From eac1b775511f96a73ff7bbae571ff297d57fd578 Mon Sep 17 00:00:00 2001 From: Gsunshine Date: Mon, 23 Sep 2024 18:46:35 -0400 Subject: [PATCH 01/60] Add AMP gradscalar. --- ct_train.py | 5 ++-- training/ct_training_loop.py | 52 +++++++++++++++++++----------------- 2 files changed, 30 insertions(+), 27 deletions(-) diff --git a/ct_train.py b/ct_train.py index d9976a5b..f7251870 100644 --- a/ct_train.py +++ b/ct_train.py @@ -78,7 +78,8 @@ 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_gradscaler', help='Enable torch.cuda.amp.GradScaler, NOTE overwritting loss_scale set by --ls', metavar='BOOL', type=bool, default=False, show_default=True) +@click.option('--amp', help='Enable torch.cuda.amp.GradScaler for mixed precision training, \ + NOTE overwritting loss_scale 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) @@ -165,7 +166,7 @@ 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, enable_gradscaler=opts.enable_gradscaler) + c.update(loss_scaling=opts.ls, cudnn_benchmark=opts.bench, enable_tf32=opts.tf32, enable_gradscaler=opts.enable_amp) 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(mid_t=opts.mid_t, metrics=opts.metrics, sample_ticks=opts.sample_every, eval_ticks=opts.eval_every) diff --git a/training/ct_training_loop.py b/training/ct_training_loop.py index 4f5f0b80..10c4d4b0 100644 --- a/training/ct_training_loop.py +++ b/training/ct_training_loop.py @@ -128,8 +128,8 @@ def training_loop( 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_gradscaler = False, # Enable torch.cuda.amp.GradScaler + enable_tf32 = False, # Enable tf32 for A100/H100 GPUs? + enable_amp = False, # Enable torch.cuda.amp.GradScaler device = torch.device('cuda'), ): # Initialize. @@ -169,8 +169,9 @@ 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 - dist.print0(f'GradScaler enabled: {enable_gradscaler}') - if enable_gradscaler: + # Automatic Mixed Precision + dist.print0(f'GradScaler enabled: {enable_amp} for mixed preicision 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(f'Setting up GradScaler...') @@ -206,10 +207,11 @@ def training_loop( data = torch.load(resume_state_dump, map_location=torch.device('cpu')) misc.copy_params_and_buffers(src_module=data['net'], dst_module=net, require_all=True) optimizer.load_state_dict(data['optimizer_state']) - if enable_gradscaler: + 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}"...') - # Although not loading the state_dict of the GradScaler works well, loading it can improve reproducibility. scaler.load_state_dict(data['gradscaler_state']) else: dist.print0(f'GradScaler state is not found in "{resume_state_dump}", using the default state.') @@ -269,24 +271,26 @@ def update_scheduler(loss_fn): loss = loss_fn(net=ddp, images=images, labels=labels, augment_pipe=augment_pipe) training_stats.report('Loss/loss', loss) - if enable_gradscaler: + if enable_amp: scaler.scale(loss.mean()).backward() else: - # loss.sum().mul(loss_scaling / batch_gpu_total).backward() loss.mul(loss_scaling).mean().backward() - if enable_gradscaler: - # TODO Is torch.nan_to_num(param.grad, nan=0, posinf=1e5, neginf=-1e5, out=param.grad) needed when using GradScaler? + # 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) + + # 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. + if enable_amp: scaler.step(optimizer) scaler.update() else: - # Update weights. - # for g in optimizer.param_groups: - # g['lr'] = optimizer_kwargs['lr'] * min(cur_nimg / max(lr_rampup_kimg * 1000, 1e-8), 1) - 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() + optimizer.step() # Update EMA. if ema_halflife_kimg is not None: @@ -341,10 +345,9 @@ def update_scheduler(loss_fn): # 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: - if enable_gradscaler: - data = dict(net=net, optimizer_state=optimizer.state_dict(), gradscaler_state=scaler.state_dict()) - else: - data = dict(net=net, optimizer_state=optimizer.state_dict()) + data = dict(net=net, optimizer_state=optimizer.state_dict()) + if enable_amp: + data.update(gradscaler_state=scaler.state_dict()) torch.save(data, os.path.join(run_dir, f'training-state-{cur_tick:06d}.pt')) # Save latest checkpoints @@ -363,10 +366,9 @@ def update_scheduler(loss_fn): del data # conserve memory if dist.get_rank() == 0: - if enable_gradscaler: - data = dict(net=net, optimizer_state=optimizer.state_dict(), gradscaler_state=scaler.state_dict()) - else: - data = dict(net=net, optimizer_state=optimizer.state_dict()) + data = dict(net=net, optimizer_state=optimizer.state_dict()) + if enable_amp: + data.update(gradscaler_state=scaler.state_dict()) torch.save(data, os.path.join(run_dir, f'training-state-latest.pt')) # Sample Img From 48e3f4781e4a5a4c9387da6e9363bd50d335ece2 Mon Sep 17 00:00:00 2001 From: Gsunshine Date: Mon, 23 Sep 2024 18:57:41 -0400 Subject: [PATCH 02/60] Update README. --- README.md | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/README.md b/README.md index 18449702..d232ef27 100644 --- a/README.md +++ b/README.md @@ -14,6 +14,7 @@ Try your own [Consistency Models](https://arxiv.org/abs/2303.01469)! You only ne Baking more in the oven. 🙃 +- **2024.09.23** - Add Gradscaler for Mixed Precision Training. To use mixed precision with GradScaler, switch to the [`amp` branch](https://github.com/locuslab/ect/tree/amp): `git checkout amp`. - **2024.04.27** - Upgrade environment to Pytorch 2.3.0. - **2024.04.12** - ECMs can now surpass SoTA GANs using 1 model step and SoTA Diffusion Models using 2 model steps on CIFAR10. Checkpoints available. @@ -46,6 +47,18 @@ bash run_ecm.sh --desc bs128.200k Replace NGPUs and PORT with the number of GPUs used for training and the port number for DDP sync. +### Half Precision Training + +In this branch, we enable fp16 training with AMP GradScaler for more stable training dynamics. +To enable fp16 and GradScaler, add the following arguments to your script: + +```bash +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). +Full support for Automatic Mixed Precision (AMP) will be added later. + ## Evaluation Run the following command to calculate FID of a pretrained checkpoint. From a2238829fa46c28272213217be7a411684f0a91f Mon Sep 17 00:00:00 2001 From: hjjjs4vbmv-netizen Date: Tue, 14 Jul 2026 23:53:59 +0800 Subject: [PATCH 03/60] fix: restore AMP CLI and metrics-none behavior --- .gitignore | 17 +++++++++++++---- ct_train.py | 4 ++-- training/ct_training_loop.py | 2 +- 3 files changed, 16 insertions(+), 7 deletions(-) diff --git a/.gitignore b/.gitignore index 3857786f..f3250f37 100644 --- a/.gitignore +++ b/.gitignore @@ -161,10 +161,19 @@ cython_debug/ # Project-related -datasets - -ct-runs -ct-evals +datasets/ +runs/ +checkpoints/ +ct-runs/ +ct-evals/ +wandb/ +*.pkl +*.pt +*.pth +*.ckpt +*.safetensors +*.zip +*.pyc slurm* debug.sh diff --git a/ct_train.py b/ct_train.py index f7251870..1906ec4b 100644 --- a/ct_train.py +++ b/ct_train.py @@ -78,7 +78,7 @@ 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('--amp', help='Enable torch.cuda.amp.GradScaler for mixed precision training, \ +@click.option('--enable_amp', '--amp', 'enable_amp', help='Enable torch.cuda.amp.GradScaler for mixed precision training, \ NOTE overwritting loss_scale 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) @@ -166,7 +166,7 @@ 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, enable_gradscaler=opts.enable_amp) + 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=opts.snap, state_dump_ticks=opts.dump, ckpt_ticks=opts.ckpt, double_ticks=opts.double) c.update(mid_t=opts.mid_t, metrics=opts.metrics, sample_ticks=opts.sample_every, eval_ticks=opts.eval_every) diff --git a/training/ct_training_loop.py b/training/ct_training_loop.py index 10c4d4b0..f89bf66f 100644 --- a/training/ct_training_loop.py +++ b/training/ct_training_loop.py @@ -380,7 +380,7 @@ 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={}, From 36dcae6f5416b2e1be770e70f6f81a20b2d2cb6b Mon Sep 17 00:00:00 2001 From: hjjjs4vbmv-netizen Date: Wed, 15 Jul 2026 11:59:52 +0800 Subject: [PATCH 04/60] chore: integrate reproducible engineering workflow --- artifacts/day2/collaborator_audit.md | 128 +++++++++++ artifacts/day2/integration_report.md | 126 +++++++++++ conda-matpool.yml | 18 ++ docs/DAY1_A.md | 202 ++++++++++++++++++ download_checkpoint.sh | 125 +++++++++++ prepare_data.sh | 147 +++++++++++++ scripts/check_environment.py | 138 ++++++++++++ scripts/smoke_engineering_100steps.sh | 295 ++++++++++++++++++++++++++ scripts/verify_assets.py | 221 +++++++++++++++++++ scripts/verify_smoke_run.py | 136 ++++++++++++ setup_env.sh | 127 +++++++++++ 11 files changed, 1663 insertions(+) create mode 100644 artifacts/day2/collaborator_audit.md create mode 100644 artifacts/day2/integration_report.md create mode 100644 conda-matpool.yml create mode 100644 docs/DAY1_A.md create mode 100755 download_checkpoint.sh create mode 100755 prepare_data.sh create mode 100755 scripts/check_environment.py create mode 100755 scripts/smoke_engineering_100steps.sh create mode 100755 scripts/verify_assets.py create mode 100755 scripts/verify_smoke_run.py create mode 100755 setup_env.sh 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/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/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/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' "${OUTPUT}" diff --git a/prepare_data.sh b/prepare_data.sh new file mode 100755 index 00000000..7d9bf252 --- /dev/null +++ b/prepare_data.sh @@ -0,0 +1,147 @@ +#!/usr/bin/env bash + +# Download CIFAR-10, verify the official archive, and convert it to EDM ZIP format. + +set -Eeuo pipefail + +ROOT_DIR="$(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/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/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/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_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}" From 57cef9cc9c4a9b1d29602e52b47b1e032bcf48a2 Mon Sep 17 00:00:00 2001 From: w10800 <1793719632@qq.com> Date: Wed, 15 Jul 2026 16:54:17 +0800 Subject: [PATCH 05/60] Validate Role A clean-container workflow --- artifacts/day3/role_a_environment.json | 39 +++++++++++ artifacts/day3/role_a_smoke_report.json | 61 +++++++++++++++++ docs/ROLE_A_CLEAN_CONTAINER_VALIDATION.md | 82 +++++++++++++++++++++++ 3 files changed, 182 insertions(+) create mode 100644 artifacts/day3/role_a_environment.json create mode 100644 artifacts/day3/role_a_smoke_report.json create mode 100644 docs/ROLE_A_CLEAN_CONTAINER_VALIDATION.md diff --git a/artifacts/day3/role_a_environment.json b/artifacts/day3/role_a_environment.json new file mode 100644 index 00000000..029a4705 --- /dev/null +++ b/artifacts/day3/role_a_environment.json @@ -0,0 +1,39 @@ +{ + "schema_version": 1, + "git_commit": "7c638a4d1f293222ec9eb8ad86cffc03fdcb7282", + "branch": "role-a/clean-container-validation", + "git_dirty": false, + "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": { + "accelerate": "0.27.2", + "diffusers": "0.26.3", + "huggingface-hub": "0.23.4", + "numpy": "1.26.4", + "scipy": "1.12.0", + "torch": "2.3.0" + }, + "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", + "environment_adjustment": "The pre-existing ect environment had huggingface-hub 0.20.3 from the superseded branch. It was upgraded to the main-branch-required 0.23.4 before the final setup_env.sh validation.", + "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..0dc175c8 --- /dev/null +++ b/artifacts/day3/role_a_smoke_report.json @@ -0,0 +1,61 @@ +{ + "schema_version": 1, + "test_kind": "engineering_connectivity_only", + "official_ect_baseline": false, + "git_commit": "7c638a4d1f293222ec9eb8ad86cffc03fdcb7282", + "branch": "role-a/clean-container-validation", + "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", + "checkpoint_path": "/mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl", + "checkpoint_sha256": "4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da", + "check_only": "passed", + "dry_run": "passed", + "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_means": { + "fresh": [11.838935947418213, 17.023774967267357], + "resume": [16.92818015435338] + }, + "wall_time_seconds": 180, + "peak_vram_mib": 5563, + "peak_reserved_vram_mib": 5616, + "run_directory": "/mnt/ect_project/runs/engineering-smoke/7c638a4d-20260715T083443Z", + "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.", + "The console prints a NaN before training_stats.default_collector.update() at the initialization and resume-final maintenance points; this is a reporting-order artifact, not a non-finite recorded training loss.", + "The resume log explicitly records loading GradScaler state from the fresh training state." + ], + "unresolved_issues": [ + "env.yml does not explicitly pin huggingface-hub==0.23.4 even though scripts/check_environment.py enforces that exact version." + ], + "final_status": "passed" +} diff --git a/docs/ROLE_A_CLEAN_CONTAINER_VALIDATION.md b/docs/ROLE_A_CLEAN_CONTAINER_VALIDATION.md new file mode 100644 index 00000000..fb73193b --- /dev/null +++ b/docs/ROLE_A_CLEAN_CONTAINER_VALIDATION.md @@ -0,0 +1,82 @@ +# Role A clean-container validation + +## Result + +Role A engineering validation passed on one MatPool A100 container from the frozen integration baseline: + +- branch: `role-a/clean-container-validation` +- commit: `7c638a4d1f293222ec9eb8ad86cffc03fdcb7282` +- 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/7c638a4d-20260715T083443Z` + +This is an engineering connectivity test only. It is not the official fixed ECT baseline and it did not run FID or KID. + +## Commands actually run + +After checking out the required commit and creating the role branch, the following validation commands were run: + +```bash +bash setup_env.sh +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 container already contained an `ect` environment prepared against a superseded branch. The first `setup_env.sh` invocation correctly rejected `huggingface-hub 0.20.3`; it was reconciled to the required version and the exact setup command was rerun: + +```bash +python -m pip install --index-url https://pypi.tuna.tsinghua.edu.cn/simple huggingface-hub==0.23.4 +bash setup_env.sh +``` + +The second invocation passed with a clean Git worktree. No training-code 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 official EDM checkpoint SHA-256 matched `4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da`. + +## Smoke results + +| Check | Result | +| --- | --- | +| 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 | 180 seconds | +| Peak allocated VRAM | 5,563 MiB | +| Peak reserved VRAM | 5,616 MiB | + +Both phases generated a numbered network snapshot and training state. 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 17.0237749673 +- Resume: 16.9281801544 + +The console has `loss nan` at the initialization maintenance point and the resume-final maintenance point. In `training/ct_training_loop.py`, the status line reads the default collector before `default_collector.update()` is called, so a new process can print an empty collector even though the subsequently written statistics are finite. This is a reporting-order observation; no core training file was changed. + +## Storage and Git hygiene + +All datasets, checkpoints, training states, complete logs, and generated images remain under `/mnt/ect_project`. Git contains only the two compact Day 3 JSON summaries and this document. No FID-50k job was launched and no large file is included in the branch. + +## Follow-up observation + +`scripts/check_environment.py` requires exactly `huggingface-hub==0.23.4`, while `env.yml` currently leaves that transitive dependency unpinned. A newly resolved environment could therefore install a different version and fail validation. This should be resolved by the integration owner before declaring the environment specification permanently frozen. From 892e4978ccd5b4e7b9c7f98c61d3b1658d20b7df Mon Sep 17 00:00:00 2001 From: edwards365 Date: Wed, 15 Jul 2026 17:05:43 +0800 Subject: [PATCH 06/60] Add reproducible fixed-seed evaluation workflow --- README.md | 21 +++ ct_eval.py | 13 +- .../preliminary_seed42_fp32_8ksteps/README.md | 16 ++ .../checkpoint.txt | 1 + .../final_metrics_summary.csv | 3 + .../metadata.json | 18 ++ .../sample_seed42_nfe1.png | Bin 0 -> 571147 bytes .../sample_seed42_nfe2_mid0821.png | Bin 0 -> 563282 bytes .../speed_benchmark.txt | 24 +++ scripts/evaluate_checkpoint.sh | 17 ++ scripts/sample_checkpoint.sh | 14 ++ scripts/sample_fixed_seeds.py | 158 ++++++++++++++++++ tests/test_fixed_seed_sampling.py | 35 ++++ 13 files changed, 317 insertions(+), 3 deletions(-) create mode 100644 results/preliminary_seed42_fp32_8ksteps/README.md create mode 100644 results/preliminary_seed42_fp32_8ksteps/checkpoint.txt create mode 100644 results/preliminary_seed42_fp32_8ksteps/final_metrics_summary.csv create mode 100644 results/preliminary_seed42_fp32_8ksteps/metadata.json create mode 100644 results/preliminary_seed42_fp32_8ksteps/sample_seed42_nfe1.png create mode 100644 results/preliminary_seed42_fp32_8ksteps/sample_seed42_nfe2_mid0821.png create mode 100644 results/preliminary_seed42_fp32_8ksteps/speed_benchmark.txt create mode 100644 scripts/evaluate_checkpoint.sh create mode 100644 scripts/sample_checkpoint.sh create mode 100644 scripts/sample_fixed_seeds.py create mode 100644 tests/test_fixed_seed_sampling.py diff --git a/README.md b/README.md index 561918d9..269db366 100644 --- a/README.md +++ b/README.md @@ -78,6 +78,27 @@ 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 batch sizes 8 and 16 produce pixel-identical results for both NFE=1 and NFE=2: + +```bash +bash scripts/sample_checkpoint.sh \ + --outdir /mnt/ect_project/evaluations/ \ + --seeds 0-63 --nfe 1 2 --mid-t 0.821 \ + --batch-size 8 --verify-batch-size 16 +``` + +The output 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 +``` + ## Generative Performance ### FID Evaluation diff --git a/ct_eval.py b/ct_eval.py index b9ab7a49..fe96c8f4 100644 --- a/ct_eval.py +++ b/ct_eval.py @@ -92,6 +92,7 @@ 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') @@ -145,7 +146,7 @@ def main(**kwargs): # Trainig options. c.update(cudnn_benchmark=opts.bench) - c.update(mid_t=opts.mid_t, metrics=opts.metrics) + c.update(mid_t=() if opts.nfe == '1' else opts.mid_t, metrics=opts.metrics) # Random seed. if opts.seed is not None: @@ -274,7 +275,7 @@ 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 ): # Time step discretization. mid_t = [] if mid_t is None else mid_t @@ -283,12 +284,18 @@ def generator_fn( # t_0 = T, t_N = 0 t_steps = torch.cat([net.round_sigma(t_steps), torch.zeros_like(t_steps[:1])]) + if step_noises is not None and len(step_noises) != max(len(t_steps) - 2, 0): + raise ValueError('step_noises must contain one tensor per intermediate sampling step') + # 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 #---------------------------------------------------------------------------- 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": 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zg<09Ho5ktLVzr)B#no%q$}$UMYTC{fZZ_RWY3$mO5HiaqFE)1O0uuZF;iLPP_I6K? ze*%_OW8vsiAHVs-ckdPD^zx-G_UqNKoNjKE1$`TOQpaE!fy~9{3nCd~5s-)^dc?%Y zKxmm%rOu0>Zu9|B4WJMqn~T&#h$5g#fB-Mt&*~DCk{Vl^tSIoj8H_1*cB+b5iH9-v z{a6+-j9PP1Z&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/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..9660227c --- /dev/null +++ b/scripts/sample_fixed_seeds.py @@ -0,0 +1,158 @@ +#!/usr/bin/env python3 +"""Generate batch-independent fixed-seed samples from an ECT checkpoint.""" + +import argparse +import hashlib +import json +import pickle +import subprocess +import sys +from pathlib import Path + +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 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, batch_size, device): + shape = (net.img_channels, net.img_resolution, net.img_resolution) + batches = [] + for start in range(0, len(seeds), batch_size): + batch_seeds = seeds[start:start + batch_size] + latents, step_noises = seeded_inputs(batch_seeds, shape, nfe - 1) + images = 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], + ) + batches.append(images.cpu()) + images = torch.cat(batches).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 assert_batch_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 = [seed for seed, a, b in zip(seeds, first_images, second_images) if image_bytes(a) != image_bytes(b)] + if mismatches: + raise RuntimeError(f"NFE={nfe} differs across batch sizes {first}/{second} for seeds: {mismatches}") + return first_images + + +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 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("--batch-size", type=int, default=8) + parser.add_argument("--verify-batch-size", type=int, default=16) + 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") + args.outdir.mkdir(parents=True, exist_ok=True) + + with dnnlib.util.open_url(args.network, verbose=True) as handle: + checkpoint_bytes = handle.read() + checkpoint_sha256 = hashlib.sha256(checkpoint_bytes).hexdigest() + data = pickle.loads(checkpoint_bytes) + net = data["ema"].eval().requires_grad_(False).to(args.device) + + manifest = [] + with torch.no_grad(): + for nfe in args.nfe: + images = assert_batch_equivalence( + net, seeds, nfe, args.mid_t, args.batch_size, + args.verify_batch_size, args.device, + ) + mode_dir = args.outdir / f"nfe{nfe}" + for seed, image in zip(seeds, images): + path = mode_dir / "images" / f"seed{seed:06d}.png" + save_rgb(image, path) + manifest.append((sha256_file(path), path.relative_to(args.outdir).as_posix())) + grid_path = mode_dir / "grid_8x8.png" + save_grid(images, grid_path) + manifest.append((sha256_file(grid_path), grid_path.relative_to(args.outdir).as_posix())) + + metadata = { + "checkpoint": args.network, + "checkpoint_sha256": checkpoint_sha256, + "evaluation_git_commit": git_commit(), + "seeds": seeds, + "nfe": args.nfe, + "mid_t": args.mid_t, + "precision": "fp16" if getattr(net, "use_fp16", False) else "fp32", + "gpu": torch.cuda.get_device_name(0) if torch.cuda.is_available() else "cpu", + "image_format": "32x32 RGB" if net.img_resolution == 32 and net.img_channels == 3 else f"{net.img_resolution}x{net.img_resolution} channels={net.img_channels}", + "batch_sizes_verified": [args.batch_size, args.verify_batch_size], + "batch_independent_sha256": True, + } + metadata_path = args.outdir / "metadata.json" + metadata_path.write_text(json.dumps(metadata, indent=2) + "\n", encoding="utf-8") + manifest.append((sha256_file(metadata_path), metadata_path.name)) + (args.outdir / "sha256_manifest.txt").write_text( + "".join(f"{digest} {name}\n" for digest, name in manifest), encoding="utf-8" + ) + print(json.dumps(metadata, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tests/test_fixed_seed_sampling.py b/tests/test_fixed_seed_sampling.py new file mode 100644 index 00000000..10693923 --- /dev/null +++ b/tests/test_fixed_seed_sampling.py @@ -0,0 +1,35 @@ +import unittest + +import numpy as np +import torch + +from scripts.sample_fixed_seeds import generate_uint8 + + +class DummyNet(torch.nn.Module): + img_channels = 3 + img_resolution = 32 + + def round_sigma(self, sigma): + return sigma + + def forward(self, x, sigma, class_labels=None): + del sigma, class_labels + return torch.tanh(x / 80) + + +class FixedSeedSamplingTest(unittest.TestCase): + def test_batches_are_pixel_identical_for_nfe1_and_nfe2(self): + net = DummyNet().eval() + seeds = list(range(64)) + for nfe in [1, 2]: + with self.subTest(nfe=nfe): + batch8 = generate_uint8(net, seeds, nfe, 0.821, 8, "cpu") + batch16 = generate_uint8(net, seeds, nfe, 0.821, 16, "cpu") + self.assertEqual(batch8.shape, (64, 3, 32, 32)) + self.assertEqual(batch8.dtype, np.uint8) + np.testing.assert_array_equal(batch8, batch16) + + +if __name__ == "__main__": + unittest.main() From 9ebc6c3006340a77989b383e0475eb1a9e3a0d03 Mon Sep 17 00:00:00 2001 From: edwards365 Date: Wed, 15 Jul 2026 17:23:17 +0800 Subject: [PATCH 07/60] Make fixed-seed sampling batch invariant --- scripts/sample_fixed_seeds.py | 24 ++++++++++++++---------- tests/test_fixed_seed_sampling.py | 5 ++++- 2 files changed, 18 insertions(+), 11 deletions(-) diff --git a/scripts/sample_fixed_seeds.py b/scripts/sample_fixed_seeds.py index 9660227c..2f1e4d56 100644 --- a/scripts/sample_fixed_seeds.py +++ b/scripts/sample_fixed_seeds.py @@ -50,18 +50,21 @@ def seeded_inputs(seeds, shape, intermediate_steps): def generate_uint8(net, seeds, nfe, mid_t, batch_size, device): shape = (net.img_channels, net.img_resolution, net.img_resolution) - batches = [] + images = [] for start in range(0, len(seeds), batch_size): batch_seeds = seeds[start:start + batch_size] - latents, step_noises = seeded_inputs(batch_seeds, shape, nfe - 1) - images = 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], - ) - batches.append(images.cpu()) - images = torch.cat(batches).numpy() + # Keep model forwards at batch=1. cuDNN may select different convolution + # plans for different batch shapes, which can change quantized pixels. + for seed in batch_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) @@ -143,6 +146,7 @@ def main(): "gpu": torch.cuda.get_device_name(0) if torch.cuda.is_available() else "cpu", "image_format": "32x32 RGB" if net.img_resolution == 32 and net.img_channels == 3 else f"{net.img_resolution}x{net.img_resolution} channels={net.img_channels}", "batch_sizes_verified": [args.batch_size, args.verify_batch_size], + "model_forward_batch_size": 1, "batch_independent_sha256": True, } metadata_path = args.outdir / "metadata.json" diff --git a/tests/test_fixed_seed_sampling.py b/tests/test_fixed_seed_sampling.py index 10693923..4425bcbf 100644 --- a/tests/test_fixed_seed_sampling.py +++ b/tests/test_fixed_seed_sampling.py @@ -15,7 +15,10 @@ def round_sigma(self, sigma): def forward(self, x, sigma, class_labels=None): del sigma, class_labels - return torch.tanh(x / 80) + # 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): From 6576c16816a32ad25c1ee9ecad72e49b70fe2495 Mon Sep 17 00:00:00 2001 From: edwards365 Date: Wed, 15 Jul 2026 17:44:07 +0800 Subject: [PATCH 08/60] Add fixed-seed sampling artifacts --- .../metadata.json | 85 ++++++++++++ .../nfe1/grid_8x8.png | Bin 0 -> 143881 bytes .../nfe2/grid_8x8.png | Bin 0 -> 143480 bytes .../sha256_manifest.txt | 131 ++++++++++++++++++ 4 files changed, 216 insertions(+) create mode 100644 results/fixed_seeds_0_63_fp32_8ksteps/metadata.json create mode 100644 results/fixed_seeds_0_63_fp32_8ksteps/nfe1/grid_8x8.png create mode 100644 results/fixed_seeds_0_63_fp32_8ksteps/nfe2/grid_8x8.png create mode 100644 results/fixed_seeds_0_63_fp32_8ksteps/sha256_manifest.txt 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", + 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+ee78b151bfc536b07a74b79bbbce84720eef9b86dadc24d3c82f96c21726c6b7 nfe2/images/seed000063.png +5796f819d7e46b01ec04d3cda7700f38a3d3fc3e68725be0f498b43a373f701f nfe2/grid_8x8.png +0a39cbd0d2f5f99afe5f29c4d786ef8715ed4f3afc3c18187297b49dbff673a8 metadata.json From 956c6f41390e9e26885e689eb4c3b66d45957c38 Mon Sep 17 00:00:00 2001 From: w10800 <1793719632@qq.com> Date: Thu, 16 Jul 2026 09:34:17 +0800 Subject: [PATCH 09/60] Pin Hugging Face Hub for clean setup --- env.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/env.yml b/env.yml index 006367f4..a3df83d4 100644 --- a/env.yml +++ b/env.yml @@ -20,3 +20,4 @@ dependencies: - pyspng - diffusers==0.26.3 - accelerate==0.27.2 + - huggingface-hub==0.23.4 From 0167aca752713698cb89c5486d1d84e6247562bb Mon Sep 17 00:00:00 2001 From: w10800 <1793719632@qq.com> Date: Thu, 16 Jul 2026 10:13:52 +0800 Subject: [PATCH 10/60] docs: refresh clean-container validation evidence --- artifacts/day3/role_a_environment.json | 46 ++++++++++++++--- artifacts/day3/role_a_smoke_report.json | 50 +++++++++++++++---- docs/ROLE_A_CLEAN_CONTAINER_VALIDATION.md | 61 ++++++++++++++--------- 3 files changed, 115 insertions(+), 42 deletions(-) diff --git a/artifacts/day3/role_a_environment.json b/artifacts/day3/role_a_environment.json index 029a4705..74f3de2a 100644 --- a/artifacts/day3/role_a_environment.json +++ b/artifacts/day3/role_a_environment.json @@ -1,8 +1,18 @@ { - "schema_version": 1, - "git_commit": "7c638a4d1f293222ec9eb8ad86cffc03fdcb7282", + "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, @@ -13,19 +23,40 @@ "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", - "numpy": "1.26.4", - "scipy": "1.12.0", - "torch": "2.3.0" + "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], + "resolution": [ + 32, + 32 + ], "color_mode": "RGB", "zip_crc_ok": true, "sha256": "08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372", @@ -34,6 +65,7 @@ "checkpoint_path": "/mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl", "checkpoint_sha256": "4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da", "checkpoint_validation": "passed", - "environment_adjustment": "The pre-existing ect environment had huggingface-hub 0.20.3 from the superseded branch. It was upgraded to the main-branch-required 0.23.4 before the final setup_env.sh validation.", + "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 index 0dc175c8..1a46a57c 100644 --- a/artifacts/day3/role_a_smoke_report.json +++ b/artifacts/day3/role_a_smoke_report.json @@ -1,19 +1,34 @@ { - "schema_version": 1, + "schema_version": 2, "test_kind": "engineering_connectivity_only", "official_ect_baseline": false, - "git_commit": "7c638a4d1f293222ec9eb8ad86cffc03fdcb7282", "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", - "check_only": "passed", + "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, @@ -36,26 +51,39 @@ "resume_growth_tracker_increment": 100 }, "loss_finite": true, + "loss_sample_counts": { + "fresh": 1000, + "resume": 1000 + }, "loss_means": { - "fresh": [11.838935947418213, 17.023774967267357], - "resume": [16.92818015435338] + "fresh": [ + 11.838935947418213, + 17.023772768939686 + ], + "resume": [ + 16.928153270974754 + ] }, - "wall_time_seconds": 180, + "wall_time_seconds": 185, "peak_vram_mib": 5563, "peak_reserved_vram_mib": 5616, - "run_directory": "/mnt/ect_project/runs/engineering-smoke/7c638a4d-20260715T083443Z", + "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.", - "The console prints a NaN before training_stats.default_collector.update() at the initialization and resume-final maintenance points; this is a reporting-order artifact, not a non-finite recorded training loss.", - "The resume log explicitly records loading GradScaler state from the fresh training state." + "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": [ - "env.yml does not explicitly pin huggingface-hub==0.23.4 even though scripts/check_environment.py enforces that exact version." + "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/docs/ROLE_A_CLEAN_CONTAINER_VALIDATION.md b/docs/ROLE_A_CLEAN_CONTAINER_VALIDATION.md index fb73193b..f31dfca8 100644 --- a/docs/ROLE_A_CLEAN_CONTAINER_VALIDATION.md +++ b/docs/ROLE_A_CLEAN_CONTAINER_VALIDATION.md @@ -2,23 +2,36 @@ ## Result -Role A engineering validation passed on one MatPool A100 container from the frozen integration baseline: +Role A engineering validation passed on one MatPool A100 container using a newly created Conda environment: - branch: `role-a/clean-container-validation` -- commit: `7c638a4d1f293222ec9eb8ad86cffc03fdcb7282` +- 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/7c638a4d-20260715T083443Z` +- 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 -After checking out the required commit and creating the role branch, the following validation commands were 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 +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 @@ -26,14 +39,7 @@ bash scripts/smoke_engineering_100steps.sh --dry-run --port 29521 bash scripts/smoke_engineering_100steps.sh --port 29521 ``` -The container already contained an `ect` environment prepared against a superseded branch. The first `setup_env.sh` invocation correctly rejected `huggingface-hub 0.20.3`; it was reconciled to the required version and the exact setup command was rerun: - -```bash -python -m pip install --index-url https://pypi.tuna.tsinghua.edu.cn/simple huggingface-hub==0.23.4 -bash setup_env.sh -``` - -The second invocation passed with a clean Git worktree. No training-code file was changed. +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 @@ -43,40 +49,47 @@ The default persistent paths were used: - 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 official EDM checkpoint SHA-256 matched `4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da`. +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 | | --- | --- | -| check-only | PASS | +| 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 | +| GradScaler restored | yes; explicit restore message in Resume log | | Formal metrics | disabled (`metrics=[]`) | -| Wall time | 180 seconds | +| 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. 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. +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 17.0237749673 -- Resume: 16.9281801544 +- 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 console has `loss nan` at the initialization maintenance point and the resume-final maintenance point. In `training/ct_training_loop.py`, the status line reads the default collector before `default_collector.update()` is called, so a new process can print an empty collector even though the subsequently written statistics are finite. This is a reporting-order observation; no core training file was changed. +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 -All datasets, checkpoints, training states, complete logs, and generated images remain under `/mnt/ect_project`. Git contains only the two compact Day 3 JSON summaries and this document. No FID-50k job was launched and no large file is included in the branch. +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. -## Follow-up observation +## Unresolved issues -`scripts/check_environment.py` requires exactly `huggingface-hub==0.23.4`, while `env.yml` currently leaves that transitive dependency unpinned. A newly resolved environment could therefore install a different version and fail validation. This should be resolved by the integration owner before declaring the environment specification permanently frozen. +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. From 2e3659051a9985ede8b6dbac2530e28c7253080c Mon Sep 17 00:00:00 2001 From: edwards365 Date: Thu, 16 Jul 2026 10:19:22 +0800 Subject: [PATCH 11/60] Close fixed-seed evaluation acceptance gaps --- README.md | 9 +- docs/EVALUATION_PROTOCOL.md | 101 +++++++++++++ .../fixed_seeds_0_63_fp32_8ksteps/README.md | 14 ++ scripts/sample_fixed_seeds.py | 140 ++++++++++++++---- tests/test_fixed_seed_sampling.py | 83 ++++++++++- 5 files changed, 308 insertions(+), 39 deletions(-) create mode 100644 docs/EVALUATION_PROTOCOL.md create mode 100644 results/fixed_seeds_0_63_fp32_8ksteps/README.md diff --git a/README.md b/README.md index 269db366..d4828140 100644 --- a/README.md +++ b/README.md @@ -80,13 +80,16 @@ bash eval_ecm.sh --resume ### Fixed-seed evaluation -Role D sampling uses seeds 0-63 and verifies that batch sizes 8 and 16 produce pixel-identical results for both NFE=1 and NFE=2: +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/ \ + --outdir /mnt/ect_project/evaluations \ --seeds 0-63 --nfe 1 2 --mid-t 0.821 \ - --batch-size 8 --verify-batch-size 16 + --work-group-size 8 --verify-work-group-size 16 \ + --precision fp32 ``` The output 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. diff --git a/docs/EVALUATION_PROTOCOL.md b/docs/EVALUATION_PROTOCOL.md new file mode 100644 index 00000000..f6ab9ebc --- /dev/null +++ b/docs/EVALUATION_PROTOCOL.md @@ -0,0 +1,101 @@ +# Evaluation protocol + +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 + +- Generate one 32x32 RGB PNG for each seed in 0-63. +- Run NFE=1 and NFE=2, using `mid_t=0.821` for NFE=2. +- 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 by the full checkpoint SHA256. +- Select precision explicitly. Use `fp32` for this acceptance smoke; use + `checkpoint` only when intentionally preserving checkpoint-native precision. + +The sampler writes to `//`. A run fails before +publishing metadata if either work-group or repeated-run determinism fails. + +## 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/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da/ +``` + +Verify the image counts and manifest after the command completes: + +```bash +RESULT=/mnt/ect_project/evaluations/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da +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. Its purpose is limited to checkpoint loading, fixed-seed generation, +output completeness, work-group invariance, and repeated-run determinism. +Historical FID/KID values remain preliminary evidence only. Formal metrics are +deferred until the B/C checkpoint and comparison protocol are finalized. + +## 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/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/scripts/sample_fixed_seeds.py b/scripts/sample_fixed_seeds.py index 2f1e4d56..0c62bf6e 100644 --- a/scripts/sample_fixed_seeds.py +++ b/scripts/sample_fixed_seeds.py @@ -1,5 +1,5 @@ #!/usr/bin/env python3 -"""Generate batch-independent fixed-seed samples from an ECT checkpoint.""" +"""Generate work-group-independent fixed-seed samples from a checkpoint.""" import argparse import hashlib @@ -7,6 +7,7 @@ import pickle import subprocess import sys +import time from pathlib import Path import numpy as np @@ -48,14 +49,14 @@ def seeded_inputs(seeds, shape, intermediate_steps): return torch.stack(latents), [torch.stack(items) for items in noises] -def generate_uint8(net, seeds, nfe, mid_t, batch_size, device): +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), batch_size): - batch_seeds = seeds[start:start + batch_size] + 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 batch_seeds: + for seed in work_group_seeds: latents, step_noises = seeded_inputs([seed], shape, nfe - 1) image = generator_fn( net, @@ -72,15 +73,35 @@ def image_bytes(image): return image.transpose(1, 2, 0).tobytes() -def assert_batch_equivalence(net, seeds, nfe, mid_t, first, second, device): +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 = [seed for seed, a, b in zip(seeds, first_images, second_images) if image_bytes(a) != image_bytes(b)] + mismatches = differing_seeds(seeds, first_images, second_images) if mismatches: - raise RuntimeError(f"NFE={nfe} differs across batch sizes {first}/{second} for seeds: {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) @@ -96,6 +117,36 @@ def save_grid(images, path, columns=8): 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 main(): parser = argparse.ArgumentParser() parser.add_argument("--network", required=True, help="Checkpoint PKL path or URL") @@ -103,59 +154,90 @@ def main(): 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("--batch-size", type=int, default=8) - parser.add_argument("--verify-batch-size", type=int, default=16) + 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") - args.outdir.mkdir(parents=True, exist_ok=True) - + 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() + run_dir = args.outdir / checkpoint_sha256 + 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: - images = assert_batch_equivalence( - net, seeds, nfe, args.mid_t, args.batch_size, - args.verify_batch_size, args.device, + 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 = args.outdir / f"nfe{nfe}" - for seed, image in zip(seeds, images): - path = mode_dir / "images" / f"seed{seed:06d}.png" - save_rgb(image, path) - manifest.append((sha256_file(path), path.relative_to(args.outdir).as_posix())) - grid_path = mode_dir / "grid_8x8.png" - save_grid(images, grid_path) - manifest.append((sha256_file(grid_path), grid_path.relative_to(args.outdir).as_posix())) + 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": None 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 = { "checkpoint": args.network, "checkpoint_sha256": checkpoint_sha256, + "output_directory": str(run_dir), "evaluation_git_commit": git_commit(), "seeds": seeds, + "seed_list": seeds, + "seed_count": len(seeds), + "image_count": len(seeds) * len(args.nfe), "nfe": args.nfe, "mid_t": args.mid_t, - "precision": "fp16" if getattr(net, "use_fp16", False) else "fp32", + "modes": modes, + "elapsed_seconds": elapsed_seconds, + "generator_implementation": "ct_eval.generator_fn", + "precision_requested": args.precision, + "precision": effective_precision, "gpu": torch.cuda.get_device_name(0) if torch.cuda.is_available() else "cpu", "image_format": "32x32 RGB" if net.img_resolution == 32 and net.img_channels == 3 else f"{net.img_resolution}x{net.img_resolution} channels={net.img_channels}", - "batch_sizes_verified": [args.batch_size, args.verify_batch_size], - "model_forward_batch_size": 1, - "batch_independent_sha256": True, + "work_group_sizes_verified": [ + args.work_group_size, + args.verify_work_group_size, + ], + "forward_batch_size": 1, + "work_group_independent_sha256": True, + "repeat_runs_verified": 2, + "repeat_run_deterministic_sha256": True, } - metadata_path = args.outdir / "metadata.json" + 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)) - (args.outdir / "sha256_manifest.txt").write_text( + (run_dir / "sha256_manifest.txt").write_text( "".join(f"{digest} {name}\n" for digest, name in manifest), encoding="utf-8" ) print(json.dumps(metadata, indent=2)) + print(f"Results written to {run_dir}") if __name__ == "__main__": diff --git a/tests/test_fixed_seed_sampling.py b/tests/test_fixed_seed_sampling.py index 4425bcbf..ae708cec 100644 --- a/tests/test_fixed_seed_sampling.py +++ b/tests/test_fixed_seed_sampling.py @@ -1,14 +1,25 @@ import unittest +from pathlib import Path +from tempfile import TemporaryDirectory import numpy as np +import PIL.Image import torch -from scripts.sample_fixed_seeds import generate_uint8 +from scripts.sample_fixed_seeds import ( + assert_repeat_equivalence, + assert_work_group_equivalence, + configure_precision, + generate_uint8, + save_mode_outputs, + sha256_file, +) class DummyNet(torch.nn.Module): img_channels = 3 img_resolution = 32 + use_fp16 = False def round_sigma(self, sigma): return sigma @@ -22,16 +33,74 @@ def forward(self, x, sigma, class_labels=None): class FixedSeedSamplingTest(unittest.TestCase): - def test_batches_are_pixel_identical_for_nfe1_and_nfe2(self): + def test_precision_is_explicit_and_reported(self): + net = DummyNet().eval() + self.assertEqual(configure_precision(net, "checkpoint", "cpu"), "fp32") + self.assertEqual(configure_precision(net, "fp32", "cpu"), "fp32") + with self.assertRaisesRegex(ValueError, "requires a CUDA device"): + configure_precision(net, "fp16", "cpu") + + def test_work_groups_are_pixel_identical_for_nfe1_and_nfe2(self): + net = DummyNet().eval() + seeds = list(range(64)) + for nfe in [1, 2]: + with self.subTest(nfe=nfe): + group8 = generate_uint8(net, seeds, nfe, 0.821, 8, "cpu") + group16 = generate_uint8(net, 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): net = DummyNet().eval() seeds = list(range(64)) for nfe in [1, 2]: with self.subTest(nfe=nfe): - batch8 = generate_uint8(net, seeds, nfe, 0.821, 8, "cpu") - batch16 = generate_uint8(net, seeds, nfe, 0.821, 16, "cpu") - self.assertEqual(batch8.shape, (64, 3, 32, 32)) - self.assertEqual(batch8.dtype, np.uint8) - np.testing.assert_array_equal(batch8, batch16) + reference = assert_work_group_equivalence( + net, seeds, nfe, 0.821, 8, 16, "cpu" + ) + assert_repeat_equivalence( + net, seeds, nfe, 0.821, 8, reference, "cpu" + ) + + def test_repeated_png_sha256_is_identical(self): + net = DummyNet().eval() + seeds = list(range(64)) + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + for nfe in [1, 2]: + with self.subTest(nfe=nfe): + first = generate_uint8(net, seeds, nfe, 0.821, 8, "cpu") + second = generate_uint8(net, seeds, nfe, 0.821, 8, "cpu") + first_paths = save_mode_outputs(first, seeds, root / "first" / f"nfe{nfe}") + second_paths = save_mode_outputs(second, 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): + net = DummyNet().eval() + seeds = list(range(64)) + self.assertEqual(seeds, list(range(64))) + self.assertEqual(len(seeds), len(set(seeds))) + + with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + for nfe in [1, 2]: + with self.subTest(nfe=nfe): + images = generate_uint8(net, seeds, nfe, 0.821, 8, "cpu") + paths = save_mode_outputs(images, 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)) if __name__ == "__main__": From 59d39a5418bcc97aef69bce140a31b657ed6f582 Mon Sep 17 00:00:00 2001 From: edwards365 Date: Thu, 16 Jul 2026 10:34:49 +0800 Subject: [PATCH 12/60] Add official EDM fixed-seed smoke artifacts --- .../metadata.json | 175 ++++++++++++++++++ .../nfe1/grid_8x8.png | Bin 0 -> 91023 bytes .../nfe2/grid_8x8.png | Bin 0 -> 64976 bytes .../sha256_manifest.txt | 131 +++++++++++++ 4 files changed, 306 insertions(+) create mode 100644 results/official_edm_smoke/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da/metadata.json create mode 100644 results/official_edm_smoke/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da/nfe1/grid_8x8.png create mode 100644 results/official_edm_smoke/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da/nfe2/grid_8x8.png create mode 100644 results/official_edm_smoke/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da/sha256_manifest.txt diff --git a/results/official_edm_smoke/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da/metadata.json b/results/official_edm_smoke/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da/metadata.json new file mode 100644 index 00000000..5798099c --- /dev/null +++ b/results/official_edm_smoke/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da/metadata.json @@ -0,0 +1,175 @@ +{ + "checkpoint": "/mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl", + "checkpoint_sha256": "4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da", + "output_directory": "/mnt/ect_project/evaluations/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da", + "evaluation_git_commit": "2e3659051a9985ede8b6dbac2530e28c7253080c", + "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 + ], + "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, 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zn&5AK1_XhpM9E{=vZ|b^Y&)^`Jk`8r5GVa7K?hCwed0xuC#zCACY8{()Gw0frS~bc zLK1NhM>Ke=)h2sKWUO^b!-+&6%@!#PL)K-A3&RHz1NrLR;c;>^mlt)+7>5?_skIjU z6w>{WA)<3>ZQCBcoMwO|F5q_q4n^bp+8d^_04xr+!@-3Dq%*EM7;$Umq>dyL)lvH$ zofBem Date: Thu, 16 Jul 2026 10:49:06 +0800 Subject: [PATCH 13/60] Refine deterministic evaluation protocol schema --- README.md | 2 +- docs/EVALUATION_PROTOCOL.md | 50 +++++++++--- scripts/sample_fixed_seeds.py | 111 ++++++++++++++++++-------- tests/test_fixed_seed_sampling.py | 126 ++++++++++++++++++++++++------ 4 files changed, 222 insertions(+), 67 deletions(-) diff --git a/README.md b/README.md index d4828140..9012505e 100644 --- a/README.md +++ b/README.md @@ -92,7 +92,7 @@ bash scripts/sample_checkpoint.sh \ --precision fp32 ``` -The output 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 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`: diff --git a/docs/EVALUATION_PROTOCOL.md b/docs/EVALUATION_PROTOCOL.md index f6ab9ebc..c0f953aa 100644 --- a/docs/EVALUATION_PROTOCOL.md +++ b/docs/EVALUATION_PROTOCOL.md @@ -24,18 +24,45 @@ 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. -- Run NFE=1 and NFE=2, using `mid_t=0.821` for NFE=2. +- 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 by the full checkpoint SHA256. +- 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 `//`. A run fails before -publishing metadata if either work-group or repeated-run determinism fails. +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 @@ -71,13 +98,13 @@ bash scripts/sample_checkpoint.sh \ The expected output root is: ```text -/mnt/ect_project/evaluations/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da/ +/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/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da +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) @@ -88,10 +115,13 @@ 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. Its purpose is limited to checkpoint loading, fixed-seed generation, -output completeness, work-group invariance, and repeated-run determinism. -Historical FID/KID values remain preliminary evidence only. Formal metrics are -deferred until the B/C checkpoint and comparison protocol are finalized. +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 +FID-50k runs only after the final checkpoint and comparison protocol are +frozen. ## Git artifact policy diff --git a/scripts/sample_fixed_seeds.py b/scripts/sample_fixed_seeds.py index 0c62bf6e..aaedca1d 100644 --- a/scripts/sample_fixed_seeds.py +++ b/scripts/sample_fixed_seeds.py @@ -5,10 +5,12 @@ 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 @@ -38,6 +40,13 @@ def git_commit(): 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)] @@ -147,6 +156,61 @@ def configure_precision(net, requested, device): 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") @@ -170,7 +234,8 @@ def main(): with dnnlib.util.open_url(args.network, verbose=True) as handle: checkpoint_bytes = handle.read() checkpoint_sha256 = hashlib.sha256(checkpoint_bytes).hexdigest() - run_dir = args.outdir / checkpoint_sha256 + 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) @@ -197,45 +262,27 @@ def main(): modes.append({ "name": f"nfe{nfe}", "nfe": nfe, - "mid_t": None if nfe == 1 else args.mid_t, + "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 = { - "checkpoint": args.network, - "checkpoint_sha256": checkpoint_sha256, - "output_directory": str(run_dir), - "evaluation_git_commit": git_commit(), - "seeds": seeds, - "seed_list": seeds, - "seed_count": len(seeds), - "image_count": len(seeds) * len(args.nfe), - "nfe": args.nfe, - "mid_t": args.mid_t, - "modes": modes, - "elapsed_seconds": elapsed_seconds, - "generator_implementation": "ct_eval.generator_fn", - "precision_requested": args.precision, - "precision": effective_precision, - "gpu": torch.cuda.get_device_name(0) if torch.cuda.is_available() else "cpu", - "image_format": "32x32 RGB" if net.img_resolution == 32 and net.img_channels == 3 else f"{net.img_resolution}x{net.img_resolution} channels={net.img_channels}", - "work_group_sizes_verified": [ - args.work_group_size, - args.verify_work_group_size, - ], - "forward_batch_size": 1, - "work_group_independent_sha256": True, - "repeat_runs_verified": 2, - "repeat_run_deterministic_sha256": True, - } + 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)) - (run_dir / "sha256_manifest.txt").write_text( - "".join(f"{digest} {name}\n" for digest, name in manifest), encoding="utf-8" - ) + write_manifest(manifest, run_dir / "sha256_manifest.txt") print(json.dumps(metadata, indent=2)) print(f"Results written to {run_dir}") diff --git a/tests/test_fixed_seed_sampling.py b/tests/test_fixed_seed_sampling.py index ae708cec..94a7ed82 100644 --- a/tests/test_fixed_seed_sampling.py +++ b/tests/test_fixed_seed_sampling.py @@ -1,4 +1,5 @@ import unittest +from argparse import Namespace from pathlib import Path from tempfile import TemporaryDirectory @@ -9,10 +10,14 @@ 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, ) @@ -33,64 +38,75 @@ def forward(self, x, sigma, class_labels=None): class FixedSeedSamplingTest(unittest.TestCase): + def setUp(self): + self.net = DummyNet().eval() + self.seeds = list(range(64)) + def test_precision_is_explicit_and_reported(self): - net = DummyNet().eval() - self.assertEqual(configure_precision(net, "checkpoint", "cpu"), "fp32") - self.assertEqual(configure_precision(net, "fp32", "cpu"), "fp32") + 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(net, "fp16", "cpu") + 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_work_groups_are_pixel_identical_for_nfe1_and_nfe2(self): - net = DummyNet().eval() - seeds = list(range(64)) for nfe in [1, 2]: with self.subTest(nfe=nfe): - group8 = generate_uint8(net, seeds, nfe, 0.821, 8, "cpu") - group16 = generate_uint8(net, seeds, nfe, 0.821, 16, "cpu") + 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): - net = DummyNet().eval() - seeds = list(range(64)) for nfe in [1, 2]: with self.subTest(nfe=nfe): reference = assert_work_group_equivalence( - net, seeds, nfe, 0.821, 8, 16, "cpu" + self.net, self.seeds, nfe, 0.821, 8, 16, "cpu" ) assert_repeat_equivalence( - net, seeds, nfe, 0.821, 8, reference, "cpu" + self.net, self.seeds, nfe, 0.821, 8, reference, "cpu" ) def test_repeated_png_sha256_is_identical(self): - net = DummyNet().eval() - seeds = list(range(64)) with TemporaryDirectory() as temp_dir: root = Path(temp_dir) for nfe in [1, 2]: with self.subTest(nfe=nfe): - first = generate_uint8(net, seeds, nfe, 0.821, 8, "cpu") - second = generate_uint8(net, seeds, nfe, 0.821, 8, "cpu") - first_paths = save_mode_outputs(first, seeds, root / "first" / f"nfe{nfe}") - second_paths = save_mode_outputs(second, seeds, root / "second" / f"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): - net = DummyNet().eval() - seeds = list(range(64)) - self.assertEqual(seeds, list(range(64))) - self.assertEqual(len(seeds), len(set(seeds))) + 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(net, seeds, nfe, 0.821, 8, "cpu") - paths = save_mode_outputs(images, seeds, root / f"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( @@ -102,6 +118,68 @@ def test_protocol_outputs_are_complete_rgb_images(self): 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() From d95094b2e49e15a616a6b5c5dfc7a6676a91f833 Mon Sep 17 00:00:00 2001 From: edwards365 Date: Thu, 16 Jul 2026 11:03:20 +0800 Subject: [PATCH 14/60] chore: validate evaluation workflow on latest main --- .../metadata.json | 175 ------------------ .../metadata.json | 116 ++++++++++++ .../nfe1/grid_8x8.png | Bin .../nfe2/grid_8x8.png | Bin .../sha256_manifest.txt | 2 +- 5 files changed, 117 insertions(+), 176 deletions(-) delete mode 100644 results/official_edm_smoke/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da/metadata.json create mode 100644 results/official_edm_smoke/edm-cifar10-32x32-uncond-vp-4d5dcc1f1d0d/metadata.json rename results/official_edm_smoke/{4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da => edm-cifar10-32x32-uncond-vp-4d5dcc1f1d0d}/nfe1/grid_8x8.png (100%) rename results/official_edm_smoke/{4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da => edm-cifar10-32x32-uncond-vp-4d5dcc1f1d0d}/nfe2/grid_8x8.png (100%) rename results/official_edm_smoke/{4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da => edm-cifar10-32x32-uncond-vp-4d5dcc1f1d0d}/sha256_manifest.txt (99%) diff --git a/results/official_edm_smoke/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da/metadata.json b/results/official_edm_smoke/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da/metadata.json deleted file mode 100644 index 5798099c..00000000 --- a/results/official_edm_smoke/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da/metadata.json +++ /dev/null @@ -1,175 +0,0 @@ -{ - "checkpoint": "/mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl", - "checkpoint_sha256": "4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da", - "output_directory": "/mnt/ect_project/evaluations/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da", - "evaluation_git_commit": "2e3659051a9985ede8b6dbac2530e28c7253080c", - "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 - ], - "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 - ], - "seed_count": 64, - "image_count": 128, - "nfe": [ - 1, - 2 - ], - "mid_t": 0.821, - "modes": [ - { - "name": "nfe1", - "nfe": 1, - "mid_t": null, - "image_count": 64, - "elapsed_seconds": 4.476461 - }, - { - "name": "nfe2", - "nfe": 2, - "mid_t": 0.821, - "image_count": 64, - "elapsed_seconds": 7.324607 - } - ], - "elapsed_seconds": 11.801117, - "generator_implementation": "ct_eval.generator_fn", - "precision_requested": "fp32", - "precision": "fp32", - "gpu": "NVIDIA A100-PCIE-40GB", - "image_format": "32x32 RGB", - "work_group_sizes_verified": [ - 8, - 16 - ], - "forward_batch_size": 1, - "work_group_independent_sha256": true, - "repeat_runs_verified": 2, - "repeat_run_deterministic_sha256": true -} diff --git a/results/official_edm_smoke/edm-cifar10-32x32-uncond-vp-4d5dcc1f1d0d/metadata.json b/results/official_edm_smoke/edm-cifar10-32x32-uncond-vp-4d5dcc1f1d0d/metadata.json new file mode 100644 index 00000000..9158a752 --- /dev/null +++ b/results/official_edm_smoke/edm-cifar10-32x32-uncond-vp-4d5dcc1f1d0d/metadata.json @@ -0,0 +1,116 @@ +{ + "schema_version": "1.0", + "evaluation_git_commit": "521eae939df1fd29e66c6938a80e9831373dc6c2", + "checkpoint_path": "/mnt/ect_project/pretrained/edm-cifar10-32x32-uncond-vp.pkl", + "checkpoint_sha256": "4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da", + "checkpoint_id": "edm-cifar10-32x32-uncond-vp-4d5dcc1f1d0d", + "output_directory": "/mnt/ect_project/evaluations/edm-cifar10-32x32-uncond-vp-4d5dcc1f1d0d", + "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": 11.774366, + "elapsed_seconds_by_mode": { + "nfe1": 4.38635, + "nfe2": 7.387926 + }, + "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, + "verified_modes": [ + "nfe1", + "nfe2" + ] +} diff --git a/results/official_edm_smoke/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da/nfe1/grid_8x8.png b/results/official_edm_smoke/edm-cifar10-32x32-uncond-vp-4d5dcc1f1d0d/nfe1/grid_8x8.png similarity index 100% rename from results/official_edm_smoke/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da/nfe1/grid_8x8.png rename to results/official_edm_smoke/edm-cifar10-32x32-uncond-vp-4d5dcc1f1d0d/nfe1/grid_8x8.png diff --git a/results/official_edm_smoke/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da/nfe2/grid_8x8.png b/results/official_edm_smoke/edm-cifar10-32x32-uncond-vp-4d5dcc1f1d0d/nfe2/grid_8x8.png similarity index 100% rename from results/official_edm_smoke/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da/nfe2/grid_8x8.png rename to results/official_edm_smoke/edm-cifar10-32x32-uncond-vp-4d5dcc1f1d0d/nfe2/grid_8x8.png diff --git a/results/official_edm_smoke/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da/sha256_manifest.txt b/results/official_edm_smoke/edm-cifar10-32x32-uncond-vp-4d5dcc1f1d0d/sha256_manifest.txt similarity index 99% rename from results/official_edm_smoke/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da/sha256_manifest.txt rename to results/official_edm_smoke/edm-cifar10-32x32-uncond-vp-4d5dcc1f1d0d/sha256_manifest.txt index 33455a93..1a4f46ee 100644 --- a/results/official_edm_smoke/4d5dcc1f1d0d41c8934ad21626eeddbdc0460182becf9fc059a0631b1eedb4da/sha256_manifest.txt +++ b/results/official_edm_smoke/edm-cifar10-32x32-uncond-vp-4d5dcc1f1d0d/sha256_manifest.txt @@ -128,4 +128,4 @@ d07f10a3f584092573203db53727c8d2736c807a4d4f804e7f9170b77e321aa9 nfe2/images/se 3549523b482ad856d93198b5ddbe6aec3981440f5cd61097b0c91bce2a2d00b2 nfe2/images/seed000062.png c3857a13658b43faed063120e2ba593c1ead4e5a8b1d37346d74fae5ed9497e8 nfe2/images/seed000063.png 2a1ebcb1f2a587e19332a71e55ca112c1ed3ca6a354eb037cbac5cd8bd14afff nfe2/grid_8x8.png -505f4a0480add60aa2070d4ed2cb86dc2fc035d176ed2ec3d9ab4c46e61a4d28 metadata.json +eeecc69850092a8245a64ebf2e46df23be634e5aeeebfc76e4f858f1b962e643 metadata.json From cb91b4fba52fe50f3b55e00521a3218a1519ecac Mon Sep 17 00:00:00 2001 From: kangw24 <280028295+kangw24@users.noreply.github.com> Date: Fri, 17 Jul 2026 17:25:48 +0800 Subject: [PATCH 15/60] Add paired fixed/adaptive training and evidence infrastructure Add the clean Role B paired-training runner, telemetry, resume identity gates, evidence collector, tests, and A100 smoke artifact. --- .gitignore | 1 + artifacts/role_b/paired_resume_smoke.json | 16 + ct_train.py | 10 +- docs/PAIRED_TRAINING_PROTOCOL.md | 135 +++ results/fixed_baseline_preliminary/README.md | 31 + .../fixed_baseline_preliminary/metadata.json | 33 + .../train_summary.csv | 126 +++ scripts/collect_fixed_baseline_results.py | 12 + scripts/collect_schedule_results.py | 620 ++++++++++++++ scripts/run_fixed_baseline.sh | 6 + scripts/run_schedule_experiment.sh | 454 ++++++++++ tests/test_paired_training_infra.py | 780 ++++++++++++++++++ training/ct_training_loop.py | 198 ++++- 13 files changed, 2409 insertions(+), 13 deletions(-) create mode 100644 artifacts/role_b/paired_resume_smoke.json create mode 100644 docs/PAIRED_TRAINING_PROTOCOL.md create mode 100644 results/fixed_baseline_preliminary/README.md create mode 100644 results/fixed_baseline_preliminary/metadata.json create mode 100644 results/fixed_baseline_preliminary/train_summary.csv create mode 100755 scripts/collect_fixed_baseline_results.py create mode 100755 scripts/collect_schedule_results.py create mode 100755 scripts/run_fixed_baseline.sh create mode 100755 scripts/run_schedule_experiment.sh create mode 100644 tests/test_paired_training_infra.py diff --git a/.gitignore b/.gitignore index f3250f37..02b89428 100644 --- a/.gitignore +++ b/.gitignore @@ -164,6 +164,7 @@ cython_debug/ datasets/ runs/ checkpoints/ +pretrained/ ct-runs/ ct-evals/ wandb/ 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/ct_train.py b/ct_train.py index 724680d6..89ab6ed1 100644 --- a/ct_train.py +++ b/ct_train.py @@ -190,7 +190,15 @@ 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/PAIRED_TRAINING_PROTOCOL.md b/docs/PAIRED_TRAINING_PROTOCOL.md new file mode 100644 index 00000000..75eba1f5 --- /dev/null +++ b/docs/PAIRED_TRAINING_PROTOCOL.md @@ -0,0 +1,135 @@ +# Paired Training Protocol (Role B) + +Canonical protocol for fair fixed (`sigmoid`) vs adaptive (`adaptive_v1`) ECT +training comparisons. This supersedes ad-hoc `baseline_protocol.md` notes for +paired-run engineering. + +## Scope + +Role B owns: + +- shared runner / collector +- per-attempted-iteration telemetry +- experiment directory hygiene (no mixed runs) +- reproducible metadata packaging + +Role C owns the `adaptive_v1` schedule implementation. Until that lands on +`main`, `--schedule adaptive_v1` will fail at CLI validation; the runner still +accepts the flag so B/C stay parameter-aligned. + +## Frozen paired knobs + +Except for `--schedule` / `--mapping`, both arms use: + +| Knob | Value | +| --- | --- | +| Dataset | CIFAR-10 32×32 EDM ZIP | +| Teacher | EDM CIFAR-10 uncond VP transfer | +| Cond | False | +| Arch / precond | ddpmpp / ect | +| Optim / lr | RAdam / 1e-4 | +| Batch / batch-gpu | 128 / 16 | +| Dropout / augment | 0.2 / 0 | +| q / k / b / c | 256 / 8 / 1 / 0 | +| double | 10000 | +| ema_beta | 0.9993 | +| seed | 0 | +| Precision | FP16 + GradScaler | +| Metrics | none | + +## Modes + +| Mode | Duration (Mimg) | Attempted iterations @ batch 128 | Intent | +| --- | --- | --- | --- | +| `dry-run` | n/a | n/a | Print resolved params + exact command | +| `activation` | 0.004 | 32 (ends at 4.096 kimg after batch rounding; protocol text “~31” is the pre-rounding estimate) | Verify adaptive controller activates before stability | +| `stability` | 0.016 | 125 | Engineering stability evidence | +| `baseline` | 0.128 | 1000 | Formal paired baseline budget | + +## Runner + +```bash +bash scripts/run_schedule_experiment.sh \ + --mode stability \ + --schedule sigmoid + +bash scripts/run_schedule_experiment.sh \ + --mode stability \ + --schedule adaptive_v1 +``` + +Except for `--schedule` (and Role C adaptive-internal knobs once on `main`), every other frozen knob is identical. + +Default unique outdirs: + +```text +$ECT_RUNS_ROOT/ +├── sigmoid-activation--/ +├── 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. + +## 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` +- `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. +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. + +Adaptive-only fields (`loss_ema`, `correction`, …) are reserved for Role C. + +## Collector + +```bash +python scripts/collect_schedule_results.py \ + --run-dir /path/to/run \ + --outdir results/fixed_baseline_v1 \ + --mode stability \ + --schedule sigmoid \ + --data "$ECT_DATA_PATH" \ + --transfer "$ECT_TRANSFER_PATH" +``` + +`scripts/collect_fixed_baseline_results.py` remains a compatibility wrapper. + +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/results/fixed_baseline_preliminary/README.md b/results/fixed_baseline_preliminary/README.md new file mode 100644 index 00000000..04869e21 --- /dev/null +++ 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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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_schedule_results.py b/scripts/collect_schedule_results.py new file mode 100755 index 00000000..b14ffa71 --- /dev/null +++ b/scripts/collect_schedule_results.py @@ -0,0 +1,620 @@ +#!/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 + + +OUTPUT_FIELDS = ( + "attempted_iteration", + "successful_optimizer_steps", + "processed_kimg", + "loss", + "grad_scale", + "step_skipped", + "schedule", + "stage", + "seconds", + "peak_vram_mib", +) + +MODE_DURATION_MIMG = { + "activation": 0.004, + "stability": 0.016, + "baseline": 0.128, +} + + +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 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 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 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}") + + 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 + + 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) + 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", ""), + "seconds": float(row["elapsed_sec"]), + "peak_vram_mib": peak_gb * 1024.0, + } + ) + + attempted = len(packaged) + successful = attempted - skipped + 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}" + ) + + 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}") + 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, + "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"], + "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/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 "$@" diff --git a/scripts/run_schedule_experiment.sh b/scripts/run_schedule_experiment.sh new file mode 100755 index 00000000..bc6816a3 --- /dev/null +++ b/scripts/run_schedule_experiment.sh @@ -0,0 +1,454 @@ +#!/usr/bin/env bash + +# Paired experiment runner (Role B) — owner of fixed/adaptive comparison infra. +# Same frozen hyperparameters for both schedules; only --schedule (and Role C +# adaptive-internal knobs once available) may differ. +# +# Duration (Mimg) → total_kimg = int(duration * 1000); discrete batch completion +# activation 0.004 → 4 kimg target → 32 attempted iterations @ batch 128 (4096 images) +# stability 0.016 → 16 kimg → 125 attempted iterations @ batch 128 +# baseline 0.128 → 128 kimg → 1000 attempted iterations @ batch 128 +# +# Fresh runs always use a unique empty directory and pass --transfer only. +# Resume requires --resume, reuses that run directory, and must NOT pass --transfer. +# Fixed and adaptive never share an output directory. + +set -euo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +ENV_NAME="${ECT_ENV_NAME:-ect}" + +MODE="" +SCHEDULE="" +RESUME="" +OUTDIR_OVERRIDE="" + +usage() { + cat <<'EOF' +Usage: + bash scripts/run_schedule_experiment.sh \ + --schedule {sigmoid|adaptive_v1} \ + --mode {dry-run|activation|stability|baseline} \ + [--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 + ;; + -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: $0 --mode {dry-run|activation|stability|baseline}" >&2 + exit 2 + ;; +esac + +# Frozen paired knobs — identical for sigmoid and adaptive_v1. +DATA="${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 +SEED=0 +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="${OUTDIR_OVERRIDE}" + return + fi + # Unique per (schedule, mode, commit, time): never mixes sigmoid with adaptive_v1. + OUTDIR="${RUNS_ROOT}/${SCHEDULE_SLUG}-${MODE}-${GIT_SHA_SHORT}-${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/tests/test_paired_training_infra.py b/tests/test_paired_training_infra.py new file mode 100644 index 00000000..673d4fcd --- /dev/null +++ b/tests/test_paired_training_infra.py @@ -0,0 +1,780 @@ +#!/usr/bin/env python3 +"""Lightweight infra tests for Role B paired-training runner/collector.""" + +from __future__ import annotations + +import csv +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, +) -> 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: + writer = csv.DictWriter( + handle, + fieldnames=[ + "attempted_iteration", + "successful_optimizer_steps", + "processed_nimg", + "processed_kimg", + "loss", + "grad_scale", + "step_skipped", + "schedule", + "stage", + "elapsed_sec", + "peak_vram_gb", + ], + ) + writer.writeheader() + writer.writerow( + { + "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, + "elapsed_sec": "1.0", + "peak_vram_gb": "1.5", + } + ) + + (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") + + +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_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 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/training/ct_training_loop.py b/training/ct_training_loop.py index 162270ba..bca30f59 100644 --- a/training/ct_training_loop.py +++ b/training/ct_training_loop.py @@ -1,4 +1,5 @@ import os +import csv import time import copy import json @@ -15,6 +16,23 @@ from metrics import metric_main +# Per-attempted-iteration CSV for paired fixed/adaptive comparisons. +# schedule / stage are recorded every row so collectors do not scrape logs. +# Adaptive-only columns (loss_ema, correction, ...) are added by Role C later. +_TRAIN_SUMMARY_FIELDS = ( + 'attempted_iteration', + 'successful_optimizer_steps', + 'processed_nimg', + 'processed_kimg', + 'loss', + 'grad_scale', + 'step_skipped', + 'schedule', + 'stage', + 'elapsed_sec', + 'peak_vram_gb', +) + #---------------------------------------------------------------------------- def setup_snapshot_image_grid(training_set, random_seed=0): @@ -202,11 +220,32 @@ 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 + 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')) 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 enable_amp: if 'gradscaler_state' in data: # NOTE(aiihn): Although not loading the state_dict of the GradScaler works well, @@ -241,13 +280,79 @@ 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 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: + with open(summary_path, 'rt', newline='') as handle: + rows = list(csv.DictReader(handle)) + if not rows: + raise RuntimeError(f'resume requested but {summary_path} has no data rows') + 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}' + ) + 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.') @@ -255,14 +360,46 @@ 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(): + # 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, + elapsed_sec=elapsed_base_sec + (time.time() - start_time), + ) + if hasattr(loss_fn, 'schedule_state_dict'): + data['loss_fn_state'] = loss_fn.schedule_state_dict() + 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 = [] for round_idx in range(num_accumulation_rounds): with misc.ddp_sync(ddp, (round_idx == num_accumulation_rounds - 1)): images, labels = next(dataset_iterator) @@ -270,6 +407,7 @@ 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()) training_stats.report('Loss/loss', loss) if enable_amp: scaler.scale(loss.mean()).backward() @@ -291,13 +429,35 @@ def update_scheduler(loss_fn): # for g in optimizer.param_groups: # g['lr'] = optimizer_kwargs['lr'] * min(cur_nimg / max(lr_rampup_kimg * 1000, 1e-8), 1) - # Update weights. + # 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_mean = float(torch.cat([x.flatten() for x in loss_batches]).mean().cpu()) + 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: ema_halflife_nimg = ema_halflife_kimg * 1000 @@ -309,6 +469,22 @@ def update_scheduler(loss_fn): # Perform maintenance tasks once per tick. cur_nimg += batch_size + 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, + 'elapsed_sec': f'{elapsed_sec:.6f}', + 'peak_vram_gb': f'{peak_vram_gb:.6f}', + }) + train_summary_csv.flush() + done = (cur_nimg >= total_kimg * 1000) if (not done) and (cur_tick != 0) and (cur_nimg < tick_start_nimg + kimg_per_tick * 1000): continue @@ -319,6 +495,8 @@ 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}"] @@ -351,10 +529,7 @@ def update_scheduler(loss_fn): # 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: - data = dict(net=net, optimizer_state=optimizer.state_dict()) - if enable_amp: - data.update(gradscaler_state=scaler.state_dict()) - torch.save(data, os.path.join(run_dir, f'training-state-{cur_tick:06d}.pt')) + torch.save(build_training_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: @@ -372,10 +547,7 @@ def update_scheduler(loss_fn): del data # conserve memory if dist.get_rank() == 0: - data = dict(net=net, optimizer_state=optimizer.state_dict()) - if enable_amp: - data.update(gradscaler_state=scaler.state_dict()) - torch.save(data, os.path.join(run_dir, f'training-state-latest.pt')) + torch.save(build_training_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: @@ -444,6 +616,8 @@ 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...') From 5344a5c97ab461b640ad5c5413cbf57eec527c2a Mon Sep 17 00:00:00 2001 From: Alicia Date: Tue, 21 Jul 2026 10:23:57 +0800 Subject: [PATCH 16/60] Add loss-EMA adaptive t-to-r schedule and activation evidence Add the loss-EMA feedback-adaptive schedule, fixed-schedule parity tests, adaptive telemetry, exact resume support, activation gating, canonical dataset identity, and clean A100 activation evidence. Role B reviewed head c138bd7f015a04394a902ece14b03693c8962076: 81 tests passed with no blocking issues. --- artifacts/cifar10_canonical_identity.json | 79 +++ ct_train.py | 43 +- docs/PAIRED_TRAINING_PROTOCOL.md | 46 +- docs/SCHEDULES.md | 161 +++++ .../README.md | 53 ++ .../metadata.json | 57 ++ .../train_summary.csv | 33 ++ scripts/collect_schedule_results.py | 333 +++++++++++ tests/test_adaptive_signal_updates.py | 188 ++++++ tests/test_paired_training_infra.py | 550 +++++++++++++++++- tests/test_schedules.py | 415 +++++++++++++ tests/test_training_cli_compat.py | 92 +++ training/ct_training_loop.py | 420 ++++++++++++- training/loss.py | 84 ++- training/schedules.py | 350 +++++++++++ 15 files changed, 2865 insertions(+), 39 deletions(-) create mode 100644 artifacts/cifar10_canonical_identity.json create mode 100644 docs/SCHEDULES.md create mode 100644 results/adaptive_v1_activation_a100_cb84a934/README.md create mode 100644 results/adaptive_v1_activation_a100_cb84a934/metadata.json create mode 100644 results/adaptive_v1_activation_a100_cb84a934/train_summary.csv create mode 100644 tests/test_adaptive_signal_updates.py create mode 100644 tests/test_schedules.py create mode 100644 tests/test_training_cli_compat.py create mode 100644 training/schedules.py diff --git a/artifacts/cifar10_canonical_identity.json b/artifacts/cifar10_canonical_identity.json new file mode 100644 index 00000000..1a608603 --- /dev/null +++ b/artifacts/cifar10_canonical_identity.json @@ -0,0 +1,79 @@ +{ + "schema_version": 1, + "asset_id": "cifar10-32x32-canonical-08c9ed1b2b1c", + "asset_type": "edm_imagefolder_zip", + "canonical_status": "recovered_and_verified", + "verified_at_utc": "2026-07-20T11:03:55Z", + "verification_host": "qpAEJL", + "canonical_archive": { + "path": "/mnt/ect_project/datasets/cifar10-32x32.zip", + "bytes": 166000134, + "sha256": "08c9ed1b2b1c523268dc0f05a0569dd654209aea46197e3f56ec149dd714f372", + "zip_crc_ok": true, + "member_count": 50001, + "compression_methods": { + "stored": 50001 + }, + "distinct_zip_timestamps": 6 + }, + "official_source_archive": { + "path": "/mnt/ect_project/datasets/cifar-10-python.tar.gz", + "bytes": 170498071, + "md5": "c58f30108f718f92721af3b95e74349a", + "expected_md5": "c58f30108f718f92721af3b95e74349a", + "md5_ok": true + }, + "semantic_identity": { + "method": "edm_zip_semantic_identity_v1", + "member_names_sha256": "e94bed3934ef284769555b78a38c8536845b2a1a30c0657934b7b2bb161a74eb", + "member_contents_sha256": "89acb714d98b6a51b2e4fcfc10d65d682d00c22a37a2edad8dbec0e02f05298f", + "png_member_bytes_sha256": "7c0bdb2e1ec770bbaa70066ac99368df5b80471b2a4fd81e40252ad2cf80c9e7", + "dataset_json_raw_sha256": "260c37e04df4cff7cf74f29970e1b3090973d2a03b79f695d7622471d182b52d", + "labels_canonical_sha256": "3eeb6437c221a8102b86adc451a21586d43c5013c642cc39a3147823104ece9a", + "label_pairs_sha256": "9453fff06b2ec02708ae903a5417f49f75c8adf42ab78c61c6e7fb628be0164a" + }, + "dataset_validation": { + "images": 50000, + "labels": 50000, + "resolution": [ + 32, + 32 + ], + "color_mode": "RGB", + "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/ct_train.py b/ct_train.py index 89ab6ed1..dee0baca 100644 --- a/ct_train.py +++ b/ct_train.py @@ -36,6 +36,27 @@ def convert(self, value, param, ctx): return [] return value.split(',') + +def normalize_schedule_name(_ctx, _param, value): + return 'adaptive_v1' if value == 'adaptive-v1' else 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, + ) + #---------------------------------------------------------------------------- @click.command() @@ -65,10 +86,23 @@ 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', 'adaptive_v1', 'adaptive-v1']), + callback=normalize_schedule_name, default='sigmoid', 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('--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) @@ -118,7 +152,7 @@ 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. @@ -168,7 +202,8 @@ def main(**kwargs): 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, enable_amp=opts.enable_amp) - 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(kimg_per_tick=opts.tick, snapshot_ticks=opts.snap, state_dump_ticks=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. diff --git a/docs/PAIRED_TRAINING_PROTOCOL.md b/docs/PAIRED_TRAINING_PROTOCOL.md index 75eba1f5..2d5ce8a9 100644 --- a/docs/PAIRED_TRAINING_PROTOCOL.md +++ b/docs/PAIRED_TRAINING_PROTOCOL.md @@ -79,7 +79,7 @@ Rules: 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. +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 @@ -94,15 +94,31 @@ attempted iteration: - `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. -Adaptive-only fields (`loss_ema`, `correction`, …) are reserved for Role C. +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 @@ -118,6 +134,32 @@ python scripts/collect_schedule_results.py \ `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 diff --git a/docs/SCHEDULES.md b/docs/SCHEDULES.md new file mode 100644 index 00000000..b0e53664 --- /dev/null +++ b/docs/SCHEDULES.md @@ -0,0 +1,161 @@ +# 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；三种调度均保持官方 + `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 | +| `adaptive_v1` | 官方 `sigmoid` ratio + loss EMA 驱动的有界修正 | Role C 实验 v1 | + +**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)`。 + +`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。 + +从旧的 11 列 `train_summary.csv` 续训时，只接受完全匹配的旧标准表头。训练循环 +会先保存 `.pre-telemetry.bak`，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。 + +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 # 打印三种调度的 r/t 表 +``` diff --git 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, + "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/ect_project/src/recurrence_of_ect-cb84a934-clean/ct_train.py --data=/root/ect-assets/cifar10-32x32-canonical-08c9ed1b2b1c.zip --outdir=/root/ect-runs/paired-training-v1/adaptive-v1-activation-cb84a934-20260720T153954Z --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": 31.16120934, + "final_signal_updates": 8, + "first_adapted_pair_iteration": 13, + "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 +++ b/results/adaptive_v1_activation_a100_cb84a934/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 +1,0,0.128000,15.97663820,65536,true,adaptive_v1,0,1,,,0,0,0,0.985617611763,0.0143823882369,7.852284,5916.343296 +2,0,0.256000,15.02455795,32768,true,adaptive_v1,0,1,,,0,0,0,0.985632538854,0.0143674611461,9.081361,2136.822784 +3,0,0.384000,16.38218355,16384,true,adaptive_v1,0,1,,,0,0,0,0.985938287727,0.0140617122734,9.786533,2136.822784 +4,0,0.512000,17.54660499,8192,true,adaptive_v1,0,1,16.2324961722,,0,1,0,0.985108195351,0.0148918046487,10.498530,2136.822784 +5,0,0.640000,15.65527940,4096,true,adaptive_v1,0,1,16.2324961722,,0,1,0,0.986100688498,0.0138993115016,11.209856,2136.822784 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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/scripts/collect_schedule_results.py b/scripts/collect_schedule_results.py index b14ffa71..a9c8ecef 100755 --- a/scripts/collect_schedule_results.py +++ b/scripts/collect_schedule_results.py @@ -22,6 +22,16 @@ 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", @@ -31,6 +41,8 @@ "step_skipped", "schedule", "stage", + "next_loop_cur_tick", + *TELEMETRY_FIELDS, "seconds", "peak_vram_mib", ) @@ -41,6 +53,9 @@ "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. @@ -55,6 +70,66 @@ def expected_final_nimg(duration_mimg: float, global_batch: int) -> int: 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}") @@ -136,6 +211,40 @@ 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}") @@ -257,6 +366,111 @@ def extract_duration_from_command(exact_command: str) -> float | None: 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], @@ -394,6 +608,12 @@ def main(argv: list[str] | None = None) -> None: 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) @@ -413,6 +633,10 @@ def main(argv: list[str] | None = None) -> None: 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"]) @@ -428,6 +652,75 @@ def main(argv: list[str] | None = None) -> None: 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"])), @@ -438,13 +731,24 @@ def main(argv: list[str] | None = None) -> None: "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: @@ -472,6 +776,11 @@ def main(argv: list[str] | None = None) -> None: 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 @@ -500,6 +809,13 @@ def main(argv: list[str] | None = None) -> None: 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 @@ -570,6 +886,15 @@ def main(argv: list[str] | None = None) -> None: "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, @@ -594,6 +919,14 @@ def main(argv: list[str] | None = None) -> None: "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}", } diff --git a/tests/test_adaptive_signal_updates.py b/tests/test_adaptive_signal_updates.py new file mode 100644 index 00000000..ecc72f23 --- /dev/null +++ b/tests/test_adaptive_signal_updates.py @@ -0,0 +1,188 @@ +import csv +import tempfile +import unittest +from pathlib import Path + +import torch + +from training.ct_training_loop import ( + AdaptiveSignalWindow, + _LEGACY_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}, + {'r_over_t_mean': 0.8, 'gap_mean': 0.2}, + ], + device=torch.device('cpu'), + ) + self.assertAlmostEqual(metrics['r_over_t_mean'], 0.7) + self.assertAlmostEqual(metrics['gap_mean'], 0.3) + + 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', + ): + 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_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_paired_training_infra.py b/tests/test_paired_training_infra.py index 673d4fcd..c9634872 100644 --- a/tests/test_paired_training_infra.py +++ b/tests/test_paired_training_infra.py @@ -4,6 +4,7 @@ from __future__ import annotations import csv +import json import os import subprocess import sys @@ -34,6 +35,8 @@ def write_minimal_run( 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 @@ -90,9 +93,7 @@ def _sha(path: Path | None, fallback: bytes) -> str: 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: - writer = csv.DictWriter( - handle, - fieldnames=[ + fieldnames = [ "attempted_iteration", "successful_optimizer_steps", "processed_nimg", @@ -102,13 +103,23 @@ def _sha(path: Path | None, fallback: bytes) -> str: "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() - writer.writerow( - { + row = { "attempted_iteration": 1, "successful_optimizer_steps": 1, "processed_nimg": nimg, @@ -118,16 +129,126 @@ def _sha(path: Path | None, fallback: bytes) -> str: "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)]) @@ -388,6 +509,419 @@ def _assets(self, tmp: Path) -> tuple[Path, Path]: 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) diff --git a/tests/test_schedules.py b/tests/test_schedules.py new file mode 100644 index 00000000..95bd1d71 --- /dev/null +++ b/tests/test_schedules.py @@ -0,0 +1,415 @@ +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', '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', + } + 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 = torch.tensor([0.5, 1.5], dtype=torch.float64) + loss_fn._record_schedule_runtime_pair(t=t, r=r) + metrics = loss_fn.schedule_runtime_metrics() + self.assertEqual(metrics['r_over_t_mean'], 0.625) + self.assertEqual(metrics['gap_mean'], 0.375) + + 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_training_cli_compat.py b/tests/test_training_cli_compat.py new file mode 100644 index 00000000..5aec6266 --- /dev/null +++ b/tests/test_training_cli_compat.py @@ -0,0 +1,92 @@ +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) + + 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', 'adaptive_v1']: + 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', + ) + + 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_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/training/ct_training_loop.py b/training/ct_training_loop.py index bca30f59..381c17c2 100644 --- a/training/ct_training_loop.py +++ b/training/ct_training_loop.py @@ -2,9 +2,12 @@ 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 @@ -17,8 +20,44 @@ from metrics import metric_main # Per-attempted-iteration CSV for paired fixed/adaptive comparisons. -# schedule / stage are recorded every row so collectors do not scrape logs. -# Adaptive-only columns (loss_ema, correction, ...) are added by Role C later. +# 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', +) + _TRAIN_SUMMARY_FIELDS = ( 'attempted_iteration', 'successful_optimizer_steps', @@ -29,10 +68,231 @@ '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', ) +#---------------------------------------------------------------------------- + +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' + 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 + + +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] + 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_runtime_pairs(metric_batches, device): + """Average public r/t telemetry across accumulation rounds and ranks.""" + r_values = [float(metrics['r_over_t_mean']) for metrics in metric_batches] + gap_values = [float(metrics['gap_mean']) for metrics in metric_batches] + r_values = [value for value in r_values if math.isfinite(value)] + gap_values = [value for value in gap_values if math.isfinite(value)] + totals = torch.tensor( + [sum(r_values), len(r_values), sum(gap_values), len(gap_values)], + dtype=torch.float64, + device=device, + ) + if dist.get_world_size() > 1: + torch.distributed.all_reduce(totals) + r_count = float(totals[1]) + gap_count = float(totals[3]) + return { + 'r_over_t_mean': float(totals[0] / r_count) if r_count > 0 else float('nan'), + 'gap_mean': float(totals[2] / gap_count) if gap_count > 0 else float('nan'), + } + + #---------------------------------------------------------------------------- def setup_snapshot_image_grid(training_set, random_seed=0): @@ -140,6 +400,7 @@ 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. @@ -225,6 +486,7 @@ def training_loop( 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}"...') @@ -246,6 +508,8 @@ def training_loop( 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, @@ -304,16 +568,36 @@ def training_loop( schedule_name = getattr(loss_fn, 'adj', None) if schedule_name is None: schedule_name = loss_kwargs.get('adj', 'unknown') + adaptive_signal_window = ( + AdaptiveSignalWindow(adaptive_update_kimg, start_nimg=cur_nimg) + if schedule_name == 'adaptive_v1' else 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: - with open(summary_path, 'rt', newline='') as handle: - rows = list(csv.DictReader(handle)) - if not rows: - raise RuntimeError(f'resume requested but {summary_path} has no data rows') + 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)))) @@ -333,6 +617,29 @@ def training_loop( 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( @@ -361,7 +668,7 @@ 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(): + 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 @@ -374,10 +681,17 @@ def build_training_state(): cur_nimg=cur_nimg, cur_tick=cur_tick + 1, tick_start_nimg=cur_nimg, - elapsed_sec=elapsed_base_sec + (time.time() - start_time), + # 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 @@ -400,6 +714,7 @@ def build_training_state(): # Accumulate gradients. optimizer.zero_grad(set_to_none=True) loss_batches = [] + schedule_metric_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) @@ -408,6 +723,7 @@ def build_training_state(): 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()) training_stats.report('Loss/loss', loss) if enable_amp: scaler.scale(loss.mean()).backward() @@ -448,7 +764,10 @@ def build_training_state(): if not step_skipped: successful_optimizer_steps += 1 - loss_mean = float(torch.cat([x.flatten() for x in loss_batches]).mean().cpu()) + 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) @@ -467,8 +786,40 @@ def build_training_state(): 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: + 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']) + + # 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) + 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, @@ -480,13 +831,21 @@ def build_training_state(): '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}", 'elapsed_sec': f'{elapsed_sec:.6f}', 'peak_vram_gb': f'{peak_vram_gb:.6f}', }) train_summary_csv.flush() - done = (cur_nimg >= total_kimg * 1000) - if (not done) and (cur_tick != 0) and (cur_nimg < tick_start_nimg + kimg_per_tick * 1000): + # Perform maintenance tasks once per tick. + if not maintenance_due: continue # Print status line, accumulating the same information in training_stats. @@ -527,12 +886,32 @@ def build_training_state(): 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(build_training_state(), 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(): @@ -546,8 +925,15 @@ def build_training_state(): 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(build_training_state(), 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: diff --git a/training/loss.py b/training/loss.py index 9a1007a5..bd39d2fa 100644 --- a/training/loss.py +++ b/training/loss.py @@ -5,22 +5,33 @@ 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): 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, + ) + self.schedule = get_schedule(adj, **schedule_kwargs) self.q = q self.stage = 0 @@ -30,12 +41,68 @@ 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') 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), + } + + def _record_schedule_runtime_pair(self, t, r): + with torch.no_grad(): + valid = torch.isfinite(t) & torch.isfinite(r) & (t > 0) + if not bool(valid.any()): + self._runtime_r_over_t_mean = float('nan') + self._runtime_gap_mean = float('nan') + return + valid_t = t[valid].to(torch.float64) + valid_r = r[valid].to(torch.float64) + self._runtime_r_over_t_mean = float((valid_r / valid_t).mean().cpu()) + self._runtime_gap_mean = float(((valid_t - valid_r) / valid_t).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 +120,8 @@ 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) diff --git a/training/schedules.py b/training/schedules.py new file mode 100644 index 00000000..4f96fe3a --- /dev/null +++ b/training/schedules.py @@ -0,0 +1,350 @@ +"""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. + 'adaptive_v1' Official sigmoid ratio plus a bounded correction driven by + the EMA of the globally aggregated training loss. + +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 + +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 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) + +#---------------------------------------------------------------------------- +# Experimental schedules (Role C). Changes relative to the official fixed +# schedules live below this line only. + +@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(), + } + +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())) From ef4aa3142eb2049bd5d541d18d7278cdf758029c Mon Sep 17 00:00:00 2001 From: kangw24 <280028295+kangw24@users.noreply.github.com> Date: Tue, 21 Jul 2026 12:06:42 +0800 Subject: [PATCH 17/60] Add canonical paired A100 activation and stability evidence Add canonical-runtime paired sigmoid/adaptive activation and fresh 16 kimg stability evidence, including comparison summaries, controller telemetry, and checkpoint identities for Role D evaluation. --- .../README.md | 53 ++++ .../metadata.json | 57 ++++ .../train_summary.csv | 33 +++ .../README.md | 53 ++++ .../metadata.json | 57 ++++ .../train_summary.csv | 126 +++++++++ results/paired_comparison_a100_5344a5c9.json | 245 ++++++++++++++++++ results/paired_comparison_a100_5344a5c9.md | 57 ++++ .../README.md | 53 ++++ .../metadata.json | 57 ++++ .../train_summary.csv | 33 +++ .../sigmoid_stability_a100_5344a5c9/README.md | 52 ++++ .../metadata.json | 57 ++++ .../train_summary.csv | 126 +++++++++ 14 files changed, 1059 insertions(+) create mode 100644 results/adaptive_v1_activation_a100_5344a5c9/README.md create mode 100644 results/adaptive_v1_activation_a100_5344a5c9/metadata.json create mode 100644 results/adaptive_v1_activation_a100_5344a5c9/train_summary.csv create mode 100644 results/adaptive_v1_stability_a100_5344a5c9/README.md create mode 100644 results/adaptive_v1_stability_a100_5344a5c9/metadata.json create mode 100644 results/adaptive_v1_stability_a100_5344a5c9/train_summary.csv create mode 100644 results/paired_comparison_a100_5344a5c9.json create mode 100644 results/paired_comparison_a100_5344a5c9.md create mode 100644 results/sigmoid_activation_a100_5344a5c9/README.md create mode 100644 results/sigmoid_activation_a100_5344a5c9/metadata.json create mode 100644 results/sigmoid_activation_a100_5344a5c9/train_summary.csv create mode 100644 results/sigmoid_stability_a100_5344a5c9/README.md create mode 100644 results/sigmoid_stability_a100_5344a5c9/metadata.json create mode 100644 results/sigmoid_stability_a100_5344a5c9/train_summary.csv 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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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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@@ -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/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, + 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+122,113,15.616000,26.55014968,128,false,sigmoid,0,1,,,0,0,0,0.985934026339,0.0140659736614,101.848956,2562.343936 +123,114,15.744000,27.73538589,128,false,sigmoid,0,1,,,0,0,0,0.985068636282,0.0149313637183,102.590893,2562.343936 +124,115,15.872000,26.71504211,128,false,sigmoid,0,1,,,0,0,0,0.985571943847,0.0144280561529,103.341092,2562.343936 +125,116,16.000000,26.38569498,128,false,sigmoid,0,2,,,0,0,0,0.985990140347,0.0140098596528,104.098172,2562.343936 From 4b80a4f78f954a93aa5d4a022b20efe661a7b184 Mon Sep 17 00:00:00 2001 From: w10800 <1793719632@qq.com> Date: Tue, 21 Jul 2026 17:55:46 +0800 Subject: [PATCH 18/60] Add final paired 16 kimg fixed-seed evaluation Add the canonical fixed-vs-adaptive NFE=1/2 evaluation evidence, deterministic manifests, method grids, same-seed comparison grids, and final provenance documentation. --- .../FINAL_EVALUATION_README.md | 110 +++++++++++++++ .../metadata.json | 116 ++++++++++++++++ .../nfe1/grid_8x8.png | Bin 0 -> 89603 bytes .../nfe2/grid_8x8.png | Bin 0 -> 154344 bytes .../sha256_manifest.txt | 131 ++++++++++++++++++ .../comparison_nfe1_fixed_vs_adaptive.png | Bin 0 -> 168964 bytes .../comparison_nfe2_fixed_vs_adaptive.png | Bin 0 -> 298366 bytes .../metadata.json | 116 ++++++++++++++++ .../nfe1/grid_8x8.png | Bin 0 -> 91014 bytes .../nfe2/grid_8x8.png | Bin 0 -> 154088 bytes .../sha256_manifest.txt | 131 ++++++++++++++++++ 11 files changed, 604 insertions(+) create mode 100644 results/final_paired_16k/FINAL_EVALUATION_README.md create mode 100644 results/final_paired_16k/adaptive_v1_16k_canonical-7d162808dc98/metadata.json create mode 100644 results/final_paired_16k/adaptive_v1_16k_canonical-7d162808dc98/nfe1/grid_8x8.png create mode 100644 results/final_paired_16k/adaptive_v1_16k_canonical-7d162808dc98/nfe2/grid_8x8.png create mode 100644 results/final_paired_16k/adaptive_v1_16k_canonical-7d162808dc98/sha256_manifest.txt create mode 100644 results/final_paired_16k/comparison_nfe1_fixed_vs_adaptive.png create mode 100644 results/final_paired_16k/comparison_nfe2_fixed_vs_adaptive.png create mode 100644 results/final_paired_16k/sigmoid_16k_canonical-32aa46615846/metadata.json create mode 100644 results/final_paired_16k/sigmoid_16k_canonical-32aa46615846/nfe1/grid_8x8.png create mode 100644 results/final_paired_16k/sigmoid_16k_canonical-32aa46615846/nfe2/grid_8x8.png create mode 100644 results/final_paired_16k/sigmoid_16k_canonical-32aa46615846/sha256_manifest.txt diff --git a/results/final_paired_16k/FINAL_EVALUATION_README.md b/results/final_paired_16k/FINAL_EVALUATION_README.md new file mode 100644 index 00000000..08ef3dd9 --- /dev/null +++ b/results/final_paired_16k/FINAL_EVALUATION_README.md @@ -0,0 +1,110 @@ +# Final paired 16 kimg fixed-seed evaluation + +## Material Passport + +- Mode: `validate` +- Verification status: `VERIFIED` +- Training commit: `5344a5c97ab461b640ad5c5413cbf57eec527c2a` +- Evaluation code commit: `ef4aa3142eb2049bd5d541d18d7278cdf758029c` +- 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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