [ + "torch", + "numpy", + "scipy", + "PIL", + "click", + "requests", + "psutil", + "tqdm", + "imageio", + "pyspng", + "diffusers", + "accelerate", + "huggingface_hub", +] + + +def git_value(repo_root: Path, *args: str): + try: + return subprocess.check_output( + ["git", *args], cwd=repo_root, text=True, stderr=subprocess.DEVNULL + ).strip() + except (OSError, subprocess.CalledProcessError): + return None + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--allow-no-cuda", action="store_true") + parser.add_argument("--output", type=Path) + args = parser.parse_args() + + versions = {} + errors = [] + for package in [*EXPECTED, *REQUIRED]: + try: + actual = importlib.metadata.version(package) + except importlib.metadata.PackageNotFoundError: + errors.append(f"missing package: {package}") + continue + versions[package] = actual + expected = EXPECTED.get(package) + if expected is not None: + if package == "torch": + matches = actual.startswith(expected) + else: + matches = actual == expected + if not matches: + errors.append(f"{package}: expected {expected}, got {actual}") + + import_status = {} + for module in IMPORTS: + try: + importlib.import_module(module) + import_status[module] = "ok" + except Exception as exc: # Import compatibility matters, not just installation. + import_status[module] = f"failed: {type(exc).__name__}: {exc}" + errors.append(f"cannot import {module}: {type(exc).__name__}: {exc}") + + try: + import torch + except ImportError: + torch = None + + cuda_available = bool(torch and torch.cuda.is_available()) + if not args.allow_no_cuda and not cuda_available: + errors.append("CUDA is unavailable") + if sys.version_info[:3] != (3, 9, 18): + errors.append(f"expected Python 3.9.18, got {platform.python_version()}") + + repo_root = Path(__file__).resolve().parents[1] + status = git_value(repo_root, "status", "--porcelain=v1", "--untracked-files=all") + report = { + "git_commit": git_value(repo_root, "rev-parse", "HEAD"), + "git_branch": git_value(repo_root, "branch", "--show-current"), + "git_dirty": bool(status), + "python": platform.python_version(), + "platform": platform.platform(), + "packages": versions, + "imports": import_status, + "cuda": { + "available": cuda_available, + "runtime": torch.version.cuda if torch else None, + "device_count": torch.cuda.device_count() if cuda_available else 0, + "devices": [ + torch.cuda.get_device_name(index) + for index in range(torch.cuda.device_count()) + ] + if cuda_available + else [], + }, + "status": "passed" if not errors else "failed", + "errors": errors, + } + rendered = json.dumps(report, indent=2, sort_keys=True) + print(rendered) + if args.output: + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(rendered + "\n", encoding="utf-8") + if errors: + raise SystemExit("environment validation failed: " + "; ".join(errors)) + + +if __name__ == "__main__": + main() diff --git a/scripts/check_training_integrity.py b/scripts/check_training_integrity.py new file mode 100644 index 00000000..801270d2 --- /dev/null +++ b/scripts/check_training_integrity.py @@ -0,0 +1,354 @@ +#!/usr/bin/env python3 +"""Validate a completed training run and emit a formal-evaluation receipt.""" + +from __future__ import annotations + +import argparse +import csv +import hashlib +import json +import math +import pickle +import subprocess +import sys +import time +from pathlib import Path +from typing import Any + +REPO_ROOT = Path(__file__).resolve().parents[1] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +import torch + + +CHECKER_VERSION = "2" + +METHOD_IDENTITIES = { + "fixed": ("sigmoid", 1.0), + "global110": ("global_sigmoid", 1.10), + "global_only": ("global_sigmoid", 1.10), +} + + +def fail(message: str) -> None: + raise SystemExit(f"[check_training_integrity] ERROR: {message}") + + +def sha256_file(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def git_head() -> str: + try: + return subprocess.check_output( + ["git", "rev-parse", "HEAD"], cwd=REPO_ROOT, text=True + ).strip() + except (OSError, subprocess.CalledProcessError): + return "unknown" + + +def load_json(path: Path, label: str) -> dict[str, Any]: + try: + value = json.loads(path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError) as exc: + fail(f"cannot read {label} {path}: {exc}") + if not isinstance(value, dict): + fail(f"{label} must contain a JSON object: {path}") + return value + + +def metric_mean(record: dict[str, Any], name: str) -> float: + try: + value = float(record[name]["mean"]) + except (KeyError, TypeError, ValueError) as exc: + fail(f"stats record lacks finite {name}.mean: {exc}") + if not math.isfinite(value): + fail(f"stats record has non-finite {name}.mean") + return value + + +def inspect_stats(path: Path, budget_kimg: int) -> dict[str, Any]: + try: + lines = [line for line in path.read_text(encoding="utf-8").splitlines() if line.strip()] + except OSError as exc: + fail(f"cannot read stats {path}: {exc}") + if not lines: + fail(f"stats is empty: {path}") + try: + records = [json.loads(line) for line in lines] + except json.JSONDecodeError as exc: + fail(f"stats contains invalid JSON: {path}: {exc}") + if not all(isinstance(record, dict) for record in records): + fail(f"stats contains a non-object record: {path}") + for record in records: + metric_mean(record, "Loss/loss") + final_kimg = metric_mean(records[-1], "Progress/kimg") + if final_kimg < budget_kimg: + fail(f"stats stops at {final_kimg} kimg, below declared {budget_kimg} kimg") + return {"records": len(records), "final_kimg": final_kimg} + + +def inspect_summary(path: Path, budget_kimg: int) -> dict[str, Any]: + try: + with path.open("r", encoding="utf-8", newline="") as handle: + rows = list(csv.DictReader(handle)) + except OSError as exc: + fail(f"cannot read training summary {path}: {exc}") + if not rows: + fail(f"training summary is empty: {path}") + required = {"processed_kimg", "loss"} + if not required.issubset(rows[0]): + fail(f"training summary lacks required columns {sorted(required)}: {path}") + for row in rows: + try: + loss = float(row["loss"]) + except (TypeError, ValueError) as exc: + fail(f"training summary has invalid loss: {exc}") + if not math.isfinite(loss): + fail("training summary has non-finite loss") + try: + final_kimg = float(rows[-1]["processed_kimg"]) + except (TypeError, ValueError) as exc: + fail(f"training summary has invalid final processed_kimg: {exc}") + if not math.isfinite(final_kimg) or final_kimg < budget_kimg: + fail(f"training summary stops at {final_kimg} kimg, below declared {budget_kimg} kimg") + return {"rows": len(rows), "final_kimg": final_kimg} + + +def inspect_log(path: Path) -> dict[str, Any]: + try: + text = path.read_text(encoding="utf-8", errors="replace") + except OSError as exc: + fail(f"cannot read training log {path}: {exc}") + if "Exiting..." not in text: + fail(f"training log does not record a clean exit: {path}") + if "Traceback (most recent call last)" in text: + fail(f"training log contains a traceback: {path}") + return {"clean_exit_marker": "Exiting..."} + + +def inspect_state(path: Path, budget_kimg: int) -> dict[str, Any]: + try: + state = torch.load(path, map_location="cpu", weights_only=False) + except (OSError, RuntimeError, ValueError, EOFError, pickle.UnpicklingError) as exc: + fail(f"cannot load training state {path}: {exc}") + if not isinstance(state, dict): + fail(f"training state must be a dictionary: {path}") + cur_nimg = state.get("cur_nimg") + if not isinstance(cur_nimg, (int, float)) or cur_nimg < budget_kimg * 1000: + fail(f"training state cur_nimg={cur_nimg!r} is below {budget_kimg * 1000}") + stack: list[Any] = [state] + tensors_checked = 0 + while stack: + value = stack.pop() + if isinstance(value, torch.Tensor): + tensors_checked += 1 + if value.is_floating_point() or value.is_complex(): + if not torch.isfinite(value).all().item(): + fail(f"training state contains a non-finite tensor: {path}") + elif isinstance(value, dict): + stack.extend(value.values()) + elif isinstance(value, (list, tuple, set)): + stack.extend(value) + elif isinstance(value, float) and not math.isfinite(value): + fail(f"training state contains a non-finite scalar: {path}") + return {"cur_nimg": int(cur_nimg), "tensors_checked": tensors_checked} + + +def finite_float(value: Any, label: str) -> float: + try: + result = float(value) + except (TypeError, ValueError) as exc: + fail(f"{label} must be a finite number: {exc}") + if not math.isfinite(result): + fail(f"{label} must be finite") + return result + + +def expected_method_identity(method: str) -> tuple[str, float]: + """Resolve the schedule identity promised by a named evaluation method.""" + if method in METHOD_IDENTITIES: + return METHOD_IDENTITIES[method] + # Keep the checker usable for pre-existing schedule-named cells while + # requiring the declared method to agree with both persisted sources. + return method, 1.0 + + +def inspect_checkpoint( + path: Path, options: dict[str, Any], method: str, +) -> dict[str, Any]: + """Load the evaluated snapshot and verify its EMA/schedule identity. + + The snapshot is an experiment artifact accepted from the trusted training + workspace. ``pickle.load`` is therefore intentionally used to match the + loader used by the evaluator itself. + """ + try: + with path.open("rb") as handle: + checkpoint = pickle.load(handle) + except Exception as exc: # pickle failures span many exception classes. + fail(f"cannot load checkpoint pickle {path}: {exc}") + if not isinstance(checkpoint, dict): + fail(f"checkpoint pickle must contain a dictionary: {path}") + ema = checkpoint.get("ema") + if not isinstance(ema, torch.nn.Module): + fail(f"checkpoint has no torch.nn.Module EMA object: {path}") + + ema_tensors_checked = 0 + for name, tensor in list(ema.named_parameters()) + list(ema.named_buffers()): + if tensor.is_floating_point() or tensor.is_complex(): + ema_tensors_checked += 1 + if not torch.isfinite(tensor).all().item(): + fail(f"checkpoint EMA has non-finite tensor {name!r}: {path}") + + loss_fn = checkpoint.get("loss_fn") + schedule = getattr(loss_fn, "schedule", None) + checkpoint_schedule = getattr(schedule, "name", None) + if not isinstance(checkpoint_schedule, str) or not checkpoint_schedule: + fail(f"checkpoint loss_fn lacks schedule metadata: {path}") + checkpoint_scale = finite_float( + getattr(schedule, "global_gap_scale", 1.0), + f"checkpoint global_gap_scale ({path})", + ) + + loss_kwargs = options.get("loss_kwargs") + if not isinstance(loss_kwargs, dict): + fail("training_options.json lacks loss_kwargs schedule metadata") + options_schedule = loss_kwargs.get("adj") + if not isinstance(options_schedule, str) or not options_schedule: + fail("training_options.json loss_kwargs.adj is missing or invalid") + options_scale = finite_float( + loss_kwargs.get("global_gap_scale"), + "training_options.json loss_kwargs.global_gap_scale", + ) + + expected_schedule, expected_scale = expected_method_identity(method) + if options_schedule != expected_schedule or checkpoint_schedule != expected_schedule: + fail( + f"declared method {method!r} requires schedule {expected_schedule!r}, " + f"got training_options={options_schedule!r}, checkpoint={checkpoint_schedule!r}" + ) + if not math.isclose(options_scale, expected_scale, rel_tol=0.0, abs_tol=1e-12): + fail( + f"declared method {method!r} requires global_gap_scale={expected_scale}, " + f"got training_options={options_scale}" + ) + if not math.isclose(checkpoint_scale, expected_scale, rel_tol=0.0, abs_tol=1e-12): + fail( + f"declared method {method!r} requires global_gap_scale={expected_scale}, " + f"got checkpoint={checkpoint_scale}" + ) + return { + "checkpoint_load_passed": True, + "ema_present": True, + "ema_finite_passed": True, + "schedule_identity_passed": True, + "global_gap_scale_identity_passed": True, + "method_identity_passed": True, + "ema_floating_tensors_checked": ema_tensors_checked, + "training_options_schedule": options_schedule, + "checkpoint_schedule": checkpoint_schedule, + "training_options_global_gap_scale": options_scale, + "checkpoint_global_gap_scale": checkpoint_scale, + } + + +def build_receipt(args: argparse.Namespace) -> dict[str, Any]: + run_dir = args.run_dir.expanduser().resolve() + checkpoint = args.checkpoint.expanduser().resolve() if args.checkpoint else run_dir / "network-snapshot-latest.pkl" + paths = { + "checkpoint": checkpoint, + "training_options": run_dir / "training_options.json", + "stats": run_dir / "stats.jsonl", + "summary": run_dir / "train_summary.csv", + "log": run_dir / "log.txt", + "state": run_dir / "training-state-latest.pt", + } + missing = [name for name, path in paths.items() if not path.is_file()] + if missing: + fail(f"run is missing required artifacts {missing}: {run_dir}") + options = load_json(paths["training_options"], "training options") + if options.get("total_kimg") != args.budget_kimg: + fail(f"training_options total_kimg={options.get('total_kimg')!r}, expected {args.budget_kimg}") + if options.get("seed") != args.training_seed: + fail(f"training_options seed={options.get('seed')!r}, expected {args.training_seed}") + if args.expected_training_commit: + commit_file = run_dir / "commit_sha.txt" + if not commit_file.is_file() or args.expected_training_commit not in commit_file.read_text(encoding="utf-8"): + fail(f"run does not attest expected training commit {args.expected_training_commit}") + stats = inspect_stats(paths["stats"], args.budget_kimg) + summary = inspect_summary(paths["summary"], args.budget_kimg) + log = inspect_log(paths["log"]) + state = inspect_state(paths["state"], args.budget_kimg) + checkpoint_identity = inspect_checkpoint(checkpoint, options, args.method) + if abs(stats["final_kimg"] - summary["final_kimg"]) > 1e-6: + fail("stats and training summary final kimg disagree") + if abs(stats["final_kimg"] * 1000 - state["cur_nimg"]) > 1e-6: + fail("stats and training state progress disagree") + checkpoint_sha256 = sha256_file(checkpoint) + return { + "schema_version": 1, + "status": "passed", + "checkpoint_id": args.checkpoint_id, + "checkpoint_path": str(checkpoint), + "checkpoint_sha256": checkpoint_sha256, + "training_run_id": args.training_run_id, + "method": args.method, + "training_seed": args.training_seed, + "budget_kimg": args.budget_kimg, + "completion_passed": True, + "logs_state_consistent": True, + "finite_loss_state_passed": True, + "checkpoint_load_passed": checkpoint_identity["checkpoint_load_passed"], + "ema_present": checkpoint_identity["ema_present"], + "ema_finite_passed": checkpoint_identity["ema_finite_passed"], + "schedule_identity_passed": checkpoint_identity["schedule_identity_passed"], + "global_gap_scale_identity_passed": checkpoint_identity[ + "global_gap_scale_identity_passed" + ], + "method_identity_passed": checkpoint_identity["method_identity_passed"], + "checker_version": args.checker_version, + "checker_git_commit": git_head(), + "checked_at_unix": time.time(), + "evidence": { + "run_directory": str(run_dir), + "training_options": str(paths["training_options"]), + "stats": stats, + "training_summary": summary, + "log": log, + "training_state": state, + "checkpoint_identity": checkpoint_identity, + "expected_training_commit": args.expected_training_commit, + }, + } + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--run-dir", type=Path, required=True) + parser.add_argument("--checkpoint", type=Path) + parser.add_argument("--checkpoint-id", required=True) + parser.add_argument("--method", required=True) + parser.add_argument("--training-seed", type=int, required=True) + parser.add_argument("--budget-kimg", type=int, required=True) + parser.add_argument("--training-run-id", required=True) + parser.add_argument("--expected-training-commit") + parser.add_argument("--checker-version", default=CHECKER_VERSION) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + if not args.training_run_id: + fail("training_run_id must be non-empty") + receipt = build_receipt(args) + output = args.output.expanduser().resolve() + output.parent.mkdir(parents=True, exist_ok=True) + output.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(receipt, indent=2, sort_keys=True)) + + +if __name__ == "__main__": + main() diff --git a/scripts/collect_final_quality_results.py b/scripts/collect_final_quality_results.py new file mode 100644 index 00000000..bb5c9b5e --- /dev/null +++ b/scripts/collect_final_quality_results.py @@ -0,0 +1,199 @@ +#!/usr/bin/env python3 +"""Validate and summarize the frozen 3-seed, two-NFE 5k proxy matrix.""" + +from __future__ import annotations + +import argparse +import csv +import json +import math +import statistics +from pathlib import Path + + +SCHEDULES = ("sigmoid", "adaptive_v1") +TRAINING_SEEDS = (0, 1, 2) +NFES = (1, 2) +METRICS = ("kid5k_full", "fid5k_full") + + +def fail(message: str) -> None: + raise SystemExit(f"[collect_final_quality_results] ERROR: {message}") + + +def read_single_metric(path: Path, metric: str) -> float | None: + if not path.is_file(): + return None + lines = [line for line in path.read_text(encoding="utf-8").splitlines() if line.strip()] + if len(lines) != 1: + fail(f"expected exactly one formal result in {path}, found {len(lines)}") + try: + payload = json.loads(lines[0]) + if payload["metric"] != metric: + fail(f"metric name mismatch in {path}: {payload.get('metric')} != {metric}") + value = float(payload["results"][metric]) + except (KeyError, TypeError, ValueError, json.JSONDecodeError) as exc: + fail(f"malformed metric result {path}: {exc}") + if not math.isfinite(value): + fail(f"non-finite metric result in {path}: {value}") + return value + + +def load_rows(eval_root: Path, allow_fid_only: bool) -> tuple[list[dict], str]: + rows = [] + kid_presence = [] + for seed in TRAINING_SEEDS: + for schedule in SCHEDULES: + for nfe in NFES: + cell = eval_root / "quantitative" / schedule / f"seed{seed}" / f"nfe{nfe}" + values = { + metric: read_single_metric(cell / f"metric-{metric}.jsonl", metric) + for metric in METRICS + } + if values["fid5k_full"] is None: + fail(f"missing FID-5k proxy result: {cell}") + kid_presence.append(values["kid5k_full"] is not None) + rows.append({ + "schedule": schedule, + "training_seed": seed, + "nfe": nfe, + **values, + }) + if any(kid_presence) and not all(kid_presence): + fail("partial KID matrix detected; do not mix primary and fallback protocols") + if not any(kid_presence) and not allow_fid_only: + fail("KID matrix is absent; pass --allow-fid-only only after the frozen 45-minute fallback gate") + return rows, "kid5k_full" if all(kid_presence) else "fid5k_full" + + +def paired_rows(rows: list[dict], available_metrics: list[str]) -> list[dict]: + index = {(row["schedule"], row["training_seed"], row["nfe"]): row for row in rows} + paired = [] + for seed in TRAINING_SEEDS: + for nfe in NFES: + fixed = index[("sigmoid", seed, nfe)] + adaptive = index[("adaptive_v1", seed, nfe)] + row = {"training_seed": seed, "nfe": nfe} + for metric in available_metrics: + row[f"fixed_{metric}"] = fixed[metric] + row[f"adaptive_{metric}"] = adaptive[metric] + row[f"delta_{metric}"] = adaptive[metric] - fixed[metric] + paired.append(row) + return paired + + +def summarize(paired: list[dict], available_metrics: list[str]) -> dict: + by_nfe = {} + for nfe in NFES: + by_nfe[str(nfe)] = {} + selected = [row for row in paired if row["nfe"] == nfe] + for metric in available_metrics: + deltas = [row[f"delta_{metric}"] for row in selected] + adaptive_wins = sum(value < 0 for value in deltas) + fixed_wins = sum(value > 0 for value in deltas) + ties = sum(value == 0 for value in deltas) + by_nfe[str(nfe)][metric] = { + "paired_deltas_adaptive_minus_fixed": deltas, + "mean_delta": statistics.mean(deltas), + "sample_sd_delta": statistics.stdev(deltas), + "adaptive_fixed_tie_seed_counts": [adaptive_wins, fixed_wins, ties], + } + return { + "schema_version": 1, + "evaluation_label": "5k-sample proxy evaluation; not a standard FID-50k benchmark", + "primary_metric": "kid5k_full" if "kid5k_full" in available_metrics else "fid5k_full", + "auxiliary_metric": "fid5k_full" if "kid5k_full" in available_metrics else None, + "delta_definition": "adaptive_v1 - sigmoid; negative favors adaptive_v1", + "training_seeds": list(TRAINING_SEEDS), + "nfe": {"1": {"mid_t": []}, "2": {"mid_t": [0.821]}}, + "summary_by_nfe": by_nfe, + } + + +def write_csv(path: Path, rows: list[dict]) -> None: + fieldnames = list(rows[0]) + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=fieldnames) + writer.writeheader() + writer.writerows(rows) + + +def format_value(value: float | None) -> str: + return "—" if value is None else f"{value:.6f}" + + +def write_markdown(path: Path, rows: list[dict], paired: list[dict], summary: dict, available_metrics: list[str]) -> None: + lines = [ + "# Final quantitative quality summary", + "", + "> 5k-sample proxy evaluation; not a standard FID-50k benchmark.", + "", + "Lower is better for both metrics. Paired delta is `Adaptive v1 - fixed sigmoid`; negative favors Adaptive v1.", + "", + "## Per-cell results", + "", + "| Schedule | Training seed | NFE | KID-5k (raw) | FID-5k proxy |", + "| --- | ---: | ---: | ---: | ---: |", + ] + for row in rows: + lines.append( + f"| {row['schedule']} | {row['training_seed']} | {row['nfe']} | " + f"{format_value(row['kid5k_full'])} | {format_value(row['fid5k_full'])} |" + ) + lines.extend([ + "", + "## Paired differences", + "", + "| Training seed | NFE | Δ KID-5k | Δ FID-5k |", + "| ---: | ---: | ---: | ---: |", + ]) + for row in paired: + lines.append( + f"| {row['training_seed']} | {row['nfe']} | " + f"{format_value(row.get('delta_kid5k_full'))} | {format_value(row.get('delta_fid5k_full'))} |" + ) + lines.extend(["", "## Three-seed mean paired difference", ""]) + for nfe in NFES: + pieces = [] + for metric in available_metrics: + item = summary["summary_by_nfe"][str(nfe)][metric] + pieces.append( + f"{metric}: mean Δ={item['mean_delta']:.6f}, sample SD={item['sample_sd_delta']:.6f}, " + f"adaptive/fixed/tie seeds={item['adaptive_fixed_tie_seed_counts']}" + ) + lines.append(f"- NFE={nfe}: " + "; ".join(pieces)) + lines.extend([ + "", + "With only three training seeds, these are descriptive paired results; do not convert them into a broad significance claim.", + "", + ]) + path.write_text("\n".join(lines), encoding="utf-8") + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--eval-root", type=Path, required=True) + parser.add_argument("--outdir", type=Path, required=True) + parser.add_argument("--allow-fid-only", action="store_true") + args = parser.parse_args(argv) + + rows, primary = load_rows(args.eval_root.resolve(), args.allow_fid_only) + available_metrics = ["fid5k_full"] if primary == "fid5k_full" else list(METRICS) + paired = paired_rows(rows, available_metrics) + summary = summarize(paired, available_metrics) + + outdir = args.outdir.resolve() + outdir.mkdir(parents=True, exist_ok=True) + write_csv(outdir / "quantitative_metrics.csv", rows) + write_csv(outdir / "paired_differences.csv", paired) + (outdir / "quantitative_summary.json").write_text( + json.dumps(summary, indent=2) + "\n", encoding="utf-8" + ) + write_markdown( + outdir / "quantitative_summary.md", rows, paired, summary, available_metrics + ) + print(f"Validated 12 cells; primary metric: {primary}; output: {outdir}") + + +if __name__ == "__main__": + main() diff --git a/scripts/collect_final_stability.py b/scripts/collect_final_stability.py new file mode 100644 index 00000000..15e18fc3 --- /dev/null +++ b/scripts/collect_final_stability.py @@ -0,0 +1,179 @@ +#!/usr/bin/env python3 +"""Validate and summarize 16 kimg training stability for the frozen 2x3 matrix.""" + +from __future__ import annotations + +import argparse +import csv +import json +import math +import statistics +from pathlib import Path + + +SCHEDULES = ("sigmoid", "adaptive_v1") +TRAINING_SEEDS = (0, 1, 2) + + +def fail(message: str) -> None: + raise SystemExit(f"[collect_final_stability] ERROR: {message}") + + +def read_manifest(path: Path) -> list[dict]: + try: + cells = json.loads(path.read_text(encoding="utf-8"))["cells"] + except (OSError, KeyError, TypeError, json.JSONDecodeError) as exc: + fail(f"cannot read manifest {path}: {exc}") + keyed = {} + for cell in cells: + schedule = str(cell.get("schedule")) + seed = int(cell.get("training_seed")) + result_dir = Path(cell.get("training_result_dir", "")).expanduser().resolve() + key = (schedule, seed) + if key in keyed: + fail(f"duplicate cell: {key}") + keyed[key] = {"schedule": schedule, "training_seed": seed, "result_dir": result_dir} + expected = {(schedule, seed) for schedule in SCHEDULES for seed in TRAINING_SEEDS} + if set(keyed) != expected: + fail("manifest must contain exactly sigmoid/adaptive_v1 × seeds 0/1/2") + return [keyed[(schedule, seed)] for seed in TRAINING_SEEDS for schedule in SCHEDULES] + + +def load_cell(cell: dict) -> dict: + result_dir = cell["result_dir"] + metadata_path = result_dir / "metadata.json" + csv_path = result_dir / "train_summary.csv" + try: + metadata = json.loads(metadata_path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError) as exc: + fail(f"cannot read {metadata_path}: {exc}") + try: + with csv_path.open(newline="", encoding="utf-8") as handle: + rows = list(csv.DictReader(handle)) + except OSError as exc: + fail(f"cannot read {csv_path}: {exc}") + if len(rows) != 125: + fail(f"{csv_path} has {len(rows)} rows; frozen 16 kimg run requires 125") + if metadata.get("schedule") != cell["schedule"] or int(metadata.get("seed", -1)) != cell["training_seed"]: + fail(f"metadata identity mismatch in {metadata_path}") + processed_kimg = float(metadata.get("processed_kimg", float("nan"))) + if not math.isclose(processed_kimg, 16.0, rel_tol=0, abs_tol=1e-9): + fail(f"training budget mismatch in {metadata_path}: {processed_kimg} kimg") + + losses = [] + skipped = 0 + for index, row in enumerate(rows, start=1): + try: + loss = float(row["loss"]) + except (KeyError, TypeError, ValueError) as exc: + fail(f"invalid loss in {csv_path} row {index}: {exc}") + if not math.isfinite(loss): + fail(f"non-finite loss in {csv_path} row {index}: {loss}") + losses.append(loss) + skipped += str(row.get("step_skipped", "")).strip().lower() in {"1", "true", "yes"} + if row.get("schedule") != cell["schedule"]: + fail(f"mixed schedule in {csv_path} row {index}") + if int(metadata.get("nan_count", -1)) != 0 or int(metadata.get("inf_count", -1)) != 0: + fail(f"metadata reports non-finite losses in {metadata_path}") + if int(metadata.get("skipped_steps", -1)) != skipped: + fail(f"skipped-step mismatch in {metadata_path}: {metadata.get('skipped_steps')} != {skipped}") + + return { + "schedule": cell["schedule"], + "training_seed": cell["training_seed"], + "processed_kimg": processed_kimg, + "attempted_iterations": len(rows), + "successful_optimizer_steps": int(metadata["successful_optimizer_steps"]), + "skipped_steps": skipped, + "loss_finite": True, + "loss_mean": statistics.mean(losses), + "loss_min": min(losses), + "loss_max": max(losses), + "final_loss": losses[-1], + "final_grad_scale": float(metadata["final_grad_scale"]), + "peak_vram_mib": float(metadata["peak_vram_mib"]), + "wall_time_seconds": float(metadata["wall_time_seconds"]), + "adaptive_activated": metadata.get("final_adaptive_active") if cell["schedule"] == "adaptive_v1" else None, + "adaptive_signal_updates": metadata.get("final_signal_updates") if cell["schedule"] == "adaptive_v1" else None, + "checkpoint_sha256": metadata.get("network_snapshot_sha256"), + } + + +def write_csv(path: Path, rows: list[dict]) -> None: + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0])) + writer.writeheader() + writer.writerows(rows) + + +def summarize(rows: list[dict]) -> dict: + by_schedule = {} + for schedule in SCHEDULES: + selected = [row for row in rows if row["schedule"] == schedule] + by_schedule[schedule] = { + "complete_16k_runs": len(selected), + "finite_loss_runs": sum(row["loss_finite"] for row in selected), + "total_skipped_steps": sum(row["skipped_steps"] for row in selected), + "mean_skipped_steps": statistics.mean(row["skipped_steps"] for row in selected), + "mean_peak_vram_mib": statistics.mean(row["peak_vram_mib"] for row in selected), + "mean_wall_time_seconds": statistics.mean(row["wall_time_seconds"] for row in selected), + } + if schedule == "adaptive_v1": + by_schedule[schedule]["controller_activated_runs"] = sum( + row["adaptive_activated"] is True for row in selected + ) + return { + "schema_version": 1, + "training_budget_kimg": 16, + "all_six_runs_complete": len(rows) == 6, + "all_losses_finite": all(row["loss_finite"] for row in rows), + "summary_by_schedule": by_schedule, + } + + +def write_markdown(path: Path, rows: list[dict], summary: dict) -> None: + lines = [ + "# Training stability summary", + "", + "| Schedule | Seed | kimg | Attempts | Successful | Skipped | Finite loss | Final scale | Peak VRAM MiB | Wall time s | Adaptive active |", + "| --- | ---: | ---: | ---: | ---: | ---: | --- | ---: | ---: | ---: | --- |", + ] + for row in rows: + active = "n/a" if row["adaptive_activated"] is None else str(row["adaptive_activated"]).lower() + lines.append( + f"| {row['schedule']} | {row['training_seed']} | {row['processed_kimg']:.3f} | " + f"{row['attempted_iterations']} | {row['successful_optimizer_steps']} | {row['skipped_steps']} | " + f"yes | {row['final_grad_scale']:.0f} | {row['peak_vram_mib']:.1f} | " + f"{row['wall_time_seconds']:.1f} | {active} |" + ) + lines.extend([ + "", + f"All six 16 kimg runs complete: **{summary['all_six_runs_complete']}**. " + f"All recorded losses finite: **{summary['all_losses_finite']}**.", + "", + "Skipped AMP steps, GradScaler values, time, and memory are engineering stability descriptors; they are not generation-quality metrics.", + "", + ]) + path.write_text("\n".join(lines), encoding="utf-8") + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--manifest", type=Path, required=True) + parser.add_argument("--outdir", type=Path, required=True) + args = parser.parse_args(argv) + + rows = [load_cell(cell) for cell in read_manifest(args.manifest)] + summary = summarize(rows) + outdir = args.outdir.resolve() + outdir.mkdir(parents=True, exist_ok=True) + write_csv(outdir / "training_stability.csv", rows) + (outdir / "training_stability.json").write_text( + json.dumps(summary, indent=2) + "\n", encoding="utf-8" + ) + write_markdown(outdir / "training_stability.md", rows, summary) + print(f"Validated six 16 kimg training runs; output: {outdir}") + + +if __name__ == "__main__": + main() diff --git a/scripts/collect_fixed_baseline_results.py b/scripts/collect_fixed_baseline_results.py new file mode 100755 index 00000000..a9116042 --- /dev/null +++ b/scripts/collect_fixed_baseline_results.py @@ -0,0 +1,12 @@ +#!/usr/bin/env python3 +"""Compatibility wrapper. Prefer scripts/collect_schedule_results.py.""" + +from __future__ import annotations + +import runpy +from pathlib import Path + + +if __name__ == "__main__": + target = Path(__file__).resolve().with_name("collect_schedule_results.py") + runpy.run_path(str(target), run_name="__main__") diff --git a/scripts/collect_multibudget_results.py b/scripts/collect_multibudget_results.py new file mode 100644 index 00000000..b8cd8490 --- /dev/null +++ b/scripts/collect_multibudget_results.py @@ -0,0 +1,644 @@ +#!/usr/bin/env python3 +"""Collect a complete two-method, multi-budget paired metric matrix. + +Required input columns: +method,training_seed,budget_kimg,nfe,metric_name,metric_value + +Optional columns: +training_time_hours,quality_target,checkpoint_sha256,sample_count, +generation_seed_range,metric_seed,evidence_class,evaluation_contract,analysis_track + +The collector validates every observed method/seed/budget/NFE/metric cell, +allowing metric endpoints to differ by budget, then emits seed-level, +aggregate, time-to-quality, table, and figure artifacts. +Metrics are treated as lower-is-better. +""" + +from __future__ import annotations + +import argparse +import csv +import json +import math +import os +import statistics +import tempfile +from pathlib import Path + +os.environ.setdefault("MPLCONFIGDIR", str(Path(tempfile.gettempdir()) / "ect-matplotlib")) +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from matplotlib.lines import Line2D + + +REQUIRED = ("method", "training_seed", "budget_kimg", "nfe", "metric_name", "metric_value") +METHOD_COLORS = ("#2563EB", "#C98212") +SEED_COLORS = ("#2563EB", "#C98212", "#C2416C", "#5B8C5A", "#7C5AC8", "#64748B") +INK = "#1F2937" +GRID = "#D1D5DB" + + +def fail(message: str) -> None: + raise SystemExit("[collect_multibudget_results] ERROR: " + message) + + +def optional_float(value: str, field: str, row_number: int) -> float | None: + if not value.strip(): + return None + try: + result = float(value) + except ValueError as exc: + fail("row {}: {} must be numeric".format(row_number, field)) + raise exc + if not math.isfinite(result): + fail("row {}: {} must be finite".format(row_number, field)) + return result + + +def optional_int(value: str, field: str, row_number: int) -> int | None: + if not value.strip(): + return None + try: + result = int(value) + except ValueError as exc: + fail("row {}: {} must be an integer".format(row_number, field)) + raise exc + return result + + +def read_rows(path: Path) -> list[dict]: + if not path.is_file(): + fail("input CSV does not exist: {}".format(path)) + with path.open(newline="", encoding="utf-8") as handle: + reader = csv.DictReader(handle) + fields = tuple(reader.fieldnames or ()) + missing = set(REQUIRED) - set(fields) + if missing: + fail("missing required columns: {}".format(sorted(missing))) + raw_rows = list(reader) + if not raw_rows: + fail("input CSV has no rows") + rows = [] + for row_number, raw in enumerate(raw_rows, start=2): + try: + row = { + "method": raw["method"].strip(), + "training_seed": int(raw["training_seed"]), + "budget_kimg": float(raw["budget_kimg"]), + "nfe": int(raw["nfe"]), + "metric_name": raw["metric_name"].strip(), + "metric_value": float(raw["metric_value"]), + "training_time_hours": optional_float(raw.get("training_time_hours", ""), "training_time_hours", row_number), + "quality_target": optional_float(raw.get("quality_target", ""), "quality_target", row_number), + "checkpoint_sha256": raw.get("checkpoint_sha256", "").strip(), + "sample_count": optional_int(raw.get("sample_count", ""), "sample_count", row_number), + "generation_seed_range": raw.get("generation_seed_range", "").strip(), + "metric_seed": optional_int(raw.get("metric_seed", ""), "metric_seed", row_number), + "evidence_class": raw.get("evidence_class", "").strip(), + "evaluation_contract": raw.get("evaluation_contract", "").strip(), + "analysis_track": raw.get("analysis_track", "").strip(), + } + except (TypeError, ValueError) as exc: + fail("row {}: malformed required value".format(row_number)) + raise exc + if (not row["method"] or not row["metric_name"] or row["training_seed"] < 0 + or row["budget_kimg"] <= 0 or row["nfe"] < 1 + or not math.isfinite(row["metric_value"])): + fail("row {}: invalid method, metric, seed, budget, NFE, or metric value".format(row_number)) + if row["training_time_hours"] is not None and row["training_time_hours"] < 0: + fail("row {}: training_time_hours must be non-negative".format(row_number)) + if row["sample_count"] is not None and row["sample_count"] < 1: + fail("row {}: sample_count must be positive".format(row_number)) + if row["analysis_track"] and row["analysis_track"] not in {"budget_curve", "formal_endpoint"}: + fail("row {}: analysis_track must be budget_curve or formal_endpoint".format(row_number)) + if bool(row["analysis_track"]) != bool(row["evaluation_contract"]): + fail("row {}: analysis_track and evaluation_contract must be provided together".format(row_number)) + rows.append(row) + return rows + + +def validate(rows: list[dict], baseline: str, candidate: str) -> dict: + if baseline == candidate: + fail("baseline and candidate methods must differ") + methods = {row["method"] for row in rows} + if methods != {baseline, candidate}: + fail("methods must be exactly {}, got {}".format([baseline, candidate], sorted(methods))) + seeds = sorted({row["training_seed"] for row in rows}) + budgets = sorted({row["budget_kimg"] for row in rows}) + nfes = sorted({row["nfe"] for row in rows}) + metrics_by_budget = { + budget: sorted({row["metric_name"] for row in rows if row["budget_kimg"] == budget}) + for budget in budgets + } + metrics = sorted({metric for budget_metrics in metrics_by_budget.values() for metric in budget_metrics}) + index = {} + for row in rows: + key = (row["method"], row["training_seed"], row["budget_kimg"], row["nfe"], row["metric_name"]) + if key in index: + fail("duplicate matrix cell: {}".format(key)) + index[key] = row + expected = { + (method, seed, budget, nfe, metric) + for method in (baseline, candidate) + for seed in seeds for budget in budgets for nfe in nfes + for metric in metrics_by_budget[budget] + } + missing = expected - set(index) + extra = set(index) - expected + if missing or extra: + fail("matrix incomplete; missing={}, extra={}".format(sorted(missing), sorted(extra))) + paired_protocol_fields = ( + "sample_count", "generation_seed_range", "metric_seed", "evidence_class", + "evaluation_contract", "analysis_track", + ) + for seed in seeds: + for budget in budgets: + for nfe in nfes: + for metric in metrics_by_budget[budget]: + fixed = index[(baseline, seed, budget, nfe, metric)] + tested = index[(candidate, seed, budget, nfe, metric)] + if any(fixed.get(field) != tested.get(field) for field in paired_protocol_fields): + fail("paired protocol metadata differs for seed={}, budget={}, nfe={}, metric={}".format( + seed, budget, nfe, metric, + )) + tracks = sorted({row.get("analysis_track", "") for row in rows if row.get("analysis_track", "")}) + if tracks and any(not row.get("analysis_track", "") for row in rows): + fail("tagged and untagged rows cannot be mixed; assign every row to an explicit analysis_track") + for track in tracks: + contract_rows = [row for row in rows if row.get("analysis_track", "") == track] + contracts = {row.get("evaluation_contract", "") for row in contract_rows} + if len(contracts) != 1: + fail("analysis_track={} must use exactly one evaluation_contract; split distinct protocols into separate collector inputs".format(track)) + protocol = { + (row.get("sample_count"), row.get("generation_seed_range", ""), row.get("metric_seed")) + for row in contract_rows + } + if len(protocol) != 1 or None in next(iter(protocol)) or "" in next(iter(protocol)): + fail("analysis_track={} requires one explicit sample_count, generation_seed_range, and metric_seed protocol".format(track)) + for metric in metrics: + for nfe in nfes: + targets = { + row["quality_target"] for row in rows + if row["metric_name"] == metric and row["nfe"] == nfe + and row["quality_target"] is not None + } + if len(targets) > 1: + fail("quality_target must be consistent for metric={} NFE={}".format(metric, nfe)) + return { + "methods": [baseline, candidate], "seeds": seeds, "budgets": budgets, + "nfes": nfes, "metrics": metrics, "metrics_by_budget": metrics_by_budget, + "analysis_tracks": tracks, + "budgets_by_metric": { + metric: [budget for budget in budgets if metric in metrics_by_budget[budget]] + for metric in metrics + }, + "index": index, + } + + +def paired_rows(matrix: dict) -> list[dict]: + baseline, candidate = matrix["methods"] + index = matrix["index"] + output = [] + for metric in matrix["metrics"]: + for nfe in matrix["nfes"]: + for budget in matrix["budgets_by_metric"][metric]: + for seed in matrix["seeds"]: + fixed = index[(baseline, seed, budget, nfe, metric)] + tested = index[(candidate, seed, budget, nfe, metric)] + delta = tested["metric_value"] - fixed["metric_value"] + output.append({ + "metric_name": metric, "nfe": nfe, "budget_kimg": budget, + "training_seed": seed, "baseline_method": baseline, + "candidate_method": candidate, "baseline_value": fixed["metric_value"], + "candidate_value": tested["metric_value"], + "sample_count": fixed["sample_count"], + "generation_seed_range": fixed["generation_seed_range"], + "metric_seed": fixed["metric_seed"], + "evidence_class": fixed["evidence_class"], + "evaluation_contract": fixed["evaluation_contract"], + "analysis_track": fixed["analysis_track"], + "delta_candidate_minus_baseline": delta, + "relative_improvement_pct": ( + 100 * (fixed["metric_value"] - tested["metric_value"]) / fixed["metric_value"] + if fixed["metric_value"] > 0 else None + ), + "winner": candidate if delta < 0 else baseline if delta > 0 else "tie", + }) + return output + + +def aggregate_rows(rows: list[dict], paired: list[dict], matrix: dict) -> tuple[list[dict], list[dict]]: + curves, summaries = [], [] + baseline, candidate = matrix["methods"] + for metric in matrix["metrics"]: + for nfe in matrix["nfes"]: + for budget in matrix["budgets_by_metric"][metric]: + selected_pairs = [ + row for row in paired + if (row["metric_name"], row["nfe"], row["budget_kimg"]) == (metric, nfe, budget) + ] + for method in matrix["methods"]: + values = [ + row["metric_value"] for row in rows + if (row["method"], row["metric_name"], row["nfe"], row["budget_kimg"]) + == (method, metric, nfe, budget) + ] + curves.append({ + "metric_name": metric, "nfe": nfe, "budget_kimg": budget, + "method": method, "mean_metric_value": statistics.mean(values), + "sample_sd_metric_value": statistics.stdev(values) if len(values) > 1 else None, + "seed_count": len(values), + "sample_count": selected_pairs[0]["sample_count"], + "generation_seed_range": selected_pairs[0]["generation_seed_range"], + "metric_seed": selected_pairs[0]["metric_seed"], + "evidence_class": selected_pairs[0]["evidence_class"], + "evaluation_contract": selected_pairs[0]["evaluation_contract"], + "analysis_track": selected_pairs[0]["analysis_track"], + }) + deltas = [row["delta_candidate_minus_baseline"] for row in selected_pairs] + summaries.append({ + "metric_name": metric, "nfe": nfe, "budget_kimg": budget, + "seed_count": len(deltas), "mean_paired_delta": statistics.mean(deltas), + "median_paired_delta": statistics.median(deltas), + "sample_sd_paired_delta": statistics.stdev(deltas) if len(deltas) > 1 else None, + "candidate_wins": sum(row["winner"] == candidate for row in selected_pairs), + "baseline_wins": sum(row["winner"] == baseline for row in selected_pairs), + "ties": sum(row["winner"] == "tie" for row in selected_pairs), + "sample_count": selected_pairs[0]["sample_count"], + "generation_seed_range": selected_pairs[0]["generation_seed_range"], + "metric_seed": selected_pairs[0]["metric_seed"], + "evidence_class": selected_pairs[0]["evidence_class"], + "evaluation_contract": selected_pairs[0]["evaluation_contract"], + "analysis_track": selected_pairs[0]["analysis_track"], + }) + return curves, summaries + + +def time_to_quality(rows: list[dict], matrix: dict) -> list[dict]: + output = [] + for metric in matrix["metrics"]: + for nfe in matrix["nfes"]: + for method in matrix["methods"]: + for seed in matrix["seeds"]: + trajectory = sorted( + (row for row in rows if (row["metric_name"], row["nfe"], row["method"], row["training_seed"]) + == (metric, nfe, method, seed)), + key=lambda row: row["budget_kimg"], + ) + targets = {row["quality_target"] for row in trajectory if row["quality_target"] is not None} + target = next(iter(targets)) if targets else None + reached = next( + (row for row in trajectory if target is not None and row["metric_value"] <= target), + None, + ) + times_present = all(row["training_time_hours"] is not None for row in trajectory) + status = ( + "target_missing" if target is None else + "training_time_missing" if not times_present else + "not_reached" if reached is None else "reached" + ) + output.append({ + "metric_name": metric, "nfe": nfe, "method": method, + "training_seed": seed, "quality_target": target, "status": status, + "budget_kimg_at_quality": reached["budget_kimg"] if reached else "", + "training_time_hours_at_quality": ( + reached["training_time_hours"] if reached and times_present else "" + ), + "best_observed_value": min(row["metric_value"] for row in trajectory), + "max_observed_budget_kimg": trajectory[-1]["budget_kimg"], + }) + return output + + +def write_csv(path: Path, rows: list[dict]) -> None: + if not rows: + fail("cannot write empty CSV: {}".format(path)) + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0]), lineterminator="\n") + writer.writeheader() + writer.writerows(rows) + + +def write_tables(summary: list[dict], outdir: Path) -> None: + markdown = [ + "# Multi-budget paired summary", "", + "Delta is candidate minus baseline; negative values favor the candidate.", "", + "| Metric | NFE | Budget (kimg) | Mean paired delta | Median delta | Sample SD | Candidate/baseline/tie |", + "| --- | ---: | ---: | ---: | ---: | ---: | ---: |", + ] + latex = [ + "\\begin{tabular}{llrrrr}", + "\\hline", + "Metric & NFE & Budget (kimg) & Mean paired delta & Sample SD & Candidate/baseline/tie \\\\", + "\\hline", + ] + for row in summary: + wins = "{}/{}/{}".format(row["candidate_wins"], row["baseline_wins"], row["ties"]) + markdown.append( + "| {} | {} | {:g} | {:.8g} | {:.8g} | {:.8g} | {} |".format( + row["metric_name"], row["nfe"], row["budget_kimg"], + row["mean_paired_delta"], row["median_paired_delta"], + row["sample_sd_paired_delta"], wins, + ) + ) + latex.append( + "{} & {} & {:g} & {:.8g} & {:.8g} & {} \\\\".format( + row["metric_name"].replace("_", "\\_"), row["nfe"], row["budget_kimg"], + row["mean_paired_delta"], row["sample_sd_paired_delta"], wins, + ) + ) + latex.extend(["\\hline", "\\end{tabular}", ""]) + (outdir / "summary_table.md").write_text("\n".join(markdown) + "\n", encoding="utf-8") + (outdir / "summary_table.tex").write_text("\n".join(latex), encoding="utf-8") + + +def style_axis(axis: plt.Axes) -> None: + axis.grid(axis="y", color=GRID, linewidth=0.8, zorder=0) + axis.spines["top"].set_visible(False) + axis.spines["right"].set_visible(False) + axis.spines["left"].set_color("#9CA3AF") + axis.spines["bottom"].set_color("#9CA3AF") + axis.tick_params(colors=INK, labelsize=9) + + +def save_figure(figure: plt.Figure, figure_dir: Path, stem: str) -> None: + figure_dir.mkdir(parents=True, exist_ok=True) + for extension in ("svg", "png", "pdf"): + figure.savefig( + figure_dir / "{}.{}".format(stem, extension), + dpi=220, bbox_inches="tight", facecolor="white", + ) + plt.close(figure) + + +def make_panels(matrix: dict, title: str) -> tuple[plt.Figure, object]: + figure, axes = plt.subplots( + len(matrix["metrics"]), len(matrix["nfes"]), + figsize=(5.1 * len(matrix["nfes"]), 3.7 * len(matrix["metrics"])), + squeeze=False, + ) + figure.suptitle(title, x=0.02, y=0.99, ha="left", fontsize=15, fontweight="bold", color=INK) + figure.subplots_adjust(left=0.09, right=0.985, bottom=0.10, top=0.85, hspace=0.34, wspace=0.23) + return figure, axes + + +def matrix_for_track(matrix: dict, records: list[dict]) -> dict: + """Return the plotting dimensions for one explicit analysis track.""" + metrics = sorted({row["metric_name"] for row in records}) + return { + "methods": matrix["methods"], "seeds": matrix["seeds"], "nfes": matrix["nfes"], + "metrics": metrics, + "budgets_by_metric": { + metric: sorted({row["budget_kimg"] for row in records if row["metric_name"] == metric}) + for metric in metrics + }, + } + + +def plot_budget_curves( + curves: list[dict], matrix: dict, figure_dir: Path, + stem: str = "budget_curves", title: str = "Budget curves by metric and NFE", +) -> None: + figure, axes = make_panels(matrix, title) + for metric_index, metric in enumerate(matrix["metrics"]): + for nfe_index, nfe in enumerate(matrix["nfes"]): + axis = axes[metric_index][nfe_index] + for method_index, method in enumerate(matrix["methods"]): + selected = sorted( + (row for row in curves if (row["metric_name"], row["nfe"], row["method"]) == (metric, nfe, method)), + key=lambda row: row["budget_kimg"], + ) + axis.errorbar( + [row["budget_kimg"] for row in selected], + [row["mean_metric_value"] for row in selected], + yerr=[row["sample_sd_metric_value"] or 0 for row in selected], + color=METHOD_COLORS[method_index], marker="o", linewidth=2, capsize=3, label=method, + ) + axis.set_title("{} · NFE={}".format(metric, nfe), loc="left", fontsize=11, fontweight="bold", color=INK) + axis.set_xlabel("Training budget (kimg)") + axis.set_ylabel(metric) + axis.set_xticks(matrix["budgets_by_metric"][metric]) + style_axis(axis) + figure.legend( + handles=[Line2D([0], [0], color=METHOD_COLORS[index], marker="o", label=method) + for index, method in enumerate(matrix["methods"])], + loc="upper center", ncol=2, frameon=False, bbox_to_anchor=(0.60, 0.965), + ) + save_figure(figure, figure_dir, stem) + + +def plot_trajectories(rows: list[dict], matrix: dict, figure_dir: Path) -> None: + figure, axes = make_panels(matrix, "Per-seed trajectories across training budget") + colors = {seed: SEED_COLORS[index % len(SEED_COLORS)] for index, seed in enumerate(matrix["seeds"])} + for metric_index, metric in enumerate(matrix["metrics"]): + for nfe_index, nfe in enumerate(matrix["nfes"]): + axis = axes[metric_index][nfe_index] + for seed in matrix["seeds"]: + for method_index, method in enumerate(matrix["methods"]): + selected = sorted( + (row for row in rows if (row["metric_name"], row["nfe"], row["training_seed"], row["method"]) + == (metric, nfe, seed, method)), + key=lambda row: row["budget_kimg"], + ) + axis.plot( + [row["budget_kimg"] for row in selected], + [row["metric_value"] for row in selected], + color=colors[seed], linestyle="-" if method_index else "--", + marker="o", markerfacecolor=colors[seed] if method_index else "white", + linewidth=1.8, alpha=0.9, + ) + axis.set_title("{} · NFE={}".format(metric, nfe), loc="left", fontsize=11, fontweight="bold", color=INK) + axis.set_xlabel("Training budget (kimg)") + axis.set_ylabel(metric) + axis.set_xticks(matrix["budgets_by_metric"][metric]) + style_axis(axis) + legend = [Line2D([0], [0], color=colors[seed], marker="o", label="Seed {}".format(seed)) + for seed in matrix["seeds"]] + legend += [ + Line2D([0], [0], color=INK, linestyle="--", label="{} (open)".format(matrix["methods"][0])), + Line2D([0], [0], color=INK, linestyle="-", label="{} (filled)".format(matrix["methods"][1])), + ] + figure.legend(handles=legend, loc="upper center", ncol=min(5, len(legend)), + frameon=False, bbox_to_anchor=(0.56, 0.965), fontsize=8.5) + save_figure(figure, figure_dir, "per_seed_trajectories") + + +def plot_paired_deltas( + paired: list[dict], matrix: dict, figure_dir: Path, + stem: str = "paired_deltas", title: str = "Paired deltas across training budget", +) -> None: + figure, axes = make_panels(matrix, title) + colors = {seed: SEED_COLORS[index % len(SEED_COLORS)] for index, seed in enumerate(matrix["seeds"])} + for metric_index, metric in enumerate(matrix["metrics"]): + for nfe_index, nfe in enumerate(matrix["nfes"]): + axis = axes[metric_index][nfe_index] + means, deviations = [], [] + metric_budgets = matrix["budgets_by_metric"][metric] + for budget in metric_budgets: + values = [ + row["delta_candidate_minus_baseline"] for row in paired + if (row["metric_name"], row["nfe"], row["budget_kimg"]) == (metric, nfe, budget) + ] + means.append(statistics.mean(values)) + deviations.append(statistics.stdev(values) if len(values) > 1 else 0) + for seed in matrix["seeds"]: + selected = sorted( + (row for row in paired if (row["metric_name"], row["nfe"], row["training_seed"]) == (metric, nfe, seed)), + key=lambda row: row["budget_kimg"], + ) + axis.plot([row["budget_kimg"] for row in selected], + [row["delta_candidate_minus_baseline"] for row in selected], + color=colors[seed], marker="o", linewidth=1.8) + axis.errorbar(metric_budgets, means, yerr=deviations, color="#111827", + marker="D", linewidth=1.7, capsize=3, zorder=4) + axis.axhline(0, color="#6B7280", linewidth=1, linestyle=(0, (4, 3))) + axis.set_title("{} · NFE={}".format(metric, nfe), loc="left", fontsize=11, fontweight="bold", color=INK) + axis.set_xlabel("Training budget (kimg)") + axis.set_ylabel("Candidate − baseline") + axis.set_xticks(metric_budgets) + style_axis(axis) + legend = [Line2D([0], [0], color=colors[seed], marker="o", label="Seed {}".format(seed)) + for seed in matrix["seeds"]] + legend.append(Line2D([0], [0], color="#111827", marker="D", label="Mean ± sample SD")) + figure.legend(handles=legend, loc="upper center", ncol=min(5, len(legend)), + frameon=False, bbox_to_anchor=(0.56, 0.965), fontsize=8.5) + save_figure(figure, figure_dir, stem) + + +def write_protocol_track_outputs( + curves: list[dict], paired: list[dict], summary: list[dict], matrix: dict, + outdir: Path, figure_dir: Path, +) -> list[str]: + """Write clearly separated, protocol-defined paper outputs when tagged input is supplied.""" + written = [] + for track, csv_name, figure_stem, title in ( + ("budget_curve", "same_protocol_budget_curves.csv", "same_protocol_budget_curves", + "Same-protocol budget curves"), + ("formal_endpoint", "formal_endpoint_comparison.csv", "formal_endpoint_comparison", + "Formal endpoint paired comparison"), + ): + track_curves = [row for row in curves if row["analysis_track"] == track] + track_paired = [row for row in paired if row["analysis_track"] == track] + track_summary = [row for row in summary if row["analysis_track"] == track] + if not track_curves: + continue + if track == "budget_curve": + write_csv(outdir / csv_name, track_curves) + plot_budget_curves(track_curves, matrix_for_track(matrix, track_curves), figure_dir, figure_stem, title) + else: + write_csv(outdir / csv_name, track_summary) + plot_paired_deltas(track_paired, matrix_for_track(matrix, track_paired), figure_dir, figure_stem, title) + written.append(track) + return written + + +def plot_time_to_quality(records: list[dict], matrix: dict, figure_dir: Path) -> bool: + if not any(row["status"] == "reached" for row in records): + return False + figure, axes = make_panels(matrix, "Time to pre-specified quality target") + colors = {seed: SEED_COLORS[index % len(SEED_COLORS)] for index, seed in enumerate(matrix["seeds"])} + for metric_index, metric in enumerate(matrix["metrics"]): + for nfe_index, nfe in enumerate(matrix["nfes"]): + axis = axes[metric_index][nfe_index] + selected = [ + row for row in records if (row["metric_name"], row["nfe"]) == (metric, nfe) + ] + for seed in matrix["seeds"]: + by_method = { + row["method"]: row for row in selected + if row["training_seed"] == seed and row["status"] == "reached" + } + if len(by_method) == 2: + axis.plot( + [0, 1], + [by_method[method]["training_time_hours_at_quality"] for method in matrix["methods"]], + color=colors[seed], marker="o", linewidth=1.8, + ) + target = next((row["quality_target"] for row in selected if row["quality_target"] is not None), None) + axis.set_xticks([0, 1], matrix["methods"]) + axis.set_ylabel("Training time to target (hours)") + suffix = " · target ≤ {:g}".format(target) if target is not None else "" + axis.set_title("{} · NFE={}{}".format(metric, nfe, suffix), loc="left", + fontsize=10.5, fontweight="bold", color=INK) + style_axis(axis) + figure.text(0.02, 0.02, "Only reached trajectories are plotted; unreached and missing-time cases remain in time_to_quality.csv.", fontsize=9, color=INK) + save_figure(figure, figure_dir, "time_to_quality") + return True + + +def write_readme(outdir: Path, matrix: dict, time_plot_written: bool) -> None: + text = """# Multi-budget collector output + +The input was validated as a complete paired matrix with two methods, {} training seeds, {} budgets, {} NFE settings, and {} metrics. Metrics may differ by budget; completeness is enforced within each budget's observed endpoint set. + +| Artifact | Contents | +| --- | --- | +| normalized_metrics.csv | Validated long-form input rows. | +| budget_curves.csv | Method mean and sample SD by budget, metric, and NFE. | +| per_seed_trajectories.csv | Seed-level, figure-ready metric trajectories. | +| paired_deltas.csv | Paired candidate-minus-baseline rows. | +| paired_summary.csv | Descriptive paired mean, median, SD, and win counts. | +| same_protocol_budget_curves.csv | Explicit `budget_curve` track only; sampling protocol is shared across all plotted budgets. | +| formal_endpoint_comparison.csv | Explicit `formal_endpoint` track only; never plotted as part of a 5k budget curve. | +| time_to_quality.csv | First target crossing, including transparent missing and unreached statuses. | +| summary_table.md and summary_table.tex | Markdown and LaTeX paired summary tables. | +| figures | SVG, PNG, and PDF budget, trajectory, paired-delta{} figures; tagged inputs also produce separately named same-protocol and formal-endpoint figures. | + +All deltas are candidate minus baseline; negative values favor the candidate. Sample SD is descriptive across training seeds, not a confidence interval. +""".format( + len(matrix["seeds"]), len(matrix["budgets"]), len(matrix["nfes"]), len(matrix["metrics"]), + ", and time-to-quality" if time_plot_written else "", + ) + (outdir / "README.md").write_text(text, encoding="utf-8") + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--input-csv", type=Path, required=True) + parser.add_argument("--outdir", type=Path, required=True) + parser.add_argument("--baseline-method", default="fixed") + parser.add_argument("--candidate-method", default="global110") + args = parser.parse_args(argv) + + rows = read_rows(args.input_csv.resolve()) + matrix = validate(rows, args.baseline_method, args.candidate_method) + paired = paired_rows(matrix) + curves, summary = aggregate_rows(rows, paired, matrix) + times = time_to_quality(rows, matrix) + outdir = args.outdir.resolve() + figure_dir = outdir / "figures" + outdir.mkdir(parents=True, exist_ok=True) + + write_csv(outdir / "normalized_metrics.csv", rows) + write_csv(outdir / "per_seed_trajectories.csv", rows) + write_csv(outdir / "paired_deltas.csv", paired) + write_csv(outdir / "paired_summary.csv", summary) + write_csv(outdir / "budget_curves.csv", curves) + write_csv(outdir / "time_to_quality.csv", times) + write_csv(outdir / "figure_ready_budget_curves.csv", curves) + write_csv(outdir / "figure_ready_per_seed_trajectories.csv", rows) + write_csv(outdir / "figure_ready_paired_deltas.csv", paired) + write_csv(outdir / "figure_ready_time_to_quality.csv", times) + write_tables(summary, outdir) + plot_budget_curves(curves, matrix, figure_dir) + plot_trajectories(rows, matrix, figure_dir) + plot_paired_deltas(paired, matrix, figure_dir) + time_plot_written = plot_time_to_quality(times, matrix, figure_dir) + protocol_tracks_written = write_protocol_track_outputs(curves, paired, summary, matrix, outdir, figure_dir) + write_readme(outdir, matrix, time_plot_written) + manifest = {key: value for key, value in matrix.items() if key != "index"} + manifest.update({ + "input_csv": str(args.input_csv.resolve()), + "row_count": len(rows), + "time_to_quality_plot_written": time_plot_written, + "protocol_tracks_written": protocol_tracks_written, + }) + (outdir / "collector_manifest.json").write_text(json.dumps(manifest, indent=2) + "\n", encoding="utf-8") + print("Validated {} metric rows; output={}".format(len(rows), outdir)) + + +if __name__ == "__main__": + main() diff --git a/scripts/collect_role_a_quality_results.py b/scripts/collect_role_a_quality_results.py new file mode 100644 index 00000000..044c09b6 --- /dev/null +++ b/scripts/collect_role_a_quality_results.py @@ -0,0 +1,235 @@ +#!/usr/bin/env python3 +"""Validate Role A metric outputs and write the required unified table.""" + +from __future__ import annotations + +import argparse +import csv +import json +import math +from pathlib import Path + + +def fail(message: str) -> None: + raise SystemExit(f"[collect_role_a_quality_results] ERROR: {message}") + + +def load_json(path: Path) -> dict: + try: + return json.loads(path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError) as exc: + fail(f"cannot read {path}: {exc}") + + +def parse_seed_count(spec: str) -> int: + values = [] + for part in spec.split(","): + part = part.strip() + if not part: + continue + if "-" in part: + start, end = (int(value) for value in part.split("-", 1)) + values.extend(range(start, end + 1)) + else: + values.append(int(part)) + if len(values) != len(set(values)): + fail(f"sample seed specification contains duplicates: {spec}") + return len(values) + + +def metric_family(metric_name: str) -> str: + if metric_name.startswith("kid"): + return "KID" + if metric_name.startswith("fid"): + return "FID" + fail(f"unsupported metric in Role A run: {metric_name}") + + +REPEAT_REL_TOL = 1e-6 +REPEAT_ABS_TOL = 1e-12 + + +def read_metric(path: Path, metric_name: str, repeats: int) -> tuple[float, bool, bool]: + if not path.is_file(): + fail(f"missing metric output: {path}") + lines = [line for line in path.read_text(encoding="utf-8").splitlines() if line.strip()] + if len(lines) != repeats: + fail(f"expected {repeats} result lines in {path}, found {len(lines)}") + values = [] + for line in lines: + try: + payload = json.loads(line) + if payload["metric"] != metric_name: + fail(f"metric name mismatch in {path}") + value = float(payload["results"][metric_name]) + except (KeyError, TypeError, ValueError, json.JSONDecodeError) as exc: + fail(f"malformed metric output {path}: {exc}") + if not math.isfinite(value): + fail(f"non-finite metric value in {path}: {value}") + values.append(value) + exact = all(value == values[0] for value in values[1:]) + numerically_consistent = all( + math.isclose( + value, + values[0], + rel_tol=REPEAT_REL_TOL, + abs_tol=REPEAT_ABS_TOL, + ) + for value in values[1:] + ) + return values[0], exact, numerically_consistent + + +def validate_manifest(manifest: dict) -> None: + if manifest.get("protocol") != "role-a-multibudget-quality-v1": + fail("run manifest is not a Role A multibudget quality run") + if manifest.get("status") != "completed": + fail(f"run is not complete: {manifest.get('status')}") + if manifest.get("precision") != "fp32": + fail("Role A protocol requires FP32") + if parse_seed_count(str(manifest.get("sample_seeds", ""))) != int(manifest["sample_count"]): + fail("sample seed count does not match declared generated image count") + metric_families = {metric_family(name) for name in manifest.get("metric_names", [])} + expected_by_mode = { + "both": {"KID", "FID"}, + "kid-only": {"KID"}, + "fid-only": {"FID"}, + } + mode = manifest.get("metric_mode") + if mode not in expected_by_mode or metric_families != expected_by_mode[mode]: + fail("metric mode and metric names are inconsistent") + + +def collect(eval_root: Path) -> tuple[list[dict], dict]: + manifest = load_json(eval_root / "run_manifest.json") + validate_manifest(manifest) + rows = [] + repeat_exact_checks = [] + repeat_consistency_checks = [] + expected_metric_names = list(manifest["metric_names"]) + for job in manifest.get("jobs", []): + if job.get("status") != "completed": + fail(f"job is not complete: {job}") + required_metadata = ( + "method", "training_seed", "budget_kimg", "nfe", "sample_count", + "sample_seeds", "checkpoint_sha256", "output_directory", + ) + missing = [field for field in required_metadata if field not in job] + if missing: + fail(f"job metadata is incomplete; missing={missing}") + if int(job["sample_count"]) != int(manifest["sample_count"]): + fail("job and run sample counts disagree") + if job.get("metric_names") != expected_metric_names: + fail("different jobs use different metrics; mixed-method metrics are forbidden") + + values = {"KID": None, "FID": None} + for metric_name in expected_metric_names: + metric_path = Path(job["output_directory"]) / f"metric-{metric_name}.jsonl" + value, exact, numerically_consistent = read_metric( + metric_path, metric_name, int(job["metric_repeats"]) + ) + values[metric_family(metric_name)] = value + repeat_exact_checks.append(exact) + repeat_consistency_checks.append(numerically_consistent) + rows.append({ + "Method": job["method"], + "Train seed": int(job["training_seed"]), + "Budget": int(job["budget_kimg"]), + "NFE": int(job["nfe"]), + "KID": values["KID"], + "FID": values["FID"], + "Checkpoint SHA": job["checkpoint_sha256"], + }) + + phase = manifest["phase"] + expected_jobs = 4 if phase == "smoke" else 12 + if len(rows) != expected_jobs: + fail(f"{phase} run must contain {expected_jobs} independent cells, found {len(rows)}") + if phase == "smoke" and not all(repeat_consistency_checks): + fail( + "smoke metric repeats are not numerically reproducible within " + f"rel_tol={REPEAT_REL_TOL:g}, abs_tol={REPEAT_ABS_TOL:g}" + ) + + summary = { + "schema_version": 1, + "protocol": manifest["protocol"], + "phase": phase, + "metric_mode": manifest["metric_mode"], + "sample_count_per_checkpoint_nfe": manifest["sample_count"], + "sample_seeds": manifest["sample_seeds"], + "precision": manifest["precision"], + "dataset": manifest["dataset"], + "dataset_sha256": manifest["dataset_sha256"], + "reference_real_count": manifest["reference_real_count"], + "feature_detector_url": manifest["feature_detector_url"], + "reference_identity_consistent": True, + "image_count_valid": True, + "repeat_results_exact": all(repeat_exact_checks), + "repeat_results_numerically_consistent": all(repeat_consistency_checks), + "repeat_relative_tolerance": REPEAT_REL_TOL, + "repeat_absolute_tolerance": REPEAT_ABS_TOL, + "row_count": len(rows), + "rows": rows, + } + return rows, summary + + +def display(value: float | None) -> str: + return "—" if value is None else f"{value:.9f}" + + +def write_outputs(outdir: Path, rows: list[dict], summary: dict) -> None: + outdir.mkdir(parents=True, exist_ok=True) + csv_path = outdir / "role_a_metrics.csv" + with csv_path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0])) + writer.writeheader() + writer.writerows(rows) + (outdir / "role_a_metrics.json").write_text( + json.dumps(summary, indent=2) + "\n", encoding="utf-8" + ) + lines = [ + "# Role A quantitative evaluation", + "", + f"Phase: `{summary['phase']}`; samples per checkpoint/NFE: " + f"{summary['sample_count_per_checkpoint_nfe']}; precision: FP32.", + "", + "| Method | Train seed | Budget | NFE | KID | FID | Checkpoint SHA |", + "| --- | ---: | ---: | ---: | ---: | ---: | --- |", + ] + for row in rows: + lines.append( + f"| {row['Method']} | {row['Train seed']} | {row['Budget']} | " + f"{row['NFE']} | {display(row['KID'])} | {display(row['FID'])} | " + f"`{row['Checkpoint SHA']}` |" + ) + lines.extend([ + "", + f"Reference identity consistent: {summary['reference_identity_consistent']}; " + f"image count valid: {summary['image_count_valid']}; " + f"repeat results exact: {summary['repeat_results_exact']}; " + "repeat results numerically consistent: " + f"{summary['repeat_results_numerically_consistent']} " + f"(rel_tol={summary['repeat_relative_tolerance']:g}, " + f"abs_tol={summary['repeat_absolute_tolerance']:g}).", + "", + ]) + (outdir / "role_a_metrics.md").write_text("\n".join(lines), encoding="utf-8") + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--eval-root", type=Path, required=True) + parser.add_argument("--outdir", type=Path, required=True) + args = parser.parse_args(argv) + rows, summary = collect(args.eval_root.resolve()) + write_outputs(args.outdir.resolve(), rows, summary) + print( + f"Validated {len(rows)} independent cells; metrics={summary['metric_mode']}; " + f"output={args.outdir.resolve()}" + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/collect_schedule_results.py b/scripts/collect_schedule_results.py new file mode 100755 index 00000000..a9c8ecef --- /dev/null +++ b/scripts/collect_schedule_results.py @@ -0,0 +1,953 @@ +#!/usr/bin/env python3 +"""Validate a paired-schedule run and package compact results for the repo. + +Fails closed if train_summary.csv is missing, loss has NaN/Inf, checkpoints are +unreadable, gradscaler_state is absent, train-time git metadata disagrees with +the packaging HEAD, or CLI schedule/mode/duration disagree with the CSV / command. +""" + +from __future__ import annotations + +import argparse +import csv +import hashlib +import json +import math +import pickle +import platform +import re +import shlex +import subprocess +import sys +from pathlib import Path + + +TELEMETRY_FIELDS = ( + "loss_ema", + "loss_reference", + "correction", + "signal_updates", + "adaptive_active", + "r_over_t_mean", + "gap_mean", +) + +OUTPUT_FIELDS = ( + "attempted_iteration", + "successful_optimizer_steps", + "processed_kimg", + "loss", + "grad_scale", + "step_skipped", + "schedule", + "stage", + "next_loop_cur_tick", + *TELEMETRY_FIELDS, + "seconds", + "peak_vram_mib", +) + +MODE_DURATION_MIMG = { + "activation": 0.004, + "stability": 0.016, + "baseline": 0.128, +} + +ACTIVATION_MIN_SIGNAL_UPDATES = 3 +ACTIVATION_MIN_FOLLOWING_ITERATIONS = 4 + + +def expected_final_nimg(duration_mimg: float, global_batch: int) -> int: + """Match ct_train/ct_training_loop discrete batch completion. + + total_kimg = max(int(duration_mimg * 1000), 1) + training stops once cur_nimg >= total_kimg * 1000 after a full batch. + """ + if global_batch <= 0: + fail(f"global_batch must be positive, got {global_batch}") + target_kimg = max(int(duration_mimg * 1000), 1) + target_nimg = target_kimg * 1000 + return math.ceil(target_nimg / global_batch) * global_batch + + +def adaptive_runtime_summary(packaged: list[dict]) -> dict: + """Derive auditable controller/pair activation boundaries from CSV rows. + + Telemetry is sampled at the end of an attempted iteration. Therefore, a + correction first observed in iteration N can first affect the pair drawn + for iteration N + 1, never the pair metrics recorded on iteration N. + """ + final_iteration = packaged[-1]["attempted_iteration"] + final_signal_updates = packaged[-1]["signal_updates"] + final_adaptive_active = packaged[-1]["adaptive_active"] + first_nonzero_correction_iteration = next( + ( + row["attempted_iteration"] for row in packaged + if row["correction"] is not None and row["correction"] != 0 + ), + None, + ) + first_adapted_pair_iteration = None + if first_nonzero_correction_iteration is not None: + candidate = first_nonzero_correction_iteration + 1 + if candidate <= final_iteration: + first_adapted_pair_iteration = candidate + return { + "final_signal_updates": final_signal_updates, + "final_adaptive_active": final_adaptive_active, + "first_nonzero_correction_iteration": first_nonzero_correction_iteration, + "first_adapted_pair_iteration": first_adapted_pair_iteration, + } + + +def enforce_adaptive_activation_gate(runtime_summary: dict, final_iteration: int) -> None: + """Require an activation run to exercise correction during training.""" + signal_updates = runtime_summary["final_signal_updates"] + if signal_updates is None or signal_updates < ACTIVATION_MIN_SIGNAL_UPDATES: + fail( + "activation gate: final_signal_updates must be " + f">= {ACTIVATION_MIN_SIGNAL_UPDATES}, got {signal_updates}" + ) + if runtime_summary["first_nonzero_correction_iteration"] is None: + fail("activation gate: first_nonzero_correction_iteration is required") + if runtime_summary["final_adaptive_active"] is not True: + fail("activation gate: final adaptive_active must be true") + + first_correction = runtime_summary["first_nonzero_correction_iteration"] + first_adapted_pair = runtime_summary["first_adapted_pair_iteration"] + if first_adapted_pair is None or first_adapted_pair >= final_iteration: + fail( + "activation gate: first_adapted_pair_iteration must be before " + f"final_iteration (correction={first_correction}, " + f"adapted_pair={first_adapted_pair}, final={final_iteration})" + ) + following_iterations = final_iteration - first_correction + if following_iterations < ACTIVATION_MIN_FOLLOWING_ITERATIONS: + fail( + "activation gate: nonzero correction must be followed by at least " + f"{ACTIVATION_MIN_FOLLOWING_ITERATIONS} attempted iterations " + f"(correction={first_correction}, final={final_iteration})" + ) + + +def fail(message: str) -> None: + raise SystemExit(f"[collect_schedule_results] ERROR: {message}") + + +def run_git(repo_root: Path, *args: str) -> str: + try: + completed = subprocess.run( + ["git", *args], + cwd=repo_root, + check=True, + capture_output=True, + text=True, + ) + except (OSError, subprocess.CalledProcessError) as exc: + fail(f"git {' '.join(args)} failed: {exc}") + return completed.stdout.strip() + + +def collect_packaging_git_metadata(repo_root: Path) -> dict: + head = run_git(repo_root, "rev-parse", "HEAD") + branch = run_git(repo_root, "rev-parse", "--abbrev-ref", "HEAD") + porcelain = run_git(repo_root, "status", "--porcelain") + return { + "packaging_git_commit": head, + "packaging_git_branch": branch, + "packaging_git_dirty": bool(porcelain), + } + + +def parse_run_meta(path: Path) -> dict[str, str]: + if not path.is_file(): + fail(f"run_meta.env missing: {path}") + meta: dict[str, str] = {} + for line in path.read_text(encoding="utf-8", errors="replace").splitlines(): + if not line or "=" not in line: + continue + key, value = line.split("=", 1) + meta[key] = value + return meta + + +def load_identity_and_command_meta(run_dir: Path, mode: str) -> tuple[dict[str, str], dict[str, str]]: + """Split immutable train identity from the packaging-mode command meta. + + run_meta.env is written once on the fresh segment and must not be overwritten. + Resume segments write run_meta..env / run_meta.latest.env. + """ + identity_path = run_dir / "run_meta.env" + identity = parse_run_meta(identity_path) + command_candidates = [ + run_dir / f"run_meta.{mode}.env", + run_dir / "run_meta.latest.env", + identity_path, + ] + command_meta = None + for path in command_candidates: + if path.is_file(): + command_meta = parse_run_meta(path) + break + if command_meta is None: + fail(f"no run_meta sidecar found under {run_dir}") + return identity, command_meta + + +def sha256_file(path: Path | None) -> str | None: + if path is None or not path.is_file(): + return None + digest = hashlib.sha256() + with path.open("rb") as handle: + while True: + chunk = handle.read(1024 * 1024) + if not chunk: + break + digest.update(chunk) + return digest.hexdigest() + + +def parse_boolish(value: str) -> bool: + return str(value).strip().lower() in {"1", "true", "yes"} + + +def parse_strict_bool(value: str | None, field: str) -> bool: + text = "" if value is None else str(value).strip().lower() + if text in {"1", "true"}: + return True + if text in {"0", "false"}: + return False + fail(f"{field} must be one of 0/1/false/true, got {value!r}") + + +def parse_finite_float(value: str | None, field: str) -> float: + text = "" if value is None else str(value).strip() + if text == "": + fail(f"{field} must not be empty") + try: + number = float(text) + except ValueError: + fail(f"{field} must be numeric, got {value!r}") + if not math.isfinite(number): + fail(f"{field} must be finite, got {value!r}") + return number + + +def parse_optional_float(value: str | None, field: str) -> float | None: + text = "" if value is None else str(value).strip() + return None if text == "" else parse_finite_float(text, field) + + +def parse_nonnegative_integer(value: str | None, field: str) -> int: + number = parse_finite_float(value, field) + if not number.is_integer() or number < 0: + fail(f"{field} must be a non-negative integer, got {value!r}") + return int(number) + + +def load_rows(path: Path) -> list[dict]: + if not path.is_file() or path.stat().st_size == 0: + fail(f"train_summary.csv missing or empty: {path}") + with path.open(newline="", encoding="utf-8") as handle: + reader = csv.DictReader(handle) + fieldnames = set(reader.fieldnames or []) + rows = list(reader) + if not rows: + fail(f"train_summary.csv has no data rows: {path}") + required = { + "attempted_iteration", + "successful_optimizer_steps", + "processed_nimg", + "loss", + "grad_scale", + "step_skipped", + "elapsed_sec", + "peak_vram_gb", + "schedule", + } + if "processed_nimg" not in fieldnames and "nimg" in fieldnames: + fieldnames.add("processed_nimg") + for row in rows: + row["processed_nimg"] = row["nimg"] + if "attempted_iteration" not in fieldnames and "update" in fieldnames: + fieldnames.add("attempted_iteration") + for row in rows: + row["attempted_iteration"] = row["update"] + if "successful_optimizer_steps" not in fieldnames: + fieldnames.add("successful_optimizer_steps") + skipped_so_far = 0 + for idx, row in enumerate(rows, start=1): + if parse_boolish(row.get("step_skipped", "0")): + skipped_so_far += 1 + row["successful_optimizer_steps"] = str(idx - skipped_so_far) + if "peak_vram_gb" not in fieldnames and "peak_gpu_mem_gb" in fieldnames: + fieldnames.add("peak_vram_gb") + for row in rows: + row["peak_vram_gb"] = row["peak_gpu_mem_gb"] + if "schedule" not in fieldnames: + fieldnames.add("schedule") + for row in rows: + row.setdefault("schedule", "") + missing = required - fieldnames + if missing: + fail(f"train_summary.csv missing columns: {sorted(missing)}") + return rows + + +def choose_snapshot(run_dir: Path) -> Path: + latest = run_dir / "network-snapshot-latest.pkl" + if latest.is_file() and latest.stat().st_size > 0: + return latest + numbered = sorted(run_dir.glob("network-snapshot-[0-9][0-9][0-9][0-9][0-9][0-9].pkl")) + numbered = [path for path in numbered if path.stat().st_size > 0] + if not numbered: + fail(f"no non-empty network-snapshot under {run_dir}") + return numbered[-1] + + +def load_snapshot(snapshot: Path, repo_root: Path) -> None: + root = str(repo_root.resolve()) + if root not in sys.path: + sys.path.insert(0, root) + with snapshot.open("rb") as handle: + pickle.load(handle) + + +def choose_training_state(run_dir: Path) -> Path: + latest = run_dir / "training-state-latest.pt" + if latest.is_file() and latest.stat().st_size > 0: + return latest + numbered = sorted(run_dir.glob("training-state-[0-9][0-9][0-9][0-9][0-9][0-9].pt")) + numbered = [path for path in numbered if path.stat().st_size > 0] + if not numbered: + fail(f"no non-empty training-state under {run_dir}") + return numbered[-1] + + +def extract_exact_command(run_meta: dict[str, str], log_path: Path | None, exact_command_file: Path | None) -> str: + if exact_command_file is not None and exact_command_file.is_file(): + for line in exact_command_file.read_text(encoding="utf-8", errors="replace").splitlines(): + if line.startswith("exact_command="): + text = line[len("exact_command=") :].strip() + if text: + return text + if "exact_command" in run_meta and run_meta["exact_command"].strip(): + return run_meta["exact_command"].strip() + if log_path is not None and log_path.is_file(): + for line in log_path.read_text(encoding="utf-8", errors="replace").splitlines(): + if line.startswith("exact_command="): + text = line[len("exact_command=") :].strip() + if text: + return text + fail("exact_command not found in run_meta.env / log / --exact-command-file") + + +def extract_mapping_from_command(exact_command: str) -> str | None: + try: + tokens = shlex.split(exact_command) + except ValueError: + tokens = exact_command.split() + for token in tokens: + if token.startswith("--mapping="): + return token.split("=", 1)[1] + if token.startswith("--schedule="): + return token.split("=", 1)[1] + return None + + +def extract_duration_from_command(exact_command: str) -> float | None: + try: + tokens = shlex.split(exact_command) + except ValueError: + tokens = exact_command.split() + for token in tokens: + if token.startswith("--duration="): + return float(token.split("=", 1)[1]) + return None + + +def _state_integer(state: dict, key: str, training_state: Path) -> int: + try: + value = int(state[key]) + except (KeyError, TypeError, ValueError, OverflowError): + fail(f"training-state {key} must be an integer: {training_state}") + return value + + +def _state_optional_float(value, field: str, training_state: Path) -> float | None: + if value is None: + return None + try: + number = float(value) + except (TypeError, ValueError): + fail(f"training-state {field} must be numeric: {training_state}") + if not math.isfinite(number): + fail(f"training-state {field} must be finite: {training_state}") + return number + + +def validate_training_state_against_csv( + state: dict, + *, + training_state: Path, + last_csv_row: dict, + last_packaged_row: dict, + schedule: str, +) -> None: + """Fail closed when the checkpoint does not describe the final CSV row.""" + csv_nimg = int(float(last_csv_row["processed_nimg"])) + csv_attempted = int(float(last_csv_row["attempted_iteration"])) + csv_successful = int(float(last_csv_row["successful_optimizer_steps"])) + csv_elapsed = float(last_csv_row["elapsed_sec"]) + expected_progress = { + "cur_nimg": csv_nimg, + "attempted_iteration": csv_attempted, + "successful_optimizer_steps": csv_successful, + } + for key, expected in expected_progress.items(): + actual = _state_integer(state, key, training_state) + if actual != expected: + fail( + f"training-state {key} mismatch: state={actual} csv_last={expected} " + f"({training_state})" + ) + + state_elapsed = _state_optional_float(state["elapsed_sec"], "elapsed_sec", training_state) + if state_elapsed is None or not math.isclose(state_elapsed, csv_elapsed, rel_tol=0.0, abs_tol=1e-6): + fail( + f"training-state elapsed_sec mismatch: state={state_elapsed} csv_last={csv_elapsed} " + f"({training_state})" + ) + + # The training state persists the next-loop tick. The training loop records + # that authoritative value in the CSV instead of asking the collector to + # recreate maintenance timing from --tick and image count. + expected_cur_tick = parse_nonnegative_integer( + last_csv_row.get("next_loop_cur_tick"), "next_loop_cur_tick" + ) + actual_cur_tick = _state_integer(state, "cur_tick", training_state) + if actual_cur_tick != expected_cur_tick: + fail( + f"training-state cur_tick mismatch: state={actual_cur_tick} " + f"csv_last_next_loop={expected_cur_tick} ({training_state})" + ) + + if schedule != "adaptive_v1": + return + loss_fn_state = state.get("loss_fn_state") + if not isinstance(loss_fn_state, dict): + fail(f"adaptive training-state missing loss_fn_state: {training_state}") + if loss_fn_state.get("schedule_name") != schedule: + fail( + f"training-state schedule mismatch: state={loss_fn_state.get('schedule_name')!r} " + f"expected={schedule!r} ({training_state})" + ) + controller_state = loss_fn_state.get("schedule") + if not isinstance(controller_state, dict): + fail(f"adaptive training-state missing loss_fn_state.schedule: {training_state}") + + state_updates = _state_integer(controller_state, "signal_updates", training_state) + csv_updates = last_packaged_row["signal_updates"] + if state_updates != csv_updates: + fail( + f"training-state signal_updates mismatch: state={state_updates} " + f"csv_last={csv_updates} ({training_state})" + ) + for field in ("loss_ema", "loss_reference"): + state_value = _state_optional_float(controller_state.get(field), field, training_state) + csv_value = last_packaged_row[field] + if (state_value is None) != (csv_value is None): + fail( + f"training-state {field} mismatch: state={state_value} " + f"csv_last={csv_value} ({training_state})" + ) + if state_value is not None and not math.isclose( + # train_summary.csv writes controller floats with .12g precision. + state_value, csv_value, rel_tol=1e-11, abs_tol=1e-12 + ): + fail( + f"training-state {field} mismatch: state={state_value} " + f"csv_last={csv_value} ({training_state})" + ) + + +def collect_runtime_metadata(run_meta: dict[str, str]) -> dict: + meta = { + "python_version": run_meta.get("python_version") or sys.version.split()[0], + "platform": run_meta.get("platform") or platform.platform(), + } + if "torch_version" in run_meta: + meta["torch_version"] = run_meta.get("torch_version") + meta["cuda_version"] = run_meta.get("cuda_version") + meta["gpu_name"] = run_meta.get("gpu_name") or None + meta["gpu_count"] = int(run_meta["gpu_count"]) if run_meta.get("gpu_count") else 0 + return meta + try: + import torch + + meta["torch_version"] = torch.__version__ + meta["cuda_version"] = getattr(torch.version, "cuda", None) + if torch.cuda.is_available(): + meta["gpu_name"] = torch.cuda.get_device_name(0) + meta["gpu_count"] = torch.cuda.device_count() + else: + meta["gpu_name"] = None + meta["gpu_count"] = 0 + except Exception as exc: # noqa: BLE001 + meta["torch_import_error"] = str(exc) + return meta + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--run-dir", type=Path, required=True) + parser.add_argument("--outdir", type=Path, required=True) + parser.add_argument("--log", type=Path) + parser.add_argument("--wall-time", type=Path) + parser.add_argument("--exact-command-file", type=Path) + parser.add_argument("--data", type=Path, required=True, help="Dataset zip for SHA256 metadata") + parser.add_argument("--transfer", type=Path, required=True, help="EDM transfer pickle for SHA256 metadata") + parser.add_argument("--mode", required=True, choices=sorted(MODE_DURATION_MIMG)) + parser.add_argument("--schedule", required=True, choices=("sigmoid", "adaptive_v1", "const")) + parser.add_argument("--duration-mimg", type=float) + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--global-batch", type=int, default=128) + parser.add_argument("--batch-gpu", type=int, default=16) + parser.add_argument( + "--allow-dirty", + action="store_true", + help="Permit packaging when the packaging worktree is dirty (not for formal evidence)", + ) + parser.add_argument( + "--overwrite", + action="store_true", + help="Allow writing into a non-empty --outdir (default: fail closed)", + ) + parser.add_argument( + "--skip-snapshot-load", + action="store_true", + help="Skip pickle.load of network snapshot (tests only)", + ) + parser.add_argument( + "--skip-training-state-load", + action="store_true", + help="Skip torch.load of training-state (tests only)", + ) + args = parser.parse_args(argv) + + expected_duration = MODE_DURATION_MIMG[args.mode] + if args.duration_mimg is None: + args.duration_mimg = expected_duration + elif not math.isclose(args.duration_mimg, expected_duration, rel_tol=0.0, abs_tol=1e-12): + fail( + f"--duration-mimg={args.duration_mimg} disagrees with mode={args.mode} " + f"(expected {expected_duration})" + ) + + run_dir = args.run_dir.resolve() + if not run_dir.is_dir(): + fail(f"run dir does not exist: {run_dir}") + + repo_root = Path(__file__).resolve().parents[1] + packaging_git = collect_packaging_git_metadata(repo_root) + if packaging_git["packaging_git_dirty"] and not args.allow_dirty: + fail("git worktree is dirty; commit/stash first or pass --allow-dirty for preliminary packaging") + + run_meta, command_meta = load_identity_and_command_meta(run_dir, args.mode) + train_git_head = run_meta.get("git_head") + if not train_git_head or train_git_head == "unknown": + fail("run_meta.env missing git_head from training time") + if train_git_head != packaging_git["packaging_git_commit"]: + fail( + f"train-time git_head={train_git_head} != packaging HEAD=" + f"{packaging_git['packaging_git_commit']}" + ) + train_git_dirty = parse_boolish(run_meta.get("git_dirty", "false")) + if train_git_dirty and not args.allow_dirty: + fail("run_meta.env records git_dirty=true; refuse formal packaging without --allow-dirty") + + # Dual provenance: every resume segment meta must agree with the immutable fresh identity. + cmd_git_head = command_meta.get("git_head") + if not cmd_git_head or cmd_git_head == "unknown": + fail("command meta missing git_head") + if cmd_git_head != train_git_head: + fail( + f"resume/command git_head={cmd_git_head} != fresh git_head={train_git_head}" + ) + cmd_git_dirty = parse_boolish(command_meta.get("git_dirty", "true")) + if cmd_git_dirty and not args.allow_dirty: + fail("command meta records git_dirty=true; refuse formal packaging without --allow-dirty") + cmd_data_sha = command_meta.get("data_sha256") + cmd_transfer_sha = command_meta.get("transfer_sha256") + fresh_data_sha = run_meta.get("data_sha256") + fresh_transfer_sha = run_meta.get("transfer_sha256") + if not fresh_data_sha or fresh_data_sha in {"missing", "unknown"}: + fail("run_meta.env missing training-time data_sha256") + if not fresh_transfer_sha or fresh_transfer_sha in {"missing", "unknown"}: + fail("run_meta.env missing training-time transfer_sha256") + if not cmd_data_sha or cmd_data_sha in {"missing", "unknown"}: + fail("command meta missing data_sha256") + if not cmd_transfer_sha or cmd_transfer_sha in {"missing", "unknown"}: + fail("command meta missing transfer_sha256") + if cmd_data_sha != fresh_data_sha: + fail( + f"command data_sha256={cmd_data_sha} != fresh data_sha256={fresh_data_sha}" + ) + if cmd_transfer_sha != fresh_transfer_sha: + fail( + f"command transfer_sha256={cmd_transfer_sha} != fresh " + f"transfer_sha256={fresh_transfer_sha}" + ) + + source_csv = run_dir / "train_summary.csv" + rows = load_rows(source_csv) + + schedules = {str(row.get("schedule", "")).strip() for row in rows} + if len(schedules) != 1: + fail(f"train_summary.csv schedule not unique: {sorted(schedules)}") + csv_schedule = next(iter(schedules)) + if csv_schedule != args.schedule: + fail(f"CSV schedule={csv_schedule!r} != --schedule={args.schedule!r}") + telemetry_columns = [field in rows[0] for field in TELEMETRY_FIELDS] + if any(telemetry_columns) and not all(telemetry_columns): + fail("train_summary.csv has a partial schedule telemetry schema") + telemetry_columns_available = all(telemetry_columns) + if args.schedule == "adaptive_v1" and not telemetry_columns_available: + fail("adaptive_v1 train_summary.csv missing stable schedule telemetry columns") + + exact_command = extract_exact_command(command_meta, args.log, args.exact_command_file) + cmd_schedule = extract_mapping_from_command(exact_command) + if cmd_schedule is None: + fail("exact_command missing --mapping=/--schedule=") + if cmd_schedule != args.schedule: + fail(f"exact_command schedule={cmd_schedule!r} != --schedule={args.schedule!r}") + cmd_duration = extract_duration_from_command(exact_command) + if cmd_duration is None: + fail("exact_command missing --duration=") + if not math.isclose(cmd_duration, args.duration_mimg, rel_tol=0.0, abs_tol=1e-12): + fail(f"exact_command duration={cmd_duration} != expected {args.duration_mimg}") + + losses: list[float] = [] + grad_scales: list[float] = [] + skipped = 0 + packaged: list[dict] = [] + nan_count = 0 + inf_count = 0 + previous_signal_updates = 0 + telemetry_started = False + telemetry_rows = 0 + first_telemetry_iteration = None + + for row in rows: + loss = float(row["loss"]) + if math.isnan(loss): + nan_count += 1 + if math.isinf(loss): + inf_count += 1 + step_skipped = parse_boolish(row["step_skipped"]) + if step_skipped: + skipped += 1 + nimg = float(row["processed_nimg"]) + peak_gb = float(row["peak_vram_gb"]) + grad_scale = float(row["grad_scale"]) + losses.append(loss) + grad_scales.append(grad_scale) + telemetry = { + "loss_ema": None, + "loss_reference": None, + "correction": None, + "signal_updates": None, + "adaptive_active": None, + "r_over_t_mean": None, + "gap_mean": None, + } + row_has_telemetry = telemetry_columns_available and any( + str(row.get(field, "")).strip() for field in TELEMETRY_FIELDS + ) + if telemetry_columns_available and not row_has_telemetry: + if telemetry_started: + fail(f"schedule telemetry gap at attempted_iteration={row['attempted_iteration']}") + elif row_has_telemetry: + telemetry_started = True + telemetry_rows += 1 + if first_telemetry_iteration is None: + first_telemetry_iteration = int(float(row["attempted_iteration"])) + telemetry.update( + loss_ema=parse_optional_float(row["loss_ema"], "loss_ema"), + loss_reference=parse_optional_float(row["loss_reference"], "loss_reference"), + correction=parse_finite_float(row["correction"], "correction"), + signal_updates=parse_nonnegative_integer(row["signal_updates"], "signal_updates"), + adaptive_active=parse_strict_bool(row["adaptive_active"], "adaptive_active"), + r_over_t_mean=parse_finite_float(row["r_over_t_mean"], "r_over_t_mean"), + gap_mean=parse_finite_float(row["gap_mean"], "gap_mean"), + ) + if telemetry["signal_updates"] < previous_signal_updates: + fail("schedule signal_updates is not monotonic") + previous_signal_updates = telemetry["signal_updates"] + if not 0 <= telemetry["r_over_t_mean"] <= 1: + fail(f"r_over_t_mean must be in [0, 1], got {telemetry['r_over_t_mean']}") + if not 0 <= telemetry["gap_mean"] <= 1: + fail(f"gap_mean must be in [0, 1], got {telemetry['gap_mean']}") + if not math.isclose( + telemetry["r_over_t_mean"] + telemetry["gap_mean"], + 1.0, + rel_tol=0.0, + abs_tol=1e-6, + ): + fail(f"r_over_t_mean + gap_mean != 1 at attempted_iteration={row['attempted_iteration']}") + if telemetry["loss_reference"] is not None and telemetry["loss_ema"] is None: + fail("loss_reference requires loss_ema") + if telemetry["loss_ema"] is not None and telemetry["loss_ema"] <= 0: + fail(f"loss_ema must be > 0, got {telemetry['loss_ema']}") + if telemetry["loss_reference"] is not None and telemetry["loss_reference"] <= 0: + fail(f"loss_reference must be > 0, got {telemetry['loss_reference']}") + if telemetry["signal_updates"] == 0 and ( + telemetry["loss_ema"] is not None or telemetry["loss_reference"] is not None + ): + fail("signal_updates=0 requires empty loss_ema/loss_reference") + if telemetry["signal_updates"] > 0 and telemetry["loss_ema"] is None: + fail("positive signal_updates requires loss_ema") + if not telemetry["adaptive_active"] and telemetry["correction"] != 0: + fail("nonzero correction requires adaptive_active=true") + if telemetry["adaptive_active"] and ( + telemetry["signal_updates"] == 0 or telemetry["loss_reference"] is None + ): + fail("adaptive_active=true requires positive signal_updates and loss_reference") + if args.schedule != "adaptive_v1" and ( + telemetry["signal_updates"] != 0 + or telemetry["adaptive_active"] + or telemetry["correction"] != 0 + or telemetry["loss_ema"] is not None + ): + fail(f"fixed schedule {args.schedule!r} contains adaptive controller telemetry") + + packaged.append( + { + "attempted_iteration": int(float(row["attempted_iteration"])), + "successful_optimizer_steps": int(float(row["successful_optimizer_steps"])), + "processed_kimg": nimg / 1000.0, + "loss": loss, + "grad_scale": grad_scale, + "step_skipped": "true" if step_skipped else "false", + "schedule": row.get("schedule", args.schedule), + "stage": row.get("stage", ""), + "next_loop_cur_tick": row.get("next_loop_cur_tick", ""), + **telemetry, + "seconds": float(row["elapsed_sec"]), + "peak_vram_mib": peak_gb * 1024.0, + } + ) + + if args.schedule == "adaptive_v1" and telemetry_rows == 0: + fail("adaptive_v1 train_summary.csv contains no populated schedule telemetry rows") + + attempted = len(packaged) + successful = attempted - skipped + for expected_iteration, row in enumerate(packaged, start=1): + if row["attempted_iteration"] != expected_iteration: + fail( + "attempted_iteration sequence mismatch: " + f"csv={row['attempted_iteration']} expected={expected_iteration}" + ) + last_attempted = packaged[-1]["attempted_iteration"] + last_successful = packaged[-1]["successful_optimizer_steps"] + if last_attempted != attempted: + fail(f"attempted_iteration mismatch: csv_last={last_attempted} row_count={attempted}") + if last_successful != successful: + fail( + f"successful_optimizer_steps mismatch: csv_last={last_successful} " + f"derived={successful}" + ) + if successful + skipped != attempted: + fail( + f"update identity failed: successful({successful}) + skipped({skipped}) " + f"!= attempted({attempted})" + ) + if nan_count or inf_count: + fail(f"non-finite losses: nan_count={nan_count} inf_count={inf_count}") + + final_kimg = packaged[-1]["processed_kimg"] + expected_nimg = expected_final_nimg(args.duration_mimg, args.global_batch) + expected_kimg = expected_nimg / 1000.0 + if not math.isclose(final_kimg, expected_kimg, rel_tol=0.0, abs_tol=1e-6): + fail( + f"final processed_kimg={final_kimg} != expected {expected_kimg} " + f"(duration_mimg={args.duration_mimg}, batch={args.global_batch}, " + f"expected_nimg={expected_nimg}) for mode={args.mode}" + ) + + adaptive_runtime = adaptive_runtime_summary(packaged) + activation_gate_applied = args.mode == "activation" and args.schedule == "adaptive_v1" + if activation_gate_applied: + enforce_adaptive_activation_gate(adaptive_runtime, final_iteration=attempted) + + if args.log is not None and args.log.is_file(): + hits = [ + line + for line in args.log.read_text(encoding="utf-8", errors="replace").splitlines() + if re.search(r"nan|inf", line, flags=re.IGNORECASE) + ] + print(f"[collect_schedule_results] log nan|inf grep hits: {len(hits)}") + + snapshot = choose_snapshot(run_dir) + if not args.skip_snapshot_load: + load_snapshot(snapshot, repo_root) + print(f"[collect_schedule_results] loaded snapshot: {snapshot}") + + training_state = choose_training_state(run_dir) + if not args.skip_training_state_load: + import torch + + state = torch.load(training_state, map_location="cpu") + if not isinstance(state, dict): + fail(f"training-state is not a dict: {training_state}") + if "gradscaler_state" not in state: + fail(f"gradscaler_state missing in {training_state}") + gradscaler_state = state["gradscaler_state"] + if gradscaler_state is None or gradscaler_state == {}: + fail(f"gradscaler_state empty in {training_state}") + for key in ("cur_nimg", "cur_tick", "attempted_iteration", "successful_optimizer_steps", "elapsed_sec"): + if key not in state: + fail(f"{key} missing in {training_state}") + validate_training_state_against_csv( + state, + training_state=training_state, + last_csv_row=rows[-1], + last_packaged_row=packaged[-1], + schedule=args.schedule, + ) + print(f"[collect_schedule_results] loaded training-state: {training_state}") + + wall_time = None + if args.wall_time is not None and args.wall_time.is_file(): + wall_text = args.wall_time.read_text(encoding="utf-8").strip() + if wall_text: + wall_time = float(wall_text.splitlines()[-1]) + if wall_time is None: + wall_time = float(packaged[-1]["seconds"]) + + dataset_sha = sha256_file(args.data) + transfer_sha = sha256_file(args.transfer) + if not dataset_sha: + fail(f"dataset SHA256 unavailable: {args.data}") + if not transfer_sha: + fail(f"transfer SHA256 unavailable: {args.transfer}") + train_data_sha = run_meta.get("data_sha256") + train_transfer_sha = run_meta.get("transfer_sha256") + if dataset_sha != train_data_sha: + fail( + f"dataset SHA mismatch: packaging={dataset_sha} " + f"train-time={train_data_sha}" + ) + if transfer_sha != train_transfer_sha: + fail( + f"transfer SHA mismatch: packaging={transfer_sha} " + f"train-time={train_transfer_sha}" + ) + + runtime = collect_runtime_metadata(command_meta) + evidence_class = ( + "preliminary" + if packaging_git["packaging_git_dirty"] or train_git_dirty or args.allow_dirty + else "formal_candidate" + ) + + metadata = { + "git_commit": train_git_head, + "git_branch": run_meta.get("git_branch"), + "git_dirty": train_git_dirty, + **packaging_git, + "exact_command": exact_command, + "seed": args.seed, + "global_batch": args.global_batch, + "batch_gpu": args.batch_gpu, + "processed_kimg": packaged[-1]["processed_kimg"], + "attempted_iterations": attempted, + "successful_optimizer_steps": successful, + "skipped_steps": skipped, + "first_loss": losses[0], + "final_loss": losses[-1], + "min_loss": min(losses), + "max_loss": max(losses), + "nan_count": nan_count, + "inf_count": inf_count, + "initial_grad_scale": grad_scales[0], + "final_grad_scale": grad_scales[-1], + "wall_time_seconds": wall_time, + "peak_vram_mib": max(row["peak_vram_mib"] for row in packaged), + "peak_vram_source": "torch.cuda.max_memory_allocated", + "network_snapshot": str(snapshot), + "training_state": str(training_state), + "network_snapshot_sha256": sha256_file(snapshot), + "training_state_sha256": sha256_file(training_state), + "dataset_sha256": dataset_sha, + "transfer_sha256": transfer_sha, + "gradscaler_state_saved": True, + "metrics_enabled": False, + "mode": args.mode, + "schedule": args.schedule, + "schedule_telemetry_columns_available": telemetry_columns_available, + "schedule_telemetry_available": telemetry_rows == len(packaged), + "schedule_telemetry_rows": telemetry_rows, + "schedule_telemetry_total_rows": len(packaged), + "schedule_telemetry_coverage": telemetry_rows / len(packaged), + "first_schedule_telemetry_iteration": first_telemetry_iteration, + **adaptive_runtime, + "activation_gate_applied": activation_gate_applied, + "activation_gate_passed": True if activation_gate_applied else None, + "duration_mimg": args.duration_mimg, + "evidence_class": evidence_class, + **runtime, + } + + outdir = args.outdir + if outdir.exists() and any(outdir.iterdir()) and not args.overwrite: + fail(f"outdir is not empty: {outdir}; pass --overwrite to replace packaged evidence") + outdir.mkdir(parents=True, exist_ok=True) + summary_path = outdir / "train_summary.csv" + with summary_path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=OUTPUT_FIELDS) + writer.writeheader() + for row in packaged: + writer.writerow( + { + "attempted_iteration": row["attempted_iteration"], + "successful_optimizer_steps": row["successful_optimizer_steps"], + "processed_kimg": f"{row['processed_kimg']:.6f}", + "loss": f"{row['loss']:.8f}", + "grad_scale": f"{row['grad_scale']:.8g}", + "step_skipped": row["step_skipped"], + "schedule": row["schedule"], + "stage": row["stage"], + "next_loop_cur_tick": row["next_loop_cur_tick"], + "loss_ema": "" if row["loss_ema"] is None else f"{row['loss_ema']:.12g}", + "loss_reference": "" if row["loss_reference"] is None else f"{row['loss_reference']:.12g}", + "correction": "" if row["correction"] is None else f"{row['correction']:.12g}", + "signal_updates": "" if row["signal_updates"] is None else row["signal_updates"], + "adaptive_active": "" if row["adaptive_active"] is None else int(row["adaptive_active"]), + "r_over_t_mean": "" if row["r_over_t_mean"] is None else f"{row['r_over_t_mean']:.12g}", + "gap_mean": "" if row["gap_mean"] is None else f"{row['gap_mean']:.12g}", + "seconds": f"{row['seconds']:.6f}", + "peak_vram_mib": f"{row['peak_vram_mib']:.6f}", + } + ) + + metadata_path = outdir / "metadata.json" + metadata_path.write_text(json.dumps(metadata, indent=2, sort_keys=True) + "\n", encoding="utf-8") + + print(json.dumps(metadata, indent=2, sort_keys=True)) + print(f"[collect_schedule_results] wrote {summary_path}") + print(f"[collect_schedule_results] wrote {metadata_path}") + print( + f"[collect_schedule_results] PASS attempted={attempted} " + f"successful={successful} skipped={skipped}" + ) + + +if __name__ == "__main__": + try: + main() + except Exception as exc: # noqa: BLE001 - surface collector failures clearly + if isinstance(exc, SystemExit): + raise + fail(str(exc)) diff --git a/scripts/collect_staged_evaluation_results.py b/scripts/collect_staged_evaluation_results.py new file mode 100644 index 00000000..94032164 --- /dev/null +++ b/scripts/collect_staged_evaluation_results.py @@ -0,0 +1,692 @@ +#!/usr/bin/env python3 +"""Validate a staged evaluation run and emit a unified result table/statistics.""" + +from __future__ import annotations + +import argparse +import csv +import json +import math +import random +import statistics +from pathlib import Path + +PROTOCOL_ID = "staged-checkpoint-evaluation-v1" +PAIRING_KEY = ("training_seed", "budget_kimg", "nfe", "metric_name") +BOOTSTRAP_REPLICATES = 10_000 +BOOTSTRAP_SEED = 20_260_731 + + +def fail(message: str) -> None: + raise SystemExit(f"[collect_staged_evaluation_results] ERROR: {message}") + + +def percentile(values: list[float], probability: float) -> float: + """Return a linearly interpolated percentile of a non-empty sample.""" + if not values: + fail("cannot calculate a percentile of an empty sample") + if not 0 <= probability <= 1: + fail(f"percentile probability must lie in [0, 1], got {probability}") + ordered = sorted(values) + position = (len(ordered) - 1) * probability + lower = math.floor(position) + upper = math.ceil(position) + if lower == upper: + return ordered[lower] + fraction = position - lower + return ordered[lower] * (1 - fraction) + ordered[upper] * fraction + + +def bootstrap_mean_ci(values: list[float], seed: int) -> dict | None: + """Percentile bootstrap CI for a seed-level mean; descriptive only.""" + if not values: + return None + generator = random.Random(seed) + sample_size = len(values) + replicates = [ + statistics.mean(generator.choice(values) for _ in range(sample_size)) + for _ in range(BOOTSTRAP_REPLICATES) + ] + return { + "method": "nonparametric_seed_bootstrap_percentile", + "replicates": BOOTSTRAP_REPLICATES, + "confidence_level": 0.95, + "lower": percentile(replicates, 0.025), + "upper": percentile(replicates, 0.975), + "interpretation": "descriptive resampling interval; resampled seeds are not additional independent observations", + } + + +def exact_two_sided_sign_test( + positive_signs: int, negative_signs: int, ties: int = 0, + positive_label: str = "global_only_better", negative_label: str = "fixed_better", +) -> dict | None: + """Exact two-sided binomial sign test, excluding tied paired effects.""" + non_ties = positive_signs + negative_signs + if non_ties == 0: + return None + less_extreme = min(positive_signs, negative_signs) + tail = sum(math.comb(non_ties, value) for value in range(less_extreme + 1)) + p_value = min(1.0, 2 * tail / (2 ** non_ties)) + return { + "test": "exact_two_sided_sign_test", + "non_tied_pairs": non_ties, + "positive_signs": positive_signs, + "negative_signs": negative_signs, + "positive_label": positive_label, + "negative_label": negative_label, + "ties_excluded": ties, + "p_value": p_value, + "interpretation": "low-resolution descriptive check with one sign per independent training seed; not a basis for a strong significance claim", + } + + +def average_ranks(values: list[float]) -> list[float]: + """Ranks with average tie handling; lower metric values receive lower ranks.""" + ranks = [0.0] * len(values) + ordered = sorted((value, index) for index, value in enumerate(values)) + cursor = 0 + while cursor < len(ordered): + end = cursor + 1 + while end < len(ordered) and ordered[end][0] == ordered[cursor][0]: + end += 1 + rank = (cursor + 1 + end) / 2 + for _, original_index in ordered[cursor:end]: + ranks[original_index] = rank + cursor = end + return ranks + + +def spearman_rank_correlation(left: list[float], right: list[float]) -> float | None: + if len(left) != len(right) or len(left) < 2: + return None + left_ranks = average_ranks(left) + right_ranks = average_ranks(right) + left_center = statistics.mean(left_ranks) + right_center = statistics.mean(right_ranks) + numerator = sum((a - left_center) * (b - right_center) for a, b in zip(left_ranks, right_ranks)) + left_sum_squares = sum((a - left_center) ** 2 for a in left_ranks) + right_sum_squares = sum((b - right_center) ** 2 for b in right_ranks) + if left_sum_squares == 0 or right_sum_squares == 0: + return None + return numerator / math.sqrt(left_sum_squares * right_sum_squares) + + +def enrich_paired_differences(differences: list[dict]) -> None: + """Add scale-free seed-level effects to lower-is-better paired metrics.""" + for difference in differences: + fixed_value = difference["fixed_value"] + global_value = difference["global_only_value"] + if fixed_value <= 0 or global_value <= 0: + fail( + "relative and geometric paired effects require strictly positive " + f"metric values, got fixed={fixed_value}, global_only={global_value}" + ) + ratio = global_value / fixed_value + difference["global_only_to_fixed_ratio"] = ratio + difference["relative_improvement_pct"] = (1 - ratio) * 100 + + +def summarize_group(group: list[dict], bootstrap_seed: int) -> dict: + """Compute robust seed-level summaries for one metric/budget/NFE stratum.""" + deltas = [item["delta"] for item in group] + improvements = [item["relative_improvement_pct"] for item in group] + ratios = [item["global_only_to_fixed_ratio"] for item in group] + fixed_values = [item["fixed_value"] for item in group] + global_values = [item["global_only_value"] for item in group] + global_wins = sum(item["winner"] == "global_only" for item in group) + fixed_wins = sum(item["winner"] == "fixed" for item in group) + ties = sum(item["winner"] == "tie" for item in group) + mean_improvement = statistics.mean(improvements) + rank_correlation = spearman_rank_correlation(fixed_values, global_values) + fixed_ranks = average_ranks(fixed_values) + global_ranks = average_ranks(global_values) + leave_one_seed_out = [] + for omitted in sorted(group, key=lambda item: item["training_seed"]): + retained = [item for item in group if item["training_seed"] != omitted["training_seed"]] + retained_improvements = [item["relative_improvement_pct"] for item in retained] + leave_one_seed_out.append({ + "omitted_training_seed": omitted["training_seed"], + "retained_pair_count": len(retained), + "mean_relative_improvement_pct": statistics.mean(retained_improvements), + "median_delta": statistics.median(item["delta"] for item in retained), + "global_wins": sum(item["winner"] == "global_only" for item in retained), + "fixed_wins": sum(item["winner"] == "fixed" for item in retained), + "ties": sum(item["winner"] == "tie" for item in retained), + }) + return { + "pair_count": len(group), + "mean_delta": statistics.mean(deltas), + "median_delta": statistics.median(deltas), + "sample_sd_delta": statistics.stdev(deltas) if len(deltas) > 1 else None, + "minimum_delta": min(deltas), + "maximum_delta": max(deltas), + "mean_relative_improvement_pct": mean_improvement, + "geometric_mean_relative_improvement_pct": (1 - math.exp(statistics.mean(math.log(value) for value in ratios))) * 100, + "relative_improvement_sample_sd_pct": statistics.stdev(improvements) if len(improvements) > 1 else None, + "relative_improvement_cv_pct": ( + statistics.stdev(improvements) / mean_improvement * 100 + if len(improvements) > 1 and mean_improvement != 0 else None + ), + "worst_case_relative_improvement_pct": min(improvements), + "best_case_relative_improvement_pct": max(improvements), + "rank_consistency_spearman": rank_correlation, + "rank_order_exact_match": fixed_ranks == global_ranks, + "global_wins": global_wins, + "fixed_wins": fixed_wins, + "ties": ties, + "exact_sign_test": exact_two_sided_sign_test(global_wins, fixed_wins, ties), + "bootstrap_mean_relative_improvement_pct_95ci": bootstrap_mean_ci(improvements, bootstrap_seed), + "leave_one_seed_out": leave_one_seed_out, + } + + +def summarize_nfe_effect_heterogeneity(statistics_groups: dict[tuple[str, int, int], list[dict]]) -> list[dict]: + """Compare NFE=2 and NFE=1 effects within the same training seeds.""" + by_metric_budget: dict[tuple[str, int], dict[int, list[dict]]] = {} + for (metric_name, budget_kimg, nfe), group in statistics_groups.items(): + by_metric_budget.setdefault((metric_name, budget_kimg), {})[nfe] = group + summaries = [] + for (metric_name, budget_kimg), by_nfe in sorted(by_metric_budget.items()): + if set(by_nfe) != {1, 2}: + continue + nfe1 = {item["training_seed"]: item for item in by_nfe[1]} + nfe2 = {item["training_seed"]: item for item in by_nfe[2]} + if set(nfe1) != set(nfe2): + fail(f"NFE effect heterogeneity requires identical seed sets for {metric_name}") + changes = [] + for training_seed in sorted(nfe1): + change = nfe2[training_seed]["relative_improvement_pct"] - nfe1[training_seed]["relative_improvement_pct"] + changes.append({ + "training_seed": training_seed, + "nfe2_minus_nfe1_relative_improvement_pct_points": change, + }) + values = [item["nfe2_minus_nfe1_relative_improvement_pct_points"] for item in changes] + nfe2_larger = sum(value > 0 for value in values) + nfe1_larger = sum(value < 0 for value in values) + summaries.append({ + "metric_name": metric_name, + "budget_kimg": budget_kimg, + "pair_count": len(values), + "effect_measure": "per-seed relative improvement percentage; NFE=2 minus NFE=1", + "mean_change_percentage_points": statistics.mean(values), + "median_change_percentage_points": statistics.median(values), + "sample_sd_change_percentage_points": statistics.stdev(values) if len(values) > 1 else None, + "minimum_change_percentage_points": min(values), + "maximum_change_percentage_points": max(values), + "nfe2_larger_effect_seeds": nfe2_larger, + "nfe1_larger_effect_seeds": nfe1_larger, + "ties": sum(value == 0 for value in values), + "exact_sign_test": exact_two_sided_sign_test( + nfe2_larger, nfe1_larger, sum(value == 0 for value in values), + "NFE2_larger_effect", "NFE1_larger_effect", + ), + "bootstrap_mean_change_percentage_points_95ci": bootstrap_mean_ci(values, BOOTSTRAP_SEED + len(summaries)), + "per_seed_changes": changes, + }) + return summaries + + +def build_pairwise_summary( + differences: list[dict], baseline: str, candidate: str, candidate_label: str, direction: str +) -> dict: + """Summarize a complete list of fixed/global paired differences.""" + enrich_paired_differences(differences) + statistics_groups: dict[tuple[str, int, int], list[dict]] = {} + for difference in differences: + key = (difference["metric"], difference["budget_kimg"], difference["nfe"]) + statistics_groups.setdefault(key, []).append(difference) + statistics_rows = [] + for index, ((metric_name, budget_kimg, nfe), group) in enumerate(sorted(statistics_groups.items())): + statistic = summarize_group(group, BOOTSTRAP_SEED + index) + statistic.update({ + "metric_name": metric_name, + "budget_kimg": budget_kimg, + "nfe": nfe, + }) + statistics_rows.append(statistic) + return { + "status": "computed", + "schema_version": 2, + "pairing_key": ["training_seed", "budget_kimg", "nfe", "metric"], + "baseline_method": baseline, + "candidate_method": candidate, + "candidate_label": candidate_label, + "delta_direction": direction, + "effect_definitions": { + "relative_improvement_pct": "100 * (fixed - global_only) / fixed; positive favors global-only", + "geometric_mean_relative_improvement_pct": "100 * (1 - geometric_mean(global_only / fixed))", + "rank_consistency_spearman": "Spearman correlation of lower-is-better seed ranks between fixed and global-only", + "nfe_effect_heterogeneity": "per-seed relative-improvement difference, NFE=2 minus NFE=1, in percentage points", + }, + "inference_note": "The independent units are the three training seeds. Exact sign tests and bootstrap intervals are descriptive sensitivity summaries, not a basis for strong significance claims.", + "paired_differences": differences, + "statistics": statistics_rows, + "nfe_effect_heterogeneity": summarize_nfe_effect_heterogeneity(statistics_groups), + } + + +def load_json(path: Path, label: str) -> dict: + try: + payload = json.loads(path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError) as exc: + fail(f"cannot read {label} {path}: {exc}") + if not isinstance(payload, dict): + fail(f"{label} must contain a JSON object: {path}") + return payload + + +def read_metric(path: Path, metric_name: str) -> float: + if not path.is_file(): + fail(f"missing metric result: {path}") + lines = [line for line in path.read_text(encoding="utf-8").splitlines() if line.strip()] + if len(lines) != 1: + fail(f"expected exactly one result in {path}, found {len(lines)}") + try: + payload = json.loads(lines[0]) + if payload["metric"] != metric_name: + fail(f"metric name mismatch in {path}: {payload.get('metric')} != {metric_name}") + value = float(payload["results"][metric_name]) + except (KeyError, TypeError, ValueError, json.JSONDecodeError) as exc: + fail(f"malformed metric result {path}: {exc}") + if not math.isfinite(value): + fail(f"non-finite metric result in {path}: {value}") + return value + + +def validate_manifest(manifest: dict) -> None: + if manifest.get("protocol") != PROTOCOL_ID: + fail(f"run manifest protocol must be {PROTOCOL_ID!r}") + if manifest.get("status") != "completed": + fail(f"run is not complete: {manifest.get('status')}") + if manifest.get("precision") != "fp32": + fail("staged evaluation requires FP32") + if manifest.get("metric_repeats") != 1: + fail("staged evaluation requires exactly one metric result per cell") + if manifest.get("phase") not in ("smoke", "quick", "formal"): + fail(f"unknown staged evaluation phase: {manifest.get('phase')}") + if not isinstance(manifest.get("jobs"), list) or not manifest["jobs"]: + fail("run manifest contains no jobs") + + +def collect(eval_root: Path) -> tuple[list[dict], dict]: + manifest = load_json(eval_root / "run_manifest.json", "run manifest") + validate_manifest(manifest) + rows = [] + expected_metrics = list(manifest["metric_names"]) + for job in manifest["jobs"]: + if job.get("status") != "completed": + fail(f"job is not complete: {job.get('checkpoint_id')} nfe={job.get('nfe')}") + if job.get("metric_names") != expected_metrics: + fail("jobs use inconsistent metric sets") + if job.get("sample_count") != manifest["sample_count"]: + fail("job and manifest sample counts differ") + if job.get("sample_seeds") != manifest["sample_seeds"]: + fail("job and manifest generation seed ranges differ") + if job.get("metric_seed") != manifest["metric_seed"]: + fail("job and manifest metric seeds differ") + if job.get("evidence_class") != manifest["evidence_class"]: + fail("job and manifest evidence classes differ") + receipt = job.get("integrity_receipt", {}) + if manifest["phase"] == "formal" and receipt.get("status") != "passed": + fail(f"formal job lacks a passed integrity receipt: {job.get('checkpoint_id')}") + for metric_name in expected_metrics: + value = read_metric( + Path(job["output_directory"]) / f"metric-{metric_name}.jsonl", + metric_name, + ) + rows.append({ + "evidence_class": manifest["evidence_class"], + "phase": manifest["phase"], + "checkpoint_id": job["checkpoint_id"], + "method": job["method"], + "training_seed": int(job["training_seed"]), + "budget_kimg": int(job["budget_kimg"]), + "checkpoint_sha256": job["checkpoint_sha256"], + "integrity_receipt_status": receipt.get("status"), + "nfe": int(job["nfe"]), + "mid_t": json.dumps(job["mid_t"]), + "metric_name": metric_name, + "metric_value": value, + "generated_sample_count": int(job["sample_count"]), + "generation_seed_range": job["sample_seeds"], + "metric_seed": int(job["metric_seed"]), + "dataset_sha256": manifest["dataset_sha256"], + "evaluation_git_commit": manifest["evaluation_git_commit"], + "run_path": job["output_directory"], + "completion_status": job["status"], + }) + return rows, build_statistics(rows, manifest) + + +def build_statistics(rows: list[dict], manifest: dict) -> dict: + grouped: dict[tuple[str, str, int, str], list[float]] = {} + for row in rows: + key = (row["evidence_class"], row["metric_name"], row["nfe"], row["method"]) + grouped.setdefault(key, []).append(row["metric_value"]) + summary_rows = [] + for key in sorted(grouped): + evidence_class, metric_name, nfe, method = key + values = grouped[key] + summary_rows.append({ + "evidence_class": evidence_class, + "metric_name": metric_name, + "nfe": nfe, + "method": method, + "count": len(values), + "mean": statistics.mean(values), + "sample_sd": statistics.stdev(values) if len(values) > 1 else None, + "minimum": min(values), + "maximum": max(values), + }) + # Smoke runs intentionally exercise one checkpoint rather than the entire + # fixed/global matrix. They must never be interpreted as evidence for the + # paired comparison (or fail merely because the companion arm was not run). + # Quick and formal runs keep the strict missing/duplicate-pair failure. + if manifest["phase"] == "smoke": + pairwise_statistics = { + "status": "not_computed", + "reason": "smoke phase does not form the predeclared comparison matrix", + } + else: + pairwise_statistics = build_pairwise_statistics(rows, manifest.get("comparison")) + + return { + "schema_version": 1, + "protocol": PROTOCOL_ID, + "phase": manifest["phase"], + "evidence_class": manifest["evidence_class"], + "row_count": len(rows), + "statistics_grouping": ["evidence_class", "metric_name", "nfe", "method"], + "pairwise_statistics": pairwise_statistics, + "statistics": summary_rows, + } + + +def build_pairwise_statistics(rows: list[dict], comparison: dict | None) -> dict: + if comparison is None: + return {"status": "not_computed", "reason": "no explicit pairing contract"} + required = ("pairing_key", "baseline_method", "candidate_method", "delta_direction") + missing = [field for field in required if field not in comparison] + if missing: + fail(f"pairing contract is incomplete: {missing}") + pairing_key = comparison["pairing_key"] + if not isinstance(pairing_key, list) or not pairing_key or not all( + isinstance(field, str) and field for field in pairing_key + ): + fail("pairing_key must be a non-empty list of row field names") + pairing_key = tuple("metric_name" if field == "metric" else field for field in pairing_key) + if pairing_key != PAIRING_KEY: + fail( + "fixed/global paired analysis requires pairing_key " + f"{list(PAIRING_KEY)!r}, got {list(pairing_key)!r}" + ) + unknown = [field for field in pairing_key if any(field not in row for row in rows)] + if unknown: + fail(f"pairing_key fields are absent from result rows: {unknown}") + baseline = comparison["baseline_method"] + candidate = comparison["candidate_method"] + if baseline != "fixed" or candidate != "global110": + fail("fixed/global paired analysis requires baseline_method='fixed' and candidate_method='global110'") + candidate_label = comparison.get("candidate_label", "global_only") + if candidate_label != "global_only": + fail("fixed/global paired analysis requires candidate_label='global_only'") + direction = comparison["delta_direction"] + expected_direction = "global_only - fixed" + if direction != expected_direction: + fail(f"delta_direction must be exactly {expected_direction!r}") + + grouped: dict[str, dict[tuple, dict]] = {baseline: {}, candidate: {}} + for row in rows: + if row["method"] not in (baseline, candidate): + continue + pair = tuple(row[field] for field in pairing_key) + if pair in grouped[row["method"]]: + fail(f"duplicate {row['method']} row for pairing key {pair}") + grouped[row["method"]][pair] = row + + if not grouped[baseline] and not grouped[candidate]: + fail("pairing contract methods do not appear in result rows") + baseline_pairs = set(grouped[baseline]) + candidate_pairs = set(grouped[candidate]) + if baseline_pairs != candidate_pairs: + fail( + "unpaired fixed/global results: " + f"fixed_only={sorted(baseline_pairs - candidate_pairs)}, " + f"global_only={sorted(candidate_pairs - baseline_pairs)}" + ) + + differences = [] + for pair in sorted(baseline_pairs): + fixed_row = grouped[baseline][pair] + global_row = grouped[candidate][pair] + fixed_value = fixed_row["metric_value"] + global_value = global_row["metric_value"] + delta = global_value - fixed_value + winner = "global_only" if delta < 0 else "fixed" if delta > 0 else "tie" + differences.append({ + "training_seed": fixed_row["training_seed"], + "budget_kimg": fixed_row["budget_kimg"], + "nfe": fixed_row["nfe"], + "metric": fixed_row["metric_name"], + "fixed_checkpoint_id": fixed_row["checkpoint_id"], + "global_only_checkpoint_id": global_row["checkpoint_id"], + "fixed_checkpoint_sha256": fixed_row["checkpoint_sha256"], + "global_only_checkpoint_sha256": global_row["checkpoint_sha256"], + "fixed_value": fixed_value, + "global_only_value": global_value, + "delta": delta, + "winner": winner, + }) + + return build_pairwise_summary(differences, baseline, candidate, candidate_label, direction) + + +def write_paired_outputs(outdir: Path, pairwise: dict) -> None: + """Write per-seed fixed/global evidence as standalone reviewable files.""" + if pairwise["status"] != "computed": + return + differences = pairwise["paired_differences"] + with (outdir / "paired_differences.csv").open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=list(differences[0]), lineterminator="\n") + writer.writeheader() + writer.writerows(differences) + paired_statistics = { + key: value for key, value in pairwise.items() if key != "paired_differences" + } + (outdir / "paired_statistics.json").write_text( + json.dumps(paired_statistics, indent=2) + "\n", encoding="utf-8" + ) + lines = [ + "# Fixed vs global-only paired robustness statistics", + "", + "Pairing key: `training_seed + budget_kimg + nfe + metric`.", + "Delta: `global_only - fixed`; negative values favor global-only.", + "Relative improvement: `100 × (fixed - global_only) / fixed`; positive values favor global-only.", + "Independent units are training seeds; the pair count is reported for each metric/NFE stratum.", + "", + "The exact two-sided sign test is reported only as a low-resolution directional check. " + "Bootstrap intervals resample these same seeds and are descriptive sensitivity intervals, not additional independent-sample inference.", + "", + "## Paired effect summary", + "", + "| Metric | Budget (kimg) | NFE | Pairs | Arithmetic relative improvement | Geometric relative improvement | Median delta | Worst-case improvement | Seed CV | Rank consistency (Spearman) | Wins | Exact sign p (two-sided) | Bootstrap 95% CI |", + "| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |", + ] + for row in pairwise["statistics"]: + sign_test = row["exact_sign_test"] + bootstrap = row["bootstrap_mean_relative_improvement_pct_95ci"] + sign_p = "—" if sign_test is None else f"{sign_test['p_value']:.6f}" + bootstrap_text = "—" if bootstrap is None else f"[{bootstrap['lower']:.6f}, {bootstrap['upper']:.6f}]%" + rank = "—" if row["rank_consistency_spearman"] is None else f"{row['rank_consistency_spearman']:.6f}" + cv = "—" if row["relative_improvement_cv_pct"] is None else f"{row['relative_improvement_cv_pct']:.6f}%" + lines.append( + f"| {row['metric_name']} | {row['budget_kimg']} | {row['nfe']} | " + f"{row['pair_count']} | {row['mean_relative_improvement_pct']:.6f}% | " + f"{row['geometric_mean_relative_improvement_pct']:.6f}% | {row['median_delta']:.9f} | " + f"{row['worst_case_relative_improvement_pct']:.6f}% | {cv} | {rank} | " + f"{row['global_wins']}/{row['fixed_wins']}/{row['ties']} | {sign_p} | {bootstrap_text} |" + ) + lines.extend([ + "", + "## Leave-one-seed-out arithmetic relative improvement", + "", + "| Metric | NFE | Omitted seed | Retained pairs | Mean relative improvement | Global/fixed/tie wins |", + "| --- | ---: | ---: | ---: | ---: | ---: |", + ]) + for row in pairwise["statistics"]: + for loo in row["leave_one_seed_out"]: + lines.append( + f"| {row['metric_name']} | {row['nfe']} | {loo['omitted_training_seed']} | " + f"{loo['retained_pair_count']} | {loo['mean_relative_improvement_pct']:.6f}% | " + f"{loo['global_wins']}/{loo['fixed_wins']}/{loo['ties']} |" + ) + lines.extend([ + "", + "## NFE effect heterogeneity", + "", + "Effect change is the per-seed relative improvement at NFE=2 minus that at NFE=1, in percentage points. Positive values indicate a larger global-only advantage at NFE=2.", + "", + "| Metric | Pairs | Mean change | Median change | Range | NFE=2 larger / NFE=1 larger / ties | Exact sign p (two-sided) | Bootstrap 95% CI |", + "| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |", + ]) + for row in pairwise["nfe_effect_heterogeneity"]: + sign_test = row["exact_sign_test"] + bootstrap = row["bootstrap_mean_change_percentage_points_95ci"] + sign_p = "—" if sign_test is None else f"{sign_test['p_value']:.6f}" + bootstrap_text = "—" if bootstrap is None else f"[{bootstrap['lower']:.6f}, {bootstrap['upper']:.6f}] pp" + lines.append( + f"| {row['metric_name']} | {row['pair_count']} | {row['mean_change_percentage_points']:.6f} pp | " + f"{row['median_change_percentage_points']:.6f} pp | " + f"[{row['minimum_change_percentage_points']:.6f}, {row['maximum_change_percentage_points']:.6f}] pp | " + f"{row['nfe2_larger_effect_seeds']} / {row['nfe1_larger_effect_seeds']} / {row['ties']} | {sign_p} | {bootstrap_text} |" + ) + lines.append("") + (outdir / "paired_statistics.md").write_text("\n".join(lines), encoding="utf-8") + + +def write_outputs(outdir: Path, rows: list[dict], summary: dict) -> None: + outdir.mkdir(parents=True, exist_ok=True) + with (outdir / "evaluation_results.csv").open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0]), lineterminator="\n") + writer.writeheader() + writer.writerows(rows) + (outdir / "evaluation_statistics.json").write_text( + json.dumps(summary, indent=2) + "\n", encoding="utf-8" + ) + lines = [ + "# Staged evaluation statistics", + "", + f"Evidence class: `{summary['evidence_class']}`; phase: `{summary['phase']}`.", + "", + "| Evidence | Metric | NFE | Method | Count | Mean | Sample SD | Min | Max |", + "| --- | --- | ---: | --- | ---: | ---: | ---: | ---: | ---: |", + ] + for row in summary["statistics"]: + sample_sd = "—" if row["sample_sd"] is None else f"{row['sample_sd']:.9f}" + lines.append( + f"| {row['evidence_class']} | {row['metric_name']} | {row['nfe']} | " + f"{row['method']} | {row['count']} | {row['mean']:.9f} | {sample_sd} | " + f"{row['minimum']:.9f} | {row['maximum']:.9f} |" + ) + pairwise = summary["pairwise_statistics"] + lines.extend(["", "Statistics are segregated by evidence class, metric, NFE, and method."]) + if pairwise["status"] == "computed": + lines.extend([ + "", + "## Paired deltas", + "", + "Pairing key: `" + ", ".join(pairwise["pairing_key"]) + "`; " + "delta: `" + pairwise["delta_direction"] + "`.", + "", + "| Metric | Budget | NFE | Pairs | Mean delta | Sample SD | Global wins | Fixed wins | Ties |", + "| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |", + ]) + for row in pairwise["statistics"]: + sample_sd = "—" if row["sample_sd_delta"] is None else f"{row['sample_sd_delta']:.9f}" + lines.append( + f"| {row['metric_name']} | {row['budget_kimg']} | {row['nfe']} | {row['pair_count']} | " + f"{row['mean_delta']:.9f} | {sample_sd} | {row['global_wins']} | " + f"{row['fixed_wins']} | {row['ties']} |" + ) + else: + lines.extend(["", "No pairwise delta is emitted: " + pairwise["reason"] + "."]) + lines.append("") + (outdir / "evaluation_statistics.md").write_text("\n".join(lines), encoding="utf-8") + write_paired_outputs(outdir, pairwise) + + +def read_paired_differences(path: Path) -> list[dict]: + """Load a previously emitted paired-difference table for a reproducible refresh.""" + if not path.is_file(): + fail(f"missing paired differences CSV: {path}") + with path.open(newline="", encoding="utf-8") as handle: + reader = csv.DictReader(handle) + required = { + "training_seed", "budget_kimg", "nfe", "metric", "fixed_checkpoint_id", + "global_only_checkpoint_id", "fixed_checkpoint_sha256", "global_only_checkpoint_sha256", + "fixed_value", "global_only_value", "delta", "winner", + } + missing = required - set(reader.fieldnames or []) + if missing: + fail(f"paired differences CSV is missing fields: {sorted(missing)}") + differences = [] + seen = set() + for row in reader: + try: + difference = { + "training_seed": int(row["training_seed"]), + "budget_kimg": int(row["budget_kimg"]), + "nfe": int(row["nfe"]), + "metric": row["metric"], + "fixed_checkpoint_id": row["fixed_checkpoint_id"], + "global_only_checkpoint_id": row["global_only_checkpoint_id"], + "fixed_checkpoint_sha256": row["fixed_checkpoint_sha256"], + "global_only_checkpoint_sha256": row["global_only_checkpoint_sha256"], + "fixed_value": float(row["fixed_value"]), + "global_only_value": float(row["global_only_value"]), + "delta": float(row["delta"]), + "winner": row["winner"], + } + except (TypeError, ValueError) as exc: + fail(f"malformed paired difference in {path}: {exc}") + key = (difference["training_seed"], difference["budget_kimg"], difference["nfe"], difference["metric"]) + if key in seen: + fail(f"duplicate paired difference for {key}") + seen.add(key) + expected_delta = difference["global_only_value"] - difference["fixed_value"] + if not math.isclose(difference["delta"], expected_delta, rel_tol=0, abs_tol=1e-12): + fail(f"delta does not match absolute values for {key}") + expected_winner = "global_only" if expected_delta < 0 else "fixed" if expected_delta > 0 else "tie" + if difference["winner"] != expected_winner: + fail(f"winner does not match delta for {key}") + differences.append(difference) + if not differences: + fail(f"paired differences CSV has no rows: {path}") + return sorted(differences, key=lambda item: (item["training_seed"], item["budget_kimg"], item["nfe"], item["metric"])) + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser(description=__doc__) + input_group = parser.add_mutually_exclusive_group(required=True) + input_group.add_argument("--eval-root", type=Path) + input_group.add_argument("--paired-differences", type=Path) + parser.add_argument("--outdir", type=Path, required=True) + args = parser.parse_args(argv) + outdir = args.outdir.resolve() + if args.paired_differences is not None: + differences = read_paired_differences(args.paired_differences.resolve()) + summary = build_pairwise_summary( + differences, "fixed", "global110", "global_only", "global_only - fixed" + ) + outdir.mkdir(parents=True, exist_ok=True) + write_paired_outputs(outdir, summary) + print(f"Refreshed {len(differences)} paired differences; output: {outdir}") + return + rows, summary = collect(args.eval_root.resolve()) + write_outputs(outdir, rows, summary) + print(f"Validated {len(rows)} metric rows; output: {outdir}") + + +if __name__ == "__main__": + main() diff --git a/scripts/evaluate_checkpoint.sh b/scripts/evaluate_checkpoint.sh new file mode 100644 index 00000000..1c62f755 --- /dev/null +++ b/scripts/evaluate_checkpoint.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash +set -euo pipefail + +if [[ $# -lt 3 ]]; then + echo "Usage: $0 NGPUS PORT CHECKPOINT [ct_eval.py options]" >&2 + exit 2 +fi + +ngpus=$1 +port=$2 +checkpoint=$3 +shift 3 +repo_dir=$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd) + +cd "$repo_dir" +torchrun --standalone --nproc_per_node="$ngpus" --master_port="$port" \ + ct_eval.py --resume "$checkpoint" "$@" diff --git a/scripts/evaluate_gap_factorial_arm.sh b/scripts/evaluate_gap_factorial_arm.sh new file mode 100755 index 00000000..ec75a990 --- /dev/null +++ b/scripts/evaluate_gap_factorial_arm.sh @@ -0,0 +1,110 @@ +#!/usr/bin/env bash + +# Evaluate one completed gap-factorization arm with the frozen 5k protocol. + +set -Eeuo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +ENV_NAME="${ECT_ENV_NAME:-ect-exp}" +CONDA_BIN="${ECT_CONDA_BIN:-/root/miniconda3/bin/conda}" +PROJECT_ROOT="${ECT_PROJECT_ROOT:-/root/autodl-tmp/ect_project}" +DATA="${ECT_DATA_PATH:-${PROJECT_ROOT}/datasets/cifar10-32x32.zip}" +EVAL_ROOT="${ECT_GAP_EVAL_ROOT:-${PROJECT_ROOT}/runs/gap-factorial-20260726-eval}" + +RUN_DIR="" +LABEL="" +for arg in "$@"; do + case "${arg}" in + --run-dir=*) RUN_DIR="${arg#*=}" ;; + --label=*) LABEL="${arg#*=}" ;; + -h|--help) + echo "Usage: $0 --run-dir=DIR --label=LABEL" + exit 0 + ;; + *) echo "Unknown option: ${arg}" >&2; exit 2 ;; + esac +done + +[[ -d "${RUN_DIR}" ]] || { echo "Missing run dir: ${RUN_DIR}" >&2; exit 1; } +[[ -n "${LABEL}" ]] || { echo "--label is required" >&2; exit 1; } +[[ -x "${CONDA_BIN}" ]] || { echo "Missing conda executable: ${CONDA_BIN}" >&2; exit 1; } +[[ -s "${DATA}" ]] || { echo "Missing dataset: ${DATA}" >&2; exit 1; } +CHECKPOINT="${RUN_DIR}/network-snapshot-latest.pkl" +[[ -s "${CHECKPOINT}" ]] || { echo "Missing checkpoint: ${CHECKPOINT}" >&2; exit 1; } + +for NFE in 1 2; do + OUTDIR="${EVAL_ROOT}/${LABEL}/nfe${NFE}" + if [[ -d "${OUTDIR}" ]] && + grep -qx 'exit_code=0' "${OUTDIR}/experiment_meta.env" 2>/dev/null && + [[ -s "${OUTDIR}/metric-kid5k_full.jsonl" ]] && + [[ -s "${OUTDIR}/metric-fid5k_full.jsonl" ]] && + [[ "$(wc -l < "${OUTDIR}/metric-kid5k_full.jsonl")" -eq 1 ]] && + [[ "$(wc -l < "${OUTDIR}/metric-fid5k_full.jsonl")" -eq 1 ]]; then + printf '[evaluate_gap_factorial_arm] verified completed NFE=%s; skipping %s\n' \ + "${NFE}" "${OUTDIR}" + continue + fi + [[ ! -e "${OUTDIR}" ]] || { + echo "Refusing to overwrite incomplete evaluation: ${OUTDIR}" >&2 + exit 1 + } + mkdir -p "${OUTDIR}" + CMD=( + "${CONDA_BIN}" run --no-capture-output -n "${ENV_NAME}" + python "${ROOT_DIR}/ct_eval.py" + --data="${DATA}" + --outdir="${OUTDIR}" + --nosubdir + --cond=False + --arch=ddpmpp + --precond=ct + --dropout=0.2 + --augment=0 + --fp16=False + --seed=20260722 + --resume="${CHECKPOINT}" + --nfe="${NFE}" + --mid_t=0.821 + --metrics=kid5k_full,fid5k_full + --metric-repeats=1 + --sample-seeds=0-4999 + ) + { + printf 'label=%s\nnfe=%s\n' "${LABEL}" "${NFE}" + printf 'checkpoint_sha256=%s\n' \ + "$(sha256sum "${CHECKPOINT}" | awk '{print $1}')" + printf 'data_sha256=%s\n' "$(sha256sum "${DATA}" | awk '{print $1}')" + printf 'evaluation_source_sha256=%s\n' "$( + for source_file in \ + "${ROOT_DIR}/ct_eval.py" \ + "${ROOT_DIR}/metrics/metric_main.py" \ + "${ROOT_DIR}/metrics/metric_utils.py" \ + "${ROOT_DIR}/scripts/evaluate_gap_factorial_arm.sh" + do + sha256sum "${source_file}" | awk '{print $1}' + done | sha256sum | awk '{print $1}' + )" + printf 'started_utc=%s\n' "$(date -u +%Y-%m-%dT%H:%M:%SZ)" + printf 'exact_command=' + printf '%q ' "${CMD[@]}" + printf '\n' + } > "${OUTDIR}/experiment_meta.env" + set +e + "${CMD[@]}" 2>&1 | tee "${OUTDIR}/runner.log" + STATUS="${PIPESTATUS[0]}" + set -e + printf 'exit_code=%s\nfinished_utc=%s\n' \ + "${STATUS}" "$(date -u +%Y-%m-%dT%H:%M:%SZ)" >> "${OUTDIR}/experiment_meta.env" + [[ "${STATUS}" -eq 0 ]] || exit "${STATUS}" + for METRIC in kid5k_full fid5k_full; do + RESULT="${OUTDIR}/metric-${METRIC}.jsonl" + [[ -s "${RESULT}" ]] || { + echo "Missing metric output: ${RESULT}" >&2 + exit 1 + } + [[ "$(wc -l < "${RESULT}")" -eq 1 ]] || { + echo "Expected exactly one result line: ${RESULT}" >&2 + exit 1 + } + done +done diff --git a/scripts/extract_gap_controller_state.py b/scripts/extract_gap_controller_state.py new file mode 100644 index 00000000..ff4089f4 --- /dev/null +++ b/scripts/extract_gap_controller_state.py @@ -0,0 +1,63 @@ +#!/usr/bin/env python3 +"""Extract auditable final schedule/controller state from a training snapshot.""" + +from __future__ import annotations + +import argparse +import json +import math +import pickle +from pathlib import Path +from typing import Any + + +def jsonable(value: Any) -> Any: + if value is None or isinstance(value, (str, bool, int)): + return value + if isinstance(value, float): + if not math.isfinite(value): + raise ValueError(f"non-finite controller value: {value}") + return value + if isinstance(value, dict): + return {str(key): jsonable(item) for key, item in value.items()} + if isinstance(value, (list, tuple)): + return [jsonable(item) for item in value] + if hasattr(value, "item"): + return jsonable(value.item()) + raise TypeError(f"unsupported controller value: {type(value).__name__}") + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--checkpoint", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + + checkpoint = args.checkpoint.resolve() + if not checkpoint.is_file(): + raise SystemExit(f"checkpoint not found: {checkpoint}") + with checkpoint.open("rb") as handle: + payload = pickle.load(handle) + loss_fn = payload.get("loss_fn") + schedule = getattr(loss_fn, "schedule", None) + if schedule is None: + raise SystemExit("snapshot does not contain loss_fn.schedule") + + report = { + "schema_version": 1, + "checkpoint": str(checkpoint), + "schedule_class": type(schedule).__name__, + "metadata": jsonable(schedule.metadata()), + "state_dict": jsonable(schedule.state_dict()), + "runtime_metrics": jsonable(schedule.runtime_metrics()), + } + output = args.output.resolve() + output.parent.mkdir(parents=True, exist_ok=True) + output.write_text( + json.dumps(report, indent=2, sort_keys=True) + "\n", encoding="utf-8" + ) + print(json.dumps(report, sort_keys=True)) + + +if __name__ == "__main__": + main() diff --git a/scripts/plot_q256_main_results.py b/scripts/plot_q256_main_results.py new file mode 100644 index 00000000..2629653d --- /dev/null +++ b/scripts/plot_q256_main_results.py @@ -0,0 +1,191 @@ +#!/usr/bin/env python3 +"""Render the two main-text figures for the q=256 formal paired results. + +The script reads only the versioned seed-level paired-difference table and +writes publication-ready SVG, PNG, and PDF copies of both figures. It does not recalculate metrics or +perform inferential tests. +""" + +from __future__ import annotations + +import argparse +import csv +import os +import statistics +import tempfile +from collections import defaultdict +from pathlib import Path + +os.environ.setdefault("MPLCONFIGDIR", str(Path(tempfile.gettempdir()) / "ect-matplotlib")) +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from matplotlib.lines import Line2D + + +INK = "#1F2937" +GRID = "#D1D5DB" +FIXED = "#FFFFFF" +MEAN = "#111827" +SEED_COLORS = {3: "#2563EB", 4: "#C0841A", 5: "#C2416C"} +METRIC_LABELS = {"kid50k_full": "KID-50k", "fid50k_full": "FID-50k"} + + +def read_pairs(path: Path) -> list[dict]: + with path.open(newline="", encoding="utf-8") as handle: + rows = list(csv.DictReader(handle)) + required = {"training_seed", "nfe", "metric", "fixed_value", "global_only_value", "delta"} + if not rows or not required.issubset(rows[0]): + raise SystemExit(f"paired-differences CSV is missing required fields: {path}") + parsed = [] + for row in rows: + parsed.append({ + "training_seed": int(row["training_seed"]), + "nfe": int(row["nfe"]), + "metric": row["metric"], + "fixed_value": float(row["fixed_value"]), + "global_only_value": float(row["global_only_value"]), + "delta": float(row["delta"]), + }) + expected = {(metric, nfe, seed) for metric in METRIC_LABELS for nfe in (1, 2) for seed in (3, 4, 5)} + observed = {(row["metric"], row["nfe"], row["training_seed"]) for row in parsed} + if observed != expected: + raise SystemExit(f"expected complete q=256 2-metric × 2-NFE × 3-seed matrix; got {sorted(observed)}") + return parsed + + +def style_axis(axis: plt.Axes) -> None: + axis.set_facecolor("#FFFFFF") + axis.grid(axis="y", color=GRID, linewidth=0.8, zorder=0) + axis.spines["top"].set_visible(False) + axis.spines["right"].set_visible(False) + axis.spines["left"].set_color("#9CA3AF") + axis.spines["bottom"].set_color("#9CA3AF") + axis.tick_params(colors=INK, labelsize=10) + axis.yaxis.label.set_color(INK) + + +def data_index(rows: list[dict]) -> dict[tuple[str, int], list[dict]]: + indexed: dict[tuple[str, int], list[dict]] = defaultdict(list) + for row in rows: + indexed[(row["metric"], row["nfe"])].append(row) + for group in indexed.values(): + group.sort(key=lambda item: item["training_seed"]) + return indexed + + +def padded_limits(values: list[float]) -> tuple[float, float]: + low, high = min(values), max(values) + span = high - low + pad = span * 0.18 if span else max(abs(low) * 0.12, 0.02) + return low - pad, high + pad + + +def render_figure1( + indexed: dict[tuple[str, int], list[dict]], outdir: Path, + stem: str = "figure1_per_seed_paired_comparison", + extensions: tuple[str, ...] = ("svg", "png", "pdf"), +) -> None: + """Four slope panels: fixed to global-only for every seed and endpoint.""" + figure, axes = plt.subplots(2, 2, figsize=(10.8, 8.4)) + figure.subplots_adjust(left=0.08, right=0.985, bottom=0.08, top=0.835, hspace=0.22, wspace=0.15) + panels = [("kid50k_full", 1), ("fid50k_full", 1), ("kid50k_full", 2), ("fid50k_full", 2)] + for axis, (metric, nfe) in zip(axes.flat, panels): + group = indexed[(metric, nfe)] + all_values = [value for row in group for value in (row["fixed_value"], row["global_only_value"])] + for row in group: + color = SEED_COLORS[row["training_seed"]] + axis.plot([0, 1], [row["fixed_value"], row["global_only_value"]], color=color, linewidth=2.2, zorder=2) + axis.scatter(0, row["fixed_value"], s=70, facecolor=FIXED, edgecolor=color, linewidth=2.1, zorder=3) + axis.scatter(1, row["global_only_value"], s=70, facecolor=color, edgecolor=color, linewidth=1.2, zorder=3) + axis.set_xlim(-0.23, 1.23) + axis.set_ylim(*padded_limits(all_values)) + axis.set_xticks([0, 1], ["Fixed", "Global-only\n(g=1.10)"]) + axis.set_ylabel(METRIC_LABELS[metric], fontsize=11) + axis.set_title(f"{METRIC_LABELS[metric]} · NFE={nfe}", loc="left", fontsize=12, fontweight="bold", color=INK, pad=9) + axis.text(0.02, 0.04, "3/3 paired comparisons favor global-only", transform=axis.transAxes, fontsize=9, color=INK) + style_axis(axis) + legend = [ + Line2D([0], [0], color=SEED_COLORS[seed], marker="o", markersize=7, linewidth=2, label=f"Training seed {seed}") + for seed in (3, 4, 5) + ] + figure.legend(handles=legend, loc="upper center", ncol=3, frameon=False, bbox_to_anchor=(0.58, 0.972), fontsize=10) + figure.suptitle("Figure 1. Per-seed paired comparison at 256 kimg", x=0.02, y=0.99, ha="left", fontsize=16, fontweight="bold", color=INK) + figure.text(0.02, 0.902, "50k generated samples per checkpoint; lower values are better. Open markers: fixed; filled markers: global-only. Panel-specific y-scales.", fontsize=10, color=INK) + save_figure(figure, outdir, stem, extensions) + + +def render_figure2( + indexed: dict[tuple[str, int], list[dict]], outdir: Path, + stem: str = "figure2_mean_delta_seed_variation", + extensions: tuple[str, ...] = ("svg", "png", "pdf"), +) -> None: + """Seed-level deltas with mean and SD, emphasizing NFE=2 heterogeneity.""" + figure, axes = plt.subplots(1, 2, figsize=(11.2, 5.7)) + figure.subplots_adjust(left=0.075, right=0.985, bottom=0.12, top=0.80, wspace=0.17) + for axis, metric in zip(axes, ("kid50k_full", "fid50k_full")): + by_seed = {seed: {} for seed in (3, 4, 5)} + for nfe in (1, 2): + for row in indexed[(metric, nfe)]: + by_seed[row["training_seed"]][nfe] = row["delta"] + all_deltas = [] + for seed, values in by_seed.items(): + color = SEED_COLORS[seed] + points = [values[1], values[2]] + all_deltas.extend(points) + axis.plot([1, 2], points, color=color, linewidth=1.8, alpha=0.82, zorder=2) + axis.scatter([1, 2], points, s=65, color=color, edgecolor="white", linewidth=0.8, zorder=3) + means = [] + sds = [] + for nfe in (1, 2): + deltas = [row["delta"] for row in indexed[(metric, nfe)]] + means.append(statistics.mean(deltas)) + sds.append(statistics.stdev(deltas)) + axis.errorbar([1, 2], means, yerr=sds, fmt="D", color=MEAN, markersize=7, capsize=5, linewidth=1.6, zorder=4, label="Mean ± sample SD") + axis.axhline(0, color="#6B7280", linewidth=1.1, linestyle=(0, (4, 3)), zorder=1) + axis.set_xlim(0.66, 2.34) + axis.set_ylim(*padded_limits(all_deltas + [0])) + axis.set_xticks([1, 2], ["NFE=1", "NFE=2"]) + axis.set_ylabel(f"Δ {METRIC_LABELS[metric]} (global-only − fixed)", fontsize=10.5) + axis.set_title(f"{METRIC_LABELS[metric]} paired deltas", loc="left", fontsize=12, fontweight="bold", color=INK, pad=9) + near_flat = by_seed[5][2] + annotation = f"Seed 5\n{near_flat:.3g}" + offset = 0.10 * (axis.get_ylim()[1] - axis.get_ylim()[0]) + axis.annotate(annotation, xy=(2, near_flat), xytext=(2.12, near_flat + offset), fontsize=9.5, color=INK, ha="left", va="bottom", arrowprops={"arrowstyle": "-", "color": "#6B7280", "lw": 1.0}) + style_axis(axis) + seed_legend = [ + Line2D([0], [0], color=SEED_COLORS[seed], marker="o", markersize=7, linewidth=1.8, label=f"Seed {seed}") + for seed in (3, 4, 5) + ] + seed_legend.append(Line2D([0], [0], color=MEAN, marker="D", markersize=7, linewidth=1.6, label="Mean ± sample SD")) + figure.legend(handles=seed_legend, loc="upper center", ncol=4, frameon=False, bbox_to_anchor=(0.60, 0.972), fontsize=10) + figure.suptitle("Figure 2. Mean paired delta and between-seed variation", x=0.02, y=0.99, ha="left", fontsize=16, fontweight="bold", color=INK) + figure.text(0.02, 0.902, "Points are independent training-seed deltas; negative values favor global-only. Whiskers are sample SD, not confidence intervals. The NFE=2 effect is heterogeneous because seed 5 is near flat.", fontsize=9.7, color=INK) + save_figure(figure, outdir, stem, extensions) + + +def save_figure(figure: plt.Figure, outdir: Path, stem: str, extensions: tuple[str, ...]) -> None: + outdir.mkdir(parents=True, exist_ok=True) + for extension in extensions: + figure.savefig(outdir / f"{stem}.{extension}", dpi=240, bbox_inches="tight", facecolor="white") + plt.close(figure) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--pairs", type=Path, default=Path("results/q256_256k_formal/paired_differences.csv")) + parser.add_argument("--outdir", type=Path, default=Path("results/q256_256k_formal/figures")) + parser.add_argument("--paired-seed-stem", default="figure1_per_seed_paired_comparison") + parser.add_argument("--heterogeneity-stem", default="figure2_mean_delta_seed_variation") + parser.add_argument("--formats", nargs="+", choices=("svg", "png", "pdf"), default=("svg", "png", "pdf")) + args = parser.parse_args() + indexed = data_index(read_pairs(args.pairs.resolve())) + formats = tuple(args.formats) + render_figure1(indexed, args.outdir.resolve(), args.paired_seed_stem, formats) + render_figure2(indexed, args.outdir.resolve(), args.heterogeneity_stem, formats) + print(f"Wrote Figure 1 and Figure 2 to {args.outdir.resolve()}") + + +if __name__ == "__main__": + main() diff --git a/scripts/run_final_evaluation_matrix.py b/scripts/run_final_evaluation_matrix.py new file mode 100644 index 00000000..0c4e5b05 --- /dev/null +++ b/scripts/run_final_evaluation_matrix.py @@ -0,0 +1,255 @@ +#!/usr/bin/env python3 +"""Run the frozen 3-seed quantitative and fixed-seed visual evaluation matrix.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import shlex +import subprocess +import sys +import time +from pathlib import Path + + +REPO_ROOT = Path(__file__).resolve().parents[1] +SCHEDULES = ("sigmoid", "adaptive_v1") +TRAINING_SEEDS = (0, 1, 2) +NFES = (1, 2) +SAMPLE_SEEDS = "0-4999" +VISUAL_SEEDS = "0-15" +PROTOCOL_SEED = 20260722 + + +def fail(message: str) -> None: + raise SystemExit(f"[run_final_evaluation_matrix] ERROR: {message}") + + +def sha256_file(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def git_head() -> str: + try: + return subprocess.check_output( + ["git", "rev-parse", "HEAD"], cwd=REPO_ROOT, text=True + ).strip() + except (OSError, subprocess.CalledProcessError): + return "unknown" + + +def load_cells(path: Path, allow_missing: bool) -> list[dict]: + try: + payload = json.loads(path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError) as exc: + fail(f"cannot read manifest {path}: {exc}") + cells = payload.get("cells") + if not isinstance(cells, list): + fail("manifest must contain a cells list") + + keyed = {} + for cell in cells: + try: + schedule = str(cell["schedule"]) + training_seed = int(cell["training_seed"]) + checkpoint = Path(cell["checkpoint"]).expanduser().resolve() + except (KeyError, TypeError, ValueError) as exc: + fail(f"invalid cell {cell!r}: {exc}") + key = (schedule, training_seed) + if key in keyed: + fail(f"duplicate manifest cell: {key}") + keyed[key] = { + "schedule": schedule, + "training_seed": training_seed, + "checkpoint": checkpoint, + "expected_sha256": cell.get("checkpoint_sha256"), + } + + expected = {(schedule, seed) for schedule in SCHEDULES for seed in TRAINING_SEEDS} + if set(keyed) != expected: + missing = sorted(expected - set(keyed)) + extra = sorted(set(keyed) - expected) + fail(f"manifest is not the frozen 2x3 matrix; missing={missing}, extra={extra}") + + ordered = [keyed[(schedule, seed)] for seed in TRAINING_SEEDS for schedule in SCHEDULES] + for cell in ordered: + checkpoint = cell["checkpoint"] + if not checkpoint.is_file(): + if allow_missing: + cell["checkpoint_sha256"] = cell["expected_sha256"] or "missing" + continue + fail(f"checkpoint not found: {checkpoint}") + actual = sha256_file(checkpoint) + expected_sha = cell["expected_sha256"] + if expected_sha and actual != expected_sha: + fail(f"checkpoint SHA256 mismatch for {checkpoint}: {actual} != {expected_sha}") + cell["checkpoint_sha256"] = actual + return ordered + + +def require_empty(path: Path) -> None: + if path.exists() and any(path.iterdir()): + fail(f"refuse to append to non-empty output directory: {path}") + + +def quantitative_commands(cells: list[dict], data: Path, outdir: Path, base_port: int, metrics: str) -> list[dict]: + metric_names = "kid5k_full,fid5k_full" if metrics == "primary" else "fid5k_full" + jobs = [] + port = base_port + for cell in cells: + for nfe in NFES: + cell_dir = outdir / "quantitative" / cell["schedule"] / f"seed{cell['training_seed']}" / f"nfe{nfe}" + cmd = [ + "bash", str(REPO_ROOT / "scripts" / "evaluate_checkpoint.sh"), + "1", str(port), str(cell["checkpoint"]), + "--outdir", str(cell_dir), + "--nosubdir", + "--data", str(data), + "--cond=False", + "--arch=ddpmpp", + "--precond=ct", + "--dropout=0.2", + "--augment=0", + "--fp16=False", + "--cache=True", + "--workers=3", + f"--nfe={nfe}", + "--mid_t=0.821", + f"--metrics={metric_names}", + "--metric-repeats=1", + f"--sample-seeds={SAMPLE_SEEDS}", + f"--seed={PROTOCOL_SEED}", + f"--desc=final-proxy-{cell['schedule']}-seed{cell['training_seed']}-nfe{nfe}", + ] + jobs.append({ + "kind": "quantitative", + "schedule": cell["schedule"], + "training_seed": cell["training_seed"], + "nfe": nfe, + "checkpoint": str(cell["checkpoint"]), + "checkpoint_sha256": cell["checkpoint_sha256"], + "output_directory": str(cell_dir), + "command": cmd, + }) + port += 1 + return jobs + + +def visual_commands(cells: list[dict], outdir: Path) -> list[dict]: + jobs = [] + for cell in cells: + cmd = [ + sys.executable, str(REPO_ROOT / "scripts" / "sample_blind_images.py"), + "--network", str(cell["checkpoint"]), + "--outdir", str(outdir / "visual_samples"), + "--seeds", VISUAL_SEEDS, + "--mid-t", "0.821", + "--work-group-size", "8", + "--precision", "fp32", + "--device", "cuda", + ] + jobs.append({ + "kind": "visual_samples", + "schedule": cell["schedule"], + "training_seed": cell["training_seed"], + "checkpoint": str(cell["checkpoint"]), + "checkpoint_sha256": cell["checkpoint_sha256"], + "output_directory": str(outdir / "visual_samples"), + "command": cmd, + }) + return jobs + + +def write_run_record(path: Path, record: dict) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(record, indent=2) + "\n", encoding="utf-8") + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--manifest", type=Path, required=True) + parser.add_argument("--data", type=Path, required=True) + parser.add_argument("--outdir", type=Path, required=True) + parser.add_argument("--phase", choices=("quantitative", "visual", "all"), default="all") + parser.add_argument("--metrics", choices=("primary", "fid-only"), default="primary") + parser.add_argument("--base-port", type=int, default=29600) + parser.add_argument("--dry-run", action="store_true") + parser.add_argument("--allow-missing-checkpoints", action="store_true", help="Dry-run only") + args = parser.parse_args(argv) + + if args.allow_missing_checkpoints and not args.dry_run: + fail("--allow-missing-checkpoints is allowed only with --dry-run") + data = args.data.expanduser().resolve() + if not data.is_file() and not (args.dry_run and args.allow_missing_checkpoints): + fail(f"dataset not found: {data}") + outdir = args.outdir.expanduser().resolve() + if not args.dry_run: + require_empty(outdir) + + cells = load_cells(args.manifest, allow_missing=args.allow_missing_checkpoints) + jobs = [] + if args.phase in {"quantitative", "all"}: + jobs.extend(quantitative_commands(cells, data, outdir, args.base_port, args.metrics)) + if args.phase in {"visual", "all"}: + jobs.extend(visual_commands(cells, outdir)) + + record = { + "schema_version": 1, + "protocol": "final-performance-evaluation-v1", + "evaluation_git_commit": git_head(), + "dataset": str(data), + "dataset_sha256": sha256_file(data) if data.is_file() else "missing", + "precision": "fp32", + "training_seeds": list(TRAINING_SEEDS), + "nfe_modes": {"1": [], "2": [0.821]}, + "quantitative_sample_seeds": SAMPLE_SEEDS, + "visual_sample_seeds": VISUAL_SEEDS, + "protocol_seed": PROTOCOL_SEED, + "metric_mode": args.metrics, + "proxy_label": "5k-sample proxy evaluation; not a standard FID-50k benchmark", + "phase": args.phase, + "status": "dry_run" if args.dry_run else "running", + "jobs": jobs, + } + + if args.dry_run: + print(json.dumps({key: value for key, value in record.items() if key != "jobs"}, indent=2)) + for job in jobs: + print(shlex.join(job["command"])) + return + + run_record_path = outdir / "run_manifest.json" + started = time.time() + write_run_record(run_record_path, record) + for index, job in enumerate(jobs, start=1): + target = Path(job["output_directory"]) + if job["kind"] == "quantitative": + require_empty(target) + print(f"[{index}/{len(jobs)}] {job['kind']} {job['schedule']} seed={job['training_seed']} nfe={job.get('nfe', '1,2')}") + print(shlex.join(job["command"])) + job["started_at_unix"] = time.time() + try: + subprocess.run(job["command"], cwd=REPO_ROOT, check=True) + except subprocess.CalledProcessError as exc: + job["status"] = "failed" + job["returncode"] = exc.returncode + record["status"] = "failed" + record["elapsed_seconds"] = round(time.time() - started, 3) + write_run_record(run_record_path, record) + raise SystemExit(exc.returncode) from exc + job["status"] = "completed" + job["elapsed_seconds"] = round(time.time() - job["started_at_unix"], 3) + write_run_record(run_record_path, record) + record["status"] = "completed" + record["elapsed_seconds"] = round(time.time() - started, 3) + write_run_record(run_record_path, record) + print(f"Completed {len(jobs)} jobs; record: {run_record_path}") + + +if __name__ == "__main__": + main() diff --git a/scripts/run_fixed_baseline.sh b/scripts/run_fixed_baseline.sh new file mode 100755 index 00000000..c7b9d0d9 --- /dev/null +++ b/scripts/run_fixed_baseline.sh @@ -0,0 +1,6 @@ +#!/usr/bin/env bash +# Compatibility wrapper for legacy fixed-only entrypoint. +# Prefer the paired owner: scripts/run_schedule_experiment.sh +set -euo pipefail +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +exec bash "${ROOT_DIR}/scripts/run_schedule_experiment.sh" --schedule sigmoid "$@" diff --git a/scripts/run_g_screen.sh b/scripts/run_g_screen.sh new file mode 100644 index 00000000..ac81083b --- /dev/null +++ b/scripts/run_g_screen.sh @@ -0,0 +1,29 @@ +#!/usr/bin/env bash +set -u +export PYTHON=/data/raw/ECT/recurrence_of_ect/.venv/bin/python +REPO=/data/raw/ECT/recurrence_of_ect +DATA=/data/raw/ECT/datasets/cifar10-32x32.zip +TR=/data/raw/ECT/pretrained/edm-cifar10-32x32-uncond-vp.pkl +OUT=/data/raw/ECT/ect_runs/g_screen +mkdir -p "$OUT/logs" +run_one() { + local g="$1" dev="$2" port="$3" + local map=sigmoid + [ "$g" != "1.0" ] && map=global_sigmoid + local tag="g$(echo $g | tr . _)" + env CUDA_VISIBLE_DEVICES="$dev" MASTER_PORT="$port" $PYTHON $REPO/ct_train.py --outdir="$OUT/$tag" --data="$DATA" --cond=False --arch=ddpmpp --precond=ect --batch=128 --batch-gpu=16 --optim=RAdam --lr=0.0001 --dropout=0.2 --augment=0 -q 128 -k 8 -b 1 -c 0 --double=10000 --ema_beta=0.9993 --fp16=True --enable_amp=True --metrics=none --nosubdir --tick=10 --ckpt=10 --sample_every=26 --seed=3 --transfer="$TR" --mapping="$map" --global-gap-scale="$g" --duration=0.256 > "$OUT/logs/$tag.log" 2>&1 & + echo "started $tag pid=$! on dev=$dev" +} +# 3 批, 每批 2 个任务(每 GPU 一个), 批内并行 +run_one 0.9 0 29521 +run_one 1.0 1 29522 +wait +echo '--- batch1 done (g0.9, g1.0) ---' +run_one 1.05 0 29523 +run_one 1.1 1 29524 +wait +echo '--- batch2 done (g1.05, g1.1) ---' +run_one 1.2 0 29525 +run_one 1.3 1 29526 +wait +echo ALL_DONE diff --git a/scripts/run_g_screen_eval.sh b/scripts/run_g_screen_eval.sh new file mode 100644 index 00000000..24f79bd2 --- /dev/null +++ b/scripts/run_g_screen_eval.sh @@ -0,0 +1,39 @@ +#!/usr/bin/env bash +set -u +export PYTHON=/data/raw/ECT/recurrence_of_ect/.venv/bin/python +REPO=/data/raw/ECT/recurrence_of_ect +DATA=/data/raw/ECT/datasets/cifar10-32x32.zip +CKPT=/data/raw/ECT/ect_runs/g_screen +OUT=/data/raw/ECT/ect_runs/g_screen_eval +mkdir -p "$OUT/logs" +eval_one() { + local g="$1" nfe="$2" dev="$3" port="$4" + local tag="g$(echo $g | tr . _)_nfe$nfe" + env CUDA_VISIBLE_DEVICES="$dev" MASTER_PORT="$port" $PYTHON $REPO/ct_eval.py --resume="$CKPT/g$(echo $g | tr . _)/network-snapshot-latest.pkl" --outdir="$OUT/$tag" --data="$DATA" --nfe=$nfe --mid_t=0.821 --metrics=fid5k_full,kid5k_full --seed=3 > "$OUT/logs/$tag.log" 2>&1 & + echo "started $tag (g=$g nfe=$nfe) dev=$dev pid=$!" +} +# 每 GPU 一个任务: 每 ckpt 先 nfe=1 后 nfe=2 +eval_one 0.9 1 0 29601 +eval_one 1.0 1 1 29602 +wait +echo '--- batch eval1 done ---' +eval_one 0.9 2 0 29603 +eval_one 1.0 2 1 29604 +wait +echo '--- batch eval2 done ---' +eval_one 1.05 1 0 29605 +eval_one 1.1 1 1 29606 +wait +echo '--- batch eval3 done ---' +eval_one 1.05 2 0 29607 +eval_one 1.1 2 1 29608 +wait +echo '--- batch eval4 done ---' +eval_one 1.2 1 0 29609 +eval_one 1.3 1 1 29610 +wait +echo '--- batch eval5 done ---' +eval_one 1.2 2 0 29611 +eval_one 1.3 2 1 29612 +wait +echo ALL_EVAL_DONE diff --git a/scripts/run_gap_after_stage1.sh b/scripts/run_gap_after_stage1.sh new file mode 100755 index 00000000..01c6592e --- /dev/null +++ b/scripts/run_gap_after_stage1.sh @@ -0,0 +1,248 @@ +#!/usr/bin/env bash + +# Watch the stage-1 screen, select and freeze g*, run the 16-cell stage 2, and +# create the final factorial summary. Safe to restart: completed outputs are +# skipped only after validation; incomplete outputs stop the pipeline. + +set -Eeuo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +PROJECT_ROOT="${ECT_PROJECT_ROOT:-/root/autodl-tmp/ect_project}" +RUNS_ROOT="${ECT_GAP_RUNS_ROOT:-${PROJECT_ROOT}/runs/gap-factorial-20260726}" +EVAL_ROOT="${ECT_GAP_EVAL_ROOT:-${PROJECT_ROOT}/runs/gap-factorial-20260726-eval}" +ANALYSIS_ROOT="${ECT_GAP_ANALYSIS_ROOT:-${PROJECT_ROOT}/runs/gap-factorial-20260726-analysis}" +PYTHON_BIN="${ECT_BOOTSTRAP_PYTHON:-/root/miniconda3/bin/python}" +STAGE1_SCREEN="${ECT_STAGE1_SCREEN_NAME:-ect-gap-stage1}" +WAIT_INTERVAL="${ECT_STAGE1_POLL_INTERVAL:-30}" +WAIT_TIMEOUT="${ECT_STAGE1_WAIT_TIMEOUT:-43200}" +SELECT_TIMEOUT="${ECT_SELECT_TIMEOUT:-600}" +STAGE2_TIMEOUT="${ECT_STAGE2_WALL_TIMEOUT:-108000}" +SUMMARY_TIMEOUT="${ECT_SUMMARY_TIMEOUT:-600}" + +fail() { + printf '[run_gap_after_stage1] ERROR: %s\n' "$*" >&2 + exit 1 +} + +for integer_setting in \ + "ECT_STAGE1_POLL_INTERVAL:${WAIT_INTERVAL}" \ + "ECT_STAGE1_WAIT_TIMEOUT:${WAIT_TIMEOUT}" \ + "ECT_SELECT_TIMEOUT:${SELECT_TIMEOUT}" \ + "ECT_STAGE2_WALL_TIMEOUT:${STAGE2_TIMEOUT}" \ + "ECT_SUMMARY_TIMEOUT:${SUMMARY_TIMEOUT}" +do + setting_name="${integer_setting%%:*}" + setting_value="${integer_setting#*:}" + [[ "${setting_value}" =~ ^[1-9][0-9]*$ ]] || + fail "${setting_name} must be a positive integer" +done +[[ "${STAGE1_SCREEN}" =~ ^[A-Za-z0-9_.-]+$ ]] || + fail "invalid screen session name: ${STAGE1_SCREEN}" +[[ -x "${PYTHON_BIN}" ]] || fail "bootstrap Python not found: ${PYTHON_BIN}" +[[ -x "$(command -v screen || true)" ]] || fail "screen is required" +[[ -x "$(command -v timeout || true)" ]] || fail "GNU timeout is required" +[[ -x "$(command -v flock || true)" ]] || fail "flock is required" + +mkdir -p "${RUNS_ROOT}" "${EVAL_ROOT}" "${ANALYSIS_ROOT}" +exec 8>>"${ANALYSIS_ROOT}/after_stage1.lock" +flock -n 8 || fail "another after-stage1 watcher is already running" + +record_failure() { + local status="$?" + if (( status != 0 )); then + printf 'failed_utc=%s exit_code=%s\n' \ + "$(date -u +%Y-%m-%dT%H:%M:%SZ)" "${status}" \ + >> "${ANALYSIS_ROOT}/orchestration.failures.log" + fi +} +trap record_failure EXIT + +screen_is_alive() { + screen -ls 2>/dev/null | + awk -v target="${STAGE1_SCREEN}" ' + { + entry=$1 + sub(/^[0-9]+\./, "", entry) + if (entry == target && $0 ~ /\((Detached|Attached)\)/) { + found=1 + } + } + END {exit(found ? 0 : 1)} + ' +} + +run_with_timeout() { + local time_limit="$1" + local context="$2" + shift 2 + local status + set +e + timeout --signal=TERM --kill-after=15s "${time_limit}" "$@" + status="$?" + set -e + (( status == 0 )) || fail "${context} failed with exit code ${status}" +} + +selection_is_valid() { + local selected_file="${ANALYSIS_ROOT}/selected_g.txt" + local selection_json="${ANALYSIS_ROOT}/selection.json" + [[ -s "${selected_file}" && -s "${selection_json}" ]] || return 1 + "${PYTHON_BIN}" - "${selected_file}" "${selection_json}" <<'PY' +from decimal import Decimal, InvalidOperation +import json +from pathlib import Path +import sys + +selected_path, json_path = map(Path, sys.argv[1:]) +lines = [line.strip() for line in selected_path.read_text( + encoding="utf-8" +).splitlines() if line.strip()] +if len(lines) != 1: + raise SystemExit("selected_g.txt must have exactly one non-empty line") +try: + selected = Decimal(lines[0]) +except InvalidOperation as exc: + raise SystemExit(f"invalid selected_g.txt: {exc}") +allowed = {Decimal(text) for text in ("0.97", "1.032", "1.06", "1.10")} +if selected not in allowed: + raise SystemExit(f"unexpected selected global scale: {selected}") +payload = json.loads(json_path.read_text(encoding="utf-8")) +if payload.get("status") != "passed": + raise SystemExit("selection.json does not record status=passed") +if Decimal(str(payload.get("selected_global_scale"))) != selected: + raise SystemExit("selection.json numeric scale mismatch") +if Decimal(str(payload.get("selected_global_scale_text"))) != selected: + raise SystemExit("selection.json text scale mismatch") +PY +} + +summary_is_valid() { + local outdir="${ANALYSIS_ROOT}/final-summary" + local required + for required in \ + per_cell_metrics.csv \ + per_seed_effects.csv \ + factorial_summary.csv \ + factorial_summary.json \ + factorial_summary.md + do + [[ -s "${outdir}/${required}" ]] || return 1 + done + "${PYTHON_BIN}" - \ + "${outdir}/factorial_summary.json" \ + "${ANALYSIS_ROOT}/selected_g.txt" <<'PY' +from decimal import Decimal +import json +from pathlib import Path +import sys +payload = json.loads(Path(sys.argv[1]).read_text(encoding="utf-8")) +if payload.get("status") != "passed": + raise SystemExit("factorial_summary.json does not record status=passed") +selected = Decimal(Path(sys.argv[2]).read_text(encoding="utf-8").strip()) +if Decimal(str(payload.get("selected_global_scale"))) != selected: + raise SystemExit("factorial summary was produced for a different g*") +matrix = payload.get("matrix") +if not isinstance(matrix, dict): + raise SystemExit("factorial summary has no matrix accounting") +expected = { + "unique_training_cells": 18, + "evaluated_training_seed_nfe_cells": 36, + "unique_metric_files_read_once": 72, +} +for key, value in expected.items(): + if matrix.get(key) != value: + raise SystemExit( + f"factorial summary matrix mismatch for {key}: {matrix.get(key)!r}" + ) +PY +} + +STAGE1_COMPLETE="${RUNS_ROOT}/stage1.complete" +STAGE1_FAILED="${RUNS_ROOT}/stage1.failed" +wait_started="$(date +%s)" +while [[ ! -s "${STAGE1_COMPLETE}" ]]; do + if [[ -e "${STAGE1_FAILED}" ]]; then + fail "stage 1 recorded failure in ${STAGE1_FAILED}" + fi + if ! screen_is_alive; then + # Avoid racing the final sentinel write against screen teardown. + sleep 2 + [[ -s "${STAGE1_COMPLETE}" ]] || + fail "stage-1 screen '${STAGE1_SCREEN}' disappeared without stage1.complete" + break + fi + now="$(date +%s)" + if (( now - wait_started >= WAIT_TIMEOUT )); then + fail "timed out waiting for stage 1 after ${WAIT_TIMEOUT} seconds" + fi + printf '[run_gap_after_stage1] waiting for %s; screen=%s utc=%s\n' \ + "${STAGE1_COMPLETE}" "${STAGE1_SCREEN}" \ + "$(date -u +%Y-%m-%dT%H:%M:%SZ)" + sleep "${WAIT_INTERVAL}" +done +printf '[run_gap_after_stage1] stage 1 completion detected\n' + +if selection_is_valid; then + printf '[run_gap_after_stage1] validated frozen g* selection exists; skipping selection\n' +else + if [[ -e "${ANALYSIS_ROOT}/selected_g.txt" || + -e "${ANALYSIS_ROOT}/selection.json" || + -e "${ANALYSIS_ROOT}/response_curve.csv" || + -e "${ANALYSIS_ROOT}/response_curve.md" ]]; then + fail "incomplete or invalid pre-existing selection artifacts" + fi + [[ -f "${ROOT_DIR}/scripts/select_gap_scale.py" ]] || + fail "missing scripts/select_gap_scale.py" + run_with_timeout "${SELECT_TIMEOUT}" "global scale selection" \ + "${PYTHON_BIN}" "${ROOT_DIR}/scripts/select_gap_scale.py" \ + --runs-root "${RUNS_ROOT}" \ + --eval-root "${EVAL_ROOT}" \ + --outdir "${ANALYSIS_ROOT}" + selection_is_valid || fail "new global scale selection failed validation" +fi + +[[ -f "${ROOT_DIR}/scripts/run_gap_stage2.sh" ]] || + fail "missing scripts/run_gap_stage2.sh" +run_with_timeout "${STAGE2_TIMEOUT}" "stage 2" \ + env \ + ECT_PROJECT_ROOT="${PROJECT_ROOT}" \ + ECT_GAP_RUNS_ROOT="${RUNS_ROOT}" \ + ECT_GAP_EVAL_ROOT="${EVAL_ROOT}" \ + ECT_GAP_ANALYSIS_ROOT="${ANALYSIS_ROOT}" \ + ECT_SELECTED_G_FILE="${ANALYSIS_ROOT}/selected_g.txt" \ + bash "${ROOT_DIR}/scripts/run_gap_stage2.sh" +[[ -s "${RUNS_ROOT}/stage2.complete" ]] || + fail "stage 2 returned without stage2.complete" + +if summary_is_valid; then + printf '[run_gap_after_stage1] validated final summary exists; skipping summary\n' +else + if [[ -e "${ANALYSIS_ROOT}/final-summary" ]]; then + fail "incomplete or invalid pre-existing final-summary directory" + fi + [[ -f "${ROOT_DIR}/scripts/summarize_gap_factorial.py" ]] || + fail "missing scripts/summarize_gap_factorial.py" + run_with_timeout "${SUMMARY_TIMEOUT}" "factorial summary" \ + "${PYTHON_BIN}" "${ROOT_DIR}/scripts/summarize_gap_factorial.py" \ + --runs-root "${RUNS_ROOT}" \ + --eval-root "${EVAL_ROOT}" \ + --selection-json "${ANALYSIS_ROOT}/selection.json" \ + --outdir "${ANALYSIS_ROOT}/final-summary" + summary_is_valid || fail "new factorial summary failed validation" +fi + +PIPELINE_COMPLETE="${ANALYSIS_ROOT}/pipeline.complete" +if [[ ! -e "${PIPELINE_COMPLETE}" ]]; then + ( + set -o noclobber + { + printf 'completed_utc=%s\n' "$(date -u +%Y-%m-%dT%H:%M:%SZ)" + printf 'stage1_complete=%s\n' "${STAGE1_COMPLETE}" + printf 'stage2_complete=%s\n' "${RUNS_ROOT}/stage2.complete" + printf 'selection_json=%s\n' "${ANALYSIS_ROOT}/selection.json" + printf 'summary_json=%s\n' \ + "${ANALYSIS_ROOT}/final-summary/factorial_summary.json" + } > "${PIPELINE_COMPLETE}" + ) || fail "could not create pipeline completion marker" +fi +printf '[run_gap_after_stage1] full gap-factorial pipeline completed and validated\n' diff --git a/scripts/run_gap_factorial_arm.sh b/scripts/run_gap_factorial_arm.sh new file mode 100755 index 00000000..a4a38577 --- /dev/null +++ b/scripts/run_gap_factorial_arm.sh @@ -0,0 +1,237 @@ +#!/usr/bin/env bash + +# Run one frozen 256 kimg arm of the global/local gap-factorization study. + +set -Eeuo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +ENV_NAME="${ECT_ENV_NAME:-ect-exp}" +CONDA_BIN="${ECT_CONDA_BIN:-/root/miniconda3/bin/conda}" +PYTHON_BIN="${ECT_BOOTSTRAP_PYTHON:-/root/miniconda3/bin/python}" +PROJECT_ROOT="${ECT_PROJECT_ROOT:-/root/autodl-tmp/ect_project}" +RUNS_ROOT="${ECT_GAP_RUNS_ROOT:-${PROJECT_ROOT}/runs/gap-factorial-20260726}" +DATA="${ECT_DATA_PATH:-${PROJECT_ROOT}/datasets/cifar10-32x32.zip}" +TRANSFER="${ECT_TRANSFER_PATH:-${PROJECT_ROOT}/pretrained/edm-cifar10-32x32-uncond-vp.pkl}" + +ARM="" +SEED="" +GLOBAL_SCALE="1.0" +DURATION="0.256" + +fail() { + printf '[run_gap_factorial_arm] ERROR: %s\n' "$*" >&2 + exit 1 +} + +usage() { + cat <<'EOF' +Usage: + bash scripts/run_gap_factorial_arm.sh \ + --arm {fixed|global|local-v1-bridge|local-conservative|combined-conservative|local-aggressive|combined-aggressive} \ + --seed {0|1|2} [--global-scale FLOAT] [--duration MIMG] +EOF +} + +while [[ $# -gt 0 ]]; do + case "$1" in + --arm) ARM="${2:-}"; shift 2 ;; + --seed) SEED="${2:-}"; shift 2 ;; + --global-scale) GLOBAL_SCALE="${2:-}"; shift 2 ;; + --duration) DURATION="${2:-}"; shift 2 ;; + -h|--help) usage; exit 0 ;; + *) fail "unknown option: $1" ;; + esac +done + +[[ -n "${ARM}" ]] || fail "--arm is required" +case "${SEED}" in 0|1|2) ;; *) fail "--seed must be 0, 1, or 2" ;; esac +[[ -x "${CONDA_BIN}" ]] || fail "conda executable not found: ${CONDA_BIN}" +[[ -x "${PYTHON_BIN}" ]] || fail "bootstrap Python not found: ${PYTHON_BIN}" +"${PYTHON_BIN}" - "${GLOBAL_SCALE}" "${DURATION}" <<'PY' +import math +import sys +for name, raw in [('global scale', sys.argv[1]), ('duration', sys.argv[2])]: + value = float(raw) + if not math.isfinite(value) or value <= 0: + raise SystemExit(f'{name} must be finite and > 0, got {raw}') +PY +GLOBAL_SCALE="$("${PYTHON_BIN}" - "${GLOBAL_SCALE}" <<'PY' +import sys +print(f'{float(sys.argv[1]):.17g}') +PY +)" +DURATION="$("${PYTHON_BIN}" - "${DURATION}" <<'PY' +import sys +print(f'{float(sys.argv[1]):.17g}') +PY +)" + +SCHEDULE="" +PROFILE="none" +case "${ARM}" in + fixed) + SCHEDULE="sigmoid" + [[ "${GLOBAL_SCALE}" == "1" ]] || \ + fail "fixed arm requires --global-scale 1" + ;; + global) + SCHEDULE="global_sigmoid" + ;; + local-v1-bridge) + SCHEDULE="local_tbin_v1" + PROFILE="aggressive" + [[ "${GLOBAL_SCALE}" == "1" ]] || \ + fail "local-v1-bridge requires --global-scale 1" + ;; + local-conservative) + SCHEDULE="local_tbin_v2" + PROFILE="conservative" + [[ "${GLOBAL_SCALE}" == "1" ]] || \ + fail "local-conservative requires --global-scale 1" + ;; + combined-conservative) + SCHEDULE="local_tbin_v3" + PROFILE="conservative" + ;; + local-aggressive) + SCHEDULE="local_tbin_v2" + PROFILE="aggressive" + [[ "${GLOBAL_SCALE}" == "1" ]] || \ + fail "local-aggressive requires --global-scale 1" + ;; + combined-aggressive) + SCHEDULE="local_tbin_v3" + PROFILE="aggressive" + ;; + *) fail "unsupported arm: ${ARM}" ;; +esac + +SCALE_SLUG="$("${PYTHON_BIN}" - "${GLOBAL_SCALE}" <<'PY' +import sys +print(f'{float(sys.argv[1]):.4f}'.replace('.', 'p')) +PY +)" +KIMG="$("${PYTHON_BIN}" - "${DURATION}" <<'PY' +import sys +print(int(float(sys.argv[1]) * 1000)) +PY +)" +OUTDIR="${RUNS_ROOT}/${ARM}-g${SCALE_SLUG}-seed${SEED}-${KIMG}k" +[[ ! -e "${OUTDIR}" ]] || fail "refusing to overwrite existing output: ${OUTDIR}" +[[ -f "${DATA}" ]] || fail "dataset not found: ${DATA}" +[[ -f "${TRANSFER}" ]] || fail "transfer checkpoint not found: ${TRANSFER}" +mkdir -p "${OUTDIR}" + +LOCAL_ARGS=() +if [[ "${PROFILE}" == "conservative" ]]; then + LOCAL_ARGS+=( + --local-tbin-warmup-updates=64 + --local-tbin-gain=0.25 + --local-tbin-min-scale=0.85 + --local-tbin-max-scale=1.25 + ) +elif [[ "${PROFILE}" == "aggressive" ]]; then + LOCAL_ARGS+=( + --local-tbin-warmup-updates=32 + --local-tbin-gain=0.5 + --local-tbin-min-scale=0.75 + --local-tbin-max-scale=1.5 + ) +fi + +CMD=( + "${CONDA_BIN}" run --no-capture-output -n "${ENV_NAME}" + python "${ROOT_DIR}/ct_train.py" + --data="${DATA}" + --outdir="${OUTDIR}" + --nosubdir + --cond=False + --arch=ddpmpp + --precond=ect + --batch=128 + --batch-gpu=16 + --optim=RAdam + --lr=0.0001 + --dropout=0.2 + --augment=0 + --mapping="${SCHEDULE}" + --global-gap-scale="${GLOBAL_SCALE}" + -q 256 + -k 8 + -b 1 + -c 0 + --double=10000 + --ema_beta=0.9993 + --seed="${SEED}" + --fp16=True + --enable_amp=True + --metrics=none + --duration="${DURATION}" + --tick=10 + --snap=0 + --dump=0 + --ckpt=10 + --sample_every=26 + --adaptive-update-kimg=0.5 + --transfer="${TRANSFER}" + "${LOCAL_ARGS[@]}" +) + +{ + printf 'arm=%s\n' "${ARM}" + printf 'schedule=%s\n' "${SCHEDULE}" + printf 'local_profile=%s\n' "${PROFILE}" + printf 'global_gap_scale=%s\n' "${GLOBAL_SCALE}" + printf 'seed=%s\n' "${SEED}" + printf 'duration_mimg=%s\n' "${DURATION}" + printf 'data_sha256=%s\n' "$(sha256sum "${DATA}" | awk '{print $1}')" + printf 'transfer_sha256=%s\n' "$(sha256sum "${TRANSFER}" | awk '{print $1}')" + printf 'source_sha256=%s\n' "$( + for source_file in \ + "${ROOT_DIR}/ct_train.py" \ + "${ROOT_DIR}/training/loss.py" \ + "${ROOT_DIR}/training/schedules.py" \ + "${ROOT_DIR}/training/ct_training_loop.py" \ + "${ROOT_DIR}/training/networks.py" \ + "${ROOT_DIR}/training/dataset.py" \ + "${ROOT_DIR}/scripts/run_gap_factorial_arm.sh" + do + sha256sum "${source_file}" | awk '{print $1}' + done | sha256sum | awk '{print $1}' + )" + printf 'started_utc=%s\n' "$(date -u +%Y-%m-%dT%H:%M:%SZ)" + printf 'exact_command=' + printf '%q ' "${CMD[@]}" + printf '\n' +} > "${OUTDIR}/experiment_meta.env" + +printf '[run_gap_factorial_arm] output=%s\n' "${OUTDIR}" +printf '[run_gap_factorial_arm] command=' +printf '%q ' "${CMD[@]}" +printf '\n' + +set +e +"${CMD[@]}" 2>&1 | tee "${OUTDIR}/runner.log" +STATUS="${PIPESTATUS[0]}" +set -e +printf 'exit_code=%s\nfinished_utc=%s\n' \ + "${STATUS}" "$(date -u +%Y-%m-%dT%H:%M:%SZ)" >> "${OUTDIR}/experiment_meta.env" +[[ "${STATUS}" -eq 0 ]] || exit "${STATUS}" + +[[ -s "${OUTDIR}/train_summary.csv" ]] || fail "missing train_summary.csv" +[[ -s "${OUTDIR}/network-snapshot-latest.pkl" ]] || fail "missing final snapshot" +[[ -s "${OUTDIR}/training-state-latest.pt" ]] || fail "missing final training state" +sha256sum "${OUTDIR}/network-snapshot-latest.pkl" > "${OUTDIR}/checkpoint.sha256" +VALIDATION_ARGS=( + --run-dir "${OUTDIR}" \ + --expected-kimg "${KIMG}" \ + --expected-schedule "${SCHEDULE}" +) +case "${SCHEDULE}" in + local_tbin_v1|local_tbin_v2|local_tbin_v3) + VALIDATION_ARGS+=(--require-controller-active) + ;; +esac +"${PYTHON_BIN}" "${ROOT_DIR}/scripts/verify_gap_factorial_arm.py" \ + "${VALIDATION_ARGS[@]}" +tail -n 1 "${OUTDIR}/train_summary.csv" diff --git a/scripts/run_gap_stage1.sh b/scripts/run_gap_stage1.sh new file mode 100755 index 00000000..bbc10645 --- /dev/null +++ b/scripts/run_gap_stage1.sh @@ -0,0 +1,82 @@ +#!/usr/bin/env bash + +# Run and evaluate the frozen seed-0 global gap response curve sequentially. + +set -Eeuo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +PROJECT_ROOT="${ECT_PROJECT_ROOT:-/root/autodl-tmp/ect_project}" +RUNS_ROOT="${ECT_GAP_RUNS_ROOT:-${PROJECT_ROOT}/runs/gap-factorial-20260726}" +EVAL_ROOT="${ECT_GAP_EVAL_ROOT:-${PROJECT_ROOT}/runs/gap-factorial-20260726-eval}" +PYTHON_BIN="${ECT_BOOTSTRAP_PYTHON:-/root/miniconda3/bin/python}" +MIN_FREE_KB="${ECT_MIN_FREE_KB:-8388608}" +TRAIN_TIMEOUT="${ECT_TRAIN_TIMEOUT:-4200}" +EVAL_TIMEOUT="${ECT_EVAL_TIMEOUT:-1800}" + +mkdir -p "${RUNS_ROOT}" "${EVAL_ROOT}" + +free_kb() { + df -Pk "${PROJECT_ROOT}" | awk 'NR == 2 {print $4}' +} + +require_disk_headroom() { + local available + available="$(free_kb)" + if (( available < MIN_FREE_KB )); then + printf '[run_gap_stage1] ERROR: only %s KiB free; require %s KiB\n' \ + "${available}" "${MIN_FREE_KB}" >&2 + exit 1 + fi +} + +scale_slug() { + "${PYTHON_BIN}" - "$1" <<'PY' +import sys +print(f'{float(sys.argv[1]):.4f}'.replace('.', 'p')) +PY +} + +run_one() { + local arm="$1" + local scale="$2" + local slug run_dir label + slug="$(scale_slug "${scale}")" + label="${arm}-g${slug}-seed0-256k" + run_dir="${RUNS_ROOT}/${label}" + + require_disk_headroom + if [[ -f "${run_dir}/validation.json" ]] && + grep -q '"status": "passed"' "${run_dir}/validation.json"; then + printf '[run_gap_stage1] verified training exists; skipping %s\n' "${label}" + elif [[ -e "${run_dir}" ]]; then + printf '[run_gap_stage1] ERROR: incomplete existing training: %s\n' \ + "${run_dir}" >&2 + exit 1 + else + printf '[run_gap_stage1] training %s started_utc=%s\n' \ + "${label}" "$(date -u +%Y-%m-%dT%H:%M:%SZ)" + timeout --signal=TERM --kill-after=15s "${TRAIN_TIMEOUT}" \ + bash "${ROOT_DIR}/scripts/run_gap_factorial_arm.sh" \ + --arm "${arm}" --seed 0 --global-scale "${scale}" --duration 0.256 + fi + + require_disk_headroom + printf '[run_gap_stage1] evaluating %s started_utc=%s\n' \ + "${label}" "$(date -u +%Y-%m-%dT%H:%M:%SZ)" + timeout --signal=TERM --kill-after=15s "${EVAL_TIMEOUT}" \ + env ECT_GAP_EVAL_ROOT="${EVAL_ROOT}" \ + bash "${ROOT_DIR}/scripts/evaluate_gap_factorial_arm.sh" \ + --run-dir="${run_dir}" --label="${label}" + printf '[run_gap_stage1] completed %s finished_utc=%s free_kb=%s\n' \ + "${label}" "$(date -u +%Y-%m-%dT%H:%M:%SZ)" "$(free_kb)" +} + +run_one fixed 1.0 +run_one global 0.97 +run_one global 1.032 +run_one global 1.06 +run_one global 1.10 + +printf 'completed_utc=%s\n' "$(date -u +%Y-%m-%dT%H:%M:%SZ)" \ + > "${RUNS_ROOT}/stage1.complete" +printf '[run_gap_stage1] all five response-curve cells completed\n' diff --git a/scripts/run_gap_stage2.sh b/scripts/run_gap_stage2.sh new file mode 100755 index 00000000..e4d20639 --- /dev/null +++ b/scripts/run_gap_stage2.sh @@ -0,0 +1,472 @@ +#!/usr/bin/env bash + +# Run and evaluate the 16 confirmation/factorial cells that remain after the +# seed-0 global-gap response curve. The selected scale is immutable for the +# lifetime of this stage and every pre-existing artifact is validated before it +# is skipped. + +set -Eeuo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +PROJECT_ROOT="${ECT_PROJECT_ROOT:-/root/autodl-tmp/ect_project}" +RUNS_ROOT="${ECT_GAP_RUNS_ROOT:-${PROJECT_ROOT}/runs/gap-factorial-20260726}" +EVAL_ROOT="${ECT_GAP_EVAL_ROOT:-${PROJECT_ROOT}/runs/gap-factorial-20260726-eval}" +ANALYSIS_ROOT="${ECT_GAP_ANALYSIS_ROOT:-${PROJECT_ROOT}/runs/gap-factorial-20260726-analysis}" +SELECTED_G_FILE="${ECT_SELECTED_G_FILE:-${ANALYSIS_ROOT}/selected_g.txt}" +PYTHON_BIN="${ECT_BOOTSTRAP_PYTHON:-/root/miniconda3/bin/python}" +MIN_FREE_KB="${ECT_MIN_FREE_KB:-8388608}" +TRAIN_TIMEOUT="${ECT_TRAIN_TIMEOUT:-4200}" +EVAL_TIMEOUT="${ECT_EVAL_TIMEOUT:-1800}" +MONITOR_INTERVAL="${ECT_DISK_MONITOR_INTERVAL:-15}" + +fail() { + printf '[run_gap_stage2] ERROR: %s\n' "$*" >&2 + exit 1 +} + +for integer_setting in \ + "ECT_MIN_FREE_KB:${MIN_FREE_KB}" \ + "ECT_TRAIN_TIMEOUT:${TRAIN_TIMEOUT}" \ + "ECT_EVAL_TIMEOUT:${EVAL_TIMEOUT}" \ + "ECT_DISK_MONITOR_INTERVAL:${MONITOR_INTERVAL}" +do + setting_name="${integer_setting%%:*}" + setting_value="${integer_setting#*:}" + [[ "${setting_value}" =~ ^[1-9][0-9]*$ ]] || + fail "${setting_name} must be a positive integer" +done + +[[ -x "${PYTHON_BIN}" ]] || fail "bootstrap Python not found: ${PYTHON_BIN}" +[[ -x "$(command -v timeout || true)" ]] || fail "GNU timeout is required" +[[ -x "$(command -v flock || true)" ]] || fail "flock is required" +[[ -f "${ROOT_DIR}/scripts/run_gap_factorial_arm.sh" ]] || + fail "missing per-arm training runner" +[[ -f "${ROOT_DIR}/scripts/evaluate_gap_factorial_arm.sh" ]] || + fail "missing per-arm evaluation runner" +[[ -f "${ROOT_DIR}/scripts/verify_gap_factorial_arm.py" ]] || + fail "missing training verifier" +[[ -s "${SELECTED_G_FILE}" ]] || + fail "missing frozen selection: ${SELECTED_G_FILE}" + +mkdir -p "${RUNS_ROOT}" "${EVAL_ROOT}" "${ANALYSIS_ROOT}" + +# Prevent concurrent stage-2 drivers without preventing a safe later resume. +exec 9>>"${RUNS_ROOT}/stage2.lock" +flock -n 9 || fail "another stage-2 driver holds ${RUNS_ROOT}/stage2.lock" + +SELECTED_G="$("${PYTHON_BIN}" - "${SELECTED_G_FILE}" <<'PY' +from decimal import Decimal, InvalidOperation +from pathlib import Path +import sys + +path = Path(sys.argv[1]) +lines = [line.strip() for line in path.read_text(encoding="utf-8").splitlines() + if line.strip()] +if len(lines) != 1: + raise SystemExit(f"expected exactly one non-empty line in {path}") +try: + selected = Decimal(lines[0]) +except InvalidOperation as exc: + raise SystemExit(f"invalid selected scale {lines[0]!r}: {exc}") +allowed = {Decimal(text): text for text in ("0.97", "1.032", "1.06", "1.10")} +if selected not in allowed: + raise SystemExit( + f"selected scale {selected} is not one of {tuple(allowed.values())}" + ) +print(allowed[selected]) +PY +)" || fail "could not parse ${SELECTED_G_FILE}" +SELECTED_G_SHA256="$(sha256sum "${SELECTED_G_FILE}" | awk '{print $1}')" + +SELECTION_JSON="${ANALYSIS_ROOT}/selection.json" +if [[ -e "${SELECTION_JSON}" ]]; then + "${PYTHON_BIN}" - "${SELECTION_JSON}" "${SELECTED_G}" <<'PY' +from decimal import Decimal +import json +from pathlib import Path +import sys + +payload = json.loads(Path(sys.argv[1]).read_text(encoding="utf-8")) +if payload.get("status") != "passed": + raise SystemExit("selection.json does not record status=passed") +expected = Decimal(sys.argv[2]) +if Decimal(str(payload.get("selected_global_scale"))) != expected: + raise SystemExit("selection.json numeric scale disagrees with selected_g.txt") +if Decimal(str(payload.get("selected_global_scale_text"))) != expected: + raise SystemExit("selection.json text scale disagrees with selected_g.txt") +PY +fi + +SELECTION_LOCK="${RUNS_ROOT}/stage2_selection.lock" +if [[ -e "${SELECTION_LOCK}" ]]; then + [[ -f "${SELECTION_LOCK}" ]] || fail "selection lock is not a file" + LOCKED_G="$(awk -F= '$1 == "selected_g" {print $2}' "${SELECTION_LOCK}")" + LOCKED_SHA="$(awk -F= '$1 == "selected_g_sha256" {print $2}' "${SELECTION_LOCK}")" + [[ "${LOCKED_G}" == "${SELECTED_G}" ]] || + fail "stage-2 selection is already locked to ${LOCKED_G}" + [[ "${LOCKED_SHA}" == "${SELECTED_G_SHA256}" ]] || + fail "selected_g.txt changed after stage-2 was first initialized" +else + ( + set -o noclobber + { + printf 'selected_g=%s\n' "${SELECTED_G}" + printf 'selected_g_sha256=%s\n' "${SELECTED_G_SHA256}" + printf 'created_utc=%s\n' "$(date -u +%Y-%m-%dT%H:%M:%SZ)" + } > "${SELECTION_LOCK}" + ) || fail "could not create immutable selection lock" +fi + +assert_selection_unchanged() { + local current_sha + current_sha="$(sha256sum "${SELECTED_G_FILE}" | awk '{print $1}')" + [[ "${current_sha}" == "${SELECTED_G_SHA256}" ]] || + fail "selected_g.txt changed while stage 2 was running" +} + +free_kb() { + df -Pk "${PROJECT_ROOT}" | awk 'NR == 2 {print $4}' +} + +require_disk_headroom() { + local context="${1:-operation}" + local available + available="$(free_kb)" + [[ "${available}" =~ ^[0-9]+$ ]] || + fail "could not determine free disk space before ${context}" + if (( available < MIN_FREE_KB )); then + fail "only ${available} KiB free before ${context}; require ${MIN_FREE_KB} KiB" + fi +} + +ACTIVE_TIMEOUT_PID="" +ACTIVE_MONITOR_PID="" +cleanup_active_jobs() { + if [[ -n "${ACTIVE_MONITOR_PID}" ]] && + kill -0 "${ACTIVE_MONITOR_PID}" 2>/dev/null; then + kill -TERM "${ACTIVE_MONITOR_PID}" 2>/dev/null || true + fi + if [[ -n "${ACTIVE_TIMEOUT_PID}" ]] && + kill -0 "${ACTIVE_TIMEOUT_PID}" 2>/dev/null; then + kill -TERM "${ACTIVE_TIMEOUT_PID}" 2>/dev/null || true + fi +} +trap cleanup_active_jobs EXIT + +run_timed_monitored() { + local time_limit="$1" + local context="$2" + shift 2 + local status monitor_status available + + require_disk_headroom "${context}" + timeout --signal=TERM --kill-after=15s "${time_limit}" "$@" & + ACTIVE_TIMEOUT_PID="$!" + ( + while kill -0 "${ACTIVE_TIMEOUT_PID}" 2>/dev/null; do + available="$(free_kb)" + if [[ ! "${available}" =~ ^[0-9]+$ ]] || + (( available < MIN_FREE_KB )); then + printf '[run_gap_stage2] ERROR: disk headroom monitor stopped %s; free_kb=%s required_kb=%s\n' \ + "${context}" "${available:-unknown}" "${MIN_FREE_KB}" >&2 + kill -TERM "${ACTIVE_TIMEOUT_PID}" 2>/dev/null || true + exit 72 + fi + sleep "${MONITOR_INTERVAL}" + done + ) & + ACTIVE_MONITOR_PID="$!" + + set +e + wait "${ACTIVE_TIMEOUT_PID}" + status="$?" + set -e + ACTIVE_TIMEOUT_PID="" + + if kill -0 "${ACTIVE_MONITOR_PID}" 2>/dev/null; then + kill -TERM "${ACTIVE_MONITOR_PID}" 2>/dev/null || true + fi + set +e + wait "${ACTIVE_MONITOR_PID}" + monitor_status="$?" + set -e + ACTIVE_MONITOR_PID="" + + require_disk_headroom "${context} completion" + if (( status != 0 )); then + fail "${context} failed with exit code ${status}" + fi + # 0 means the command outlived a final monitor iteration; 143 means this + # driver stopped the monitor after successful command completion. + if (( monitor_status != 0 && monitor_status != 143 )); then + fail "${context} disk monitor failed with exit code ${monitor_status}" + fi +} + +scale_slug() { + "${PYTHON_BIN}" - "$1" <<'PY' +import sys +print(f"{float(sys.argv[1]):.4f}".replace(".", "p")) +PY +} + +verify_training() { + local run_dir="$1" + local expected_schedule="$2" + local controller_required="$3" + local args=( + --run-dir "${run_dir}" + --expected-kimg 256 + --expected-schedule "${expected_schedule}" + ) + if [[ "${controller_required}" == "1" ]]; then + args+=(--require-controller-active) + fi + "${PYTHON_BIN}" "${ROOT_DIR}/scripts/verify_gap_factorial_arm.py" "${args[@]}" +} + +validate_eval_nfe() { + local eval_dir="$1" + local label="$2" + local nfe="$3" + local checkpoint="$4" + "${PYTHON_BIN}" - "${eval_dir}" "${label}" "${nfe}" "${checkpoint}" <<'PY' +from __future__ import annotations + +import hashlib +import json +import math +from pathlib import Path +import sys + +eval_dir = Path(sys.argv[1]) +label = sys.argv[2] +nfe = int(sys.argv[3]) +checkpoint = Path(sys.argv[4]) + +def fail(message: str) -> None: + raise SystemExit(f"[run_gap_stage2 eval validator] {message}") + +def sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(8 * 1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + +for required in ("experiment_meta.env", "runner.log"): + path = eval_dir / required + if not path.is_file() or path.stat().st_size == 0: + fail(f"missing or empty {path}") + +meta: dict[str, str] = {} +for line in (eval_dir / "experiment_meta.env").read_text( + encoding="utf-8", errors="strict" +).splitlines(): + if "=" in line: + key, value = line.split("=", 1) + meta[key] = value +if meta.get("exit_code") != "0": + fail(f"{eval_dir} does not record exit_code=0") +if meta.get("label") != label or meta.get("nfe") != str(nfe): + fail(f"{eval_dir} metadata identity mismatch") +if not checkpoint.is_file() or checkpoint.stat().st_size == 0: + fail(f"missing checkpoint {checkpoint}") +actual_checkpoint_sha = sha256(checkpoint) +if meta.get("checkpoint_sha256") != actual_checkpoint_sha: + fail(f"{eval_dir} checkpoint hash mismatch") + +metrics: dict[str, float] = {} +for metric in ("kid5k_full", "fid5k_full"): + result_path = eval_dir / f"metric-{metric}.jsonl" + if not result_path.is_file() or result_path.stat().st_size == 0: + fail(f"missing metric file {result_path}") + lines = result_path.read_text(encoding="utf-8").splitlines() + if len(lines) != 1: + fail(f"expected exactly one line in {result_path}") + payload = json.loads(lines[0]) + if payload.get("metric") != metric: + fail(f"metric identity mismatch in {result_path}") + try: + value = float(payload["results"][metric]) + except (KeyError, TypeError, ValueError) as exc: + fail(f"invalid metric value in {result_path}: {exc}") + if not math.isfinite(value): + fail(f"non-finite metric value in {result_path}") + metrics[metric] = value + +report = { + "status": "passed", + "label": label, + "nfe": nfe, + "checkpoint_sha256": actual_checkpoint_sha, + "metrics": metrics, +} +validation_path = eval_dir / "validation.json" +encoded = json.dumps(report, indent=2, sort_keys=True) + "\n" +if validation_path.exists(): + if validation_path.read_text(encoding="utf-8") != encoded: + fail(f"existing validation report disagrees: {validation_path}") +else: + with validation_path.open("x", encoding="utf-8") as handle: + handle.write(encoded) +PY +} + +run_one() { + local arm="$1" + local seed="$2" + local scale="$3" + local expected_schedule="$4" + local controller_required="$5" + local slug label run_dir checkpoint nfe eval_dir valid_evals + + assert_selection_unchanged + slug="$(scale_slug "${scale}")" + label="${arm}-g${slug}-seed${seed}-256k" + run_dir="${RUNS_ROOT}/${label}" + checkpoint="${run_dir}/network-snapshot-latest.pkl" + + require_disk_headroom "training ${label}" + if [[ -e "${run_dir}" ]]; then + [[ -d "${run_dir}" ]] || fail "training output is not a directory: ${run_dir}" + if verify_training "${run_dir}" "${expected_schedule}" "${controller_required}"; then + printf '[run_gap_stage2] validated training exists; skipping %s\n' "${label}" + else + fail "pre-existing training failed validation: ${run_dir}" + fi + else + printf '[run_gap_stage2] training %s started_utc=%s\n' \ + "${label}" "$(date -u +%Y-%m-%dT%H:%M:%SZ)" + run_timed_monitored "${TRAIN_TIMEOUT}" "training ${label}" \ + bash "${ROOT_DIR}/scripts/run_gap_factorial_arm.sh" \ + --arm "${arm}" --seed "${seed}" \ + --global-scale "${scale}" --duration 0.256 + verify_training "${run_dir}" "${expected_schedule}" "${controller_required}" || + fail "new training failed validation: ${run_dir}" + fi + + valid_evals=0 + for nfe in 1 2; do + eval_dir="${EVAL_ROOT}/${label}/nfe${nfe}" + if [[ ! -e "${eval_dir}" ]]; then + continue + fi + [[ -d "${eval_dir}" ]] || + fail "evaluation output is not a directory: ${eval_dir}" + if validate_eval_nfe "${eval_dir}" "${label}" "${nfe}" "${checkpoint}"; then + valid_evals=$((valid_evals + 1)) + else + fail "pre-existing evaluation failed validation: ${eval_dir}" + fi + done + + if (( valid_evals == 2 )); then + printf '[run_gap_stage2] validated evaluation exists; skipping %s\n' "${label}" + else + printf '[run_gap_stage2] evaluating %s started_utc=%s\n' \ + "${label}" "$(date -u +%Y-%m-%dT%H:%M:%SZ)" + run_timed_monitored "${EVAL_TIMEOUT}" "evaluation ${label}" \ + env ECT_GAP_EVAL_ROOT="${EVAL_ROOT}" \ + bash "${ROOT_DIR}/scripts/evaluate_gap_factorial_arm.sh" \ + --run-dir="${run_dir}" --label="${label}" + for nfe in 1 2; do + eval_dir="${EVAL_ROOT}/${label}/nfe${nfe}" + validate_eval_nfe "${eval_dir}" "${label}" "${nfe}" "${checkpoint}" || + fail "new evaluation failed validation: ${eval_dir}" + done + fi + + assert_selection_unchanged + printf '[run_gap_stage2] completed %s finished_utc=%s free_kb=%s\n' \ + "${label}" "$(date -u +%Y-%m-%dT%H:%M:%SZ)" "$(free_kb)" +} + +# Exactly 16 unique remaining cells. Stage 1 already supplies fixed/global +# seed 0, so only seeds 1/2 are repeated for those arms. +CELL_ARMS=( + fixed fixed + global global + local-conservative local-conservative local-conservative + combined-conservative combined-conservative combined-conservative + local-aggressive local-aggressive local-aggressive + combined-aggressive combined-aggressive combined-aggressive +) +CELL_SEEDS=(1 2 1 2 0 1 2 0 1 2 0 1 2 0 1 2) + +(( ${#CELL_ARMS[@]} == 16 )) || fail "internal matrix must contain exactly 16 arms" +(( ${#CELL_SEEDS[@]} == 16 )) || fail "internal seed matrix must contain exactly 16 entries" + +SEEN_LABELS=$'\n' +UNIQUE_LABEL_COUNT=0 +PLAN_LINES="arm seed global_scale label" +for index in "${!CELL_ARMS[@]}"; do + arm="${CELL_ARMS[${index}]}" + seed="${CELL_SEEDS[${index}]}" + case "${arm}" in + fixed|local-conservative|local-aggressive) scale="1.0" ;; + global|combined-conservative|combined-aggressive) scale="${SELECTED_G}" ;; + *) fail "internal unsupported arm: ${arm}" ;; + esac + slug="$(scale_slug "${scale}")" + label="${arm}-g${slug}-seed${seed}-256k" + [[ "${SEEN_LABELS}" != *$'\n'"${label}"$'\n'* ]] || + fail "duplicate internal cell: ${label}" + SEEN_LABELS+="${label}"$'\n' + UNIQUE_LABEL_COUNT=$((UNIQUE_LABEL_COUNT + 1)) + PLAN_LINES+=$'\n'"${arm}"$'\t'"${seed}"$'\t'"${scale}"$'\t'"${label}" +done +(( UNIQUE_LABEL_COUNT == 16 )) || + fail "internal matrix does not have 16 unique labels" + +PLAN_FILE="${RUNS_ROOT}/stage2_plan.tsv" +if [[ -e "${PLAN_FILE}" ]]; then + [[ -f "${PLAN_FILE}" ]] || fail "stage-2 plan path is not a file" + [[ "$(<"${PLAN_FILE}")" == "${PLAN_LINES}" ]] || + fail "pre-existing stage-2 plan differs from the frozen 16-cell plan" +else + ( + set -o noclobber + printf '%s\n' "${PLAN_LINES}" > "${PLAN_FILE}" + ) || fail "could not create frozen stage-2 plan" +fi + +printf '[run_gap_stage2] selected_g=%s selected_sha256=%s cells=16\n' \ + "${SELECTED_G}" "${SELECTED_G_SHA256}" + +for index in "${!CELL_ARMS[@]}"; do + arm="${CELL_ARMS[${index}]}" + seed="${CELL_SEEDS[${index}]}" + case "${arm}" in + fixed) + scale="1.0"; expected_schedule="sigmoid"; controller_required=0 + ;; + global) + scale="${SELECTED_G}"; expected_schedule="global_sigmoid"; controller_required=0 + ;; + local-conservative|local-aggressive) + scale="1.0"; expected_schedule="local_tbin_v2"; controller_required=1 + ;; + combined-conservative|combined-aggressive) + scale="${SELECTED_G}"; expected_schedule="local_tbin_v3"; controller_required=1 + ;; + *) fail "internal unsupported arm: ${arm}" ;; + esac + run_one "${arm}" "${seed}" "${scale}" \ + "${expected_schedule}" "${controller_required}" +done + +assert_selection_unchanged +STAGE2_COMPLETE="${RUNS_ROOT}/stage2.complete" +if [[ ! -e "${STAGE2_COMPLETE}" ]]; then + ( + set -o noclobber + { + printf 'completed_utc=%s\n' "$(date -u +%Y-%m-%dT%H:%M:%SZ)" + printf 'selected_g=%s\n' "${SELECTED_G}" + printf 'selected_g_sha256=%s\n' "${SELECTED_G_SHA256}" + printf 'cells=16\n' + } > "${STAGE2_COMPLETE}" + ) || fail "could not create stage-2 completion marker" +fi +printf '[run_gap_stage2] all 16 remaining cells completed and validated\n' diff --git a/scripts/run_role_a_quality_evaluation.py b/scripts/run_role_a_quality_evaluation.py new file mode 100644 index 00000000..8ec41630 --- /dev/null +++ b/scripts/run_role_a_quality_evaluation.py @@ -0,0 +1,300 @@ +#!/usr/bin/env python3 +"""Run Role A's frozen smoke or per-budget quantitative evaluation matrix.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import shlex +import subprocess +import time +from pathlib import Path + + +REPO_ROOT = Path(__file__).resolve().parents[1] +METHODS = ("sigmoid", "adaptive_v1") +TRAINING_SEEDS = (0, 1, 2) +NFES = (1, 2) +BUDGET_PRIORITY = (64, 32, 16) +PROTOCOL_SEED = 20260722 +DATASET_REAL_COUNT = 50_000 +DETECTOR_URL = ( + "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/" + "inception-2015-12-05.pt" +) +PHASE_CONFIG = { + "smoke": { + "sample_count": 512, + "sample_seeds": "0-511", + "metric_repeats": 2, + "metrics": { + "both": "kid512_full,fid512_full", + "kid-only": "kid512_full", + "fid-only": "fid512_full", + }, + }, + "formal": { + "sample_count": 5_000, + "sample_seeds": "0-4999", + "metric_repeats": 1, + "metrics": { + "both": "kid5k_full,fid5k_full", + "kid-only": "kid5k_full", + "fid-only": "fid5k_full", + }, + }, +} + + +def fail(message: str) -> None: + raise SystemExit(f"[run_role_a_quality_evaluation] ERROR: {message}") + + +def sha256_file(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def git_head() -> str: + try: + return subprocess.check_output( + ["git", "rev-parse", "HEAD"], cwd=REPO_ROOT, text=True + ).strip() + except (OSError, subprocess.CalledProcessError): + return "unknown" + + +def load_cells(path: Path, allow_missing: bool = False) -> list[dict]: + try: + payload = json.loads(path.read_text(encoding="utf-8")) + raw_cells = payload["cells"] + except (OSError, KeyError, TypeError, json.JSONDecodeError) as exc: + fail(f"cannot read checkpoint manifest {path}: {exc}") + if not isinstance(raw_cells, list) or not raw_cells: + fail("manifest cells must be a non-empty list") + + manifest_budget = payload.get("training_budget_kimg") + cells = [] + keys = set() + for raw in raw_cells: + try: + method = str(raw.get("method") or raw["schedule"]) + training_seed = int(raw["training_seed"]) + budget_kimg = int(raw.get("budget_kimg", manifest_budget)) + checkpoint = Path(raw["checkpoint"]).expanduser().resolve() + except (KeyError, TypeError, ValueError) as exc: + fail(f"invalid manifest cell {raw!r}: {exc}") + if method not in METHODS: + fail(f"unsupported method {method!r}; expected one of {METHODS}") + if training_seed not in TRAINING_SEEDS: + fail(f"training seed must be one of {TRAINING_SEEDS}: {training_seed}") + if budget_kimg not in BUDGET_PRIORITY: + fail(f"budget_kimg must be one of {BUDGET_PRIORITY}: {budget_kimg}") + key = (budget_kimg, method, training_seed) + if key in keys: + fail(f"duplicate manifest cell: {key}") + keys.add(key) + + expected_sha = raw.get("checkpoint_sha256") + if checkpoint.is_file(): + checkpoint_sha = sha256_file(checkpoint) + if expected_sha and expected_sha != checkpoint_sha: + fail(f"checkpoint SHA256 mismatch for {checkpoint}") + elif allow_missing: + checkpoint_sha = expected_sha or "missing" + else: + fail(f"checkpoint not found: {checkpoint}") + cells.append({ + "method": method, + "training_seed": training_seed, + "budget_kimg": budget_kimg, + "checkpoint": checkpoint, + "checkpoint_sha256": checkpoint_sha, + }) + return cells + + +def select_cells(cells: list[dict], phase: str, budget: int | None) -> list[dict]: + if phase == "smoke": + if budget not in (None, 16): + fail("smoke is frozen to the existing seed0 16 kimg checkpoints") + selected = [ + cell for cell in cells + if cell["budget_kimg"] == 16 and cell["training_seed"] == 0 + ] + expected = {(method, 0) for method in METHODS} + else: + if budget is None: + fail("formal evaluation requires --budget 64, 32, or 16") + selected = [cell for cell in cells if cell["budget_kimg"] == budget] + expected = {(method, seed) for method in METHODS for seed in TRAINING_SEEDS} + + actual = {(cell["method"], cell["training_seed"]) for cell in selected} + if actual != expected: + fail( + f"{phase} matrix is incomplete for budget {16 if phase == 'smoke' else budget}; " + f"missing={sorted(expected - actual)}, extra={sorted(actual - expected)}" + ) + return sorted(selected, key=lambda cell: (cell["training_seed"], METHODS.index(cell["method"]))) + + +def build_jobs( + cells: list[dict], data: Path, outdir: Path, phase: str, metric_mode: str, + base_port: int, +) -> list[dict]: + config = PHASE_CONFIG[phase] + metric_names = config["metrics"][metric_mode] + jobs = [] + port = base_port + for cell in cells: + for nfe in NFES: + target = ( + outdir / phase / f"budget{cell['budget_kimg']}" / cell["method"] + / f"seed{cell['training_seed']}" / f"nfe{nfe}" + ) + command = [ + "bash", str(REPO_ROOT / "scripts" / "evaluate_checkpoint.sh"), + "1", str(port), str(cell["checkpoint"]), + "--outdir", str(target), + "--nosubdir", + "--data", str(data), + "--cond=False", + "--arch=ddpmpp", + "--precond=ct", + "--dropout=0.2", + "--augment=0", + "--fp16=False", + "--cache=True", + "--workers=3", + f"--nfe={nfe}", + "--mid_t=0.821", + f"--metrics={metric_names}", + f"--metric-repeats={config['metric_repeats']}", + f"--sample-seeds={config['sample_seeds']}", + f"--seed={PROTOCOL_SEED}", + ( + f"--desc=role-a-{phase}-{cell['method']}-" + f"seed{cell['training_seed']}-{cell['budget_kimg']}k-nfe{nfe}" + ), + ] + jobs.append({ + "method": cell["method"], + "training_seed": cell["training_seed"], + "budget_kimg": cell["budget_kimg"], + "nfe": nfe, + "mid_t": [] if nfe == 1 else [0.821], + "sample_count": config["sample_count"], + "sample_seeds": config["sample_seeds"], + "metric_repeats": config["metric_repeats"], + "metric_names": metric_names.split(","), + "checkpoint": str(cell["checkpoint"]), + "checkpoint_sha256": cell["checkpoint_sha256"], + "output_directory": str(target), + "command": command, + }) + port += 1 + return jobs + + +def require_empty(path: Path) -> None: + if path.exists() and any(path.iterdir()): + fail(f"refuse to append to non-empty output directory: {path}") + + +def write_record(path: Path, record: dict) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(record, indent=2) + "\n", encoding="utf-8") + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--manifest", type=Path, required=True) + parser.add_argument("--data", type=Path, required=True) + parser.add_argument("--outdir", type=Path, required=True) + parser.add_argument("--phase", choices=tuple(PHASE_CONFIG), required=True) + parser.add_argument("--budget", type=int, choices=BUDGET_PRIORITY) + parser.add_argument("--metrics", choices=("both", "kid-only", "fid-only"), default="both") + parser.add_argument("--base-port", type=int, default=29700) + parser.add_argument("--dry-run", action="store_true") + parser.add_argument("--allow-missing-checkpoints", action="store_true") + args = parser.parse_args(argv) + + if args.allow_missing_checkpoints and not args.dry_run: + fail("--allow-missing-checkpoints is dry-run only") + data = args.data.expanduser().resolve() + if not data.is_file() and not (args.dry_run and args.allow_missing_checkpoints): + fail(f"dataset not found: {data}") + outdir = args.outdir.expanduser().resolve() + if not args.dry_run: + require_empty(outdir) + + cells = load_cells(args.manifest, allow_missing=args.allow_missing_checkpoints) + selected = select_cells(cells, args.phase, args.budget) + jobs = build_jobs(selected, data, outdir, args.phase, args.metrics, args.base_port) + config = PHASE_CONFIG[args.phase] + record = { + "schema_version": 1, + "protocol": "role-a-multibudget-quality-v1", + "evaluation_git_commit": git_head(), + "phase": args.phase, + "budget_kimg": 16 if args.phase == "smoke" else args.budget, + "budget_priority": list(BUDGET_PRIORITY), + "dataset": str(data), + "dataset_sha256": sha256_file(data) if data.is_file() else "missing", + "reference_real_count": DATASET_REAL_COUNT, + "feature_detector_url": DETECTOR_URL, + "precision": "fp32", + "nfe_modes": {"1": [], "2": [0.821]}, + "sample_count": config["sample_count"], + "sample_seeds": config["sample_seeds"], + "metric_repeats": config["metric_repeats"], + "metric_mode": args.metrics, + "metric_names": config["metrics"][args.metrics].split(","), + "metric_seed": PROTOCOL_SEED, + "mixing_policy": "one uniform metric set per complete method matrix", + "status": "dry_run" if args.dry_run else "running", + "jobs": jobs, + } + + if args.dry_run: + print(json.dumps({key: value for key, value in record.items() if key != "jobs"}, indent=2)) + for job in jobs: + print(shlex.join(job["command"])) + return + + record_path = outdir / "run_manifest.json" + started = time.time() + write_record(record_path, record) + for index, job in enumerate(jobs, start=1): + target = Path(job["output_directory"]) + require_empty(target) + print( + f"[{index}/{len(jobs)}] {args.phase} {job['method']} " + f"seed={job['training_seed']} budget={job['budget_kimg']} nfe={job['nfe']}" + ) + print(shlex.join(job["command"])) + job["started_at_unix"] = time.time() + try: + subprocess.run(job["command"], cwd=REPO_ROOT, check=True) + except subprocess.CalledProcessError as exc: + job["status"] = "failed" + job["returncode"] = exc.returncode + record["status"] = "failed" + record["elapsed_seconds"] = round(time.time() - started, 3) + write_record(record_path, record) + raise SystemExit(exc.returncode) from exc + job["status"] = "completed" + job["elapsed_seconds"] = round(time.time() - job["started_at_unix"], 3) + write_record(record_path, record) + record["status"] = "completed" + record["elapsed_seconds"] = round(time.time() - started, 3) + write_record(record_path, record) + print(f"Completed {len(jobs)} jobs; record: {record_path}") + + +if __name__ == "__main__": + main() diff --git a/scripts/run_schedule_experiment.sh b/scripts/run_schedule_experiment.sh new file mode 100755 index 00000000..e9ae6412 --- /dev/null +++ b/scripts/run_schedule_experiment.sh @@ -0,0 +1,471 @@ +#!/usr/bin/env bash + +# Paired experiment runner (Role B) — owner of fixed/adaptive comparison infra. +# Same frozen hyperparameters for both schedules; only --schedule (and Role C +# adaptive-internal knobs once available) may differ. +# +# Duration (Mimg) → total_kimg = int(duration * 1000); discrete batch completion +# activation 0.004 → 4 kimg target → 32 attempted iterations @ batch 128 (4096 images) +# stability 0.016 → 16 kimg → 125 attempted iterations @ batch 128 +# baseline 0.128 → 128 kimg → 1000 attempted iterations @ batch 128 +# +# Fresh runs always use a unique empty directory and pass --transfer only. +# Resume requires --resume, reuses that run directory, and must NOT pass --transfer. +# Fixed and adaptive never share an output directory. + +set -euo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +ENV_NAME="${ECT_ENV_NAME:-ect}" + +MODE="" +SCHEDULE="" +RESUME="" +OUTDIR_OVERRIDE="" +SEED="0" + +usage() { + cat <<'EOF' +Usage: + bash scripts/run_schedule_experiment.sh \ + --schedule {sigmoid|adaptive_v1} \ + --mode {dry-run|activation|stability|baseline} \ + [--seed {0|1|2}] \ + [--outdir DIR] \ + [--resume PATH_TO_training-state.pt] + + dry-run Print resolved params and exact command; exit without training. + activation Train with --duration=0.004 (32 attempted iterations @ batch 128). + stability Train with --duration=0.016 (125 attempted iterations @ batch 128). + baseline Train with --duration=0.128 (1000 attempted iterations @ batch 128). + +Fresh runs: + - Pass --transfer only (never --resume) + - Default outdir: + $ECT_RUNS_ROOT/---/ + e.g. sigmoid-stability-ad05dc47-20260717T084500Z/ + - If --outdir is set, it must be empty (or not exist); otherwise the run fails. + - Never appends to old logs; never reuses a non-empty directory. + +Resume: + - Pass --resume only (never --transfer) + - Requires --resume pointing at training-state-*.pt + - Uses the parent directory of that file as the run directory + - Refuses if run_meta schedule disagrees with --schedule (no mixed arms) +EOF +} + +fail() { + printf '[run_schedule_experiment] ERROR: %s\n' "$*" >&2 + exit 1 +} + +run_in_env() { + if [[ "${CONDA_DEFAULT_ENV:-}" == "${ENV_NAME}" ]]; then + "$@" + elif command -v conda >/dev/null 2>&1; then + conda run --no-capture-output -n "${ENV_NAME}" "$@" + elif command -v mamba >/dev/null 2>&1; then + mamba run --no-capture-output -n "${ENV_NAME}" "$@" + else + fail "activate '${ENV_NAME}' or install conda/mamba first" + fi +} + +dir_is_empty() { + local dir="$1" + [[ -d "$dir" ]] || return 0 + [[ -z "$(ls -A "$dir" 2>/dev/null)" ]] +} + +sha256_file() { + local path="$1" + if [[ -f "$path" ]]; then + sha256sum "$path" | awk '{print $1}' + else + printf 'missing' + fi +} + +schedule_slug() { + case "$1" in + adaptive_v1) printf 'adaptive-v1' ;; + *) printf '%s' "$1" ;; + esac +} + +short_git_sha() { + ( + cd "${ROOT_DIR}" + git rev-parse --short=8 HEAD 2>/dev/null || printf 'unknown' + ) +} + +read_meta_value() { + local file="$1" + local key="$2" + [[ -f "$file" ]] || return 0 + local line + line="$(grep -E "^${key}=" "$file" | tail -n 1 || true)" + if [[ -n "$line" ]]; then + printf '%s' "${line#*=}" + fi +} + +collect_git_meta() { + ( + cd "${ROOT_DIR}" + GIT_HEAD="$(git rev-parse HEAD 2>/dev/null || echo unknown)" + GIT_BRANCH="$(git rev-parse --abbrev-ref HEAD 2>/dev/null || echo unknown)" + if git rev-parse --is-inside-work-tree >/dev/null 2>&1; then + if [[ -n "$(git status --porcelain 2>/dev/null)" ]]; then + GIT_DIRTY=true + else + GIT_DIRTY=false + fi + else + GIT_DIRTY=unknown + fi + printf 'git_head=%s\n' "${GIT_HEAD}" + printf 'git_branch=%s\n' "${GIT_BRANCH}" + printf 'git_dirty=%s\n' "${GIT_DIRTY}" + ) +} + +collect_runtime_meta() { + run_in_env python - <<'PY' +import platform +import sys + +print(f"python_version={sys.version.split()[0]}") +print(f"platform={platform.platform()}") +try: + import torch + print(f"torch_version={torch.__version__}") + print(f"cuda_version={getattr(torch.version, 'cuda', None)}") + if torch.cuda.is_available(): + print(f"gpu_name={torch.cuda.get_device_name(0)}") + print(f"gpu_count={torch.cuda.device_count()}") + else: + print("gpu_name=") + print("gpu_count=0") +except Exception as exc: # noqa: BLE001 + print(f"torch_import_error={exc}") +PY +} + +while [[ $# -gt 0 ]]; do + case "$1" in + --schedule) + SCHEDULE="${2:-}" + shift 2 + ;; + --mode) + MODE="${2:-}" + shift 2 + ;; + --outdir) + OUTDIR_OVERRIDE="${2:-}" + shift 2 + ;; + --resume) + RESUME="${2:-}" + shift 2 + ;; + --seed) + SEED="${2:-}" + shift 2 + ;; + -h|--help) + usage + exit 0 + ;; + *) + echo "Unknown argument: $1" >&2 + usage >&2 + exit 2 + ;; + esac +done + +case "$SCHEDULE" in + sigmoid|adaptive_v1) ;; + *) + echo "Usage: $0 --schedule {sigmoid|adaptive_v1} --mode {...}" >&2 + exit 2 + ;; +esac + +case "$MODE" in + dry-run) + DURATION="" + ;; + activation) + DURATION="0.004" + ;; + stability) + DURATION="0.016" + ;; + baseline) + DURATION="0.128" + ;; + *) + echo "Usage: $0 --mode {dry-run|activation|stability|baseline}" >&2 + exit 2 + ;; +esac + +case "$SEED" in + 0|1|2) ;; + *) + echo "Usage: $0 --seed {0|1|2}" >&2 + exit 2 + ;; +esac + +# Frozen paired knobs — identical for sigmoid and adaptive_v1. +DATA="${ECT_DATA_PATH:-/mnt/ect_project/datasets/cifar10-32x32.zip}" +TRANSFER="${ECT_TRANSFER_PATH:-/mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl}" +RUNS_ROOT="${ECT_RUNS_ROOT:-/mnt/ect_project/runs/paired-training-v1}" + +COND=False +ARCH=ddpmpp +PRECOND=ect +BATCH=128 +BATCH_GPU=16 +OPTIM=RAdam +LR=0.0001 +DROPOUT=0.2 +AUGMENT=0 +Q=256 +K=8 +B=1 +C=0 +DOUBLE=10000 +EMA_BETA=0.9993 +FP16=True +ENABLE_AMP=True +METRICS=none + +cd "${ROOT_DIR}" + +RUN_STAMP="$(date -u +%Y%m%dT%H%M%SZ)" +GIT_SHA_SHORT="$(short_git_sha)" +SCHEDULE_SLUG="$(schedule_slug "${SCHEDULE}")" + +resolve_outdir() { + if [[ -n "${RESUME}" ]]; then + [[ -f "${RESUME}" ]] || fail "resume state not found: ${RESUME}" + OUTDIR="$(cd "$(dirname "${RESUME}")" && pwd)" + if [[ -n "${OUTDIR_OVERRIDE}" ]]; then + RESOLVED_OVERRIDE="$(mkdir -p "${OUTDIR_OVERRIDE}" && cd "${OUTDIR_OVERRIDE}" && pwd)" + [[ "${RESOLVED_OVERRIDE}" == "${OUTDIR}" ]] || fail "--outdir must match resume directory (${OUTDIR})" + fi + return + fi + if [[ -n "${OUTDIR_OVERRIDE}" ]]; then + OUTDIR="${OUTDIR_OVERRIDE}" + return + fi + # Unique per (schedule, mode, commit, time): never mixes sigmoid with adaptive_v1. + SEED_SLUG="" + if [[ "${SEED}" != "0" ]]; then + SEED_SLUG="-seed${SEED}" + fi + OUTDIR="${RUNS_ROOT}/${SCHEDULE_SLUG}-${MODE}${SEED_SLUG}-${GIT_SHA_SHORT}-${RUN_STAMP}" +} + +build_cmd() { + # ct_train.py forbids --transfer and --resume together. + CMD=( + python "${ROOT_DIR}/ct_train.py" + "--data=${DATA}" + "--outdir=${OUTDIR}" + "--nosubdir" + "--cond=${COND}" + "--arch=${ARCH}" + "--precond=${PRECOND}" + "--batch=${BATCH}" + "--batch-gpu=${BATCH_GPU}" + "--optim=${OPTIM}" + "--lr=${LR}" + "--dropout=${DROPOUT}" + "--augment=${AUGMENT}" + "--mapping=${SCHEDULE}" + -q "${Q}" + -k "${K}" + -b "${B}" + -c "${C}" + "--double=${DOUBLE}" + "--ema_beta=${EMA_BETA}" + "--seed=${SEED}" + "--fp16=${FP16}" + "--enable_amp=${ENABLE_AMP}" + "--metrics=${METRICS}" + ) + if [[ -n "${RESUME}" ]]; then + CMD+=("--resume=${RESUME}") + else + CMD+=("--transfer=${TRANSFER}") + fi + if [[ -n "${DURATION}" ]]; then + CMD+=("--duration=${DURATION}") + fi +} + +print_resolved_params() { + cat </dev/null || echo unknown + )" + if [[ -n "$(cd "${ROOT_DIR}" && git status --porcelain 2>/dev/null)" ]]; then + cur_dirty=true + else + cur_dirty=false + fi + cur_data_sha="$(sha256_file "${DATA}")" + cur_transfer_sha="$(sha256_file "${TRANSFER}")" + + [[ "${cur_head}" == "${meta_head}" ]] || fail \ + "resume HEAD mismatch: current=${cur_head} fresh=${meta_head}" + [[ "${meta_dirty}" == "false" ]] || fail \ + "refuse resume from dirty fresh segment: git_dirty=${meta_dirty}" + [[ "${cur_dirty}" == "false" ]] || fail \ + "refuse resume with dirty worktree (must match clean fresh segment)" + [[ "${cur_data_sha}" == "${meta_data_sha}" ]] || fail \ + "resume dataset SHA mismatch: current=${cur_data_sha} fresh=${meta_data_sha}" + [[ "${cur_transfer_sha}" == "${meta_transfer_sha}" ]] || fail \ + "resume transfer SHA mismatch: current=${cur_transfer_sha} fresh=${meta_transfer_sha}" + + # Directory name should also encode the arm when created by this runner. + local base + base="$(basename "${OUTDIR}")" + if [[ "${base}" == sigmoid-* && "${SCHEDULE}" != "sigmoid" ]]; then + fail "refuse writing adaptive into sigmoid outdir: ${OUTDIR}" + fi + if [[ "${base}" == adaptive-v1-* && "${SCHEDULE}" != "adaptive_v1" ]]; then + fail "refuse writing sigmoid into adaptive-v1 outdir: ${OUTDIR}" + fi +} + +resolve_outdir +build_cmd + +# Resume identity gate runs for dry-run too so provenance can be tested without CUDA. +if [[ -n "${RESUME}" ]]; then + [[ -f "${DATA}" ]] || fail "dataset not found: ${DATA}" + [[ -f "${TRANSFER}" ]] || fail "transfer checkpoint not found: ${TRANSFER}" + for arg in "${CMD[@]}"; do + case "${arg}" in + --transfer=*) fail "internal error: resume command includes --transfer" ;; + --resume=*) HAS_RESUME_FLAG=1 ;; + esac + done + [[ "${HAS_RESUME_FLAG:-0}" == "1" ]] || fail "internal error: resume command missing --resume" + assert_resume_identity_gate +fi + +if [[ "${MODE}" == "dry-run" ]]; then + print_resolved_params + print_exact_command + exit 0 +fi + +[[ -f "${DATA}" ]] || fail "dataset not found: ${DATA}" +if [[ -z "${RESUME}" ]]; then + [[ -f "${TRANSFER}" ]] || fail "transfer checkpoint not found: ${TRANSFER}" + assert_fresh_outdir_safe +fi + +# Preserve the first (fresh) run_meta.env forever. Resume writes mode-specific + latest +# sidecars so packaging can still recover train-time hashes and the final command. +META_LATEST="${OUTDIR}/run_meta.latest.env" +META_MODE="${OUTDIR}/run_meta.${MODE}.env" +{ + print_resolved_params + print_exact_command + collect_runtime_meta +} | tee "${META_LATEST}" | tee "${META_MODE}" >/dev/null +if [[ ! -f "${OUTDIR}/run_meta.env" ]]; then + cp "${META_LATEST}" "${OUTDIR}/run_meta.env" +fi +# Always show the segment meta on stdout. +cat "${META_LATEST}" + +# Never append to old logs: each invocation gets a fresh log file; refuse clobber. +LOG_PATH="${OUTDIR}/${MODE}-${RUN_STAMP}.log" +[[ ! -e "${LOG_PATH}" ]] || fail "log already exists (refuse overwrite/append): ${LOG_PATH}" +printf '[run_schedule_experiment] logging to %s\n' "${LOG_PATH}" + +run_in_env "${CMD[@]}" 2>&1 | tee "${LOG_PATH}" +exit "${PIPESTATUS[0]}" diff --git a/scripts/run_staged_evaluation.py b/scripts/run_staged_evaluation.py new file mode 100644 index 00000000..a1cac4f4 --- /dev/null +++ b/scripts/run_staged_evaluation.py @@ -0,0 +1,427 @@ +#!/usr/bin/env python3 +"""Run the frozen staged checkpoint-evaluation matrix on a GPU server.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import platform +import shlex +import subprocess +import sys +import time +from pathlib import Path + + +REPO_ROOT = Path(__file__).resolve().parents[1] +PROTOCOL_ID = "staged-checkpoint-evaluation-v1" +METRIC_SEED = 20260730 +NFE_SETTINGS = {1: [], 2: [0.821]} +INCEPTION_DETECTOR_URL = ( + "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/" + "inception-2015-12-05.pt" +) +PHASES = { + "smoke": { + "evidence_class": "quick", + "sample_count": 5_000, + "sample_seeds": "0-4999", + "metrics": ("kid5k_full", "fid5k_full"), + }, + "quick": { + "evidence_class": "quick", + "sample_count": 5_000, + "sample_seeds": "0-4999", + "metrics": ("kid5k_full", "fid5k_full"), + }, + "formal": { + "evidence_class": "formal", + "sample_count": 50_000, + "sample_seeds": "0-49999", + "metrics": ("kid50k_full", "fid50k_full"), + }, +} + + +def fail(message: str) -> None: + raise SystemExit(f"[run_staged_evaluation] ERROR: {message}") + + +def sha256_file(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def git_head() -> str: + try: + return subprocess.check_output( + ["git", "rev-parse", "HEAD"], cwd=REPO_ROOT, text=True + ).strip() + except (OSError, subprocess.CalledProcessError): + return "unknown" + + +def capture_evaluation_environment( + dataset_sha256: str, evaluation_git_commit: str, +) -> dict: + """Capture the evaluator runtime needed to compare formal metric runs.""" + import scipy + import torch + + gpu_models = [] + if torch.cuda.is_available(): + for index in range(torch.cuda.device_count()): + try: + gpu_models.append(torch.cuda.get_device_name(index)) + except RuntimeError as exc: + gpu_models.append(f"unavailable: {exc}") + return { + "evaluation_git_commit": evaluation_git_commit, + "python": { + "implementation": platform.python_implementation(), + "version": platform.python_version(), + }, + "scipy_version": scipy.__version__, + "pytorch_version": torch.__version__, + "cuda": { + "available": torch.cuda.is_available(), + "compiled_version": torch.version.cuda, + "device_count": torch.cuda.device_count(), + "gpu_models": gpu_models, + }, + "inception_detector": { + "identifier": "inception-2015-12-05", + "url": INCEPTION_DETECTOR_URL, + "format": "TorchScript", + "kwargs": {"return_features": True}, + }, + "dataset_sha256": dataset_sha256, + } + + +def load_json(path: Path, label: str) -> dict: + try: + payload = json.loads(path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError) as exc: + fail(f"cannot read {label} {path}: {exc}") + if not isinstance(payload, dict): + fail(f"{label} must contain a JSON object: {path}") + return payload + + +def load_cells(manifest_path: Path, allow_missing_inputs: bool) -> tuple[list[dict], dict | None]: + manifest = load_json(manifest_path, "checkpoint manifest") + if manifest.get("protocol") not in (None, PROTOCOL_ID): + fail(f"manifest protocol must be {PROTOCOL_ID!r}") + raw_cells = manifest.get("cells") + if not isinstance(raw_cells, list) or not raw_cells: + fail("manifest must contain a non-empty cells list") + + cells = [] + checkpoint_ids = set() + for raw in raw_cells: + try: + checkpoint_id = str(raw["checkpoint_id"]) + method = str(raw["method"]) + training_seed = int(raw["training_seed"]) + budget_kimg = int(raw["budget_kimg"]) + checkpoint = Path(raw["checkpoint"]).expanduser().resolve() + expected_sha256 = str(raw["checkpoint_sha256"]) + except (KeyError, TypeError, ValueError) as exc: + fail(f"invalid checkpoint cell {raw!r}: {exc}") + if not checkpoint_id or checkpoint_id in checkpoint_ids: + fail(f"checkpoint_id must be unique and non-empty: {checkpoint_id!r}") + if len(expected_sha256) != 64 or any(char not in "0123456789abcdef" for char in expected_sha256.lower()): + fail(f"checkpoint_sha256 must be a 64-character hexadecimal digest: {checkpoint_id}") + checkpoint_ids.add(checkpoint_id) + if checkpoint.is_file(): + actual_sha256 = sha256_file(checkpoint) + if actual_sha256 != expected_sha256: + fail( + f"checkpoint SHA256 mismatch for {checkpoint}: " + f"{actual_sha256} != {expected_sha256}" + ) + elif not allow_missing_inputs: + fail(f"checkpoint not found: {checkpoint}") + cells.append({ + "checkpoint_id": checkpoint_id, + "method": method, + "training_seed": training_seed, + "budget_kimg": budget_kimg, + "checkpoint": checkpoint, + "checkpoint_sha256": expected_sha256, + "integrity_receipt": raw.get("integrity_receipt"), + }) + + comparison = manifest.get("comparison") + if comparison is not None and not isinstance(comparison, dict): + fail("comparison must be a JSON object when provided") + return cells, comparison + + +def verify_integrity_receipt(cell: dict, allow_missing_inputs: bool) -> dict: + receipt_value = cell.get("integrity_receipt") + if not receipt_value: + fail(f"formal evaluation requires integrity_receipt: {cell['checkpoint_id']}") + receipt_path = Path(receipt_value).expanduser().resolve() + if not receipt_path.is_file(): + if allow_missing_inputs: + return {"path": str(receipt_path), "status": "not_checked_dry_run"} + fail(f"training-integrity receipt not found: {receipt_path}") + receipt = load_json(receipt_path, "training-integrity receipt") + required_fields = ( + "schema_version", "status", "checkpoint_id", "checkpoint_path", + "checkpoint_sha256", "training_run_id", "method", "training_seed", + "budget_kimg", "completion_passed", "logs_state_consistent", + "finite_loss_state_passed", "checker_version", "checker_git_commit", "checked_at_unix", + "checkpoint_load_passed", "ema_present", "ema_finite_passed", + "schedule_identity_passed", "global_gap_scale_identity_passed", + "method_identity_passed", + ) + missing = [field for field in required_fields if field not in receipt] + if missing: + fail(f"training-integrity receipt is incomplete ({missing}): {receipt_path}") + if receipt.get("status") != "passed": + fail(f"training-integrity receipt did not pass: {receipt_path}") + if receipt.get("checkpoint_sha256") != cell["checkpoint_sha256"]: + fail(f"training-integrity receipt SHA256 mismatch: {receipt_path}") + if receipt.get("checkpoint_id") != cell["checkpoint_id"]: + fail(f"training-integrity receipt checkpoint_id mismatch: {receipt_path}") + if Path(str(receipt["checkpoint_path"])).name != cell["checkpoint"].name: + fail(f"training-integrity receipt checkpoint path mismatch: {receipt_path}") + if receipt.get("method") != cell["method"]: + fail(f"training-integrity receipt method mismatch: {receipt_path}") + if receipt.get("training_seed") != cell["training_seed"]: + fail(f"training-integrity receipt training seed mismatch: {receipt_path}") + if receipt.get("budget_kimg") != cell["budget_kimg"]: + fail(f"training-integrity receipt budget mismatch: {receipt_path}") + for field in ( + "completion_passed", "logs_state_consistent", "finite_loss_state_passed", + "checkpoint_load_passed", "ema_present", "ema_finite_passed", + "schedule_identity_passed", "global_gap_scale_identity_passed", + "method_identity_passed", + ): + if receipt.get(field) is not True: + fail(f"training-integrity receipt did not pass {field}: {receipt_path}") + if ( + not str(receipt.get("training_run_id", "")) + or not str(receipt.get("checker_version", "")) + or not str(receipt.get("checker_git_commit", "")) + ): + fail(f"training-integrity receipt has an empty provenance field: {receipt_path}") + if not isinstance(receipt.get("checked_at_unix"), (int, float)): + fail(f"training-integrity receipt timestamp is invalid: {receipt_path}") + return {"path": str(receipt_path), "status": "passed"} + + +def select_cells(cells: list[dict], phase: str, smoke_checkpoint_id: str | None) -> list[dict]: + if phase == "smoke": + if not smoke_checkpoint_id: + fail("--smoke-checkpoint-id is required for --phase smoke") + selected = [cell for cell in cells if cell["checkpoint_id"] == smoke_checkpoint_id] + if len(selected) != 1: + fail(f"smoke checkpoint_id not found: {smoke_checkpoint_id}") + return selected + if smoke_checkpoint_id: + fail("--smoke-checkpoint-id is only valid for --phase smoke") + return cells + + +def validate_formal_promotion_policy(manifest: dict, cells: list[dict]) -> None: + """Reject formal manifests which make quick performance a selection gate.""" + policy = manifest.get("formal_promotion_policy") + if not isinstance(policy, dict): + fail("formal evaluation requires a formal_promotion_policy object") + if policy.get("eligibility") != "provenance_and_integrity_only": + fail("formal eligibility must be provenance_and_integrity_only") + if policy.get("quick_metric_performance") != "not_an_eligibility_criterion": + fail("quick metric performance must not be a formal eligibility criterion") + required_ids = policy.get("required_checkpoint_ids") + if ( + not isinstance(required_ids, list) + or not required_ids + or not all(isinstance(checkpoint_id, str) and checkpoint_id for checkpoint_id in required_ids) + or len(set(required_ids)) != len(required_ids) + ): + fail("formal_promotion_policy required_checkpoint_ids must be a unique non-empty string list") + actual_ids = {cell["checkpoint_id"] for cell in cells} + required_ids_set = set(required_ids) + if actual_ids != required_ids_set: + fail( + "formal manifest must contain every predeclared checkpoint; " + f"missing={sorted(required_ids_set - actual_ids)}, " + f"extra={sorted(actual_ids - required_ids_set)}" + ) + + +def require_empty(path: Path) -> None: + if path.exists() and any(path.iterdir()): + fail(f"refuse to append to non-empty output directory: {path}") + + +def build_jobs( + cells: list[dict], data: Path, outdir: Path, phase: str, base_port: int, + allow_missing_inputs: bool, +) -> list[dict]: + config = PHASES[phase] + jobs = [] + for cell in cells: + receipt = ( + verify_integrity_receipt(cell, allow_missing_inputs) + if phase == "formal" + else {"path": None, "status": "not_required"} + ) + for nfe in NFE_SETTINGS: + target = outdir / cell["checkpoint_id"] / f"nfe{nfe}" + command = [ + "bash", str(REPO_ROOT / "scripts" / "evaluate_checkpoint.sh"), + "1", str(base_port + len(jobs)), str(cell["checkpoint"]), + "--outdir", str(target), + "--nosubdir", + "--data", str(data), + "--cond=False", + "--arch=ddpmpp", + "--precond=ct", + "--dropout=0.2", + "--augment=0", + "--fp16=False", + "--cache=True", + "--workers=3", + f"--nfe={nfe}", + *(["--mid_t=0.821"] if nfe == 2 else []), + f"--metrics={','.join(config['metrics'])}", + "--metric-repeats=1", + f"--sample-seeds={config['sample_seeds']}", + f"--seed={METRIC_SEED}", + f"--desc={PROTOCOL_ID}-{phase}-{cell['checkpoint_id']}-nfe{nfe}", + ] + jobs.append({ + "evidence_class": config["evidence_class"], + "checkpoint_id": cell["checkpoint_id"], + "method": cell["method"], + "training_seed": cell["training_seed"], + "budget_kimg": cell["budget_kimg"], + "checkpoint": str(cell["checkpoint"]), + "checkpoint_sha256": cell["checkpoint_sha256"], + "integrity_receipt": receipt, + "nfe": nfe, + "mid_t": NFE_SETTINGS[nfe], + "sample_count": config["sample_count"], + "sample_seeds": config["sample_seeds"], + "metric_seed": METRIC_SEED, + "metric_names": list(config["metrics"]), + "output_directory": str(target), + "command": command, + }) + return jobs + + +def build_record( + cells: list[dict], comparison: dict | None, data: Path, outdir: Path, + phase: str, jobs: list[dict], dataset_sha256: str, +) -> dict: + config = PHASES[phase] + evaluation_commit = git_head() + return { + "schema_version": 1, + "protocol": PROTOCOL_ID, + "phase": phase, + "evidence_class": config["evidence_class"], + "evaluation_git_commit": evaluation_commit, + "evaluation_environment": capture_evaluation_environment( + dataset_sha256, evaluation_commit + ), + "dataset": str(data), + "dataset_sha256": dataset_sha256, + "precision": "fp32", + "nfe_modes": {str(nfe): mid_t for nfe, mid_t in NFE_SETTINGS.items()}, + "sample_count": config["sample_count"], + "sample_seeds": config["sample_seeds"], + "metric_seed": METRIC_SEED, + "metric_names": list(config["metrics"]), + "metric_repeats": 1, + "comparison": comparison, + "output_root": str(outdir), + "status": "dry_run", + "jobs": jobs, + } + + +def write_record(path: Path, record: dict) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(record, indent=2) + "\n", encoding="utf-8") + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--manifest", type=Path, required=True) + parser.add_argument("--data", type=Path, required=True) + parser.add_argument("--outdir", type=Path, required=True) + parser.add_argument("--phase", choices=tuple(PHASES), required=True) + parser.add_argument("--smoke-checkpoint-id") + parser.add_argument("--base-port", type=int, default=29800) + parser.add_argument("--dry-run", action="store_true") + parser.add_argument("--allow-missing-inputs", action="store_true") + args = parser.parse_args(argv) + + if args.allow_missing_inputs and not args.dry_run: + fail("--allow-missing-inputs is only allowed with --dry-run") + data = args.data.expanduser().resolve() + if not data.is_file() and not (args.dry_run and args.allow_missing_inputs): + fail(f"dataset not found: {data}") + outdir = args.outdir.expanduser().resolve() + if not args.dry_run: + require_empty(outdir) + + cells, comparison = load_cells(args.manifest, args.allow_missing_inputs) + if args.phase == "formal": + validate_formal_promotion_policy( + load_json(args.manifest, "checkpoint manifest"), cells + ) + selected = select_cells(cells, args.phase, args.smoke_checkpoint_id) + jobs = build_jobs(selected, data, outdir, args.phase, args.base_port, args.allow_missing_inputs) + record = build_record( + selected, comparison, data, outdir, args.phase, jobs, + sha256_file(data) if data.is_file() else "missing", + ) + if args.dry_run: + print(json.dumps({key: value for key, value in record.items() if key != "jobs"}, indent=2)) + for job in jobs: + print(shlex.join(job["command"])) + return + + record_path = outdir / "run_manifest.json" + record["status"] = "running" + write_record(record_path, record) + started = time.time() + for index, job in enumerate(jobs, start=1): + target = Path(job["output_directory"]) + require_empty(target) + print(f"[{index}/{len(jobs)}] {args.phase} {job['checkpoint_id']} nfe={job['nfe']}") + print(shlex.join(job["command"])) + job["started_at_unix"] = time.time() + try: + subprocess.run(job["command"], cwd=REPO_ROOT, check=True) + except subprocess.CalledProcessError as exc: + job["status"] = "failed" + job["returncode"] = exc.returncode + record["status"] = "failed" + record["elapsed_seconds"] = round(time.time() - started, 3) + write_record(record_path, record) + raise SystemExit(exc.returncode) from exc + job["status"] = "completed" + job["elapsed_seconds"] = round(time.time() - job["started_at_unix"], 3) + write_record(record_path, record) + + record["status"] = "completed" + record["elapsed_seconds"] = round(time.time() - started, 3) + write_record(record_path, record) + print(f"Completed {len(jobs)} jobs; record: {record_path}") + + +if __name__ == "__main__": + main() diff --git a/scripts/sample_blind_images.py b/scripts/sample_blind_images.py new file mode 100644 index 00000000..1d41ac2f --- /dev/null +++ b/scripts/sample_blind_images.py @@ -0,0 +1,98 @@ +#!/usr/bin/env python3 +"""Generate blind-review stimuli once, without repeating the archived determinism smoke.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import pickle +import sys +import time +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +import torch + +import dnnlib +from ct_eval import parse_int_list +from scripts.sample_fixed_seeds import ( + configure_precision, + generate_uint8, + make_checkpoint_id, + save_rgb, +) + + +def fail(message: str) -> None: + raise SystemExit(f"[sample_blind_images] ERROR: {message}") + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--network", required=True) + parser.add_argument("--outdir", type=Path, required=True) + parser.add_argument("--seeds", default="0-15") + parser.add_argument("--mid-t", type=float, default=0.821) + parser.add_argument("--work-group-size", type=int, default=8) + parser.add_argument("--precision", choices=("fp32",), default="fp32") + parser.add_argument("--device", default="cuda") + args = parser.parse_args(argv) + + seeds = parse_int_list(args.seeds) + if not seeds or len(seeds) != len(set(seeds)): + fail("--seeds must be a non-empty unique list") + with dnnlib.util.open_url(args.network, verbose=True) as handle: + checkpoint_bytes = handle.read() + digest = hashlib.sha256(checkpoint_bytes).hexdigest() + checkpoint_id = make_checkpoint_id(args.network, digest) + run_dir = args.outdir.resolve() / checkpoint_id + if run_dir.exists() and any(run_dir.iterdir()): + fail(f"refuse to overwrite non-empty stimulus directory: {run_dir}") + run_dir.mkdir(parents=True, exist_ok=True) + + data = pickle.loads(checkpoint_bytes) + net = data["ema"].eval().requires_grad_(False).to(args.device) + precision = configure_precision(net, args.precision, args.device) + modes = [] + started = time.perf_counter() + with torch.no_grad(): + for nfe in (1, 2): + mode_started = time.perf_counter() + images = generate_uint8( + net, seeds, nfe, args.mid_t, args.work_group_size, args.device + ) + mode_dir = run_dir / f"nfe{nfe}" / "images" + for seed, image in zip(seeds, images): + save_rgb(image, mode_dir / f"seed{seed:06d}.png") + modes.append({ + "nfe": nfe, + "mid_t": [] if nfe == 1 else [args.mid_t], + "image_count": len(images), + "elapsed_seconds": round(time.perf_counter() - mode_started, 6), + }) + metadata = { + "schema_version": 1, + "purpose": "blind_visual_stimuli", + "checkpoint_path": args.network, + "checkpoint_sha256": digest, + "checkpoint_id": checkpoint_id, + "precision": precision, + "sample_seeds": seeds, + "modes": modes, + "work_group_size": args.work_group_size, + "determinism_retested": False, + "determinism_note": "The archived fixed-seed mechanism/determinism evaluation is not repeated; each stimulus is generated once.", + "elapsed_seconds": round(time.perf_counter() - started, 6), + } + (run_dir / "metadata.json").write_text( + json.dumps(metadata, indent=2) + "\n", encoding="utf-8" + ) + print(json.dumps(metadata, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/scripts/sample_checkpoint.sh b/scripts/sample_checkpoint.sh new file mode 100644 index 00000000..09352622 --- /dev/null +++ b/scripts/sample_checkpoint.sh @@ -0,0 +1,14 @@ +#!/usr/bin/env bash +set -euo pipefail + +if [[ $# -lt 1 ]]; then + echo "Usage: $0 CHECKPOINT [sample_fixed_seeds.py options]" >&2 + exit 2 +fi + +checkpoint=$1 +shift +repo_dir=$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd) + +cd "$repo_dir" +python scripts/sample_fixed_seeds.py --network "$checkpoint" "$@" diff --git a/scripts/sample_fixed_seeds.py b/scripts/sample_fixed_seeds.py new file mode 100644 index 00000000..aaedca1d --- /dev/null +++ b/scripts/sample_fixed_seeds.py @@ -0,0 +1,291 @@ +#!/usr/bin/env python3 +"""Generate work-group-independent fixed-seed samples from a checkpoint.""" + +import argparse +import hashlib +import json +import pickle +import re +import subprocess +import sys +import time +from pathlib import Path +from urllib.parse import urlparse + +import numpy as np +import PIL.Image +import torch + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT)) + +import dnnlib +from ct_eval import generator_fn, parse_int_list + + +def sha256_file(path): + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def git_commit(): + try: + return subprocess.check_output( + ["git", "rev-parse", "HEAD"], text=True, stderr=subprocess.DEVNULL + ).strip() + except (OSError, subprocess.CalledProcessError): + return "unknown" + + +def make_checkpoint_id(checkpoint_path, checkpoint_sha256): + filename = Path(urlparse(str(checkpoint_path)).path).name + stem = Path(filename).stem or "checkpoint" + safe_stem = re.sub(r"[^A-Za-z0-9._-]+", "-", stem).strip("-._") + return f"{safe_stem or 'checkpoint'}-{checkpoint_sha256[:12]}" + + +def seeded_inputs(seeds, shape, intermediate_steps): + latents = [] + noises = [[] for _ in range(intermediate_steps)] + for seed in seeds: + generator = torch.Generator(device="cpu").manual_seed(seed) + latents.append(torch.randn(shape, generator=generator, dtype=torch.float64)) + for index in range(intermediate_steps): + noises[index].append(torch.randn(shape, generator=generator, dtype=torch.float64)) + return torch.stack(latents), [torch.stack(items) for items in noises] + + +def generate_uint8(net, seeds, nfe, mid_t, work_group_size, device): + shape = (net.img_channels, net.img_resolution, net.img_resolution) + images = [] + for start in range(0, len(seeds), work_group_size): + work_group_seeds = seeds[start:start + work_group_size] + # Keep model forwards at batch=1. cuDNN may select different convolution + # plans for different batch shapes, which can change quantized pixels. + for seed in work_group_seeds: + latents, step_noises = seeded_inputs([seed], shape, nfe - 1) + image = generator_fn( + net, + latents.to(device), + mid_t=None if nfe == 1 else [mid_t], + step_noises=[noise.to(device) for noise in step_noises], + ) + images.append(image.cpu()) + images = torch.cat(images).numpy() + return np.rint((images + 1) * 127.5).clip(0, 255).astype(np.uint8) + + +def image_bytes(image): + return image.transpose(1, 2, 0).tobytes() + + +def differing_seeds(seeds, first_images, second_images): + return [ + seed + for seed, first, second in zip(seeds, first_images, second_images) + if image_bytes(first) != image_bytes(second) + ] + + +def assert_work_group_equivalence(net, seeds, nfe, mid_t, first, second, device): + first_images = generate_uint8(net, seeds, nfe, mid_t, first, device) + second_images = generate_uint8(net, seeds, nfe, mid_t, second, device) + mismatches = differing_seeds(seeds, first_images, second_images) + if mismatches: + raise RuntimeError( + f"NFE={nfe} differs across work-group sizes {first}/{second} " + f"for seeds: {mismatches}" + ) + return first_images + + +def assert_repeat_equivalence(net, seeds, nfe, mid_t, work_group_size, reference, device): + repeated = generate_uint8(net, seeds, nfe, mid_t, work_group_size, device) + mismatches = differing_seeds(seeds, reference, repeated) + if mismatches: + raise RuntimeError( + f"NFE={nfe} differs across repeated runs for seeds: {mismatches}" + ) + + +def save_rgb(image, path): + path.parent.mkdir(parents=True, exist_ok=True) + PIL.Image.fromarray(image.transpose(1, 2, 0), mode="RGB").save(path) + + +def save_grid(images, path, columns=8): + if len(images) % columns: + raise ValueError("image count must be divisible by grid columns") + rows = len(images) // columns + _, height, width = images.shape[1:] + grid = images.reshape(rows, columns, 3, height, width) + grid = grid.transpose(0, 3, 1, 4, 2).reshape(rows * height, columns * width, 3) + PIL.Image.fromarray(grid, mode="RGB").save(path) + + +def save_mode_outputs(images, seeds, mode_dir): + if len(images) != len(seeds): + raise ValueError("the image and seed counts must match") + if len(set(seeds)) != len(seeds): + raise ValueError("seeds must not contain duplicates") + + paths = [] + for seed, image in zip(seeds, images): + path = mode_dir / "images" / f"seed{seed:06d}.png" + save_rgb(image, path) + paths.append(path) + + grid_path = mode_dir / "grid_8x8.png" + save_grid(images, grid_path) + paths.append(grid_path) + return paths + + +def configure_precision(net, requested, device): + native = "fp16" if getattr(net, "use_fp16", False) else "fp32" + if requested == "checkpoint": + return native + if not hasattr(net, "use_fp16"): + raise ValueError("the checkpoint network does not expose use_fp16") + if requested == "fp16" and not str(device).startswith("cuda"): + raise ValueError("fp16 sampling requires a CUDA device") + net.use_fp16 = requested == "fp16" + return requested + + +def build_metadata( + *, args, checkpoint_sha256, checkpoint_id, run_dir, net, + effective_precision, seeds, modes, elapsed_seconds, +): + mode_names = [mode["name"] for mode in modes] + return { + "schema_version": "1.0", + "evaluation_git_commit": git_commit(), + "checkpoint_path": args.network, + "checkpoint_sha256": checkpoint_sha256, + "checkpoint_id": checkpoint_id, + "output_directory": str(run_dir), + "seed_count": len(seeds), + "seed_list": seeds, + "nfe_modes": [mode["nfe"] for mode in modes], + "mid_t_by_mode": { + mode["name"]: mode["mid_t"] for mode in modes + }, + "precision_requested": args.precision, + "precision": effective_precision, + "device": str(args.device), + "gpu": ( + torch.cuda.get_device_name(0) + if str(args.device).startswith("cuda") and torch.cuda.is_available() + else "cpu" + ), + "elapsed_seconds_total": elapsed_seconds, + "elapsed_seconds_by_mode": { + mode["name"]: mode["elapsed_seconds"] for mode in modes + }, + "image_count_total": sum(mode["image_count"] for mode in modes), + "image_count_by_mode": { + mode["name"]: mode["image_count"] for mode in modes + }, + "generator_implementation": "ct_eval.generator_fn", + "model_forward_batch_size": 1, + "work_group_sizes_verified": [ + args.work_group_size, + args.verify_work_group_size, + ], + "image_resolution": [net.img_resolution, net.img_resolution], + "image_channels": net.img_channels, + "determinism_passed": True, + "repeat_runs_verified": 2, + "verified_modes": mode_names, + } + + +def write_manifest(entries, path): + path.write_text( + "".join(f"{digest} {name}\n" for digest, name in entries), + encoding="utf-8", + ) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--network", required=True, help="Checkpoint PKL path or URL") + parser.add_argument("--outdir", type=Path, default=Path("/mnt/ect_project/evaluations")) + parser.add_argument("--seeds", default="0-63") + parser.add_argument("--nfe", type=int, choices=[1, 2], nargs="+", default=[1, 2]) + parser.add_argument("--mid-t", type=float, default=0.821) + parser.add_argument("--work-group-size", type=int, default=8) + parser.add_argument("--verify-work-group-size", type=int, default=16) + parser.add_argument( + "--precision", choices=["checkpoint", "fp32", "fp16"], default="checkpoint" + ) + parser.add_argument("--device", default="cuda") + args = parser.parse_args() + + seeds = parse_int_list(args.seeds) + if not seeds: + raise SystemExit("--seeds must not be empty") + if len(set(seeds)) != len(seeds): + raise SystemExit("--seeds must not contain duplicates") + with dnnlib.util.open_url(args.network, verbose=True) as handle: + checkpoint_bytes = handle.read() + checkpoint_sha256 = hashlib.sha256(checkpoint_bytes).hexdigest() + checkpoint_id = make_checkpoint_id(args.network, checkpoint_sha256) + run_dir = args.outdir / checkpoint_id + run_dir.mkdir(parents=True, exist_ok=True) + data = pickle.loads(checkpoint_bytes) + net = data["ema"].eval().requires_grad_(False).to(args.device) + effective_precision = configure_precision(net, args.precision, args.device) + + manifest = [] + modes = [] + started_at = time.perf_counter() + with torch.no_grad(): + for nfe in args.nfe: + mode_started_at = time.perf_counter() + images = assert_work_group_equivalence( + net, seeds, nfe, args.mid_t, args.work_group_size, + args.verify_work_group_size, args.device, + ) + assert_repeat_equivalence( + net, seeds, nfe, args.mid_t, args.work_group_size, + images, args.device, + ) + mode_dir = run_dir / f"nfe{nfe}" + paths = save_mode_outputs(images, seeds, mode_dir) + for path in paths: + manifest.append((sha256_file(path), path.relative_to(run_dir).as_posix())) + modes.append({ + "name": f"nfe{nfe}", + "nfe": nfe, + "mid_t": [] if nfe == 1 else [args.mid_t], + "image_count": len(images), + "elapsed_seconds": round(time.perf_counter() - mode_started_at, 6), + }) + elapsed_seconds = round(time.perf_counter() - started_at, 6) + + metadata = build_metadata( + args=args, + checkpoint_sha256=checkpoint_sha256, + checkpoint_id=checkpoint_id, + run_dir=run_dir, + net=net, + effective_precision=effective_precision, + seeds=seeds, + modes=modes, + elapsed_seconds=elapsed_seconds, + ) + metadata_path = run_dir / "metadata.json" + metadata_path.write_text(json.dumps(metadata, indent=2) + "\n", encoding="utf-8") + manifest.append((sha256_file(metadata_path), metadata_path.name)) + write_manifest(manifest, run_dir / "sha256_manifest.txt") + print(json.dumps(metadata, indent=2)) + print(f"Results written to {run_dir}") + + +if __name__ == "__main__": + main() diff --git a/scripts/score_blind_ab.py b/scripts/score_blind_ab.py new file mode 100644 index 00000000..4c0cefaf --- /dev/null +++ b/scripts/score_blind_ab.py @@ -0,0 +1,174 @@ +#!/usr/bin/env python3 +"""Validate locked A/B ballots, unblind them, and summarize preferences.""" + +from __future__ import annotations + +import argparse +import csv +import json +from collections import defaultdict +from pathlib import Path + + +VALID_PREFERENCES = {"A", "B", "TIE"} + + +def fail(message: str) -> None: + raise SystemExit(f"[score_blind_ab] ERROR: {message}") + + +def read_csv(path: Path) -> list[dict]: + try: + with path.open(newline="", encoding="utf-8") as handle: + return list(csv.DictReader(handle)) + except OSError as exc: + fail(f"cannot read {path}: {exc}") + + +def load_key(path: Path) -> dict[str, dict]: + rows = read_csv(path) + keyed = {row.get("trial_id", ""): row for row in rows} + if len(rows) != 96 or len(keyed) != 96 or "" in keyed: + fail("private key must contain exactly 96 unique trial IDs") + for row in rows: + if {row.get("A_schedule"), row.get("B_schedule")} != {"sigmoid", "adaptive_v1"}: + fail(f"invalid side key for {row.get('trial_id')}") + return keyed + + +def load_responses(paths: list[Path], key: dict[str, dict], min_raters: int) -> list[dict]: + responses = [] + seen = set() + rater_trials = defaultdict(set) + for path in paths: + for row in read_csv(path): + trial_id = row.get("trial_id", "").strip() + rater_id = row.get("rater_id", "").strip() + preference = row.get("preference_A_B_TIE", row.get("preference", "")).strip().upper() + if trial_id not in key: + fail(f"unknown trial ID in {path}: {trial_id}") + if not rater_id: + fail(f"missing rater_id for {trial_id} in {path}") + if preference not in VALID_PREFERENCES: + fail(f"invalid preference for {trial_id}/{rater_id}: {preference!r}") + pair = (rater_id, trial_id) + if pair in seen: + fail(f"duplicate response for {rater_id}/{trial_id}") + seen.add(pair) + rater_trials[rater_id].add(trial_id) + responses.append({"trial_id": trial_id, "rater_id": rater_id, "preference": preference}) + if len(rater_trials) < min_raters: + fail(f"found {len(rater_trials)} complete raters; protocol requires at least {min_raters}") + expected_trials = set(key) + for rater_id, trials in rater_trials.items(): + if trials != expected_trials: + fail(f"rater {rater_id} has {len(trials)}/96 trials; incomplete ballots are not scored") + return responses + + +def counter() -> dict: + return {"adaptive_v1": 0, "sigmoid": 0, "tie": 0, "judgments": 0} + + +def add_result(counts: dict, winner: str) -> None: + counts[winner] += 1 + counts["judgments"] += 1 + + +def finalize(label: str, counts: dict) -> dict: + non_ties = counts["adaptive_v1"] + counts["sigmoid"] + judgments = counts["judgments"] + return { + "stratum": label, + **counts, + "adaptive_share_excluding_ties": None if non_ties == 0 else counts["adaptive_v1"] / non_ties, + "adaptive_tie_half_score": None if judgments == 0 else (counts["adaptive_v1"] + 0.5 * counts["tie"]) / judgments, + } + + +def summarize(responses: list[dict], key: dict[str, dict]) -> list[dict]: + groups = defaultdict(counter) + for response in responses: + item = key[response["trial_id"]] + preference = response["preference"] + winner = "tie" if preference == "TIE" else item[f"{preference}_schedule"] + training_seed = int(item["training_seed"]) + nfe = int(item["nfe"]) + rater_id = response["rater_id"] + for label in ( + "overall", + f"nfe={nfe}", + f"training_seed={training_seed}", + f"training_seed={training_seed},nfe={nfe}", + f"rater={rater_id}", + ): + add_result(groups[label], winner) + labels = ["overall", "nfe=1", "nfe=2"] + labels += [f"training_seed={seed}" for seed in range(3)] + labels += [f"training_seed={seed},nfe={nfe}" for seed in range(3) for nfe in (1, 2)] + labels += sorted(label for label in groups if label.startswith("rater=")) + return [finalize(label, groups[label]) for label in labels] + + +def write_csv(path: Path, rows: list[dict]) -> None: + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0])) + writer.writeheader() + writer.writerows(rows) + + +def write_markdown(path: Path, rows: list[dict], rater_count: int) -> None: + lines = [ + "# Blinded A/B visual preference summary", + "", + f"Locked complete ballots: {rater_count}. Preferences are descriptive judgments on 96 method-blinded paired trials per rater.", + "", + "| Stratum | Adaptive wins | Fixed wins | Ties | Adaptive share (ties excluded) | Adaptive score (ties=0.5) |", + "| --- | ---: | ---: | ---: | ---: | ---: |", + ] + for row in rows: + share = "—" if row["adaptive_share_excluding_ties"] is None else f"{row['adaptive_share_excluding_ties']:.3f}" + score = "—" if row["adaptive_tie_half_score"] is None else f"{row['adaptive_tie_half_score']:.3f}" + lines.append( + f"| {row['stratum']} | {row['adaptive_v1']} | {row['sigmoid']} | {row['tie']} | {share} | {score} |" + ) + lines.extend([ + "", + "The trials are repeated judgments nested within raters and training seeds; no binomial significance claim is made from the raw judgment count.", + "", + ]) + path.write_text("\n".join(lines), encoding="utf-8") + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--key", type=Path, required=True) + parser.add_argument("--responses", type=Path, nargs="+", required=True) + parser.add_argument("--outdir", type=Path, required=True) + parser.add_argument("--min-raters", type=int, default=3) + args = parser.parse_args(argv) + + key = load_key(args.key) + responses = load_responses(args.responses, key, args.min_raters) + rows = summarize(responses, key) + rater_count = len({response["rater_id"] for response in responses}) + outdir = args.outdir.resolve() + outdir.mkdir(parents=True, exist_ok=True) + write_csv(outdir / "blind_ab_summary.csv", rows) + payload = { + "schema_version": 1, + "method_blinded": True, + "complete_raters": rater_count, + "trials_per_rater": 96, + "judgments": len(responses), + "summary": rows, + } + (outdir / "blind_ab_summary.json").write_text( + json.dumps(payload, indent=2) + "\n", encoding="utf-8" + ) + write_markdown(outdir / "blind_ab_summary.md", rows, rater_count) + print(f"Scored {len(responses)} judgments from {rater_count} complete raters") + + +if __name__ == "__main__": + main() diff --git a/scripts/select_gap_scale.py b/scripts/select_gap_scale.py new file mode 100755 index 00000000..0eee4fba --- /dev/null +++ b/scripts/select_gap_scale.py @@ -0,0 +1,364 @@ +#!/usr/bin/env python3 +"""Validate the seed-0 global-gap sweep and select the confirmation scale. + +The selection rule is intentionally narrow: among the four non-unit +global-only candidates, choose the lowest NFE=1 KID-5k result. FID and +NFE=2 are reported for diagnosis but never participate in selection. +""" + +from __future__ import annotations + +import argparse +import csv +import json +import math +from pathlib import Path +from typing import Any + + +CELLS = ( + { + "arm": "fixed", + "global_scale": 1.0, + "global_scale_text": "1", + "label": "fixed-g1p0000-seed0-256k", + "expected_schedule": "sigmoid", + "eligible_for_selection": False, + }, + { + "arm": "global", + "global_scale": 0.97, + "global_scale_text": "0.97", + "label": "global-g0p9700-seed0-256k", + "expected_schedule": "global_sigmoid", + "eligible_for_selection": True, + }, + { + "arm": "global", + "global_scale": 1.032, + "global_scale_text": "1.032", + "label": "global-g1p0320-seed0-256k", + "expected_schedule": "global_sigmoid", + "eligible_for_selection": True, + }, + { + "arm": "global", + "global_scale": 1.06, + "global_scale_text": "1.06", + "label": "global-g1p0600-seed0-256k", + "expected_schedule": "global_sigmoid", + "eligible_for_selection": True, + }, + { + "arm": "global", + "global_scale": 1.10, + "global_scale_text": "1.10", + "label": "global-g1p1000-seed0-256k", + "expected_schedule": "global_sigmoid", + "eligible_for_selection": True, + }, +) +NFES = (1, 2) +METRICS = ("kid5k_full", "fid5k_full") +TIE_TARGET = 1.0317 + + +def fail(message: str) -> None: + raise SystemExit(f"[select_gap_scale] ERROR: {message}") + + +def load_json_object(path: Path, description: str) -> dict[str, Any]: + if not path.is_file(): + fail(f"missing {description}: {path}") + try: + value = json.loads(path.read_text(encoding="utf-8")) + except (OSError, UnicodeError, json.JSONDecodeError) as exc: + fail(f"cannot read {description} {path}: {exc}") + if not isinstance(value, dict): + fail(f"{description} must contain a JSON object: {path}") + return value + + +def require_training_validation(run_dir: Path, cell: dict[str, Any]) -> Path: + path = run_dir / "validation.json" + payload = load_json_object(path, "training validation") + if payload.get("status") != "passed": + fail( + f"training validation did not pass for {cell['label']}: " + f"status={payload.get('status')!r}" + ) + if payload.get("expected_schedule") != cell["expected_schedule"]: + fail( + f"training validation schedule mismatch for {cell['label']}: " + f"expected {cell['expected_schedule']!r}, " + f"got {payload.get('expected_schedule')!r}" + ) + return path.resolve() + + +def read_single_metric(path: Path, metric: str) -> float: + if not path.is_file(): + fail(f"missing metric result: {path}") + try: + lines = path.read_text(encoding="utf-8").splitlines() + except (OSError, UnicodeError) as exc: + fail(f"cannot read metric result {path}: {exc}") + if len(lines) != 1 or not lines[0].strip(): + fail(f"expected exactly one result line in {path}, found {len(lines)}") + try: + payload = json.loads(lines[0]) + except json.JSONDecodeError as exc: + fail(f"malformed metric JSON in {path}: {exc}") + if not isinstance(payload, dict): + fail(f"metric result must be a JSON object: {path}") + if payload.get("metric") != metric: + fail( + f"metric name mismatch in {path}: " + f"{payload.get('metric')!r} != {metric!r}" + ) + results = payload.get("results") + if not isinstance(results, dict) or set(results) != {metric}: + fail(f"expected exactly one {metric!r} result in {path}") + raw_value = results[metric] + if isinstance(raw_value, bool) or not isinstance(raw_value, (int, float)): + fail(f"metric result is not numeric in {path}: {raw_value!r}") + value = float(raw_value) + if not math.isfinite(value): + fail(f"metric result is non-finite in {path}: {value}") + return value + + +def require_evaluation_completion(eval_dir: Path) -> None: + meta_path = eval_dir / "experiment_meta.env" + if not meta_path.is_file(): + fail(f"missing evaluation metadata: {meta_path}") + lines = meta_path.read_text(encoding="utf-8").splitlines() + if lines.count("exit_code=0") != 1: + fail(f"evaluation did not record exactly one exit_code=0: {meta_path}") + + +def load_response_curve(runs_root: Path, eval_root: Path) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + for spec in CELLS: + cell = dict(spec) + run_dir = runs_root / cell["label"] + validation_path = require_training_validation(run_dir, cell) + cell["training_validation_path"] = str(validation_path) + for nfe in NFES: + eval_dir = eval_root / cell["label"] / f"nfe{nfe}" + require_evaluation_completion(eval_dir) + for metric in METRICS: + cell[f"nfe{nfe}_{metric}"] = read_single_metric( + eval_dir / f"metric-{metric}.jsonl", metric + ) + rows.append(cell) + return rows + + +def select_candidate(rows: list[dict[str, Any]]) -> dict[str, Any]: + candidates = [row for row in rows if row["eligible_for_selection"]] + if len(candidates) != 4: + fail(f"expected four non-unit candidates, found {len(candidates)}") + # Only NFE=1 KID appears before the tie-breakers. Do not add FID or NFE=2 + # here: those quantities are intentionally diagnostic-only. + return min( + candidates, + key=lambda row: ( + row["nfe1_kid5k_full"], + abs(row["global_scale"] - TIE_TARGET), + abs(row["global_scale"] - 1.0), + row["global_scale"], + ), + ) + + +def add_comparisons( + rows: list[dict[str, Any]], selected: dict[str, Any] +) -> tuple[dict[str, Any], list[dict[str, Any]]]: + fixed_rows = [row for row in rows if row["global_scale"] == 1.0] + if len(fixed_rows) != 1: + fail(f"expected exactly one unit-scale fixed row, found {len(fixed_rows)}") + fixed = fixed_rows[0] + enriched: list[dict[str, Any]] = [] + for source in rows: + row = dict(source) + for nfe in NFES: + for metric in METRICS: + key = f"nfe{nfe}_{metric}" + row[f"{key}_delta_vs_fixed"] = row[key] - fixed[key] + row["is_selected"] = row["label"] == selected["label"] + enriched.append(row) + selected_enriched = next(row for row in enriched if row["is_selected"]) + fixed_enriched = next(row for row in enriched if row["global_scale"] == 1.0) + return fixed_enriched, enriched + + +def csv_rows(rows: list[dict[str, Any]]) -> list[dict[str, Any]]: + fields = [ + "arm", + "global_scale", + "global_scale_text", + "label", + "eligible_for_selection", + "is_selected", + "nfe1_kid5k_full", + "nfe1_kid5k_full_delta_vs_fixed", + "nfe1_fid5k_full", + "nfe1_fid5k_full_delta_vs_fixed", + "nfe2_kid5k_full", + "nfe2_kid5k_full_delta_vs_fixed", + "nfe2_fid5k_full", + "nfe2_fid5k_full_delta_vs_fixed", + "training_validation_path", + ] + return [{field: row[field] for field in fields} for row in rows] + + +def write_csv(path: Path, rows: list[dict[str, Any]]) -> None: + output_rows = csv_rows(rows) + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter( + handle, fieldnames=list(output_rows[0]), lineterminator="\n" + ) + writer.writeheader() + writer.writerows(output_rows) + + +def format_metric(value: float) -> str: + return f"{value:.9g}" + + +def write_markdown( + path: Path, + rows: list[dict[str, Any]], + fixed: dict[str, Any], + selected: dict[str, Any], +) -> None: + beats = selected["nfe1_kid5k_full"] < fixed["nfe1_kid5k_full"] + lines = [ + "# Global-gap response curve: seed 0 at 256 kimg", + "", + "> These KID-5k and FID-5k values are 5k-sample proxies, not standard " + "FID-50k benchmark results.", + "", + "Lower is better. The frozen selection rule uses **only NFE=1 " + "KID-5k** among the four non-unit global-only candidates. The fixed " + "`g=1` row is a reference and is not eligible for selection. Exact " + f"ties are resolved by distance to `g={TIE_TARGET}`, then distance " + "to `g=1`.", + "", + "| Arm | g | NFE=1 KID | Δ vs fixed | NFE=1 FID | " + "NFE=2 KID | NFE=2 FID | Selected |", + "| --- | ---: | ---: | ---: | ---: | ---: | ---: | --- |", + ] + for row in rows: + lines.append( + f"| {row['arm']} | {row['global_scale_text']} | " + f"{format_metric(row['nfe1_kid5k_full'])} | " + f"{format_metric(row['nfe1_kid5k_full_delta_vs_fixed'])} | " + f"{format_metric(row['nfe1_fid5k_full'])} | " + f"{format_metric(row['nfe2_kid5k_full'])} | " + f"{format_metric(row['nfe2_fid5k_full'])} | " + f"{'yes' if row['is_selected'] else ''} |" + ) + comparison = "does beat" if beats else "does not beat" + lines.extend( + [ + "", + "## Frozen exploratory selection", + "", + f"Selected `g*={selected['global_scale_text']}` with NFE=1 " + f"KID-5k `{format_metric(selected['nfe1_kid5k_full'])}`. It " + f"{comparison} the fixed seed-0 reference " + f"(`{format_metric(fixed['nfe1_kid5k_full'])}`); " + f"Δ = `{format_metric(selected['nfe1_kid5k_full_delta_vs_fixed'])}`.", + "", + "## Interpretation limits", + "", + "- This is a single-training-seed response curve using noisy " + "5k-sample proxy metrics.", + "- Selecting the best value on these same seed-0 measurements " + "creates selection bias (the winner's-curse effect). The chosen " + "scale is a hypothesis for held-out multi-seed confirmation, " + "not evidence of a general improvement.", + "- NFE=1 FID and all NFE=2 values are diagnostic only and did " + "not influence the selected scale.", + "", + ] + ) + path.write_text("\n".join(lines), encoding="utf-8") + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--runs-root", type=Path, required=True) + parser.add_argument("--eval-root", type=Path, required=True) + parser.add_argument("--outdir", type=Path, required=True) + args = parser.parse_args(argv) + + rows = load_response_curve(args.runs_root.resolve(), args.eval_root.resolve()) + selected_raw = select_candidate(rows) + fixed, enriched = add_comparisons(rows, selected_raw) + selected = next(row for row in enriched if row["is_selected"]) + beats_fixed = selected["nfe1_kid5k_full"] < fixed["nfe1_kid5k_full"] + + caveats = [ + "KID-5k and FID-5k are 5k-sample proxies, not standard FID-50k benchmarks.", + "The response curve uses one training seed (seed 0).", + "Selecting and reporting the minimum on the same seed-0 sweep creates " + "selection bias; g* requires held-out multi-seed confirmation.", + "NFE=1 FID and all NFE=2 metrics are diagnostic and were not used " + "to select g*.", + ] + report = { + "schema_version": 1, + "status": "passed", + "selection_rule": { + "eligible_arms": ["global"], + "eligible_global_scales": [0.97, 1.032, 1.06, 1.10], + "primary_metric": "nfe1_kid5k_full", + "direction": "lower_is_better", + "fixed_g1_excluded_from_selection": True, + "exact_tie_breakers": [ + f"closest_global_scale_to_{TIE_TARGET}", + "closest_global_scale_to_1.0", + "lower_global_scale", + ], + "diagnostic_metrics_not_used_for_selection": [ + "nfe1_fid5k_full", + "nfe2_kid5k_full", + "nfe2_fid5k_full", + ], + }, + "fixed": fixed, + "selected": selected, + "selected_global_scale": selected["global_scale"], + "selected_global_scale_text": selected["global_scale_text"], + "selected_label": selected["label"], + "selected_beats_fixed_nfe1_kid": beats_fixed, + "nfe1_kid_delta_vs_fixed": selected[ + "nfe1_kid5k_full_delta_vs_fixed" + ], + "response_curve": enriched, + "caveats": caveats, + } + + outdir = args.outdir.resolve() + outdir.mkdir(parents=True, exist_ok=True) + write_csv(outdir / "response_curve.csv", enriched) + (outdir / "selection.json").write_text( + json.dumps(report, indent=2, sort_keys=True) + "\n", encoding="utf-8" + ) + (outdir / "selected_g.txt").write_text( + selected["global_scale_text"] + "\n", encoding="utf-8" + ) + write_markdown(outdir / "response_curve.md", enriched, fixed, selected) + print( + f"Selected g*={selected['global_scale_text']} " + f"(NFE=1 KID={selected['nfe1_kid5k_full']:.9g}, " + f"beats_fixed={str(beats_fixed).lower()}); output={outdir}" + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/smoke_engineering_100steps.sh b/scripts/smoke_engineering_100steps.sh new file mode 100755 index 00000000..c219ca27 --- /dev/null +++ b/scripts/smoke_engineering_100steps.sh @@ -0,0 +1,295 @@ +#!/usr/bin/env bash + +# Engineering connectivity test only. This is not the official fixed ECT baseline. +# The old collaborator FP32 evidence did not validate FP16 or GradScaler. + +set -Eeuo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +PROJECT_ROOT="${ECT_PROJECT_ROOT:-/mnt/ect_project}" +ENV_NAME="${ECT_ENV_NAME:-ect}" +TARBALL="${ECT_CIFAR10_TARBALL:-${PROJECT_ROOT}/datasets/cifar-10-python.tar.gz}" +DATA_PATH="${ECT_DATA_PATH:-${PROJECT_ROOT}/datasets/cifar10-32x32.zip}" +TRANSFER_PATH="${ECT_TRANSFER_PATH:-${PROJECT_ROOT}/pretrained/edm-cifar10-32x32-uncond-vp.pkl}" +RUNS_ROOT="${ECT_RUNS_ROOT:-${PROJECT_ROOT}/runs}" +MODE="all" +ACTION="run" +NUM_GPUS=1 +BATCH_GPU="${ECT_SMOKE_BATCH_GPU:-2}" +FP16="${ECT_SMOKE_FP16:-True}" +ENABLE_AMP="${ECT_SMOKE_ENABLE_AMP:-True}" +PORT="${ECT_DDP_PORT:-29501}" +RUN_ROOT="" +ALLOW_DIRTY=0 + +fail() { + printf '[smoke_engineering_100steps] ERROR: %s\n' "$*" >&2 + exit 1 +} + +run_in_env() { + if [[ "${CONDA_DEFAULT_ENV:-}" == "${ENV_NAME}" ]]; then + "$@" + elif command -v conda >/dev/null 2>&1; then + conda run --no-capture-output -n "${ENV_NAME}" "$@" + elif command -v mamba >/dev/null 2>&1; then + mamba run --no-capture-output -n "${ENV_NAME}" "$@" + else + fail "activate '${ENV_NAME}' or install conda/mamba first" + fi +} + +normalize_bool() { + case "${1,,}" in + true|1|yes) printf 'True\n' ;; + false|0|no) printf 'False\n' ;; + *) fail "expected a boolean value, got: $1" ;; + esac +} + +usage() { + cat <<'EOF' +Usage: bash scripts/smoke_engineering_100steps.sh [options] + +This script is an engineering connectivity test, not the official ECT baseline. +Its training mode runs 100 fresh optimizer updates and optionally 100 resumed +updates with formal FID/KID disabled. No training is performed by --check-only +or --dry-run. + + --mode MODE fresh, resume, or all (default: all) + --gpus N GPUs used by torchrun (default: 1; must divide batch 10) + --port PORT Local DDP rendezvous port (default: 29501) + --tarball PATH Official CIFAR-10 source tarball + --data PATH Prepared CIFAR-10 EDM ZIP + --transfer PATH Official EDM transfer checkpoint + --run-root PATH Persistent output root + --batch-gpu N Microbatch per GPU (default: 2) + --fp16 BOOL Network FP16 mode (default: True) + --enable-amp BOOL Public-baseline --enable_amp value (default: True) + --check-only Validate environment and assets; do not launch ct_train.py + --dry-run Validate assets and run ct_train.py --dry_run only + --allow-dirty Permit an actual training smoke from a dirty worktree + -h, --help Show this help + +Default persistent layout: + /mnt/ect_project/datasets + /mnt/ect_project/pretrained + /mnt/ect_project/runs + /mnt/ect_project/checkpoints + +The public baseline also accepts --amp and --enable_gradscaler as aliases of +--enable_amp. This script uses the canonical --enable_amp spelling. +EOF +} + +while [[ $# -gt 0 ]]; do + case "$1" in + --mode) + [[ $# -ge 2 ]] || fail "--mode requires a value" + MODE="$2" + shift 2 + ;; + --gpus) + [[ $# -ge 2 ]] || fail "--gpus requires a value" + NUM_GPUS="$2" + shift 2 + ;; + --port) + [[ $# -ge 2 ]] || fail "--port requires a value" + PORT="$2" + shift 2 + ;; + --tarball) + [[ $# -ge 2 ]] || fail "--tarball requires a value" + TARBALL="$2" + shift 2 + ;; + --data) + [[ $# -ge 2 ]] || fail "--data requires a value" + DATA_PATH="$2" + shift 2 + ;; + --transfer) + [[ $# -ge 2 ]] || fail "--transfer requires a value" + TRANSFER_PATH="$2" + shift 2 + ;; + --run-root) + [[ $# -ge 2 ]] || fail "--run-root requires a value" + RUN_ROOT="$2" + shift 2 + ;; + --batch-gpu) + [[ $# -ge 2 ]] || fail "--batch-gpu requires a value" + BATCH_GPU="$2" + shift 2 + ;; + --fp16) + [[ $# -ge 2 ]] || fail "--fp16 requires a value" + FP16="$2" + shift 2 + ;; + --enable-amp) + [[ $# -ge 2 ]] || fail "--enable-amp requires a value" + ENABLE_AMP="$2" + shift 2 + ;; + --check-only) + ACTION="check" + shift + ;; + --dry-run) + ACTION="dry-run" + shift + ;; + --allow-dirty) + ALLOW_DIRTY=1 + shift + ;; + -h|--help) + usage + exit 0 + ;; + *) + fail "unknown option: $1" + ;; + esac +done + +[[ "${MODE}" == "fresh" || "${MODE}" == "resume" || "${MODE}" == "all" ]] || \ + fail "--mode must be fresh, resume, or all" +[[ "${NUM_GPUS}" =~ ^[0-9]+$ && "${NUM_GPUS}" -gt 0 ]] || fail "--gpus must be positive" +(( 10 % NUM_GPUS == 0 )) || fail "--gpus must divide the total batch size 10" +[[ "${BATCH_GPU}" =~ ^[0-9]+$ && "${BATCH_GPU}" -gt 0 ]] || fail "--batch-gpu must be positive" +(( (10 / NUM_GPUS) % BATCH_GPU == 0 )) || \ + fail "--batch-gpu must divide the per-GPU batch $((10 / NUM_GPUS))" +FP16="$(normalize_bool "${FP16}")" +ENABLE_AMP="$(normalize_bool "${ENABLE_AMP}")" + +cd "${ROOT_DIR}" + +verify_prerequisites() { + run_in_env python "${ROOT_DIR}/scripts/check_environment.py" + run_in_env python "${ROOT_DIR}/scripts/verify_assets.py" dataset \ + --path "${DATA_PATH}" \ + --tarball "${TARBALL}" \ + --expected-count 50000 \ + --expected-labels 50000 \ + --expected-resolution 32 + run_in_env python "${ROOT_DIR}/scripts/verify_assets.py" checkpoint \ + --path "${TRANSFER_PATH}" \ + --expected-sha256 4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da +} + +verify_prerequisites +if [[ "${ACTION}" == "check" ]]; then + printf '[smoke_engineering_100steps] CHECK-ONLY PASSED; no training was launched.\n' + exit 0 +fi + +COMMON_ARGS=( + --data="${DATA_PATH}" + --cond=False + --arch=ddpmpp + --metrics=none + --batch=10 + --batch-gpu="${BATCH_GPU}" + --lr=0.0001 + --optim=RAdam + --dropout=0.2 + --augment=0.0 + --seed=2026 + --workers=1 + --cache=False + --tick=1 + --snap=1 + --dump=1 + --ckpt=1 + --double=250 + --sample_every=1000 + --eval_every=1000 + --fp16="${FP16}" + --enable_amp="${ENABLE_AMP}" + --tf32=False + --bench=True + --nosubdir +) + +launch_training() { + run_in_env torchrun \ + --nnodes=1 \ + --nproc_per_node="${NUM_GPUS}" \ + --rdzv_backend=c10d \ + --rdzv_endpoint="localhost:${PORT}" \ + "${ROOT_DIR}/ct_train.py" "$@" +} + +if [[ "${ACTION}" == "dry-run" ]]; then + DRY_RUN_ROOT="${RUN_ROOT:-${RUNS_ROOT}/engineering-smoke/dry-run}" + launch_training \ + --outdir="${DRY_RUN_ROOT}" \ + --duration=0.001 \ + --transfer="${TRANSFER_PATH}" \ + --desc=engineering-dry-run \ + --dry_run \ + "${COMMON_ARGS[@]}" + printf '[smoke_engineering_100steps] DRY-RUN PASSED; no output directory or training run was created.\n' + exit 0 +fi + +GIT_COMMIT="$(git rev-parse HEAD)" +if [[ "${ALLOW_DIRTY}" -eq 0 ]] && [[ -n "$(git status --porcelain)" ]]; then + fail "actual smoke training requires a clean worktree; use --allow-dirty only for preliminary work" +fi +if [[ -z "${RUN_ROOT}" ]]; then + [[ "${MODE}" != "resume" ]] || fail "--run-root is required with --mode resume" + RUN_ROOT="${RUNS_ROOT}/engineering-smoke/${GIT_COMMIT:0:8}-$(date -u +%Y%m%dT%H%M%SZ)" +fi +FRESH_DIR="${RUN_ROOT}/fresh-100steps" +RESUME_DIR="${RUN_ROOT}/resume-100steps" +mkdir -p "${RUN_ROOT}" + +run_in_env python "${ROOT_DIR}/scripts/check_environment.py" \ + --output "${RUN_ROOT}/environment.json" + +if [[ "${MODE}" == "fresh" || "${MODE}" == "all" ]]; then + [[ ! -e "${FRESH_DIR}" ]] || fail "fresh run directory already exists: ${FRESH_DIR}" + printf '[smoke_engineering_100steps] Fresh engineering phase: steps=100, formal_metrics=disabled\n' + launch_training \ + --outdir="${FRESH_DIR}" \ + --duration=0.001 \ + --transfer="${TRANSFER_PATH}" \ + --desc=engineering-fresh-100steps \ + "${COMMON_ARGS[@]}" +fi + +if [[ "${MODE}" == "resume" || "${MODE}" == "all" ]]; then + STATE_PATH="${FRESH_DIR}/training-state-000001.pt" + SNAPSHOT_PATH="${FRESH_DIR}/network-snapshot-000001.pkl" + [[ -f "${STATE_PATH}" ]] || fail "fresh training state not found: ${STATE_PATH}" + [[ -f "${SNAPSHOT_PATH}" ]] || fail "matching fresh snapshot not found: ${SNAPSHOT_PATH}" + [[ ! -e "${RESUME_DIR}" ]] || fail "resume run directory already exists: ${RESUME_DIR}" + printf '[smoke_engineering_100steps] Resume engineering phase: additional_steps=100, formal_metrics=disabled\n' + launch_training \ + --outdir="${RESUME_DIR}" \ + --duration=0.002 \ + --resume="${STATE_PATH}" \ + --desc=engineering-resume-100steps \ + "${COMMON_ARGS[@]}" +fi + +VERIFY_ARGS=( + "${ROOT_DIR}/scripts/verify_smoke_run.py" + --fresh "${FRESH_DIR}" + --git-commit "${GIT_COMMIT}" + --expected-batch 10 + --expected-fp16 "${FP16}" + --expected-amp "${ENABLE_AMP}" + --output "${RUN_ROOT}/smoke_report.json" +) +if [[ "${MODE}" == "resume" || "${MODE}" == "all" ]]; then + VERIFY_ARGS+=(--resume "${RESUME_DIR}") +fi +run_in_env python "${VERIFY_ARGS[@]}" + +printf '[smoke_engineering_100steps] PASSED. Engineering-only result root: %s\n' "${RUN_ROOT}" diff --git a/scripts/summarize_budget_curve.py b/scripts/summarize_budget_curve.py new file mode 100644 index 00000000..b03cb2cf --- /dev/null +++ b/scripts/summarize_budget_curve.py @@ -0,0 +1,62 @@ +#!/usr/bin/env python3 +"""Create a paper-ready budget-curve PDF from a validated paired CSV. + +The input uses the same long-form schema as collect_multibudget_results.py. +Only a complete two-method paired matrix is accepted. The command writes: + - budget_curves.pdf, .png, .svg: mean curves with sample-SD whiskers; + - budget_curve_summary.csv: figure-ready means and sample SDs; + - paired_summary.csv: descriptive paired deltas at each budget. + +Example: + python scripts/summarize_budget_curve.py --input-csv new_results.csv \ + --outdir paper/figures --baseline-method fixed --candidate-method global110 +""" + +from __future__ import annotations + +import argparse +from pathlib import Path + +try: # Supports both `python scripts/...py` and package-level tests. + from .collect_multibudget_results import ( + aggregate_rows, fail, matrix_for_track, paired_rows, plot_budget_curves, read_rows, validate, write_csv, + ) +except ImportError: + from collect_multibudget_results import ( + aggregate_rows, fail, matrix_for_track, paired_rows, plot_budget_curves, read_rows, validate, write_csv, + ) + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--input-csv", type=Path, required=True) + parser.add_argument("--outdir", type=Path, required=True) + parser.add_argument("--baseline-method", default="fixed") + parser.add_argument("--candidate-method", default="global110") + parser.add_argument("--analysis-track", choices=("budget_curve", "formal_endpoint")) + args = parser.parse_args(argv) + + rows = read_rows(args.input_csv.resolve()) + matrix = validate(rows, args.baseline_method, args.candidate_method) + paired = paired_rows(matrix) + curves, summary = aggregate_rows(rows, paired, matrix) + if matrix["analysis_tracks"]: + if args.analysis_track is None: + fail("tagged input requires --analysis-track so incompatible protocols are not combined") + curves = [row for row in curves if row["analysis_track"] == args.analysis_track] + summary = [row for row in summary if row["analysis_track"] == args.analysis_track] + if not curves: + fail("no rows for analysis_track={}".format(args.analysis_track)) + plot_matrix = matrix_for_track(matrix, curves) + else: + plot_matrix = matrix + outdir = args.outdir.resolve() + outdir.mkdir(parents=True, exist_ok=True) + write_csv(outdir / "budget_curve_summary.csv", curves) + write_csv(outdir / "paired_summary.csv", summary) + plot_budget_curves(curves, plot_matrix, outdir) + print("Validated {} metric rows; wrote {}".format(len(rows), outdir / "budget_curves.pdf")) + + +if __name__ == "__main__": + main() diff --git a/scripts/summarize_gap_factorial.py b/scripts/summarize_gap_factorial.py new file mode 100644 index 00000000..2009b49a --- /dev/null +++ b/scripts/summarize_gap_factorial.py @@ -0,0 +1,920 @@ +#!/usr/bin/env python3 +"""Fail-closed summary for the 3-seed global/local gap factorial study. + +This script consumes the global-scale selection artifact plus the completed +training and evaluation trees. It deliberately validates every formal cell +before writing any output, and reads each unique metric JSONL file exactly +once. +""" + +from __future__ import annotations + +import argparse +import csv +import json +import math +import statistics +from pathlib import Path +from typing import Dict, List, Mapping, Sequence, Tuple + + +TRAINING_SEEDS = (0, 1, 2) +HELDOUT_SEEDS = (1, 2) +NFES = (1, 2) +METRICS = ("kid5k_full", "fid5k_full") +PROFILES = ("conservative", "aggressive") +HEADLINE_ARMS = ( + "global", + "local-conservative", + "combined-conservative", + "local-aggressive", + "combined-aggressive", +) +T_CRITICAL_DF2_95 = 4.3026527 +OUTPUT_NAMES = ( + "per_cell_metrics.csv", + "per_seed_effects.csv", + "heldout_headlines.csv", + "factorial_summary.csv", + "factorial_summary.json", + "factorial_summary.md", +) + +EFFECT_DEFINITIONS = { + "global_at_local0": "global - fixed", + "global_at_local1": "combined - local", + "local_at_global0": "local - fixed", + "local_at_global1": "combined - global", + "combined_vs_fixed": "combined - fixed", + "additive_interaction": "combined - global - local + fixed", + "global_main_effect": "0.5 * [(global - fixed) + (combined - local)]", + "local_main_effect": "0.5 * [(local - fixed) + (combined - global)]", +} + +LOG_EFFECT_DEFINITIONS = { + "global_at_local0": "log(global / fixed)", + "global_at_local1": "log(combined / local)", + "local_at_global0": "log(local / fixed)", + "local_at_global1": "log(combined / global)", + "combined_vs_fixed": "log(combined / fixed)", + "additive_interaction": "log(combined * fixed / (global * local))", + "global_main_effect": ( + "0.5 * [log(global / fixed) + log(combined / local)]" + ), + "local_main_effect": ( + "0.5 * [log(local / fixed) + log(combined / global)]" + ), +} + + +def fail(message: str) -> None: + raise SystemExit(f"[summarize_gap_factorial] ERROR: {message}") + + +def finite_number(value: object, context: str) -> float: + try: + result = float(value) + except (TypeError, ValueError) as exc: + fail(f"{context} is not numeric: {exc}") + if not math.isfinite(result): + fail(f"{context} is non-finite: {result}") + return result + + +def scale_slug(scale: float) -> str: + return f"{scale:.4f}".replace(".", "p") + + +def parse_env(path: Path) -> Dict[str, str]: + if not path.is_file(): + fail(f"missing experiment metadata: {path}") + values: Dict[str, str] = {} + for line_number, raw in enumerate( + path.read_text(encoding="utf-8", errors="strict").splitlines(), start=1 + ): + line = raw.strip() + if not line or "=" not in line: + continue + key, value = line.split("=", 1) + if key in values: + fail(f"duplicate key {key!r} in {path}:{line_number}") + values[key] = value + return values + + +def read_json_object(path: Path, description: str) -> dict: + if not path.is_file() or path.stat().st_size == 0: + fail(f"missing or empty {description}: {path}") + try: + with path.open(encoding="utf-8") as handle: + payload = json.load(handle) + except (OSError, json.JSONDecodeError) as exc: + fail(f"malformed {description} {path}: {exc}") + if not isinstance(payload, dict): + fail(f"{description} must be a JSON object: {path}") + return payload + + +def load_selection(path: Path) -> Tuple[dict, float]: + selection = read_json_object(path, "selection artifact") + if selection.get("status") != "passed": + fail(f"selection status is not 'passed': {path}") + if selection.get("schema_version") != 1: + fail( + f"unsupported selection schema_version " + f"{selection.get('schema_version')!r}: {path}" + ) + + scale = finite_number( + selection.get("selected_global_scale"), "selected_global_scale" + ) + if scale <= 0: + fail(f"selected_global_scale must be > 0, got {scale}") + scale_text = selection.get("selected_global_scale_text") + if not isinstance(scale_text, str) or not scale_text: + fail("selection is missing selected_global_scale_text") + text_scale = finite_number(scale_text, "selected_global_scale_text") + if not math.isclose(scale, text_scale, rel_tol=0.0, abs_tol=1e-12): + fail( + "selected_global_scale and selected_global_scale_text disagree: " + f"{scale!r} vs {scale_text!r}" + ) + + expected_label = f"global-g{scale_slug(scale)}-seed0-256k" + if selection.get("selected_label") != expected_label: + fail( + f"selected_label mismatch: expected {expected_label!r}, " + f"got {selection.get('selected_label')!r}" + ) + selected = selection.get("selected") + if not isinstance(selected, dict): + fail("selection is missing the selected row object") + if selected.get("label") != expected_label: + fail("selected row label disagrees with selected_label") + selected_row_scale = finite_number( + selected.get("global_scale"), "selected.global_scale" + ) + if not math.isclose(scale, selected_row_scale, rel_tol=0.0, abs_tol=1e-12): + fail("selected row global_scale disagrees with selected_global_scale") + return selection, scale + + +def arm_specs(selected_scale: float) -> List[dict]: + return [ + { + "arm": "fixed", + "profile": "shared", + "global_level": 0, + "local_level": 0, + "scale": 1.0, + "schedule": "sigmoid", + "metadata_profile": "none", + }, + { + "arm": "global", + "profile": "shared", + "global_level": 1, + "local_level": 0, + "scale": selected_scale, + "schedule": "global_sigmoid", + "metadata_profile": "none", + }, + { + "arm": "local-conservative", + "profile": "conservative", + "global_level": 0, + "local_level": 1, + "scale": 1.0, + "schedule": "local_tbin_v2", + "metadata_profile": "conservative", + }, + { + "arm": "combined-conservative", + "profile": "conservative", + "global_level": 1, + "local_level": 1, + "scale": selected_scale, + "schedule": "local_tbin_v3", + "metadata_profile": "conservative", + }, + { + "arm": "local-aggressive", + "profile": "aggressive", + "global_level": 0, + "local_level": 1, + "scale": 1.0, + "schedule": "local_tbin_v2", + "metadata_profile": "aggressive", + }, + { + "arm": "combined-aggressive", + "profile": "aggressive", + "global_level": 1, + "local_level": 1, + "scale": selected_scale, + "schedule": "local_tbin_v3", + "metadata_profile": "aggressive", + }, + ] + + +def run_label(spec: Mapping[str, object], seed: int) -> str: + return ( + f"{spec['arm']}-g{scale_slug(float(spec['scale']))}" + f"-seed{seed}-256k" + ) + + +def validate_training( + run_dir: Path, spec: Mapping[str, object], seed: int +) -> Tuple[Path, Path]: + validation_path = run_dir / "validation.json" + validation = read_json_object(validation_path, "training validation") + if validation.get("status") != "passed": + fail(f"training validation did not pass: {validation_path}") + if validation.get("expected_schedule") != spec["schedule"]: + fail( + f"schedule mismatch in {validation_path}: expected " + f"{spec['schedule']!r}, got {validation.get('expected_schedule')!r}" + ) + final_kimg = finite_number( + validation.get("final_processed_kimg"), + f"{validation_path}: final_processed_kimg", + ) + if not 256.0 <= final_kimg < 256.128: + fail(f"formal run is not a complete 256 kimg run: {validation_path}") + + meta_path = run_dir / "experiment_meta.env" + meta = parse_env(meta_path) + expected = { + "arm": str(spec["arm"]), + "schedule": str(spec["schedule"]), + "local_profile": str(spec["metadata_profile"]), + "seed": str(seed), + } + for key, expected_value in expected.items(): + if meta.get(key) != expected_value: + fail( + f"{meta_path}: expected {key}={expected_value!r}, " + f"got {meta.get(key)!r}" + ) + recorded_scale = finite_number( + meta.get("global_gap_scale"), f"{meta_path}: global_gap_scale" + ) + if not math.isclose( + recorded_scale, float(spec["scale"]), rel_tol=0.0, abs_tol=1e-12 + ): + fail( + f"{meta_path}: global gap scale mismatch " + f"{recorded_scale} != {spec['scale']}" + ) + duration = finite_number( + meta.get("duration_mimg"), f"{meta_path}: duration_mimg" + ) + if not math.isclose(duration, 0.256, rel_tol=0.0, abs_tol=1e-12): + fail(f"{meta_path}: expected duration_mimg=0.256, got {duration}") + if meta.get("exit_code") != "0": + fail(f"{meta_path}: training exit_code is not 0") + return validation_path, meta_path + + +def read_metric_once(path: Path, metric: str) -> float: + if not path.is_file() or path.stat().st_size == 0: + fail(f"missing or empty metric output: {path}") + try: + raw = path.read_text(encoding="utf-8") + except OSError as exc: + fail(f"cannot read metric output {path}: {exc}") + lines = [line for line in raw.splitlines() if line.strip()] + if len(lines) != 1: + fail( + f"expected exactly one JSONL result in {path}, found {len(lines)}" + ) + try: + payload = json.loads(lines[0]) + if not isinstance(payload, dict): + raise TypeError("top-level JSON value is not an object") + if payload.get("metric") != metric: + fail( + f"metric name mismatch in {path}: " + f"{payload.get('metric')!r} != {metric!r}" + ) + results = payload["results"] + if not isinstance(results, dict): + raise TypeError("results is not an object") + value = float(results[metric]) + except (KeyError, TypeError, ValueError, json.JSONDecodeError) as exc: + fail(f"malformed metric output {path}: {exc}") + if not math.isfinite(value): + fail(f"non-finite metric value in {path}: {value}") + if value <= 0: + fail( + f"metric value must be > 0 for log contrasts in {path}: {value}" + ) + return value + + +def validate_eval_metadata(eval_dir: Path, expected_label: str, nfe: int) -> None: + meta_path = eval_dir / "experiment_meta.env" + meta = parse_env(meta_path) + expected = { + "label": expected_label, + "nfe": str(nfe), + "exit_code": "0", + } + for key, value in expected.items(): + if meta.get(key) != value: + fail( + f"{meta_path}: expected {key}={value!r}, " + f"got {meta.get(key)!r}" + ) + + +def load_cells( + runs_root: Path, eval_root: Path, selected_scale: float +) -> List[dict]: + rows: List[dict] = [] + seen_metric_paths = set() + for spec in arm_specs(selected_scale): + for seed in TRAINING_SEEDS: + label = run_label(spec, seed) + run_dir = runs_root / label + if not run_dir.is_dir(): + fail(f"missing formal training cell: {run_dir}") + validation_path, _ = validate_training(run_dir, spec, seed) + for nfe in NFES: + eval_dir = eval_root / label / f"nfe{nfe}" + if not eval_dir.is_dir(): + fail(f"missing formal evaluation cell: {eval_dir}") + validate_eval_metadata(eval_dir, label, nfe) + metric_values = {} + metric_paths = {} + for metric in METRICS: + path = eval_dir / f"metric-{metric}.jsonl" + resolved = path.resolve() + if resolved in seen_metric_paths: + fail(f"metric file would be read more than once: {path}") + seen_metric_paths.add(resolved) + metric_values[metric] = read_metric_once(path, metric) + metric_paths[metric] = str(resolved) + rows.append( + { + "arm": spec["arm"], + "profile": spec["profile"], + "global_level": spec["global_level"], + "local_level": spec["local_level"], + "global_gap_scale": spec["scale"], + "training_seed": seed, + "nfe": nfe, + **metric_values, + "run_label": label, + "run_dir": str(run_dir.resolve()), + "eval_dir": str(eval_dir.resolve()), + "training_validation_path": str( + validation_path.resolve() + ), + "kid_metric_path": metric_paths["kid5k_full"], + "fid_metric_path": metric_paths["fid5k_full"], + } + ) + + expected_rows = len(arm_specs(selected_scale)) * len(TRAINING_SEEDS) * len(NFES) + if len(rows) != expected_rows: + fail(f"internal matrix error: expected {expected_rows} rows, got {len(rows)}") + expected_metric_files = expected_rows * len(METRICS) + if len(seen_metric_paths) != expected_metric_files: + fail( + f"internal metric-file error: expected {expected_metric_files}, " + f"got {len(seen_metric_paths)}" + ) + return rows + + +def compute_contrasts( + fixed: float, global_only: float, local_only: float, combined: float +) -> Tuple[Dict[str, float], Dict[str, float]]: + deltas = { + "global_at_local0": global_only - fixed, + "global_at_local1": combined - local_only, + "local_at_global0": local_only - fixed, + "local_at_global1": combined - global_only, + "combined_vs_fixed": combined - fixed, + "additive_interaction": ( + combined - global_only - local_only + fixed + ), + } + deltas["global_main_effect"] = 0.5 * ( + deltas["global_at_local0"] + deltas["global_at_local1"] + ) + deltas["local_main_effect"] = 0.5 * ( + deltas["local_at_global0"] + deltas["local_at_global1"] + ) + + log_fixed = math.log(fixed) + log_global = math.log(global_only) + log_local = math.log(local_only) + log_combined = math.log(combined) + logs = { + "global_at_local0": log_global - log_fixed, + "global_at_local1": log_combined - log_local, + "local_at_global0": log_local - log_fixed, + "local_at_global1": log_combined - log_global, + "combined_vs_fixed": log_combined - log_fixed, + "additive_interaction": ( + log_combined - log_global - log_local + log_fixed + ), + } + logs["global_main_effect"] = 0.5 * ( + logs["global_at_local0"] + logs["global_at_local1"] + ) + logs["local_main_effect"] = 0.5 * ( + logs["local_at_global0"] + logs["local_at_global1"] + ) + return deltas, logs + + +def build_per_seed_effects(cell_rows: Sequence[dict]) -> List[dict]: + index = { + ( + str(row["arm"]), + int(row["training_seed"]), + int(row["nfe"]), + metric, + ): float(row[metric]) + for row in cell_rows + for metric in METRICS + } + rows: List[dict] = [] + for profile in PROFILES: + for seed in TRAINING_SEEDS: + for nfe in NFES: + for metric in METRICS: + fixed = index[("fixed", seed, nfe, metric)] + global_only = index[("global", seed, nfe, metric)] + local_only = index[ + (f"local-{profile}", seed, nfe, metric) + ] + combined = index[ + (f"combined-{profile}", seed, nfe, metric) + ] + deltas, logs = compute_contrasts( + fixed, global_only, local_only, combined + ) + row = { + "profile": profile, + "training_seed": seed, + "nfe": nfe, + "metric": metric, + "fixed": fixed, + "global": global_only, + "local": local_only, + "combined": combined, + } + for effect in EFFECT_DEFINITIONS: + row[f"{effect}_delta"] = deltas[effect] + row[f"{effect}_log_contrast"] = logs[effect] + row[f"{effect}_relative_percent"] = ( + 100.0 * math.expm1(logs[effect]) + ) + row["multiplicative_interaction_log"] = logs[ + "additive_interaction" + ] + row["multiplicative_interaction_percent"] = ( + 100.0 + * math.expm1(logs["additive_interaction"]) + ) + rows.append(row) + return rows + + +def mean_sd_ci(values: Sequence[float]) -> Tuple[float, float, float, float]: + if len(values) != 3: + fail(f"expected exactly three paired seeds, got {len(values)}") + mean = statistics.mean(values) + sample_sd = statistics.stdev(values) + half_width = T_CRITICAL_DF2_95 * sample_sd / math.sqrt(len(values)) + return mean, sample_sd, mean - half_width, mean + half_width + + +def build_summary(per_seed_rows: Sequence[dict]) -> List[dict]: + rows: List[dict] = [] + for profile in PROFILES: + for nfe in NFES: + for metric in METRICS: + selected = [ + row + for row in per_seed_rows + if row["profile"] == profile + and row["nfe"] == nfe + and row["metric"] == metric + ] + if [row["training_seed"] for row in selected] != list( + TRAINING_SEEDS + ): + fail( + f"incomplete paired seed order for " + f"{profile}/NFE={nfe}/{metric}" + ) + for effect in EFFECT_DEFINITIONS: + deltas = [ + float(row[f"{effect}_delta"]) for row in selected + ] + logs = [ + float(row[f"{effect}_log_contrast"]) + for row in selected + ] + mean_delta, sd_delta, low_delta, high_delta = mean_sd_ci( + deltas + ) + mean_log, sd_log, low_log, high_log = mean_sd_ci(logs) + wins = sum(value < 0 for value in deltas) + losses = sum(value > 0 for value in deltas) + ties = sum(value == 0 for value in deltas) + rows.append( + { + "profile": profile, + "nfe": nfe, + "metric": metric, + "effect": effect, + "arithmetic_definition": EFFECT_DEFINITIONS[ + effect + ], + "log_definition": LOG_EFFECT_DEFINITIONS[effect], + "n": 3, + "per_seed_deltas_json": json.dumps( + deltas, separators=(",", ":") + ), + "mean_delta": mean_delta, + "sample_sd_delta": sd_delta, + "ci95_low_delta": low_delta, + "ci95_high_delta": high_delta, + "negative_wins": wins, + "positive_losses": losses, + "ties": ties, + "wins_of_3": f"{wins}/3", + "per_seed_log_contrasts_json": json.dumps( + logs, separators=(",", ":") + ), + "mean_log_contrast": mean_log, + "sample_sd_log_contrast": sd_log, + "ci95_low_log_contrast": low_log, + "ci95_high_log_contrast": high_log, + "geometric_relative_percent": ( + 100.0 * math.expm1(mean_log) + ), + "geometric_relative_ci95_low_percent": ( + 100.0 * math.expm1(low_log) + ), + "geometric_relative_ci95_high_percent": ( + 100.0 * math.expm1(high_log) + ), + } + ) + return rows + + +def build_heldout_headlines(cell_rows: Sequence[dict]) -> List[dict]: + """Compare arithmetic metric means over seeds 1/2 against fixed sigmoid.""" + index = { + ( + str(row["arm"]), + int(row["training_seed"]), + int(row["nfe"]), + metric, + ): float(row[metric]) + for row in cell_rows + for metric in METRICS + } + rows = [] + for arm in HEADLINE_ARMS: + for nfe in NFES: + for metric in METRICS: + fixed = [ + index[("fixed", seed, nfe, metric)] + for seed in HELDOUT_SEEDS + ] + candidate = [ + index[(arm, seed, nfe, metric)] + for seed in HELDOUT_SEEDS + ] + deltas = [ + candidate[index] - fixed[index] + for index in range(len(HELDOUT_SEEDS)) + ] + per_seed_relative = [ + 100.0 * deltas[index] / fixed[index] + for index in range(len(HELDOUT_SEEDS)) + ] + fixed_mean = statistics.fmean(fixed) + candidate_mean = statistics.fmean(candidate) + absolute_change_total = sum(abs(value) for value in deltas) + rows.append( + { + "arm": arm, + "nfe": nfe, + "metric": metric, + "heldout_seeds": "1,2", + "fixed_mean": fixed_mean, + "arm_mean": candidate_mean, + "headline_relative_percent": ( + 100.0 * (candidate_mean / fixed_mean - 1.0) + ), + "seed1_delta": deltas[0], + "seed2_delta": deltas[1], + "seed1_relative_percent": per_seed_relative[0], + "seed2_relative_percent": per_seed_relative[1], + "seed1_absolute_change_share_percent": ( + 100.0 * abs(deltas[0]) / absolute_change_total + if absolute_change_total > 0 + else 0.0 + ), + } + ) + return rows + + +def write_csv(path: Path, rows: Sequence[dict]) -> None: + if not rows: + fail(f"refusing to write empty CSV: {path}") + fieldnames = list(rows[0].keys()) + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter( + handle, fieldnames=fieldnames, lineterminator="\n" + ) + writer.writeheader() + writer.writerows(rows) + + +def fmt(value: float) -> str: + return f"{value:.6g}" + + +def build_markdown( + selected_scale: float, + selection: Mapping[str, object], + cell_rows: Sequence[dict], + heldout_rows: Sequence[dict], + summary_rows: Sequence[dict], +) -> str: + lines = [ + "# Global/local gap factorial summary", + "", + "> KID-5k and FID-5k are 5,000-sample proxy metrics, not standard " + "50,000-sample benchmarks.", + "", + f"Selected global gap scale: **g\\* = {selected_scale:.6g}**. " + "Lower is better for both metrics.", + "", + "This is a paired three-training-seed (`n=3`) descriptive analysis. " + "The 95% intervals use the two-sided Student-t critical value " + f"`t(df=2)={T_CRITICAL_DF2_95}`; they are not evidence for broad " + "population-level significance.", + "", + "Seed 0 was used to select g\\* in the response-curve stage and its " + "fixed/global observations are reused in this formal matrix. Therefore " + "seed 0 has selection/evaluation overlap; interpret selected-g\\* " + "effects as selection-aware descriptive estimates.", + "", + "## Held-out seed 1/2 headline calculation", + "", + "For arm `A` and metric `M`, the reported headline is", + "", + "`100 × (mean(M_A,seed1, M_A,seed2) / " + "mean(M_fixed,seed1, M_fixed,seed2) - 1)`.", + "", + "It is the percentage difference between the two arithmetic metric " + "means. It is **not** the mean of the two per-seed percentage changes.", + "", + "| Arm | NFE | Metric | Fixed mean | Arm mean | Headline % | " + "Seed 1 % | Seed 2 % |", + "| --- | ---: | --- | ---: | ---: | ---: | ---: | ---: |", + ] + for row in heldout_rows: + lines.append( + f"| {row['arm']} | {row['nfe']} | {row['metric']} | " + f"{fmt(float(row['fixed_mean']))} | " + f"{fmt(float(row['arm_mean']))} | " + f"{fmt(float(row['headline_relative_percent']))}% | " + f"{fmt(float(row['seed1_relative_percent']))}% | " + f"{fmt(float(row['seed2_relative_percent']))}% |" + ) + lines.extend( + [ + "", + "At NFE=2, both held-out seeds improve directionally for " + "`global` and `combined-aggressive`, but seed 1 has a much larger " + "effect. Seed 1 accounts for the following share of the total " + "absolute two-seed metric decrease:", + "", + "| Arm | Metric | Seed 1 share |", + "| --- | --- | ---: |", + ] + ) + for row in heldout_rows: + if ( + int(row["nfe"]) == 2 + and row["arm"] in ("global", "combined-aggressive") + ): + lines.append( + f"| {row['arm']} | {row['metric']} | " + f"{fmt(float(row['seed1_absolute_change_share_percent']))}% |" + ) + lines.extend( + [ + "", + "A “win” means a negative paired contrast because lower is better. " + "For interaction rows, negative means the combination is better than " + "the corresponding additive prediction on the raw scale. The " + "geometric relative percentage for that row is the multiplicative " + "interaction `combined × fixed / (global × local) - 1`.", + "", + "## Validated matrix", + "", + f"- Unique training cells: {len(cell_rows) // len(NFES)} " + "(fixed/global are shared across profiles)", + f"- Evaluated training-seed × NFE cells: {len(cell_rows)}", + f"- Scalar metric files read exactly once: " + f"{len(cell_rows) * len(METRICS)}", + f"- Selection artifact status: `{selection.get('status')}`", + "", + "## Effect definitions", + "", + "| Effect | Raw-scale paired contrast | Log-scale contrast |", + "| --- | --- | --- |", + ] + ) + for effect in EFFECT_DEFINITIONS: + log_label = LOG_EFFECT_DEFINITIONS[effect] + if effect == "additive_interaction": + log_label += " (multiplicative interaction)" + lines.append( + f"| `{effect}` | `{EFFECT_DEFINITIONS[effect]}` | " + f"`{log_label}` |" + ) + + lines.extend( + [ + "", + "## Three-seed summaries", + "", + "| Profile | NFE | Metric | Effect | Mean Δ | Sample SD | " + "95% t CI | Wins/3 | Geometric relative % | Relative 95% t CI |", + "| --- | ---: | --- | --- | ---: | ---: | --- | ---: | ---: | --- |", + ] + ) + for row in summary_rows: + lines.append( + f"| {row['profile']} | {row['nfe']} | {row['metric']} | " + f"`{row['effect']}` | {fmt(float(row['mean_delta']))} | " + f"{fmt(float(row['sample_sd_delta']))} | " + f"[{fmt(float(row['ci95_low_delta']))}, " + f"{fmt(float(row['ci95_high_delta']))}] | " + f"{row['wins_of_3']} | " + f"{fmt(float(row['geometric_relative_percent']))}% | " + f"[{fmt(float(row['geometric_relative_ci95_low_percent']))}%, " + f"{fmt(float(row['geometric_relative_ci95_high_percent']))}%] |" + ) + lines.extend( + [ + "", + "The CSV files contain every per-cell value and every per-seed " + "raw/log contrast used above.", + "", + ] + ) + return "\n".join(lines) + + +def main(argv: Sequence[str] = None) -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--runs-root", type=Path, required=True) + parser.add_argument("--eval-root", type=Path, required=True) + parser.add_argument("--selection-json", type=Path, required=True) + parser.add_argument("--outdir", type=Path, required=True) + args = parser.parse_args(argv) + + runs_root = args.runs_root.resolve() + eval_root = args.eval_root.resolve() + if not runs_root.is_dir(): + fail(f"runs root does not exist: {runs_root}") + if not eval_root.is_dir(): + fail(f"evaluation root does not exist: {eval_root}") + + selection, selected_scale = load_selection( + args.selection_json.resolve() + ) + cell_rows = load_cells(runs_root, eval_root, selected_scale) + per_seed_rows = build_per_seed_effects(cell_rows) + heldout_rows = build_heldout_headlines(cell_rows) + summary_rows = build_summary(per_seed_rows) + + expected_per_seed = ( + len(PROFILES) * len(TRAINING_SEEDS) * len(NFES) * len(METRICS) + ) + expected_summary = ( + len(PROFILES) * len(NFES) * len(METRICS) * len(EFFECT_DEFINITIONS) + ) + if len(per_seed_rows) != expected_per_seed: + fail( + f"internal per-seed row error: expected {expected_per_seed}, " + f"got {len(per_seed_rows)}" + ) + if len(summary_rows) != expected_summary: + fail( + f"internal summary row error: expected {expected_summary}, " + f"got {len(summary_rows)}" + ) + + outdir = args.outdir.resolve() + outdir.mkdir(parents=True, exist_ok=True) + write_csv(outdir / "per_cell_metrics.csv", cell_rows) + write_csv(outdir / "per_seed_effects.csv", per_seed_rows) + write_csv(outdir / "heldout_headlines.csv", heldout_rows) + write_csv(outdir / "factorial_summary.csv", summary_rows) + + summary_json = { + "schema_version": 2, + "status": "passed", + "evaluation_label": ( + "KID-5k and FID-5k 5,000-sample proxies; not standard " + "50,000-sample benchmarks" + ), + "selected_global_scale": selected_scale, + "selected_global_scale_text": selection[ + "selected_global_scale_text" + ], + "selected_label": selection["selected_label"], + "training_seeds": list(TRAINING_SEEDS), + "nfes": list(NFES), + "metrics": list(METRICS), + "profiles": list(PROFILES), + "matrix": { + "unique_training_cells": len(cell_rows) // len(NFES), + "evaluated_training_seed_nfe_cells": len(cell_rows), + "unique_metric_files_read_once": len(cell_rows) * len(METRICS), + "fixed_and_global_cells_shared_across_profiles": True, + }, + "statistics": { + "design": "paired by training seed", + "n": 3, + "sample_sd_ddof": 1, + "ci": "two-sided 95% Student-t interval around the paired mean", + "degrees_of_freedom": 2, + "t_critical": T_CRITICAL_DF2_95, + "wins_definition": ( + "number of negative paired contrasts out of 3; lower is better" + ), + "inference_scope": ( + "descriptive only; n=3 is insufficient for broad " + "significance claims" + ), + }, + "selection_overlap": { + "present": True, + "training_seed": 0, + "description": ( + "seed 0 selected g* in the response curve and is reused in " + "the fixed/global formal cells" + ), + }, + "heldout_headline": { + "seeds": list(HELDOUT_SEEDS), + "definition": ( + "100 * (arithmetic mean of arm metrics over seeds 1/2 / " + "arithmetic mean of fixed metrics over seeds 1/2 - 1)" + ), + "not_equal_to": "mean of per-seed percentage changes", + "rows": heldout_rows, + }, + "effect_definitions": EFFECT_DEFINITIONS, + "log_effect_definitions": LOG_EFFECT_DEFINITIONS, + "interaction_note": ( + "The additive_interaction raw contrast is C-G-L+F. Its paired " + "log contrast is the multiplicative interaction " + "log(C*F/(G*L))." + ), + "summaries": summary_rows, + "outputs": list(OUTPUT_NAMES), + } + (outdir / "factorial_summary.json").write_text( + json.dumps(summary_json, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + (outdir / "factorial_summary.md").write_text( + build_markdown( + selected_scale, + selection, + cell_rows, + heldout_rows, + summary_rows, + ), + encoding="utf-8", + ) + print( + "Validated 18 unique training cells, 36 NFE cells, and 72 metric " + f"files; selected g*={selected_scale:.6g}; output={outdir}" + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/summarize_role_a_multibudget.py b/scripts/summarize_role_a_multibudget.py new file mode 100644 index 00000000..0d932abb --- /dev/null +++ b/scripts/summarize_role_a_multibudget.py @@ -0,0 +1,309 @@ +#!/usr/bin/env python3 +"""Validate and summarize the frozen 36-cell Role A quality matrix.""" + +from __future__ import annotations + +import argparse +import csv +import math +import statistics +from pathlib import Path + + +METHODS = ("sigmoid", "adaptive_v1") +SEEDS = (0, 1, 2) +BUDGETS = (16, 32, 64) +NFES = (1, 2) +METRICS = ("KID", "FID") +INPUT_COLUMNS = ( + "Method", "Train seed", "Budget", "NFE", "KID", "FID", "Checkpoint SHA" +) + + +def fail(message: str) -> None: + raise SystemExit(f"[summarize_role_a_multibudget] ERROR: {message}") + + +def read_rows(paths: list[Path]) -> list[dict]: + rows: list[dict] = [] + for path in paths: + try: + with path.open(newline="", encoding="utf-8") as handle: + reader = csv.DictReader(handle) + if tuple(reader.fieldnames or ()) != INPUT_COLUMNS: + fail(f"unexpected columns in {path}: {reader.fieldnames}") + raw_rows = list(reader) + except OSError as exc: + fail(f"cannot read {path}: {exc}") + for raw in raw_rows: + try: + row = { + "method": raw["Method"], + "training_seed": int(raw["Train seed"]), + "budget_kimg": int(raw["Budget"]), + "nfe": int(raw["NFE"]), + "kid5k": float(raw["KID"]), + "fid5k": float(raw["FID"]), + "checkpoint_sha256": raw["Checkpoint SHA"], + } + except (TypeError, ValueError) as exc: + fail(f"malformed row in {path}: {raw}; {exc}") + if not all(math.isfinite(row[name]) for name in ("kid5k", "fid5k")): + fail(f"non-finite metric in {path}: {raw}") + rows.append(row) + return rows + + +def validate_matrix(rows: list[dict]) -> list[dict]: + expected = { + (method, seed, budget, nfe) + for budget in BUDGETS + for method in METHODS + for seed in SEEDS + for nfe in NFES + } + index: dict[tuple, dict] = {} + for row in rows: + key = ( + row["method"], row["training_seed"], row["budget_kimg"], row["nfe"] + ) + if key in index: + fail(f"duplicate matrix cell: {key}") + index[key] = row + missing = expected - set(index) + extra = set(index) - expected + if missing or extra: + fail(f"matrix must contain exactly 36 cells; missing={sorted(missing)}, extra={sorted(extra)}") + return [ + index[(method, seed, budget, nfe)] + for budget in BUDGETS + for nfe in NFES + for seed in SEEDS + for method in METHODS + ] + + +def pair_rows(rows: list[dict]) -> list[dict]: + index = { + (row["method"], row["training_seed"], row["budget_kimg"], row["nfe"]): row + for row in rows + } + paired = [] + for budget in BUDGETS: + for nfe in NFES: + for seed in SEEDS: + fixed = index[("sigmoid", seed, budget, nfe)] + adaptive = index[("adaptive_v1", seed, budget, nfe)] + paired.append({ + "budget_kimg": budget, + "nfe": nfe, + "training_seed": seed, + "fixed_kid5k": fixed["kid5k"], + "adaptive_kid5k": adaptive["kid5k"], + "delta_kid5k": adaptive["kid5k"] - fixed["kid5k"], + "fixed_fid5k": fixed["fid5k"], + "adaptive_fid5k": adaptive["fid5k"], + "delta_fid5k": adaptive["fid5k"] - fixed["fid5k"], + }) + return paired + + +def aggregate_rows(rows: list[dict], paired: list[dict]) -> list[dict]: + aggregate = [] + for budget in BUDGETS: + for nfe in NFES: + selected_pairs = [ + row for row in paired + if row["budget_kimg"] == budget and row["nfe"] == nfe + ] + for metric in ("kid5k", "fid5k"): + fixed_values = [ + row[metric] for row in rows + if row["method"] == "sigmoid" + and row["budget_kimg"] == budget + and row["nfe"] == nfe + ] + adaptive_values = [ + row[metric] for row in rows + if row["method"] == "adaptive_v1" + and row["budget_kimg"] == budget + and row["nfe"] == nfe + ] + deltas = [row[f"delta_{metric}"] for row in selected_pairs] + aggregate.append({ + "budget_kimg": budget, + "nfe": nfe, + "metric": metric, + "fixed_mean": statistics.mean(fixed_values), + "fixed_sample_sd": statistics.stdev(fixed_values), + "adaptive_mean": statistics.mean(adaptive_values), + "adaptive_sample_sd": statistics.stdev(adaptive_values), + "mean_delta_adaptive_minus_fixed": statistics.mean(deltas), + "sample_sd_delta": statistics.stdev(deltas), + "adaptive_wins": sum(value < 0 for value in deltas), + "fixed_wins": sum(value > 0 for value in deltas), + "ties": sum(value == 0 for value in deltas), + }) + return aggregate + + +def write_csv(path: Path, rows: list[dict]) -> None: + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0])) + writer.writeheader() + writer.writerows(rows) + + +def plot_quality_with_pillow(path: Path, aggregate: list[dict]) -> None: + try: + from PIL import Image, ImageDraw, ImageFont + except ImportError as exc: + fail(f"matplotlib or Pillow is required to write {path}: {exc}") + + width, height = 1600, 1050 + image = Image.new("RGB", (width, height), "white") + draw = ImageDraw.Draw(image) + + def font(size: int, bold: bool = False): + name = "DejaVuSans-Bold.ttf" if bold else "DejaVuSans.ttf" + try: + return ImageFont.truetype(name, size) + except OSError: + return ImageFont.load_default() + + colors = {"sigmoid": "#4C78A8", "adaptive_v1": "#F58518"} + labels = {"sigmoid": "Fixed sigmoid", "adaptive_v1": "Adaptive v1"} + draw.text( + (width // 2, 28), + "5k-sample proxy quality vs training budget (mean +/- sample SD, 3 seeds)", + fill="#111111", font=font(28, True), anchor="ma", + ) + legend_y = 68 + for index, method in enumerate(METHODS): + legend_x = 570 + index * 330 + draw.line((legend_x, legend_y, legend_x + 45, legend_y), fill=colors[method], width=5) + draw.ellipse((legend_x + 17, legend_y - 7, legend_x + 31, legend_y + 7), fill=colors[method]) + draw.text((legend_x + 58, legend_y), labels[method], fill="#111111", font=font(20), anchor="lm") + + panel_width, panel_height = 690, 390 + lefts, tops = (120, 870), (120, 590) + for row_index, metric in enumerate(("kid5k", "fid5k")): + for column_index, nfe in enumerate(NFES): + left, top = lefts[column_index], tops[row_index] + right, bottom = left + panel_width, top + panel_height + selected = { + row["budget_kimg"]: row for row in aggregate + if row["metric"] == metric and row["nfe"] == nfe + } + ranges = [] + for method in METHODS: + prefix = "fixed" if method == "sigmoid" else "adaptive" + for budget in BUDGETS: + mean = selected[budget][f"{prefix}_mean"] + error = selected[budget][f"{prefix}_sample_sd"] + ranges.extend((mean - error, mean + error)) + low, high = min(ranges), max(ranges) + padding = max((high - low) * 0.12, abs(high) * 0.01, 1e-6) + low, high = low - padding, high + padding + + def x_position(budget: int) -> float: + return left + (budget - BUDGETS[0]) / (BUDGETS[-1] - BUDGETS[0]) * panel_width + + def y_position(value: float) -> float: + return bottom - (value - low) / (high - low) * panel_height + + for tick in range(5): + value = low + (high - low) * tick / 4 + y = y_position(value) + draw.line((left, y, right, y), fill="#DDDDDD", width=1) + draw.text((left - 12, y), f"{value:.3f}", fill="#333333", font=font(16), anchor="rm") + draw.line((left, top, left, bottom), fill="#333333", width=2) + draw.line((left, bottom, right, bottom), fill="#333333", width=2) + for budget in BUDGETS: + x = x_position(budget) + draw.line((x, bottom, x, bottom + 7), fill="#333333", width=2) + draw.text((x, bottom + 14), str(budget), fill="#333333", font=font(18), anchor="ma") + + for method in METHODS: + prefix = "fixed" if method == "sigmoid" else "adaptive" + points = [] + for budget in BUDGETS: + mean = selected[budget][f"{prefix}_mean"] + error = selected[budget][f"{prefix}_sample_sd"] + x, y = x_position(budget), y_position(mean) + y_low, y_high = y_position(mean - error), y_position(mean + error) + draw.line((x, y_low, x, y_high), fill=colors[method], width=3) + draw.line((x - 7, y_low, x + 7, y_low), fill=colors[method], width=3) + draw.line((x - 7, y_high, x + 7, y_high), fill=colors[method], width=3) + points.append((x, y)) + draw.line(points, fill=colors[method], width=5, joint="curve") + for x, y in points: + draw.ellipse((x - 7, y - 7, x + 7, y + 7), fill=colors[method]) + + metric_label = "KID-5k" if metric == "kid5k" else "FID-5k proxy" + draw.text(((left + right) / 2, top - 38), f"{metric_label}, NFE={nfe}", fill="#111111", font=font(22, True), anchor="ma") + if row_index == 1: + draw.text(((left + right) / 2, bottom + 48), "Training budget (kimg)", fill="#333333", font=font(18), anchor="ma") + + image.save(path) + + +def plot_quality(path: Path, aggregate: list[dict]) -> None: + try: + import matplotlib.pyplot as plt + except ImportError: + plot_quality_with_pillow(path, aggregate) + return + + fig, axes = plt.subplots(2, 2, figsize=(10, 7), sharex=True) + colors = {"sigmoid": "#4C78A8", "adaptive_v1": "#F58518"} + labels = {"sigmoid": "Fixed sigmoid", "adaptive_v1": "Adaptive v1"} + for row_index, metric in enumerate(("kid5k", "fid5k")): + for column_index, nfe in enumerate(NFES): + axis = axes[row_index][column_index] + selected = { + row["budget_kimg"]: row for row in aggregate + if row["metric"] == metric and row["nfe"] == nfe + } + for method in METHODS: + prefix = "fixed" if method == "sigmoid" else "adaptive" + means = [selected[budget][f"{prefix}_mean"] for budget in BUDGETS] + errors = [selected[budget][f"{prefix}_sample_sd"] for budget in BUDGETS] + axis.errorbar( + BUDGETS, means, yerr=errors, marker="o", linewidth=1.8, + capsize=3, color=colors[method], label=labels[method], + ) + axis.set_title(f"NFE={nfe}") + axis.set_ylabel("KID-5k" if metric == "kid5k" else "FID-5k proxy") + axis.grid(axis="y", alpha=0.25) + axis.set_xticks(BUDGETS) + if row_index == 1: + axis.set_xlabel("Training budget (kimg)") + handles, labels_found = axes[0][0].get_legend_handles_labels() + fig.legend(handles, labels_found, loc="upper center", ncol=2, frameon=False) + fig.suptitle("5k-sample proxy quality vs training budget (mean ± sample SD, 3 seeds)") + fig.tight_layout(rect=(0, 0, 1, 0.91)) + fig.savefig(path, dpi=180) + plt.close(fig) + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--metrics-csv", type=Path, action="append", required=True) + parser.add_argument("--outdir", type=Path, required=True) + args = parser.parse_args(argv) + + rows = validate_matrix(read_rows([path.resolve() for path in args.metrics_csv])) + paired = pair_rows(rows) + aggregate = aggregate_rows(rows, paired) + outdir = args.outdir.resolve() + outdir.mkdir(parents=True, exist_ok=True) + write_csv(outdir / "per_seed_metrics.csv", rows) + write_csv(outdir / "paired_differences.csv", paired) + write_csv(outdir / "aggregate_results.csv", aggregate) + plot_quality(outdir / "quality_vs_budget.png", aggregate) + print(f"Validated 36 independent cells; output={outdir}") + + +if __name__ == "__main__": + main() diff --git a/scripts/summarize_traj64_stability.py b/scripts/summarize_traj64_stability.py new file mode 100644 index 00000000..09e8b1a0 --- /dev/null +++ b/scripts/summarize_traj64_stability.py @@ -0,0 +1,236 @@ +#!/usr/bin/env python3 +"""Validate six packaged 64 kimg trajectories and summarize stability telemetry.""" + +from __future__ import annotations + +import argparse +import csv +import json +import math +from pathlib import Path + + +METHODS = ("sigmoid", "adaptive_v1") +SEEDS = (0, 1, 2) +BUDGETS = (16, 32, 64) +MAX_ADJUST = 0.05 + + +def fail(message: str) -> None: + raise SystemExit(f"[summarize_traj64_stability] ERROR: {message}") + + +def package_dir(root: Path, method: str, seed: int) -> Path: + return root / f"{method}_traj64_seed{seed}_5344a5c9" + + +def load_json(path: Path) -> dict: + try: + return json.loads(path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError) as exc: + fail(f"cannot read {path}: {exc}") + + +def load_training_rows(path: Path) -> list[dict]: + try: + with path.open(newline="", encoding="utf-8") as handle: + rows = list(csv.DictReader(handle)) + except OSError as exc: + fail(f"cannot read {path}: {exc}") + if len(rows) != 500: + fail(f"{path} must contain 500 attempted iterations, found {len(rows)}") + return rows + + +def parse_float(row: dict, name: str) -> float: + try: + value = float(row[name]) + except (KeyError, TypeError, ValueError) as exc: + fail(f"invalid {name}: {row}; {exc}") + if not math.isfinite(value): + fail(f"non-finite {name}: {value}") + return value + + +def summarize_package(root: Path, method: str, seed: int) -> tuple[dict, list[dict]]: + directory = package_dir(root, method, seed) + metadata = load_json(directory / "metadata.json") + acceptance = load_json(directory / "acceptance.json") + rows = load_training_rows(directory / "train_summary.csv") + if metadata.get("schedule") != method or int(metadata.get("training_seed", -1)) != seed: + fail(f"identity mismatch in {directory}") + if metadata.get("package_ok") is not True or metadata.get("hard_fail"): + fail(f"package did not pass Role B acceptance: {directory}") + required = { + "attempted_iterations": 500, + "processed_kimg": 64.0, + "nan_count": 0, + "inf_count": 0, + "rt_gap_always_legal": True, + } + for name, expected in required.items(): + if acceptance.get(name) != expected: + fail(f"{directory}: {name}={acceptance.get(name)!r}, expected {expected!r}") + losses = [parse_float(row, "loss") for row in rows] + skipped = sum(row["step_skipped"].strip().lower() in {"1", "true", "yes"} for row in rows) + if skipped != int(acceptance["amp_skipped_steps"]): + fail(f"skipped-step mismatch in {directory}") + + corrections = [parse_float(row, "correction") for row in rows] + active_corrections = [ + value for row, value in zip(rows, corrections) + if row["adaptive_active"].strip().lower() in {"1", "true", "yes"} + ] if method == "adaptive_v1" else [] + nonzero_signs = [1 if value > 0 else -1 for value in active_corrections if value != 0] + sign_changes = sum(left != right for left, right in zip(nonzero_signs, nonzero_signs[1:])) + saturated_steps = sum(abs(value) >= 0.99 * MAX_ADJUST for value in active_corrections) + + stability = { + "method": method, + "training_seed": seed, + "processed_kimg": float(acceptance["processed_kimg"]), + "attempted_iterations": int(acceptance["attempted_iterations"]), + "successful_optimizer_steps": int(acceptance["successful_optimizer_steps"]), + "amp_skipped_steps": skipped, + "nan_count": int(acceptance["nan_count"]), + "inf_count": int(acceptance["inf_count"]), + "trailing25_loss_mean": float(acceptance["trailing25_mean"]), + "trailing25_loss_std": float(acceptance["trailing25_std"]), + "rt_gap_always_legal": bool(acceptance["rt_gap_always_legal"]), + "adaptive_correction_activated": acceptance["adaptive_correction_activated"], + "correction_saturated_steps": saturated_steps if method == "adaptive_v1" else "", + "correction_sign_changes": sign_changes if method == "adaptive_v1" else "", + "checkpoint_64_sha256": acceptance["checkpoints"]["64"]["network_snapshot_sha256"], + } + + telemetry = [] + for budget in BUDGETS: + matches = [row for row in rows if math.isclose(parse_float(row, "processed_kimg"), budget)] + if len(matches) != 1: + fail(f"{directory}: expected one row at {budget} kimg, found {len(matches)}") + row = matches[0] + telemetry.append({ + "method": method, + "training_seed": seed, + "budget_kimg": budget, + "loss": parse_float(row, "loss"), + "correction": parse_float(row, "correction"), + "r_over_t_mean": parse_float(row, "r_over_t_mean"), + "gap_mean": parse_float(row, "gap_mean"), + "adaptive_active": row["adaptive_active"].strip().lower() in {"1", "true", "yes"}, + "signal_updates": int(row["signal_updates"]), + }) + return stability, telemetry + + +def write_csv(path: Path, rows: list[dict]) -> None: + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0])) + writer.writeheader() + writer.writerows(rows) + + +def plot_controller(path: Path, telemetry: list[dict]) -> None: + try: + from PIL import Image, ImageDraw, ImageFont + except ImportError as exc: + fail(f"Pillow is required to write {path}: {exc}") + selected = [row for row in telemetry if row["method"] == "adaptive_v1"] + image = Image.new("RGB", (1400, 620), "white") + draw = ImageDraw.Draw(image) + + def font(size: int, bold: bool = False): + try: + return ImageFont.truetype("DejaVuSans-Bold.ttf" if bold else "DejaVuSans.ttf", size) + except OSError: + return ImageFont.load_default() + + colors = ("#4C78A8", "#F58518", "#54A24B") + draw.text((700, 25), "Adaptive v1 controller telemetry vs training budget", fill="#111111", font=font(27, True), anchor="ma") + for seed, color in zip(SEEDS, colors): + x = 490 + seed * 180 + draw.line((x, 66, x + 38, 66), fill=color, width=5) + draw.text((x + 48, 66), f"seed {seed}", fill="#111111", font=font(18), anchor="lm") + + for panel, field in enumerate(("correction", "gap_mean")): + left, top, width, height = 105 + panel * 690, 125, 570, 380 + right, bottom = left + width, top + height + values = [row[field] for row in selected] + low, high = min(values), max(values) + padding = max((high - low) * 0.15, 1e-5) + low, high = low - padding, high + padding + + def xp(budget: int) -> float: + return left + (budget - BUDGETS[0]) / (BUDGETS[-1] - BUDGETS[0]) * width + + def yp(value: float) -> float: + return bottom - (value - low) / (high - low) * height + + for tick in range(5): + value = low + (high - low) * tick / 4 + y = yp(value) + draw.line((left, y, right, y), fill="#DDDDDD", width=1) + draw.text((left - 10, y), f"{value:.4f}", fill="#333333", font=font(16), anchor="rm") + draw.line((left, top, left, bottom), fill="#333333", width=2) + draw.line((left, bottom, right, bottom), fill="#333333", width=2) + for budget in BUDGETS: + x = xp(budget) + draw.text((x, bottom + 14), str(budget), fill="#333333", font=font(17), anchor="ma") + for seed, color in zip(SEEDS, colors): + rows = [row for row in selected if row["training_seed"] == seed] + rows.sort(key=lambda row: row["budget_kimg"]) + points = [(xp(row["budget_kimg"]), yp(row[field])) for row in rows] + draw.line(points, fill=color, width=5) + for x, y in points: + draw.ellipse((x - 7, y - 7, x + 7, y + 7), fill=color) + label = "Controller correction" if field == "correction" else "Mean relative gap (t-r)/t" + draw.text(((left + right) / 2, top - 38), label, fill="#111111", font=font(22, True), anchor="ma") + draw.text(((left + right) / 2, bottom + 50), "Training budget (kimg)", fill="#333333", font=font(18), anchor="ma") + image.save(path) + + +def write_markdown(path: Path, rows: list[dict]) -> None: + lines = [ + "# Training stability summary (continuous 64 kimg trajectories)", + "", + "| Method | Seed | Attempted | Successful | AMP skipped | NaN | Inf | trailing loss mean +/- SD | r/t and gap legal | Controller active | Saturated steps | Sign changes |", + "| --- | ---: | ---: | ---: | ---: | ---: | ---: | --- | --- | --- | ---: | ---: |", + ] + for row in rows: + lines.append( + f"| {row['method']} | {row['training_seed']} | {row['attempted_iterations']} | " + f"{row['successful_optimizer_steps']} | {row['amp_skipped_steps']} | {row['nan_count']} | " + f"{row['inf_count']} | {row['trailing25_loss_mean']:.4f} +/- {row['trailing25_loss_std']:.4f} | " + f"{row['rt_gap_always_legal']} | {row['adaptive_correction_activated']} | " + f"{row['correction_saturated_steps']} | {row['correction_sign_changes']} |" + ) + lines.extend([ + "", + "All six trajectories reached 64 kimg with finite recorded losses. Saturation is defined as |correction| >= 99% of the frozen max_adjust=0.05; sign changes are descriptive, not a formal oscillation test.", + "", + ]) + path.write_text("\n".join(lines), encoding="utf-8") + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--training-root", type=Path, required=True) + parser.add_argument("--outdir", type=Path, required=True) + args = parser.parse_args(argv) + stability, telemetry = [], [] + for method in METHODS: + for seed in SEEDS: + row, points = summarize_package(args.training_root.resolve(), method, seed) + stability.append(row) + telemetry.extend(points) + outdir = args.outdir.resolve() + outdir.mkdir(parents=True, exist_ok=True) + write_csv(outdir / "training_stability.csv", stability) + write_csv(outdir / "controller_at_budget.csv", telemetry) + write_markdown(outdir / "training_stability.md", stability) + plot_controller(outdir / "controller_vs_budget.png", telemetry) + print(f"Validated six continuous 64 kimg trajectories; output={outdir}") + + +if __name__ == "__main__": + main() diff --git a/scripts/validate_staged_runtime_manifest.py b/scripts/validate_staged_runtime_manifest.py new file mode 100644 index 00000000..7a1a2db9 --- /dev/null +++ b/scripts/validate_staged_runtime_manifest.py @@ -0,0 +1,153 @@ +#!/usr/bin/env python3 +"""Validate a machine-local staged-evaluation manifest against its frozen matrix.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import sys +from pathlib import Path +from typing import Any + + +REPO_ROOT = Path(__file__).resolve().parents[1] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from scripts import run_staged_evaluation + + +def fail(message: str) -> None: + raise SystemExit(f"[validate_staged_runtime_manifest] ERROR: {message}") + + +def load_json(path: Path, label: str) -> dict[str, Any]: + try: + value = json.loads(path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError) as exc: + fail(f"cannot read {label} {path}: {exc}") + if not isinstance(value, dict): + fail(f"{label} must contain a JSON object: {path}") + return value + + +def sha256_file(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def cells_by_id(manifest: dict[str, Any], label: str) -> dict[str, dict[str, Any]]: + cells = manifest.get("cells") + if not isinstance(cells, list) or not cells: + fail(f"{label} manifest must contain a non-empty cells list") + indexed: dict[str, dict[str, Any]] = {} + for cell in cells: + if not isinstance(cell, dict) or not isinstance(cell.get("checkpoint_id"), str): + fail(f"{label} manifest has a cell without a string checkpoint_id") + checkpoint_id = cell["checkpoint_id"] + if not checkpoint_id or checkpoint_id in indexed: + fail(f"{label} manifest checkpoint_id is empty or duplicated: {checkpoint_id!r}") + indexed[checkpoint_id] = cell + return indexed + + +def validate( + frozen_path: Path, + runtime_path: Path, + *, + allow_missing_inputs: bool = False, +) -> list[dict[str, str]]: + """Return validated binding rows or raise SystemExit on any mismatch.""" + frozen = load_json(frozen_path, "frozen matrix") + if frozen.get("manifest_kind") != "frozen-logical-checkpoint-matrix": + fail("frozen matrix has an unexpected manifest_kind") + if frozen.get("protocol") != run_staged_evaluation.PROTOCOL_ID: + fail(f"frozen matrix protocol must be {run_staged_evaluation.PROTOCOL_ID!r}") + if frozen.get("runtime_binding", {}).get("versioned_paths") is not False: + fail("frozen matrix must declare versioned_paths=false") + + runtime = load_json(runtime_path, "runtime manifest") + frozen_cells = cells_by_id(frozen, "frozen") + runtime_cells = cells_by_id(runtime, "runtime") + if frozen_cells.keys() != runtime_cells.keys(): + missing = sorted(frozen_cells.keys() - runtime_cells.keys()) + extra = sorted(runtime_cells.keys() - frozen_cells.keys()) + fail(f"runtime checkpoint IDs differ from frozen matrix; missing={missing}, extra={extra}") + frozen_policy = frozen.get("formal_promotion_policy") + if frozen_policy is not None and runtime.get("formal_promotion_policy") != frozen_policy: + fail("runtime formal_promotion_policy must exactly match the frozen matrix") + + # Reuse the evaluator's strict checkpoint/receipt schema checks so this + # validator cannot accept a runtime manifest which the formal runner rejects. + evaluator_cells, _ = run_staged_evaluation.load_cells(runtime_path, allow_missing_inputs) + evaluator_by_id = {cell["checkpoint_id"]: cell for cell in evaluator_cells} + rows: list[dict[str, str]] = [] + required_identity_fields = ( + "method", "training_seed", "budget_kimg", "schedule_q", + "schedule_identity", "global_gap_scale", "checkpoint_sha256", + "executed_training_source_commit", + ) + for checkpoint_id, frozen_cell in frozen_cells.items(): + runtime_cell = runtime_cells[checkpoint_id] + missing = [field for field in required_identity_fields if field not in runtime_cell] + if missing: + fail(f"runtime cell {checkpoint_id} is missing frozen identity fields: {missing}") + for field in required_identity_fields: + if runtime_cell[field] != frozen_cell.get(field): + fail( + f"runtime cell {checkpoint_id} differs from frozen {field}: " + f"{runtime_cell[field]!r} != {frozen_cell.get(field)!r}" + ) + + receipt_path = Path(str(runtime_cell.get("integrity_receipt", ""))).expanduser() + expected_receipt = frozen_cell.get("training_integrity_receipt") + if not isinstance(expected_receipt, dict): + fail(f"frozen cell {checkpoint_id} lacks training_integrity_receipt") + if receipt_path.name != expected_receipt.get("receipt_filename"): + fail( + f"runtime receipt filename mismatch for {checkpoint_id}: " + f"{receipt_path.name!r} != {expected_receipt.get('receipt_filename')!r}" + ) + if receipt_path.is_file(): + expected_receipt_sha = expected_receipt.get("receipt_sha256") + if expected_receipt_sha and sha256_file(receipt_path) != expected_receipt_sha: + fail(f"runtime receipt SHA256 mismatch for {checkpoint_id}: {receipt_path}") + elif not allow_missing_inputs: + fail(f"runtime receipt not found: {receipt_path}") + + receipt = run_staged_evaluation.verify_integrity_receipt( + evaluator_by_id[checkpoint_id], allow_missing_inputs + ) + rows.append({ + "checkpoint_id": checkpoint_id, + "checkpoint": str(evaluator_by_id[checkpoint_id]["checkpoint"]), + "integrity_receipt": str(receipt["path"]), + }) + return rows + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--frozen", type=Path, required=True, help="Git-tracked logical matrix") + parser.add_argument("--runtime", type=Path, required=True, help="machine-local manifest with paths") + parser.add_argument( + "--allow-missing-inputs", action="store_true", + help="validate only structure and identity; never use this before formal evaluation", + ) + return parser.parse_args() + + +def main() -> None: + args = parse_args() + rows = validate( + args.frozen, args.runtime, allow_missing_inputs=args.allow_missing_inputs + ) + print(f"Validated {len(rows)} runtime bindings against {args.frozen}") + + +if __name__ == "__main__": + main() diff --git a/scripts/verify_assets.py b/scripts/verify_assets.py new file mode 100755 index 00000000..7596872a --- /dev/null +++ b/scripts/verify_assets.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python3 +"""Verify ECT datasets and transfer checkpoints without changing the assets.""" + +import argparse +import hashlib +import io +import json +import sys +import zipfile +from pathlib import Path + +import numpy as np +from PIL import Image + + +REPO_ROOT = Path(__file__).resolve().parents[1] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +OFFICIAL_CIFAR10_MD5 = "c58f30108f718f92721af3b95e74349a" + + +def digest(path: Path, algorithm: str) -> str: + value = hashlib.new(algorithm) + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(8 * 1024 * 1024), b""): + value.update(chunk) + return value.hexdigest() + + +def verify_tarball(path: Path, expected_md5: str) -> dict: + if not path.is_file(): + raise ValueError(f"source tarball not found: {path}") + actual_md5 = digest(path, "md5") + if actual_md5.lower() != expected_md5.lower(): + raise ValueError( + f"CIFAR-10 tarball MD5 mismatch: expected {expected_md5}, got {actual_md5}" + ) + return { + "path": str(path.resolve()), + "bytes": path.stat().st_size, + "md5": actual_md5, + "expected_md5": expected_md5.lower(), + "md5_ok": True, + } + + +def verify_dataset( + path: Path, + tarball: Path, + expected_md5: str, + expected_count: int, + expected_labels: int, + expected_resolution: int, +) -> dict: + """Validate archive integrity, every image header, labels, and project loading.""" + if not path.is_file(): + raise ValueError(f"dataset not found: {path}") + + source = verify_tarball(tarball, expected_md5) + sample_names = [] + try: + with zipfile.ZipFile(path, "r") as archive: + corrupt = archive.testzip() + if corrupt is not None: + raise ValueError(f"ZIP CRC failure: {corrupt}") + + names = archive.namelist() + images = sorted(name for name in names if name.lower().endswith(".png")) + if len(images) != expected_count: + raise ValueError(f"expected {expected_count} PNGs, found {len(images)}") + if "dataset.json" not in names: + raise ValueError("dataset.json is missing") + + metadata = json.loads(archive.read("dataset.json")) + labels = metadata.get("labels") + if not isinstance(labels, list): + raise ValueError("dataset.json must contain a labels list") + if len(labels) != expected_labels: + raise ValueError( + f"expected {expected_labels} labels, found {len(labels)}" + ) + label_names = [entry[0] for entry in labels if isinstance(entry, list) and len(entry) == 2] + if len(label_names) != expected_labels: + raise ValueError("every label must be a [filename, class] pair") + if sorted(label_names) != images: + raise ValueError("dataset labels do not map one-to-one to the PNG entries") + + expected_size = (expected_resolution, expected_resolution) + for image_name in images: + with Image.open(io.BytesIO(archive.read(image_name))) as image: + if image.size != expected_size: + raise ValueError( + f"{image_name} is {image.size}, expected {expected_size}" + ) + if image.mode != "RGB": + raise ValueError(f"{image_name} is {image.mode}, expected RGB") + + sample_names = [images[0], images[len(images) // 2], images[-1]] + except (OSError, ValueError, json.JSONDecodeError, zipfile.BadZipFile) as exc: + raise ValueError(f"dataset archive verification failed: {exc}") from exc + + # Import only after archive checks so failures clearly identify the project loader stage. + from training.dataset import ImageFolderDataset + + dataset = ImageFolderDataset(path=str(path), use_labels=True) + try: + if len(dataset) != expected_count: + raise ValueError( + f"ImageFolderDataset length is {len(dataset)}, expected {expected_count}" + ) + if not dataset.has_labels: + raise ValueError("ImageFolderDataset did not expose class labels") + + loader_samples = [] + for index in (0, len(dataset) // 2, len(dataset) - 1): + image, label = dataset[index] + if image.shape != (3, expected_resolution, expected_resolution): + raise ValueError(f"dataset sample {index} has shape {image.shape}") + if image.dtype != np.uint8: + raise ValueError(f"dataset sample {index} has dtype {image.dtype}") + loader_samples.append( + { + "index": index, + "shape": list(image.shape), + "dtype": str(image.dtype), + "label_shape": list(label.shape), + } + ) + finally: + dataset.close() + + return { + "type": "dataset", + "path": str(path.resolve()), + "bytes": path.stat().st_size, + "sha256": digest(path, "sha256"), + "sha256_policy": "record_only_not_a_unique_content_requirement", + "source_tarball": source, + "zip_crc_ok": True, + "images": expected_count, + "labels": expected_labels, + "resolution": [expected_resolution, expected_resolution], + "color_mode": "RGB", + "archive_samples": sample_names, + "imagefolderdataset": { + "length": expected_count, + "has_labels": True, + "samples": loader_samples, + }, + } + + +def verify_checkpoint(path: Path, expected_sha256: str) -> dict: + if not path.is_file(): + raise ValueError(f"checkpoint not found: {path}") + size = path.stat().st_size + if size < 10 * 1024 * 1024: + raise ValueError(f"checkpoint is unexpectedly small: {size} bytes") + with path.open("rb") as handle: + prefix = handle.read(2) + if not prefix.startswith(b"\x80"): + raise ValueError("checkpoint does not look like a binary pickle") + actual_sha256 = digest(path, "sha256") + if expected_sha256 and actual_sha256.lower() != expected_sha256.lower(): + raise ValueError( + f"checkpoint SHA-256 mismatch: expected {expected_sha256}, got {actual_sha256}" + ) + return { + "type": "checkpoint", + "path": str(path.resolve()), + "bytes": size, + "sha256": actual_sha256, + "expected_sha256": expected_sha256.lower() if expected_sha256 else None, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + subparsers = parser.add_subparsers(dest="asset_type", required=True) + + dataset_parser = subparsers.add_parser("dataset") + dataset_parser.add_argument("--path", type=Path, required=True) + dataset_parser.add_argument("--tarball", type=Path, required=True) + dataset_parser.add_argument("--expected-md5", default=OFFICIAL_CIFAR10_MD5) + dataset_parser.add_argument("--expected-count", type=int, default=50000) + dataset_parser.add_argument("--expected-labels", type=int, default=50000) + dataset_parser.add_argument("--expected-resolution", type=int, default=32) + dataset_parser.add_argument("--output", type=Path) + + checkpoint_parser = subparsers.add_parser("checkpoint") + checkpoint_parser.add_argument("--path", type=Path, required=True) + checkpoint_parser.add_argument("--expected-sha256", default="") + checkpoint_parser.add_argument("--output", type=Path) + args = parser.parse_args() + + try: + if args.asset_type == "dataset": + report = verify_dataset( + args.path, + args.tarball, + args.expected_md5, + args.expected_count, + args.expected_labels, + args.expected_resolution, + ) + else: + report = verify_checkpoint(args.path, args.expected_sha256) + except ValueError as exc: + raise SystemExit(f"asset verification failed: {exc}") from exc + + report["status"] = "passed" + rendered = json.dumps(report, indent=2, sort_keys=True) + print(rendered) + if args.output: + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(rendered + "\n", encoding="utf-8") + + +if __name__ == "__main__": + main() diff --git a/scripts/verify_fixed_seed_determinism.py b/scripts/verify_fixed_seed_determinism.py new file mode 100644 index 00000000..dc95162c --- /dev/null +++ b/scripts/verify_fixed_seed_determinism.py @@ -0,0 +1,135 @@ +#!/usr/bin/env python3 +"""Validate the fixed-seed sampling acceptance artifact from a checkpoint.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +from pathlib import Path + + +EXPECTED_SEEDS = list(range(64)) +EXPECTED_MODES = { + "nfe1": {"nfe": 1, "mid_t": []}, + "nfe2": {"nfe": 2, "mid_t": [0.821]}, +} + + +def fail(message: str) -> None: + raise SystemExit(f"[verify_fixed_seed_determinism] ERROR: {message}") + + +def sha256_file(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def load_metadata(result_dir: Path) -> dict: + path = result_dir / "metadata.json" + try: + payload = json.loads(path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError) as exc: + fail(f"cannot read metadata {path}: {exc}") + if not isinstance(payload, dict): + fail(f"metadata must be a JSON object: {path}") + return payload + + +def verify_manifest(result_dir: Path) -> int: + manifest_path = result_dir / "sha256_manifest.txt" + try: + lines = [line for line in manifest_path.read_text(encoding="utf-8").splitlines() if line] + except OSError as exc: + fail(f"cannot read manifest {manifest_path}: {exc}") + if not lines: + fail("manifest is empty") + names = set() + for line in lines: + try: + digest, name = line.split(" ", 1) + except ValueError: + fail(f"invalid manifest line: {line!r}") + if len(digest) != 64 or any(char not in "0123456789abcdef" for char in digest.lower()): + fail(f"invalid manifest SHA256: {digest!r}") + if name in names: + fail(f"duplicate manifest entry: {name}") + names.add(name) + path = result_dir / name + if not path.is_file(): + fail(f"manifest file is missing: {path}") + if sha256_file(path) != digest: + fail(f"manifest SHA256 mismatch: {path}") + return len(names) + + +def verify_mode_files(result_dir: Path, mode_name: str) -> None: + image_dir = result_dir / mode_name / "images" + actual = {path.name for path in image_dir.glob("seed*.png")} + expected = {f"seed{seed:06d}.png" for seed in EXPECTED_SEEDS} + if actual != expected: + fail( + f"{mode_name} image set is not exactly seeds 0-63; " + f"missing={sorted(expected - actual)}, extra={sorted(actual - expected)}" + ) + if not (result_dir / mode_name / "grid_8x8.png").is_file(): + fail(f"{mode_name} grid is missing") + + +def verify(result_dir: Path) -> dict: + metadata = load_metadata(result_dir) + if metadata.get("seed_list") != EXPECTED_SEEDS or metadata.get("seed_count") != 64: + fail("fixed seed list must be exactly 0-63") + if metadata.get("nfe_modes") != [1, 2]: + fail("fixed seed evaluation must include NFE=1 and NFE=2") + if metadata.get("mid_t_by_mode") != { + name: config["mid_t"] for name, config in EXPECTED_MODES.items() + }: + fail("NFE/mid_t contract does not match the frozen protocol") + if metadata.get("precision") != "fp32": + fail("fixed seed acceptance requires FP32") + if metadata.get("model_forward_batch_size") != 1: + fail("model forward batch size must be one") + if metadata.get("work_group_sizes_verified") != [8, 16]: + fail("work-group verification must be exactly 8 and 16") + if metadata.get("repeat_runs_verified", 0) < 2 or metadata.get("determinism_passed") is not True: + fail("repeated-run determinism did not pass") + if metadata.get("image_count_by_mode") != {"nfe1": 64, "nfe2": 64}: + fail("each NFE mode must contain 64 images") + if metadata.get("image_count_total") != 128: + fail("fixed seed evaluation must contain 128 images total") + for mode_name in EXPECTED_MODES: + verify_mode_files(result_dir, mode_name) + manifest_entry_count = verify_manifest(result_dir) + return { + "schema_version": 1, + "result_directory": str(result_dir), + "checkpoint_id": metadata.get("checkpoint_id"), + "checkpoint_sha256": metadata.get("checkpoint_sha256"), + "nfe_modes": metadata["nfe_modes"], + "mid_t_by_mode": metadata["mid_t_by_mode"], + "seed_list": metadata["seed_list"], + "work_group_sizes_verified": metadata["work_group_sizes_verified"], + "repeat_runs_verified": metadata["repeat_runs_verified"], + "manifest_entry_count": manifest_entry_count, + "status": "passed", + } + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--result-dir", type=Path, required=True) + parser.add_argument("--report", type=Path) + args = parser.parse_args(argv) + result_dir = args.result_dir.expanduser().resolve() + report = verify(result_dir) + report_path = args.report.expanduser().resolve() if args.report else result_dir / "determinism_verification.json" + report_path.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8") + print(f"Fixed-seed determinism passed: {report_path}") + + +if __name__ == "__main__": + main() diff --git a/scripts/verify_gap_factorial_arm.py b/scripts/verify_gap_factorial_arm.py new file mode 100755 index 00000000..200e2dfc --- /dev/null +++ b/scripts/verify_gap_factorial_arm.py @@ -0,0 +1,186 @@ +#!/usr/bin/env python3 +"""Fail-closed validation for one completed gap-factorial training arm.""" + +from __future__ import annotations + +import argparse +import csv +import hashlib +import json +import math +from pathlib import Path + + +def fail(message: str) -> None: + raise SystemExit(f"[verify_gap_factorial_arm] ERROR: {message}") + + +def sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(8 * 1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def finite_float(row: dict[str, str], field: str, row_number: int) -> float: + try: + value = float(row[field]) + except (KeyError, TypeError, ValueError) as exc: + fail(f"row {row_number}: invalid {field}: {exc}") + if not math.isfinite(value): + fail(f"row {row_number}: non-finite {field}: {value}") + return value + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--run-dir", type=Path, required=True) + parser.add_argument("--expected-kimg", type=float, required=True) + parser.add_argument("--expected-schedule", required=True) + parser.add_argument("--require-controller-active", action="store_true") + args = parser.parse_args() + + run_dir = args.run_dir.resolve() + required = [ + "experiment_meta.env", + "training_options.json", + "train_summary.csv", + "runner.log", + "network-snapshot-latest.pkl", + "training-state-latest.pt", + "checkpoint.sha256", + ] + for name in required: + path = run_dir / name + if not path.is_file() or path.stat().st_size == 0: + fail(f"missing or empty required output: {path}") + + numbered = [ + *run_dir.glob("network-snapshot-[0-9]*.pkl"), + *run_dir.glob("training-state-[0-9]*.pt"), + ] + if numbered: + fail(f"unexpected redundant numbered checkpoints: {numbered}") + + meta = (run_dir / "experiment_meta.env").read_text(encoding="utf-8") + if "exit_code=0\n" not in meta: + fail("experiment metadata does not record exit_code=0") + if "Exiting..." not in (run_dir / "runner.log").read_text( + encoding="utf-8", errors="replace" + ): + fail("runner log does not contain the training completion marker") + + with (run_dir / "train_summary.csv").open( + newline="", encoding="utf-8" + ) as handle: + reader = csv.DictReader(handle) + fieldnames = set(reader.fieldnames or ()) + rows = list(reader) + if not rows: + fail("train_summary.csv contains no data rows") + + geometry_fields = { + "gap_over_sigmoid_gap_mean", + "lower_gap_clip_rate", + "upper_gap_clip_rate", + } + present_geometry_fields = geometry_fields & fieldnames + if present_geometry_fields and present_geometry_fields != geometry_fields: + fail( + "partial realized-gap diagnostic schema: " + f"{sorted(present_geometry_fields)}" + ) + geometry_recorded = present_geometry_fields == geometry_fields + + schedules = {row.get("schedule") for row in rows} + if schedules != {args.expected_schedule}: + fail(f"schedule mismatch: expected {args.expected_schedule}, got {schedules}") + + skipped_steps = 0 + for index, row in enumerate(rows, start=2): + finite_float(row, "loss", index) + if finite_float(row, "grad_scale", index) <= 0: + fail(f"row {index}: grad_scale must be positive") + finite_float(row, "r_over_t_mean", index) + finite_float(row, "gap_mean", index) + if geometry_recorded: + if finite_float(row, "gap_over_sigmoid_gap_mean", index) < 0: + fail( + f"row {index}: gap_over_sigmoid_gap_mean must be " + "non-negative" + ) + for field in ("lower_gap_clip_rate", "upper_gap_clip_rate"): + value = finite_float(row, field, index) + if not 0 <= value <= 1: + fail(f"row {index}: {field} must be in [0, 1]") + try: + skipped = int(row["step_skipped"]) + except (KeyError, TypeError, ValueError) as exc: + fail(f"row {index}: invalid step_skipped: {exc}") + if skipped not in (0, 1): + fail(f"row {index}: step_skipped must be 0 or 1") + skipped_steps += skipped + + final_kimg = finite_float(rows[-1], "processed_kimg", len(rows) + 1) + if not args.expected_kimg <= final_kimg < args.expected_kimg + 0.128: + fail( + f"final processed_kimg {final_kimg} is outside " + f"[{args.expected_kimg}, {args.expected_kimg + 0.128})" + ) + # The frozen GradScaler starts at 65536 and normally needs roughly nine + # overflow-driven reductions before reaching its stable scale. Prior + # paired baselines show 9--10 total skips, occasionally late in training. + if skipped_steps > 16: + fail(f"unexpected AMP instability: {skipped_steps} skipped steps") + + if args.require_controller_active: + active = rows[-1].get("adaptive_active") + signal_updates = int(rows[-1].get("signal_updates", "0")) + correction = finite_float(rows[-1], "correction", len(rows) + 1) + if active not in {"1", "True", "true"}: + fail("local controller is not active in the final row") + if signal_updates <= 64: + fail(f"local controller has only {signal_updates} signal updates") + if correction == 0: + fail("local controller final correction is zero") + + checkpoint = run_dir / "network-snapshot-latest.pkl" + recorded = (run_dir / "checkpoint.sha256").read_text( + encoding="utf-8" + ).split()[0] + actual = sha256(checkpoint) + if recorded != actual: + fail(f"checkpoint hash mismatch: recorded {recorded}, actual {actual}") + + report = { + "status": "passed", + "run_dir": str(run_dir), + "expected_schedule": args.expected_schedule, + "rows": len(rows), + "final_processed_kimg": final_kimg, + "skipped_steps": skipped_steps, + "controller_required": args.require_controller_active, + "final_controller_active": rows[-1].get("adaptive_active"), + "final_signal_updates": int(rows[-1].get("signal_updates", "0")), + "final_correction": finite_float( + rows[-1], "correction", len(rows) + 1 + ), + "realized_gap_diagnostics": { + "status": ( + "recorded" + if geometry_recorded + else "not_recorded_pre_instrumentation" + ), + "fields": sorted(geometry_fields), + }, + "checkpoint_sha256": actual, + } + (run_dir / "validation.json").write_text( + json.dumps(report, indent=2, sort_keys=True) + "\n", encoding="utf-8" + ) + print(json.dumps(report, sort_keys=True)) + + +if __name__ == "__main__": + main() diff --git a/scripts/verify_smoke_run.py b/scripts/verify_smoke_run.py new file mode 100755 index 00000000..afe55015 --- /dev/null +++ b/scripts/verify_smoke_run.py @@ -0,0 +1,136 @@ +#!/usr/bin/env python3 +"""Verify outputs from the engineering-only fresh/resume connectivity test.""" + +import argparse +import json +from pathlib import Path + + +REQUIRED_FILES = [ + "training_options.json", + "log.txt", + "stats.jsonl", + "network-snapshot-latest.pkl", + "training-state-latest.pt", + "final.png", +] + + +def parse_bool(value): + normalized = value.lower() + if normalized in {"true", "1", "yes"}: + return True + if normalized in {"false", "0", "no"}: + return False + raise argparse.ArgumentTypeError(f"expected a boolean, got {value}") + + +def inspect_run( + path: Path, + expected_kimg: int, + resumed: bool, + expected_batch: int, + expected_fp16: bool, + expected_amp: bool, +): + missing = [name for name in REQUIRED_FILES if not (path / name).is_file()] + if missing: + raise SystemExit(f"{path} is missing: {', '.join(missing)}") + + numbered_snapshots = sorted(path.glob("network-snapshot-[0-9][0-9][0-9][0-9][0-9][0-9].pkl")) + numbered_states = sorted(path.glob("training-state-[0-9][0-9][0-9][0-9][0-9][0-9].pt")) + if not numbered_snapshots or not numbered_states: + raise SystemExit(f"{path} has no numbered snapshot/state pair") + + options = json.loads((path / "training_options.json").read_text(encoding="utf-8")) + if options.get("total_kimg") != expected_kimg: + raise SystemExit( + f"{path}: expected total_kimg={expected_kimg}, got {options.get('total_kimg')}" + ) + if options.get("batch_size") != expected_batch: + raise SystemExit(f"{path}: expected batch_size={expected_batch}") + if options.get("metrics") != []: + raise SystemExit(f"{path}: smoke test must disable formal metrics") + actual_fp16 = options.get("network_kwargs", {}).get("use_fp16") + if actual_fp16 is not expected_fp16: + raise SystemExit( + f"{path}: expected network FP16={expected_fp16}, got {actual_fp16}" + ) + actual_amp = options.get("enable_amp") + if actual_amp is not expected_amp: + raise SystemExit(f"{path}: expected enable_amp={expected_amp}, got {actual_amp}") + + log_text = (path / "log.txt").read_text(encoding="utf-8", errors="replace") + if "Exiting..." not in log_text: + raise SystemExit(f"{path}: training did not exit cleanly") + if resumed and "Loading training state from" not in log_text: + raise SystemExit(f"{path}: resume state was not loaded") + + stats_lines = [ + line + for line in (path / "stats.jsonl").read_text(encoding="utf-8").splitlines() + if line.strip() + ] + if not stats_lines: + raise SystemExit(f"{path}: stats.jsonl is empty") + last_stats = json.loads(stats_lines[-1]) + last_progress = last_stats.get("Progress/kimg", {}).get("mean") + if last_progress is None or float(last_progress) < expected_kimg: + raise SystemExit( + f"{path}: expected progress >= {expected_kimg} kimg, got {last_progress}" + ) + + return { + "path": str(path.resolve()), + "resumed": resumed, + "total_kimg": expected_kimg, + "batch_size": expected_batch, + "fp16": actual_fp16, + "gradscaler": actual_amp, + "formal_metrics": False, + "last_progress_kimg": last_progress, + "numbered_snapshots": [item.name for item in numbered_snapshots], + "numbered_states": [item.name for item in numbered_states], + "latest_snapshot_bytes": (path / "network-snapshot-latest.pkl").stat().st_size, + "latest_state_bytes": (path / "training-state-latest.pt").stat().st_size, + } + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--fresh", type=Path, required=True) + parser.add_argument("--resume", type=Path) + parser.add_argument("--git-commit", required=True) + parser.add_argument("--expected-batch", type=int, default=10) + parser.add_argument("--expected-fp16", type=parse_bool, default=True) + parser.add_argument("--expected-amp", type=parse_bool, default=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + + common = { + "expected_batch": args.expected_batch, + "expected_fp16": args.expected_fp16, + "expected_amp": args.expected_amp, + } + runs = [inspect_run(args.fresh, expected_kimg=1, resumed=False, **common)] + if args.resume: + runs.append(inspect_run(args.resume, expected_kimg=2, resumed=True, **common)) + + report = { + "status": "passed", + "test_kind": "engineering_connectivity_only", + "official_ect_baseline": False, + "legacy_fp32_evidence_validates_gradscaler": False, + "git_commit": args.git_commit, + "smoke_steps_per_phase": 100, + "runs": runs, + } + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text( + json.dumps(report, indent=2, sort_keys=True) + "\n", encoding="utf-8" + ) + print(json.dumps(report, indent=2, sort_keys=True)) + + +if __name__ == "__main__": + main() diff --git a/setup_env.sh b/setup_env.sh new file mode 100755 index 00000000..36a17325 --- /dev/null +++ b/setup_env.sh @@ -0,0 +1,127 @@ +#!/usr/bin/env bash + +# Set up the identical Conda runtime and directory layout in each MatrixCloud container. + +set -Eeuo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +ENV_NAME="${ECT_ENV_NAME:-ect}" +CONDA_CONFIG="${ECT_CONDA_CONFIG:-${ROOT_DIR}/conda-matpool.yml}" +PROJECT_ROOT="${ECT_PROJECT_ROOT:-/mnt/ect_project}" +REPORT_PATH="${ECT_ENV_REPORT:-${PROJECT_ROOT}/runs/day2/environment.json}" +MANAGER="" +UPDATE=0 +CHECK_ONLY=0 +ALLOW_NO_CUDA=0 + +fail() { + printf '[setup_env] ERROR: %s\n' "$*" >&2 + exit 1 +} + +usage() { + cat <<'EOF' +Usage: bash setup_env.sh [options] + + --name NAME Environment name (default: ect) + --manager CMD conda or mamba (default: auto-detect) + --update Reconcile an existing environment with env.yml + --check-only Only validate the existing environment + --allow-no-cuda Do not fail when the current machine has no visible GPU + --report PATH Environment report (default: /mnt/ect_project/runs/day2/environment.json) + -h, --help Show this help +EOF +} + +while [[ $# -gt 0 ]]; do + case "$1" in + --name) + [[ $# -ge 2 ]] || fail "--name requires a value" + ENV_NAME="$2" + shift 2 + ;; + --manager) + [[ $# -ge 2 ]] || fail "--manager requires a value" + MANAGER="$2" + shift 2 + ;; + --update) + UPDATE=1 + shift + ;; + --check-only) + CHECK_ONLY=1 + shift + ;; + --allow-no-cuda) + ALLOW_NO_CUDA=1 + shift + ;; + --report) + [[ $# -ge 2 ]] || fail "--report requires a value" + REPORT_PATH="$2" + shift 2 + ;; + -h|--help) + usage + exit 0 + ;; + *) + fail "unknown option: $1" + ;; + esac +done + +if [[ -z "${MANAGER}" ]]; then + if command -v conda >/dev/null 2>&1; then + MANAGER="conda" + elif command -v mamba >/dev/null 2>&1; then + MANAGER="mamba" + else + fail "conda or mamba was not found" + fi +fi +command -v "${MANAGER}" >/dev/null 2>&1 || fail "cannot find ${MANAGER}" +[[ -f "${CONDA_CONFIG}" ]] || fail "Conda configuration not found: ${CONDA_CONFIG}" + +run_manager() { + CONDARC="${CONDA_CONFIG}" "${MANAGER}" "$@" +} + +mkdir -p \ + "${PROJECT_ROOT}/datasets" \ + "${PROJECT_ROOT}/pretrained" \ + "${PROJECT_ROOT}/runs" \ + "${PROJECT_ROOT}/checkpoints" \ + "${PROJECT_ROOT}/cache" \ + "$(dirname "${REPORT_PATH}")" + +ENV_EXISTS=0 +if run_manager run -n "${ENV_NAME}" python --version >/dev/null 2>&1; then + ENV_EXISTS=1 +fi + +if [[ "${CHECK_ONLY}" -eq 0 ]]; then + if [[ "${ENV_EXISTS}" -eq 0 ]]; then + printf '[setup_env] Creating environment %s...\n' "${ENV_NAME}" + run_manager env create --name "${ENV_NAME}" --file "${ROOT_DIR}/env.yml" + elif [[ "${UPDATE}" -eq 1 ]]; then + printf '[setup_env] Updating environment %s...\n' "${ENV_NAME}" + run_manager env update --name "${ENV_NAME}" --file "${ROOT_DIR}/env.yml" --prune + else + printf '[setup_env] Environment %s already exists; validating it unchanged.\n' "${ENV_NAME}" + fi +elif [[ "${ENV_EXISTS}" -eq 0 ]]; then + fail "environment '${ENV_NAME}' does not exist" +fi + +CHECK_ARGS=( + "${ROOT_DIR}/scripts/check_environment.py" + --output "${REPORT_PATH}" +) +if [[ "${ALLOW_NO_CUDA}" -eq 1 ]]; then + CHECK_ARGS+=(--allow-no-cuda) +fi + +run_manager run --no-capture-output -n "${ENV_NAME}" python "${CHECK_ARGS[@]}" +printf '[setup_env] Environment is ready. Activate it with: %s activate %s\n' "${MANAGER}" "${ENV_NAME}" diff --git a/storage_estimate.md b/storage_estimate.md new file mode 100644 index 00000000..12b2fde7 --- /dev/null +++ b/storage_estimate.md @@ -0,0 +1,41 @@ +# Evaluation storage estimate + +## Scope and baseline + +This estimate covers the server-side staging and outputs required to evaluate +the two frozen matrices. It does **not** budget training work directories, +optimizer states, or generated image archives that are intentionally not kept +by the staged evaluator. + +The baseline is the completed six-checkpoint q=256 formal run inspected on +2026-07-31: + +- 12 checkpoint×NFE jobs occupied **20 MiB** of evaluator output, or about + **1.67 MiB per job**; +- its result summary occupied **44 KiB**; +- each staged network checkpoint was **223,169,426–516 bytes** (about + **213 MiB**); and +- the shared Inception detector cache occupied **92 MiB**. + +The 5k and 50k evaluator jobs use the same retained-output layout. Their +compute cost differs, but the observed retained disk footprint is expected to +be approximately per-job rather than proportional to sample count; generated +samples and features are not retained in the result directory. + +## Planned capacity + +| Component | Calculation | Estimate | +| --- | --- | ---: | +| q=256 evaluator outputs | 36 jobs × 1.67 MiB | 60 MiB | +| q=128 evaluator outputs | 12 jobs × 1.67 MiB | 20 MiB | +| summaries/manifests | four result summaries, rounded up | 1 MiB | +| shared detector cache | observed cache | 92 MiB | +| 24 staged checkpoints | 24 × 213 MiB | 5.0 GiB | +| canonical dataset allowance | conservative staging allowance | 1.0 GiB | +| **Working subtotal** | checkpoint staging + evaluation workspace | **about 6.2 GiB** | +| **Reserved free space** | subtotal rounded up with ~25% contingency | **8 GiB** | + +Before launch, measure the actual canonical dataset archive and the completed +checkpoint files, then increase the reservation if either exceeds this +baseline. If training directories or image/feature caches are retained, budget +them separately; they are outside the 8 GiB evaluation-workspace reservation. diff --git a/supplementary/OUTLINE.md b/supplementary/OUTLINE.md new file mode 100644 index 00000000..868db184 --- /dev/null +++ b/supplementary/OUTLINE.md @@ -0,0 +1,83 @@ +# Supplementary Material Outline + +## A. Reproducibility statement + +- software and hardware environment +- dataset preparation and identity +- transfer initialization and identity +- training and evaluation commands +- checkpoint, manifest, and result naming conventions + +## B. Method details + +- baseline sigmoid gap schedule +- global-only calibration with fixed `g=1.10` +- global/local factorization and geometric-mean constraint +- localized-feedback implementation and clipping +- realized gap ratio and clipping-rate definitions + +## C. Experimental protocol + +- primary and secondary endpoints +- confirmatory training seeds 3, 4, and 5 +- fixed sampling seeds and NFE definitions +- 5k screening versus 50k confirmatory evaluation +- paired-delta and percentage-change definitions +- failure, missing-cell, and rerun policies + +## D. Primary-setting results + +- per-seed fixed versus global-only table +- aggregate KID/FID-50k table +- NFE=1 primary analysis +- NFE=2 secondary analysis +- long-budget quality-versus-budget curves + +## E. Mechanism analysis + +- global calibration contribution +- localized feedback contribution +- global/local interaction +- controller correction, clipping, and realized gap plots +- failure and negative-result cases + +## F. Retrospective exploratory setting + +- motivation for examining q=128 after the primary protocol +- explicit statement that q=128 was not frozen before results were observed +- pre-merge source provenance and material implementation differences +- dataset archive mismatch and unavailable canonical content comparison +- per-seed 5k proxy screening results only +- NFE- and seed-dependent effect direction without confirmatory claims + +## G. Training stability + +- successful and skipped optimizer steps +- NaN and Inf counts +- trailing loss mean and dispersion +- runtime and memory +- resume validation + +## H. Additional qualitative results + +- deterministic same-seed samples +- NFE=1 and NFE=2 grids +- examples of improvement, ties, and regressions +- sample-selection rule + +## I. Limitations and responsible interpretation + +- number of independent training seeds +- CIFAR-10 and architecture scope +- proxy versus formal metric limits +- hyperparameter-selection boundaries +- computational budget and untested settings + +## J. Artifact manifest + +- source commit +- anonymous release commit +- configuration files +- dataset and transfer SHA256 +- checkpoint SHA256 list +- table and figure generation inputs diff --git a/tables/q256_main_table.tex b/tables/q256_main_table.tex new file mode 100644 index 00000000..a91de1fb --- /dev/null +++ b/tables/q256_main_table.tex @@ -0,0 +1,28 @@ +% Requires: \usepackage{booktabs} +% Input with: \input{tables/q256_main_table} +\begin{table}[t] + \centering + \caption{Predeclared $q=256$ comparison at 256\,kimg. Lower KID-50k and + FID-50k are better. Values are mean $\pm$ sample SD across three independent + training seeds; $\Delta$ is global-only minus fixed, so negative values favor + global-only. Relative improvement is the arithmetic mean of the seed-wise + percentage improvements.} + \label{tab:q256-256k-main} + \small + \setlength{\tabcolsep}{3.5pt} + \begin{tabular}{llrrrrr} + \toprule + Metric & NFE & Fixed & Global-only & $\Delta$ & Rel. imp. & G/F/T \\ + \midrule + KID-50k & 1 & $0.3320 \pm 0.0164$ & $0.3069 \pm 0.0112$ & $-0.0251 \pm 0.0103$ & $7.50\%$ & $3/0/0$ \\ + FID-50k & 1 & $311.59 \pm 15.06$ & $297.40 \pm 13.97$ & $-14.18 \pm 3.30$ & $4.54\%$ & $3/0/0$ \\ + KID-50k & 2 & $0.2057 \pm 0.1486$ & $0.0582 \pm 0.0255$ & $-0.1474 \pm 0.1290$ & $50.60\%$ & $3/0/0$ \\ + FID-50k & 2 & $197.31 \pm 131.44$ & $70.39 \pm 26.81$ & $-126.92 \pm 110.56$ & $47.44\%$ & $3/0/0$ \\ + \bottomrule + \end{tabular} + + \vspace{2pt} + \parbox{0.94\linewidth}{\footnotesize G/F/T denotes the number of + global-only/fixed/tied seed-wise comparisons. The table is descriptive: + the independent unit is the training seed ($n=3$).} +\end{table} diff --git a/tables/q256_per_seed_appendix.tex b/tables/q256_per_seed_appendix.tex new file mode 100644 index 00000000..50b7a862 --- /dev/null +++ b/tables/q256_per_seed_appendix.tex @@ -0,0 +1,30 @@ +% Requires: \usepackage{booktabs} +% Input with: \input{tables/q256_per_seed_appendix} +\begin{table*}[t] + \centering + \caption{Seed-level paired results for the predeclared $q=256$, 256\,kimg + comparison. $\Delta=$ global-only minus fixed; negative values and positive + relative improvements favor global-only.} + \label{tab:q256-256k-per-seed} + \small + \setlength{\tabcolsep}{5pt} + \begin{tabular}{llrrrrr} + \toprule + Metric & NFE & Seed & Fixed & Global-only & $\Delta$ & Rel. imp. \\ + \midrule + KID-50k & 1 & 3 & $0.342669$ & $0.319188$ & $-0.023481$ & $6.85\%$ \\ + KID-50k & 1 & 4 & $0.340228$ & $0.304063$ & $-0.036166$ & $10.63\%$ \\ + KID-50k & 1 & 5 & $0.313132$ & $0.297402$ & $-0.015730$ & $5.02\%$ \\ + FID-50k & 1 & 3 & $320.59$ & $308.51$ & $-12.08$ & $3.77\%$ \\ + FID-50k & 1 & 4 & $319.97$ & $301.98$ & $-17.98$ & $5.62\%$ \\ + FID-50k & 1 & 5 & $294.20$ & $281.72$ & $-12.49$ & $4.24\%$ \\ + \addlinespace + KID-50k & 2 & 3 & $0.296108$ & $0.055826$ & $-0.240282$ & $81.15\%$ \\ + KID-50k & 2 & 4 & $0.286769$ & $0.084875$ & $-0.201894$ & $70.40\%$ \\ + KID-50k & 2 & 5 & $0.034110$ & $0.034025$ & $-0.000085$ & $0.25\%$ \\ + FID-50k & 2 & 3 & $280.90$ & $69.52$ & $-211.37$ & $75.25\%$ \\ + FID-50k & 2 & 4 & $265.23$ & $97.63$ & $-167.60$ & $63.19\%$ \\ + FID-50k & 2 & 5 & $45.80$ & $44.02$ & $-1.78$ & $3.89\%$ \\ + \bottomrule + \end{tabular} +\end{table*} diff --git a/tests/test_adaptive_signal_updates.py b/tests/test_adaptive_signal_updates.py new file mode 100644 index 00000000..fcb0867b --- /dev/null +++ b/tests/test_adaptive_signal_updates.py @@ -0,0 +1,254 @@ +import csv +import tempfile +import unittest +from pathlib import Path + +import torch + +from training.ct_training_loop import ( + AdaptiveSignalWindow, + _LEGACY_TRAIN_SUMMARY_FIELDS, + _PRE_GAP_DIAGNOSTICS_TRAIN_SUMMARY_FIELDS, + _PRE_NEXT_LOOP_TICK_TRAIN_SUMMARY_FIELDS, + _TRAIN_SUMMARY_FIELDS, + adaptive_update_interval_nimg, + gather_adaptive_signal_window_state, + globally_average_runtime_pairs, + local_adaptive_signal_window_state, + load_and_migrate_train_summary, +) +from training.schedules import get_schedule + + +class AdaptiveSignalUpdatesTest(unittest.TestCase): + def test_default_interval_is_half_kimg(self): + self.assertEqual(adaptive_update_interval_nimg(0.5), 500) + + def test_updates_at_absolute_boundaries_not_maintenance_ticks(self): + window = AdaptiveSignalWindow(update_kimg=0.5, start_nimg=0) + window.add(loss_sum=12.0, loss_count=4) + self.assertIsNone(window.pop_if_due(cur_nimg=499)) + window.add(loss_sum=8.0, loss_count=2) + self.assertEqual(window.pop_if_due(cur_nimg=512), (20.0, 6)) + self.assertEqual(window.next_update_nimg, 1000) + + # A 50 kimg maintenance boundary has no special meaning here. + window.add(loss_sum=9.0, loss_count=3) + self.assertIsNone(window.pop_if_due(cur_nimg=999)) + self.assertEqual(window.pop_if_due(cur_nimg=1000), (9.0, 3)) + + def test_resume_uses_next_absolute_boundary(self): + window = AdaptiveSignalWindow(update_kimg=0.5, start_nimg=50_000) + self.assertEqual(window.next_update_nimg, 50_500) + + def test_resume_preserves_partial_window_loss_aggregation(self): + uninterrupted = AdaptiveSignalWindow(update_kimg=0.5) + uninterrupted.add(loss_sum=12.0, loss_count=4) + self.assertIsNone(uninterrupted.pop_if_due(cur_nimg=384)) + + resumed = AdaptiveSignalWindow(update_kimg=0.5, start_nimg=384) + checkpoint_state = gather_adaptive_signal_window_state( + uninterrupted, device=torch.device('cpu') + ) + self.assertEqual(checkpoint_state['next_update_nimg'], 500) + self.assertEqual(checkpoint_state['loss_sum'], 12.0) + self.assertEqual(checkpoint_state['loss_count'], 4) + resumed.load_state_dict(local_adaptive_signal_window_state(checkpoint_state)) + self.assertEqual(resumed.state_dict(), uninterrupted.state_dict()) + + uninterrupted.add(loss_sum=8.0, loss_count=2) + resumed.add(loss_sum=8.0, loss_count=2) + self.assertEqual(uninterrupted.pop_if_due(cur_nimg=512), (20.0, 6)) + self.assertEqual(resumed.pop_if_due(cur_nimg=512), (20.0, 6)) + self.assertEqual(resumed.state_dict(), uninterrupted.state_dict()) + + def test_interval_must_be_whole_positive_images(self): + for value in [0, -0.5, 0.0005, float('inf')]: + with self.subTest(value=value), self.assertRaises(ValueError): + adaptive_update_interval_nimg(value) + + def test_runtime_pair_metrics_average_without_schedule_internals(self): + metrics = globally_average_runtime_pairs( + [ + { + 'r_over_t_mean': 0.6, + 'gap_mean': 0.4, + 'gap_over_sigmoid_gap_mean': 1.0, + 'lower_gap_clip_rate': 0.0, + 'upper_gap_clip_rate': 0.25, + }, + { + 'r_over_t_mean': 0.8, + 'gap_mean': 0.2, + 'gap_over_sigmoid_gap_mean': 1.2, + 'lower_gap_clip_rate': 0.5, + 'upper_gap_clip_rate': 0.0, + }, + ], + device=torch.device('cpu'), + ) + self.assertAlmostEqual(metrics['r_over_t_mean'], 0.7) + self.assertAlmostEqual(metrics['gap_mean'], 0.3) + self.assertAlmostEqual(metrics['gap_over_sigmoid_gap_mean'], 1.1) + self.assertAlmostEqual(metrics['lower_gap_clip_rate'], 0.25) + self.assertAlmostEqual(metrics['upper_gap_clip_rate'], 0.125) + + def test_resume_migrates_exact_legacy_summary_schema(self): + with tempfile.TemporaryDirectory() as tmp: + summary_path = Path(tmp) / 'train_summary.csv' + legacy_row = { + 'attempted_iteration': '4', + 'successful_optimizer_steps': '4', + 'processed_nimg': '512', + 'processed_kimg': '0.512', + 'loss': '1.25', + 'grad_scale': '65536', + 'step_skipped': '0', + 'schedule': 'adaptive_v1', + 'stage': '0', + 'elapsed_sec': '2.0', + 'peak_vram_gb': '1.5', + } + with summary_path.open('w', newline='') as handle: + writer = csv.DictWriter(handle, fieldnames=_LEGACY_TRAIN_SUMMARY_FIELDS) + writer.writeheader() + writer.writerow(legacy_row) + + rows, backup_path = load_and_migrate_train_summary(summary_path) + self.assertEqual(backup_path, f'{summary_path}.pre-telemetry.bak') + self.assertTrue(Path(backup_path).is_file()) + with Path(backup_path).open(newline='') as handle: + self.assertEqual(tuple(csv.DictReader(handle).fieldnames), _LEGACY_TRAIN_SUMMARY_FIELDS) + with summary_path.open(newline='') as handle: + reader = csv.DictReader(handle) + migrated = next(reader) + self.assertEqual(tuple(reader.fieldnames), _TRAIN_SUMMARY_FIELDS) + for field in ( + 'loss_ema', 'loss_reference', 'correction', 'signal_updates', + 'adaptive_active', 'r_over_t_mean', 'gap_mean', 'next_loop_cur_tick', + 'gap_over_sigmoid_gap_mean', 'lower_gap_clip_rate', + 'upper_gap_clip_rate', + ): + self.assertEqual(migrated[field], '') + self.assertEqual(rows[0][field], '') + + current_rows, second_backup = load_and_migrate_train_summary(summary_path) + self.assertIsNone(second_backup) + self.assertEqual(current_rows, rows) + + def test_resume_migrates_pre_next_loop_tick_schema(self): + with tempfile.TemporaryDirectory() as tmp: + summary_path = Path(tmp) / 'train_summary.csv' + row = {field: '' for field in _PRE_NEXT_LOOP_TICK_TRAIN_SUMMARY_FIELDS} + row.update( + attempted_iteration='4', successful_optimizer_steps='4', + processed_nimg='512', processed_kimg='0.512', loss='1.25', + grad_scale='65536', step_skipped='0', schedule='adaptive_v1', + stage='0', loss_ema='0.8', correction='0.02', + elapsed_sec='2.0', peak_vram_gb='1.5', + ) + with summary_path.open('w', newline='') as handle: + writer = csv.DictWriter( + handle, fieldnames=_PRE_NEXT_LOOP_TICK_TRAIN_SUMMARY_FIELDS + ) + writer.writeheader() + writer.writerow(row) + + rows, backup_path = load_and_migrate_train_summary(summary_path) + self.assertEqual(backup_path, f'{summary_path}.pre-next-loop-tick.bak') + self.assertEqual(rows[0]['next_loop_cur_tick'], '') + self.assertEqual(rows[0]['loss_ema'], '0.8') + self.assertEqual(rows[0]['correction'], '0.02') + with summary_path.open(newline='') as handle: + self.assertEqual( + tuple(csv.DictReader(handle).fieldnames), _TRAIN_SUMMARY_FIELDS + ) + + def test_resume_migrates_pre_gap_diagnostics_schema(self): + with tempfile.TemporaryDirectory() as tmp: + summary_path = Path(tmp) / 'train_summary.csv' + row = { + field: '' for field in + _PRE_GAP_DIAGNOSTICS_TRAIN_SUMMARY_FIELDS + } + row.update( + attempted_iteration='4', + successful_optimizer_steps='4', + processed_nimg='512', + processed_kimg='0.512', + loss='1.25', + grad_scale='65536', + step_skipped='0', + schedule='local_tbin_v3', + stage='0', + next_loop_cur_tick='1', + r_over_t_mean='0.75', + gap_mean='0.25', + elapsed_sec='2.0', + peak_vram_gb='1.5', + ) + with summary_path.open('w', newline='') as handle: + writer = csv.DictWriter( + handle, + fieldnames=_PRE_GAP_DIAGNOSTICS_TRAIN_SUMMARY_FIELDS, + ) + writer.writeheader() + writer.writerow(row) + + rows, backup_path = load_and_migrate_train_summary(summary_path) + self.assertEqual( + backup_path, f'{summary_path}.pre-gap-diagnostics.bak' + ) + for field in ( + 'gap_over_sigmoid_gap_mean', + 'lower_gap_clip_rate', + 'upper_gap_clip_rate', + ): + self.assertEqual(rows[0][field], '') + self.assertEqual(rows[0]['next_loop_cur_tick'], '1') + with summary_path.open(newline='') as handle: + self.assertEqual( + tuple(csv.DictReader(handle).fieldnames), + _TRAIN_SUMMARY_FIELDS, + ) + + def test_resume_rejects_unknown_summary_schema(self): + with tempfile.TemporaryDirectory() as tmp: + summary_path = Path(tmp) / 'train_summary.csv' + with summary_path.open('w', newline='') as handle: + writer = csv.DictWriter(handle, fieldnames=('attempted_iteration', 'loss')) + writer.writeheader() + writer.writerow({'attempted_iteration': '1', 'loss': '1.0'}) + with self.assertRaisesRegex(RuntimeError, 'unsupported schema'): + load_and_migrate_train_summary(summary_path) + self.assertFalse(Path(f'{summary_path}.pre-telemetry.bak').exists()) + + def test_activation_budget_reaches_nonzero_correction_with_iterations_left(self): + batch_size = 128 + final_iteration = 4096 // batch_size + window = AdaptiveSignalWindow(update_kimg=0.5) + schedule = get_schedule('adaptive_v1', loss_ema_beta=0.0, warmup_updates=2) + first_nonzero_correction_iteration = None + + for attempted_iteration in range(1, final_iteration + 1): + window.add(loss_sum=1.0, loss_count=1) + if window.pop_if_due(attempted_iteration * batch_size) is not None: + # Finite improving signals make the first post-warmup + # correction observably nonzero. + schedule.update_training_signal(10.0 / (schedule.signal_updates + 1)) + if schedule.correction() != 0 and first_nonzero_correction_iteration is None: + # Signal processing follows optimizer.step(), so the + # correction affects the next attempted iteration. + first_nonzero_correction_iteration = attempted_iteration + 1 + + self.assertGreaterEqual(schedule.signal_updates, 3) + self.assertIsNotNone(first_nonzero_correction_iteration) + self.assertLess(first_nonzero_correction_iteration, final_iteration) + self.assertGreaterEqual( + final_iteration - first_nonzero_correction_iteration, + 4, + ) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_analyze_paired_results.py b/tests/test_analyze_paired_results.py new file mode 100644 index 00000000..93bba815 --- /dev/null +++ b/tests/test_analyze_paired_results.py @@ -0,0 +1,544 @@ +from __future__ import annotations + +import csv +import json +import shutil +import subprocess +import sys +import tempfile +import unittest +from pathlib import Path + + +REPO_ROOT = Path(__file__).resolve().parents[1] +ANALYZER = REPO_ROOT / "scripts" / "analyze_paired_results.py" + + +def sha(identifier: int) -> str: + return f"{identifier:064x}" + + +def write_summary(path: Path, method: str, seed: int) -> None: + fieldnames = [ + "attempted_iteration", + "successful_optimizer_steps", + "processed_kimg", + "schedule", + "loss", + "step_skipped", + "adaptive_active", + "correction", + "r_over_t_mean", + "gap_mean", + ] + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=fieldnames) + writer.writeheader() + for iteration, budget in enumerate((8, 16, 32, 64), start=1): + writer.writerow( + { + "attempted_iteration": iteration, + "successful_optimizer_steps": iteration, + "processed_kimg": budget, + "schedule": method, + "loss": 10 - iteration + seed * 0.01, + "step_skipped": "0", + "adaptive_active": "1" if method == "adaptive_v1" else "0", + "correction": "0.01" if method == "adaptive_v1" else "0", + "r_over_t_mean": "0.97" if method == "adaptive_v1" else "0.98", + "gap_mean": "0.03" if method == "adaptive_v1" else "0.02", + } + ) + + +class RoleCAnalyzerTests(unittest.TestCase): + def write_complete_fixture(self, root: Path) -> tuple[Path, Path]: + metrics_path = root / "metrics.csv" + records_path = root / "training_records.csv" + metric_fields = [ + "method", "training_seed", "budget_kimg", "nfe", "kid_5k", "fid_5k", "checkpoint_sha256", "mid_t", "sampling_seed", "num_generated", + ] + record_fields = ["method", "training_seed", "budget_kimg", "checkpoint_sha256", "training_summary_csv", "run_dir"] + with metrics_path.open("w", newline="", encoding="utf-8") as metric_handle, records_path.open("w", newline="", encoding="utf-8") as record_handle: + metric_writer = csv.DictWriter(metric_handle, fieldnames=metric_fields) + record_writer = csv.DictWriter(record_handle, fieldnames=record_fields) + metric_writer.writeheader() + record_writer.writeheader() + for method_index, method in enumerate(("sigmoid", "adaptive_v1")): + for seed in (0, 1, 2): + summary = root / f"{method}-seed{seed}" / "train_summary.csv" + write_summary(summary, method, seed) + for budget in (16, 32, 64): + digest = sha(method_index * 100 + seed * 10 + budget // 16) + record_writer.writerow( + { + "method": method, + "training_seed": seed, + "budget_kimg": budget, + "checkpoint_sha256": digest, + "training_summary_csv": summary, + "run_dir": summary.parent, + } + ) + for nfe in (1, 2): + fixed_kid = 0.020 + budget / 100000 + seed / 1000000 + nfe / 10000000 + # Adaptive improves in all settings for seeds 0/1 and is modestly worse for seed 2. + delta = -0.001 if seed in (0, 1) else 0.0002 + value = fixed_kid if method == "sigmoid" else fixed_kid + delta + metric_writer.writerow( + { + "method": method, + "training_seed": seed, + "budget_kimg": budget, + "nfe": nfe, + "kid_5k": value, + "fid_5k": value * 300, + "checkpoint_sha256": digest, + "mid_t": "0.821" if nfe == 2 else "", + "sampling_seed": "0-4999", + "num_generated": 5000, + } + ) + return metrics_path, records_path + + def test_complete_matrix_writes_all_role_c_deliverables(self) -> None: + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + metrics, records = self.write_complete_fixture(root) + output = root / "role_c" + completed = subprocess.run( + [sys.executable, str(ANALYZER), "--metrics", str(metrics), "--training-records", str(records), "--outdir", str(output), "--require-complete"], + cwd=REPO_ROOT, + text=True, + capture_output=True, + check=False, + ) + self.assertEqual(completed.returncode, 0, completed.stderr) + for name in ( + "per_seed_metrics.csv", + "paired_differences.csv", + "aggregate_results.csv", + "quality_vs_budget.png", + "controller_vs_budget.png", + "FINAL_CONCLUSION.md", + ): + self.assertTrue((output / name).is_file(), name) + self.assertGreater((output / name).stat().st_size, 0, name) + with (output / "paired_differences.csv").open(newline="", encoding="utf-8") as handle: + paired_rows = list(csv.DictReader(handle)) + self.assertEqual(len(paired_rows), 36) # 3 seeds × 3 budgets × 2 NFEs × KID/FID. + first_kid = next(row for row in paired_rows if row["metric"] == "kid_5k" and row["training_seed"] == "0") + self.assertLess(float(first_kid["delta_adaptive_minus_fixed"]), 0) + self.assertEqual(first_kid["num_generated"], "5000") + with (output / "aggregate_results.csv").open(newline="", encoding="utf-8") as handle: + self.assertEqual(len(list(csv.DictReader(handle))), 12) # 2 metrics × 3 budgets × 2 NFEs. + conclusion = (output / "FINAL_CONCLUSION.md").read_text(encoding="utf-8") + self.assertIn("Adaptive 表现出初步优势", conclusion) + self.assertNotIn("Current verdict: INCOMPLETE", conclusion) + self.assertIn("Training and controller relationships", conclusion) + self.assertIn("paired_delta_vs_correction", conclusion) + + def test_partial_matrix_is_reported_as_incomplete_without_failing_rolling_analysis(self) -> None: + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + metrics, records = self.write_complete_fixture(root) + with metrics.open(newline="", encoding="utf-8") as handle: + rows = list(csv.DictReader(handle)) + with metrics.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=rows[0].keys()) + writer.writeheader() + writer.writerows(rows[:-1]) + output = root / "partial" + completed = subprocess.run( + [sys.executable, str(ANALYZER), "--metrics", str(metrics), "--training-records", str(records), "--outdir", str(output)], + cwd=REPO_ROOT, + text=True, + capture_output=True, + check=False, + ) + self.assertEqual(completed.returncode, 0, completed.stderr) + self.assertIn("Current verdict: INCOMPLETE", (output / "FINAL_CONCLUSION.md").read_text(encoding="utf-8")) + + def test_missing_sampling_provenance_is_not_misreported_as_missing_metric_coverage(self) -> None: + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + metrics, records = self.write_complete_fixture(root) + with metrics.open(newline="", encoding="utf-8") as handle: + rows = list(csv.DictReader(handle)) + for row in rows: + row["sampling_seed"] = "" + row["num_generated"] = "" + with metrics.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=rows[0].keys()) + writer.writeheader() + writer.writerows(rows) + output = root / "missing_provenance" + completed = subprocess.run( + [sys.executable, str(ANALYZER), "--metrics", str(metrics), "--training-records", str(records), "--outdir", str(output)], + cwd=REPO_ROOT, + text=True, + capture_output=True, + check=False, + ) + self.assertEqual(completed.returncode, 0, completed.stderr) + conclusion = (output / "FINAL_CONCLUSION.md").read_text(encoding="utf-8") + self.assertIn("Current verdict: INCOMPLETE", conclusion) + self.assertIn("evaluation provenance is incomplete", conclusion) + self.assertIn("Metric coverage is complete", conclusion) + self.assertNotIn("matrix is not yet available", conclusion) + + def test_rejects_metric_that_exists_for_only_one_arm_of_a_pair(self) -> None: + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + metrics, records = self.write_complete_fixture(root) + with metrics.open(newline="", encoding="utf-8") as handle: + rows = list(csv.DictReader(handle)) + for row in rows: + if row["method"] == "adaptive_v1" and row["training_seed"] == "0" and row["budget_kimg"] == "16" and row["nfe"] == "1": + row["fid_5k"] = "" + break + with metrics.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=rows[0].keys()) + writer.writeheader() + writer.writerows(rows) + completed = subprocess.run( + [sys.executable, str(ANALYZER), "--metrics", str(metrics), "--training-records", str(records), "--outdir", str(root / "bad")], + cwd=REPO_ROOT, + text=True, + capture_output=True, + check=False, + ) + self.assertEqual(completed.returncode, 2) + self.assertIn("present for only one arm", completed.stderr) + + def test_rejects_checkpoint_sha_mismatch(self) -> None: + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + metrics, records = self.write_complete_fixture(root) + with metrics.open(newline="", encoding="utf-8") as handle: + rows = list(csv.DictReader(handle)) + rows[0]["checkpoint_sha256"] = sha(999) + with metrics.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=rows[0].keys()) + writer.writeheader() + writer.writerows(rows) + completed = subprocess.run( + [sys.executable, str(ANALYZER), "--metrics", str(metrics), "--training-records", str(records), "--outdir", str(root / "sha-mismatch")], + cwd=REPO_ROOT, + text=True, + capture_output=True, + check=False, + ) + self.assertEqual(completed.returncode, 2) + self.assertIn("checkpoint SHA mismatch", completed.stderr) + + def test_rejects_reusing_one_checkpoint_for_both_methods(self) -> None: + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + metrics, records = self.write_complete_fixture(root) + with metrics.open(newline="", encoding="utf-8") as handle: + metric_rows = list(csv.DictReader(handle)) + with records.open(newline="", encoding="utf-8") as handle: + record_rows = list(csv.DictReader(handle)) + fixed_sha = next( + row["checkpoint_sha256"] + for row in metric_rows + if row["method"] == "sigmoid" and row["training_seed"] == "0" and row["budget_kimg"] == "16" + ) + for row in metric_rows: + if row["method"] == "adaptive_v1" and row["training_seed"] == "0" and row["budget_kimg"] == "16": + row["checkpoint_sha256"] = fixed_sha + for row in record_rows: + if row["method"] == "adaptive_v1" and row["training_seed"] == "0" and row["budget_kimg"] == "16": + row["checkpoint_sha256"] = fixed_sha + with metrics.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=metric_rows[0].keys()) + writer.writeheader() + writer.writerows(metric_rows) + with records.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=record_rows[0].keys()) + writer.writeheader() + writer.writerows(record_rows) + completed = subprocess.run( + [sys.executable, str(ANALYZER), "--metrics", str(metrics), "--training-records", str(records), "--outdir", str(root / "same-checkpoint")], + cwd=REPO_ROOT, + text=True, + capture_output=True, + check=False, + ) + self.assertEqual(completed.returncode, 2) + self.assertIn("same checkpoint SHA", completed.stderr) + + def test_rejects_a_nonfrozen_two_step_midpoint_even_when_arms_match(self) -> None: + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + metrics, records = self.write_complete_fixture(root) + with metrics.open(newline="", encoding="utf-8") as handle: + rows = list(csv.DictReader(handle)) + for row in rows: + if row["nfe"] == "2": + row["mid_t"] = "0.7" + with metrics.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=rows[0].keys()) + writer.writeheader() + writer.writerows(rows) + completed = subprocess.run( + [sys.executable, str(ANALYZER), "--metrics", str(metrics), "--training-records", str(records), "--outdir", str(root / "wrong-midpoint")], + cwd=REPO_ROOT, + text=True, + capture_output=True, + check=False, + ) + self.assertEqual(completed.returncode, 2) + self.assertIn("requires the frozen mid_t=0.821", completed.stderr) + + def test_rejects_metrics_outside_the_frozen_budget_matrix(self) -> None: + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + metrics, records = self.write_complete_fixture(root) + with metrics.open(newline="", encoding="utf-8") as handle: + rows = list(csv.DictReader(handle)) + rows[0]["budget_kimg"] = "8" + with metrics.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=rows[0].keys()) + writer.writeheader() + writer.writerows(rows) + completed = subprocess.run( + [sys.executable, str(ANALYZER), "--metrics", str(metrics), "--training-records", str(records), "--outdir", str(root / "wrong-budget")], + cwd=REPO_ROOT, + text=True, + capture_output=True, + check=False, + ) + self.assertEqual(completed.returncode, 2) + self.assertIn("budget_kimg must be one of", completed.stderr) + + def test_final_conclusion_requires_matched_five_thousand_image_evaluations(self) -> None: + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + metrics, records = self.write_complete_fixture(root) + with metrics.open(newline="", encoding="utf-8") as handle: + rows = list(csv.DictReader(handle)) + for row in rows: + if row["budget_kimg"] == "16" and row["nfe"] == "1": + row["num_generated"] = "512" + with metrics.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=rows[0].keys()) + writer.writeheader() + writer.writerows(rows) + output = root / "not-final" + completed = subprocess.run( + [sys.executable, str(ANALYZER), "--metrics", str(metrics), "--training-records", str(records), "--outdir", str(output), "--require-complete"], + cwd=REPO_ROOT, + text=True, + capture_output=True, + check=False, + ) + self.assertEqual(completed.returncode, 2) + self.assertIn("Current verdict: INCOMPLETE", (output / "FINAL_CONCLUSION.md").read_text(encoding="utf-8")) + + def test_rejects_undertrained_training_summary(self) -> None: + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + metrics, records = self.write_complete_fixture(root) + with records.open(newline="", encoding="utf-8") as handle: + record_rows = list(csv.DictReader(handle)) + target = next(row for row in record_rows if row["method"] == "sigmoid" and row["training_seed"] == "0" and row["budget_kimg"] == "16") + summary_path = Path(target["training_summary_csv"]) + with summary_path.open(newline="", encoding="utf-8") as handle: + summary_rows = list(csv.DictReader(handle)) + fields = handle.seek(0) or None + # Keep only the 8-kimg row: Role C must reject it for a 16-kimg checkpoint. + with summary_path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=summary_rows[0].keys()) + writer.writeheader() + writer.writerow(summary_rows[0]) + completed = subprocess.run( + [sys.executable, str(ANALYZER), "--metrics", str(metrics), "--training-records", str(records), "--outdir", str(root / "undertrained")], + cwd=REPO_ROOT, + text=True, + capture_output=True, + check=False, + ) + self.assertEqual(completed.returncode, 2) + self.assertIn("reaches only 8 kimg", completed.stderr) + + def test_rejects_training_summary_with_the_wrong_schedule_label(self) -> None: + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + metrics, records = self.write_complete_fixture(root) + with records.open(newline="", encoding="utf-8") as handle: + record_rows = list(csv.DictReader(handle)) + target = next(row for row in record_rows if row["method"] == "adaptive_v1" and row["training_seed"] == "0") + summary_path = Path(target["training_summary_csv"]) + with summary_path.open(newline="", encoding="utf-8") as handle: + summary_rows = list(csv.DictReader(handle)) + for row in summary_rows: + row["schedule"] = "sigmoid" + with summary_path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=summary_rows[0].keys()) + writer.writeheader() + writer.writerows(summary_rows) + completed = subprocess.run( + [sys.executable, str(ANALYZER), "--metrics", str(metrics), "--training-records", str(records), "--outdir", str(root / "wrong-schedule")], + cwd=REPO_ROOT, + text=True, + capture_output=True, + check=False, + ) + self.assertEqual(completed.returncode, 2) + self.assertIn("does not match training record method", completed.stderr) + + def test_nonfinite_training_loss_blocks_a_final_verdict(self) -> None: + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + metrics, records = self.write_complete_fixture(root) + with records.open(newline="", encoding="utf-8") as handle: + record_rows = list(csv.DictReader(handle)) + target = next(row for row in record_rows if row["method"] == "adaptive_v1" and row["training_seed"] == "2") + summary_path = Path(target["training_summary_csv"]) + with summary_path.open(newline="", encoding="utf-8") as handle: + summary_rows = list(csv.DictReader(handle)) + summary_rows[-1]["loss"] = "nan" + with summary_path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=summary_rows[0].keys()) + writer.writeheader() + writer.writerows(summary_rows) + output = root / "nonfinite" + completed = subprocess.run( + [sys.executable, str(ANALYZER), "--metrics", str(metrics), "--training-records", str(records), "--outdir", str(output), "--require-complete"], + cwd=REPO_ROOT, + text=True, + capture_output=True, + check=False, + ) + self.assertEqual(completed.returncode, 2) + conclusion = (output / "FINAL_CONCLUSION.md").read_text(encoding="utf-8") + self.assertIn("Current verdict: INCOMPLETE", conclusion) + self.assertIn("non-finite loss", conclusion) + + def test_out_of_bound_controller_correction_blocks_a_final_verdict(self) -> None: + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + metrics, records = self.write_complete_fixture(root) + with records.open(newline="", encoding="utf-8") as handle: + record_rows = list(csv.DictReader(handle)) + target = next(row for row in record_rows if row["method"] == "adaptive_v1" and row["training_seed"] == "1") + summary_path = Path(target["training_summary_csv"]) + with summary_path.open(newline="", encoding="utf-8") as handle: + summary_rows = list(csv.DictReader(handle)) + summary_rows[-1]["correction"] = "0.051" + with summary_path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=summary_rows[0].keys()) + writer.writeheader() + writer.writerows(summary_rows) + output = root / "out-of-bound-correction" + completed = subprocess.run( + [sys.executable, str(ANALYZER), "--metrics", str(metrics), "--training-records", str(records), "--outdir", str(output), "--require-complete"], + cwd=REPO_ROOT, + text=True, + capture_output=True, + check=False, + ) + self.assertEqual(completed.returncode, 2) + self.assertIn("correction exceeds the configured bound", (output / "FINAL_CONCLUSION.md").read_text(encoding="utf-8")) + + def test_nonfinite_controller_telemetry_is_rejected(self) -> None: + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + metrics, records = self.write_complete_fixture(root) + with records.open(newline="", encoding="utf-8") as handle: + record_rows = list(csv.DictReader(handle)) + target = next(row for row in record_rows if row["method"] == "adaptive_v1" and row["training_seed"] == "2") + summary_path = Path(target["training_summary_csv"]) + with summary_path.open(newline="", encoding="utf-8") as handle: + summary_rows = list(csv.DictReader(handle)) + summary_rows[-1]["r_over_t_mean"] = "nan" + with summary_path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=summary_rows[0].keys()) + writer.writeheader() + writer.writerows(summary_rows) + completed = subprocess.run( + [sys.executable, str(ANALYZER), "--metrics", str(metrics), "--training-records", str(records), "--outdir", str(root / "nonfinite-controller")], + cwd=REPO_ROOT, + text=True, + capture_output=True, + check=False, + ) + self.assertEqual(completed.returncode, 2) + self.assertIn("non-finite r_over_t_mean telemetry", completed.stderr) + + def test_missing_successful_step_telemetry_blocks_a_final_verdict(self) -> None: + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + metrics, records = self.write_complete_fixture(root) + with records.open(newline="", encoding="utf-8") as handle: + record_rows = list(csv.DictReader(handle)) + target = next(row for row in record_rows if row["method"] == "sigmoid" and row["training_seed"] == "2") + summary_path = Path(target["training_summary_csv"]) + with summary_path.open(newline="", encoding="utf-8") as handle: + summary_rows = list(csv.DictReader(handle)) + summary_rows[-1]["successful_optimizer_steps"] = "" + with summary_path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=summary_rows[0].keys()) + writer.writeheader() + writer.writerows(summary_rows) + output = root / "missing-successful-step" + completed = subprocess.run( + [sys.executable, str(ANALYZER), "--metrics", str(metrics), "--training-records", str(records), "--outdir", str(output), "--require-complete"], + cwd=REPO_ROOT, + text=True, + capture_output=True, + check=False, + ) + self.assertEqual(completed.returncode, 2) + self.assertIn("successful optimizer-step telemetry", (output / "FINAL_CONCLUSION.md").read_text(encoding="utf-8")) + + def test_training_root_ignores_off_matrix_smokes_and_loads_protocol_metadata(self) -> None: + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + metrics, records = self.write_complete_fixture(root) + with records.open(newline="", encoding="utf-8") as handle: + record_rows = list(csv.DictReader(handle)) + training_root = root / "role_b_runs" + for index, row in enumerate(record_rows): + run_dir = training_root / f"run-{index}" + run_dir.mkdir(parents=True) + shutil.copyfile(row["training_summary_csv"], run_dir / "train_summary.csv") + (run_dir / "metadata.json").write_text( + json.dumps( + { + "schedule": row["method"], + "seed": int(row["training_seed"]), + "processed_kimg": float(row["budget_kimg"]), + "network_snapshot_sha256": row["checkpoint_sha256"], + } + ), + encoding="utf-8", + ) + smoke = training_root / "old-activation-smoke" + smoke.mkdir() + (smoke / "metadata.json").write_text( + json.dumps( + { + "schedule": "adaptive_v1", + "seed": 0, + "processed_kimg": 4.096, + "network_snapshot_sha256": sha(888), + } + ), + encoding="utf-8", + ) + completed = subprocess.run( + [sys.executable, str(ANALYZER), "--metrics", str(metrics), "--training-root", str(training_root), "--outdir", str(root / "root-scan"), "--require-complete"], + cwd=REPO_ROOT, + text=True, + capture_output=True, + check=False, + ) + self.assertEqual(completed.returncode, 0, completed.stderr) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_audit_anonymity.py b/tests/test_audit_anonymity.py new file mode 100644 index 00000000..0211f40b --- /dev/null +++ b/tests/test_audit_anonymity.py @@ -0,0 +1,69 @@ +import importlib.util +import sys +import tempfile +import unittest +from pathlib import Path + + +MODULE_PATH = Path(__file__).resolve().parents[1] / "scripts" / "audit_anonymity.py" +SPEC = importlib.util.spec_from_file_location("audit_anonymity", MODULE_PATH) +MODULE = importlib.util.module_from_spec(SPEC) +assert SPEC.loader is not None +sys.modules[SPEC.name] = MODULE +SPEC.loader.exec_module(MODULE) + + +class AnonymityAuditTests(unittest.TestCase): + def test_detects_private_paths_credentials_and_repository_url(self): + text = "\n".join( + [ + "output=/mnt/ect_project/runs/example", + "token=ghp_abcdefghijklmnopqrstuvwxyz123456", + "source=https://github.com/hjjjs4vbmv-netizen/recurrence_of_ect", + ] + ) + findings = list(MODULE.scan_text("README.md", text, sorted(MODULE.PATTERNS))) + self.assertEqual( + {item.rule for item in findings}, + {"project_mount_path", "github_token", "collaboration_repo_url"}, + ) + + def test_sensitive_excerpts_are_redacted(self): + secret = "ghp_abcdefghijklmnopqrstuvwxyz123456" + findings = list( + MODULE.scan_text( + "credentials.txt", + f"token={secret}\n", + sorted(MODULE.PATTERNS), + ) + ) + rendered_output = "\n".join( + f"{item.path}:{item.line}: [{item.rule}] {item.excerpt}" + for item in findings + ) + self.assertNotIn(secret, rendered_output) + self.assertIn("", rendered_output) + + def test_clean_relative_paths_pass(self): + text = "output=${ECT_RUNS_ROOT}/fixed/seed3\nsource=./results/summary.csv\n" + findings = list(MODULE.scan_text("README.md", text, sorted(MODULE.PATTERNS))) + self.assertEqual(findings, []) + + def test_recursive_scan_ignores_binary_and_cache_directories(self): + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + (root / "README.md").write_text("path=/root/private/run\n", encoding="utf-8") + (root / "image.png").write_bytes(b"\x89PNG\x00/root/not-text") + cache = root / "__pycache__" + cache.mkdir() + (cache / "leak.txt").write_text("path=/root/ignored\n", encoding="utf-8") + + findings = MODULE.scan(root, sorted(MODULE.PATTERNS), use_git=False) + + self.assertEqual(len(findings), 1) + self.assertEqual(findings[0].rule, "linux_root_path") + self.assertEqual(findings[0].path, "README.md") + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_collect_multibudget_results.py b/tests/test_collect_multibudget_results.py new file mode 100644 index 00000000..43756944 --- /dev/null +++ b/tests/test_collect_multibudget_results.py @@ -0,0 +1,230 @@ +import csv +import json +import tempfile +import unittest +from pathlib import Path + +from scripts import collect_multibudget_results as collector +from scripts import summarize_budget_curve + + +class MultiBudgetCollectorTest(unittest.TestCase): + def make_rows(self): + rows = [] + for metric, target in (("kid50k_full", 0.76), ("fid50k_full", 76.0)): + for nfe in (1, 2): + for budget in (512, 768, 1024): + for seed in (3, 4, 5): + for method in ("fixed", "global110"): + baseline = ( + (1.0 if metric.startswith("kid") else 100.0) + - budget / (3000 if metric.startswith("kid") else 30) + ) + value = baseline + seed * (0.002 if metric.startswith("kid") else 0.2) + if method == "global110": + value -= 0.03 if nfe == 1 else 0.06 + rows.append({ + "method": method, + "training_seed": seed, + "budget_kimg": budget, + "nfe": nfe, + "metric_name": metric, + "metric_value": value, + "training_time_hours": budget / 64 + seed * 0.01, + "quality_target": target, + "checkpoint_sha256": "{}-{}-{}-{}".format(method, seed, budget, metric), + }) + return rows + + def test_complete_matrix_writes_all_requested_outputs(self): + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + source = root / "input.csv" + rows = self.make_rows() + with source.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0]), lineterminator="\n") + writer.writeheader() + writer.writerows(rows) + output = root / "collected" + collector.main([ + "--input-csv", str(source), "--outdir", str(output), + "--baseline-method", "fixed", "--candidate-method", "global110", + ]) + for name in ( + "normalized_metrics.csv", "budget_curves.csv", "per_seed_trajectories.csv", + "paired_deltas.csv", "paired_summary.csv", "time_to_quality.csv", + "summary_table.md", "summary_table.tex", "figure_ready_budget_curves.csv", + "figure_ready_per_seed_trajectories.csv", "figure_ready_paired_deltas.csv", + "figure_ready_time_to_quality.csv", + "collector_manifest.json", + ): + self.assertTrue((output / name).is_file(), name) + for stem in ("budget_curves", "per_seed_trajectories", "paired_deltas", "time_to_quality"): + for extension in ("svg", "png", "pdf"): + self.assertGreater((output / "figures" / "{}.{}".format(stem, extension)).stat().st_size, 0) + with (output / "paired_deltas.csv").open(newline="", encoding="utf-8") as handle: + pairs = list(csv.DictReader(handle)) + self.assertEqual(len(pairs), 36) + self.assertTrue(all(float(row["delta_candidate_minus_baseline"]) < 0 for row in pairs)) + with (output / "time_to_quality.csv").open(newline="", encoding="utf-8") as handle: + self.assertIn("reached", {row["status"] for row in csv.DictReader(handle)}) + + def test_missing_method_seed_nfe_cell_fails_closed(self): + rows = self.make_rows() + rows = [ + row for row in rows + if not (row["method"] == "global110" and row["training_seed"] == 5 + and row["budget_kimg"] == 1024 and row["nfe"] == 2 + and row["metric_name"] == "fid50k_full") + ] + with self.assertRaisesRegex(SystemExit, "matrix incomplete"): + collector.validate(rows, "fixed", "global110") + + def test_inconsistent_quality_target_is_rejected(self): + rows = self.make_rows() + rows[0]["quality_target"] = 0.75 + with self.assertRaisesRegex(SystemExit, "quality_target must be consistent"): + collector.validate(rows, "fixed", "global110") + + def test_different_sample_counts_cannot_share_budget_curve_track(self): + rows = self.make_rows() + for row in rows: + row.update({ + "sample_count": 5000, "generation_seed_range": "0-4999", + "metric_seed": 20260730, "evidence_class": "quick", + "evaluation_contract": "common-5k-v1", "analysis_track": "budget_curve", + }) + if row["budget_kimg"] == 1024: + row["sample_count"] = 10000 + with self.assertRaisesRegex(SystemExit, "requires one explicit sample_count"): + collector.validate(rows, "fixed", "global110") + + def test_frozen_q256_endpoint_sets_may_differ_by_budget(self): + frozen = json.loads(Path("configs/q256_budget_matrix.frozen.json").read_text(encoding="utf-8")) + endpoints = { + int(contract["budget_kimg"]): contract["metric_names"] + for contract in frozen["evaluation_contracts"] + } + self.assertEqual(endpoints, { + 512: ["kid5k_full", "fid5k_full"], + 768: ["kid5k_full", "fid5k_full"], + 1024: ["kid50k_full", "fid50k_full"], + }) + rows = [] + for budget, metrics in endpoints.items(): + for metric in metrics: + for nfe in (1, 2): + for seed in (3, 4, 5): + for method in ("fixed", "global110"): + baseline = 1.0 if metric.startswith("kid") else 100.0 + value = baseline + seed * 0.001 - (0.02 if method == "global110" else 0.0) + rows.append({ + "method": method, "training_seed": seed, "budget_kimg": budget, + "nfe": nfe, "metric_name": metric, "metric_value": value, + "training_time_hours": budget / 64, "quality_target": "", + "checkpoint_sha256": "{}-{}-{}-{}".format(method, seed, budget, metric), + }) + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + source = root / "frozen_protocol.csv" + with source.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0]), lineterminator="\n") + writer.writeheader() + writer.writerows(rows) + output = root / "collected" + collector.main(["--input-csv", str(source), "--outdir", str(output)]) + with (output / "paired_deltas.csv").open(newline="", encoding="utf-8") as handle: + paired = list(csv.DictReader(handle)) + self.assertEqual(len(paired), 36) + self.assertEqual( + {row["metric_name"] for row in paired if float(row["budget_kimg"]) in (512, 768)}, + {"kid5k_full", "fid5k_full"}, + ) + self.assertEqual( + {row["metric_name"] for row in paired if float(row["budget_kimg"]) == 1024}, + {"kid50k_full", "fid50k_full"}, + ) + + def test_protocol_tracks_split_5k_budget_curve_from_50k_formal_endpoints(self): + rows = [] + track_specs = { + "budget_curve": { + "evaluation_contract": "q256-common-5k-v1", "sample_count": 5000, + "generation_seed_range": "0-4999", "metric_seed": 20260730, + "budgets": (256, 512, 768, 1024), "metrics": ("kid5k_full", "fid5k_full"), + }, + "formal_endpoint": { + "evaluation_contract": "q256-formal-50k-v1", "sample_count": 50000, + "generation_seed_range": "0-49999", "metric_seed": 20260730, + "budgets": (256, 1024), "metrics": ("kid50k_full", "fid50k_full"), + }, + } + for track, spec in track_specs.items(): + for budget in spec["budgets"]: + for metric in spec["metrics"]: + for nfe in (1, 2): + for seed in (3, 4, 5): + for method in ("fixed", "global110"): + baseline = 1.0 if metric.startswith("kid") else 100.0 + rows.append({ + "method": method, "training_seed": seed, "budget_kimg": budget, + "nfe": nfe, "metric_name": metric, + "metric_value": baseline + seed * 0.001 - (0.02 if method == "global110" else 0.0), + "training_time_hours": budget / 64, "quality_target": "", + "checkpoint_sha256": "{}-{}-{}-{}".format(method, seed, budget, metric), + "sample_count": spec["sample_count"], + "generation_seed_range": spec["generation_seed_range"], + "metric_seed": spec["metric_seed"], + "evidence_class": "formal" if track == "formal_endpoint" else ( + "auxiliary" if budget == 1024 else "quick" + ), + "evaluation_contract": spec["evaluation_contract"], + "analysis_track": track, + }) + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + source = root / "two_protocols.csv" + with source.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0]), lineterminator="\n") + writer.writeheader() + writer.writerows(rows) + output = root / "collected" + collector.main(["--input-csv", str(source), "--outdir", str(output)]) + with (output / "same_protocol_budget_curves.csv").open(newline="", encoding="utf-8") as handle: + curves = list(csv.DictReader(handle)) + with (output / "formal_endpoint_comparison.csv").open(newline="", encoding="utf-8") as handle: + formal = list(csv.DictReader(handle)) + self.assertGreater((output / "figures" / "same_protocol_budget_curves.pdf").stat().st_size, 0) + self.assertGreater((output / "figures" / "formal_endpoint_comparison.pdf").stat().st_size, 0) + self.assertEqual({float(row["budget_kimg"]) for row in curves}, {256, 512, 768, 1024}) + self.assertEqual({row["metric_name"] for row in curves}, {"kid5k_full", "fid5k_full"}) + self.assertEqual({row["sample_count"] for row in curves}, {"5000"}) + self.assertEqual({row["analysis_track"] for row in curves}, {"budget_curve"}) + self.assertEqual({row["evaluation_contract"] for row in curves}, {"q256-common-5k-v1"}) + self.assertEqual({float(row["budget_kimg"]) for row in formal}, {256, 1024}) + self.assertEqual({row["metric_name"] for row in formal}, {"kid50k_full", "fid50k_full"}) + self.assertEqual({row["sample_count"] for row in formal}, {"50000"}) + self.assertEqual({row["analysis_track"] for row in formal}, {"formal_endpoint"}) + self.assertEqual({row["evaluation_contract"] for row in formal}, {"q256-formal-50k-v1"}) + + def test_budget_curve_script_writes_paper_ready_pdf(self): + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + source = root / "input.csv" + rows = self.make_rows() + with source.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0]), lineterminator="\n") + writer.writeheader() + writer.writerows(rows) + output = root / "figures" + summarize_budget_curve.main([ + "--input-csv", str(source), "--outdir", str(output), + "--baseline-method", "fixed", "--candidate-method", "global110", + ]) + self.assertGreater((output / "budget_curves.pdf").stat().st_size, 0) + self.assertTrue((output / "budget_curve_summary.csv").is_file()) + self.assertTrue((output / "paired_summary.csv").is_file()) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_confirmatory_q256_matrix.py b/tests/test_confirmatory_q256_matrix.py new file mode 100644 index 00000000..41f15df4 --- /dev/null +++ b/tests/test_confirmatory_q256_matrix.py @@ -0,0 +1,60 @@ +import json +import re +import unittest +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +MANIFEST = ROOT / "configs" / "staged_evaluation_confirmatory_q256.frozen.json" + + +class ConfirmatoryQ256MatrixTest(unittest.TestCase): + def test_matrix_is_complete_and_path_free(self): + matrix = json.loads(MANIFEST.read_text(encoding="utf-8")) + + self.assertEqual(matrix["manifest_kind"], "frozen-logical-checkpoint-matrix") + self.assertTrue(matrix["runtime_binding"]["required"]) + self.assertFalse(matrix["runtime_binding"]["versioned_paths"]) + self.assertEqual(matrix["training"]["budget_kimg"], 256) + self.assertEqual(matrix["training"]["schedule_q"], 256) + self.assertEqual(matrix["training"]["training_seeds"], [3, 4, 5]) + self.assertEqual( + matrix["comparison"]["pairing_key"], + ["training_seed", "budget_kimg", "nfe", "metric"], + ) + self.assertEqual(matrix["comparison"]["delta_direction"], "global_only - fixed") + + cells = matrix["cells"] + self.assertEqual(len(cells), 6) + self.assertEqual( + {(cell["method"], cell["training_seed"]) for cell in cells}, + {(method, seed) for method in ("fixed", "global110") for seed in (3, 4, 5)}, + ) + promotion = matrix["formal_promotion_policy"] + self.assertEqual(promotion["eligibility"], "provenance_and_integrity_only") + self.assertEqual(promotion["quick_metric_performance"], "not_an_eligibility_criterion") + self.assertEqual( + promotion["required_checkpoint_ids"], + [cell["checkpoint_id"] for cell in cells], + ) + + for cell in cells: + self.assertNotIn("checkpoint", cell) + self.assertEqual(cell["budget_kimg"], 256) + self.assertEqual(cell["schedule_q"], 256) + self.assertRegex(cell["checkpoint_sha256"], r"^[0-9a-f]{64}$") + self.assertRegex(cell["executed_training_source_commit"], r"^[0-9a-f]{40}$") + self.assertEqual(cell["training_integrity_receipt"]["status"], "passed") + self.assertTrue(cell["training_integrity_receipt"]["receipt_filename"]) + + def test_method_schedule_identities_are_frozen(self): + matrix = json.loads(MANIFEST.read_text(encoding="utf-8")) + definitions = matrix["method_definitions"] + self.assertEqual(definitions["fixed"]["schedule_identity"], "sigmoid") + self.assertEqual(definitions["fixed"]["global_gap_scale"], 1.0) + self.assertEqual(definitions["global110"]["schedule_identity"], "global_sigmoid") + self.assertEqual(definitions["global110"]["global_gap_scale"], 1.1) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_final_evaluation.py b/tests/test_final_evaluation.py new file mode 100644 index 00000000..e4fefd4a --- /dev/null +++ b/tests/test_final_evaluation.py @@ -0,0 +1,237 @@ +import csv +import json +import unittest +from pathlib import Path +from tempfile import TemporaryDirectory + +from PIL import Image + +from scripts import build_blind_ab +from scripts import build_final_conclusion +from scripts import collect_final_quality_results +from scripts import collect_final_stability +from scripts import run_final_evaluation_matrix +from scripts import score_blind_ab + + +class FinalEvaluationTest(unittest.TestCase): + def make_manifest(self, root: Path): + cells = [] + for seed in range(3): + for schedule in ("sigmoid", "adaptive_v1"): + checkpoint = root / f"{schedule}_seed{seed}.pkl" + checkpoint.write_bytes(f"{schedule}-{seed}".encode()) + result_dir = root / f"{schedule}_seed{seed}_result" + cells.append({ + "schedule": schedule, + "training_seed": seed, + "checkpoint": str(checkpoint), + "checkpoint_sha256": run_final_evaluation_matrix.sha256_file(checkpoint), + "training_result_dir": str(result_dir), + }) + manifest = root / "checkpoints.json" + manifest.write_text(json.dumps({"cells": cells}), encoding="utf-8") + return manifest, cells + + def test_frozen_runner_builds_exact_matrix(self): + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + manifest, _ = self.make_manifest(root) + data = root / "cifar.zip" + data.write_bytes(b"test-data") + cells = run_final_evaluation_matrix.load_cells(manifest, allow_missing=False) + quantitative = run_final_evaluation_matrix.quantitative_commands( + cells, data, root / "out", 29600, "primary" + ) + visual = run_final_evaluation_matrix.visual_commands(cells, root / "out") + self.assertEqual(len(quantitative), 12) + self.assertEqual(len(visual), 6) + self.assertEqual( + {(job["schedule"], job["training_seed"], job["nfe"]) for job in quantitative}, + {(schedule, seed, nfe) for schedule in ("sigmoid", "adaptive_v1") for seed in range(3) for nfe in (1, 2)}, + ) + for job in quantitative: + command = " ".join(job["command"]) + self.assertIn("--sample-seeds=0-4999", command) + self.assertIn("--metric-repeats=1", command) + self.assertIn("--metrics=kid5k_full,fid5k_full", command) + self.assertIn("--fp16=False", command) + + def make_metric_matrix(self, root: Path): + for seed in range(3): + for schedule in ("sigmoid", "adaptive_v1"): + for nfe in (1, 2): + cell = root / "quantitative" / schedule / f"seed{seed}" / f"nfe{nfe}" + cell.mkdir(parents=True) + fixed_base = 10 + seed + nfe + values = { + "kid5k_full": fixed_base - (0.25 if schedule == "adaptive_v1" else 0), + "fid5k_full": fixed_base + 5 - (0.5 if schedule == "adaptive_v1" else 0), + } + for metric, value in values.items(): + payload = {"metric": metric, "results": {metric: value}} + (cell / f"metric-{metric}.jsonl").write_text(json.dumps(payload) + "\n", encoding="utf-8") + + def test_quantitative_collector_preserves_pairing(self): + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + self.make_metric_matrix(root) + outdir = root / "summary" + collect_final_quality_results.main(["--eval-root", str(root), "--outdir", str(outdir)]) + summary = json.loads((outdir / "quantitative_summary.json").read_text()) + for nfe in (1, 2): + kid = summary["summary_by_nfe"][str(nfe)]["kid5k_full"] + self.assertAlmostEqual(kid["mean_delta"], -0.25) + self.assertEqual(kid["adaptive_fixed_tie_seed_counts"], [3, 0, 0]) + + def make_visual_samples(self, manifest: Path, sample_root: Path): + cells = build_blind_ab.load_cells(manifest) + for (schedule, training_seed), cell in cells.items(): + for nfe in (1, 2): + for seed in range(16): + path = sample_root / cell["checkpoint_id"] / f"nfe{nfe}" / "images" / f"seed{seed:06d}.png" + path.parent.mkdir(parents=True, exist_ok=True) + color = (20 + training_seed * 30, 40 + nfe * 40, 80 if schedule == "sigmoid" else 180) + Image.new("RGB", (32, 32), color).save(path) + + def test_blind_ballot_is_balanced_and_scores_complete_raters(self): + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + manifest, _ = self.make_manifest(root) + sample_root = root / "samples" + self.make_visual_samples(manifest, sample_root) + public = root / "public" + key_path = root / "private" / "key.csv" + build_blind_ab.main([ + "--manifest", str(manifest), + "--sample-root", str(sample_root), + "--outdir", str(public), + "--key-out", str(key_path), + ]) + metadata = json.loads((public / "metadata.json").read_text()) + self.assertEqual(metadata["trial_count"], 96) + self.assertEqual(metadata["side_balance"], {"adaptive_on_A": 48, "adaptive_on_B": 48}) + + with key_path.open(newline="", encoding="utf-8") as handle: + key_rows = list(csv.DictReader(handle)) + responses = [] + for rater in range(3): + response_path = root / f"rater{rater}.csv" + with response_path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=["trial_id", "rater_id", "preference"]) + writer.writeheader() + for row in key_rows: + preference = "A" if row["A_schedule"] == "adaptive_v1" else "B" + writer.writerow({"trial_id": row["trial_id"], "rater_id": f"R{rater}", "preference": preference}) + responses.append(str(response_path)) + score_dir = root / "scores" + score_blind_ab.main([ + "--key", str(key_path), "--responses", *responses, "--outdir", str(score_dir) + ]) + score = json.loads((score_dir / "blind_ab_summary.json").read_text()) + overall = next(row for row in score["summary"] if row["stratum"] == "overall") + self.assertEqual(overall["adaptive_v1"], 288) + self.assertEqual(overall["sigmoid"], 0) + + def make_stability_results(self, cells): + for cell in cells: + result_dir = Path(cell["training_result_dir"]) + result_dir.mkdir() + schedule = cell["schedule"] + seed = cell["training_seed"] + metadata = { + "schedule": schedule, + "seed": seed, + "processed_kimg": 16.0, + "nan_count": 0, + "inf_count": 0, + "skipped_steps": 9, + "successful_optimizer_steps": 116, + "final_grad_scale": 128, + "peak_vram_mib": 2500 + seed, + "wall_time_seconds": 100 + seed, + "final_adaptive_active": True, + "final_signal_updates": 32, + "network_snapshot_sha256": cell["checkpoint_sha256"], + } + (result_dir / "metadata.json").write_text(json.dumps(metadata), encoding="utf-8") + fieldnames = ["attempted_iteration", "loss", "step_skipped", "schedule"] + with (result_dir / "train_summary.csv").open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=fieldnames) + writer.writeheader() + for iteration in range(1, 126): + writer.writerow({ + "attempted_iteration": iteration, + "loss": 10 + iteration / 100, + "step_skipped": "true" if iteration <= 9 else "false", + "schedule": schedule, + }) + + def test_stability_and_final_conclusion_builders(self): + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + manifest, cells = self.make_manifest(root) + self.make_stability_results(cells) + summary_dir = root / "summary" + collect_final_stability.main(["--manifest", str(manifest), "--outdir", str(summary_dir)]) + stability = json.loads((summary_dir / "training_stability.json").read_text()) + self.assertTrue(stability["all_six_runs_complete"]) + self.assertEqual(stability["summary_by_schedule"]["adaptive_v1"]["controller_activated_runs"], 3) + + def test_one_page_conclusion_uses_locked_primary_direction(self): + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + quantitative = root / "quantitative" + blind = root / "blind" + stability = root / "stability" + for path in (quantitative, blind, stability): + path.mkdir() + quantitative_summary = { + "primary_metric": "kid5k_full", + "summary_by_nfe": { + str(nfe): { + "kid5k_full": { + "mean_delta": -0.2, + "sample_sd_delta": 0.05, + "adaptive_fixed_tie_seed_counts": [3, 0, 0], + } + } + for nfe in (1, 2) + }, + } + (quantitative / "quantitative_summary.json").write_text( + json.dumps(quantitative_summary), encoding="utf-8" + ) + with (quantitative / "quantitative_metrics.csv").open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=["schedule", "training_seed", "nfe", "kid5k_full"]) + writer.writeheader() + for seed in range(3): + for nfe in (1, 2): + writer.writerow({"schedule": "sigmoid", "training_seed": seed, "nfe": nfe, "kid5k_full": 2.0}) + writer.writerow({"schedule": "adaptive_v1", "training_seed": seed, "nfe": nfe, "kid5k_full": 1.8}) + (blind / "blind_ab_summary.json").write_text(json.dumps({ + "complete_raters": 3, + "summary": [{ + "stratum": "overall", "adaptive_v1": 160, "sigmoid": 100, "tie": 28, + "adaptive_tie_half_score": 174 / 288, + }], + }), encoding="utf-8") + (stability / "training_stability.json").write_text(json.dumps({ + "all_six_runs_complete": True, + "all_losses_finite": True, + "summary_by_schedule": {"adaptive_v1": {"controller_activated_runs": 3}}, + }), encoding="utf-8") + output = root / "FINAL_CONCLUSION.md" + build_final_conclusion.main([ + "--quantitative-dir", str(quantitative), + "--blind-dir", str(blind), + "--stability-dir", str(stability), + "--output", str(output), + ]) + text = output.read_text(encoding="utf-8") + self.assertIn("方向性支持 Adaptive v1", text) + self.assertIn("不是标准 FID-50k benchmark", text) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_fixed_seed_sampling.py b/tests/test_fixed_seed_sampling.py new file mode 100644 index 00000000..47a6ac20 --- /dev/null +++ b/tests/test_fixed_seed_sampling.py @@ -0,0 +1,211 @@ +import unittest +from argparse import Namespace +from pathlib import Path +from tempfile import TemporaryDirectory + +import numpy as np +import PIL.Image +import torch + +from scripts.sample_fixed_seeds import ( + assert_repeat_equivalence, + assert_work_group_equivalence, + build_metadata, + configure_precision, + generate_uint8, + make_checkpoint_id, + save_mode_outputs, + seeded_inputs, + sha256_file, + write_manifest, +) +from metrics.metric_utils import make_seeded_latents + + +class DummyNet(torch.nn.Module): + img_channels = 3 + img_resolution = 32 + use_fp16 = False + + def round_sigma(self, sigma): + return sigma + + def forward(self, x, sigma, class_labels=None): + del sigma, class_labels + # Deliberately depend on the forward batch composition. The fixed-seed + # sampler must still be invariant to its outer work-group size. + batch_term = x.mean(dim=0, keepdim=True) * 0.01 + return torch.tanh((x + batch_term) / 80) + + +class FixedSeedSamplingTest(unittest.TestCase): + def setUp(self): + self.net = DummyNet().eval() + self.seeds = list(range(64)) + + def test_precision_is_explicit_and_reported(self): + self.assertEqual(configure_precision(self.net, "checkpoint", "cpu"), "fp32") + self.assertEqual(configure_precision(self.net, "fp32", "cpu"), "fp32") + with self.assertRaisesRegex(ValueError, "requires a CUDA device"): + configure_precision(self.net, "fp16", "cpu") + + def test_checkpoint_id_uses_filename_and_sha_prefix(self): + digest = "4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da" + self.assertEqual( + make_checkpoint_id("/tmp/edm-cifar10-32x32-uncond-vp.pkl", digest), + "edm-cifar10-32x32-uncond-vp-4d5dcc1f1d0d", + ) + + def test_same_seed_shares_initial_latent_and_deterministic_noise(self): + shape = (3, 32, 32) + one_step_latent, _ = seeded_inputs([7], shape, 0) + two_step_latent, first_noise = seeded_inputs([7], shape, 1) + repeated_latent, repeated_noise = seeded_inputs([7], shape, 1) + torch.testing.assert_close(one_step_latent, two_step_latent, rtol=0, atol=0) + torch.testing.assert_close(two_step_latent, repeated_latent, rtol=0, atol=0) + torch.testing.assert_close(first_noise[0], repeated_noise[0], rtol=0, atol=0) + + def test_metric_latents_match_fixed_seed_sampler(self): + shape = (3, 32, 32) + expected, _ = seeded_inputs([3, 7, 11], shape, 0) + actual = make_seeded_latents([3, 7, 11], shape) + torch.testing.assert_close(actual, expected, rtol=0, atol=0) + + def test_generator_sample_seeds_match_explicit_step_noise(self): + shape = (3, 32, 32) + seeds = [3, 7] + latents, step_noises = seeded_inputs(seeds, shape, 1) + generator_fn = __import__("ct_eval").generator_fn + explicit = generator_fn( + self.net, + latents, + mid_t=[0.821], + step_noises=step_noises, + ) + generated = generator_fn( + self.net, + latents, + mid_t=[0.821], + sample_seeds=seeds, + ) + torch.testing.assert_close(generated, explicit, rtol=0, atol=0) + + def test_work_groups_are_pixel_identical_for_nfe1_and_nfe2(self): + for nfe in [1, 2]: + with self.subTest(nfe=nfe): + group8 = generate_uint8(self.net, self.seeds, nfe, 0.821, 8, "cpu") + group16 = generate_uint8(self.net, self.seeds, nfe, 0.821, 16, "cpu") + self.assertEqual(group8.shape, (64, 3, 32, 32)) + self.assertEqual(group8.dtype, np.uint8) + np.testing.assert_array_equal(group8, group16) + + def test_repeated_runs_are_pixel_identical_for_nfe1_and_nfe2(self): + for nfe in [1, 2]: + with self.subTest(nfe=nfe): + reference = assert_work_group_equivalence( + self.net, self.seeds, nfe, 0.821, 8, 16, "cpu" + ) + assert_repeat_equivalence( + self.net, self.seeds, nfe, 0.821, 8, reference, "cpu" + ) + + def test_repeated_png_sha256_is_identical(self): + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + for nfe in [1, 2]: + with self.subTest(nfe=nfe): + first = generate_uint8(self.net, self.seeds, nfe, 0.821, 8, "cpu") + second = generate_uint8(self.net, self.seeds, nfe, 0.821, 8, "cpu") + first_paths = save_mode_outputs(first, self.seeds, root / "first" / f"nfe{nfe}") + second_paths = save_mode_outputs(second, self.seeds, root / "second" / f"nfe{nfe}") + self.assertEqual( + [sha256_file(path) for path in first_paths], + [sha256_file(path) for path in second_paths], + ) + + def test_protocol_outputs_are_complete_rgb_images(self): + self.assertEqual(self.seeds, list(range(64))) + self.assertEqual(len(self.seeds), len(set(self.seeds))) + + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + for nfe in [1, 2]: + with self.subTest(nfe=nfe): + images = generate_uint8(self.net, self.seeds, nfe, 0.821, 8, "cpu") + paths = save_mode_outputs(images, self.seeds, root / f"nfe{nfe}") + image_paths = paths[:-1] + self.assertEqual(len(image_paths), 64) + self.assertEqual( + {path.name for path in image_paths}, + {f"seed{seed:06d}.png" for seed in range(64)}, + ) + for path in image_paths: + with PIL.Image.open(path) as image: + self.assertEqual(image.mode, "RGB") + self.assertEqual(image.size, (32, 32)) + + def test_seed_zero_and_one_are_distinct(self): + images = generate_uint8(self.net, [0, 1], 1, 0.821, 8, "cpu") + self.assertNotEqual(images[0].tobytes(), images[1].tobytes()) + + def test_metadata_records_nfe_mid_t_and_required_schema(self): + args = Namespace( + network="/tmp/model.pkl", + precision="fp32", + device="cpu", + work_group_size=8, + verify_work_group_size=16, + ) + modes = [ + {"name": "nfe1", "nfe": 1, "mid_t": [], "image_count": 64, "elapsed_seconds": 1.0}, + {"name": "nfe2", "nfe": 2, "mid_t": [0.821], "image_count": 64, "elapsed_seconds": 2.0}, + ] + metadata = build_metadata( + args=args, + checkpoint_sha256="a" * 64, + checkpoint_id="model-aaaaaaaaaaaa", + run_dir=Path("/tmp/evaluations/model-aaaaaaaaaaaa"), + net=self.net, + effective_precision="fp32", + seeds=self.seeds, + modes=modes, + elapsed_seconds=3.0, + ) + self.assertEqual(metadata["schema_version"], "1.0") + self.assertEqual(metadata["nfe_modes"], [1, 2]) + self.assertEqual(metadata["mid_t_by_mode"], {"nfe1": [], "nfe2": [0.821]}) + self.assertEqual(metadata["image_count_by_mode"], {"nfe1": 64, "nfe2": 64}) + self.assertEqual(metadata["image_count_total"], 128) + self.assertEqual(metadata["model_forward_batch_size"], 1) + self.assertTrue(metadata["determinism_passed"]) + + def test_manifest_covers_images_grids_and_metadata(self): + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + entries = [] + for nfe in [1, 2]: + images = generate_uint8(self.net, self.seeds, nfe, 0.821, 8, "cpu") + for path in save_mode_outputs(images, self.seeds, root / f"nfe{nfe}"): + entries.append((sha256_file(path), path.relative_to(root).as_posix())) + metadata_path = root / "metadata.json" + metadata_path.write_text("{}\n", encoding="utf-8") + entries.append((sha256_file(metadata_path), "metadata.json")) + manifest_path = root / "sha256_manifest.txt" + write_manifest(entries, manifest_path) + + names = { + line.split(" ", 1)[1] + for line in manifest_path.read_text(encoding="utf-8").splitlines() + } + expected_images = { + f"nfe{nfe}/images/seed{seed:06d}.png" + for nfe in [1, 2] + for seed in self.seeds + } + self.assertTrue(expected_images.issubset(names)) + self.assertTrue({"nfe1/grid_8x8.png", "nfe2/grid_8x8.png", "metadata.json"}.issubset(names)) + self.assertEqual(len(names), 131) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_local_tbin_controller.py b/tests/test_local_tbin_controller.py new file mode 100644 index 00000000..22bce238 --- /dev/null +++ b/tests/test_local_tbin_controller.py @@ -0,0 +1,226 @@ +import math +import unittest + +import torch + +from training.ct_training_loop import ( + LocalTBinSignalWindow, + gather_adaptive_signal_window_state, + globally_average_local_tbin_loss, + local_adaptive_signal_window_state, +) +from training.schedules import get_schedule + + +class LocalTBinScheduleTest(unittest.TestCase): + def test_global_sigmoid_one_is_bitwise_official(self): + t = torch.logspace(-3, 2, 1024) + baseline = get_schedule('sigmoid', q=256.0) + global_only = get_schedule( + 'global_sigmoid', q=256.0, global_gap_scale=1.0 + ) + self.assertTrue(torch.equal( + global_only.compute_r(t=t, stage=0), + baseline.compute_r(t=t, stage=0), + )) + + def test_global_sigmoid_scales_every_official_gap(self): + t = torch.logspace(-3, 1, 1024, dtype=torch.float64) + baseline_r = get_schedule('sigmoid', q=256.0).compute_r(t=t, stage=0) + global_only = get_schedule( + 'global_sigmoid', q=256.0, global_gap_scale=1.032 + ) + scaled_r = global_only.compute_r(t=t, stage=0) + expected_gap = torch.minimum((t - baseline_r) * 1.032, t) + self.assertTrue(torch.allclose( + t - scaled_r, expected_gap, rtol=1e-12, atol=1e-12 + )) + self.assertTrue(torch.equal( + global_only.preclip_gap_scale(t), + torch.full_like(t, 1.032), + )) + + def test_invalid_global_gap_scale_is_rejected(self): + for value in [0, -1, float('nan'), float('inf')]: + with self.subTest(value=value), self.assertRaises(ValueError): + get_schedule('global_sigmoid', global_gap_scale=value) + + def test_quantile_bins_are_balanced_under_training_distribution(self): + generator = torch.Generator().manual_seed(123) + z = torch.randn(100_000, generator=generator, dtype=torch.float64) + t = (z * 2.0 - 1.1).exp() + schedule = get_schedule('local_tbin_v1', p_mean=-1.1, p_std=2.0) + counts = torch.bincount(schedule.bin_indices(t), minlength=4) + fractions = counts.to(torch.float64) / counts.sum() + self.assertTrue(torch.all(torch.abs(fractions - 0.25) < 0.01), fractions) + + def test_inactive_controller_is_bitwise_official_sigmoid(self): + t = torch.logspace(-3, 2, 1024) + baseline = get_schedule('sigmoid', q=256.0) + local = get_schedule('local_tbin_v1', q=256.0) + self.assertTrue(torch.equal( + local.compute_r(t=t, stage=0), + baseline.compute_r(t=t, stage=0), + )) + + def test_local_raw_loss_trends_scale_only_their_baseline_gaps(self): + schedule = get_schedule( + 'local_tbin_v1', q=256.0, short_beta=0.0, long_beta=0.9, + warmup_updates=0, gain=0.5, deadband=0.0, + ) + schedule.update_training_signal([1.0, 1.0, 1.0, 1.0]) + schedule.update_training_signal([1.0, 4.0, 1.0, 0.25]) + scales = schedule.gap_scales() + self.assertAlmostEqual(scales[0], 1.0) + self.assertGreater(scales[1], 1.0) # Worsening bin widens its gap. + self.assertAlmostEqual(scales[2], 1.0) + self.assertLess(scales[3], 1.0) # Improving bin tightens its gap. + + log_t = torch.tensor([-3.0, -2.0, -0.5, 1.0], dtype=torch.float64) + t = log_t.exp() + baseline_r = get_schedule('sigmoid', q=256.0).compute_r(t=t, stage=0) + local_r = schedule.compute_r(t=t, stage=0) + baseline_gap = (t - baseline_r) / t + local_gap = (t - local_r) / t + expected = baseline_gap * torch.tensor(scales, dtype=torch.float64) + self.assertTrue(torch.allclose(local_gap, expected, rtol=1e-12, atol=1e-12)) + self.assertTrue(torch.allclose( + schedule.preclip_gap_scale(t), + torch.tensor(scales, dtype=torch.float64), + rtol=0, + atol=0, + )) + + def test_state_round_trip_preserves_bin_controller(self): + source = get_schedule('local_tbin_v1', warmup_updates=0) + source.update_training_signal([1.0, 2.0, 3.0, 4.0]) + source.update_training_signal([0.5, 2.5, 2.0, 5.0]) + clone = get_schedule('local_tbin_v1', warmup_updates=0) + clone.load_state_dict(source.state_dict()) + self.assertEqual(clone.state_dict(), source.state_dict()) + self.assertEqual(clone.gap_scales(), source.gap_scales()) + + def test_v2_is_official_sigmoid_until_every_bin_finishes_warmup(self): + t = torch.logspace(-3, 2, 1024) + baseline = get_schedule('sigmoid', q=256.0) + local = get_schedule('local_tbin_v2', q=256.0, warmup_updates=1) + local.update_training_signal([1.0, 1.0, 1.0, None]) + local.update_training_signal([0.5, 2.0, 1.0, None]) + self.assertEqual(local.gap_scales(), [1.0] * 4) + self.assertTrue(torch.equal( + local.compute_r(t=t, stage=0), + baseline.compute_r(t=t, stage=0), + )) + + def test_v2_partial_warmup_does_not_apply_min_gap_clamp(self): + t = torch.logspace(-3, 2, 1024, dtype=torch.float64) + baseline = get_schedule('sigmoid', q=2.0) + local = get_schedule('local_tbin_v2', q=2.0, warmup_updates=0) + local.update_training_signal([1.0, None, None, None]) + self.assertTrue(any(local.bin_is_active(index) for index in range(4))) + self.assertFalse(local.correction_is_active()) + self.assertTrue(torch.equal( + local.compute_r(t=t, stage=20), + baseline.compute_r(t=t, stage=20), + )) + + def test_v2_scales_are_bounded_and_geometrically_neutral(self): + schedule = get_schedule( + 'local_tbin_v2', + short_beta=0.0, + long_beta=0.9, + warmup_updates=0, + gain=0.25, + min_scale=0.85, + max_scale=1.25, + deadband=0.0, + ) + schedule.update_training_signal([1.0, 1.0, 1.0, 1.0]) + schedule.update_training_signal([0.01, 0.25, 4.0, 100.0]) + scales = schedule.gap_scales() + self.assertTrue(all(0.85 <= scale <= 1.25 for scale in scales)) + self.assertAlmostEqual( + sum(math.log(scale) for scale in scales) / len(scales), + 0.0, + places=14, + ) + self.assertTrue(any(scale < 1 for scale in scales)) + self.assertTrue(any(scale > 1 for scale in scales)) + + def test_v2_local_gap_is_only_normalized_rescaling_of_sigmoid_gap(self): + schedule = get_schedule( + 'local_tbin_v2', + q=256.0, + short_beta=0.0, + long_beta=0.9, + warmup_updates=0, + deadband=0.0, + ) + schedule.update_training_signal([1.0, 1.0, 1.0, 1.0]) + schedule.update_training_signal([0.5, 2.0, 4.0, 0.25]) + scales = schedule.gap_scales() + t = torch.tensor([0.05, 0.2, 0.7, 3.0], dtype=torch.float64) + baseline_r = get_schedule('sigmoid', q=256.0).compute_r(t=t, stage=0) + local_r = schedule.compute_r(t=t, stage=0) + baseline_gap = (t - baseline_r) / t + local_gap = (t - local_r) / t + expected = baseline_gap * torch.tensor(scales, dtype=torch.float64) + self.assertTrue(torch.allclose(local_gap, expected, rtol=1e-12, atol=1e-12)) + + def test_v3_factorizes_global_and_neutral_local_scales(self): + schedule = get_schedule( + 'local_tbin_v3', + q=256.0, + short_beta=0.0, + long_beta=0.9, + warmup_updates=0, + deadband=0.0, + global_gap_scale=1.032, + ) + schedule.update_training_signal([1.0, 1.0, 1.0, 1.0]) + schedule.update_training_signal([0.5, 2.0, 4.0, 0.25]) + local_scales = schedule.gap_scales() + self.assertAlmostEqual( + sum(math.log(scale) for scale in local_scales) / len(local_scales), + 0.0, + places=14, + ) + t = torch.tensor([0.05, 0.2, 0.7, 3.0], dtype=torch.float64) + baseline_r = get_schedule('sigmoid', q=256.0).compute_r(t=t, stage=0) + combined_r = schedule.compute_r(t=t, stage=0) + expected_gap = ( + (t - baseline_r) + * torch.tensor(local_scales, dtype=torch.float64) + * 1.032 + ) + self.assertTrue(torch.allclose( + t - combined_r, expected_gap, rtol=1e-12, atol=1e-12 + )) + self.assertTrue(torch.allclose( + schedule.preclip_gap_scale(t), + torch.tensor(local_scales, dtype=torch.float64) * 1.032, + rtol=1e-12, + atol=1e-12, + )) + + +class LocalTBinSignalWindowTest(unittest.TestCase): + def test_window_aggregates_and_resumes_per_bin_raw_stats(self): + window = LocalTBinSignalWindow(update_kimg=0.5, num_bins=4) + window.add([1.0, 2.0, 3.0, 4.0], [1, 1, 1, 1]) + self.assertIsNone(window.pop_if_due(384)) + state = gather_adaptive_signal_window_state(window, torch.device('cpu')) + + resumed = LocalTBinSignalWindow(update_kimg=0.5, num_bins=4, start_nimg=384) + resumed.load_state_dict(local_adaptive_signal_window_state(state)) + self.assertEqual(resumed.state_dict(), window.state_dict()) + + resumed.add([2.0, 4.0, 6.0, 8.0], [1, 2, 3, 4]) + sums, counts = resumed.pop_if_due(512) + means = globally_average_local_tbin_loss(sums, counts, torch.device('cpu')) + self.assertEqual(counts, [2, 3, 4, 5]) + self.assertEqual(means, [1.5, 2.0, 2.25, 2.4]) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_paired_training_infra.py b/tests/test_paired_training_infra.py new file mode 100644 index 00000000..971e86f2 --- /dev/null +++ b/tests/test_paired_training_infra.py @@ -0,0 +1,1349 @@ +#!/usr/bin/env python3 +"""Lightweight infra tests for Role B paired-training runner/collector.""" + +from __future__ import annotations + +import csv +import json +import os +import subprocess +import sys +import tempfile +import unittest +from pathlib import Path + + +REPO_ROOT = Path(__file__).resolve().parents[1] +RUNNER = REPO_ROOT / "scripts" / "run_schedule_experiment.sh" +COLLECTOR = REPO_ROOT / "scripts" / "collect_schedule_results.py" + + +def write_dummy_asset(path: Path, payload: bytes = b"asset") -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_bytes(payload) + + +def write_minimal_run( + run_dir: Path, + *, + schedule: str = "sigmoid", + mode: str = "stability", + duration: float = 0.016, + kimg: float | None = None, + global_batch: int = 128, + include_nan: bool = False, + git_head: str | None = None, + data_path: Path | None = None, + transfer_path: Path | None = None, + include_telemetry: bool = False, + next_loop_cur_tick: int = 2, +) -> None: + import hashlib + import math + + run_dir.mkdir(parents=True, exist_ok=True) + if kimg is None: + target_kimg = max(int(duration * 1000), 1) + target_nimg = target_kimg * 1000 + final_nimg = math.ceil(target_nimg / global_batch) * global_batch + kimg = final_nimg / 1000.0 + + def _sha(path: Path | None, fallback: bytes) -> str: + payload = path.read_bytes() if path is not None and path.is_file() else fallback + return hashlib.sha256(payload).hexdigest() + + data_sha = _sha(data_path, b"dataset") + transfer_sha = _sha(transfer_path, b"transfer") + + head = git_head or subprocess.check_output( + ["git", "rev-parse", "HEAD"], cwd=REPO_ROOT, text=True + ).strip() + branch = subprocess.check_output( + ["git", "rev-parse", "--abbrev-ref", "HEAD"], cwd=REPO_ROOT, text=True + ).strip() + dirty = bool( + subprocess.check_output(["git", "status", "--porcelain"], cwd=REPO_ROOT, text=True).strip() + ) + exact = ( + f"python {REPO_ROOT}/ct_train.py --mapping={schedule} --duration={duration} " + f"--outdir={run_dir}" + ) + (run_dir / "run_meta.env").write_text( + "\n".join( + [ + f"mode={mode}", + f"schedule={schedule}", + f"git_head={head}", + f"git_branch={branch}", + f"git_dirty={'true' if dirty else 'false'}", + f"data_sha256={data_sha}", + f"transfer_sha256={transfer_sha}", + f"exact_command={exact}", + "python_version=3.10.0", + "torch_version=2.0.0", + "cuda_version=11.8", + "gpu_name=TestGPU", + "gpu_count=1", + ] + ) + + "\n", + encoding="utf-8", + ) + + nimg = int(round(kimg * 1000)) + loss = "nan" if include_nan else "1.25" + with (run_dir / "train_summary.csv").open("w", newline="", encoding="utf-8") as handle: + fieldnames = [ + "attempted_iteration", + "successful_optimizer_steps", + "processed_nimg", + "processed_kimg", + "loss", + "grad_scale", + "step_skipped", + "schedule", + "stage", + "next_loop_cur_tick", + "elapsed_sec", + "peak_vram_gb", + ] + if include_telemetry: + fieldnames += [ + "loss_ema", + "loss_reference", + "correction", + "signal_updates", + "adaptive_active", + "r_over_t_mean", + "gap_mean", + ] + writer = csv.DictWriter(handle, fieldnames=fieldnames) + writer.writeheader() + row = { + "attempted_iteration": 1, + "successful_optimizer_steps": 1, + "processed_nimg": nimg, + "processed_kimg": f"{kimg:.6f}", + "loss": loss, + "grad_scale": "65536", + "step_skipped": 0, + "schedule": schedule, + "stage": 0, + "next_loop_cur_tick": next_loop_cur_tick, + "elapsed_sec": "1.0", + "peak_vram_gb": "1.5", + } + if include_telemetry: + row.update( + loss_ema="0.8", + loss_reference="1.0", + correction="0.02", + signal_updates="3", + adaptive_active="1", + r_over_t_mean="0.75", + gap_mean="0.25", + ) + writer.writerow(row) + + (run_dir / "network-snapshot-latest.pkl").write_bytes(b"not-a-real-pickle") + # Minimal torch-free stand-in; collector tests skip torch.load via flag. + (run_dir / "training-state-latest.pt").write_bytes(b"not-a-real-state") + + +def write_collector_training_state( + run_dir: Path, + *, + schedule: str, + cur_nimg: int = 16_000, + attempted_iteration: int = 1, + successful_optimizer_steps: int = 1, + elapsed_sec: float = 1.0, + cur_tick: int = 2, + signal_updates: int = 3, +) -> dict: + import torch + + state = { + "gradscaler_state": {"scale": 65536.0}, + "cur_nimg": cur_nimg, + "cur_tick": cur_tick, + "attempted_iteration": attempted_iteration, + "successful_optimizer_steps": successful_optimizer_steps, + "elapsed_sec": elapsed_sec, + "loss_fn_state": { + "schedule_name": schedule, + "stage": 0, + "ratio": 0.5, + "schedule": {}, + }, + } + if schedule == "adaptive_v1": + state["loss_fn_state"]["schedule"] = { + "loss_ema": 0.8, + "loss_reference": 1.0, + "signal_updates": signal_updates, + } + torch.save(state, run_dir / "training-state-latest.pt") + return state + + +def write_adaptive_activation_run( + run_dir: Path, + *, + data_path: Path, + transfer_path: Path, + final_signal_updates: int = 8, + first_nonzero_correction_iteration: int | None = 12, + final_adaptive_active: bool = True, +) -> None: + """Write a 32-iteration activation fixture with controller-state timing.""" + write_minimal_run( + run_dir, + schedule="adaptive_v1", + mode="activation", + duration=0.004, + data_path=data_path, + transfer_path=transfer_path, + include_telemetry=True, + ) + summary_path = run_dir / "train_summary.csv" + with summary_path.open(newline="", encoding="utf-8") as handle: + reader = csv.DictReader(handle) + fieldnames = reader.fieldnames + + rows = [] + for iteration in range(1, 33): + signal_updates = min(iteration // 4, final_signal_updates) + correction_active = ( + first_nonzero_correction_iteration is not None + and iteration >= first_nonzero_correction_iteration + and (iteration < 32 or final_adaptive_active) + ) + adaptive_active = signal_updates >= 3 and (iteration < 32 or final_adaptive_active) + rows.append( + { + "attempted_iteration": iteration, + "successful_optimizer_steps": iteration, + "processed_nimg": iteration * 128, + "processed_kimg": f"{iteration * 0.128:.6f}", + "loss": "1.25", + "grad_scale": "65536", + "step_skipped": "0", + "schedule": "adaptive_v1", + "stage": "0", + "next_loop_cur_tick": "1" if iteration < 32 else "2", + "elapsed_sec": f"{iteration / 10:.1f}", + "peak_vram_gb": "1.5", + "loss_ema": "0.8" if signal_updates else "", + "loss_reference": "1.0" if signal_updates else "", + "correction": "0.02" if correction_active else "0", + "signal_updates": str(signal_updates), + "adaptive_active": str(int(adaptive_active)), + "r_over_t_mean": "0.75", + "gap_mean": "0.25", + } + ) + with summary_path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=fieldnames) + writer.writeheader() + writer.writerows(rows) + + +class RunnerInfraTests(unittest.TestCase): + def test_bash_syntax(self): + subprocess.check_call(["bash", "-n", str(RUNNER)]) + + def test_dry_run_sigmoid(self): + env = os.environ.copy() + env["ECT_DATA_PATH"] = "/tmp/does-not-need-to-exist-for-dry-run.zip" + env["ECT_TRANSFER_PATH"] = "/tmp/does-not-need-to-exist-for-dry-run.pkl" + env["ECT_RUNS_ROOT"] = "/tmp/paired-runs" + completed = subprocess.run( + ["bash", str(RUNNER), "--schedule", "sigmoid", "--mode", "dry-run"], + cwd=REPO_ROOT, + env=env, + check=True, + capture_output=True, + text=True, + ) + self.assertIn("schedule=sigmoid", completed.stdout) + self.assertIn("--transfer=", completed.stdout) + self.assertNotIn("--resume=", completed.stdout) + self.assertIn("--mapping=sigmoid", completed.stdout) + self.assertRegex( + completed.stdout, + r"OUTDIR=.*/sigmoid-dry-run-[0-9a-f]{8}-[0-9]{8}T[0-9]{6}Z", + ) + + def test_dry_run_adaptive_outdir_slug(self): + env = os.environ.copy() + env["ECT_DATA_PATH"] = "/tmp/does-not-need-to-exist-for-dry-run.zip" + env["ECT_TRANSFER_PATH"] = "/tmp/does-not-need-to-exist-for-dry-run.pkl" + env["ECT_RUNS_ROOT"] = "/tmp/paired-runs" + completed = subprocess.run( + ["bash", str(RUNNER), "--schedule", "adaptive_v1", "--mode", "dry-run"], + cwd=REPO_ROOT, + env=env, + check=True, + capture_output=True, + text=True, + ) + self.assertIn("schedule_slug=adaptive-v1", completed.stdout) + self.assertRegex( + completed.stdout, + r"OUTDIR=.*/adaptive-v1-dry-run-[0-9a-f]{8}-[0-9]{8}T[0-9]{6}Z", + ) + + def test_dry_run_accepts_final_matrix_seed_one(self): + completed = subprocess.run( + [ + "bash", str(RUNNER), + "--schedule", "sigmoid", + "--mode", "dry-run", + "--seed", "1", + ], + cwd=REPO_ROOT, + capture_output=True, + text=True, + ) + self.assertEqual(completed.returncode, 0, completed.stderr) + self.assertIn("seed=1", completed.stdout) + self.assertIn("--seed=1", completed.stdout) + self.assertRegex( + completed.stdout, + r"OUTDIR=.*/sigmoid-dry-run-seed1-[0-9a-f]{8}-[0-9]{8}T[0-9]{6}Z", + ) + + def test_runner_rejects_seed_outside_final_matrix(self): + completed = subprocess.run( + [ + "bash", str(RUNNER), + "--schedule", "sigmoid", + "--mode", "dry-run", + "--seed", "3", + ], + cwd=REPO_ROOT, + capture_output=True, + text=True, + ) + self.assertNotEqual(completed.returncode, 0) + self.assertIn("--seed {0|1|2}", completed.stderr) + + def test_runner_has_no_tee_append(self): + text = RUNNER.read_text(encoding="utf-8") + # Only count real pipeline usage, not commentary. + self.assertNotRegex(text, r'(?m)^[^#\n]*\btee -a\b') + self.assertRegex(text, r'(?m)^[^#\n]*\btee "\$\{LOG_PATH\}"') + + def test_fresh_nonempty_outdir_fails(self): + with tempfile.TemporaryDirectory() as tmp: + outdir = Path(tmp) / "busy" + outdir.mkdir() + (outdir / "marker").write_text("x", encoding="utf-8") + data = Path(tmp) / "data.zip" + transfer = Path(tmp) / "transfer.pkl" + write_dummy_asset(data) + write_dummy_asset(transfer) + env = os.environ.copy() + env["ECT_DATA_PATH"] = str(data) + env["ECT_TRANSFER_PATH"] = str(transfer) + completed = subprocess.run( + [ + "bash", + str(RUNNER), + "--schedule", + "sigmoid", + "--mode", + "stability", + "--outdir", + str(outdir), + ], + cwd=REPO_ROOT, + env=env, + capture_output=True, + text=True, + ) + self.assertNotEqual(completed.returncode, 0) + self.assertTrue( + ("exists and is not empty" in completed.stderr) + or ("fresh run requires empty outdir" in completed.stderr), + completed.stderr, + ) + + def test_dry_run_resume_excludes_transfer(self): + with tempfile.TemporaryDirectory() as tmp: + tmp_path = Path(tmp) + outdir = tmp_path / "sigmoid-activation-deadbeef-20260101T000000Z" + outdir.mkdir() + resume = outdir / "training-state-latest.pt" + resume.write_bytes(b"x") + data = tmp_path / "data.zip" + transfer = tmp_path / "transfer.pkl" + write_dummy_asset(data, b"dataset") + write_dummy_asset(transfer, b"transfer") + import hashlib + + head = subprocess.check_output( + ["git", "rev-parse", "HEAD"], cwd=REPO_ROOT, text=True + ).strip() + dirty = bool( + subprocess.check_output( + ["git", "status", "--porcelain"], cwd=REPO_ROOT, text=True + ).strip() + ) + if dirty: + self.skipTest("worktree dirty; resume identity gate requires clean tree") + (outdir / "run_meta.env").write_text( + "\n".join( + [ + "schedule=sigmoid", + f"git_head={head}", + "git_dirty=false", + f"data_sha256={hashlib.sha256(b'dataset').hexdigest()}", + f"transfer_sha256={hashlib.sha256(b'transfer').hexdigest()}", + ] + ) + + "\n", + encoding="utf-8", + ) + env = os.environ.copy() + env["ECT_DATA_PATH"] = str(data) + env["ECT_TRANSFER_PATH"] = str(transfer) + completed = subprocess.run( + [ + "bash", + str(RUNNER), + "--schedule", + "sigmoid", + "--mode", + "dry-run", + "--resume", + str(resume), + ], + cwd=REPO_ROOT, + env=env, + check=True, + capture_output=True, + text=True, + ) + self.assertIn("--resume=", completed.stdout) + self.assertNotIn("--transfer=", completed.stdout) + + def test_resume_head_mismatch_fails(self): + with tempfile.TemporaryDirectory() as tmp: + tmp_path = Path(tmp) + outdir = tmp_path / "sigmoid-activation-deadbeef-20260101T000000Z" + outdir.mkdir() + resume = outdir / "training-state-latest.pt" + resume.write_bytes(b"x") + data = tmp_path / "data.zip" + transfer = tmp_path / "transfer.pkl" + write_dummy_asset(data, b"dataset") + write_dummy_asset(transfer, b"transfer") + import hashlib + + (outdir / "run_meta.env").write_text( + "\n".join( + [ + "schedule=sigmoid", + "git_head=" + ("a" * 40), + "git_dirty=false", + f"data_sha256={hashlib.sha256(b'dataset').hexdigest()}", + f"transfer_sha256={hashlib.sha256(b'transfer').hexdigest()}", + ] + ) + + "\n", + encoding="utf-8", + ) + env = os.environ.copy() + env["ECT_DATA_PATH"] = str(data) + env["ECT_TRANSFER_PATH"] = str(transfer) + completed = subprocess.run( + [ + "bash", + str(RUNNER), + "--schedule", + "sigmoid", + "--mode", + "dry-run", + "--resume", + str(resume), + ], + cwd=REPO_ROOT, + env=env, + capture_output=True, + text=True, + ) + self.assertNotEqual(completed.returncode, 0) + self.assertIn("resume HEAD mismatch", completed.stderr) + + def test_resume_hash_mismatch_fails(self): + with tempfile.TemporaryDirectory() as tmp: + tmp_path = Path(tmp) + outdir = tmp_path / "sigmoid-activation-deadbeef-20260101T000000Z" + outdir.mkdir() + resume = outdir / "training-state-latest.pt" + resume.write_bytes(b"x") + data = tmp_path / "data.zip" + transfer = tmp_path / "transfer.pkl" + write_dummy_asset(data, b"dataset") + write_dummy_asset(transfer, b"transfer") + import hashlib + + head = subprocess.check_output( + ["git", "rev-parse", "HEAD"], cwd=REPO_ROOT, text=True + ).strip() + dirty = bool( + subprocess.check_output( + ["git", "status", "--porcelain"], cwd=REPO_ROOT, text=True + ).strip() + ) + if dirty: + self.skipTest("worktree dirty; resume identity gate requires clean tree") + (outdir / "run_meta.env").write_text( + "\n".join( + [ + "schedule=sigmoid", + f"git_head={head}", + "git_dirty=false", + "data_sha256=" + ("0" * 64), + f"transfer_sha256={hashlib.sha256(b'transfer').hexdigest()}", + ] + ) + + "\n", + encoding="utf-8", + ) + env = os.environ.copy() + env["ECT_DATA_PATH"] = str(data) + env["ECT_TRANSFER_PATH"] = str(transfer) + completed = subprocess.run( + [ + "bash", + str(RUNNER), + "--schedule", + "sigmoid", + "--mode", + "dry-run", + "--resume", + str(resume), + ], + cwd=REPO_ROOT, + env=env, + capture_output=True, + text=True, + ) + self.assertNotEqual(completed.returncode, 0) + self.assertIn("resume dataset SHA mismatch", completed.stderr) + + +class CollectorInfraTests(unittest.TestCase): + def _assets(self, tmp: Path) -> tuple[Path, Path]: + data = tmp / "data.zip" + transfer = tmp / "transfer.pkl" + write_dummy_asset(data, b"dataset") + write_dummy_asset(transfer, b"transfer") + return data, transfer + + def _run_collector( + self, + run_dir: Path, + outdir: Path, + data: Path, + transfer: Path, + *, + schedule: str, + mode: str = "stability", + load_training_state: bool = False, + ) -> subprocess.CompletedProcess: + command = [ + sys.executable, + str(COLLECTOR), + "--run-dir", str(run_dir), + "--outdir", str(outdir), + "--data", str(data), + "--transfer", str(transfer), + "--mode", mode, + "--schedule", schedule, + "--allow-dirty", + "--skip-snapshot-load", + ] + if not load_training_state: + command.append("--skip-training-state-load") + return subprocess.run( + command, + cwd=REPO_ROOT, + capture_output=True, + text=True, + ) + + def test_adaptive_telemetry_is_validated_and_preserved(self): + with tempfile.TemporaryDirectory() as tmp: + tmp_path = Path(tmp) + run_dir = tmp_path / "run" + outdir = tmp_path / "out" + data, transfer = self._assets(tmp_path) + write_minimal_run( + run_dir, + schedule="adaptive_v1", + data_path=data, + transfer_path=transfer, + include_telemetry=True, + ) + completed = subprocess.run( + [ + sys.executable, + str(COLLECTOR), + "--run-dir", str(run_dir), + "--outdir", str(outdir), + "--data", str(data), + "--transfer", str(transfer), + "--mode", "stability", + "--schedule", "adaptive_v1", + "--allow-dirty", + "--skip-snapshot-load", + "--skip-training-state-load", + ], + cwd=REPO_ROOT, + capture_output=True, + text=True, + ) + self.assertEqual(completed.returncode, 0, completed.stderr) + with (outdir / "train_summary.csv").open(newline="", encoding="utf-8") as handle: + packaged = next(csv.DictReader(handle)) + self.assertEqual(packaged["signal_updates"], "3") + self.assertEqual(packaged["adaptive_active"], "1") + self.assertEqual(packaged["r_over_t_mean"], "0.75") + self.assertEqual(packaged["next_loop_cur_tick"], "2") + metadata = json.loads((outdir / "metadata.json").read_text(encoding="utf-8")) + self.assertTrue(metadata["schedule_telemetry_available"]) + self.assertEqual(metadata["final_signal_updates"], 3) + self.assertEqual(metadata["first_nonzero_correction_iteration"], 1) + + def test_training_state_must_match_final_csv_and_adaptive_telemetry(self): + cases = [ + ( + "cur_nimg", + lambda state: state.update(cur_nimg=15_872), + "training-state cur_nimg mismatch", + ), + ( + "attempted_iteration", + lambda state: state.update(attempted_iteration=2), + "training-state attempted_iteration mismatch", + ), + ( + "successful_optimizer_steps", + lambda state: state.update(successful_optimizer_steps=0), + "training-state successful_optimizer_steps mismatch", + ), + ( + "elapsed_sec", + lambda state: state.update(elapsed_sec=2.0), + "training-state elapsed_sec mismatch", + ), + ( + "cur_tick", + lambda state: state.update(cur_tick=3), + "training-state cur_tick mismatch", + ), + ( + "signal_updates", + lambda state: state["loss_fn_state"]["schedule"].update(signal_updates=2), + "training-state signal_updates mismatch", + ), + ( + "loss_ema", + lambda state: state["loss_fn_state"]["schedule"].update(loss_ema=0.7), + "training-state loss_ema mismatch", + ), + ( + "loss_reference", + lambda state: state["loss_fn_state"]["schedule"].update(loss_reference=1.1), + "training-state loss_reference mismatch", + ), + ] + with tempfile.TemporaryDirectory() as tmp: + tmp_path = Path(tmp) + data, transfer = self._assets(tmp_path) + for name, mutate, error in cases: + with self.subTest(name=name): + run_dir = tmp_path / f"run-{name}" + outdir = tmp_path / f"out-{name}" + write_minimal_run( + run_dir, + schedule="adaptive_v1", + data_path=data, + transfer_path=transfer, + include_telemetry=True, + ) + state = write_collector_training_state(run_dir, schedule="adaptive_v1") + mutate(state) + import torch + + torch.save(state, run_dir / "training-state-latest.pt") + completed = self._run_collector( + run_dir, + outdir, + data, + transfer, + schedule="adaptive_v1", + load_training_state=True, + ) + self.assertNotEqual(completed.returncode, 0) + self.assertIn(error, completed.stderr) + + def test_matching_training_state_is_accepted(self): + with tempfile.TemporaryDirectory() as tmp: + tmp_path = Path(tmp) + run_dir = tmp_path / "run" + outdir = tmp_path / "out" + data, transfer = self._assets(tmp_path) + write_minimal_run( + run_dir, + schedule="adaptive_v1", + data_path=data, + transfer_path=transfer, + include_telemetry=True, + ) + write_collector_training_state(run_dir, schedule="adaptive_v1") + completed = self._run_collector( + run_dir, + outdir, + data, + transfer, + schedule="adaptive_v1", + load_training_state=True, + ) + self.assertEqual(completed.returncode, 0, completed.stderr) + + def test_baseline_training_state_uses_recorded_tick_not_tick_formula(self): + # With batch=128 and default --tick=50 kimg, real maintenance runs at + # 0.128, 50.176, 100.224, and 128 kimg. The final checkpoint therefore + # persists cur_tick=4, whereas the removed ceil-based formula gives 3. + with tempfile.TemporaryDirectory() as tmp: + tmp_path = Path(tmp) + run_dir = tmp_path / "run" + outdir = tmp_path / "out" + data, transfer = self._assets(tmp_path) + write_minimal_run( + run_dir, + schedule="adaptive_v1", + mode="baseline", + duration=0.128, + data_path=data, + transfer_path=transfer, + include_telemetry=True, + next_loop_cur_tick=4, + ) + write_collector_training_state( + run_dir, + schedule="adaptive_v1", + cur_nimg=128_000, + cur_tick=4, + ) + completed = self._run_collector( + run_dir, + outdir, + data, + transfer, + schedule="adaptive_v1", + mode="baseline", + load_training_state=True, + ) + self.assertEqual(completed.returncode, 0, completed.stderr) + with (outdir / "train_summary.csv").open(newline="", encoding="utf-8") as handle: + self.assertEqual(next(csv.DictReader(handle))["next_loop_cur_tick"], "4") + + def test_adaptive_activation_gate_records_controller_and_pair_boundaries(self): + with tempfile.TemporaryDirectory() as tmp: + tmp_path = Path(tmp) + run_dir = tmp_path / "run" + outdir = tmp_path / "out" + data, transfer = self._assets(tmp_path) + write_adaptive_activation_run( + run_dir, + data_path=data, + transfer_path=transfer, + ) + write_collector_training_state( + run_dir, + schedule="adaptive_v1", + cur_nimg=4_096, + attempted_iteration=32, + successful_optimizer_steps=32, + elapsed_sec=3.2, + cur_tick=2, + signal_updates=8, + ) + completed = self._run_collector( + run_dir, outdir, data, transfer, + schedule="adaptive_v1", mode="activation", + load_training_state=True, + ) + self.assertEqual(completed.returncode, 0, completed.stderr) + with (run_dir / "train_summary.csv").open(newline="", encoding="utf-8") as handle: + self.assertEqual(list(csv.DictReader(handle))[-1]["next_loop_cur_tick"], "2") + metadata = json.loads((outdir / "metadata.json").read_text(encoding="utf-8")) + self.assertEqual(metadata["final_signal_updates"], 8) + self.assertTrue(metadata["final_adaptive_active"]) + self.assertEqual(metadata["first_nonzero_correction_iteration"], 12) + self.assertEqual(metadata["first_adapted_pair_iteration"], 13) + self.assertTrue(metadata["activation_gate_applied"]) + self.assertTrue(metadata["activation_gate_passed"]) + + def test_adaptive_activation_gate_rejects_nonactivated_or_late_controller(self): + cases = [ + ( + dict(final_signal_updates=2, first_nonzero_correction_iteration=None), + "final_signal_updates must be >= 3", + ), + ( + dict(first_nonzero_correction_iteration=None), + "first_nonzero_correction_iteration is required", + ), + ( + dict(final_adaptive_active=False), + "final adaptive_active must be true", + ), + ( + dict(first_nonzero_correction_iteration=31), + "first_adapted_pair_iteration must be before final_iteration", + ), + ( + dict(first_nonzero_correction_iteration=29), + "at least 4 attempted iterations", + ), + ] + for fixture_kwargs, expected_error in cases: + with self.subTest(fixture_kwargs=fixture_kwargs), tempfile.TemporaryDirectory() as tmp: + tmp_path = Path(tmp) + run_dir = tmp_path / "run" + outdir = tmp_path / "out" + data, transfer = self._assets(tmp_path) + write_adaptive_activation_run( + run_dir, + data_path=data, + transfer_path=transfer, + **fixture_kwargs, + ) + completed = self._run_collector( + run_dir, outdir, data, transfer, + schedule="adaptive_v1", mode="activation", + ) + self.assertNotEqual(completed.returncode, 0) + self.assertIn(expected_error, completed.stderr) + + def test_adaptive_telemetry_rejects_invalid_types_and_ranges(self): + cases = [ + ({"signal_updates": "3.5"}, "non-negative integer"), + ({"adaptive_active": "maybe"}, "must be one of"), + ({"correction": "nan"}, "must be finite"), + ({"r_over_t_mean": "1.1", "gap_mean": "-0.1"}, "must be in [0, 1]"), + ] + for changes, expected_error in cases: + with self.subTest(changes=changes), tempfile.TemporaryDirectory() as tmp: + tmp_path = Path(tmp) + run_dir = tmp_path / "run" + outdir = tmp_path / "out" + data, transfer = self._assets(tmp_path) + write_minimal_run( + run_dir, + schedule="adaptive_v1", + data_path=data, + transfer_path=transfer, + include_telemetry=True, + ) + summary_path = run_dir / "train_summary.csv" + with summary_path.open(newline="", encoding="utf-8") as handle: + reader = csv.DictReader(handle) + fieldnames = reader.fieldnames + row = next(reader) + row.update(changes) + with summary_path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=fieldnames) + writer.writeheader() + writer.writerow(row) + + completed = self._run_collector( + run_dir, outdir, data, transfer, schedule="adaptive_v1" + ) + self.assertNotEqual(completed.returncode, 0) + self.assertIn(expected_error, completed.stderr) + + def test_adaptive_warmup_telemetry_allows_missing_reference(self): + with tempfile.TemporaryDirectory() as tmp: + tmp_path = Path(tmp) + run_dir = tmp_path / "run" + outdir = tmp_path / "out" + data, transfer = self._assets(tmp_path) + write_minimal_run( + run_dir, + schedule="adaptive_v1", + data_path=data, + transfer_path=transfer, + include_telemetry=True, + ) + summary_path = run_dir / "train_summary.csv" + with summary_path.open(newline="", encoding="utf-8") as handle: + reader = csv.DictReader(handle) + fieldnames = reader.fieldnames + row = next(reader) + row.update( + loss_reference="", + correction="0", + signal_updates="1", + adaptive_active="0", + ) + with summary_path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=fieldnames) + writer.writeheader() + writer.writerow(row) + + completed = self._run_collector( + run_dir, outdir, data, transfer, schedule="adaptive_v1" + ) + self.assertEqual(completed.returncode, 0, completed.stderr) + + def test_migrated_telemetry_prefix_is_auditable_not_fabricated(self): + with tempfile.TemporaryDirectory() as tmp: + tmp_path = Path(tmp) + run_dir = tmp_path / "run" + outdir = tmp_path / "out" + data, transfer = self._assets(tmp_path) + write_minimal_run( + run_dir, + schedule="adaptive_v1", + data_path=data, + transfer_path=transfer, + include_telemetry=True, + ) + summary_path = run_dir / "train_summary.csv" + with summary_path.open(newline="", encoding="utf-8") as handle: + reader = csv.DictReader(handle) + fieldnames = reader.fieldnames + final_row = next(reader) + historical_row = dict(final_row) + historical_row.update( + attempted_iteration="1", + successful_optimizer_steps="1", + processed_nimg="128", + processed_kimg="0.128", + elapsed_sec="0.1", + ) + for field in ( + "loss_ema", "loss_reference", "correction", "signal_updates", + "adaptive_active", "r_over_t_mean", "gap_mean", + ): + historical_row[field] = "" + final_row.update(attempted_iteration="2", successful_optimizer_steps="2") + with summary_path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=fieldnames) + writer.writeheader() + writer.writerows([historical_row, final_row]) + + completed = self._run_collector( + run_dir, outdir, data, transfer, schedule="adaptive_v1" + ) + self.assertEqual(completed.returncode, 0, completed.stderr) + metadata = json.loads((outdir / "metadata.json").read_text(encoding="utf-8")) + self.assertTrue(metadata["schedule_telemetry_columns_available"]) + self.assertFalse(metadata["schedule_telemetry_available"]) + self.assertEqual(metadata["schedule_telemetry_rows"], 1) + self.assertEqual(metadata["schedule_telemetry_total_rows"], 2) + self.assertEqual(metadata["schedule_telemetry_coverage"], 0.5) + self.assertEqual(metadata["first_schedule_telemetry_iteration"], 2) + with (outdir / "train_summary.csv").open(newline="", encoding="utf-8") as handle: + packaged = list(csv.DictReader(handle)) + self.assertEqual(packaged[0]["signal_updates"], "") + self.assertEqual(packaged[1]["signal_updates"], "3") + + def test_missing_csv_fails(self): + with tempfile.TemporaryDirectory() as tmp: + tmp_path = Path(tmp) + run_dir = tmp_path / "run" + outdir = tmp_path / "out" + data, transfer = self._assets(tmp_path) + run_dir.mkdir() + head = subprocess.check_output( + ["git", "rev-parse", "HEAD"], cwd=REPO_ROOT, text=True + ).strip() + (run_dir / "run_meta.env").write_text( + "\n".join( + [ + f"git_head={head}", + "git_dirty=false", + f"data_sha256={__import__('hashlib').sha256(b'dataset').hexdigest()}", + f"transfer_sha256={__import__('hashlib').sha256(b'transfer').hexdigest()}", + "exact_command=python ct_train.py --mapping=sigmoid --duration=0.016", + ] + ) + + "\n", + encoding="utf-8", + ) + completed = subprocess.run( + [ + sys.executable, + str(COLLECTOR), + "--run-dir", + str(run_dir), + "--outdir", + str(outdir), + "--data", + str(data), + "--transfer", + str(transfer), + "--mode", + "stability", + "--schedule", + "sigmoid", + "--allow-dirty", + "--skip-snapshot-load", + "--skip-training-state-load", + ], + cwd=REPO_ROOT, + capture_output=True, + text=True, + ) + self.assertNotEqual(completed.returncode, 0) + self.assertIn("train_summary.csv missing", completed.stderr) + + def test_nan_fails(self): + with tempfile.TemporaryDirectory() as tmp: + tmp_path = Path(tmp) + run_dir = tmp_path / "run" + outdir = tmp_path / "out" + data, transfer = self._assets(tmp_path) + write_minimal_run(run_dir, include_nan=True, data_path=data, transfer_path=transfer) + completed = subprocess.run( + [ + sys.executable, + str(COLLECTOR), + "--run-dir", + str(run_dir), + "--outdir", + str(outdir), + "--data", + str(data), + "--transfer", + str(transfer), + "--mode", + "stability", + "--schedule", + "sigmoid", + "--allow-dirty", + "--skip-snapshot-load", + "--skip-training-state-load", + ], + cwd=REPO_ROOT, + capture_output=True, + text=True, + ) + self.assertNotEqual(completed.returncode, 0) + self.assertIn("non-finite losses", completed.stderr) + + def test_schedule_mismatch_fails(self): + with tempfile.TemporaryDirectory() as tmp: + tmp_path = Path(tmp) + run_dir = tmp_path / "run" + outdir = tmp_path / "out" + data, transfer = self._assets(tmp_path) + write_minimal_run(run_dir, schedule="sigmoid", data_path=data, transfer_path=transfer) + completed = subprocess.run( + [ + sys.executable, + str(COLLECTOR), + "--run-dir", + str(run_dir), + "--outdir", + str(outdir), + "--data", + str(data), + "--transfer", + str(transfer), + "--mode", + "stability", + "--schedule", + "adaptive_v1", + "--allow-dirty", + "--skip-snapshot-load", + "--skip-training-state-load", + ], + cwd=REPO_ROOT, + capture_output=True, + text=True, + ) + self.assertNotEqual(completed.returncode, 0) + self.assertIn("CSV schedule", completed.stderr) + + def test_resume_progress_fields_round_trip_dict(self): + # Maintenance saves next-loop values so resume matches uninterrupted training. + cur_tick = 1 + cur_nimg = 16000 + tick_start_nimg = 12800 # previous tick start; must NOT be what we persist + elapsed_base_sec = 31.3 + segment_elapsed = 10.0 + state = { + "cur_nimg": cur_nimg, + "cur_tick": cur_tick + 1, + "tick_start_nimg": cur_nimg, + "attempted_iteration": 125, + "successful_optimizer_steps": 116, + "elapsed_sec": elapsed_base_sec + segment_elapsed, + } + self.assertEqual(int(state["cur_nimg"]), 16000) + self.assertEqual(int(state["cur_tick"]), 2) + self.assertEqual(int(state["tick_start_nimg"]), 16000) + self.assertNotEqual(int(state["tick_start_nimg"]), tick_start_nimg) + self.assertGreater(float(state["elapsed_sec"]), elapsed_base_sec) + # Filename-derived estimate would be wrong for short runs. + resume_tick_from_name = 1 + kimg_per_tick = 50 + wrong = resume_tick_from_name * kimg_per_tick * 1000 + self.assertNotEqual(wrong, int(state["cur_nimg"])) + + def test_already_done_budget_is_noop(self): + cur_nimg = 16000 + total_kimg = 16 + self.assertGreaterEqual(cur_nimg, total_kimg * 1000) + + def test_collector_prefers_mode_meta_for_command(self): + with tempfile.TemporaryDirectory() as tmp: + tmp_path = Path(tmp) + run_dir = tmp_path / "run" + outdir = tmp_path / "out" + data, transfer = self._assets(tmp_path) + write_minimal_run( + run_dir, + mode="activation", + duration=0.004, + data_path=data, + transfer_path=transfer, + ) + # Simulate resume overwriting only mode/latest sidecars, preserving run_meta.env + # with activation identity hashes, while stability command lives in mode file. + import hashlib + import shutil + + identity = run_dir / "run_meta.env" + shutil.copy(identity, run_dir / "run_meta.activation.env") + data_sha = hashlib.sha256(data.read_bytes()).hexdigest() + transfer_sha = hashlib.sha256(transfer.read_bytes()).hexdigest() + head = subprocess.check_output(["git", "rev-parse", "HEAD"], cwd=REPO_ROOT, text=True).strip() + # Rewrite CSV to stability budget so packaging as stability can succeed. + write_minimal_run( + run_dir, + mode="stability", + duration=0.016, + data_path=data, + transfer_path=transfer, + ) + # Restore immutable identity with original hashes/git, but keep stability command sidecar. + (run_dir / "run_meta.env").write_text( + "\n".join( + [ + "mode=activation", + "schedule=sigmoid", + f"git_head={head}", + "git_branch=role-b/paired-training-v1", + "git_dirty=false", + f"data_sha256={data_sha}", + f"transfer_sha256={transfer_sha}", + "exact_command=python ct_train.py --mapping=sigmoid --duration=0.004", + ] + ) + + "\n", + encoding="utf-8", + ) + (run_dir / "run_meta.stability.env").write_text( + "\n".join( + [ + "mode=stability", + "schedule=sigmoid", + f"git_head={head}", + "git_dirty=false", + f"data_sha256={data_sha}", + f"transfer_sha256={transfer_sha}", + "exact_command=python ct_train.py --mapping=sigmoid --duration=0.016", + ] + ) + + "\n", + encoding="utf-8", + ) + completed = subprocess.run( + [ + sys.executable, + str(COLLECTOR), + "--run-dir", + str(run_dir), + "--outdir", + str(outdir), + "--data", + str(data), + "--transfer", + str(transfer), + "--mode", + "stability", + "--schedule", + "sigmoid", + "--allow-dirty", + "--skip-snapshot-load", + "--skip-training-state-load", + ], + cwd=REPO_ROOT, + capture_output=True, + text=True, + ) + self.assertEqual(completed.returncode, 0, completed.stderr) + + def test_collector_command_head_mismatch_fails(self): + with tempfile.TemporaryDirectory() as tmp: + tmp_path = Path(tmp) + run_dir = tmp_path / "run" + outdir = tmp_path / "out" + data, transfer = self._assets(tmp_path) + write_minimal_run(run_dir, data_path=data, transfer_path=transfer) + head = subprocess.check_output( + ["git", "rev-parse", "HEAD"], cwd=REPO_ROOT, text=True + ).strip() + import hashlib + + data_sha = hashlib.sha256(data.read_bytes()).hexdigest() + transfer_sha = hashlib.sha256(transfer.read_bytes()).hexdigest() + (run_dir / "run_meta.stability.env").write_text( + "\n".join( + [ + "mode=stability", + "schedule=sigmoid", + "git_head=" + ("b" * 40), + "git_dirty=false", + f"data_sha256={data_sha}", + f"transfer_sha256={transfer_sha}", + "exact_command=python ct_train.py --mapping=sigmoid --duration=0.016", + ] + ) + + "\n", + encoding="utf-8", + ) + # Keep immutable fresh identity at current HEAD. + lines = [] + for line in (run_dir / "run_meta.env").read_text(encoding="utf-8").splitlines(): + if line.startswith("git_head="): + lines.append(f"git_head={head}") + else: + lines.append(line) + (run_dir / "run_meta.env").write_text("\n".join(lines) + "\n", encoding="utf-8") + completed = subprocess.run( + [ + sys.executable, + str(COLLECTOR), + "--run-dir", + str(run_dir), + "--outdir", + str(outdir), + "--data", + str(data), + "--transfer", + str(transfer), + "--mode", + "stability", + "--schedule", + "sigmoid", + "--allow-dirty", + "--skip-snapshot-load", + "--skip-training-state-load", + ], + cwd=REPO_ROOT, + capture_output=True, + text=True, + ) + self.assertNotEqual(completed.returncode, 0) + self.assertIn("resume/command git_head", completed.stderr) + + def test_activation_expected_kimg_uses_batch_rounding(self): + from scripts.collect_schedule_results import expected_final_nimg + + nimg = expected_final_nimg(0.004, 128) + self.assertEqual(nimg, 4096) + self.assertEqual(nimg / 128, 32) + + def test_asset_sha_mismatch_fails(self): + with tempfile.TemporaryDirectory() as tmp: + tmp_path = Path(tmp) + run_dir = tmp_path / "run" + outdir = tmp_path / "out" + data, transfer = self._assets(tmp_path) + write_minimal_run(run_dir, data_path=data, transfer_path=transfer) + # Corrupt the recorded train-time hash. + lines = [] + for line in (run_dir / "run_meta.env").read_text(encoding="utf-8").splitlines(): + if line.startswith("data_sha256="): + lines.append("data_sha256=" + ("0" * 64)) + else: + lines.append(line) + (run_dir / "run_meta.env").write_text("\n".join(lines) + "\n", encoding="utf-8") + completed = subprocess.run( + [ + sys.executable, + str(COLLECTOR), + "--run-dir", + str(run_dir), + "--outdir", + str(outdir), + "--data", + str(data), + "--transfer", + str(transfer), + "--mode", + "stability", + "--schedule", + "sigmoid", + "--allow-dirty", + "--skip-snapshot-load", + "--skip-training-state-load", + ], + cwd=REPO_ROOT, + capture_output=True, + text=True, + ) + self.assertNotEqual(completed.returncode, 0) + self.assertIn("dataset SHA mismatch", completed.stderr) + + def test_outdir_nonempty_requires_overwrite(self): + with tempfile.TemporaryDirectory() as tmp: + tmp_path = Path(tmp) + run_dir = tmp_path / "run" + outdir = tmp_path / "out" + outdir.mkdir() + (outdir / "metadata.json").write_text("{}", encoding="utf-8") + data, transfer = self._assets(tmp_path) + write_minimal_run(run_dir, data_path=data, transfer_path=transfer) + completed = subprocess.run( + [ + sys.executable, + str(COLLECTOR), + "--run-dir", + str(run_dir), + "--outdir", + str(outdir), + "--data", + str(data), + "--transfer", + str(transfer), + "--mode", + "stability", + "--schedule", + "sigmoid", + "--allow-dirty", + "--skip-snapshot-load", + "--skip-training-state-load", + ], + cwd=REPO_ROOT, + capture_output=True, + text=True, + ) + self.assertNotEqual(completed.returncode, 0) + self.assertIn("outdir is not empty", completed.stderr) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_prospective_frozen_matrices.py b/tests/test_prospective_frozen_matrices.py new file mode 100644 index 00000000..e5b3fc9c --- /dev/null +++ b/tests/test_prospective_frozen_matrices.py @@ -0,0 +1,94 @@ +import json +import unittest +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] + + +class ProspectiveFrozenMatricesTest(unittest.TestCase): + def load(self, name: str) -> dict: + return json.loads((ROOT / "configs" / name).read_text(encoding="utf-8")) + + def assert_common_contract(self, matrix: dict, expected_q: int) -> None: + self.assertEqual(matrix["manifest_kind"], "frozen-logical-evaluation-matrix") + self.assertEqual(matrix["protocol"], "staged-checkpoint-evaluation-v1") + self.assertTrue(matrix["runtime_binding"]["required_before_evaluation"]) + self.assertFalse(matrix["runtime_binding"]["versioned_paths"]) + self.assertEqual(matrix["training"]["schedule_q"], expected_q) + self.assertEqual(matrix["training"]["training_seeds"], [3, 4, 5]) + self.assertEqual(matrix["nfe_modes"], {"1": [], "2": [0.821]}) + self.assertEqual( + matrix["comparison"]["pairing_key"], + ["training_seed", "budget_kimg", "nfe", "metric"], + ) + self.assertEqual(matrix["comparison"]["delta_direction"], "global_only - fixed") + self.assertEqual(matrix["formal_promotion_policy"]["eligibility"], "provenance_and_integrity_only") + self.assertEqual( + matrix["formal_promotion_policy"]["quick_metric_performance"], + "not_an_eligibility_criterion", + ) + self.assertEqual(matrix["method_definitions"]["fixed"]["global_gap_scale"], 1.0) + self.assertEqual(matrix["method_definitions"]["global110"]["global_gap_scale"], 1.1) + self.assertEqual(matrix["method_definitions"]["fixed"]["schedule_identity"], "sigmoid") + self.assertEqual(matrix["method_definitions"]["global110"]["schedule_identity"], "global_sigmoid") + + cells = matrix["cells"] + required = matrix["formal_promotion_policy"]["required_evaluation_checkpoint_ids"] + self.assertEqual([cell["checkpoint_id"] for cell in cells], required) + self.assertEqual(len({cell["checkpoint_id"] for cell in cells}), len(cells)) + for cell in cells: + self.assertEqual(cell["schedule_q"], expected_q) + self.assertIn(cell["method"], ("fixed", "global110")) + self.assertIn(cell["training_seed"], (3, 4, 5)) + self.assertNotIn("checkpoint", cell) + self.assertNotIn("checkpoint_sha256", cell) + + def test_q256_budget_matrix_is_complete_and_has_per_budget_metrics(self): + matrix = self.load("q256_budget_matrix.frozen.json") + self.assert_common_contract(matrix, 256) + self.assertEqual(matrix["training"]["budget_kimg"], [512, 768, 1024]) + self.assertEqual(len(matrix["cells"]), 18) + self.assertEqual( + {(cell["budget_kimg"], cell["method"], cell["training_seed"]) for cell in matrix["cells"]}, + {(budget, method, seed) for budget in (512, 768, 1024) + for method in ("fixed", "global110") for seed in (3, 4, 5)}, + ) + contracts = {contract["budget_kimg"]: contract for contract in matrix["evaluation_contracts"]} + for budget in (512, 768): + self.assertEqual(contracts[budget]["stage"], "quick") + self.assertEqual(contracts[budget]["metric_names"], ["kid5k_full", "fid5k_full"]) + self.assertEqual(contracts[budget]["sample_count"], 5000) + self.assertEqual(contracts[1024]["stage"], "formal") + self.assertEqual(contracts[1024]["metric_names"], ["kid50k_full", "fid50k_full"]) + self.assertEqual(contracts[1024]["sample_count"], 50000) + formal_ids = matrix["formal_promotion_policy"]["required_formal_checkpoint_ids"] + self.assertEqual(len(formal_ids), 6) + self.assertTrue(all("-1024k-" in checkpoint_id for checkpoint_id in formal_ids)) + + def test_q128_fresh_matrix_is_complete_and_formal(self): + matrix = self.load("q128_confirmatory_matrix.frozen.json") + self.assert_common_contract(matrix, 128) + self.assertEqual(matrix["training"]["budget_kimg"], [256]) + self.assertEqual(len(matrix["cells"]), 6) + self.assertEqual( + {(cell["method"], cell["training_seed"]) for cell in matrix["cells"]}, + {(method, seed) for method in ("fixed", "global110") for seed in (3, 4, 5)}, + ) + self.assertEqual(matrix["evaluation_contracts"], [{ + "budget_kimg": 256, + "stage": "formal", + "evidence_class": "formal", + "metric_names": ["kid50k_full", "fid50k_full"], + "sample_count": 50000, + "generation_seed_range": "0-49999", + "metric_seed": 20260730, + }]) + self.assertEqual( + matrix["formal_promotion_policy"]["required_formal_checkpoint_ids"], + matrix["formal_promotion_policy"]["required_evaluation_checkpoint_ids"], + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_q256_paper_artifacts.py b/tests/test_q256_paper_artifacts.py new file mode 100644 index 00000000..9fe32445 --- /dev/null +++ b/tests/test_q256_paper_artifacts.py @@ -0,0 +1,52 @@ +import shutil +import subprocess +import tempfile +import unittest +from pathlib import Path + +from scripts import plot_q256_main_results as q256_plots + + +REPO_ROOT = Path(__file__).resolve().parents[1] +PAIR_CSV = REPO_ROOT / "results/q256_256k_formal/paired_differences.csv" +MAIN_TABLE = REPO_ROOT / "tables/q256_main_table.tex" +APPENDIX_TABLE = REPO_ROOT / "tables/q256_per_seed_appendix.tex" + + +class Q256PaperArtifactsTest(unittest.TestCase): + def test_q256_paper_pdfs_are_reproducible_and_nonempty(self): + indexed = q256_plots.data_index(q256_plots.read_pairs(PAIR_CSV)) + with tempfile.TemporaryDirectory() as directory: + outdir = Path(directory) + q256_plots.render_figure1(indexed, outdir, "q256_paired_seed_plot", ("pdf",)) + q256_plots.render_figure2(indexed, outdir, "q256_effect_heterogeneity", ("pdf",)) + for name in ("q256_paired_seed_plot.pdf", "q256_effect_heterogeneity.pdf"): + self.assertGreater((outdir / name).stat().st_size, 0) + + @unittest.skipUnless(shutil.which("pdflatex"), "pdflatex is not available") + def test_q256_latex_tables_compile(self): + self.assertTrue(MAIN_TABLE.is_file()) + self.assertTrue(APPENDIX_TABLE.is_file()) + with tempfile.TemporaryDirectory() as directory: + build = Path(directory) + source = build / "paper_artifacts_smoke_test.tex" + source.write_text( + "\\documentclass{article}\n" + "\\usepackage[margin=0.7in]{geometry}\n" + "\\usepackage{booktabs}\n" + "\\begin{document}\n" + "\\input{tables/q256_main_table}\n" + "\\input{tables/q256_per_seed_appendix}\n" + "\\end{document}\n", + encoding="utf-8", + ) + completed = subprocess.run( + ["pdflatex", "-interaction=nonstopmode", "-halt-on-error", "-output-directory", str(build), str(source)], + cwd=REPO_ROOT, text=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, check=False, + ) + self.assertEqual(completed.returncode, 0, completed.stdout) + self.assertGreater((build / "paper_artifacts_smoke_test.pdf").stat().st_size, 0) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_role_a_quality_evaluation.py b/tests/test_role_a_quality_evaluation.py new file mode 100644 index 00000000..e845b92a --- /dev/null +++ b/tests/test_role_a_quality_evaluation.py @@ -0,0 +1,157 @@ +import csv +import json +import unittest +from pathlib import Path +from tempfile import TemporaryDirectory + +from scripts import collect_role_a_quality_results +from scripts import run_role_a_quality_evaluation + + +class RoleAQualityEvaluationTest(unittest.TestCase): + def make_manifest(self, root: Path) -> Path: + cells = [] + for budget in (64, 32, 16): + for seed in range(3): + for method in ("sigmoid", "adaptive_v1"): + checkpoint = root / f"{method}-seed{seed}-{budget}k.pkl" + checkpoint.write_bytes(f"{method}-{seed}-{budget}".encode()) + cells.append({ + "method": method, + "training_seed": seed, + "budget_kimg": budget, + "checkpoint": str(checkpoint), + "checkpoint_sha256": run_role_a_quality_evaluation.sha256_file(checkpoint), + }) + path = root / "checkpoints.json" + path.write_text(json.dumps({"cells": cells}), encoding="utf-8") + return path + + def test_smoke_is_seed0_16k_512_samples_with_exact_repeats(self): + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + cells = run_role_a_quality_evaluation.load_cells(self.make_manifest(root)) + selected = run_role_a_quality_evaluation.select_cells(cells, "smoke", 16) + jobs = run_role_a_quality_evaluation.build_jobs( + selected, root / "cifar.zip", root / "out", "smoke", "both", 29700 + ) + self.assertEqual(len(jobs), 4) + for job in jobs: + self.assertEqual(job["training_seed"], 0) + self.assertEqual(job["budget_kimg"], 16) + self.assertEqual(job["sample_count"], 512) + self.assertEqual(job["metric_repeats"], 2) + command = " ".join(job["command"]) + self.assertIn("--sample-seeds=0-511", command) + self.assertIn("--metrics=kid512_full,fid512_full", command) + self.assertIn("--fp16=False", command) + + def test_formal_requires_complete_budget_and_keeps_seed_cells_separate(self): + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + cells = run_role_a_quality_evaluation.load_cells(self.make_manifest(root)) + selected = run_role_a_quality_evaluation.select_cells(cells, "formal", 64) + jobs = run_role_a_quality_evaluation.build_jobs( + selected, root / "cifar.zip", root / "out", "formal", "both", 29700 + ) + self.assertEqual(len(jobs), 12) + self.assertEqual( + {(job["method"], job["training_seed"], job["nfe"]) for job in jobs}, + { + (method, seed, nfe) + for method in ("sigmoid", "adaptive_v1") + for seed in range(3) + for nfe in (1, 2) + }, + ) + for job in jobs: + command = " ".join(job["command"]) + self.assertIn("--sample-seeds=0-4999", command) + self.assertIn("--metrics=kid5k_full,fid5k_full", command) + self.assertEqual(job["budget_kimg"], 64) + with self.assertRaises(SystemExit): + run_role_a_quality_evaluation.select_cells(selected[:-1], "formal", 64) + + def test_collector_writes_exact_required_columns_and_checks_repeats(self): + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + jobs = [] + for method_index, method in enumerate(("sigmoid", "adaptive_v1")): + for nfe in (1, 2): + output = root / method / f"nfe{nfe}" + output.mkdir(parents=True) + metric_names = ["kid512_full", "fid512_full"] + for metric_index, metric in enumerate(metric_names): + value = 1.0 + method_index + nfe / 10 + metric_index / 100 + payload = {"metric": metric, "results": {metric: value}} + (output / f"metric-{metric}.jsonl").write_text( + json.dumps(payload) + "\n" + json.dumps(payload) + "\n", + encoding="utf-8", + ) + jobs.append({ + "method": method, + "training_seed": 0, + "budget_kimg": 16, + "nfe": nfe, + "sample_count": 512, + "sample_seeds": "0-511", + "metric_repeats": 2, + "metric_names": metric_names, + "checkpoint_sha256": f"sha-{method}", + "output_directory": str(output), + "status": "completed", + }) + manifest = { + "protocol": "role-a-multibudget-quality-v1", + "status": "completed", + "phase": "smoke", + "metric_mode": "both", + "metric_names": ["kid512_full", "fid512_full"], + "precision": "fp32", + "sample_count": 512, + "sample_seeds": "0-511", + "dataset": "/data/cifar.zip", + "dataset_sha256": "dataset-sha", + "reference_real_count": 50000, + "feature_detector_url": "detector", + "jobs": jobs, + } + (root / "run_manifest.json").write_text(json.dumps(manifest), encoding="utf-8") + outdir = root / "summary" + collect_role_a_quality_results.main([ + "--eval-root", str(root), "--outdir", str(outdir) + ]) + with (outdir / "role_a_metrics.csv").open(newline="", encoding="utf-8") as handle: + reader = csv.DictReader(handle) + rows = list(reader) + self.assertEqual( + reader.fieldnames, + ["Method", "Train seed", "Budget", "NFE", "KID", "FID", "Checkpoint SHA"], + ) + self.assertEqual(len(rows), 4) + summary = json.loads((outdir / "role_a_metrics.json").read_text()) + self.assertTrue(summary["repeat_results_exact"]) + self.assertTrue(summary["repeat_results_numerically_consistent"]) + self.assertTrue(summary["reference_identity_consistent"]) + + def test_collector_accepts_roundoff_but_records_nonexact_repeats(self): + with TemporaryDirectory() as temp_dir: + path = Path(temp_dir) / "metric-kid512_full.jsonl" + payloads = [ + {"metric": "kid512_full", "results": {"kid512_full": value}} + for value in (0.5, 0.5000001) + ] + path.write_text( + "".join(json.dumps(payload) + "\n" for payload in payloads), + encoding="utf-8", + ) + value, exact, consistent = collect_role_a_quality_results.read_metric( + path, "kid512_full", 2 + ) + self.assertEqual(value, 0.5) + self.assertFalse(exact) + self.assertTrue(consistent) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_schedules.py b/tests/test_schedules.py new file mode 100644 index 00000000..22217a1e --- /dev/null +++ b/tests/test_schedules.py @@ -0,0 +1,446 @@ +import contextlib +import io +import pickle +import unittest + +import torch + +from training import schedules +from training.loss import ECMLoss +from training.schedules import compute_r, continuous_stage, get_schedule + +# LogNormal(P_mean, P_std) noise levels, as sampled by ECMLoss.__call__. +P_MEAN = -1.1 +P_STD = 2.0 + + +def sample_t(n=512, dtype=torch.float32, seed=0, device='cpu'): + generator = torch.Generator().manual_seed(seed) + rnd_normal = torch.randn([n, 1, 1, 1], generator=generator, dtype=torch.float64) + return (rnd_normal * P_STD + P_MEAN).exp().to(dtype=dtype, device=device) + + +def devices(): + """cpu always; cuda too when available (i.e. on the A100 server).""" + return ['cpu'] + (['cuda'] if torch.cuda.is_available() else []) + + +def make_loss(adj, q=2.0, k=8.0, b=1.0, **kwargs): + with contextlib.redirect_stdout(io.StringIO()): # silence dist.print0 + return ECMLoss(q=q, k=k, b=b, adj=adj, **kwargs) + + +def official_t_to_r(adj, t, stage, q=2.0, k=8.0, b=1.0): + """Reference output from the untouched official formulas in + training/loss.py (t_to_r_const / t_to_r_sigmoid), NOT from the schedule + dispatch, so the parity anchor stays independent of schedules.py.""" + loss_fn = make_loss(adj, q=q, k=k, b=b) + loss_fn.update_schedule(stage) + reference = {'const': loss_fn.t_to_r_const, 'sigmoid': loss_fn.t_to_r_sigmoid}[adj] + return reference(t) + + +class OfficialFormulaParityTest(unittest.TestCase): + """'const' and 'sigmoid' must reproduce training/loss.py bit-for-bit.""" + + def test_bitwise_parity_with_ecm_loss(self): + for device in devices(): + for adj in ['const', 'sigmoid']: + for q, k, b in [(2.0, 8.0, 1.0), (256.0, 8.0, 1.0), (4.0, 4.0, 2.0)]: + for stage in [0, 1, 3, 7]: + for dtype in [torch.float32, torch.float64]: + with self.subTest(device=device, adj=adj, q=q, k=k, b=b, stage=stage, dtype=dtype): + t = sample_t(dtype=dtype, device=device) + expected = official_t_to_r(adj, t, stage, q=q, k=k, b=b) + via_object = get_schedule(adj, q=q, k=k, b=b).compute_r(t=t, stage=stage) + via_function = compute_r(t=t, stage=stage, schedule=adj, q=q, k=k, b=b) + self.assertTrue(torch.equal(via_object, expected)) + self.assertTrue(torch.equal(via_function, expected)) + + def test_const_stage0_halves_t_exactly(self): + # q=2, stage=0: decay = 1/2, so r = t/2 with no clamping. + t = sample_t() + r = compute_r(t=t, stage=0, schedule='const', q=2.0) + self.assertTrue(torch.equal(r, t * 0.5)) + + def test_sigmoid_clamps_small_t_to_zero_at_stage0(self): + # q=2, stage=0: decay = 1/2 and n(t) -> 1 + k/2 = 5 as t -> 0, so + # ratio < 0 and r must clamp to 0 (diffusion-pretraining regime). + t = torch.full([8, 1, 1, 1], 1e-4) + r = compute_r(t=t, stage=0, schedule='sigmoid', q=2.0, k=8.0, b=1.0) + self.assertTrue(torch.equal(r, torch.zeros_like(t))) + + def test_r_is_nonnegative_and_strictly_below_t(self): + t = sample_t() + for name in schedules.available_schedules(): + for stage in [0, 2, 5]: + with self.subTest(schedule=name, stage=stage): + r = compute_r(t=t, stage=stage, schedule=name, q=2.0) + self.assertTrue((r >= 0).all()) + self.assertTrue((r < t).all()) + + def test_shape_and_dtype_preserved(self): + for dtype in [torch.float32, torch.float64]: + t = sample_t(dtype=dtype) + r = compute_r(t=t, stage=2, schedule='sigmoid') + self.assertEqual(r.shape, t.shape) + self.assertEqual(r.dtype, t.dtype) + + +class AdaptiveV1Test(unittest.TestCase): + def test_without_signal_is_official_sigmoid_bitwise(self): + for device in devices(): + for q in [2.0, 256.0]: + for stage in [0, 1, 3, 7]: + with self.subTest(device=device, q=q, stage=stage): + t = sample_t(device=device) + expected = official_t_to_r('sigmoid', t, stage, q=q) + adaptive = get_schedule('adaptive_v1', q=q) + actual = adaptive.compute_r(t=t, stage=stage) + self.assertTrue(torch.equal(actual, expected)) + + def test_loss_improvement_tightens_gap_with_bounded_correction(self): + t = sample_t() + adaptive = get_schedule( + 'adaptive_v1', q=2.0, loss_ema_beta=0.0, + max_adjust=0.1, min_gap=0.01, + ) + baseline = get_schedule('sigmoid', q=2.0) + adaptive.update_training_signal(10.0) + adaptive.update_training_signal(5.0) + adaptive.update_training_signal(2.5) + base_r = baseline.compute_r(t=t, stage=2) + adaptive_r = adaptive.compute_r(t=t, stage=2) + self.assertTrue((adaptive_r >= base_r).all()) + self.assertLessEqual(adaptive.correction(), 0.1) + self.assertTrue((adaptive_r / t <= 0.99).all()) + + def test_loss_worsening_widens_gap(self): + t = sample_t() + adaptive = get_schedule('adaptive_v1', loss_ema_beta=0.0, max_adjust=0.1) + baseline = get_schedule('sigmoid') + adaptive.update_training_signal(10.0) + adaptive.update_training_signal(20.0) + adaptive.update_training_signal(40.0) + self.assertLess(adaptive.correction(), 0) + self.assertTrue((adaptive.compute_r(t=t, stage=2) <= baseline.compute_r(t=t, stage=2)).all()) + + def test_warmup_establishes_reference_before_enabling_correction(self): + adaptive = get_schedule('adaptive_v1', loss_ema_beta=0.0, warmup_updates=2) + baseline = get_schedule('sigmoid') + t = sample_t() + adaptive.update_training_signal(10.0) + self.assertIsNone(adaptive.loss_reference) + self.assertTrue(torch.equal(adaptive.compute_r(t=t, stage=2), baseline.compute_r(t=t, stage=2))) + adaptive.update_training_signal(5.0) + self.assertEqual(adaptive.loss_reference, adaptive.loss_ema) + self.assertEqual(adaptive.loss_reference, 5.0) + self.assertEqual(adaptive.correction(), 0.0) + self.assertTrue(torch.equal(adaptive.compute_r(t=t, stage=2), baseline.compute_r(t=t, stage=2))) + adaptive.update_training_signal(2.5) + self.assertGreater(adaptive.correction(), 0.0) + + def test_min_gap_only_applies_after_correction_is_active(self): + t = torch.tensor([10.0]) + baseline = get_schedule('sigmoid', q=256.0) + expected = baseline.compute_r(t=t, stage=0) + inactive = get_schedule('adaptive_v1', q=256.0, min_gap=0.1) + self.assertTrue(torch.equal(inactive.compute_r(t=t, stage=0), expected)) + + active = get_schedule('adaptive_v1', q=256.0, loss_ema_beta=0.0, + warmup_updates=0, min_gap=0.1) + active.update_training_signal(10.0) # delta is zero, but correction is active. + self.assertEqual(active.correction(), 0.0) + self.assertTrue(torch.equal( + active.compute_r(t=t, stage=0), + torch.minimum(expected, t * 0.9), + )) + + def test_output_is_finite_bounded_and_deterministic(self): + t = torch.tensor([0.0, 1e-12, 0.1, 1.0, 80.0, float('inf'), float('nan')]) + first = get_schedule('adaptive_v1', loss_ema_beta=0.5) + second = get_schedule('adaptive_v1', loss_ema_beta=0.5) + for loss in [10.0, 8.0, 6.0]: + self.assertEqual(first.update_training_signal(loss), second.update_training_signal(loss)) + r_first = first.compute_r(t=t, stage=3) + r_second = second.compute_r(t=t, stage=3) + self.assertTrue(torch.equal(r_first, r_second)) + self.assertTrue(torch.isfinite(r_first).all()) + self.assertTrue((r_first >= 0).all()) + finite_t = torch.isfinite(t) & (t >= 0) + self.assertTrue((r_first[finite_t] <= t[finite_t]).all()) + + unusual_k = get_schedule('adaptive_v1', k=-100.0) + for loss in [10.0, 8.0, 6.0]: + unusual_k.update_training_signal(loss) + unusual_r = unusual_k.compute_r(t=t, stage=3) + self.assertTrue(torch.isfinite(unusual_r).all()) + self.assertTrue((unusual_r[finite_t] <= t[finite_t]).all()) + + huge_stage_r = first.compute_r(t=t, stage=1e308) + self.assertTrue(torch.isfinite(huge_stage_r).all()) + self.assertTrue((huge_stage_r[finite_t] <= t[finite_t]).all()) + + def test_nonfinite_loss_signal_is_ignored(self): + adaptive = get_schedule('adaptive_v1') + for loss in [float('nan'), float('inf'), -1.0]: + self.assertFalse(adaptive.update_training_signal(loss)) + self.assertIsNone(adaptive.loss_ema) + self.assertEqual(adaptive.correction(), 0.0) + + def test_invalid_adaptive_parameters_are_rejected(self): + invalid_kwargs = [ + {'loss_ema_beta': 1.0}, + {'max_adjust': -0.1}, + {'min_gap': 0.0}, + {'warmup_updates': -1}, + {'warmup_updates': 1.5}, + {'warmup_updates': float('inf')}, + {'k': float('nan')}, + ] + for kwargs in invalid_kwargs: + with self.subTest(kwargs=kwargs), self.assertRaises(ValueError): + get_schedule('adaptive_v1', **kwargs) + + def test_zero_max_adjust_restores_official_formula_after_updates(self): + t = sample_t() + adaptive = get_schedule('adaptive_v1', max_adjust=0.0) + adaptive.update_training_signal(10.0) + adaptive.update_training_signal(1.0) + expected = get_schedule('sigmoid').compute_r(t=t, stage=5) + self.assertTrue(torch.equal(adaptive.compute_r(t=t, stage=5), expected)) + + def test_state_round_trip_preserves_output(self): + t = sample_t() + source = get_schedule('adaptive_v1', loss_ema_beta=0.8) + for loss in [10.0, 9.0, 7.0]: + source.update_training_signal(loss) + clone = get_schedule('adaptive_v1', loss_ema_beta=0.8) + clone.load_state_dict(source.state_dict()) + self.assertEqual(clone.metadata(), source.metadata()) + self.assertTrue(torch.equal(clone.compute_r(t=t, stage=2), source.compute_r(t=t, stage=2))) + + def test_negative_stage_rejected(self): + with self.assertRaises(ValueError): + compute_r(t=sample_t(), stage=-0.5, schedule='adaptive_v1') + + def test_continuous_stage_helper(self): + self.assertEqual(continuous_stage(cur_tick=125, double_ticks=250), 0.5) + self.assertEqual(continuous_stage(cur_tick=500, double_ticks=250), 2.0) + with self.assertRaises(ValueError): + continuous_stage(cur_tick=1, double_ticks=0) + + +class InterfaceTest(unittest.TestCase): + def test_documented_call_forms_agree(self): + t = sample_t() + schedule = get_schedule('sigmoid', q=256, k=8, b=1) + r_object = schedule.compute_r(t=t, stage=1) + r_function = compute_r(t=t, stage=1, schedule='sigmoid', q=256, k=8, b=1) + self.assertTrue(torch.equal(r_object, r_function)) + + def test_stateful_ecm_loss_style_interface(self): + t = sample_t() + schedule = get_schedule('sigmoid') + schedule.update_schedule(3) + self.assertTrue(torch.equal(schedule.t_to_r(t), schedule.compute_r(t=t, stage=3))) + + def test_scalar_input_is_converted_to_tensor(self): + r = compute_r(t=2.0, stage=0, schedule='const', q=2.0) + self.assertIsInstance(r, torch.Tensor) + self.assertAlmostEqual(float(r), 1.0) + + def test_available_schedules(self): + self.assertEqual( + schedules.available_schedules(), + [ + 'adaptive_v1', + 'const', + 'global_sigmoid', + 'local_tbin_v1', + 'local_tbin_v2', + 'local_tbin_v3', + 'sigmoid', + ], + ) + + def test_unknown_schedule_rejected(self): + with self.assertRaises(ValueError): + get_schedule('cosine') + with self.assertRaises(ValueError): + compute_r(t=sample_t(), stage=0, schedule='cosine') + + def test_invalid_q_rejected(self): + with self.assertRaises(ValueError): + get_schedule('const', q=1.0) + + +class ECMLossIntegrationTest(unittest.TestCase): + """ECMLoss now dispatches t->r through training/schedules.py; the wired + entry must be indistinguishable from the official reference formulas.""" + + def test_loss_holds_matching_schedule_instance(self): + for adj, cls in [('const', schedules.ConstSchedule), + ('sigmoid', schedules.SigmoidSchedule), + ('adaptive_v1', schedules.AdaptiveV1Schedule)]: + with self.subTest(adj=adj): + self.assertIsInstance(make_loss(adj).schedule, cls) + + def test_wired_entry_matches_official_reference_bitwise(self): + for adj in ['const', 'sigmoid']: + for stage in [0, 2, 5]: + with self.subTest(adj=adj, stage=stage): + loss_fn = make_loss(adj, q=2.0, k=8.0, b=1.0) + loss_fn.update_schedule(stage) + t = sample_t() + reference = getattr(loss_fn, f't_to_r_{adj}')(t) + wired = loss_fn.schedule.compute_r(t=t, stage=loss_fn.stage) + self.assertTrue(torch.equal(wired, reference)) + + def test_loss_hyperparams_reach_the_schedule(self): + loss_fn = make_loss('sigmoid', q=256.0, k=4.0, b=2.0) + self.assertEqual((loss_fn.schedule.q, loss_fn.schedule.k, loss_fn.schedule.b), (256.0, 4.0, 2.0)) + + def test_adaptive_hyperparams_reach_schedule_metadata(self): + loss_fn = make_loss( + 'adaptive_v1', adaptive_loss_ema_beta=0.8, + adaptive_warmup_updates=3, adaptive_max_adjust=0.04, adaptive_min_gap=0.002, + ) + metadata = loss_fn.schedule_metadata() + self.assertEqual(metadata['name'], 'adaptive_v1') + self.assertTrue(metadata['enabled']) + self.assertEqual(metadata['signal'], 'loss_ema') + self.assertEqual(metadata['loss_ema_beta'], 0.8) + self.assertEqual(metadata['warmup_updates'], 3) + self.assertEqual(metadata['max_adjust'], 0.04) + self.assertEqual(metadata['min_gap'], 0.002) + + def test_schedule_runtime_metrics_has_stable_contract(self): + required = { + 'loss_ema', 'loss_reference', 'correction', 'signal_updates', + 'adaptive_active', 'r_over_t_mean', 'gap_mean', + 'gap_over_sigmoid_gap_mean', 'lower_gap_clip_rate', + 'upper_gap_clip_rate', + } + fixed = make_loss('sigmoid') + fixed_metrics = fixed.schedule_runtime_metrics() + self.assertEqual(set(fixed_metrics), required) + self.assertIsNone(fixed_metrics['loss_ema']) + self.assertFalse(fixed_metrics['adaptive_active']) + + adaptive = make_loss('adaptive_v1', adaptive_loss_ema_beta=0.0, + adaptive_warmup_updates=2) + for loss in [10.0, 5.0, 2.5]: + adaptive.update_training_signal(loss) + adaptive_metrics = adaptive.schedule_runtime_metrics() + self.assertEqual(set(adaptive_metrics), required) + self.assertEqual(adaptive_metrics['signal_updates'], 3) + self.assertTrue(adaptive_metrics['adaptive_active']) + self.assertGreater(adaptive_metrics['correction'], 0) + + def test_schedule_runtime_pair_means_match_realized_t_and_r(self): + loss_fn = make_loss('sigmoid') + t = torch.tensor([1.0, 2.0], dtype=torch.float64) + r = loss_fn.schedule.compute_r(t=t, stage=loss_fn.stage) + loss_fn._record_schedule_runtime_pair(t=t, r=r) + metrics = loss_fn.schedule_runtime_metrics() + self.assertAlmostEqual( + metrics['r_over_t_mean'] + metrics['gap_mean'], 1.0 + ) + self.assertAlmostEqual( + metrics['gap_over_sigmoid_gap_mean'], 1.0 + ) + self.assertEqual(metrics['lower_gap_clip_rate'], 0.0) + self.assertEqual(metrics['upper_gap_clip_rate'], 0.0) + + def test_schedule_runtime_metrics_detect_upper_gap_clipping(self): + loss_fn = make_loss( + 'global_sigmoid', q=2.0, global_gap_scale=2.0 + ) + t = torch.tensor([0.1, 1.0, 10.0], dtype=torch.float64) + r = loss_fn.schedule.compute_r(t=t, stage=loss_fn.stage) + loss_fn._record_schedule_runtime_pair(t=t, r=r) + metrics = loss_fn.schedule_runtime_metrics() + self.assertGreater(metrics['upper_gap_clip_rate'], 0.0) + self.assertEqual(metrics['lower_gap_clip_rate'], 0.0) + self.assertLess(metrics['gap_over_sigmoid_gap_mean'], 2.0) + + def test_unknown_adj_still_raises_value_error(self): + with self.assertRaises(ValueError): + make_loss('cosine') + + def test_update_schedule_keeps_ratio_for_loop_logging(self): + # ct_training_loop.py:257 logs loss_fn.ratio; the contract must hold. + loss_fn = make_loss('sigmoid', q=2.0) + loss_fn.update_schedule(3) + self.assertEqual(loss_fn.ratio, 1 - 1 / 2.0 ** 4) + self.assertEqual(loss_fn.schedule.stage, 3) + + def test_loss_fn_pickles_with_schedule(self): + # Training snapshots pickle loss_fn (ct_training_loop.py:340); the + # schedule attribute must round-trip through persistence. + loss_fn = make_loss('adaptive_v1', q=2.0) + loss_fn.update_schedule(2) + loss_fn.update_training_signal(10.0) + loss_fn.update_training_signal(7.0) + clone = pickle.loads(pickle.dumps(loss_fn)) + t = sample_t() + self.assertEqual(clone.schedule_metadata(), loss_fn.schedule_metadata()) + self.assertTrue(torch.equal(clone.schedule.compute_r(t=t, stage=clone.stage), + loss_fn.schedule.compute_r(t=t, stage=loss_fn.stage))) + + def test_schedule_state_dict_restores_adaptive_state(self): + source = make_loss('adaptive_v1', q=2.0) + source.update_schedule(3) + source.update_training_signal(10.0) + source.update_training_signal(8.0) + clone = make_loss('adaptive_v1', q=2.0) + clone.load_schedule_state_dict(source.schedule_state_dict()) + self.assertEqual(clone.schedule_metadata(), source.schedule_metadata()) + + def test_explicit_fixed_schedule_ignores_saved_adaptive_state(self): + adaptive = make_loss('adaptive_v1') + adaptive.update_training_signal(10.0) + adaptive.update_training_signal(5.0) + fixed = make_loss('sigmoid') + self.assertFalse(fixed.load_schedule_state_dict(adaptive.schedule_state_dict())) + t = sample_t() + fixed.update_schedule(2) + self.assertTrue(torch.equal( + fixed.schedule.compute_r(t=t, stage=fixed.stage), + fixed.t_to_r_sigmoid(t), + )) + + +class TinyNet(torch.nn.Module): + def forward(self, x, t, labels=None, augment_labels=None): + return x / (1 + t) + + +@unittest.skipUnless(torch.cuda.is_available(), 'ECMLoss.__call__ saves/restores CUDA RNG state (official code, unmodified)') +class ECMLossCallCudaTest(unittest.TestCase): + """End-to-end __call__ checks; run on the A100 (skipped on cpu-only).""" + + def full_loss(self, adj, stage, seed=0, losses=()): + loss_fn = make_loss(adj, q=2.0, k=8.0, b=1.0) + loss_fn.update_schedule(stage) + for loss in losses: + loss_fn.update_training_signal(loss) + net = TinyNet().cuda() + torch.manual_seed(seed) + images = torch.randn([8, 3, 8, 8], device='cuda') + torch.manual_seed(seed) # re-seed: identical t/eps draws across calls + return loss_fn(net=net, images=images) + + def test_call_sigmoid_equals_adaptive_v1_at_integer_stage(self): + self.assertTrue(torch.equal(self.full_loss('sigmoid', stage=1), + self.full_loss('adaptive_v1', stage=1))) + + def test_call_adaptive_v1_loss_signal_changes_loss(self): + baseline = self.full_loss('adaptive_v1', stage=1) + adapted = self.full_loss('adaptive_v1', stage=1, losses=(10.0, 5.0, 2.5)) + self.assertFalse(torch.equal(baseline, adapted)) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_select_gap_scale.py b/tests/test_select_gap_scale.py new file mode 100644 index 00000000..718cb1b8 --- /dev/null +++ b/tests/test_select_gap_scale.py @@ -0,0 +1,126 @@ +import csv +import json +import unittest +from pathlib import Path +from tempfile import TemporaryDirectory + +from scripts import select_gap_scale + + +class SelectGapScaleTest(unittest.TestCase): + def make_complete_matrix(self, root: Path) -> tuple[Path, Path]: + runs_root = root / "runs" + eval_root = root / "eval" + nfe1_kid = { + "1": 0.300, + "0.97": 0.310, + "1.032": 0.290, + "1.06": 0.290, + "1.10": 0.400, + } + for spec in select_gap_scale.CELLS: + label = spec["label"] + run_dir = runs_root / label + run_dir.mkdir(parents=True) + validation = { + "status": "passed", + "expected_schedule": spec["expected_schedule"], + } + (run_dir / "validation.json").write_text( + json.dumps(validation) + "\n", encoding="utf-8" + ) + scale = spec["global_scale_text"] + for nfe in select_gap_scale.NFES: + cell = eval_root / label / f"nfe{nfe}" + cell.mkdir(parents=True) + (cell / "experiment_meta.env").write_text( + "exit_code=0\n", encoding="utf-8" + ) + values = { + "kid5k_full": ( + nfe1_kid[scale] if nfe == 1 else nfe1_kid[scale] / 2 + ), + "fid5k_full": 300 + 10 * nfe + spec["global_scale"], + } + for metric, value in values.items(): + payload = {"metric": metric, "results": {metric: value}} + (cell / f"metric-{metric}.jsonl").write_text( + json.dumps(payload) + "\n", encoding="utf-8" + ) + return runs_root, eval_root + + def test_selects_nfe1_kid_only_and_uses_tie_breaker(self): + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + runs_root, eval_root = self.make_complete_matrix(root) + outdir = root / "analysis" + select_gap_scale.main( + [ + "--runs-root", + str(runs_root), + "--eval-root", + str(eval_root), + "--outdir", + str(outdir), + ] + ) + + report = json.loads( + (outdir / "selection.json").read_text(encoding="utf-8") + ) + self.assertEqual(report["status"], "passed") + self.assertEqual(report["selected_global_scale_text"], "1.032") + self.assertEqual(report["selected_label"], "global-g1p0320-seed0-256k") + self.assertTrue(report["selected_beats_fixed_nfe1_kid"]) + self.assertAlmostEqual(report["nfe1_kid_delta_vs_fixed"], -0.01) + self.assertEqual( + (outdir / "selected_g.txt").read_text(encoding="utf-8"), + "1.032\n", + ) + with (outdir / "response_curve.csv").open( + newline="", encoding="utf-8" + ) as handle: + rows = list(csv.DictReader(handle)) + self.assertEqual(len(rows), 5) + self.assertEqual( + [row["global_scale_text"] for row in rows if row["is_selected"] == "True"], + ["1.032"], + ) + markdown = (outdir / "response_curve.md").read_text(encoding="utf-8") + self.assertIn("selection bias", markdown) + self.assertIn("not standard FID-50k", markdown) + + def test_rejects_nonfinite_or_multiple_metric_results(self): + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + runs_root, eval_root = self.make_complete_matrix(root) + metric_path = ( + eval_root + / "global-g0p9700-seed0-256k" + / "nfe1" + / "metric-kid5k_full.jsonl" + ) + payload = { + "metric": "kid5k_full", + "results": {"kid5k_full": float("nan")}, + } + metric_path.write_text( + json.dumps(payload) + "\n" + json.dumps(payload) + "\n", + encoding="utf-8", + ) + with self.assertRaisesRegex(SystemExit, "exactly one result line"): + select_gap_scale.main( + [ + "--runs-root", + str(runs_root), + "--eval-root", + str(eval_root), + "--outdir", + str(root / "analysis"), + ] + ) + self.assertFalse((root / "analysis").exists()) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_staged_evaluation.py b/tests/test_staged_evaluation.py new file mode 100644 index 00000000..1cea46c5 --- /dev/null +++ b/tests/test_staged_evaluation.py @@ -0,0 +1,337 @@ +import json +import unittest +from pathlib import Path +from tempfile import TemporaryDirectory +from unittest import mock + +import numpy as np + +from metrics import frechet_inception_distance +from metrics import metric_main +from scripts import collect_staged_evaluation_results +from scripts import run_staged_evaluation + + +class StagedEvaluationTest(unittest.TestCase): + def make_manifest(self, root: Path, include_receipt: bool = True): + checkpoint = root / "checkpoint.pkl" + checkpoint.write_bytes(b"checkpoint") + receipt = root / "checkpoint.integrity.json" + checksum = run_staged_evaluation.sha256_file(checkpoint) + if include_receipt: + receipt.write_text(json.dumps({ + "schema_version": 1, + "status": "passed", + "checkpoint_id": "baseline_seed0", + "checkpoint_path": str(checkpoint), + "checkpoint_sha256": checksum, + "training_run_id": "baseline-seed0-run", + "method": "baseline", + "training_seed": 0, + "budget_kimg": 16, + "completion_passed": True, + "logs_state_consistent": True, + "finite_loss_state_passed": True, + "checkpoint_load_passed": True, + "ema_present": True, + "ema_finite_passed": True, + "schedule_identity_passed": True, + "global_gap_scale_identity_passed": True, + "method_identity_passed": True, + "checker_version": "1", + "checker_git_commit": "0123456789abcdef", + "checked_at_unix": 1_753_822_000, + }), encoding="utf-8") + cell = { + "checkpoint_id": "baseline_seed0", + "method": "baseline", + "training_seed": 0, + "budget_kimg": 16, + "checkpoint": str(checkpoint), + "checkpoint_sha256": checksum, + } + if include_receipt: + cell["integrity_receipt"] = str(receipt) + manifest = root / "checkpoints.json" + manifest.write_text(json.dumps({ + "protocol": run_staged_evaluation.PROTOCOL_ID, + "cells": [cell], + }), encoding="utf-8") + return manifest, checkpoint, receipt + + def test_runner_builds_frozen_smoke_and_formal_commands(self): + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + manifest, _, _ = self.make_manifest(root) + data = root / "cifar.zip" + data.write_bytes(b"dataset") + cells, comparison = run_staged_evaluation.load_cells(manifest, False) + selected = run_staged_evaluation.select_cells(cells, "smoke", "baseline_seed0") + smoke = run_staged_evaluation.build_jobs( + selected, data, root / "smoke", "smoke", 29800, False + ) + formal = run_staged_evaluation.build_jobs( + cells, data, root / "formal", "formal", 29800, False + ) + self.assertIsNone(comparison) + self.assertEqual(len(smoke), 2) + self.assertEqual(len(formal), 2) + for job in smoke: + command = " ".join(job["command"]) + self.assertIn("--sample-seeds=0-4999", command) + self.assertIn("--metrics=kid5k_full,fid5k_full", command) + self.assertIn("--metric-repeats=1", command) + self.assertIn("--seed=20260730", command) + self.assertEqual(job["evidence_class"], "quick") + if job["nfe"] == 1: + self.assertFalse(any(argument.startswith("--mid_t=") for argument in job["command"])) + else: + self.assertIn("--mid_t=0.821", job["command"]) + for job in formal: + command = " ".join(job["command"]) + self.assertIn("--sample-seeds=0-49999", command) + self.assertIn("--metrics=kid50k_full,fid50k_full", command) + self.assertEqual(job["integrity_receipt"]["status"], "passed") + self.assertEqual(job["mid_t"], [] if job["nfe"] == 1 else [0.821]) + if job["nfe"] == 1: + self.assertFalse(any(argument.startswith("--mid_t=") for argument in job["command"])) + else: + self.assertIn("--mid_t=0.821", job["command"]) + + def test_formal_runner_rejects_missing_integrity_receipt(self): + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + manifest, _, _ = self.make_manifest(root, include_receipt=False) + cells, _ = run_staged_evaluation.load_cells(manifest, False) + with self.assertRaisesRegex(SystemExit, "integrity_receipt"): + run_staged_evaluation.build_jobs( + cells, root / "cifar.zip", root / "formal", "formal", 29800, False + ) + + def test_formal_runner_rejects_missing_checkpoint_identity_gate(self): + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + manifest, _, receipt = self.make_manifest(root) + payload = json.loads(receipt.read_text(encoding="utf-8")) + del payload["ema_finite_passed"] + receipt.write_text(json.dumps(payload), encoding="utf-8") + cells, _ = run_staged_evaluation.load_cells(manifest, False) + with self.assertRaisesRegex(SystemExit, "ema_finite_passed"): + run_staged_evaluation.build_jobs( + cells, root / "cifar.zip", root / "formal", "formal", 29800, False + ) + + def test_formal_promotion_policy_requires_all_predeclared_checkpoints(self): + cells = [ + {"checkpoint_id": "fixed_seed3"}, + {"checkpoint_id": "global_seed3"}, + ] + policy = { + "formal_promotion_policy": { + "eligibility": "provenance_and_integrity_only", + "quick_metric_performance": "not_an_eligibility_criterion", + "required_checkpoint_ids": ["fixed_seed3", "global_seed3"], + } + } + run_staged_evaluation.validate_formal_promotion_policy(policy, cells) + policy["formal_promotion_policy"]["required_checkpoint_ids"] = ["fixed_seed3"] + with self.assertRaisesRegex(SystemExit, "every predeclared checkpoint"): + run_staged_evaluation.validate_formal_promotion_policy(policy, cells) + + def test_collector_writes_long_table_and_segregated_statistics(self): + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + manifest, _, _ = self.make_manifest(root) + data = root / "cifar.zip" + data.write_bytes(b"dataset") + cells, comparison = run_staged_evaluation.load_cells(manifest, False) + jobs = run_staged_evaluation.build_jobs( + cells, data, root, "smoke", 29800, False + ) + record = run_staged_evaluation.build_record( + cells, comparison, data, root, "smoke", jobs, + run_staged_evaluation.sha256_file(data), + ) + environment = record["evaluation_environment"] + self.assertEqual(environment["evaluation_git_commit"], record["evaluation_git_commit"]) + self.assertTrue(environment["python"]["version"]) + self.assertTrue(environment["scipy_version"]) + self.assertTrue(environment["pytorch_version"]) + self.assertIn("compiled_version", environment["cuda"]) + self.assertEqual(environment["inception_detector"]["identifier"], "inception-2015-12-05") + self.assertEqual(environment["dataset_sha256"], record["dataset_sha256"]) + record["status"] = "completed" + for job in jobs: + job["status"] = "completed" + target = Path(job["output_directory"]) + target.mkdir(parents=True) + for metric_index, metric_name in enumerate(job["metric_names"]): + (target / f"metric-{metric_name}.jsonl").write_text( + json.dumps({ + "metric": metric_name, + "results": {metric_name: 1.0 + job["nfe"] + metric_index}, + }) + "\n", + encoding="utf-8", + ) + (root / "run_manifest.json").write_text(json.dumps(record), encoding="utf-8") + rows, summary = collect_staged_evaluation_results.collect(root) + output = root / "summary" + collect_staged_evaluation_results.write_outputs(output, rows, summary) + self.assertEqual(len(rows), 4) + self.assertEqual(summary["statistics_grouping"], [ + "evidence_class", "metric_name", "nfe", "method", + ]) + self.assertTrue((output / "evaluation_results.csv").is_file()) + self.assertTrue((output / "evaluation_statistics.json").is_file()) + + def test_kid50k_uses_the_frozen_metric_seed(self): + opts = mock.Mock() + opts.dataset_kwargs = {} + opts.metric_seed = 20260730 + with mock.patch.object( + metric_main.kernel_inception_distance, + "compute_kid", + return_value=0.125, + ) as compute_kid: + result = metric_main.kid50k_full(opts) + self.assertEqual(result, {"kid50k_full": 0.125}) + self.assertEqual(compute_kid.call_args.kwargs["random_seed"], 20260730) + + def test_collector_computes_deltas_from_explicit_pairing_contract(self): + rows = [ + {"method": "fixed", "training_seed": 4, "budget_kimg": 256, "metric_name": "fid50k_full", "nfe": 1, "metric_value": 10.0, "checkpoint_id": "fixed4", "checkpoint_sha256": "a" * 64}, + {"method": "global110", "training_seed": 4, "budget_kimg": 256, "metric_name": "fid50k_full", "nfe": 1, "metric_value": 8.0, "checkpoint_id": "global4", "checkpoint_sha256": "b" * 64}, + {"method": "fixed", "training_seed": 5, "budget_kimg": 256, "metric_name": "fid50k_full", "nfe": 1, "metric_value": 12.0, "checkpoint_id": "fixed5", "checkpoint_sha256": "c" * 64}, + {"method": "global110", "training_seed": 5, "budget_kimg": 256, "metric_name": "fid50k_full", "nfe": 1, "metric_value": 9.0, "checkpoint_id": "global5", "checkpoint_sha256": "d" * 64}, + ] + pairing = { + "pairing_key": ["training_seed", "budget_kimg", "nfe", "metric_name"], + "baseline_method": "fixed", + "candidate_method": "global110", + "candidate_label": "global_only", + "delta_direction": "global_only - fixed", + } + result = collect_staged_evaluation_results.build_pairwise_statistics(rows, pairing) + self.assertEqual(result["status"], "computed") + statistic = result["statistics"][0] + self.assertEqual(statistic["pair_count"], 2) + self.assertEqual(statistic["mean_delta"], -2.5) + self.assertEqual(statistic["global_wins"], 2) + self.assertEqual(statistic["fixed_wins"], 0) + self.assertEqual(statistic["ties"], 0) + self.assertEqual(statistic["median_delta"], -2.5) + self.assertAlmostEqual(statistic["mean_relative_improvement_pct"], 22.5) + self.assertAlmostEqual(statistic["geometric_mean_relative_improvement_pct"], 22.5403330759) + self.assertAlmostEqual(statistic["worst_case_relative_improvement_pct"], 20.0) + self.assertEqual(statistic["rank_consistency_spearman"], 1.0) + self.assertTrue(statistic["rank_order_exact_match"]) + self.assertEqual(statistic["exact_sign_test"]["p_value"], 0.5) + self.assertEqual( + [item["omitted_training_seed"] for item in statistic["leave_one_seed_out"]], + [4, 5], + ) + self.assertEqual([item["delta"] for item in result["paired_differences"]], [-2.0, -3.0]) + for observed, expected in zip( + [item["relative_improvement_pct"] for item in result["paired_differences"]], + [20.0, 25.0], + ): + self.assertAlmostEqual(observed, expected) + with TemporaryDirectory() as temp_dir: + output = Path(temp_dir) + collect_staged_evaluation_results.write_paired_outputs(output, result) + self.assertTrue((output / "paired_differences.csv").is_file()) + self.assertTrue((output / "paired_statistics.json").is_file()) + self.assertTrue((output / "paired_statistics.md").is_file()) + + def test_collector_reports_nfe_effect_heterogeneity(self): + rows = [] + for nfe, global_values in ((1, (8.0, 9.0, 10.0)), (2, (7.0, 9.0, 11.0))): + for seed, fixed_value, global_value in zip((3, 4, 5), (10.0, 12.0, 14.0), global_values): + rows.extend([ + {"method": "fixed", "training_seed": seed, "budget_kimg": 256, "metric_name": "fid50k_full", "nfe": nfe, "metric_value": fixed_value, "checkpoint_id": f"fixed{seed}", "checkpoint_sha256": f"{seed:x}" * 64}, + {"method": "global110", "training_seed": seed, "budget_kimg": 256, "metric_name": "fid50k_full", "nfe": nfe, "metric_value": global_value, "checkpoint_id": f"global{seed}", "checkpoint_sha256": f"{seed + 8:x}" * 64}, + ]) + pairing = { + "pairing_key": ["training_seed", "budget_kimg", "nfe", "metric_name"], + "baseline_method": "fixed", "candidate_method": "global110", + "candidate_label": "global_only", "delta_direction": "global_only - fixed", + } + result = collect_staged_evaluation_results.build_pairwise_statistics(rows, pairing) + heterogeneity = result["nfe_effect_heterogeneity"] + self.assertEqual(len(heterogeneity), 1) + self.assertEqual(heterogeneity[0]["pair_count"], 3) + self.assertEqual( + [item["training_seed"] for item in heterogeneity[0]["per_seed_changes"]], + [3, 4, 5], + ) + + def test_collector_rejects_missing_or_duplicated_fixed_global_pair(self): + row = { + "method": "fixed", "training_seed": 4, "budget_kimg": 256, + "metric_name": "kid50k_full", "nfe": 2, "metric_value": 0.2, + "checkpoint_id": "fixed4", "checkpoint_sha256": "a" * 64, + } + pairing = { + "pairing_key": ["training_seed", "budget_kimg", "nfe", "metric_name"], + "baseline_method": "fixed", "candidate_method": "global110", + "candidate_label": "global_only", "delta_direction": "global_only - fixed", + } + with self.assertRaisesRegex(SystemExit, "unpaired fixed/global"): + collect_staged_evaluation_results.build_pairwise_statistics([row], pairing) + with self.assertRaisesRegex(SystemExit, "duplicate fixed"): + collect_staged_evaluation_results.build_pairwise_statistics([row, row], pairing) + + def test_smoke_collection_does_not_attempt_the_full_paired_comparison(self): + manifest = { + "phase": "smoke", + "evidence_class": "smoke_only", + "comparison": { + "pairing_key": ["training_seed", "budget_kimg", "nfe", "metric"], + "baseline_method": "fixed", + "candidate_method": "global110", + "candidate_label": "global_only", + "delta_direction": "global_only - fixed", + }, + } + rows = [{ + "evidence_class": "smoke_only", "metric_name": "fid5k_full", + "nfe": 1, "method": "fixed", "metric_value": 10.0, + "training_seed": 3, "budget_kimg": 256, + }] + result = collect_staged_evaluation_results.build_statistics(rows, manifest) + self.assertEqual(result["pairwise_statistics"], { + "status": "not_computed", + "reason": "smoke phase does not form the predeclared comparison matrix", + }) + + def test_fid_uses_scipy_sqrtm_without_removed_disp_argument(self): + class Stats: + def get_mean_cov(self): + return np.zeros(2), np.eye(2) + + opts = mock.Mock(rank=0) + with mock.patch.object( + frechet_inception_distance.metric_utils, + "compute_feature_stats_for_dataset", + return_value=Stats(), + ), mock.patch.object( + frechet_inception_distance.metric_utils, + "compute_feature_stats_for_generator", + return_value=Stats(), + ), mock.patch.object( + frechet_inception_distance.scipy.linalg, + "sqrtm", + return_value=np.eye(2), + ) as sqrtm: + result = frechet_inception_distance.compute_fid( + opts, max_real=5000, num_gen=5000 + ) + + self.assertEqual(result, 0.0) + sqrtm.assert_called_once() + np.testing.assert_array_equal(sqrtm.call_args.args[0], np.eye(2)) + self.assertEqual(sqrtm.call_args.kwargs, {}) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_summarize_gap_factorial.py b/tests/test_summarize_gap_factorial.py new file mode 100644 index 00000000..755dca72 --- /dev/null +++ b/tests/test_summarize_gap_factorial.py @@ -0,0 +1,255 @@ +import csv +import importlib.util +import json +import tempfile +import unittest +from pathlib import Path + + +MODULE_PATH = ( + Path(__file__).resolve().parents[1] + / "scripts" + / "summarize_gap_factorial.py" +) +SPEC = importlib.util.spec_from_file_location( + "summarize_gap_factorial", MODULE_PATH +) +summarizer = importlib.util.module_from_spec(SPEC) +SPEC.loader.exec_module(summarizer) + + +class SummarizeGapFactorialTests(unittest.TestCase): + selected_scale = 1.032 + + def make_matrix(self, root: Path): + runs_root = root / "runs" + eval_root = root / "eval" + selection_path = root / "selection.json" + runs_root.mkdir() + eval_root.mkdir() + + selected_label = "global-g1p0320-seed0-256k" + selection = { + "schema_version": 1, + "status": "passed", + "selected_global_scale": self.selected_scale, + "selected_global_scale_text": "1.032", + "selected_label": selected_label, + "selected": { + "label": selected_label, + "global_scale": self.selected_scale, + }, + } + selection_path.write_text( + json.dumps(selection), encoding="utf-8" + ) + + arm_values = { + "fixed": 10.0, + "global": 9.0, + "local-conservative": 8.0, + "combined-conservative": 6.0, + "local-aggressive": 7.5, + "combined-aggressive": 5.5, + } + for spec in summarizer.arm_specs(self.selected_scale): + for seed in summarizer.TRAINING_SEEDS: + label = summarizer.run_label(spec, seed) + run_dir = runs_root / label + run_dir.mkdir() + validation = { + "status": "passed", + "expected_schedule": spec["schedule"], + "final_processed_kimg": 256.0, + } + (run_dir / "validation.json").write_text( + json.dumps(validation), encoding="utf-8" + ) + (run_dir / "experiment_meta.env").write_text( + "\n".join( + [ + f"arm={spec['arm']}", + f"schedule={spec['schedule']}", + f"local_profile={spec['metadata_profile']}", + f"global_gap_scale={spec['scale']}", + f"seed={seed}", + "duration_mimg=0.256", + "exit_code=0", + "", + ] + ), + encoding="utf-8", + ) + for nfe in summarizer.NFES: + eval_dir = eval_root / label / f"nfe{nfe}" + eval_dir.mkdir(parents=True) + (eval_dir / "experiment_meta.env").write_text( + f"label={label}\nnfe={nfe}\nexit_code=0\n", + encoding="utf-8", + ) + for metric_index, metric in enumerate( + summarizer.METRICS + ): + value = ( + arm_values[spec["arm"]] + + 0.1 * seed + + 0.01 * nfe + + 0.001 * metric_index + ) + payload = { + "metric": metric, + "results": {metric: value}, + } + (eval_dir / f"metric-{metric}.jsonl").write_text( + json.dumps(payload) + "\n", encoding="utf-8" + ) + return runs_root, eval_root, selection_path + + def invoke(self, runs_root, eval_root, selection_path, outdir): + summarizer.main( + [ + "--runs-root", + str(runs_root), + "--eval-root", + str(eval_root), + "--selection-json", + str(selection_path), + "--outdir", + str(outdir), + ] + ) + + def test_complete_matrix_outputs_and_contrasts(self): + with tempfile.TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + runs_root, eval_root, selection_path = self.make_matrix(root) + outdir = root / "summary" + self.invoke(runs_root, eval_root, selection_path, outdir) + + for name in summarizer.OUTPUT_NAMES: + self.assertTrue((outdir / name).is_file()) + self.assertGreater((outdir / name).stat().st_size, 0) + + with (outdir / "per_cell_metrics.csv").open( + newline="", encoding="utf-8" + ) as handle: + cells = list(csv.DictReader(handle)) + self.assertEqual(len(cells), 36) + + with (outdir / "per_seed_effects.csv").open( + newline="", encoding="utf-8" + ) as handle: + effects = list(csv.DictReader(handle)) + self.assertEqual(len(effects), 24) + target = next( + row + for row in effects + if row["profile"] == "conservative" + and row["training_seed"] == "0" + and row["nfe"] == "1" + and row["metric"] == "kid5k_full" + ) + self.assertAlmostEqual( + float(target["global_at_local0_delta"]), -1.0 + ) + self.assertAlmostEqual( + float(target["local_at_global0_delta"]), -2.0 + ) + self.assertAlmostEqual( + float(target["combined_vs_fixed_delta"]), -4.0 + ) + self.assertAlmostEqual( + float(target["additive_interaction_delta"]), -1.0 + ) + self.assertAlmostEqual( + float(target["global_main_effect_delta"]), -1.5 + ) + self.assertAlmostEqual( + float(target["local_main_effect_delta"]), -2.5 + ) + + with (outdir / "heldout_headlines.csv").open( + newline="", encoding="utf-8" + ) as handle: + headlines = list(csv.DictReader(handle)) + self.assertEqual(len(headlines), 20) + headline = next( + row + for row in headlines + if row["arm"] == "global" + and row["nfe"] == "1" + and row["metric"] == "kid5k_full" + ) + expected = 100.0 * (9.16 / 10.16 - 1.0) + self.assertAlmostEqual( + float(headline["headline_relative_percent"]), expected + ) + mean_per_seed_percent = ( + 100.0 * (9.11 / 10.11 - 1.0) + + 100.0 * (9.21 / 10.21 - 1.0) + ) / 2 + self.assertNotAlmostEqual(expected, mean_per_seed_percent) + + summary = json.loads( + (outdir / "factorial_summary.json").read_text( + encoding="utf-8" + ) + ) + self.assertEqual(summary["status"], "passed") + self.assertEqual(summary["matrix"]["unique_training_cells"], 18) + self.assertEqual( + summary["matrix"]["unique_metric_files_read_once"], 72 + ) + self.assertTrue(summary["selection_overlap"]["present"]) + self.assertEqual( + summary["heldout_headline"]["seeds"], [1, 2] + ) + self.assertEqual( + len(summary["heldout_headline"]["rows"]), 20 + ) + self.assertEqual(len(summary["summaries"]), 64) + + def test_rejects_failed_training_validation_before_writing(self): + with tempfile.TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + runs_root, eval_root, selection_path = self.make_matrix(root) + failed = ( + runs_root / "fixed-g1p0000-seed0-256k" / "validation.json" + ) + failed.write_text( + json.dumps( + { + "status": "failed", + "expected_schedule": "sigmoid", + "final_processed_kimg": 256.0, + } + ), + encoding="utf-8", + ) + outdir = root / "summary" + with self.assertRaises(SystemExit): + self.invoke(runs_root, eval_root, selection_path, outdir) + self.assertFalse(outdir.exists()) + + def test_rejects_more_than_one_metric_result(self): + with tempfile.TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + runs_root, eval_root, selection_path = self.make_matrix(root) + metric_path = ( + eval_root + / "fixed-g1p0000-seed0-256k" + / "nfe1" + / "metric-kid5k_full.jsonl" + ) + metric_path.write_text( + metric_path.read_text(encoding="utf-8") * 2, + encoding="utf-8", + ) + with self.assertRaises(SystemExit): + self.invoke( + runs_root, eval_root, selection_path, root / "summary" + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_summarize_role_a_multibudget.py b/tests/test_summarize_role_a_multibudget.py new file mode 100644 index 00000000..c9fdebc0 --- /dev/null +++ b/tests/test_summarize_role_a_multibudget.py @@ -0,0 +1,52 @@ +import csv +import tempfile +import unittest +from pathlib import Path + +from scripts import summarize_role_a_multibudget as summary + + +class RoleAMultibudgetSummaryTest(unittest.TestCase): + def make_rows(self): + rows = [] + for budget in summary.BUDGETS: + for nfe in summary.NFES: + for seed in summary.SEEDS: + for method in summary.METHODS: + adjustment = -0.01 if method == "adaptive_v1" else 0.0 + rows.append({ + "method": method, + "training_seed": seed, + "budget_kimg": budget, + "nfe": nfe, + "kid5k": 1 + seed + adjustment, + "fid5k": 10 + seed + adjustment, + "checkpoint_sha256": f"sha-{method}-{seed}-{budget}", + }) + return rows + + def test_complete_matrix_pairs_and_aggregates(self): + rows = summary.validate_matrix(self.make_rows()) + paired = summary.pair_rows(rows) + aggregate = summary.aggregate_rows(rows, paired) + self.assertEqual(len(rows), 36) + self.assertEqual(len(paired), 18) + self.assertEqual(len(aggregate), 12) + self.assertTrue(all(row["delta_kid5k"] < 0 for row in paired)) + self.assertTrue(all(row["adaptive_wins"] == 3 for row in aggregate)) + + def test_csv_requires_exact_role_a_columns(self): + with tempfile.TemporaryDirectory() as temp_dir: + path = Path(temp_dir) / "metrics.csv" + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=list(summary.INPUT_COLUMNS)) + writer.writeheader() + self.assertEqual(summary.read_rows([path]), []) + + def test_incomplete_matrix_is_rejected(self): + with self.assertRaises(SystemExit): + summary.validate_matrix(self.make_rows()[:-1]) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_training_cli_compat.py b/tests/test_training_cli_compat.py new file mode 100644 index 00000000..bceab964 --- /dev/null +++ b/tests/test_training_cli_compat.py @@ -0,0 +1,131 @@ +import contextlib +import io +import unittest + +import click +import dnnlib +import torch +from click.testing import CliRunner + +import ct_train +from training.loss import ECMLoss + + +def parse_train_args(*extra_args): + args = ['--outdir', 'out', '--data', 'dataset', *extra_args] + with ct_train.main.make_context('ct_train.py', args) as ctx: + return dict(ctx.params) + + +class TrainingCliCompatibilityTest(unittest.TestCase): + def test_no_new_option_keeps_legacy_sigmoid_default(self): + params = parse_train_args() + self.assertEqual(params['mapping'], 'sigmoid') + self.assertNotIn('schedule', params) + self.assertEqual(params['adaptive_loss_ema_beta'], 0.9) + self.assertEqual(params['adaptive_update_kimg'], 0.5) + self.assertEqual(params['adaptive_warmup_updates'], 2) + self.assertEqual(params['adaptive_max_adjust'], 0.05) + self.assertEqual(params['adaptive_min_gap'], 1e-3) + self.assertEqual(params['global_gap_scale'], 1.0) + + def test_zero_disables_numbered_snapshot_and_state_dump(self): + params = parse_train_args('--snap', '0', '--dump', '0') + self.assertEqual(params['snap'], 0) + self.assertEqual(params['dump'], 0) + + def test_legacy_mapping_option_is_preserved(self): + self.assertEqual(parse_train_args('--mapping=const')['mapping'], 'const') + self.assertEqual(parse_train_args('--mapping=sigmoid')['mapping'], 'sigmoid') + + def test_q_requires_a_value_strictly_greater_than_one(self): + for value in ['1', '0', '-2']: + with self.subTest(value=value), self.assertRaises(click.BadParameter): + parse_train_args('-q', value) + self.assertEqual(parse_train_args('-q', '1.01')['q'], 1.01) + + def test_schedule_and_mapping_are_equivalent_names(self): + for schedule in [ + 'const', 'sigmoid', 'global_sigmoid', 'adaptive_v1', + 'local_tbin_v1', 'local_tbin_v2', 'local_tbin_v3' + ]: + with self.subTest(schedule=schedule): + legacy = parse_train_args('--mapping', schedule) + current = parse_train_args('--schedule', schedule) + self.assertEqual(legacy, current) + + def test_hyphenated_adaptive_name_is_canonicalized(self): + self.assertEqual( + parse_train_args('--schedule', 'adaptive-v1')['mapping'], + 'adaptive_v1', + ) + self.assertEqual( + parse_train_args('--schedule', 'local-tbin-v1')['mapping'], + 'local_tbin_v1', + ) + self.assertEqual( + parse_train_args('--schedule', 'local-tbin-v2')['mapping'], + 'local_tbin_v2', + ) + self.assertEqual( + parse_train_args('--schedule', 'global-sigmoid')['mapping'], + 'global_sigmoid', + ) + self.assertEqual( + parse_train_args('--schedule', 'local-tbin-v3')['mapping'], + 'local_tbin_v3', + ) + + def test_help_exposes_both_option_names(self): + result = CliRunner().invoke(ct_train.main, ['--help']) + self.assertEqual(result.exit_code, 0, result.output) + self.assertIn('--schedule', result.output) + self.assertIn('--mapping', result.output) + + def test_adaptive_parameters_are_complete_in_loss_config(self): + params = parse_train_args( + '--schedule', 'adaptive_v1', + '--adaptive-loss-ema-beta', '0.8', + '--adaptive-update-kimg', '0.25', + '--adaptive-warmup-updates', '3', + '--adaptive-max-adjust', '0.04', + '--adaptive-min-gap', '0.002', + ) + loss_kwargs = ct_train.make_loss_kwargs(dnnlib.EasyDict(params)) + self.assertEqual(loss_kwargs.adj, 'adaptive_v1') + self.assertEqual(loss_kwargs.adaptive_loss_ema_beta, 0.8) + self.assertEqual(params['adaptive_update_kimg'], 0.25) + self.assertEqual(loss_kwargs.adaptive_warmup_updates, 3) + self.assertEqual(loss_kwargs.adaptive_max_adjust, 0.04) + self.assertEqual(loss_kwargs.adaptive_min_gap, 0.002) + + def test_global_gap_scale_reaches_factorized_schedules(self): + for schedule in ['global_sigmoid', 'local_tbin_v3']: + with self.subTest(schedule=schedule): + params = parse_train_args( + '--schedule', schedule, + '--global-gap-scale', '1.032', + ) + loss_kwargs = ct_train.make_loss_kwargs(dnnlib.EasyDict(params)) + self.assertEqual(loss_kwargs.adj, schedule) + self.assertEqual(loss_kwargs.global_gap_scale, 1.032) + with contextlib.redirect_stdout(io.StringIO()): + loss_fn = ECMLoss(**loss_kwargs) + self.assertEqual(loss_fn.schedule.global_gap_scale, 1.032) + + def test_explicit_sigmoid_disables_adaptive_v1(self): + params = parse_train_args('--schedule', 'sigmoid') + loss_kwargs = ct_train.make_loss_kwargs(dnnlib.EasyDict(params)) + self.assertEqual(loss_kwargs.adj, 'sigmoid') + with contextlib.redirect_stdout(io.StringIO()): + loss_fn = ECMLoss(**loss_kwargs) + loss_fn.update_schedule(3) + t = torch.tensor([0.01, 0.1, 1.0, 10.0]) + self.assertTrue(torch.equal( + loss_fn.schedule.compute_r(t=t, stage=loss_fn.stage), + loss_fn.t_to_r_sigmoid(t), + )) + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_training_integrity.py b/tests/test_training_integrity.py new file mode 100644 index 00000000..e34c99a4 --- /dev/null +++ b/tests/test_training_integrity.py @@ -0,0 +1,180 @@ +import csv +import json +import pickle +import sys +import unittest +from pathlib import Path +from tempfile import TemporaryDirectory + +import torch + +from scripts import check_training_integrity + + +class CheckpointSchedule: + def __init__(self, name: str, global_gap_scale: float = 1.0): + self.name = name + self.global_gap_scale = global_gap_scale + + +class CheckpointLoss: + def __init__(self, schedule: CheckpointSchedule): + self.schedule = schedule + + +class CheckpointEMA(torch.nn.Module): + def __init__(self, finite: bool = True): + super().__init__() + value = 1.0 if finite else float("nan") + self.weight = torch.nn.Parameter(torch.tensor([value])) + self.register_buffer("running_value", torch.tensor([value])) + + +class TrainingIntegrityTest(unittest.TestCase): + def test_checker_adds_repository_root_to_import_path(self): + self.assertEqual(sys.path[0], str(check_training_integrity.REPO_ROOT)) + + def make_run( + self, root: Path, finite: bool = True, method: str = "fixed", + checkpoint_finite: bool = True, + ) -> Path: + run = root / "run" + run.mkdir() + schedule, scale = ( + ("sigmoid", 1.0) if method == "fixed" else ("global_sigmoid", 1.10) + ) + checkpoint = { + "ema": CheckpointEMA(checkpoint_finite), + "loss_fn": CheckpointLoss(CheckpointSchedule(schedule, scale)), + } + # The evaluator stores snapshots with pickle; keep the fixture format + # identical to the actual network-snapshot-latest.pkl artifact. + with (run / "network-snapshot-latest.pkl").open("wb") as handle: + pickle.dump(checkpoint, handle) + (run / "training_options.json").write_text( + json.dumps({ + "total_kimg": 16, "seed": 7, + "loss_kwargs": {"adj": schedule, "global_gap_scale": scale}, + }), encoding="utf-8" + ) + (run / "stats.jsonl").write_text(json.dumps({ + "Loss/loss": {"mean": 1.0}, + "Progress/kimg": {"mean": 16.0}, + }) + "\n", encoding="utf-8") + with (run / "train_summary.csv").open("w", encoding="utf-8", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=["processed_kimg", "loss"]) + writer.writeheader() + writer.writerow({"processed_kimg": "16", "loss": "1.0" if finite else "nan"}) + (run / "log.txt").write_text("training complete\nExiting...\n", encoding="utf-8") + torch.save({"cur_nimg": 16000, "weight": torch.tensor([1.0])}, run / "training-state-latest.pt") + (run / "commit_sha.txt").write_text("training abcdef\n", encoding="utf-8") + return run + + def test_emits_passed_receipt_for_consistent_run(self): + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + run = self.make_run(root) + args = type("Args", (), { + "run_dir": run, "checkpoint": None, "checkpoint_id": "fixed_seed7_16k", + "method": "fixed", "training_seed": 7, "budget_kimg": 16, + "training_run_id": "fixed-seed7-16k", "expected_training_commit": "abcdef", + "checker_version": "1", + })() + receipt = check_training_integrity.build_receipt(args) + self.assertEqual(receipt["status"], "passed") + self.assertTrue(receipt["finite_loss_state_passed"]) + self.assertTrue(receipt["checkpoint_load_passed"]) + self.assertTrue(receipt["ema_present"]) + self.assertTrue(receipt["ema_finite_passed"]) + self.assertTrue(receipt["schedule_identity_passed"]) + self.assertTrue(receipt["global_gap_scale_identity_passed"]) + self.assertTrue(receipt["method_identity_passed"]) + self.assertEqual(receipt["evidence"]["training_state"]["cur_nimg"], 16000) + + def test_rejects_non_finite_loss(self): + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + run = self.make_run(root, finite=False) + args = type("Args", (), { + "run_dir": run, "checkpoint": None, "checkpoint_id": "fixed_seed7_16k", + "method": "fixed", "training_seed": 7, "budget_kimg": 16, + "training_run_id": "fixed-seed7-16k", "expected_training_commit": None, + "checker_version": "1", + })() + with self.assertRaisesRegex(SystemExit, "non-finite loss"): + check_training_integrity.build_receipt(args) + + def test_rejects_non_finite_ema(self): + with TemporaryDirectory() as temp_dir: + run = self.make_run(Path(temp_dir), checkpoint_finite=False) + args = type("Args", (), { + "run_dir": run, "checkpoint": None, "checkpoint_id": "fixed_seed7_16k", + "method": "fixed", "training_seed": 7, "budget_kimg": 16, + "training_run_id": "fixed-seed7-16k", "expected_training_commit": None, + "checker_version": "2", + })() + with self.assertRaisesRegex(SystemExit, "EMA has non-finite"): + check_training_integrity.build_receipt(args) + + def test_rejects_declared_global_method_with_fixed_schedule(self): + with TemporaryDirectory() as temp_dir: + run = self.make_run(Path(temp_dir), method="fixed") + args = type("Args", (), { + "run_dir": run, "checkpoint": None, "checkpoint_id": "global_seed7_16k", + "method": "global110", "training_seed": 7, "budget_kimg": 16, + "training_run_id": "global-seed7-16k", "expected_training_commit": None, + "checker_version": "2", + })() + with self.assertRaisesRegex(SystemExit, "requires schedule"): + check_training_integrity.build_receipt(args) + + def test_rejects_unloadable_checkpoint(self): + with TemporaryDirectory() as temp_dir: + run = self.make_run(Path(temp_dir)) + (run / "network-snapshot-latest.pkl").write_bytes(b"not a pickle") + args = type("Args", (), { + "run_dir": run, "checkpoint": None, "checkpoint_id": "fixed_seed7_16k", + "method": "fixed", "training_seed": 7, "budget_kimg": 16, + "training_run_id": "fixed-seed7-16k", "expected_training_commit": None, + "checker_version": "2", + })() + with self.assertRaisesRegex(SystemExit, "cannot load checkpoint pickle"): + check_training_integrity.build_receipt(args) + + def test_rejects_missing_ema(self): + with TemporaryDirectory() as temp_dir: + run = self.make_run(Path(temp_dir)) + checkpoint_path = run / "network-snapshot-latest.pkl" + with checkpoint_path.open("rb") as handle: + checkpoint = pickle.load(handle) + del checkpoint["ema"] + with checkpoint_path.open("wb") as handle: + pickle.dump(checkpoint, handle) + args = type("Args", (), { + "run_dir": run, "checkpoint": None, "checkpoint_id": "fixed_seed7_16k", + "method": "fixed", "training_seed": 7, "budget_kimg": 16, + "training_run_id": "fixed-seed7-16k", "expected_training_commit": None, + "checker_version": "2", + })() + with self.assertRaisesRegex(SystemExit, "EMA object"): + check_training_integrity.build_receipt(args) + + def test_rejects_global_gap_scale_identity_mismatch(self): + with TemporaryDirectory() as temp_dir: + run = self.make_run(Path(temp_dir), method="global110") + options_path = run / "training_options.json" + options = json.loads(options_path.read_text(encoding="utf-8")) + options["loss_kwargs"]["global_gap_scale"] = 1.2 + options_path.write_text(json.dumps(options), encoding="utf-8") + args = type("Args", (), { + "run_dir": run, "checkpoint": None, "checkpoint_id": "global_seed7_16k", + "method": "global110", "training_seed": 7, "budget_kimg": 16, + "training_run_id": "global-seed7-16k", "expected_training_commit": None, + "checker_version": "2", + })() + with self.assertRaisesRegex(SystemExit, "global_gap_scale=1.1"): + check_training_integrity.build_receipt(args) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_validate_staged_runtime_manifest.py b/tests/test_validate_staged_runtime_manifest.py new file mode 100644 index 00000000..2360c057 --- /dev/null +++ b/tests/test_validate_staged_runtime_manifest.py @@ -0,0 +1,123 @@ +import hashlib +import json +import unittest +from pathlib import Path +from tempfile import TemporaryDirectory + +from scripts import validate_staged_runtime_manifest + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +class ValidateStagedRuntimeManifestTest(unittest.TestCase): + def make_manifests(self, root: Path): + checkpoint = root / "network-snapshot-latest.pkl" + checkpoint.write_bytes(b"checkpoint") + receipt = root / "seed3_fixed_256k.integrity.json" + checkpoint_sha = sha256(checkpoint) + receipt.write_text(json.dumps({ + "schema_version": 1, + "status": "passed", + "checkpoint_id": "confirmatory-256k-seed3-fixed", + "checkpoint_path": str(checkpoint), + "checkpoint_sha256": checkpoint_sha, + "training_run_id": "confirmatory-256k-seed3-fixed", + "method": "fixed", + "training_seed": 3, + "budget_kimg": 256, + "completion_passed": True, + "logs_state_consistent": True, + "finite_loss_state_passed": True, + "checkpoint_load_passed": True, + "ema_present": True, + "ema_finite_passed": True, + "schedule_identity_passed": True, + "global_gap_scale_identity_passed": True, + "method_identity_passed": True, + "checker_version": "1", + "checker_git_commit": "125ece6d018e10a1c2cf13ea6e3beeda09667d23", + "checked_at_unix": 1_753_822_000, + }), encoding="utf-8") + frozen = { + "manifest_kind": "frozen-logical-checkpoint-matrix", + "protocol": "staged-checkpoint-evaluation-v1", + "runtime_binding": {"versioned_paths": False}, + "cells": [{ + "checkpoint_id": "confirmatory-256k-seed3-fixed", + "method": "fixed", + "training_seed": 3, + "budget_kimg": 256, + "schedule_q": 256, + "schedule_identity": "sigmoid", + "global_gap_scale": 1.0, + "checkpoint_sha256": checkpoint_sha, + "executed_training_source_commit": "3a0d603da97dd93ddbb6c7ce49e4a7351d54bb43", + "training_integrity_receipt": { + "receipt_filename": receipt.name, + "receipt_sha256": sha256(receipt), + }, + }], + } + runtime = { + "protocol": "staged-checkpoint-evaluation-v1", + "cells": [{ + **{key: value for key, value in frozen["cells"][0].items() + if key != "training_integrity_receipt"}, + "checkpoint": str(checkpoint), + "integrity_receipt": str(receipt), + }], + } + frozen_path = root / "frozen.json" + runtime_path = root / "runtime.json" + frozen_path.write_text(json.dumps(frozen), encoding="utf-8") + runtime_path.write_text(json.dumps(runtime), encoding="utf-8") + return frozen_path, runtime_path + + def test_accepts_exact_runtime_binding(self): + with TemporaryDirectory() as temp_dir: + frozen, runtime = self.make_manifests(Path(temp_dir)) + rows = validate_staged_runtime_manifest.validate(frozen, runtime) + self.assertEqual([row["checkpoint_id"] for row in rows], ["confirmatory-256k-seed3-fixed"]) + + def test_rejects_runtime_identity_drift(self): + with TemporaryDirectory() as temp_dir: + frozen, runtime = self.make_manifests(Path(temp_dir)) + payload = json.loads(runtime.read_text(encoding="utf-8")) + payload["cells"][0]["global_gap_scale"] = 1.2 + runtime.write_text(json.dumps(payload), encoding="utf-8") + with self.assertRaisesRegex(SystemExit, "global_gap_scale"): + validate_staged_runtime_manifest.validate(frozen, runtime) + + def test_rejects_receipt_checksum_drift(self): + with TemporaryDirectory() as temp_dir: + frozen, runtime = self.make_manifests(Path(temp_dir)) + payload = json.loads(runtime.read_text(encoding="utf-8")) + receipt = Path(payload["cells"][0]["integrity_receipt"]) + receipt.write_text(receipt.read_text(encoding="utf-8") + "\n", encoding="utf-8") + with self.assertRaisesRegex(SystemExit, "receipt SHA256"): + validate_staged_runtime_manifest.validate(frozen, runtime) + + def test_rejects_formal_promotion_policy_drift(self): + with TemporaryDirectory() as temp_dir: + frozen, runtime = self.make_manifests(Path(temp_dir)) + policy = { + "eligibility": "provenance_and_integrity_only", + "quick_metric_performance": "not_an_eligibility_criterion", + "required_checkpoint_ids": ["confirmatory-256k-seed3-fixed"], + } + frozen_payload = json.loads(frozen.read_text(encoding="utf-8")) + runtime_payload = json.loads(runtime.read_text(encoding="utf-8")) + frozen_payload["formal_promotion_policy"] = policy + runtime_payload["formal_promotion_policy"] = { + **policy, "quick_metric_performance": "metric_selected", + } + frozen.write_text(json.dumps(frozen_payload), encoding="utf-8") + runtime.write_text(json.dumps(runtime_payload), encoding="utf-8") + with self.assertRaisesRegex(SystemExit, "formal_promotion_policy"): + validate_staged_runtime_manifest.validate(frozen, runtime) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_verify_fixed_seed_determinism.py b/tests/test_verify_fixed_seed_determinism.py new file mode 100644 index 00000000..5df70d0a --- /dev/null +++ b/tests/test_verify_fixed_seed_determinism.py @@ -0,0 +1,75 @@ +import hashlib +import json +import unittest +from pathlib import Path +from tempfile import TemporaryDirectory + +from PIL import Image + +from scripts import verify_fixed_seed_determinism + + +def sha256_file(path: Path) -> str: + digest = hashlib.sha256() + digest.update(path.read_bytes()) + return digest.hexdigest() + + +class VerifyFixedSeedDeterminismTest(unittest.TestCase): + def make_result(self, root: Path) -> None: + entries = [] + for nfe in (1, 2): + mode = root / f"nfe{nfe}" + images = mode / "images" + images.mkdir(parents=True) + for seed in range(64): + path = images / f"seed{seed:06d}.png" + Image.new("RGB", (1, 1), (seed, nfe, 0)).save(path) + entries.append((sha256_file(path), path.relative_to(root).as_posix())) + grid = mode / "grid_8x8.png" + Image.new("RGB", (8, 8), (nfe, 0, 0)).save(grid) + entries.append((sha256_file(grid), grid.relative_to(root).as_posix())) + metadata = { + "checkpoint_id": "test-checkpoint", + "checkpoint_sha256": "a" * 64, + "seed_list": list(range(64)), + "seed_count": 64, + "nfe_modes": [1, 2], + "mid_t_by_mode": {"nfe1": [], "nfe2": [0.821]}, + "precision": "fp32", + "model_forward_batch_size": 1, + "work_group_sizes_verified": [8, 16], + "repeat_runs_verified": 2, + "determinism_passed": True, + "image_count_by_mode": {"nfe1": 64, "nfe2": 64}, + "image_count_total": 128, + } + metadata_path = root / "metadata.json" + metadata_path.write_text(json.dumps(metadata), encoding="utf-8") + entries.append((sha256_file(metadata_path), "metadata.json")) + (root / "sha256_manifest.txt").write_text( + "".join(f"{digest} {name}\n" for digest, name in entries), + encoding="utf-8", + ) + + def test_valid_artifact_passes_and_writes_report(self): + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + self.make_result(root) + report = verify_fixed_seed_determinism.verify(root) + self.assertEqual(report["status"], "passed") + self.assertEqual(report["manifest_entry_count"], 131) + verify_fixed_seed_determinism.main(["--result-dir", str(root)]) + self.assertTrue((root / "determinism_verification.json").is_file()) + + def test_manifest_mismatch_fails(self): + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + self.make_result(root) + (root / "nfe1" / "images" / "seed000000.png").write_bytes(b"corrupt") + with self.assertRaisesRegex(SystemExit, "manifest SHA256 mismatch"): + verify_fixed_seed_determinism.verify(root) + + +if __name__ == "__main__": + unittest.main() diff --git a/theory/novelty_and_propositions.md b/theory/novelty_and_propositions.md new file mode 100644 index 00000000..3e64498b --- /dev/null +++ b/theory/novelty_and_propositions.md @@ -0,0 +1,163 @@ +# ECT Gap Calibration: Novelty Statement & Theoretical Propositions (rev.2) + +Date: 2026-08-04; revised 2026-08-05 after the PR #33 review (REQUEST_CHANGES). +Status: stop-gradient operator corrected (finite-difference verified); +LR-matched controls added; ADCM separation counterexample constructed. + +--- + +## 1. Novelty statement (instantaneous vs finite-horizon) + +> **ADCM optimizes a state-conditioned *instantaneous* consistency criterion.** +> We do NOT claim novelty from "global multiplier vs local discretization" — +> that distinction is insufficient, since ADCM already studies finite image +> budget, training efficiency, and convergence. The candidate novelty is: +> +> **Does the instantaneous criterion determine the finite-horizon optimum +> under a specified optimizer, remaining budget, parameter-space curvature, +> and gradient-noise covariance?** +> +> Our claim is that it does **not**: two settings with the *same* instantaneous +> criterion (same population curvature $H_g$) can have *different* finite-step +> optima $g_K^\star$ because their gradient-noise covariance $\Sigma_g$ differs. +> This is a gap-dependent finite-horizon geometry that an instantaneous-only +> method cannot resolve. + +**What we explicitly do NOT claim anymore** (removed per review): +- ~~ADCM targets an infinite-data / stationary setting~~ +- ~~no global scale exists in ADCM, therefore our problem is orthogonal~~ +- ~~the U-shape establishes a gap-specific internal optimum~~ + +The honest scope, supported by the LR-matched controls (§3): +- Under a **fixed** learning rate, increasing $g$ raises the effective rate + $\eta\lambda_{\max}(H_g)\propto g^2$ until the GD stability boundary; the + observed "U-shape" is largely this effective-learning-rate effect. +- Under **LR-matched** $\eta$ (so $\eta\lambda_{\max}$ is constant across $g$), + the U-shape **collapses** in the noiseless case and becomes **monotone** in + the noisy case: the remaining $g$-dependence is the *noise-amplification* + term $\eta\nu/(\lambda(2-\eta\lambda))$, which is gap-specific but does **not** + by itself yield an internal optimum. +- The genuine finite-horizon separation comes from **anisotropic** $\Sigma_g$ + differing between loss types with identical $H_g$ (§4). + +--- + +## 2. Three propositions (revised) + +### Proposition A (Ideal-solution invariance, exact-trajectory) +Unchanged: for exact PF pairs, zero-loss solution set is independent of $g$. +(Toy: $f_\beta$ with $\beta=0$ is the ideal map, zero residual for all $g$.) + +### Proposition B (finite-horizon optimum depends on optimizer/budget/noise) +Let $H_g = \sigma_d^2\mathbb{E}_t[v_g v_g^\top]$ (curvature) and +$\Sigma_g = \sigma_d^2\mathbb{E}_t[u_g u_g^\top]$ (gradient-noise covariance per +sample), where $u_g$ is the per-sample gradient-noise feature: +- symmetric loss: $u_g = v_g$ +- stop-gradient loss: $u_g = [t, t^2]$ + +The finite-step iterate satisfies +$$\mathbb{E}[\beta_{K+1}\beta_{K+1}^\top] = (I-\eta H_g)\,\mathbb{E}[\beta_K\beta_K^\top]\,(I-\eta H_g)^\top + \eta^2 \Sigma_g.$$ +The finite-horizon error $\|\beta_K\|^2$ depends on $K$, $\beta_0$, the +eigenvectors of $H_g$, and $\Sigma_g$. Define +$$g_K^\star = \arg\min_g \mathbb{E}\|\beta_K\|^2,\qquad +g_\rho^\star = \arg\min_g \max_j|1-\eta\lambda_j(H_g)|.$$ +**$g_\rho^\star$ is budget-independent; $g_K^\star$ is budget-dependent and +also depends on $\Sigma_g$.** The previous draft conflated the two; they are +distinct and only $g_K^\star$ is the finite-horizon object. The internal-min +existence argument via $\rho_g$ alone is **not** a proof of an internal $g_K^\star$; +that requires the noise term (see Prop C / §4). + +### Proposition C (instantaneous criterion does not determine $g_K^\star$) +Two environments with **identical** $H_g$ (hence identical instantaneous +criterion / ADCM recommendation) but **different** $\Sigma_g$ yield +**different** $g_K^\star$. Counterexample constructed in §4. + +--- + +## 3. LR-matched controls (resolves review P0-2) + +With $\eta_g = \eta_1\,\lambda_{\max}(H_1)/\lambda_{\max}(H_g)$ (so +$\eta_g\lambda_{\max}(H_g)\equiv$ const, removing the effective-rate effect): + +| setting | error vs $g$ | verdict | +|---|---|---| +| noiseless | flat (spread ≈ 0) | original "U" was an LR artifact | +| noisy (isotropic) | **monotone** decreasing, no internal min | gap effect = noise amplification, not internal optimum | + +**Implication**: we withdraw the claim "an internal $g_K^\star>1$ exists from +Hessian geometry alone". The toy's honest content is: +1. fixed-LR U-shape $\approx$ effective-rate × stability boundary; +2. LR-matched monotone noise amplification; +3. true finite-horizon separation requires *anisotropic* $\Sigma_g$ (next). + +--- + +## 4. ADCM separation counterexample (resolves review P0/separation, rev.2) + +**Exact second-moment recursion (no Monte-Carlo):** +$M_{k+1}=B_g M_k B_g^\top+\eta^2\Sigma_g^{(e)},\ B_g=I-\eta H_g,\ M_0=\beta_0\beta_0^\top,\ E_K=\mathrm{Tr}(M_K).$ + +Two environments share the **same curvature $H_g$** (hence identical instantaneous +criterion $J_\text{inst}=\mathrm{Tr}(H_g)$) but differ in gradient-noise covariance: +- **env1** (symmetric loss): $\Sigma^{(1)}=H_g$ — the noise feature $v_g=[t{-}r,t^2{-}r^2]$ contains $\Delta\sim g$, so $\Sigma\propto g^2$. +- **env2** (stop-gradient loss): $\Sigma^{(2)}=\sigma_d^2\mathbb{E}[[t,t^2][t,t^2]^\top]$ — the online Jacobian $J_t=[t,t^2]$ has **no $g$ dependence**, so $\Sigma$ is $g$-independent. + +This is the real ECT physics: the stop-gradient loss shares curvature $H_g$ with +the symmetric loss (verified: both have population loss $\tfrac12\beta^\top H_g\beta$, +finite-diff rel-err $8\!\times\!10^{-2}$ MC) but its gradient-noise covariance does +**not** scale with $g$, because $g$ enters only the residual, not the online Jacobian. + +### Result (fixed $\eta$, exact recursion) +| setting | env1 $g^\star$ | env2 $g^\star$ | differ? | +|---|---|---|---| +| **realistic** (g-dep vs g-indep $\Sigma$) | 0.5 | 1.0–1.45 | **yes** | +| trace-matched (pure direction, equal Tr) | 0.5 | 0.5 | no | + +**Two honest conclusions:** +1. **Realistic separation appears**: same $H_g$, same $\eta$, exact recursion, but + $g^\star$ differs because $\Sigma^{(1)}$ grows with $g$ while $\Sigma^{(2)}$ does + not. This is gap-dependent finite-horizon geometry the instantaneous criterion + cannot predict. +2. **Pure direction (equal trace) is insufficient**: when we rescale $\Sigma^{(2)}$ + to equal trace with $\Sigma^{(1)}$, the optima coincide. So the separation does + *not* come from "different noise direction" alone — it requires the genuine + difference in *how $\Sigma_g$ depends on $g$*, which is exactly what + stop-gradient vs symmetric provides. + +Caveat: in the **LR-matched** regime ($\eta\propto 1/\lambda_{\max}(H_g)\propto 1/g^2$), +the drift $B_g$ becomes $g$-independent (since $H_g\approx g^2 H_2$), and the +realistic separation above also collapses — there $g^\star$ is driven to the +stability boundary for both. The counterexample is therefore stated for the +**fixed-$\eta$** (real-training) regime, not the LR-matched abstraction. + +--- + +## 5. Stop-gradient operator correction (review P0-3) + +**Corrected** $A_g = \sigma_d^2\,\mathbb{E}_t\big[[t,t^2]^\top\,v_g(t)^\top\big]$ +(online-branch Jacobian $J_t = [t,t^2]$, NOT $v_g$). Verified two ways: +- per-sample gradient $\mathbb{E}_z[z^2(v_g^\top\beta)[t,t^2]]$ matches $A_g\beta$ + to **rel-err $7\!\times\!10^{-12}$** (analytic $\mathbb{E}_z$); +- population-loss finite-difference gradient matches $H_g\beta$ (curvature), + confirming population loss is $\tfrac12\beta^\top H_g\beta$ — *same* as + symmetric loss. The asymmetry of $A_g$ vs $H_g$ is the source of the + different $\Sigma_g$ in §4. + +Previous CSV columns `asym_min/asym_max/asym_norm2` are recomputed from the +corrected $A_g$; old values must not be cited. + +--- + +## 6. Honest caveats & open items (anticipate reviewers) + +- The separation effect at $K{=}200$ puts sym's $g^\star$ at the boundary 1.45; + needs finer $g$ grid + more MC averaging to confirm it is interior. +- All claims are in a **linear** model with 2 parameters; not a deep-net proof + (explicitly disclaimed, matching the report's A5). +- The "signal–curvature trade-off" $O(\Delta)$/$O(\Delta^2)$ lives at the + per-pair loss level, not the population Hessian; still requires the + shared-noise surrogate-bias toy (open). +- Clipping ($\Delta=\min(g\delta_0, t-t_{\min})$) means $H_g$ is **not** exactly + a degree-4 polynomial in $g$; report polynomial-reconstruction residual + (open, small). +- Unit tests pending (review minor item). diff --git a/theory/plot_toy.py b/theory/plot_toy.py new file mode 100644 index 00000000..c7673eec --- /dev/null +++ b/theory/plot_toy.py @@ -0,0 +1,100 @@ +"""Plot toy-model results. + +Figures: + figures/toy_condition_number.pdf - g vs Hessian condition number & eta*lambda_max + figures/toy_error_vs_g.pdf - error vs g, one line per (K, noise) + figures/toy_gstar_vs_budget.pdf - optimal g* vs budget K (per noise) +""" +import os +import warnings +warnings.filterwarnings("ignore") +import numpy as np +import pandas as pd +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt + + +def main(): + os.makedirs("figures", exist_ok=True) + h = pd.read_csv("theory/toy_hessian.csv") + b = pd.read_csv("theory/toy_finite_budget.csv") + + # ---- Figure 1: condition number + stability boundary ---- + fig, axes = plt.subplots(1, 2, figsize=(11, 4.5)) + ax = axes[0] + ax.plot(h.g, h.cond, "-o", ms=3, label="condition number") + ax.set_xlabel("g") + ax.set_ylabel("condition number") + ax.set_title("Hessian condition number vs gap scale g") + ax.grid(alpha=0.3) + + ax = axes[1] + ax.plot(h.g, h.eta_lambda_max, "-o", ms=3, color="tab:red", + label=r"$\eta\,\lambda_{\max}(H_g)$") + ax.axhline(2.0, color="black", ls="--", lw=1, label="stability boundary") + ax.fill_between(h.g, 2.0, h.eta_lambda_max.max() * 1.05, + color="red", alpha=0.1) + ax.set_xlabel("g") + ax.set_ylabel(r"$\eta\,\lambda_{\max}$") + ax.set_title("Stability margin: gap over-drives GD") + ax.legend() + ax.grid(alpha=0.3) + fig.tight_layout() + fig.savefig("figures/toy_condition_number.pdf") + plt.close(fig) + + # ---- Figure 2: error vs g (one line per K, noise) ---- + fig, axes = plt.subplots(1, 3, figsize=(15, 4.5), sharey=False) + colors = plt.cm.viridis(np.linspace(0, 1, 3)) + for j, K in enumerate([50, 200, 1000]): + ax = axes[j] + for i, noise in enumerate([0, 0.01, 0.05]): + sub = b[(b.K == K) & (b.noise == noise)] + sub = sub.sort_values("g") + ax.plot(sub.g, sub.error, "-o", ms=3, color=colors[i], + label=f"noise={noise}") + gmin = sub.loc[sub.error.idxmin(), "g"] + emin = sub.error.min() + ax.plot(gmin, emin, "x", ms=8, color="red", zorder=5) + ax.annotate(f"g*={gmin:.2f}", (gmin, emin), + textcoords="offset points", xytext=(6, -4), fontsize=8) + ax.set_xlabel("g") + ax.set_ylabel("final error") + ax.set_title(f"K={K}") + ax.set_yscale("symlog", linthresh=1e-3) + ax.legend(fontsize=8) + ax.grid(alpha=0.3) + fig.suptitle("Toy-model final error vs gap scale g") + fig.tight_layout() + fig.savefig("figures/toy_error_vs_g.pdf") + plt.close(fig) + + # ---- Figure 3: g* vs budget K (per noise) ---- + fig, ax = plt.subplots(figsize=(6.5, 4.5)) + for i, noise in enumerate([0, 0.01, 0.05]): + gstars = [] + Ks = [] + for K in [50, 200, 1000]: + sub = b[(b.K == K) & (b.noise == noise)] + gmin = sub.loc[sub.error.idxmin(), "g"] + gstars.append(gmin) + Ks.append(K) + ax.plot(Ks, gstars, "-o", ms=6, color=colors[i], label=f"noise={noise}") + ax.axhline(1.0, color="black", ls="--", lw=1, label="g=1 (official)") + ax.set_xlabel("optimization budget K (iterations)") + ax.set_ylabel(r"optimal $g_K^*$") + ax.set_title(r"Internal optimal gap $g_K^*$ vs budget") + ax.legend() + ax.grid(alpha=0.3) + fig.tight_layout() + fig.savefig("figures/toy_gstar_vs_budget.pdf") + plt.close(fig) + + print("saved figures:") + for f in ["toy_condition_number", "toy_error_vs_g", "toy_gstar_vs_budget"]: + print(" figures/%s.pdf" % f) + + +if __name__ == "__main__": + main() diff --git a/theory/scan_controls.py b/theory/scan_controls.py new file mode 100644 index 00000000..7e89f28a --- /dev/null +++ b/theory/scan_controls.py @@ -0,0 +1,111 @@ +"""LR-matched control for the toy model (review P0-2). + +Tests whether the g-dependent U-shape survives a control that removes the +effective-learning-rate interpretation: + + 1. eta_scale sweep {0.05,0.1,0.25,0.5,1.0} (eta = eta_scale/lambda_max(H_1), + fixed across g) + 2. LR-matched: eta_g = eta_1 * lambda_max(H_1)/lambda_max(H_g) + => eta_g * lambda_max(H_g) == eta_1*lambda_max(H_1) = const + so the *fastest* direction rate is identical across g; + any remaining g-dependence is curvature-shape / slow-dir. + +Decision rule: if the U-shape disappears under (2), the original U was a +learning-rate artifact. If it persists, there is gap-specific geometry. + +NOTE: an earlier draft mentioned a "gradient-RMS-matched" control; it is NOT +implemented here (the LR-matched control already removes the rate confound for +this toy, and gradient-RMS matching is subsumed by LR-matching when the +gradient is H_g beta). Removed to avoid over-claiming. +""" +import os +import numpy as np +import pandas as pd +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from toy_core import (sample_t, base_gap_sigmoid, hessian_symmetric, + gd_final_error, sgd_expectation_exact, noise_cov_from_rms) + + +def build_base(seed=0, n=200000): + t = sample_t(n, rng=np.random.default_rng(seed)) + d0 = base_gap_sigmoid(t) + return t, d0 + + +def run(sigma_d=0.5, out="."): + os.makedirs(out, exist_ok=True) + t, d0 = build_base() + gs = np.arange(0.5, 2.01, 0.05) + Ks = [50, 200, 1000] + noises = [0.0, 0.01, 0.05] + eta_scales = [0.05, 0.1, 0.25, 0.5, 1.0] + + H1 = hessian_symmetric(sigma_d, t, 1.0, d0) + lam1_max = np.linalg.eigvalsh(H1)[-1] + + rows = [] + # ---- 1. eta_scale sweep (fixed-H1 normalization) ---- + for es in eta_scales: + eta1 = es / lam1_max + for g in gs: + H = hessian_symmetric(sigma_d, t, g, d0) + beta0 = np.ones(2) * 1e-2 + for K in Ks: + e = gd_final_error(H, beta0, eta1, K)[0] + rows.append(dict(control="fixed_lr", eta_scale=es, g=round(float(g),4), + K=K, noise=0.0, error=float(e))) + for nz in noises: + if nz == 0: + continue + nu = noise_cov_from_rms(H, nz) + rows.append(dict(control="fixed_lr", eta_scale=es, g=round(float(g),4), + K=K, noise=nz, error=float(sgd_expectation_exact(H, beta0, eta1, K, nu)))) + + # ---- 2. LR-matched: eta_g * lambda_max(H_g) = const = eta1*lam1_max ---- + for es in eta_scales: + target = es # eta_g * lam_max(H_g) = es (i.e. eta1*lam1_max = es) + for g in gs: + H = hessian_symmetric(sigma_d, t, g, d0) + lam_max = np.linalg.eigvalsh(H)[-1] + eta_g = target / lam_max # makes eta_g*lam_max == es for all g + beta0 = np.ones(2) * 1e-2 + for K in Ks: + e = gd_final_error(H, beta0, eta_g, K)[0] + rows.append(dict(control="lr_matched", eta_scale=es, g=round(float(g),4), + K=K, noise=0.0, error=float(e))) + for nz in noises: + if nz == 0: + continue + nu = noise_cov_from_rms(H, nz) + rows.append(dict(control="lr_matched", eta_scale=es, g=round(float(g),4), + K=K, noise=nz, error=float(sgd_expectation_exact(H, beta0, eta_g, K, nu)))) + + df = pd.DataFrame(rows) + df.to_csv(os.path.join(out, "toy_lr_controls.csv"), index=False) + + # ---- diagnostics: report optimal g* per (control, eta_scale, K, noise) ---- + print("=== optimal g* by control (eta_scale=1.0) ===") + sub = df[df.eta_scale == 1.0] + for (ctrl, K, nz), grp in sub.groupby(["control", "K", "noise"]): + gstar = grp.loc[grp.error.idxmin(), "g"] + emin = grp.error.min() + emax = grp.error.max() + spread = (emax - emin) / max(emin, 1e-30) + print(f" {ctrl:10s} K={K:4d} noise={nz}: g*={gstar:.2f} err_min={emin:.3e} spread={spread:.2f}") + + # ---- decision: does U-shape survive lr_matched? ---- + print("\n=== DECISION (does U survive LR-matching?) ===") + for K in Ks: + for nz in noises: + fixed = df[(df.control=="fixed_lr")&(df.eta_scale==1.0)&(df.K==K)&(df.noise==nz)] + matched = df[(df.control=="lr_matched")&(df.eta_scale==1.0)&(df.K==K)&(df.noise==nz)] + f_spread = (fixed.error.max()-fixed.error.min())/max(fixed.error.min(),1e-30) + m_spread = (matched.error.max()-matched.error.min())/max(matched.error.min(),1e-30) + verdict = "PERSISTS (gap-specific)" if m_spread > 0.10 else "COLLAPSES (LR artifact)" + print(f" K={K} noise={nz}: fixed_spread={f_spread:.2f} matched_spread={m_spread:.4f} -> {verdict}") + + +if __name__ == "__main__": + run(out=".") diff --git a/theory/scan_toy.py b/theory/scan_toy.py new file mode 100644 index 00000000..46cf8e6e --- /dev/null +++ b/theory/scan_toy.py @@ -0,0 +1,119 @@ +"""Scan g x K x noise for the linear-Gaussian toy model. + +Outputs: + theory/toy_hessian.csv - per-g Hessian spectrum, condition number, stability + theory/toy_finite_budget.csv - per (g, K, noise) final error + +Usage: python scan_toy.py [--out PREFIX] [--sigma-d 0.5] [--gmin 0.5 --gmax 2.0 --gstep 0.05] +""" +import argparse +import os +import numpy as np +import pandas as pd +from toy_core import (sample_t, base_gap_sigmoid, hessian_symmetric, + hessian_power_terms, gd_spectral_radius, gd_final_error, + sgd_expectation_exact, stop_gradient_operator, + asym_spectral_report, noise_cov_from_rms, + v_g) + + +def build_base(sigma_d, n_t=200000, seed=0, q=256.0): + t = sample_t(n_t, rng=np.random.default_rng(seed)) + delta0 = base_gap_sigmoid(t, q=q) + return t, delta0 + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--out", default=".", help="output dir") + ap.add_argument("--sigma-d", type=float, default=0.5) + ap.add_argument("--gmin", type=float, default=0.5) + ap.add_argument("--gmax", type=float, default=2.0) + ap.add_argument("--gstep", type=float, default=0.05) + ap.add_argument("--Ks", default="50,200,1000") + ap.add_argument("--noises", default="0,0.01,0.05") + ap.add_argument("--eta-scale", type=float, default=1.0, + help="eta = eta_scale / lambda_max(H at g=1)") + ap.add_argument("--n-t", type=int, default=200000) + args = ap.parse_args() + + sigma_d = args.sigma_d + t, delta0 = build_base(sigma_d, n_t=args.n_t) + gs = np.arange(args.gmin, args.gmax + 1e-9, args.gstep) + Ks = [int(x) for x in args.Ks.split(",")] + noises = [float(x) for x in args.noises.split(",")] + + # Hessian of the reference g=1 (normalization anchor for eta) + H1 = hessian_symmetric(sigma_d, t, 1.0, delta0) + lam1 = np.linalg.eigvalsh(H1) + lam1_max = lam1[-1] + eta = args.eta_scale / lam1_max + beta0 = np.ones(2) * 1e-2 # small initial error + + rows_hess = [] + rows_budget = [] + for g in gs: + H = hessian_symmetric(sigma_d, t, g, delta0) + lam = np.linalg.eigvalsh(H) + cond = lam[-1] / max(lam[0], 1e-15) + rho, _ = gd_spectral_radius(H, eta) + stable = bool(0 < eta * lam[-1] < 2) + # stability margin: distance of eta*lambda_max to the instability boundary 2 + stab_margin = 2.0 - eta * lam[-1] + + # stop-gradient asymmetric operator spectral report + A = stop_gradient_operator(sigma_d, t, g, delta0) + asym_spec, asym_norm = asym_spectral_report(A) + + rows_hess.append(dict( + g=round(float(g), 6), + lambda_min=float(lam[0]), lambda_max=float(lam[-1]), + cond=float(cond), eta=float(eta), + eta_lambda_max=float(eta * lam[-1]), + spectral_radius=float(rho), + stable=stable, + stability_margin=float(stab_margin), + asym_min=float(asym_spec[0]), asym_max=float(asym_spec[-1]), + asym_norm2=float(asym_norm), + )) + + # finite-budget final error for each (K, noise) + for K in Ks: + err0 = gd_final_error(H, beta0, eta, K)[0] + rows_budget.append(dict(g=round(float(g), 6), K=K, + noise=0.0, error=float(err0))) + for noise in noises: + if noise == 0: + continue + nu = noise_cov_from_rms(H, noise) + e = sgd_expectation_exact(H, beta0, eta, K, nu) + rows_budget.append(dict(g=round(float(g), 6), K=K, + noise=noise, error=float(e))) + + os.makedirs(args.out, exist_ok=True) + pd.DataFrame(rows_hess).to_csv(os.path.join(args.out, "toy_hessian.csv"), + index=False) + pd.DataFrame(rows_budget).to_csv(os.path.join(args.out, "toy_finite_budget.csv"), + index=False) + + # Power terms H2 H3 H4 (report: H_g = g^2 H2 + g^3 H3 + g^4 H4) + H2, H3, H4 = hessian_power_terms(sigma_d, t, gs, delta0) + power_info = pd.DataFrame({ + "coef": ["H2", "H3", "H4"], + "a00": [H2[0,0], H3[0,0], H4[0,0]], + "a01": [H2[0,1], H3[0,1], H4[0,1]], + "a11": [H2[1,1], H3[1,1], H4[1,1]], + }) + power_info.to_csv(os.path.join(args.out, "toy_hessian_power_terms.csv"), + index=False) + + print(f"sigma_d={sigma_d}, eta={eta:.6f} (= {args.eta_scale}/lam1_max), lam1_max={lam1_max:.4f}") + print(f"H_g power terms (g^2,g^3,g^4):") + print(power_info.to_string(index=False)) + print(f"\nhessian rows: {len(rows_hess)}, budget rows: {len(rows_budget)}") + print("saved:", os.path.join(args.out, "toy_hessian.csv"), + os.path.join(args.out, "toy_finite_budget.csv")) + + +if __name__ == "__main__": + main() diff --git a/theory/separation.py b/theory/separation.py new file mode 100644 index 00000000..feb90dd8 --- /dev/null +++ b/theory/separation.py @@ -0,0 +1,131 @@ +"""Instantaneous-statistics-insufficiency counterexample (PR #33 review rev.2). + +EXACT second-moment recursion (no Monte-Carlo noise) for two stochastic-gradient +oracles that share the SAME curvature H_g (same instantaneous criterion) but +differ in gradient-noise covariance Sigma_g: + + M_{k+1} = B_g M_k B_g^T + eta^2 Sigma_g^(e), B_g = I - eta H_g, M_0 = beta0 beta0^T + E_K(g; e) = Tr(M_K) + +We run TWO settings: + +(A) trace-matched (pure direction): Sigma^(2) rescaled to Tr == Tr(Sigma^(1)). + -> separation COLLAPSES. Pure noise DIRECTION (with equal power) is NOT + enough in this 2-param toy; the optima coincide. + +(B) realistic structure (the physics): Sigma^(1) = H_g (g-dependent, ~g^2, + from the symmetric-loss noise feature v_g which contains Delta~g); + Sigma^(2) = sigma_d^2 E[[t,t^2][t,t^2]^T] (g-INDEPENDENT, from the + stop-gradient noise feature [t,t^2] which has no g). + -> separation APPEARS: env1 g* -> small g (noise grows with g), + env2 g* -> large g (noise flat, only convergence improves). + The difference is NOT a learning-rate effect (eta is fixed, not LR-matched): + it is that the stop-gradient noise covariance does NOT scale with g while + the curvature does. This is a genuine finite-horizon gap-dependence that + the instantaneous criterion (Tr(H_g), same for both) cannot resolve. + +Setting (B) is the real counterexample; (A) is reported as an honest negative +result showing that direction alone (under equal trace) is insufficient here, +so the separation genuinely needs the g-dependent vs g-independent structure. + +Motivation for env2's g-independence: in ECT, the stop-gradient loss uses the +online-branch Jacobian J_t = [t, t^2] (d f_t / d beta), which does NOT depend +on the gap g (g only enters the residual v_g = [t-r, t^2-r^2] through r). Hence +its gradient-noise covariance is g-independent even though its population +curvature H_g is the same as the symmetric loss. See novelty_and_propositions.md +sec 5 for the A_g analysis. +""" +import os +import numpy as np +import pandas as pd +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from toy_core import sample_t, base_gap_sigmoid, hessian_symmetric + + +def exact_sgd_error(H, Sigma, eta, beta0, K): + """Exact expected squared error E||beta_K||^2 = Tr(M_K).""" + d = H.shape[0] + B = np.eye(d) - eta * H + M = np.outer(beta0, beta0) + eta2S = (eta ** 2) * Sigma + for _ in range(K): + M = B @ M @ B.T + eta2S + return float(np.trace(M)) + + +def run(sigma_d=0.5, out="."): + os.makedirs(os.path.join(out, "figures"), exist_ok=True) + t = sample_t(300000, rng=np.random.default_rng(0)) + d0 = base_gap_sigmoid(t) + H1 = hessian_symmetric(sigma_d, t, 1.0, d0) + lam1_max = np.linalg.eigvalsh(H1)[-1] + Jt = np.stack([t, t ** 2], axis=-1) + Sigma2_struct = sigma_d ** 2 * (Jt.T @ Jt / len(t)) # g-independent structure + + gs = np.arange(0.5, 1.46, 0.025) + Ks = [50, 200, 1000] + beta0 = np.ones(2) * 1e-2 + + rows = [] + summary = [] + for eta_scale in [0.25, 0.5, 1.0]: + eta = eta_scale / lam1_max + for setting in ["trace_matched", "realistic"]: + for K in Ks: + recs = [] + for g in gs: + H = hessian_symmetric(sigma_d, t, g, d0) + trH = np.trace(H) + if setting == "trace_matched": + S1 = H.copy() + S2 = Sigma2_struct * (trH / np.trace(Sigma2_struct)) + else: # realistic: g-dependent vs g-independent (no rescale) + S1 = H.copy() + S2 = Sigma2_struct.copy() + e1 = exact_sgd_error(H, S1, eta, beta0, K) + e2 = exact_sgd_error(H, S2, eta, beta0, K) + rows.append(dict(eta_scale=eta_scale, setting=setting, g=round(float(g),4), + K=K, env=1, error=e1)) + rows.append(dict(eta_scale=eta_scale, setting=setting, g=round(float(g),4), + K=K, env=2, error=e2)) + recs.append((g, e1, e2)) + g1 = min(recs, key=lambda x: x[1])[0] + g2 = min(recs, key=lambda x: x[2])[0] + summary.append(dict(eta_scale=eta_scale, setting=setting, K=K, + g1=round(g1,3), g2=round(g2,3), + differ=abs(g1-g2) > 0.02)) + + df = pd.DataFrame(rows) + df.to_csv(os.path.join(out, "toy_separation.csv"), index=False) + sdf = pd.DataFrame(summary) + sdf.to_csv(os.path.join(out, "toy_separation_summary.csv"), index=False) + + print("=== separation results (eta fixed, not LR-matched) ===") + print(sdf.to_string(index=False)) + print() + real = sdf[sdf.setting == "realistic"] + any_sep = real.differ.any() + print("=> REALISTIC separation established:" if any_sep else "=> no separation:") + print(" instantaneous criterion identical (same H_g), but g_K* differs because") + print(" Sigma^(1)~H_g (g-dep) vs Sigma^(2) g-independent (stop-grad structure).") + + # plot realistic, eta_scale=1.0 + fig, ax = plt.subplots(figsize=(6.5, 4.5)) + sub = df[(df.eta_scale == 1.0) & (df.setting == "realistic")] + for K in Ks: + s = sub[(sub.env == 1) & (sub.K == K)].sort_values("g") + p = sub[(sub.env == 2) & (sub.K == K)].sort_values("g") + ax.plot(s.g, s.error, "-", label=f"env1 Sigma~H_g (g-dep) K={K}") + ax.plot(p.g, p.error, "--", label=f"env2 Sigma~[t,t^2] (g-indep) K={K}") + ax.set_xlabel("g"); ax.set_ylabel("E||beta_K||^2 (exact)") + ax.set_yscale("log"); ax.legend(fontsize=7); ax.grid(alpha=0.3) + ax.set_title("Same H_g, different Sigma_g(g-dependence) -> different g_K*") + fig.tight_layout() + fig.savefig(os.path.join(out, "figures", "toy_separation.pdf")) + print("saved figures/toy_separation.pdf") + + +if __name__ == "__main__": + run(out=".") diff --git a/theory/test_toy_core.py b/theory/test_toy_core.py new file mode 100644 index 00000000..869b289e --- /dev/null +++ b/theory/test_toy_core.py @@ -0,0 +1,147 @@ +"""Unit tests for the toy model core (review minor item: add basic tests). + +Run: python -m pytest theory/test_toy_core.py OR python theory/test_toy_core.py +""" +import numpy as np +import sys, os +_HERE = os.path.dirname(os.path.abspath(__file__)) +sys.path.insert(0, _HERE) +# toy_core.py lives one level up (repo root of the toy) when tests run in-tree; +# when shipped under recurrence_of_ect/theory/, toy_core.py is in the same dir. +sys.path.insert(0, os.path.dirname(_HERE)) +from toy_core import (sample_t, base_gap_sigmoid, hessian_symmetric, + stop_gradient_operator, v_g, hessian_power_terms, + gd_final_error, sgd_expectation_exact, noise_cov_from_rms) + + +def _base(seed=0, n=50000): + rng = np.random.default_rng(seed) + t = sample_t(n, rng=rng) + d0 = base_gap_sigmoid(t) + return t, d0 + + +def test_v_g_second_component_equals_t2_minus_r2(): + """v_g[1] = 2 t Delta - Delta^2 must equal t^2 - r^2 with r = t - Delta.""" + t, d0 = _base() + g = 1.1 + Delta = np.minimum(g * d0, t - 1e-3) + r = t - Delta + V = v_g(t, g, d0) + np.testing.assert_allclose(V[:, 1], t**2 - r**2, rtol=1e-10) + np.testing.assert_allclose(V[:, 0], Delta, rtol=1e-10) + + +def test_hessian_symmetric_and_positive_semidefinite(): + t, d0 = _base() + H = hessian_symmetric(0.5, t, 1.0, d0) + assert H.shape == (2, 2) + assert np.allclose(H, H.T) + lam = np.linalg.eigvalsh(H) + assert lam[0] >= -1e-9, "H_g must be PSD" + + +def test_stop_gradient_operator_matches_finite_difference(): + """A_g beta must equal E_z[ per-sample stop-grad gradient ] (analytic E_z). + + Per-sample grad = z^2 (v_g^T beta) [t, t^2]; E_z[z^2] = sigma_d^2. + """ + sigma_d = 0.5 + t, d0 = _base(seed=1, n=200000) + g = 1.1 + Delta = np.minimum(g * d0, t - 1e-3) + r = t - Delta + vg = np.stack([Delta, t**2 - r**2], axis=-1) + Jt = np.stack([t, t**2], axis=-1) + beta = np.array([0.03, -0.02]) + A = stop_gradient_operator(sigma_d, t, g, d0) + # analytic E_z then E_t + grad_analytic = (sigma_d**2) * np.mean((vg @ beta)[:, None] * Jt, axis=0) + np.testing.assert_allclose(grad_analytic, A @ beta, rtol=1e-8, atol=1e-10) + + +def test_population_loss_curvature_equals_Hg(): + """Population stop-grad loss = 1/2 beta^T H_g beta (same as symmetric).""" + sigma_d = 0.5 + t, d0 = _base(seed=2, n=300000) + g = 1.2 + H = hessian_symmetric(sigma_d, t, g, d0) + beta = np.array([0.02, -0.01]) + Delta = np.minimum(g * d0, t - 1e-3) + r = t - Delta + vg = np.stack([Delta, t**2 - r**2], axis=-1) + z = np.random.default_rng(7).normal(0, sigma_d, size=len(t)) + R = z * (vg @ beta) + L_mc = 0.5 * np.mean(R**2) + L_pred = 0.5 * beta @ H @ beta + assert abs(L_mc - L_pred) / abs(L_pred) < 0.05 # MC tolerance + + +def test_hessian_power_terms_reconstruct_Hg(): + """H_g should be reconstructible from g^2 H2 + g^3 H3 + g^4 H4 (modulo clip).""" + sigma_d = 0.5 + t, d0 = _base(seed=3, n=200000) + H2, H3, H4 = hessian_power_terms(sigma_d, t, np.arange(0.5, 3.01, 0.1), d0) + for g in [0.8, 1.0, 1.3]: + H_direct = hessian_symmetric(sigma_d, t, g, d0) + H_poly = g**2 * H2 + g**3 * H3 + g**4 * H4 + # allow residual from clipping (report it, don't fail hard) + rel = np.linalg.norm(H_direct - H_poly) / np.linalg.norm(H_direct) + assert rel < 0.05, f"g={g} reconstruction rel-residual {rel}" + + +def test_lr_matched_makes_eta_lambda_max_constant(): + """LR-matched eta keeps eta*lambda_max(H_g) constant across g.""" + sigma_d = 0.5 + t, d0 = _base() + H1 = hessian_symmetric(sigma_d, t, 1.0, d0) + target = 1.0 + for g in [0.6, 1.0, 1.3]: + H = hessian_symmetric(sigma_d, t, g, d0) + eta = target / np.linalg.eigvalsh(H)[-1] + prod = eta * np.linalg.eigvalsh(H)[-1] + assert abs(prod - target) < 1e-9 + + +def test_gd_final_error_decreases_with_K(): + """More iterations -> smaller error (in the stable regime).""" + sigma_d = 0.5 + t, d0 = _base() + H = hessian_symmetric(sigma_d, t, 1.0, d0) + eta = 0.5 / np.linalg.eigvalsh(H)[-1] + beta0 = np.array([0.1, 0.1]) + e50 = gd_final_error(H, beta0, eta, 50)[0] + e1000 = gd_final_error(H, beta0, eta, 1000)[0] + assert e1000 < e50 + + +def test_separation_exact_recursion_matches_closed_form(): + """Exact recursion E_K = Tr(M_K) must match the known 1-D closed form. + + For d=1 (scalar H, Sigma, beta0): E_K = (1-eta*H)^{2K} beta0^2 + + eta^2 Sigma * (1 - (1-eta*H)^{2K}) / (eta*H*(2-eta*H)). + The matrix recursion must reproduce this exactly. + """ + from separation import exact_sgd_error + H = np.array([[3.0]]); Sig = np.array([[0.5]]); eta = 0.2; b0 = np.array([1.0]); K = 100 + rec = exact_sgd_error(H, Sig, eta, b0, K) + lam = 3.0; r = 1 - eta*lam; r2K = r**(2*K) + closed = r2K * b0[0]**2 + (eta**2 * Sig[0,0]) * (1 - r2K) / (eta*lam*(2-eta*lam)) + assert abs(rec - closed) / abs(closed) < 1e-9 + + +def _run_all(): + fns = [v for k, v in sorted(globals().items()) if k.startswith("test_")] + passed = 0 + for fn in fns: + try: + fn() + print(f"PASS {fn.__name__}") + passed += 1 + except AssertionError as e: + print(f"FAIL {fn.__name__}: {e}") + print(f"\n{passed}/{len(fns)} passed") + + +if __name__ == "__main__": + _run_all() diff --git a/theory/toy_core.py b/theory/toy_core.py new file mode 100644 index 00000000..1e9e2f6e --- /dev/null +++ b/theory/toy_core.py @@ -0,0 +1,190 @@ +"""Linear-Gaussian toy model for ECT gap calibration theory. + +Implements the math from the research report (report §0.3/0.5): + - p_t = N(0, sigma_d^2 + t^2), x_t = m(t) z, m(t) = sqrt(sigma_d^2+t^2)/sigma_d + - ideal consistency map f^*(x,t) = x / m(t) + - boundary-correct model f_beta(x,t) = (x/m(t)) (1 + beta1 t + beta2 t^2) + - same-trajectory pair residual = z v_g(t)^T beta, v_g(t) = [Delta, 2 t Delta - Delta^2]^T + - symmetric population Hessian H_g = sigma_d^2 E_t[v_g v_g^T] = g^2 H2 + g^3 H3 + g^4 H4 + - full-batch GD: beta_K = (I - eta H_g)^K beta_0, rho_g = max_j |1 - eta lambda_j(H_g)| + - noisy SGD per eigen-direction j: + E[beta_Kj^2] = (1-eta*lambda_j)^{2K} beta_0j^2 + + eta*nu_j [1 - (1-eta*lambda_j)^{2K}] / (lambda_j (2 - eta*lambda_j)) + - stop-gradient asymmetric operator A_g = E[J_t^T (J_t - J_r)] +""" +from __future__ import annotations + +import numpy as np +from numpy.linalg import eigvalsh + + +# -------------------------------------------------------------------------- +# Data / schedule primitives +# -------------------------------------------------------------------------- + +def sample_t(n, mean=-1.1, std=2.0, t_min=1e-3, t_max=100.0, rng=None): + """Sample t ~ LogNormal(mean, std), clipped to [t_min, t_max].""" + rng = rng if rng is not None else np.random.default_rng(0) + t = np.exp(rng.normal(mean, std, size=n)) + return np.clip(t, t_min, t_max) + + +def base_gap_sigmoid(t, q=256.0, k=8.0, b=1.0, stage=0.0): + """Official ECT sigmoid base gap delta0(t) = t - r_sigmoid(t). + + r/t = 1 - decay * (1 + k*sigmoid(-b t)), decay = 1/q^(stage+1). + delta0 = t - r = t * decay * (1 + k*sigmoid(-b t)). + """ + decay = 1.0 / q ** (stage + 1.0) + adj = 1.0 + k * 1.0 / (1.0 + np.exp(b * t)) + return t * decay * adj + + +# -------------------------------------------------------------------------- +# v_g, Hessian +# -------------------------------------------------------------------------- + +def v_g(t, g, delta0, t_min=1e-3): + """Feature vector v_g(t) = [Delta, 2 t Delta - Delta^2]^T. + + Delta = min(g * delta0, t - t_min). + """ + Delta = np.minimum(g * delta0, t - t_min) + v1 = Delta + v2 = 2.0 * t * Delta - Delta ** 2 + return np.stack([v1, v2], axis=-1) # (n, 2) + + +def hessian_symmetric(sigma_d, t, g, delta0, t_min=1e-3): + """H_g = sigma_d^2 E_t[v_g(t) v_g(t)^T] (population, Monte-Carlo in t).""" + V = v_g(t, g, delta0, t_min) # (n, 2) + G = np.einsum("ni,nj->ij", V, V) / len(t) + return sigma_d ** 2 * G + + +def hessian_power_terms(sigma_d, t, g, delta0, t_min=1e-3): + """Fit H_g = g^2 H2 + g^3 H3 + g^4 H4 exactly (no MC noise if same t). + + delta0 does not depend on g, so H_g = sigma_d^2 E[v_g v_g^T] is a + polynomial in g of degree <= 4. We recover H2,H3,H4 by evaluating at + g in {0,1,-1,2} and inverting the Vandermonde on the (g^2,g^3,g^4) basis. + """ + # Solve H(g) = a2*g^2 + a3*g^3 + a4*g^4 for the matrix-valued coefficients + # using least squares over a fine g-grid; exact because only 3 unknowns. + gs = np.array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0]) + Hs = np.stack([hessian_symmetric(sigma_d, t, gg, delta0, t_min) for gg in gs]) + A = np.stack([gs ** 2, gs ** 3, gs ** 4], axis=1) # (6,3) + coefs, *_ = np.linalg.lstsq(A, Hs.reshape(6, -1), rcond=None) + coefs = coefs.reshape(3, 2, 2) + return coefs[0], coefs[1], coefs[2] # H2, H3, H4 + + +# -------------------------------------------------------------------------- +# Optimization +# -------------------------------------------------------------------------- + +def gd_spectral_radius(H, eta): + lam = eigvalsh(H) + return np.max(np.abs(1.0 - eta * lam)), lam + + +def gd_final_error(H, beta0, eta, K): + """beta_K = (I - eta H)^K beta0; returns ||beta_K||^2 and ||beta_K||. + + Uses eigendecomposition with |1 - eta*lam| clipped for stability near the + instability boundary (|r| > 1 diverges; we report its magnitude). + """ + lam, V = np.linalg.eigh(H) + r = 1.0 - eta * lam + rK = r ** K + betaK = (V * rK) @ (V.T @ beta0) + return np.linalg.norm(betaK) ** 2, np.linalg.norm(betaK) + + +def sgd_expectation_exact(H, beta0, eta, K, nu_vec): + """Exact expected squared norm of beta_K under additive SGD noise. + + Per eigen-direction j: + E[beta_Kj^2] = (1-eta*lambda_j)^{2K} beta_0j^2 + + eta*nu_j [1-(1-eta*lambda_j)^{2K}] / (lambda_j (2 - eta*lambda_j)) + """ + lam, V = np.linalg.eigh(H) + beta0_proj = V.T @ beta0 + r = 1.0 - eta * lam + r2K = r ** (2 * K) + bias = r2K * beta0_proj ** 2 + denom = lam * (2.0 - eta * lam) + noise = np.where(denom > 0, eta * nu_vec * (1.0 - r2K) / np.maximum(denom, 1e-12), 0.0) + expected_sq = np.sum(bias + noise) + return expected_sq + + +# -------------------------------------------------------------------------- +# Stop-gradient asymmetric operator +# -------------------------------------------------------------------------- + +def stop_gradient_operator(sigma_d, t, g, delta0, t_min=1e-3, stage=0.0): + """Stop-gradient linear update operator for ECT. + + Model: f_beta(x_t, t) = z (1 + beta1 t + beta2 t^2) (x_t = m(t) z). + Stop-gradient loss: L = 1/2 ( f_beta(x_t,t) - sg f_beta(x_r,r) )^2. + + Pair residual (linear in beta): + f_t - sg f_r = z [ beta1 (t-r) + beta2 (t^2 - r^2) ] = z v_g(t)^T beta, + with v_g(t) = [ t-r , t^2 - r^2 ]^T, r = t - Delta, Delta = min(g*delta0, t-t_min). + + Online-branch Jacobian (d f_t / d beta): + J_t = z [ t , t^2 ]^T. + + Gradient of the stop-gradient loss: + grad_beta L = (f_t - sg f_r) * d f_t / d beta (target has no gradient) + = z^2 (v_g^T beta) [ t , t^2 ]^T + = z^2 ( J_t_col v_g^T ) beta, + so the linear operator acting on beta is z^2 [t,t^2]^T v_g^T, and its + population expectation (E[z^2] = sigma_d^2) is + + A_g = sigma_d^2 * E_t[ [t, t^2]^T v_g(t)^T ] (2x2, asymmetric). + + Note J_t_col = [t, t^2] is NOT v_g (v_g = [t-r, t^2-r^2]); the asymmetry of + A_g vs the symmetric Hessian H_g = sigma_d^2 E_t[v_g v_g^T] is exactly what + distinguishes stop-gradient ECT from the symmetric population loss. + + IMPORTANT: the population *loss* E[L] = 1/2 beta^T H_g beta is identical for + the symmetric and stop-gradient losses (curvature is H_g either way); only + the per-sample gradient NOISE differs (symmetric uses v_g, stop-gradient uses + [t, t^2]). This is the basis of the ADCM separation counterexample. + """ + Delta = np.minimum(g * delta0, t - t_min) + r = t - Delta + # residual feature v_g(t) = [t - r, t^2 - r^2] (t-r == Delta) + vg = np.stack([Delta, t ** 2 - r ** 2], axis=-1) # (n, 2) + # online-branch Jacobian column J_t = [t, t^2] (d f_beta / d beta = z*[t, t^2]) + Jt = np.stack([t, t ** 2], axis=-1) # (n, 2) + # A[i,j] = E_n[ Jt_col[i] * v_g[j] ] = E[ [t,t^2]^T (v_g)^T ] + A = np.einsum("ni,nj->ij", Jt, vg) / len(t) + return sigma_d ** 2 * A + + +def asym_spectral_report(A): + """Report symmetric part (A+A^T)/2 and antisymmetric (A-A^T)/2 spectra.""" + As = 0.5 * (A + A.T) + Aa = 0.5 * (A - A.T) + return eigvalsh(As), np.linalg.norm(Aa, ord=2) + + +# -------------------------------------------------------------------------- +# Noise model +# -------------------------------------------------------------------------- + +def noise_cov_from_rms(H, rms): + """Build additive noise covariance Sigma with given per-component RMS. + + We inject noise on the gradient as gaussian with covariance chosen so that + the *effect on beta* has per-eigenvalue noise variance nu_j = rms^2. + For the toy, the cleanest interpretation: gradient noise ~ N(0, rms^2 I) + in beta space => nu_j = rms^2 for all j (since eigenbasis of H). + Returns vector nu (per eigenvalue) and a gradient-noise sampler. + """ + d = H.shape[0] + nu = np.full(d, rms * rms) + return nu diff --git a/theory/toy_finite_budget.csv b/theory/toy_finite_budget.csv new file mode 100644 index 00000000..41dbf7fa --- /dev/null +++ b/theory/toy_finite_budget.csv @@ -0,0 +1,280 @@ +g,K,noise,error +0.5,50,0.0,9.876225374157688e-05 +0.5,50,0.01,0.0002875292735266182 +0.5,50,0.05,0.004817937748367612 +0.5,200,0.0,9.867621013107988e-05 +0.5,200,0.01,0.0008287096590410178 +0.5,200,0.05,0.018349512432879536 +0.5,1000,0.0,9.821857532293252e-05 +0.5,1000,0.01,0.003707052171110869 +0.5,1000,0.05,0.09031905847002138 +0.55,50,0.0,9.875611024302058e-05 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+0.5,trace_matched,50,0.5,0.5,False +0.5,trace_matched,200,0.5,0.5,False +0.5,trace_matched,1000,0.5,0.5,False +0.5,realistic,50,0.5,1.425,True +0.5,realistic,200,0.5,1.425,True +0.5,realistic,1000,0.5,1.425,True +1.0,trace_matched,50,0.5,0.5,False +1.0,trace_matched,200,0.5,0.5,False +1.0,trace_matched,1000,0.5,0.5,False +1.0,realistic,50,0.5,1.0,True +1.0,realistic,200,0.5,1.0,True +1.0,realistic,1000,0.5,1.0,True diff --git a/theory/verify_sg.py b/theory/verify_sg.py new file mode 100644 index 00000000..d712b26c --- /dev/null +++ b/theory/verify_sg.py @@ -0,0 +1,88 @@ +"""Verify the corrected stop-gradient operator A_g by finite differences. + +Checks (on the linear-Gaussian toy): + 1. population loss E[L] = 1/2 beta^T H_g beta (curvature == symmetric H_g) + 2. population gradient == H_g beta (== sigma_d^2 A_g ... wait: grad = sigma_d^2 A_g beta? ) + 3. per-sample gradient == z^2 (v_g^T beta) [t, t^2] -> empirical mean == sigma_d^2 A_g beta + 4. symmetric part of A_g vs H_g (they need NOT be equal) +""" +import numpy as np +from toy_core import (sample_t, base_gap_sigmoid, hessian_symmetric, + stop_gradient_operator, v_g) + + +def loss_and_grad_sample(z, t, r, beta): + """Per-sample stop-gradient loss and gradient for ONE (z, t) pair. + + f_t = z (1 + b1 t + b2 t^2), f_r = z (1 + b1 r + b2 r^2) + residual R = f_t - f_r = z [ b1 (t-r) + b2 (t^2 - r^2) ] = z v_g^T beta + L = 0.5 R^2 + grad_beta L = R * d f_t / d beta = R * z [t, t^2]^T = z^2 (v_g^T beta) [t, t^2]^T + """ + vg = np.array([t - r, t ** 2 - r ** 2]) + R = z * (vg @ beta) + L = 0.5 * R ** 2 + Jt = np.array([t, t ** 2]) + grad = (z ** 2) * (vg @ beta) * Jt + return L, grad + + +def main(): + rng = np.random.default_rng(0) + sigma_d = 0.5 + t = sample_t(200000, rng=rng) + delta0 = base_gap_sigmoid(t) + g = 1.1 + Delta = np.minimum(g * delta0, t - 1e-3) + r = t - Delta + + H = hessian_symmetric(sigma_d, t, g, delta0) + A = stop_gradient_operator(sigma_d, t, g, delta0) + print("H_g (symmetric Hessian) =\n", H) + print("A_g (stop-gradient op) =\n", A) + print("sym(A_g) =\n", 0.5 * (A + A.T)) + print("||A_g - sym(A_g)|| (antisym) =", np.linalg.norm(A - A.T) / 2) + + beta = np.array([0.03, -0.02]) + + # --- Check 1: population loss curvature == H_g --- + # Monte-Carlo population loss + z = rng.normal(0, sigma_d, size=len(t)) + # vectorized per-sample loss + vg_all = np.stack([Delta, t ** 2 - r ** 2], axis=-1) # (n,2) + R_all = z * (vg_all @ beta) # (n,) + L_mc = 0.5 * np.mean(R_all ** 2) + L_pred = 0.5 * beta @ H @ beta + print(f"\n[1] population loss: MC={L_mc:.6e} pred 0.5 b^T H_g b={L_pred:.6e} " + f"rel_err={abs(L_mc-L_pred)/abs(L_pred):.2e}") + + # --- Check 2: population gradient == H_g beta (mean of per-sample grad) --- + # per-sample grad = z^2 (v_g^T beta) [t, t^2] + Jt_all = np.stack([t, t ** 2], axis=-1) # (n,2) + grad_all = (z ** 2)[:, None] * (vg_all @ beta)[:, None] * Jt_all # (n,2) + grad_mc = grad_all.mean(axis=0) + grad_pred_H = H @ beta + print(f"[2] pop gradient: MC={grad_mc} H_g@beta={grad_pred_H} " + f"rel_err={np.linalg.norm(grad_mc-grad_pred_H)/np.linalg.norm(grad_pred_H):.2e}") + + # --- Check 3: mean per-sample grad == A_g beta (A_g already includes sigma_d^2) --- + grad_pred_A = A @ beta + print(f"[3] pop gradient: MC={grad_mc} A_g@beta={grad_pred_A} " + f"rel_err={np.linalg.norm(grad_mc-grad_pred_A)/max(np.linalg.norm(grad_pred_A),1e-15):.2e}") + print(" (A_g already folds in sigma_d^2; MC uses z^2 so it also folds it in)") + + # --- Check 4: finite-difference gradient on the population loss --- + # fd grad of E[L] should equal H_g beta (since pop loss = 0.5 b^T H_g b) + eps = 1e-5 + def poploss(bb): + Rr = z * (vg_all @ bb) + return 0.5 * np.mean(Rr ** 2) + fd = np.zeros(2) + fd[0] = (poploss(beta + [eps, 0]) - poploss(beta - [eps, 0])) / (2 * eps) + fd[1] = (poploss(beta + [0, eps]) - poploss(beta - [0, eps])) / (2 * eps) + print(f"[4] finite-diff pop grad = {fd} H_g@beta={grad_pred_H} " + f"rel_err={np.linalg.norm(fd-grad_pred_H)/np.linalg.norm(grad_pred_H):.2e}") + + +if __name__ == "__main__": + main() diff --git a/training/ct_training_loop.py b/training/ct_training_loop.py index fdafa44a..9ac2b09b 100644 --- a/training/ct_training_loop.py +++ b/training/ct_training_loop.py @@ -1,9 +1,13 @@ import os +import csv import time import copy +import filecmp import json +import math import pickle import psutil +import shutil import functools import PIL.Image import numpy as np @@ -15,6 +19,414 @@ from metrics import metric_main +# Per-attempted-iteration CSV for paired fixed/adaptive comparisons. +# Schedule telemetry comes exclusively from loss_fn.schedule_runtime_metrics(). +_LEGACY_TRAIN_SUMMARY_FIELDS = ( + 'attempted_iteration', + 'successful_optimizer_steps', + 'processed_nimg', + 'processed_kimg', + 'loss', + 'grad_scale', + 'step_skipped', + 'schedule', + 'stage', + 'elapsed_sec', + 'peak_vram_gb', +) + +# The telemetry schema predating next_loop_cur_tick. Keep this exact tuple so +# resumed runs can be migrated without guessing historical tick state. +_PRE_NEXT_LOOP_TICK_TRAIN_SUMMARY_FIELDS = ( + 'attempted_iteration', + 'successful_optimizer_steps', + 'processed_nimg', + 'processed_kimg', + 'loss', + 'grad_scale', + 'step_skipped', + 'schedule', + 'stage', + 'loss_ema', + 'loss_reference', + 'correction', + 'signal_updates', + 'adaptive_active', + 'r_over_t_mean', + 'gap_mean', + 'elapsed_sec', + 'peak_vram_gb', +) + +# Schema used by the completed 2026-07 gap-factorial runs. Keep it exact so +# those checkpoints can resume after the clipping diagnostics were added. +_PRE_GAP_DIAGNOSTICS_TRAIN_SUMMARY_FIELDS = ( + 'attempted_iteration', + 'successful_optimizer_steps', + 'processed_nimg', + 'processed_kimg', + 'loss', + 'grad_scale', + 'step_skipped', + 'schedule', + 'stage', + # The state that will be used by the next loop iteration. At a + # maintenance boundary this is also the cur_tick persisted in a checkpoint. + 'next_loop_cur_tick', + 'loss_ema', + 'loss_reference', + 'correction', + 'signal_updates', + 'adaptive_active', + 'r_over_t_mean', + 'gap_mean', + 'elapsed_sec', + 'peak_vram_gb', +) + +_TRAIN_SUMMARY_FIELDS = ( + *_PRE_GAP_DIAGNOSTICS_TRAIN_SUMMARY_FIELDS[:-2], + 'gap_over_sigmoid_gap_mean', + 'lower_gap_clip_rate', + 'upper_gap_clip_rate', + *_PRE_GAP_DIAGNOSTICS_TRAIN_SUMMARY_FIELDS[-2:], +) + +#---------------------------------------------------------------------------- + +def load_and_migrate_train_summary(summary_path): + """Load a resume CSV, upgrading only known historical schemas. + + Values absent from the original schema cannot be reconstructed, so their + migrated cells deliberately stay empty. The original file is retained + beside the upgraded CSV for auditability. + """ + with open(summary_path, 'rt', newline='') as handle: + reader = csv.DictReader(handle) + fieldnames = tuple(reader.fieldnames or ()) + rows = list(reader) + + if not rows: + raise RuntimeError(f'resume requested but {summary_path} has no data rows') + if fieldnames == _TRAIN_SUMMARY_FIELDS: + return rows, None + if fieldnames == _LEGACY_TRAIN_SUMMARY_FIELDS: + backup_path = f'{summary_path}.pre-telemetry.bak' + elif fieldnames == _PRE_NEXT_LOOP_TICK_TRAIN_SUMMARY_FIELDS: + backup_path = f'{summary_path}.pre-next-loop-tick.bak' + elif fieldnames == _PRE_GAP_DIAGNOSTICS_TRAIN_SUMMARY_FIELDS: + backup_path = f'{summary_path}.pre-gap-diagnostics.bak' + else: + raise RuntimeError( + f'resume requested but {summary_path} has an unsupported schema; ' + 'expected the current schema or an exact supported legacy schema' + ) + + if os.path.exists(backup_path): + if not filecmp.cmp(summary_path, backup_path, shallow=False): + raise RuntimeError( + f'refuse to overwrite non-matching train-summary backup: {backup_path}' + ) + else: + shutil.copy2(summary_path, backup_path) + + migrated_rows = [ + {field: row.get(field, '') for field in _TRAIN_SUMMARY_FIELDS} + for row in rows + ] + temporary_path = f'{summary_path}.telemetry-migration.tmp-{os.getpid()}' + try: + with open(temporary_path, 'wt', newline='') as handle: + writer = csv.DictWriter(handle, fieldnames=_TRAIN_SUMMARY_FIELDS) + writer.writeheader() + writer.writerows(migrated_rows) + handle.flush() + os.fsync(handle.fileno()) + os.replace(temporary_path, summary_path) + except BaseException: + if os.path.exists(temporary_path): + os.unlink(temporary_path) + raise + return migrated_rows, backup_path + +#---------------------------------------------------------------------------- + +def adaptive_update_interval_nimg(update_kimg): + """Convert an adaptive update period to an exact image-count interval.""" + update_kimg = float(update_kimg) + update_nimg = update_kimg * 1000 + if not math.isfinite(update_kimg) or update_kimg <= 0 or not update_nimg.is_integer(): + raise ValueError( + f'adaptive_update_kimg must be positive and represent whole images, got {update_kimg}' + ) + return int(update_nimg) + + +class AdaptiveSignalWindow: + """Accumulate local loss until the next absolute adaptive-update boundary. + + Windows are deliberately independent of maintenance ticks so changing + --tick does not change the controller's update frequency. + """ + + def __init__(self, update_kimg, start_nimg=0): + self.update_nimg = adaptive_update_interval_nimg(update_kimg) + start_nimg = int(start_nimg) + if start_nimg < 0: + raise ValueError(f'start_nimg must be non-negative, got {start_nimg}') + self.next_update_nimg = (start_nimg // self.update_nimg + 1) * self.update_nimg + self.loss_sum = 0.0 + self.loss_count = 0 + + def add(self, loss_sum, loss_count): + self.loss_sum += float(loss_sum) + self.loss_count += int(loss_count) + + def pop_if_due(self, cur_nimg): + cur_nimg = int(cur_nimg) + if cur_nimg < self.next_update_nimg: + return None + loss_sum, loss_count = self.loss_sum, self.loss_count + self.loss_sum = 0.0 + self.loss_count = 0 + while self.next_update_nimg <= cur_nimg: + self.next_update_nimg += self.update_nimg + return loss_sum, loss_count + + def state_dict(self): + """Return all state needed to resume a partially accumulated window.""" + return { + 'update_nimg': self.update_nimg, + 'next_update_nimg': self.next_update_nimg, + 'loss_sum': self.loss_sum, + 'loss_count': self.loss_count, + } + + def load_state_dict(self, state): + """Restore a window checkpointed after an arbitrary training step.""" + if not isinstance(state, dict): + raise ValueError('adaptive signal window state must be a dict') + required = ('update_nimg', 'next_update_nimg', 'loss_sum', 'loss_count') + missing = [name for name in required if name not in state] + if missing: + raise ValueError( + f'adaptive signal window state missing required fields: {", ".join(missing)}' + ) + update_nimg = int(state['update_nimg']) + next_update_nimg = int(state['next_update_nimg']) + loss_sum = float(state['loss_sum']) + loss_count = int(state['loss_count']) + if update_nimg != self.update_nimg: + raise ValueError( + f'adaptive signal window interval mismatch: checkpoint={update_nimg}, ' + f'current={self.update_nimg}' + ) + if next_update_nimg <= 0 or next_update_nimg % self.update_nimg != 0: + raise ValueError( + f'invalid adaptive signal window next_update_nimg: {next_update_nimg}' + ) + if loss_count < 0: + raise ValueError(f'adaptive signal window loss_count must be non-negative, got {loss_count}') + self.next_update_nimg = next_update_nimg + self.loss_sum = loss_sum + self.loss_count = loss_count + + +class LocalTBinSignalWindow: + """Accumulate raw pair-loss sums/counts for equal-probability t bins.""" + + def __init__(self, update_kimg, num_bins, start_nimg=0): + self.update_nimg = adaptive_update_interval_nimg(update_kimg) + if isinstance(num_bins, bool) or int(num_bins) != num_bins or int(num_bins) < 2: + raise ValueError(f'num_bins must be an integer >= 2, got {num_bins}') + self.num_bins = int(num_bins) + start_nimg = int(start_nimg) + if start_nimg < 0: + raise ValueError(f'start_nimg must be non-negative, got {start_nimg}') + self.next_update_nimg = (start_nimg // self.update_nimg + 1) * self.update_nimg + self.loss_sums = [0.0] * self.num_bins + self.loss_counts = [0] * self.num_bins + + def add(self, loss_sums, loss_counts): + if len(loss_sums) != self.num_bins or len(loss_counts) != self.num_bins: + raise ValueError('local t-bin signal size mismatch') + for index in range(self.num_bins): + self.loss_sums[index] += float(loss_sums[index]) + self.loss_counts[index] += int(loss_counts[index]) + + def pop_if_due(self, cur_nimg): + cur_nimg = int(cur_nimg) + if cur_nimg < self.next_update_nimg: + return None + result = (list(self.loss_sums), list(self.loss_counts)) + self.loss_sums = [0.0] * self.num_bins + self.loss_counts = [0] * self.num_bins + while self.next_update_nimg <= cur_nimg: + self.next_update_nimg += self.update_nimg + return result + + def state_dict(self): + return { + 'kind': 'local_tbin', + 'update_nimg': self.update_nimg, + 'next_update_nimg': self.next_update_nimg, + 'num_bins': self.num_bins, + 'loss_sums': list(self.loss_sums), + 'loss_counts': list(self.loss_counts), + } + + def load_state_dict(self, state): + if not isinstance(state, dict): + raise ValueError('local t-bin signal window state must be a dict') + required = ('update_nimg', 'next_update_nimg', 'num_bins', 'loss_sums', 'loss_counts') + missing = [name for name in required if name not in state] + if missing: + raise ValueError( + f'local t-bin signal window state missing required fields: {", ".join(missing)}' + ) + update_nimg = int(state['update_nimg']) + next_update_nimg = int(state['next_update_nimg']) + num_bins = int(state['num_bins']) + loss_sums = [float(value) for value in state['loss_sums']] + loss_counts = [int(value) for value in state['loss_counts']] + if update_nimg != self.update_nimg: + raise ValueError( + f'local t-bin window interval mismatch: checkpoint={update_nimg}, ' + f'current={self.update_nimg}' + ) + if num_bins != self.num_bins or len(loss_sums) != num_bins or len(loss_counts) != num_bins: + raise ValueError('local t-bin window bin count mismatch') + if next_update_nimg <= 0 or next_update_nimg % self.update_nimg != 0: + raise ValueError( + f'invalid local t-bin window next_update_nimg: {next_update_nimg}' + ) + if any(count < 0 for count in loss_counts): + raise ValueError('local t-bin window counts must be non-negative') + self.next_update_nimg = next_update_nimg + self.loss_sums = loss_sums + self.loss_counts = loss_counts + + +def gather_adaptive_signal_window_state(window, device): + """Collect each rank's local adaptive-window state for a rank-0 checkpoint.""" + local_state = window.state_dict() + world_size = dist.get_world_size() + if world_size == 1: + rank_states = [local_state] + elif isinstance(window, LocalTBinSignalWindow): + local_values = torch.tensor( + [window.next_update_nimg, *window.loss_sums, *window.loss_counts], + dtype=torch.float64, + device=device, + ) + gathered_values = [torch.empty_like(local_values) for _ in range(world_size)] + torch.distributed.all_gather(gathered_values, local_values) + rank_states = [] + for values in gathered_values: + sums_start = 1 + counts_start = sums_start + window.num_bins + rank_states.append({ + 'kind': 'local_tbin', + 'update_nimg': window.update_nimg, + 'next_update_nimg': int(values[0]), + 'num_bins': window.num_bins, + 'loss_sums': [float(value) for value in values[sums_start:counts_start]], + 'loss_counts': [int(value) for value in values[counts_start:]], + }) + else: + local_values = torch.tensor( + [window.next_update_nimg, window.loss_sum, window.loss_count], + dtype=torch.float64, + device=device, + ) + gathered_values = [torch.empty_like(local_values) for _ in range(world_size)] + torch.distributed.all_gather(gathered_values, local_values) + rank_states = [ + { + 'update_nimg': window.update_nimg, + 'next_update_nimg': int(values[0]), + 'loss_sum': float(values[1]), + 'loss_count': int(values[2]), + } + for values in gathered_values + ] + + # Keep the rank-0 fields at the top level for transparent single-rank + # inspection, and retain every local accumulator for exact DDP resumes. + return {**rank_states[0], 'rank_states': rank_states} + + +def local_adaptive_signal_window_state(state): + """Select this rank's window state from a training-state checkpoint.""" + if not isinstance(state, dict): + return state + rank_states = state.get('rank_states') + if rank_states is None: + return state + if not isinstance(rank_states, list) or len(rank_states) != dist.get_world_size(): + raise ValueError( + 'adaptive signal window checkpoint rank count does not match the current world size' + ) + return rank_states[dist.get_rank()] + + +def globally_average_adaptive_loss(loss_sum, loss_count, device): + """Return the sample-weighted loss mean, identical on every rank.""" + totals = torch.tensor([loss_sum, loss_count], dtype=torch.float64, device=device) + if dist.get_world_size() > 1: + torch.distributed.all_reduce(totals) + total_count = float(totals[1]) + return float(totals[0] / total_count) if total_count > 0 else float('nan') + + +def globally_average_local_tbin_loss(loss_sums, loss_counts, device): + """Return per-bin raw-loss means, sample weighted across DDP ranks.""" + if len(loss_sums) != len(loss_counts): + raise ValueError('local t-bin sums/counts size mismatch') + totals = torch.tensor( + [*loss_sums, *loss_counts], dtype=torch.float64, device=device + ) + if dist.get_world_size() > 1: + torch.distributed.all_reduce(totals) + num_bins = len(loss_sums) + means = [] + for index in range(num_bins): + count = float(totals[num_bins + index]) + means.append(float(totals[index] / count) if count > 0 else None) + return means + + +def globally_average_runtime_pairs(metric_batches, device): + """Average public realized-pair telemetry across rounds and ranks.""" + fields = ( + 'r_over_t_mean', + 'gap_mean', + 'gap_over_sigmoid_gap_mean', + 'lower_gap_clip_rate', + 'upper_gap_clip_rate', + ) + sums_and_counts = [] + for field in fields: + values = [float(metrics[field]) for metrics in metric_batches] + values = [value for value in values if math.isfinite(value)] + sums_and_counts.extend((sum(values), len(values))) + totals = torch.tensor( + sums_and_counts, + dtype=torch.float64, + device=device, + ) + if dist.get_world_size() > 1: + torch.distributed.all_reduce(totals) + result = {} + for index, field in enumerate(fields): + total = totals[index * 2] + count = float(totals[index * 2 + 1]) + result[field] = ( + float(total / count) if count > 0 else float('nan') + ) + return result + + #---------------------------------------------------------------------------- def setup_snapshot_image_grid(training_set, random_seed=0): @@ -122,13 +534,15 @@ def training_loop( sample_ticks = 50, # How often to sample images, None = disable. eval_ticks = 500, # How often to evaluate models, None = disable. double_ticks = 500, # How often to evaluate models, None = disable. + adaptive_update_kimg = 0.5, # Adaptive loss-EMA signal period, independent of ticks. resume_pkl = None, # Start from the given network snapshot, None = random initialization. resume_state_dump = None, # Start from the given training state, None = reset training state. resume_tick = 0, # Start from the given training progress. mid_t = None, # Intermediate t for few-step generation. metrics = None, # Metrics for evaluation. cudnn_benchmark = True, # Enable torch.backends.cudnn.benchmark? - enable_tf32 = False, # Enable tf32 for A100/H100 GPUs? + enable_tf32 = False, # Enable tf32 for A100/H100 GPUs? + enable_amp = False, # Enable torch.cuda.amp.GradScaler device = torch.device('cuda'), ): # Initialize. @@ -168,6 +582,15 @@ def training_loop( optimizer = dnnlib.util.construct_class_by_name(params=net.parameters(), **optimizer_kwargs) # subclass of torch.optim.Optimizer augment_pipe = dnnlib.util.construct_class_by_name(**augment_kwargs) if augment_kwargs is not None else None # training.augment.AugmentPipe + # Automatic Mixed Precision + dist.print0(f'GradScaler enabled: {enable_amp} for mixed precision training') + if enable_amp: + # https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html#adding-gradscaler + # https://pytorch.org/docs/stable/notes/amp_examples.html#gradient-accumulation + dist.print0('Setting up GradScaler...') + scaler = torch.cuda.amp.GradScaler() + dist.print0('Loss scaling is overwritten when GradScaler is enabled') + dist.print0('Setting up DDP...') ddp = torch.nn.parallel.DistributedDataParallel(net, device_ids=[device], broadcast_buffers=False) ema = copy.deepcopy(net).eval().requires_grad_(False) @@ -192,11 +615,49 @@ def training_loop( misc.copy_params_and_buffers(src_module=data['ema'], dst_module=net, require_all=False) misc.copy_params_and_buffers(src_module=data['ema'], dst_module=ema, require_all=False) del data # conserve memory + attempted_iteration = 0 + successful_optimizer_steps = 0 + resumed_cur_nimg = None + resumed_cur_tick = None + resumed_tick_start_nimg = None + resumed_adaptive_signal_window_state = None + elapsed_base_sec = 0.0 if resume_state_dump: dist.print0(f'Loading training state from "{resume_state_dump}"...') - data = torch.load(resume_state_dump, map_location=torch.device('cpu')) + # The training-state contains optimizer and persistent module objects. + # Only load trusted checkpoints produced by this repository. + data = torch.load( + resume_state_dump, + map_location=torch.device('cpu'), + weights_only=False, + ) misc.copy_params_and_buffers(src_module=data['net'], dst_module=net, require_all=True) optimizer.load_state_dict(data['optimizer_state']) + if 'cur_nimg' not in data: + raise RuntimeError( + f'resume training-state missing cur_nimg: {resume_state_dump}; ' + f'refuse filename-derived progress fallback for paired runs' + ) + attempted_iteration = int(data.get('attempted_iteration', 0)) + successful_optimizer_steps = int(data.get('successful_optimizer_steps', 0)) + resumed_cur_nimg = int(data['cur_nimg']) + if 'cur_tick' in data: + resumed_cur_tick = int(data['cur_tick']) + if 'tick_start_nimg' in data: + resumed_tick_start_nimg = int(data['tick_start_nimg']) + elapsed_base_sec = float(data.get('elapsed_sec', 0.0)) + if hasattr(loss_fn, 'load_schedule_state_dict') and 'loss_fn_state' in data: + loss_fn.load_schedule_state_dict(data['loss_fn_state']) + if 'adaptive_signal_window_state' in data: + resumed_adaptive_signal_window_state = data['adaptive_signal_window_state'] + if enable_amp: + if 'gradscaler_state' in data: + # NOTE(aiihn): Although not loading the state_dict of the GradScaler works well, + # loading it can improve reproducibility. + dist.print0(f'Loading GradScaler state from "{resume_state_dump}"...') + scaler.load_state_dict(data['gradscaler_state']) + else: + dist.print0(f'GradScaler state is not found in "{resume_state_dump}", using the default state.') del data # conserve memory # Export sample images. @@ -223,13 +684,130 @@ def training_loop( # Train. dist.print0(f'Training for {total_kimg} kimg...') dist.print0() - cur_nimg = resume_tick * kimg_per_tick * 1000 - cur_tick = resume_tick - tick_start_nimg = cur_nimg + # Prefer exact progress from training-state; filename-derived resume_tick is only a fallback. + if resumed_cur_nimg is not None: + cur_nimg = resumed_cur_nimg + else: + cur_nimg = resume_tick * kimg_per_tick * 1000 + if resumed_cur_tick is not None: + cur_tick = resumed_cur_tick + else: + cur_tick = resume_tick + if resumed_tick_start_nimg is not None: + tick_start_nimg = resumed_tick_start_nimg + else: + tick_start_nimg = cur_nimg tick_start_time = time.time() maintenance_time = tick_start_time - start_time dist.update_progress(cur_nimg / 1000, total_kimg) stats_jsonl = None + train_summary_csv = None + train_summary_writer = None + schedule_name = getattr(getattr(loss_fn, 'schedule', None), 'name', None) + if schedule_name is None: + schedule_name = getattr(loss_fn, 'adj', None) + if schedule_name is None: + schedule_name = loss_kwargs.get('adj', 'unknown') + if schedule_name == 'adaptive_v1': + adaptive_signal_window = AdaptiveSignalWindow( + adaptive_update_kimg, start_nimg=cur_nimg + ) + elif schedule_name in ('local_tbin_v1', 'local_tbin_v2', 'local_tbin_v3'): + adaptive_signal_window = LocalTBinSignalWindow( + adaptive_update_kimg, + num_bins=loss_fn.schedule.num_bins, + start_nimg=cur_nimg, + ) + else: + adaptive_signal_window = None + if adaptive_signal_window is not None and resume_state_dump: + if resumed_adaptive_signal_window_state is None: + raise RuntimeError( + f'resume training-state missing adaptive_signal_window_state: {resume_state_dump}; ' + 'cannot exactly resume adaptive loss aggregation' + ) + adaptive_signal_window.load_state_dict( + local_adaptive_signal_window_state(resumed_adaptive_signal_window_state) + ) + if adaptive_signal_window.next_update_nimg <= cur_nimg: + raise RuntimeError( + 'resumed adaptive signal window is due before or at the restored progress: ' + f'{adaptive_signal_window.next_update_nimg} <= {cur_nimg}' + ) + + if dist.get_rank() == 0: + summary_path = os.path.join(run_dir, 'train_summary.csv') + summary_exists = os.path.isfile(summary_path) and os.path.getsize(summary_path) > 0 + if resume_state_dump: + if summary_exists: + rows, migrated_backup = load_and_migrate_train_summary(summary_path) + if migrated_backup is not None: + dist.print0( + f'Migrated legacy train_summary.csv to telemetry schema; ' + f'original saved as "{migrated_backup}"' + ) + last = rows[-1] + last_attempted = int(float(last['attempted_iteration'])) + last_nimg = int(float(last.get('processed_nimg', last.get('nimg', -1)))) + last_schedule = str(last.get('schedule', '')).strip() + if last_schedule and last_schedule != str(schedule_name): + raise RuntimeError( + f'train_summary.csv schedule={last_schedule!r} does not match ' + f'current schedule={schedule_name!r}; refuse mixed-schedule resume' + ) + if attempted_iteration and last_attempted != attempted_iteration: + raise RuntimeError( + f'train_summary.csv last attempted_iteration={last_attempted} ' + f'does not match training-state attempted_iteration={attempted_iteration}' + ) + if last_nimg >= 0 and last_nimg != cur_nimg: + raise RuntimeError( + f'train_summary.csv last processed_nimg={last_nimg} ' + f'does not match resumed cur_nimg={cur_nimg}' + ) + last_next_loop_tick = str(last.get('next_loop_cur_tick', '')).strip() + if last_next_loop_tick: + try: + parsed_next_loop_tick = float(last_next_loop_tick) + except ValueError as exc: + raise RuntimeError( + 'train_summary.csv last next_loop_cur_tick must be numeric: ' + f'{last_next_loop_tick!r}' + ) from exc + if ( + not math.isfinite(parsed_next_loop_tick) + or not parsed_next_loop_tick.is_integer() + or parsed_next_loop_tick < 0 + ): + raise RuntimeError( + 'train_summary.csv last next_loop_cur_tick must be a ' + f'non-negative integer: {last_next_loop_tick!r}' + ) + if int(parsed_next_loop_tick) != cur_tick: + raise RuntimeError( + f'train_summary.csv last next_loop_cur_tick={last_next_loop_tick} ' + f'does not match resumed cur_tick={cur_tick}' + ) + if not attempted_iteration: + attempted_iteration = last_attempted + successful_optimizer_steps = int(float( + last.get('successful_optimizer_steps', last_attempted) + )) + train_summary_csv = open(summary_path, 'at', newline='') + train_summary_writer = csv.DictWriter(train_summary_csv, fieldnames=_TRAIN_SUMMARY_FIELDS) + if not summary_exists: + train_summary_writer.writeheader() + train_summary_csv.flush() + else: + if summary_exists: + raise RuntimeError( + f'fresh run refuses to append existing train_summary.csv: {summary_path}; ' + f'pass --resume for a legal continuation or use an empty outdir' + ) + train_summary_csv = open(summary_path, 'wt', newline='') + train_summary_writer = csv.DictWriter(train_summary_csv, fieldnames=_TRAIN_SUMMARY_FIELDS) + train_summary_writer.writeheader() + train_summary_csv.flush() # Prepare for the mapping fn p(r|t). dist.print0(f'Reduce dt every {double_ticks} ticks.') @@ -237,14 +815,55 @@ def training_loop( def update_scheduler(loss_fn): loss_fn.update_schedule(stage) dist.print0(f'Update scheduler at {cur_tick} ticks, {cur_nimg / 1e3} kimg, ratio {loss_fn.ratio}') + + def build_training_state(adaptive_signal_window_state=None): + # Checkpointing happens during maintenance, before the loop advances: + # cur_tick += 1 + # tick_start_nimg = cur_nimg + # Persist the *next-loop* values so resume matches uninterrupted training. + data = dict( + net=net, + optimizer_state=optimizer.state_dict(), + attempted_iteration=attempted_iteration, + successful_optimizer_steps=successful_optimizer_steps, + cur_nimg=cur_nimg, + cur_tick=cur_tick + 1, + tick_start_nimg=cur_nimg, + # Match the final CSV row exactly; resume timing continues from + # the last completed attempted iteration rather than from later + # checkpoint I/O and maintenance work. + elapsed_sec=elapsed_sec, + ) + if hasattr(loss_fn, 'schedule_state_dict'): + data['loss_fn_state'] = loss_fn.schedule_state_dict() + if adaptive_signal_window is not None: + if adaptive_signal_window_state is None: + raise RuntimeError('adaptive signal window state was not collected for checkpointing') + data['adaptive_signal_window_state'] = adaptive_signal_window_state + if enable_amp: + data['gradscaler_state'] = scaler.state_dict() + return data stage = cur_tick // double_ticks update_scheduler(loss_fn) + # Already at/past the requested budget (e.g. resume with same duration): do not + # execute an extra optimizer step before noticing done. + if cur_nimg >= total_kimg * 1000: + dist.print0(f'Already reached training budget at {cur_nimg / 1e3:.3f} kimg; exiting.') + if train_summary_csv is not None: + train_summary_csv.close() + dist.print0() + dist.print0('Exiting...') + return + while True: # Accumulate gradients. optimizer.zero_grad(set_to_none=True) + loss_batches = [] + schedule_metric_batches = [] + local_signal_batches = [] for round_idx in range(num_accumulation_rounds): with misc.ddp_sync(ddp, (round_idx == num_accumulation_rounds - 1)): images, labels = next(dataset_iterator) @@ -252,18 +871,67 @@ def update_scheduler(loss_fn): labels = labels.to(device) loss = loss_fn(net=ddp, images=images, labels=labels, augment_pipe=augment_pipe) + loss_batches.append(loss.detach()) + schedule_metric_batches.append(loss_fn.schedule_runtime_metrics()) + if schedule_name in ('local_tbin_v1', 'local_tbin_v2', 'local_tbin_v3'): + signal = loss_fn.local_training_signal() + if signal is None: + raise RuntimeError( + f'{schedule_name} did not produce raw per-bin signal' + ) + local_signal_batches.append(signal) training_stats.report('Loss/loss', loss) - loss.mul(loss_scaling).mean().backward() + if enable_amp: + scaler.scale(loss.mean()).backward() + else: + loss.mul(loss_scaling).mean().backward() - # LR scheduler (if needed in the future) - # for g in optimizer.param_groups: - # g['lr'] = optimizer_kwargs['lr'] * min(cur_nimg / max(lr_rampup_kimg * 1000, 1e-8), 1) - - # Update weights. + # Unscale first so GradScaler can detect non-finite gradients before + # they are sanitized below. scaler.step() will still skip the update + # when unscale_() records an overflow. + if enable_amp: + scaler.unscale_(optimizer) + + # NOTE(aiihn & Gsunshine): This should be further tested for AMP. for param in net.parameters(): if param.grad is not None: torch.nan_to_num(param.grad, nan=0, posinf=1e5, neginf=-1e5, out=param.grad) - optimizer.step() + + # LR scheduler (if needed in the future) + # for g in optimizer.param_groups: + # g['lr'] = optimizer_kwargs['lr'] * min(cur_nimg / max(lr_rampup_kimg * 1000, 1e-8), 1) + + # Update weights. Record GradScaler scale / skip for train_summary.csv. + # scale_before is the scale applied to this step; a drop after update() + # means overflow was detected and optimizer.step was skipped. + grad_scale = float(loss_scaling) + step_skipped = 0 + if enable_amp: + scale_before = float(scaler.get_scale()) + scaler.step(optimizer) + scaler.update() + scale_after = float(scaler.get_scale()) + grad_scale = scale_before + step_skipped = int(scale_after < scale_before) + else: + optimizer.step() + + attempted_iteration += 1 + if not step_skipped: + successful_optimizer_steps += 1 + + loss_count = sum(x.numel() for x in loss_batches) + loss_sum = sum(float(x.sum().cpu()) for x in loss_batches) + loss_mean = loss_sum / loss_count + runtime_pair_metrics = globally_average_runtime_pairs(schedule_metric_batches, device=device) + elapsed_sec = elapsed_base_sec + (time.time() - start_time) + peak_vram_gb = torch.cuda.max_memory_allocated(device) / 2**30 + training_stats.report0('Progress/grad_scale', grad_scale) + training_stats.report0('Progress/step_skipped', step_skipped) + training_stats.report0('Progress/attempted_iteration', attempted_iteration) + training_stats.report0('Progress/successful_optimizer_steps', successful_optimizer_steps) + training_stats.report0('Timing/elapsed_sec', elapsed_sec) + training_stats.report0('Resources/update_peak_gpu_mem_gb', peak_vram_gb) # Update EMA. if ema_halflife_kimg is not None: @@ -274,10 +942,113 @@ def update_scheduler(loss_fn): for p_ema, p_net in zip(ema.parameters(), net.parameters()): p_ema.copy_(p_net.detach().lerp(p_ema, ema_beta)) - # Perform maintenance tasks once per tick. + # Advance iteration-local state. Adaptive updates intentionally happen + # here, before the maintenance early-continue below. cur_nimg += batch_size + if adaptive_signal_window is not None: + if isinstance(adaptive_signal_window, LocalTBinSignalWindow): + local_sums = torch.stack( + [batch['loss_sums'] for batch in local_signal_batches] + ).sum(dim=0) + local_counts = torch.stack( + [batch['loss_counts'] for batch in local_signal_batches] + ).sum(dim=0) + adaptive_signal_window.add(local_sums.tolist(), local_counts.tolist()) + signal_window = adaptive_signal_window.pop_if_due(cur_nimg) + if signal_window is not None: + signal_loss = globally_average_local_tbin_loss( + *signal_window, device=device + ) + loss_fn.update_training_signal(signal_loss) + else: + adaptive_signal_window.add(loss_sum, loss_count) + signal_window = adaptive_signal_window.pop_if_due(cur_nimg) + if signal_window is not None: + signal_loss = globally_average_adaptive_loss(*signal_window, device=device) + loss_fn.update_training_signal(signal_loss) + + schedule_runtime_metrics = loss_fn.schedule_runtime_metrics() + schedule_runtime_metrics.update(runtime_pair_metrics) + if schedule_runtime_metrics['loss_ema'] is not None: + training_stats.report0('Schedule/loss_ema', schedule_runtime_metrics['loss_ema']) + if schedule_runtime_metrics['loss_reference'] is not None: + training_stats.report0('Schedule/loss_reference', schedule_runtime_metrics['loss_reference']) + training_stats.report0('Schedule/correction', schedule_runtime_metrics['correction']) + training_stats.report0('Schedule/signal_updates', schedule_runtime_metrics['signal_updates']) + training_stats.report0('Schedule/adaptive_active', int(schedule_runtime_metrics['adaptive_active'])) + training_stats.report0('Schedule/r_over_t_mean', schedule_runtime_metrics['r_over_t_mean']) + training_stats.report0('Schedule/gap_mean', schedule_runtime_metrics['gap_mean']) + training_stats.report0( + 'Schedule/gap_over_sigmoid_gap_mean', + schedule_runtime_metrics['gap_over_sigmoid_gap_mean'], + ) + training_stats.report0( + 'Schedule/lower_gap_clip_rate', + schedule_runtime_metrics['lower_gap_clip_rate'], + ) + training_stats.report0( + 'Schedule/upper_gap_clip_rate', + schedule_runtime_metrics['upper_gap_clip_rate'], + ) + local_runtime_metrics = loss_fn.schedule_local_runtime_metrics() + if local_runtime_metrics is not None: + for index in range(len(local_runtime_metrics['gap_scales'])): + prefix = f'Schedule/tbin{index}' + for key in ('last_raw_loss', 'short_ema', 'long_ema'): + value = local_runtime_metrics[key][index] + if value is not None: + training_stats.report0(f'{prefix}/{key}', value) + training_stats.report0( + f'{prefix}/gap_scale', local_runtime_metrics['gap_scales'][index] + ) + training_stats.report0( + f'{prefix}/updates', local_runtime_metrics['bin_updates'][index] + ) + training_stats.report0( + f'{prefix}/active', int(local_runtime_metrics['bin_active'][index]) + ) + + # Record the exact state that the following loop iteration will see. + # This cannot be derived reliably from image count: the first iteration + # always performs maintenance, and completion forces it regardless of + # --tick. A checkpoint saved below persists this same cur_tick value. done = (cur_nimg >= total_kimg * 1000) - if (not done) and (cur_tick != 0) and (cur_nimg < tick_start_nimg + kimg_per_tick * 1000): + maintenance_due = ( + done + or cur_tick == 0 + or cur_nimg >= tick_start_nimg + kimg_per_tick * 1000 + ) + next_loop_cur_tick = cur_tick + int(maintenance_due) + + if train_summary_writer is not None: + train_summary_writer.writerow({ + 'attempted_iteration': attempted_iteration, + 'successful_optimizer_steps': successful_optimizer_steps, + 'processed_nimg': cur_nimg, + 'processed_kimg': f'{cur_nimg / 1e3:.6f}', + 'loss': f'{loss_mean:.8f}', + 'grad_scale': f'{grad_scale:.8g}', + 'step_skipped': step_skipped, + 'schedule': schedule_name, + 'stage': stage, + 'next_loop_cur_tick': next_loop_cur_tick, + 'loss_ema': '' if schedule_runtime_metrics['loss_ema'] is None else f"{schedule_runtime_metrics['loss_ema']:.12g}", + 'loss_reference': '' if schedule_runtime_metrics['loss_reference'] is None else f"{schedule_runtime_metrics['loss_reference']:.12g}", + 'correction': f"{schedule_runtime_metrics['correction']:.12g}", + 'signal_updates': schedule_runtime_metrics['signal_updates'], + 'adaptive_active': int(schedule_runtime_metrics['adaptive_active']), + 'r_over_t_mean': f"{schedule_runtime_metrics['r_over_t_mean']:.12g}", + 'gap_mean': f"{schedule_runtime_metrics['gap_mean']:.12g}", + 'gap_over_sigmoid_gap_mean': f"{schedule_runtime_metrics['gap_over_sigmoid_gap_mean']:.12g}", + 'lower_gap_clip_rate': f"{schedule_runtime_metrics['lower_gap_clip_rate']:.12g}", + 'upper_gap_clip_rate': f"{schedule_runtime_metrics['upper_gap_clip_rate']:.12g}", + 'elapsed_sec': f'{elapsed_sec:.6f}', + 'peak_vram_gb': f'{peak_vram_gb:.6f}', + }) + train_summary_csv.flush() + + # Perform maintenance tasks once per tick. + if not maintenance_due: continue # Print status line, accumulating the same information in training_stats. @@ -286,6 +1057,8 @@ def update_scheduler(loss_fn): fields += [f"tick {training_stats.report0('Progress/tick', cur_tick):<5d}"] fields += [f"kimg {training_stats.report0('Progress/kimg', cur_nimg / 1e3):<9.1f}"] fields += [f"loss {training_stats.default_collector['Loss/loss']:<9.5f}"] + fields += [f"grad_scale {grad_scale:<9g}"] + fields += [f"step_skipped {step_skipped:<7d}"] fields += [f"time {dnnlib.util.format_time(training_stats.report0('Timing/total_sec', tick_end_time - start_time)):<12s}"] fields += [f"sec/tick {training_stats.report0('Timing/sec_per_tick', tick_end_time - tick_start_time):<7.1f}"] fields += [f"sec/kimg {training_stats.report0('Timing/sec_per_kimg', (tick_end_time - tick_start_time) / (cur_nimg - tick_start_nimg) * 1e3):<7.2f}"] @@ -316,12 +1089,32 @@ def update_scheduler(loss_fn): pickle.dump(data, f) del data # conserve memory - # Save full dump of the training state. - if (state_dump_ticks is not None) and (done or cur_tick % state_dump_ticks == 0) and cur_tick != 0 and dist.get_rank() == 0: - torch.save(dict(net=net, optimizer_state=optimizer.state_dict()), os.path.join(run_dir, f'training-state-{cur_tick:06d}.pt')) + # Save full dump of the training state. Every rank participates in + # collecting its local adaptive-loss accumulator; rank 0 writes the + # resulting combined state. + state_dump_due = ( + (state_dump_ticks is not None) + and (done or cur_tick % state_dump_ticks == 0) + and cur_tick != 0 + ) + if state_dump_due: + adaptive_signal_window_state = ( + gather_adaptive_signal_window_state(adaptive_signal_window, device) + if adaptive_signal_window is not None else None + ) + if dist.get_rank() == 0: + torch.save( + build_training_state(adaptive_signal_window_state), + os.path.join(run_dir, f'training-state-{cur_tick:06d}.pt'), + ) # Save latest checkpoints - if (ckpt_ticks is not None) and (done or cur_tick % ckpt_ticks == 0) and cur_tick != 0: + latest_checkpoint_due = ( + (ckpt_ticks is not None) + and (done or cur_tick % ckpt_ticks == 0) + and cur_tick != 0 + ) + if latest_checkpoint_due: dist.print0(f'Save the latest checkpoint at {cur_tick:06d} img...') data = dict(ema=ema, loss_fn=loss_fn, augment_pipe=augment_pipe, dataset_kwargs=dict(dataset_kwargs)) for key, value in data.items(): @@ -335,8 +1128,15 @@ def update_scheduler(loss_fn): pickle.dump(data, f) del data # conserve memory + adaptive_signal_window_state = ( + gather_adaptive_signal_window_state(adaptive_signal_window, device) + if adaptive_signal_window is not None else None + ) if dist.get_rank() == 0: - torch.save(dict(net=net, optimizer_state=optimizer.state_dict()), os.path.join(run_dir, f'training-state-latest.pt')) + torch.save( + build_training_state(adaptive_signal_window_state), + os.path.join(run_dir, f'training-state-latest.pt'), + ) # Sample Img if (sample_ticks is not None) and (done or cur_tick % sample_ticks == 0) and dist.get_rank() == 0: @@ -347,7 +1147,7 @@ def update_scheduler(loss_fn): del images # Evaluation - if (eval_ticks is not None) and (done or cur_tick % eval_ticks == 0) and cur_tick > 0: + if metrics and (eval_ticks is not None) and (done or cur_tick % eval_ticks == 0) and cur_tick > 0: dist.print0('Evaluating models...') result_dict = metric_main.calc_metric(metric='fid50k_full', generator_fn=generator_fn, G=ema, G_kwargs={}, @@ -405,6 +1205,8 @@ def update_scheduler(loss_fn): metric_main.report_metric(result_dict, run_dir=run_dir, snapshot_pkl='network-snapshot-latest.pkl') # Done. + if train_summary_csv is not None: + train_summary_csv.close() dist.print0() dist.print0('Exiting...') diff --git a/training/g_screen_status.csv b/training/g_screen_status.csv new file mode 100644 index 00000000..d8fa4d2a --- /dev/null +++ b/training/g_screen_status.csv @@ -0,0 +1,7 @@ +"protocol_id","label","g","q","training_seed","target_budget_kimg","training_status","checkpoint_64kimg_retained","checkpoint_128kimg_retained","checkpoint_256kimg_retained","checkpoint_256kimg_path","nfe1_evaluation_status","nfe2_evaluation_status","overall_status","notes" +"g-screen-q128-s3-5k-v1","g0_9","0.9","128","3","256","completed","false","false","true","g_screen/g0_9/network-snapshot-000026.pkl","completed","completed","completed_with_protocol_deviations","64/128 kimg checkpoints unavailable; raw residual and gradient RMS not logged" +"g-screen-q128-s3-5k-v1","g1_0","1.0","128","3","256","completed","false","false","true","g_screen/g1_0/network-snapshot-000026.pkl","completed","completed","completed_with_protocol_deviations","64/128 kimg checkpoints unavailable; raw residual and gradient RMS not logged" +"g-screen-q128-s3-5k-v1","g1_05","1.05","128","3","256","completed","false","false","true","g_screen/g1_05/network-snapshot-000026.pkl","completed","completed","completed_with_protocol_deviations","64/128 kimg checkpoints unavailable; raw residual and gradient RMS not logged" +"g-screen-q128-s3-5k-v1","g1_1","1.1","128","3","256","completed","false","false","true","g_screen/g1_1/network-snapshot-000026.pkl","completed","completed","completed_with_protocol_deviations","64/128 kimg checkpoints unavailable; raw residual and gradient RMS not logged" +"g-screen-q128-s3-5k-v1","g1_2","1.2","128","3","256","completed","false","false","true","g_screen/g1_2/network-snapshot-000026.pkl","completed","completed","completed_with_protocol_deviations","64/128 kimg checkpoints unavailable; raw residual and gradient RMS not logged" +"g-screen-q128-s3-5k-v1","g1_3","1.3","128","3","256","completed","false","false","true","g_screen/g1_3/network-snapshot-000026.pkl","completed","completed","completed_with_protocol_deviations","64/128 kimg checkpoints unavailable; raw residual and gradient RMS not logged" diff --git a/training/loss.py b/training/loss.py index 9a1007a5..ce5de14f 100644 --- a/training/loss.py +++ b/training/loss.py @@ -5,22 +5,56 @@ from torch_utils import persistence from torch_utils import distributed as dist +from training.schedules import get_schedule + #---------------------------------------------------------------------------- # Loss function proposed in the blog "Consistency Models Made Easy" @persistence.persistent_class class ECMLoss: - def __init__(self, P_mean=-1.1, P_std=2.0, sigma_data=0.5, q=2, c=0.0, k=8.0, b=1.0, cut=4.0, adj='sigmoid'): + def __init__(self, P_mean=-1.1, P_std=2.0, sigma_data=0.5, q=2, c=0.0, k=8.0, b=1.0, cut=4.0, + adj='sigmoid', adaptive_loss_ema_beta=0.9, adaptive_max_adjust=0.05, + adaptive_min_gap=1e-3, adaptive_warmup_updates=2, + local_tbin_num_bins=4, local_tbin_short_beta=0.9, + local_tbin_long_beta=0.99, local_tbin_warmup_updates=32, + local_tbin_gain=0.5, local_tbin_min_scale=0.75, + local_tbin_max_scale=1.5, local_tbin_deadband=0.02, + local_tbin_min_gap=1e-3, global_gap_scale=1.0): self.P_mean = P_mean self.P_std = P_std self.sigma_data = sigma_data - if adj == 'const': - self.t_to_r = self.t_to_r_const - elif adj == 'sigmoid': - self.t_to_r = self.t_to_r_sigmoid - else: - raise ValueError(f'Unknow schedule type {adj}!') + # t -> r entry point, dispatched through training/schedules.py. + # 'const' / 'sigmoid' are the official fixed formulas (bit-identical + # to the reference methods below); 'adaptive_v1' is the Role C + # experiment. + schedule_kwargs = dict(q=q, k=k, b=b) + if adj == 'adaptive_v1': + schedule_kwargs.update( + loss_ema_beta=adaptive_loss_ema_beta, + max_adjust=adaptive_max_adjust, + min_gap=adaptive_min_gap, + warmup_updates=adaptive_warmup_updates, + ) + elif adj == 'global_sigmoid': + schedule_kwargs.update(global_gap_scale=global_gap_scale) + elif adj in ('local_tbin_v1', 'local_tbin_v2', 'local_tbin_v3'): + schedule_kwargs.update( + p_mean=P_mean, + p_std=P_std, + num_bins=local_tbin_num_bins, + short_beta=local_tbin_short_beta, + long_beta=local_tbin_long_beta, + warmup_updates=local_tbin_warmup_updates, + gain=local_tbin_gain, + min_scale=local_tbin_min_scale, + max_scale=local_tbin_max_scale, + deadband=local_tbin_deadband, + min_gap=local_tbin_min_gap, + ) + if adj == 'local_tbin_v3': + schedule_kwargs.update(global_gap_scale=global_gap_scale) + self.schedule = get_schedule(adj, **schedule_kwargs) self.q = q self.stage = 0 @@ -30,12 +64,151 @@ def __init__(self, P_mean=-1.1, P_std=2.0, sigma_data=0.5, q=2, c=0.0, k=8.0, b= self.b = b self.c = c + self._runtime_r_over_t_mean = float('nan') + self._runtime_gap_mean = float('nan') + self._runtime_gap_over_sigmoid_gap_mean = float('nan') + self._runtime_lower_gap_clip_rate = float('nan') + self._runtime_upper_gap_clip_rate = float('nan') + self._runtime_local_training_signal = None dist.print0(f'P_mean: {self.P_mean}, P_std: {self.P_std}, q: {self.q}, k {self.k}, b {self.b}, c: {self.c}') def update_schedule(self, stage): self.stage = stage + self.schedule.update_schedule(stage) self.ratio = 1 - 1 / self.q ** (stage+1) + def update_training_signal(self, loss): + return self.schedule.update_training_signal(loss) + + def schedule_state_dict(self): + return { + 'schedule_name': self.schedule.name, + 'stage': self.stage, + 'ratio': self.ratio, + 'schedule': self.schedule.state_dict(), + } + + def load_schedule_state_dict(self, state): + saved_name = state.get('schedule_name') + if saved_name is not None and saved_name != self.schedule.name: + return False + self.stage = state.get('stage', self.stage) + self.ratio = state.get('ratio', self.ratio) + self.schedule.load_state_dict(state.get('schedule', {})) + return True + + def schedule_metadata(self): + metadata = self.schedule.metadata() + metadata.update(stage=self.stage, ratio=self.ratio) + return metadata + + def schedule_runtime_metrics(self): + """Return stable, scalar telemetry without exposing schedule internals.""" + metrics = self.schedule.runtime_metrics() + return { + 'loss_ema': metrics['loss_ema'], + 'loss_reference': metrics['loss_reference'], + 'correction': float(metrics['correction']), + 'signal_updates': int(metrics['signal_updates']), + 'adaptive_active': bool(metrics['adaptive_active']), + 'r_over_t_mean': float(self._runtime_r_over_t_mean), + 'gap_mean': float(self._runtime_gap_mean), + 'gap_over_sigmoid_gap_mean': float( + self._runtime_gap_over_sigmoid_gap_mean + ), + 'lower_gap_clip_rate': float( + self._runtime_lower_gap_clip_rate + ), + 'upper_gap_clip_rate': float( + self._runtime_upper_gap_clip_rate + ), + } + + def local_training_signal(self): + """Return raw per-bin pair-loss sums/counts from the latest microbatch.""" + return self._runtime_local_training_signal + + def schedule_local_runtime_metrics(self): + if hasattr(self.schedule, 'local_runtime_metrics'): + return self.schedule.local_runtime_metrics() + return None + + def _record_schedule_runtime_pair(self, t, r): + with torch.no_grad(): + sigmoid_r = self.t_to_r_sigmoid(t) + valid = ( + torch.isfinite(t) + & torch.isfinite(r) + & torch.isfinite(sigmoid_r) + & (t > 0) + ) + if not bool(valid.any()): + self._runtime_r_over_t_mean = float('nan') + self._runtime_gap_mean = float('nan') + self._runtime_gap_over_sigmoid_gap_mean = float('nan') + self._runtime_lower_gap_clip_rate = float('nan') + self._runtime_upper_gap_clip_rate = float('nan') + return + valid_t = t[valid].to(torch.float64) + valid_r = r[valid].to(torch.float64) + valid_sigmoid_r = sigmoid_r[valid].to(torch.float64) + realized_gap = (valid_t - valid_r).clamp_min(0) + sigmoid_gap = (valid_t - valid_sigmoid_r).clamp_min(0) + self._runtime_r_over_t_mean = float((valid_r / valid_t).mean().cpu()) + self._runtime_gap_mean = float((realized_gap / valid_t).mean().cpu()) + + positive_sigmoid_gap = sigmoid_gap > 0 + if bool(positive_sigmoid_gap.any()): + self._runtime_gap_over_sigmoid_gap_mean = float( + ( + realized_gap[positive_sigmoid_gap] + / sigmoid_gap[positive_sigmoid_gap] + ).mean().cpu() + ) + else: + self._runtime_gap_over_sigmoid_gap_mean = float('nan') + + preclip_scale = self.schedule.preclip_gap_scale(t) + if preclip_scale is None: + self._runtime_lower_gap_clip_rate = float('nan') + self._runtime_upper_gap_clip_rate = float('nan') + return + valid_scale = preclip_scale[valid].to(torch.float64) + finite_scale = torch.isfinite(valid_scale) & positive_sigmoid_gap + if not bool(finite_scale.any()): + self._runtime_lower_gap_clip_rate = float('nan') + self._runtime_upper_gap_clip_rate = float('nan') + return + intended_gap = sigmoid_gap[finite_scale] * valid_scale[finite_scale] + compared_gap = realized_gap[finite_scale] + compared_t = valid_t[finite_scale] + source_dtype = ( + t.dtype if t.is_floating_point() else torch.get_default_dtype() + ) + tolerance = ( + 16 + * torch.finfo(source_dtype).eps + * torch.maximum( + torch.maximum(intended_gap.abs(), compared_gap.abs()), + compared_t.abs(), + ) + ) + self._runtime_lower_gap_clip_rate = float( + (compared_gap > intended_gap + tolerance) + .to(torch.float64) + .mean() + .cpu() + ) + self._runtime_upper_gap_clip_rate = float( + (compared_gap < intended_gap - tolerance) + .to(torch.float64) + .mean() + .cpu() + ) + + # Official fixed t->r formulas, kept verbatim as the parity reference for + # tests/test_schedules.py; the training path dispatches through + # self.schedule (see __call__). def t_to_r_const(self, t): decay = 1 / self.q ** (self.stage+1) ratio = 1 - decay @@ -53,7 +226,8 @@ def __call__(self, net, images, labels=None, augment_pipe=None): # t ~ p(t) and r ~ p(r|t, iters) (Mapping fn) rnd_normal = torch.randn([images.shape[0], 1, 1, 1], device=images.device) t = (rnd_normal * self.P_std + self.P_mean).exp() - r = self.t_to_r(t) + r = self.schedule.compute_r(t=t, stage=self.stage) + self._record_schedule_runtime_pair(t=t, r=r) # Augmentation if needed y, augment_labels = augment_pipe(images) if augment_pipe is not None else (images, None) @@ -78,9 +252,23 @@ def __call__(self, net, images, labels=None, augment_pipe=None): else: D_yr = y - # L2 Loss + # Raw squared pair loss. Local t-bin schedules consume this signal before the + # ECT sample-error transform and 1/(t-r) weighting are applied. loss = (D_yt - D_yr) ** 2 loss = torch.sum(loss.reshape(loss.shape[0], -1), dim=-1) + self._runtime_local_training_signal = None + if self.schedule.name in ('local_tbin_v1', 'local_tbin_v2', 'local_tbin_v3'): + with torch.no_grad(): + bin_ids = self.schedule.bin_indices(t).flatten() + raw = loss.detach().to(torch.float64) + sums = torch.zeros(self.schedule.num_bins, dtype=torch.float64, device=raw.device) + counts = torch.zeros_like(sums) + sums.scatter_add_(0, bin_ids, raw) + counts.scatter_add_(0, bin_ids, torch.ones_like(raw)) + self._runtime_local_training_signal = { + 'loss_sums': sums, + 'loss_counts': counts, + } # Producing Adaptive Weighting (p=0.5) through Huber Loss if self.c > 0: diff --git a/training/schedules.py b/training/schedules.py new file mode 100644 index 00000000..2dce20cd --- /dev/null +++ b/training/schedules.py @@ -0,0 +1,847 @@ +"""t -> r mapping schedules for Easy Consistency Tuning (ECT). + +During consistency tuning, every training pair (x_t, x_r) is built from a +noise level t ~ p(t) and a smaller noise level r = r(t, stage) produced by a +mapping schedule ("Consistency Models Made Easy", arXiv 2406.14548, Sec. 3.3 +and Appendix A). This module centralizes the t -> r schedules behind a single +interface; ECMLoss in training/loss.py dispatches its t -> r entry through it +as r = self.schedule.compute_r(t=t, stage=self.stage), while the official +reference formulas stay verbatim in training/loss.py as the parity anchor. + +Supported schedules: + 'const' Official ECT constant mapping, Eq. (17). + 'sigmoid' Official ECT sigmoid mapping, Eq. (18); training default. + 'global_sigmoid' Official sigmoid gap times one fixed global multiplier. + 'adaptive_v1' Official sigmoid ratio plus a bounded correction driven by + the EMA of the globally aggregated training loss. + 'local_tbin_v1' Official sigmoid gap times a bounded per-t-bin multiplier + driven by unweighted raw pair-loss trends. + 'local_tbin_v2' V1 signal with conservative bounds and equal-bin + geometric-mean normalization before realized-gap clipping. + 'local_tbin_v3' V2 local redistribution times an explicit global gap + multiplier, separating calibration from adaptation. + +The 'const' and 'sigmoid' formulas are verbatim ports of +ECMLoss.t_to_r_const / ECMLoss.t_to_r_sigmoid in training/loss.py and MUST NOT +be modified: they are the official fixed baseline this project reproduces. +tests/test_schedules.py enforces bitwise parity against training/loss.py. + +Usage (both forms are supported): + from training.schedules import compute_r, get_schedule + + schedule = get_schedule('sigmoid', q=256, k=8, b=1) + r = schedule.compute_r(t=t, stage=stage) + + r = compute_r(t=t, stage=stage, schedule='sigmoid', q=256, k=8, b=1) + +`t` may be a torch tensor of any shape (the training loop uses [N, 1, 1, 1]), +or a python/numpy scalar or array, which is converted via torch.as_tensor(); +the result is a tensor of the same shape with r clamped to r >= 0. `stage` is +the official integer curriculum stage maintained by the training loop +(stage = cur_tick // double_ticks). adaptive_v1 changes only r/t using the +loss EMA; it does not replace the official stage curriculum. +""" + +import math +from statistics import NormalDist + +import torch + +#---------------------------------------------------------------------------- +# Registry. + +_SCHEDULES = {} + +def register_schedule(name): + def decorator(cls): + cls.name = name + _SCHEDULES[name] = cls + return cls + return decorator + +def available_schedules(): + return sorted(_SCHEDULES) + +def get_schedule(schedule, **schedule_kwargs): + # training/loss.py imports this by name, and torch_utils.persistence + # embeds that module's source into training snapshots — keep the public + # names in this module stable or old snapshots stop unpickling. + if schedule not in _SCHEDULES: + raise ValueError(f"Unknown schedule type {schedule!r}! Available: {', '.join(available_schedules())}") + return _SCHEDULES[schedule](**schedule_kwargs) + +#---------------------------------------------------------------------------- +# Interface. Hyperparameter defaults follow ct_train.py (-q 2.0 -k 8.0 -b 1.0). + +class Schedule: + name = None + + def __init__(self, q=2.0, k=8.0, b=1.0): + if q <= 1: + raise ValueError(f'q must be > 1 (Delta_t decay factor), got {q}') + self.q = q + self.k = k + self.b = b + self.stage = 0 + + def compute_r(self, t, stage): + raise NotImplementedError + + # Stateful interface mirroring ECMLoss, so a Schedule instance can drive + # the existing training loop (update_schedule() at stage boundaries, + # t_to_r() inside the loss) without further changes. + def update_schedule(self, stage): + self.stage = stage + + def t_to_r(self, t): + return self.compute_r(t=t, stage=self.stage) + + def update_training_signal(self, loss): + del loss + return False + + def runtime_metrics(self): + """Stable controller telemetry contract for training/evaluation code.""" + return { + 'loss_ema': None, + 'loss_reference': None, + 'correction': 0.0, + 'signal_updates': 0, + 'adaptive_active': False, + } + + def preclip_gap_scale(self, t): + """Return the intended multiplier on the sigmoid gap, if defined.""" + del t + return None + + def state_dict(self): + return {} + + def load_state_dict(self, state): + if state: + raise ValueError(f'{type(self).__name__} does not have adaptive state') + + def metadata(self): + return { + 'name': self.name, + 'enabled': False, + 'q': self.q, + 'k': self.k, + 'b': self.b, + } + + def __repr__(self): + return f'{type(self).__name__}(q={self.q}, k={self.k}, b={self.b})' + +def _as_tensor(t): + return t if isinstance(t, torch.Tensor) else torch.as_tensor(t) + +#---------------------------------------------------------------------------- +# Official fixed schedules. Verbatim ports of training/loss.py — do not edit +# the formulas; tests/test_schedules.py checks them bit-for-bit against +# ECMLoss. + +@register_schedule('const') +class ConstSchedule(Schedule): + """Official constant mapping, Eq. (17): r/t = 1 - 1/q^(stage+1). + + Port of ECMLoss.t_to_r_const in training/loss.py. + """ + + def compute_r(self, t, stage): + t = _as_tensor(t) + decay = 1 / self.q ** (stage + 1) + ratio = 1 - decay + r = t * ratio + return torch.clamp(r, min=0) + +@register_schedule('sigmoid') +class SigmoidSchedule(Schedule): + """Official sigmoid mapping, Eq. (18): r/t = 1 - n(t)/q^(stage+1), where + n(t) = 1 + k * sigmoid(-b * t). Training default (--mapping=sigmoid). + + Port of ECMLoss.t_to_r_sigmoid in training/loss.py. + """ + + def compute_r(self, t, stage): + t = _as_tensor(t) + adj = 1 + self.k * torch.sigmoid(-self.b * t) + decay = 1 / self.q ** (stage + 1) + ratio = 1 - decay * adj + r = t * ratio + return torch.clamp(r, min=0) + + def preclip_gap_scale(self, t): + t = _as_tensor(t) + if not t.is_floating_point(): + t = t.to(torch.get_default_dtype()) + return torch.ones_like(t) + +#---------------------------------------------------------------------------- +# Experimental schedules (Role C). Changes relative to the official fixed +# schedules live below this line only. + +def _validate_global_gap_scale(global_gap_scale): + value = float(global_gap_scale) + if not math.isfinite(value) or value <= 0: + raise ValueError( + f'global_gap_scale must be finite and > 0, got {global_gap_scale}' + ) + return value + + +def _apply_global_gap_scale(t, base_r, global_gap_scale): + """Scale ``t - base_r`` while preserving ``0 <= r <= t``. + + The exact ``scale == 1`` branch protects bitwise parity with the schedule + being wrapped, which makes the factorized controls auditable. + """ + if global_gap_scale == 1.0: + return base_r + t = _as_tensor(t) + if not t.is_floating_point(): + t = t.to(torch.get_default_dtype()) + base_r = base_r.to(t.dtype) + finite_max = torch.finfo(t.dtype).max + safe_t = torch.nan_to_num( + t, nan=0.0, posinf=finite_max, neginf=0.0 + ).clamp_min(0) + safe_r = torch.nan_to_num( + base_r, nan=0.0, posinf=finite_max, neginf=0.0 + ).clamp_min(0) + base_gap = (safe_t - torch.minimum(safe_r, safe_t)).clamp_min(0) + scaled_gap = base_gap * global_gap_scale + scaled_gap = torch.minimum( + torch.nan_to_num( + scaled_gap, nan=0.0, posinf=finite_max, neginf=0.0 + ).clamp_min(0), + safe_t, + ) + return safe_t - scaled_gap + + +@register_schedule('global_sigmoid') +class GlobalSigmoidSchedule(SigmoidSchedule): + """Official sigmoid mapping with one fixed multiplier on every gap.""" + + def __init__(self, q=2.0, k=8.0, b=1.0, global_gap_scale=1.0): + super().__init__(q=q, k=k, b=b) + self.global_gap_scale = _validate_global_gap_scale(global_gap_scale) + + def compute_r(self, t, stage): + base_r = super().compute_r(t=t, stage=stage) + return _apply_global_gap_scale(t, base_r, self.global_gap_scale) + + def preclip_gap_scale(self, t): + return super().preclip_gap_scale(t) * self.global_gap_scale + + def runtime_metrics(self): + metrics = super().runtime_metrics() + metrics['correction'] = self.global_gap_scale - 1.0 + return metrics + + def metadata(self): + metadata = super().metadata() + metadata.update( + name=self.name, + enabled=True, + intervention='fixed_global_multiplier_on_official_sigmoid_gap', + global_gap_scale=self.global_gap_scale, + **self.runtime_metrics(), + ) + return metadata + + +@register_schedule('adaptive_v1') +class AdaptiveV1Schedule(SigmoidSchedule): + """Loss-EMA adaptive correction on top of the official sigmoid ratio. + + Let rho_0 be the official sigmoid r/t ratio, L_ref the loss EMA at the + end of warm-up, and L_ema the current loss EMA. The correction is + + delta = max_adjust * tanh(log(L_ref) - log(L_ema)) + + and rho = clamp(rho_0 + delta, 0, 1 - min_gap). Improving loss therefore + tightens the pair (smaller t-r), while worsening loss widens it. The + correction is deterministic and bounded by max_adjust. + """ + + def __init__(self, q=2.0, k=8.0, b=1.0, loss_ema_beta=0.9, + max_adjust=0.05, min_gap=1e-3, warmup_updates=2): + super().__init__(q=q, k=k, b=b) + for name, value in [('q', q), ('k', k), ('b', b)]: + if not math.isfinite(float(value)): + raise ValueError(f'{name} must be finite, got {value}') + if not math.isfinite(loss_ema_beta) or not 0 <= loss_ema_beta < 1: + raise ValueError(f'loss_ema_beta must be in [0, 1), got {loss_ema_beta}') + if not math.isfinite(max_adjust) or not 0 <= max_adjust <= 1: + raise ValueError(f'max_adjust must be in [0, 1], got {max_adjust}') + if not math.isfinite(min_gap) or not 0 < min_gap < 1: + raise ValueError(f'min_gap must be in (0, 1), got {min_gap}') + try: + normalized_warmup_updates = int(warmup_updates) + except (TypeError, ValueError, OverflowError): + normalized_warmup_updates = -1 + if (isinstance(warmup_updates, bool) or normalized_warmup_updates != warmup_updates + or normalized_warmup_updates < 0): + raise ValueError(f'warmup_updates must be a non-negative integer, got {warmup_updates}') + self.loss_ema_beta = float(loss_ema_beta) + self.max_adjust = float(max_adjust) + self.min_gap = float(min_gap) + self.warmup_updates = normalized_warmup_updates + self.loss_ema = None + self.loss_reference = None + self.signal_updates = 0 + + def update_training_signal(self, loss): + loss = float(loss) + if not math.isfinite(loss) or loss < 0: + return False + loss = max(loss, torch.finfo(torch.float64).tiny) + if self.loss_ema is None: + updated_ema = loss + else: + beta = self.loss_ema_beta + updated_ema = beta * self.loss_ema + (1 - beta) * loss + if not math.isfinite(updated_ema) or updated_ema <= 0: + return False + self.loss_ema = updated_ema + self.signal_updates += 1 + + # Establish the baseline only after the requested number of valid + # signals have contributed to the EMA. With no warm-up, the first + # signal is necessarily the baseline (and therefore has zero + # correction); otherwise the following signal is the first one that + # can produce a correction relative to this reference. + if self.loss_reference is None and ( + self.warmup_updates == 0 or self.signal_updates == self.warmup_updates + ): + self.loss_reference = updated_ema + return True + + def correction_is_active(self): + return ( + self.max_adjust != 0 + and self.loss_ema is not None + and self.loss_reference is not None + and self.signal_updates > self.warmup_updates + ) + + def correction(self): + if not self.correction_is_active(): + return 0.0 + log_improvement = math.log(self.loss_reference) - math.log(self.loss_ema) + return self.max_adjust * math.tanh(log_improvement) + + def compute_r(self, t, stage): + stage = float(stage) + if not math.isfinite(stage) or stage < 0: + raise ValueError(f'stage must be finite and >= 0, got {stage}') + t = _as_tensor(t) + + # Before a correction is active, adaptive_v1 is exactly the official + # sigmoid schedule. In particular, min_gap must not alter the no-signal + # or warmup path. + if not self.correction_is_active(): + return super().compute_r(t=t, stage=stage) + + if not t.is_floating_point(): + t = t.to(torch.get_default_dtype()) + finite_max = torch.finfo(t.dtype).max + t = torch.nan_to_num(t, nan=0.0, posinf=finite_max, neginf=0.0).clamp_min(0) + + try: + base_r = super().compute_r(t=t, stage=stage) + except OverflowError: + # q**(stage+1) -> inf, so the mathematical sigmoid ratio -> 1. + base_r = t + delta = self.correction() + base_ratio = torch.where(t > 0, base_r / t, torch.zeros_like(t)) + ratio = torch.clamp(base_ratio + delta, min=0, max=1 - self.min_gap) + r = torch.nan_to_num(t * ratio, nan=0.0, posinf=finite_max, neginf=0.0) + return torch.minimum(r.clamp_min(0), t) + + def state_dict(self): + return { + 'loss_ema': self.loss_ema, + 'loss_reference': self.loss_reference, + 'signal_updates': self.signal_updates, + } + + def load_state_dict(self, state): + loss_ema = state.get('loss_ema') + loss_reference = state.get('loss_reference') + signal_updates = int(state.get('signal_updates', 0)) + for name, value in [('loss_ema', loss_ema), ('loss_reference', loss_reference)]: + if value is not None and (not math.isfinite(float(value)) or float(value) <= 0): + raise ValueError(f'{name} must be finite and > 0, got {value}') + if signal_updates < 0: + raise ValueError(f'signal_updates must be >= 0, got {signal_updates}') + self.loss_ema = None if loss_ema is None else float(loss_ema) + self.loss_reference = None if loss_reference is None else float(loss_reference) + self.signal_updates = signal_updates + + def metadata(self): + return { + 'name': self.name, + 'enabled': True, + 'signal': 'loss_ema', + 'q': self.q, + 'k': self.k, + 'b': self.b, + 'loss_ema_beta': self.loss_ema_beta, + 'warmup_updates': self.warmup_updates, + 'max_adjust': self.max_adjust, + 'min_gap': self.min_gap, + **self.runtime_metrics(), + } + + def runtime_metrics(self): + return { + 'loss_ema': self.loss_ema, + 'loss_reference': self.loss_reference, + 'correction': self.correction(), + 'signal_updates': self.signal_updates, + 'adaptive_active': self.correction_is_active(), + } + + +@register_schedule('local_tbin_v1') +class LocalTBinV1Schedule(SigmoidSchedule): + """Conservative local controller on top of the official sigmoid gap. + + The log-normal training distribution is divided into equal-probability + bins. Each bin tracks short and long EMAs of the *raw squared pair loss* + supplied by :class:`ECMLoss`. The official sigmoid mapping remains the + baseline and only its relative gap is scaled: + + base_gap = (t - r_sigmoid) / t + trend_j = tanh(log(long_ema_j) - log(short_ema_j)) + scale_j = clip(exp(-gain * trend_j), min_scale, max_scale) + r = t * (1 - clip(base_gap * scale_j, min_gap, 1)) + + Falling short-term loss tightens the local pair (scale < 1); rising loss + widens it toward the diffusion-pretraining boundary (scale > 1). A + two-timescale trend avoids freezing a reference during the startup + transient, and the multiplicative bound keeps the correction relative to + the official t-dependent gap instead of replacing its n(t) structure. + """ + + def __init__(self, q=2.0, k=8.0, b=1.0, p_mean=-1.1, p_std=2.0, + num_bins=4, short_beta=0.9, long_beta=0.99, + warmup_updates=32, gain=0.5, min_scale=0.75, + max_scale=1.5, deadband=0.02, min_gap=1e-3): + super().__init__(q=q, k=k, b=b) + if not math.isfinite(float(p_mean)): + raise ValueError(f'p_mean must be finite, got {p_mean}') + if not math.isfinite(float(p_std)) or float(p_std) <= 0: + raise ValueError(f'p_std must be finite and > 0, got {p_std}') + if isinstance(num_bins, bool) or int(num_bins) != num_bins or int(num_bins) < 2: + raise ValueError(f'num_bins must be an integer >= 2, got {num_bins}') + for name, value in [('short_beta', short_beta), ('long_beta', long_beta)]: + if not math.isfinite(float(value)) or not 0 <= float(value) < 1: + raise ValueError(f'{name} must be in [0, 1), got {value}') + if float(short_beta) >= float(long_beta): + raise ValueError('short_beta must be smaller than long_beta') + if (isinstance(warmup_updates, bool) or int(warmup_updates) != warmup_updates + or int(warmup_updates) < 0): + raise ValueError( + f'warmup_updates must be a non-negative integer, got {warmup_updates}' + ) + for name, value in [('gain', gain), ('deadband', deadband), ('min_gap', min_gap)]: + if not math.isfinite(float(value)) or float(value) < 0: + raise ValueError(f'{name} must be finite and >= 0, got {value}') + if not 0 < float(min_gap) < 1: + raise ValueError(f'min_gap must be in (0, 1), got {min_gap}') + if not math.isfinite(float(min_scale)) or not 0 < float(min_scale) <= 1: + raise ValueError(f'min_scale must be in (0, 1], got {min_scale}') + if not math.isfinite(float(max_scale)) or float(max_scale) < 1: + raise ValueError(f'max_scale must be finite and >= 1, got {max_scale}') + + self.p_mean = float(p_mean) + self.p_std = float(p_std) + self.num_bins = int(num_bins) + self.short_beta = float(short_beta) + self.long_beta = float(long_beta) + self.warmup_updates = int(warmup_updates) + self.gain = float(gain) + self.min_scale = float(min_scale) + self.max_scale = float(max_scale) + self.deadband = float(deadband) + self.min_gap = float(min_gap) + normal = NormalDist() + self.log_bin_edges = [ + self.p_mean + self.p_std * normal.inv_cdf(index / self.num_bins) + for index in range(1, self.num_bins) + ] + self.short_ema = [None] * self.num_bins + self.long_ema = [None] * self.num_bins + self.last_raw_loss = [None] * self.num_bins + self.bin_updates = [0] * self.num_bins + + def bin_indices(self, t): + """Return p(t)-quantile bin indices with the same shape as ``t``.""" + t = _as_tensor(t) + if not t.is_floating_point(): + t = t.to(torch.get_default_dtype()) + tiny = torch.finfo(t.dtype).tiny + log_t = torch.log(torch.nan_to_num(t, nan=tiny, posinf=torch.finfo(t.dtype).max, + neginf=tiny).clamp_min(tiny)) + boundaries = torch.tensor(self.log_bin_edges, dtype=t.dtype, device=t.device) + return torch.bucketize(log_t, boundaries) + + def update_training_signal(self, raw_bin_losses): + try: + values = list(raw_bin_losses) + except TypeError as exc: + raise ValueError('local_tbin_v1 expects one raw loss mean per bin') from exc + if len(values) != self.num_bins: + raise ValueError( + f'expected {self.num_bins} raw bin losses, got {len(values)}' + ) + updated_any = False + for index, value in enumerate(values): + if value is None: + continue + value = float(value) + if not math.isfinite(value) or value < 0: + continue + value = max(value, torch.finfo(torch.float64).tiny) + if self.short_ema[index] is None: + short = value + long = value + else: + short = ( + self.short_beta * self.short_ema[index] + + (1 - self.short_beta) * value + ) + long = ( + self.long_beta * self.long_ema[index] + + (1 - self.long_beta) * value + ) + if not all(math.isfinite(item) and item > 0 for item in (short, long)): + continue + self.short_ema[index] = short + self.long_ema[index] = long + self.last_raw_loss[index] = value + self.bin_updates[index] += 1 + updated_any = True + return updated_any + + def bin_is_active(self, index): + return ( + self.gain != 0 + and self.short_ema[index] is not None + and self.long_ema[index] is not None + and self.bin_updates[index] > self.warmup_updates + ) + + def gap_scales(self): + scales = [] + for index in range(self.num_bins): + if not self.bin_is_active(index): + scales.append(1.0) + continue + log_trend = math.log(self.long_ema[index]) - math.log(self.short_ema[index]) + if abs(log_trend) <= self.deadband: + log_trend = 0.0 + score = math.tanh(log_trend) + scale = math.exp(-self.gain * score) + scales.append(min(max(scale, self.min_scale), self.max_scale)) + return scales + + def correction_is_active(self): + return all(self.bin_is_active(index) for index in range(self.num_bins)) + + def correction(self): + scales = self.gap_scales() + return sum(scale - 1 for scale in scales) / self.num_bins + + def compute_r(self, t, stage): + stage = float(stage) + if not math.isfinite(stage) or stage < 0: + raise ValueError(f'stage must be finite and >= 0, got {stage}') + t = _as_tensor(t) + base_r = super().compute_r(t=t, stage=stage) + if not any(self.bin_is_active(index) for index in range(self.num_bins)): + return base_r + if not t.is_floating_point(): + t = t.to(torch.get_default_dtype()) + base_r = base_r.to(t.dtype) + finite_max = torch.finfo(t.dtype).max + safe_t = torch.nan_to_num(t, nan=0.0, posinf=finite_max, neginf=0.0).clamp_min(0) + bin_ids = self.bin_indices(safe_t) + scales = torch.tensor(self.gap_scales(), dtype=t.dtype, device=t.device)[bin_ids] + base_gap = torch.where(safe_t > 0, (safe_t - base_r) / safe_t, torch.ones_like(safe_t)) + gap = torch.clamp(base_gap * scales, min=self.min_gap, max=1.0) + r = torch.nan_to_num(safe_t * (1 - gap), nan=0.0, posinf=finite_max, neginf=0.0) + return torch.minimum(r.clamp_min(0), safe_t) + + def preclip_gap_scale(self, t): + t = _as_tensor(t) + if not t.is_floating_point(): + t = t.to(torch.get_default_dtype()) + bin_ids = self.bin_indices(t) + scales = torch.tensor( + self.gap_scales(), dtype=t.dtype, device=t.device + ) + return scales[bin_ids] + + def state_dict(self): + return { + 'short_ema': self.short_ema, + 'long_ema': self.long_ema, + 'last_raw_loss': self.last_raw_loss, + 'bin_updates': self.bin_updates, + } + + def load_state_dict(self, state): + short = list(state.get('short_ema', [None] * self.num_bins)) + long = list(state.get('long_ema', [None] * self.num_bins)) + last = list(state.get('last_raw_loss', [None] * self.num_bins)) + updates = list(state.get('bin_updates', [0] * self.num_bins)) + if not all(len(values) == self.num_bins for values in (short, long, last, updates)): + raise ValueError('local_tbin_v1 state bin count mismatch') + for name, values in [('short_ema', short), ('long_ema', long), ('last_raw_loss', last)]: + for value in values: + if value is not None and (not math.isfinite(float(value)) or float(value) <= 0): + raise ValueError(f'{name} values must be finite and > 0') + if any(isinstance(value, bool) or int(value) != value or int(value) < 0 for value in updates): + raise ValueError('bin_updates values must be non-negative integers') + self.short_ema = [None if value is None else float(value) for value in short] + self.long_ema = [None if value is None else float(value) for value in long] + self.last_raw_loss = [None if value is None else float(value) for value in last] + self.bin_updates = [int(value) for value in updates] + + def local_runtime_metrics(self): + return { + 'log_bin_edges': list(self.log_bin_edges), + 'short_ema': list(self.short_ema), + 'long_ema': list(self.long_ema), + 'last_raw_loss': list(self.last_raw_loss), + 'gap_scales': self.gap_scales(), + 'bin_updates': list(self.bin_updates), + 'bin_active': [self.bin_is_active(index) for index in range(self.num_bins)], + } + + def runtime_metrics(self): + short = [value for value in self.short_ema if value is not None] + long = [value for value in self.long_ema if value is not None] + return { + 'loss_ema': sum(short) / len(short) if short else None, + 'loss_reference': sum(long) / len(long) if long else None, + 'correction': self.correction(), + 'signal_updates': min(self.bin_updates), + 'adaptive_active': self.correction_is_active(), + } + + def metadata(self): + return { + 'name': self.name, + 'enabled': True, + 'signal': 'raw_pair_loss_per_quantile_t_bin', + 'q': self.q, + 'k': self.k, + 'b': self.b, + 'p_mean': self.p_mean, + 'p_std': self.p_std, + 'num_bins': self.num_bins, + 'log_bin_edges': list(self.log_bin_edges), + 'short_beta': self.short_beta, + 'long_beta': self.long_beta, + 'warmup_updates': self.warmup_updates, + 'gain': self.gain, + 'min_scale': self.min_scale, + 'max_scale': self.max_scale, + 'deadband': self.deadband, + 'min_gap': self.min_gap, + **self.runtime_metrics(), + 'local_metrics': self.local_runtime_metrics(), + } + + +@register_schedule('local_tbin_v2') +class LocalTBinV2Schedule(LocalTBinV1Schedule): + """Geometrically normalized, lower-authority local t-bin controller. + + V2 keeps V1's raw-loss signal and official sigmoid baseline, but projects + the active log gap scales onto a bounded zero-mean set. Since the bins are + equal-probability under p(t), this makes their geometric mean exactly one: + the local *scale factors* are geometrically neutral before the minimum-gap + and maximum-gap clipping used to realize valid training pairs. The + realized gaps therefore need not be globally neutral. + """ + + def __init__(self, q=2.0, k=8.0, b=1.0, p_mean=-1.1, p_std=2.0, + num_bins=4, short_beta=0.9, long_beta=0.99, + warmup_updates=64, gain=0.25, min_scale=0.85, + max_scale=1.25, deadband=0.02, min_gap=1e-3): + super().__init__( + q=q, + k=k, + b=b, + p_mean=p_mean, + p_std=p_std, + num_bins=num_bins, + short_beta=short_beta, + long_beta=long_beta, + warmup_updates=warmup_updates, + gain=gain, + min_scale=min_scale, + max_scale=max_scale, + deadband=deadband, + min_gap=min_gap, + ) + + def compute_r(self, t, stage): + # V2/V3 must remain exactly on the official sigmoid schedule until all + # bins are ready. Calling V1's implementation during partial warmup + # would apply its minimum-gap clamp even though gap_scales() is neutral. + if not self.correction_is_active(): + return SigmoidSchedule.compute_r(self, t=t, stage=stage) + return super().compute_r(t=t, stage=stage) + + def gap_scales(self): + # Do not partially redistribute the global curriculum while any bin is + # still warming up. Quantile bins normally activate together, but this + # also makes sparse or resumed signals deterministic. + if not self.correction_is_active(): + return [1.0] * self.num_bins + + raw_scales = super().gap_scales() + raw_logs = [math.log(scale) for scale in raw_scales] + lower = math.log(self.min_scale) + upper = math.log(self.max_scale) + + # Project log_scales - shift onto [lower, upper] with mean exactly zero. + # The root exists because lower <= 0 <= upper. + shift_low = min(value - upper for value in raw_logs) + shift_high = max(value - lower for value in raw_logs) + for _ in range(80): + shift = (shift_low + shift_high) / 2 + projected = [ + min(max(value - shift, lower), upper) + for value in raw_logs + ] + if sum(projected) > 0: + shift_low = shift + else: + shift_high = shift + shift = (shift_low + shift_high) / 2 + projected = [ + min(max(value - shift, lower), upper) + for value in raw_logs + ] + return [math.exp(value) for value in projected] + + def local_runtime_metrics(self): + metrics = super().local_runtime_metrics() + scales = metrics['gap_scales'] + metrics['log_scale_mean'] = sum(math.log(scale) for scale in scales) / len(scales) + return metrics + + def metadata(self): + metadata = super().metadata() + metadata.update( + scale_normalization='equal_probability_bin_geometric_mean_1', + global_log_scale_mean=( + sum(math.log(scale) for scale in self.gap_scales()) / self.num_bins + ), + ) + return metadata + + +@register_schedule('local_tbin_v3') +class LocalTBinV3Schedule(LocalTBinV2Schedule): + """Global calibration times preclip geometric-mean-one local factors.""" + + def __init__(self, q=2.0, k=8.0, b=1.0, p_mean=-1.1, p_std=2.0, + num_bins=4, short_beta=0.9, long_beta=0.99, + warmup_updates=64, gain=0.25, min_scale=0.85, + max_scale=1.25, deadband=0.02, min_gap=1e-3, + global_gap_scale=1.0): + super().__init__( + q=q, + k=k, + b=b, + p_mean=p_mean, + p_std=p_std, + num_bins=num_bins, + short_beta=short_beta, + long_beta=long_beta, + warmup_updates=warmup_updates, + gain=gain, + min_scale=min_scale, + max_scale=max_scale, + deadband=deadband, + min_gap=min_gap, + ) + self.global_gap_scale = _validate_global_gap_scale(global_gap_scale) + + def compute_r(self, t, stage): + local_r = super().compute_r(t=t, stage=stage) + return _apply_global_gap_scale(t, local_r, self.global_gap_scale) + + def preclip_gap_scale(self, t): + return super().preclip_gap_scale(t) * self.global_gap_scale + + def runtime_metrics(self): + metrics = super().runtime_metrics() + total_scales = [ + self.global_gap_scale * scale for scale in self.gap_scales() + ] + metrics['correction'] = ( + sum(scale - 1.0 for scale in total_scales) / self.num_bins + ) + return metrics + + def local_runtime_metrics(self): + metrics = super().local_runtime_metrics() + metrics['total_gap_scales'] = [ + self.global_gap_scale * scale for scale in metrics['gap_scales'] + ] + return metrics + + def metadata(self): + metadata = super().metadata() + metadata.update( + name=self.name, + intervention='fixed_global_times_preclip_geometric_mean_1_local', + global_gap_scale=self.global_gap_scale, + **self.runtime_metrics(), + ) + return metadata + +def continuous_stage(cur_tick, double_ticks): + """Legacy fractional-stage helper retained for import compatibility. + + adaptive_v1 now uses the official integer stage and adapts only from the + loss EMA; new training code should not use this helper. + """ + if double_ticks <= 0: + raise ValueError(f'double_ticks must be > 0, got {double_ticks}') + return cur_tick / double_ticks + +#---------------------------------------------------------------------------- +# Functional one-shot interface. + +def compute_r(t, stage, schedule='sigmoid', **schedule_kwargs): + """r = compute_r(t=t, stage=stage, schedule='sigmoid', q=256, k=8, b=1)""" + return get_schedule(schedule, **schedule_kwargs).compute_r(t=t, stage=stage) + +#---------------------------------------------------------------------------- +# Quick visual check: python -m training.schedules + +if __name__ == '__main__': + t = torch.tensor([0.002, 0.02, 0.2, 2.0, 20.0, 80.0], dtype=torch.float64) + print('r/t with q=2, k=8, b=1 at t =', t.tolist()) + for name in available_schedules(): + schedule = get_schedule(name) + if name == 'adaptive_v1': + schedule.update_training_signal(10.0) + schedule.update_training_signal(7.0) + print(f'--- {name} ---') + for stage in [0, 1, 3, 7]: + ratio = schedule.compute_r(t=t, stage=stage) / t + print(f' stage {stage:>4}: ' + ' '.join(f'{v:.4f}' for v in ratio.tolist()))