From 967657e7dcf0e35a95b9d31b64c3295639b2bdcf Mon Sep 17 00:00:00 2001 From: Martyn Garcia Date: Fri, 4 Sep 2026 20:57:23 -0600 Subject: [PATCH 1/4] Add covariance checks for collapsed per-mode shapes --- lib/toy_metrics.py | 53 +++++++++++++++++++++++++++++++++--- tests/test_toy_metrics.py | 57 +++++++++++++++++++++++++++++++++++++++ 2 files changed, 107 insertions(+), 3 deletions(-) create mode 100644 tests/test_toy_metrics.py diff --git a/lib/toy_metrics.py b/lib/toy_metrics.py index fe5cef6..d04bb1a 100644 --- a/lib/toy_metrics.py +++ b/lib/toy_metrics.py @@ -7,7 +7,8 @@ * how far is the model distribution from the data distribution (`sliced_w1`, cheap and GPU-resident; `exact_w1_w2`, exact but O(n^2 log n)), - * how well-calibrated is the density it puts on each mode (`mixture_nll`), + * how plausible generated samples are under the target (`mixture_nll`, + which rewards concentration and is not a calibration metric), * how balanced is its mass across the 100 modes (`mode_recall_and_hists`). @@ -172,8 +173,10 @@ def mixture_nll( log p(x) = logsumexp_k [ -||x - mu_k||^2 / (2 std^2) ] - log 100 - (d/2) log(2 pi std^2) - Lower is better; the entropy floor for perfect samples is roughly - d/2 * (1 + log(2 pi std^2)) + log 100 nats. + This measures sample plausibility, not distributional calibration. Samples + concentrated at mode centers score lower than samples from the true target. + Target samples have expected NLL approximately + d/2 * (1 + log(2 pi std^2)) + log 100 nats; this is not a lower bound. Returns: The mean NLL in nats, as a python float. @@ -372,6 +375,10 @@ def per_mode_core_ratio( Everything past the median is ignored by construction, so the estimate depends only on the half of the mass nearest the center. + This scalar assumes an isotropic Gaussian core. A matching radial median + alone cannot establish Gaussian shape or exclude collapse along one axis; + inspect `per_mode_covariance_ratios` and radial/angular structure as well. + On a genuine Gaussian this and `per_mode_std` agree, so their ratio reads directly as a tail-inflation factor. `tail_frac_10sigma` is the mass that buys that inflation: the fraction of *all* samples whose nearest-center @@ -437,3 +444,43 @@ def per_mode_core_ratio( "per_mode_core_ratio": core_std / float(std), "tail_frac_10sigma": tail_frac, } + + +def per_mode_covariance_ratios( + x: torch.Tensor, + data_std: float = 0.03, + grid_scale: float = 1.0, + min_count: int = 50, +) -> Dict[str, float]: + """Mean smallest/largest within-mode covariance eigenvalues / true variance. + + These complement the robust radial median: a line or two-point cloud can + have the correct median radius while its smallest eigenvalue is zero. + Covariances use population normalization and are tail-sensitive; report + both these ratios and the core/tail metrics. Values near one are necessary + second-moment checks, not proof of Gaussian shape. Modes below min_count + are omitted and the audited count must accompany the ratios. + """ + if not math.isfinite(data_std) or data_std <= 0: + raise ValueError("data_std must be positive and finite") + if min_count < 2: + raise ValueError("min_count must be at least 2") + with torch.no_grad(): + xf = torch.as_tensor(x).detach().to(torch.float64) + if xf.ndim != 2 or xf.shape[1] != 2: + raise ValueError("expected samples with shape (N, 2)") + centers = _grid_centers(xf.device, torch.float64, grid_scale) + nearest = torch.cdist(xf, centers).argmin(dim=1) + counts = torch.bincount(nearest, minlength=centers.shape[0]) + eigenvalues = [] + for mode in torch.nonzero(counts >= min_count).flatten(): + points = xf[nearest == mode] + residuals = points - points.mean(dim=0) + cov = residuals.T @ residuals / points.shape[0] + eigenvalues.append(torch.linalg.eigvalsh(cov).clamp_min(0) / data_std**2) + means = torch.stack(eigenvalues).mean(dim=0) if eigenvalues else None + return { + "per_mode_cov_eig_min_ratio": float(means[0]) if means is not None else float("nan"), + "per_mode_cov_eig_max_ratio": float(means[1]) if means is not None else float("nan"), + "per_mode_cov_audited_modes": len(eigenvalues), + } diff --git a/tests/test_toy_metrics.py b/tests/test_toy_metrics.py new file mode 100644 index 0000000..5cb5515 --- /dev/null +++ b/tests/test_toy_metrics.py @@ -0,0 +1,57 @@ +"""Distribution fixtures catch blind spots that a gradient check cannot.""" + +import math + +import pytest +import torch + +from lib.toy_metrics import ( + RAYLEIGH_MEDIAN_FACTOR, + _grid_centers, + exact_w1_w2, + mixture_nll, + per_mode_core_ratio, + per_mode_covariance_ratios, +) + + +def test_covariance_detects_line_collapse_that_radial_core_misses(): + centers = _grid_centers(torch.device("cpu"), torch.float64) + offsets = torch.tensor([[-1.0, 0.0], [1.0, 0.0]], dtype=torch.float64) + points = (centers[:, None, :] + offsets * 0.03 * RAYLEIGH_MEDIAN_FACTOR) + points = points.repeat_interleave(32, dim=1).reshape(-1, 2) + assert per_mode_core_ratio(points)["per_mode_core_ratio"] == pytest.approx(1.0) + cov = per_mode_covariance_ratios(points) + assert cov["per_mode_cov_audited_modes"] == 100 + assert cov["per_mode_cov_eig_min_ratio"] == pytest.approx(0.0, abs=1e-20) + assert cov["per_mode_cov_eig_max_ratio"] == pytest.approx(2 * math.log(2)) + + +def test_covariance_calibrates_on_true_gaussians(): + centers = _grid_centers(torch.device("cpu"), torch.float64) + gen = torch.Generator().manual_seed(419) + points = centers[:, None, :] + 0.03 * torch.randn(100, 1024, 2, generator=gen, dtype=torch.float64) + cov = per_mode_covariance_ratios(points.reshape(-1, 2)) + assert cov["per_mode_cov_audited_modes"] == 100 + assert cov["per_mode_cov_eig_min_ratio"] == pytest.approx(1.0, abs=0.08) + assert cov["per_mode_cov_eig_max_ratio"] == pytest.approx(1.0, abs=0.08) + + +def test_covariance_reports_missing_modes_instead_of_zero_spread(): + result = per_mode_covariance_ratios(torch.empty(0, 2)) + assert result["per_mode_cov_audited_modes"] == 0 + assert math.isnan(result["per_mode_cov_eig_min_ratio"]) + assert math.isnan(result["per_mode_cov_eig_max_ratio"]) + + +def test_nll_is_plausibility_not_calibration(): + centers = _grid_centers(torch.device("cpu"), torch.float64) + # Each point is one target standard deviation away along both dimensions. + assert mixture_nll(centers + 0.03) - mixture_nll(centers) == pytest.approx(1.0) + + +def test_exact_transport_dependency_and_known_translation(): + x = torch.tensor([[0.0, 0.0], [0.0, 1.0]], dtype=torch.float64) + w1, w2 = exact_w1_w2(x, x + torch.tensor([3.0, 0.0])) + assert w1 == pytest.approx(3.0) + assert w2 == pytest.approx(3.0) From fe19a46a6ba063cd8afb21fb97e4dab8f578c1b9 Mon Sep 17 00:00:00 2001 From: Martyn Garcia Date: Fri, 4 Sep 2026 21:01:05 -0600 Subject: [PATCH 2/4] Document reproducible setup and limits of toy distribution metrics --- docs/reproducing.md | 99 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 99 insertions(+) create mode 100644 docs/reproducing.md diff --git a/docs/reproducing.md b/docs/reproducing.md new file mode 100644 index 0000000..fb9819c --- /dev/null +++ b/docs/reproducing.md @@ -0,0 +1,99 @@ +# Reproducing and checking experiments + +Use Python 3.10 or newer. The CPU test workflow checks Python 3.11 and 3.12. +Run these commands from the repository root: + +```bash +python -m venv .venv +source .venv/bin/activate +python -m pip install -e '.[dev]' +python -m pip check +OMP_NUM_THREADS=1 MKL_NUM_THREADS=1 python -m pytest -q +``` + +`pyproject.toml` includes the dependencies needed by the experiment scripts, +including PyYAML and POT (`import ot`) for exact transport evaluation. A CUDA +installation of PyTorch is useful for full studies. For CPU-only work, install +PyTorch from its CPU wheel index before installing the project: + +```bash +python -m pip install 'torch>=2.6,<3' --index-url https://download.pytorch.org/whl/cpu +python -m pip install -e '.[dev]' +``` + +The tests exercise numerical gradient correctness, evaluation isolation, sparse +gradient penalties, particle identity bookkeeping, experiment failure/resume +behavior, and small training runs. They do not reproduce the reported full +studies or establish convergence for a new dataset. The exact-transport test +uses a distribution with a known answer so a missing solver is caught before +an expensive training run reaches its final evaluation. + +## Record the environment + +The manifest defines supported dependency ranges, not a bit-for-bit numerical +environment. Save the resolved environment and commit with every full study: + +```bash +mkdir -p results/reproduction +python -m pip freeze > results/reproduction/requirements.txt +git rev-parse HEAD > results/reproduction/commit.txt +python -c 'import torch; print(torch.__version__); print(torch.version.cuda)' > results/reproduction/torch.txt +``` + +Also retain the expanded run configurations, hardware description, summaries, +and samples. Matching a seed is insufficient for exact reproduction across +different PyTorch/CUDA versions and hardware. Historical tables were produced +before the evaluation-randomness fix and should not be expected to match new +trajectories bit for bit. + +## Controlled prior comparison + +The current comparison protocol uses the same generator, discriminator, loss, +penalty, learning rates, and training budget for three priors: + +* `particles`: a learned finite table, with its prior optimizer and VICReg term; +* `frozen_gaussian`: a fixed table initialized from a Gaussian; +* `fresh_gaussian`: newly sampled Gaussian noise for ordinary draws. + +The legacy configuration name `gaussian` continues to mean the frozen table. +It must not be interpreted as fresh continuous Gaussian sampling. The learned +table's optimizer and regularizer are part of the learned-prior intervention; +further ablations are needed to distinguish their individual effects. + +```bash +python experiments/compare_priors.py --help +python experiments/compare_priors.py +``` + +The generator emits a matched set of configurations and a protocol manifest; +use the printed run instructions for full training. Treat the old GIFs and +regularizer tables as historical recipe results, not as this controlled +three-way comparison. Use fresh seeds for confirmation after selecting a +recipe, and retain individual seed scores rather than only an aggregate. + +## Interpreting shape and diversity + +For a deterministic generator over M fixed particles, ordinary sampling can +emit at most M distinct outputs. Drawing 100,000 samples from 20,000 particles +does not create 100,000 independent locations in output space. Mode coverage, +sample plausibility, continuous diversity, and distributional fidelity are +separate properties. + +The robust core ratio estimates a radial median using an isotropic-Gaussian +conversion. A two-point cloud in each mode can pass that check while having +zero variance along one axis. The covariance eigenvalue ratios now expose +this failure: each eigenvalue is divided by the true variance, and the minimum +and maximum are averaged over sufficiently populated modes. These are +tail-sensitive population moments; read them alongside core spread, tail +mass, and the number of audited modes. Even correct covariance does not prove +Gaussian shape, so inspect radial and angular structure for stronger claims. + +Generated-sample NLL under the target rewards concentrating at mode centers. +It is a plausibility score, not a calibration test. Likewise, a confidence +interval containing zero is an inconclusive difference test, not proof of +equivalence. Establish an equivalence margin and adequate replication before +claiming two recipes perform the same. + +For future scaling studies, vary the particle count and separately test noise +around particles. Adding noise changes the model distribution and requires a +fresh comparison; the finite-support limitation is not silently removed here. From f8b59bb647976b421afccdcb64fa2da583ddf992 Mon Sep 17 00:00:00 2001 From: Martyn Garcia Date: Fri, 4 Sep 2026 21:03:53 -0600 Subject: [PATCH 3/4] Isolate evaluation RNG and add matched Gaussian prior controls --- README.md | 37 +- docs/prior-controls.md | 80 ++++ docs/reproducing.md | 8 +- examples/100gaussians.py | 213 ++-------- examples/100gaussians_no_particle_prior.py | 436 +-------------------- experiments/compare_priors.py | 120 ++++++ experiments/train_arm.py | 102 +++-- lib/grad_regularizers.py | 11 +- lib/particle_prior.py | 48 ++- lib/toy_models.py | 15 +- tests/test_prior_controls.py | 166 ++++++++ 11 files changed, 566 insertions(+), 670 deletions(-) create mode 100644 docs/prior-controls.md create mode 100644 experiments/compare_priors.py create mode 100644 tests/test_prior_controls.py diff --git a/README.md b/README.md index 21ed4bc..0723ca6 100644 --- a/README.md +++ b/README.md @@ -1,37 +1,42 @@ # ParticleGAN -**GANs don't collapse when z can move too.** +**Learnable latent particles for studying GAN mode coverage and stability.** ![100 Gaussians with Particle Prior](100gaussians.gif) ## The Problem -Traditional GANs suffer from **mode collapse**: the generator learns to produce only a subset of the data distribution, ignoring other valid modes. This happens because G must warp a *fixed* prior (usually a Gaussian) to match the data. All geometric stress concentrates in G, causing the learned manifold to fold and tear. +GANs can suffer from **mode collapse**: the generator produces only a subset of the data distribution. This project explores whether optimizing a finite latent particle cloud alongside the generator improves coverage on small, highly multimodal benchmarks. ## The Insight **What if the prior could move too?** -Instead of forcing G to do all the work, we introduce learnable "particles" in latent space. These particles move during training to match the structure of the data, absorbing geometric stress alongside G. The result: stable convergence even on highly multimodal distributions. +We introduce learnable "particles" in latent space. Both the generator and these latent vectors are optimized during training. The experiments examine how that extra flexibility interacts with discriminator regularization, optimizer dynamics, and sample quality. The results are empirical observations on these benchmarks, not a guarantee against collapse. -### Without Particles: Mode Collapse +### Historical Gaussian example ![100 Gaussians without Particle Prior](100gaussians_no_particles.gif) -*Same architecture, same hyperparameters, but with a fixed Gaussian prior — the generator collapses to a subset of modes.* +*Historical visualization from the older Gaussian example. Its architecture and training recipe differ from the particle example above, so these GIFs are not a matched prior comparison.* -## Results +## Evidence and controls -| Problem | Fixed Gaussian Prior | Particle Prior | -|---------|---------------------|----------------| -| 5 modes (text) | collapse | **converges** | -| 100 modes (2D grid) | collapse | **converges** | +The historical [regularizer study](FINDINGS.md) compares discriminator penalties within the particle model. It does not establish that a fixed Gaussian prior necessarily collapses. The current examples share one training loop and matched defaults; the only training change for the Gaussian controls is removing the learned prior and its regularizer. + +For a reproducible three-way comparison, run: + +```bash +python experiments/compare_priors.py --study-dir runs/prior_comparison --run --device cuda:0 +``` + +This runs learned particles, a frozen Gaussian table, and fresh Gaussian noise on paired seeds 23001–23003. It records configs, source revision, final samples, coverage, transport distances, and per-mode radial and covariance shape diagnostics. See [prior controls and interpretation](docs/prior-controls.md) and [reproducing the project](docs/reproducing.md). ## How It Works 1. **Particle Prior**: Instead of sampling z ~ N(0, I), we maintain a set of learnable latent vectors (particles). During training, we sample from this discrete set. -2. **Joint Optimization**: Particles are optimized alongside G and D. They naturally spread out to cover the data modes. +2. **Joint Optimization**: Particles are optimized alongside G and D. Their positions can adapt to the data modes. 3. **VICReg Regularization**: We apply variance-covariance regularization to prevent particles from collapsing to a single point, while allowing arbitrary topology (clusters, gaps, etc.). @@ -60,7 +65,7 @@ The main benchmark. 100 Gaussian modes arranged on a 10×10 grid. This is a stre python examples/100gaussians.py ``` -**With particle prior**: All 100 modes are captured — 100/100 modes with ~99% of samples within 3σ of a center after 7k steps. +The historical particle study reports runs with 100/100 modes and approximately 99% of samples within 3σ of a center after 7k steps. Coverage alone does not establish that the within-mode distribution is correct; the trainer also records shape and transport metrics. The default recipe is RpGAN (relativistic, logistic) + a one-sided cap gradient penalty on D (`relu(‖∇ₓD‖ − 1)²` on reals and fakes, coeff 1.0), Fourier-feature D, EMA evaluation, Adam β1=0, base LR 6e-4 with a delayed cosine anneal. The cap won a 420-run bake-off against the zero-centered R1/R2 penalty, which is still available with `--reg_arm a_r1r2 --reg_coeff 0.02`. See [FINDINGS.md](FINDINGS.md) for the study and [docs/convergence-tips.md](docs/convergence-tips.md) for the transferable reasoning behind each ingredient. @@ -69,14 +74,14 @@ The default recipe is RpGAN (relativistic, logistic) + a one-sided cap gradient python examples/100gaussians_no_particle_prior.py ``` -The baseline demonstrates classic mode collapse — the generator covers only a fraction of the modes. +This entrypoint uses the same architecture, losses, learning rates, schedule, and EMA as the particle example, with fresh Gaussian noise. Use `--prior frozen_gaussian` for a finite frozen-table control. The outcome depends on the recipe and seed; the baseline does not assume collapse. ## Installation ```bash git clone https://github.com/255BITS/ParticleGAN.git cd ParticleGAN -pip install torch matplotlib numpy +python -m pip install -e '.[dev]' ``` ## Project Structure @@ -119,12 +124,12 @@ from lib.gan_loss import GANLoss loss_fn = GANLoss(loss_type='hinge', mode='vanilla') d_loss = loss_fn.d_loss(d_real, d_fake) -g_loss = loss_fn.g_loss(d_real, d_fake) +g_loss = loss_fn.g_loss(d_fake) ``` ### VICRegLikeLoss (`lib/vicreg_loss.py`) -Prevents particle collapse while allowing flexible topology: +Penalizes low marginal variance and cross-dimension covariance while allowing flexible topology: ```python from lib.vicreg_loss import VICRegLikeLoss diff --git a/docs/prior-controls.md b/docs/prior-controls.md new file mode 100644 index 0000000..74d8a74 --- /dev/null +++ b/docs/prior-controls.md @@ -0,0 +1,80 @@ +# Matched prior controls + +The 100-Gaussian example and its Gaussian counterpart now call the same training +function. Architecture, discriminator loss and penalty, batch size, generator +learning rate, discriminator learning rate, run length, schedule, and generator +EMA are identical. The prior controls are: + +| Config / CLI value | Training and metric samples | Learned prior parameters | +| --- | --- | --- | +| `particles` | Uniform draws from a learned finite table | 20,000 × 4 = 80,000 by default | +| `frozen_gaussian` | Uniform draws from one fixed Gaussian table | None | +| `fresh_gaussian` | New independent Gaussian noise on every call | None | +| `gaussian` | Compatibility alias for `frozen_gaussian` | None | + +The particle arm also optimizes its table at 10× the generator learning rate, +applies VICReg, and averages its learned positions for EMA readout. The two +Gaussian controls have no prior optimizer or VICReg gradient. Generator and +discriminator parameter counts match; total trainable parameter counts differ. +This isolates the learned-prior intervention, not a fixed total capacity budget. + +Fresh Gaussian sampling keeps a fixed reference buffer exclusively for plots +requested with `fixed_first_n=True`. Ordinary `sample()` calls, coverage, sliced +W1, and final metrics all draw new Gaussian noise. Re-seeding an evaluation +generator makes the evaluation latent values repeat across checkpoints without +restricting the training distribution to a table. `sample()` returns `None` +for fresh-sample indices because those samples are not particle rows. + +The table variants can produce at most 20,000 distinct outputs through this +deterministic generator. The trainer records `unique_samples` among the 100,000 +final draws. That limit matters when extrapolating toy-benchmark results to +continuous or higher-dimensional generative modeling. + +## Reproduce a comparison + +```bash +python experiments/compare_priors.py \ + --study-dir runs/prior_comparison --run --device cuda:0 +``` + +The default comparison is nine runs: each of the three priors on seeds 23001, +23002, and 23003, with 7,000 steps, Rp logistic loss, Fourier-2 discriminator, +`b_cap` coefficient 1.0, generator LR 6e-4, discriminator LR multiplier 1.5, +Adam beta1=0, delayed cosine annealing, and EMA 0.995. These seeds are separate +from the historical study's standard seeds. They are specified in advance; +report all of them, including failed runs, instead of selecting the best seed. + +Omit `--run` to generate configs and a manifest for an external scheduler. The +manifest includes resolved config paths, source revision, whether the checkout +was dirty, Python and dependency versions, and the PyTorch CUDA build version. +Use a clean committed checkout for reported runs. Generate into a new directory +for each study; `--collect` collects completed runs from an existing manifest +without rewriting its provenance. A small `--total-steps` value is useful for +smoke tests, but is not evidence about convergence under the full recipe. + +Each run saves its exact final sample cloud, checkpoint, evaluation time series, +and summary. `comparison.json` gathers final metrics for all seed/prior pairs. +Use coverage together with high-quality fraction, transport distance, histogram +balance, radial core spread, and both covariance eigenvalue ratios. A radial +median alone can hide anisotropy or tails. Three seeds provide a modest +replication check, not a broad guarantee of stability. + +## RNG isolation and historical results + +Training now owns separate generators for real data, latent samples, and +penalty interpolation. Evaluation has its own re-seeded generator. Changing +evaluation frequency no longer changes the learned parameters; a bounded CPU +regression exercises the actual trainer for all three priors and compares every +saved model tensor and final sample array across evaluation intervals. + +This correction changes trajectories relative to historical code, which drew +training latents and diagnostic samples from the same global stream. New +summaries and comparison manifests identify the RNG scheme as +`separate_data_latent_penalty_v1`. Historical GIFs and results retain their +original meaning; they should not be relabeled as results of the corrected +matched comparison. In particular, the older no-particle GIF also used a +different architecture and stabilization recipe and does not isolate the prior. + +Checkpoints include `prior_kind`; reconstruct a prior with +`lib.particle_prior.make_prior(prior_kind, num_particles=..., z_dim=...)` before +loading its state to preserve fresh-noise versus finite-table semantics. diff --git a/docs/reproducing.md b/docs/reproducing.md index fb9819c..561cf0e 100644 --- a/docs/reproducing.md +++ b/docs/reproducing.md @@ -62,11 +62,13 @@ further ablations are needed to distinguish their individual effects. ```bash python experiments/compare_priors.py --help -python experiments/compare_priors.py +python experiments/compare_priors.py --study-dir runs/prior_comparison --run --device cuda:0 ``` -The generator emits a matched set of configurations and a protocol manifest; -use the printed run instructions for full training. Treat the old GIFs and +Omit `--run` to emit configurations and a protocol manifest for an external +scheduler. Use a new study directory for each generation. See +[the prior-control protocol](prior-controls.md) for outputs and collection. +Treat the old GIFs and regularizer tables as historical recipe results, not as this controlled three-way comparison. Use fresh seeds for confirmation after selecting a recipe, and retain individual seed scores rather than only an aggregate. diff --git a/examples/100gaussians.py b/examples/100gaussians.py index e7b1022..b85e7de 100644 --- a/examples/100gaussians.py +++ b/examples/100gaussians.py @@ -65,162 +65,16 @@ if str(_REPO_ROOT) not in sys.path: sys.path.insert(0, str(_REPO_ROOT)) -from lib.particle_prior import ParticlePrior # noqa: E402 - after the sys.path shim +from lib.particle_prior import ( # noqa: E402 + PRIOR_KINDS, ParticlePrior, canonical_prior_kind, make_prior, +) from lib.gan_loss import GANLoss # noqa: E402 from lib.grad_regularizers import GradRegularizer # noqa: E402 from lib.vicreg_loss import VICRegLikeLoss # noqa: E402 -# ========================= -# Simple MLP G / D -# ========================= - -class SimpleMLPGenerator(nn.Module): - """ - Very small MLP generator: z -> x in R^2. - - Strong enough for the toy problem but still minimal and CPU-friendly. - """ - - def __init__( - self, - z_dim: int = 4, - hidden_dim: int = 128, - n_hidden: int = 3, - out_dim: int = 2, - ) -> None: - super().__init__() - layers = [] - in_dim = z_dim - for _ in range(n_hidden): - layers.append(nn.Linear(in_dim, hidden_dim)) - layers.append(nn.LeakyReLU(0.2, inplace=True)) - in_dim = hidden_dim - layers.append(nn.Linear(in_dim, out_dim)) - self.net = nn.Sequential(*layers) - - def forward(self, z: torch.Tensor) -> torch.Tensor: - return self.net(z) - - -class SimpleMLPDiscriminator(nn.Module): - """ - Simple MLP discriminator: x in R^2 -> scalar score. - - fourier=K appends sin/cos features at frequencies pi * 2^i (i < K) per - input dimension. MLPs are spectrally biased toward low frequencies, so - without this D cannot resolve the sigma=0.03 mode structure until very - late in training and sample sharpness stalls. - """ - - def __init__( - self, - in_dim: int = 2, - hidden_dim: int = 128, - n_hidden: int = 3, - fourier: int = 2, - ) -> None: - super().__init__() - self.fourier = fourier - dim = in_dim + (2 * fourier * in_dim if fourier > 0 else 0) - if fourier > 0: - freqs = torch.pi * (2.0 ** torch.arange(fourier, dtype=torch.float32)) - self.register_buffer("freqs", freqs) - layers = [] - for _ in range(n_hidden): - layers.append(nn.Linear(dim, hidden_dim)) - layers.append(nn.LeakyReLU(0.2, inplace=True)) - dim = hidden_dim - layers.append(nn.Linear(dim, 1)) - self.net = nn.Sequential(*layers) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - h = x - if self.fourier > 0: - xf = x.unsqueeze(-1) * self.freqs # (B, in_dim, K) - h = torch.cat([h, torch.sin(xf).flatten(1), torch.cos(xf).flatten(1)], dim=1) - # Return shape (B,) for convenience. - return self.net(h).squeeze(-1) - - -# ========================= -# 100 Gaussians dataset -# ========================= - -def sample_100gaussians( - batch_size: int, - device: torch.device, - *, - generator: torch.Generator = None, - grid_scale: float = 1.0, - std: float = 0.03, -) -> torch.Tensor: - """ - Sample from a 100-Gaussian mixture: - - Centers on a 10x10 grid at coordinates: - { -4.5, -3.5, ..., 4.5 } * grid_scale - - Isotropic Gaussian noise with `std`. - - This is intentionally dense and low-variance to stress-test mode coverage. - """ - if batch_size <= 0: - raise ValueError(f"batch_size must be positive, got {batch_size}") - - if generator is None: - idx_x = torch.randint(0, 10, (batch_size,), device=device) - idx_y = torch.randint(0, 10, (batch_size,), device=device) - else: - idx_x = torch.randint(0, 10, (batch_size,), device=device, generator=generator) - idx_y = torch.randint(0, 10, (batch_size,), device=device, generator=generator) - - # Map indices 0..9 to coordinates -4.5..4.5 - centers_x = (idx_x - 4.5) * grid_scale - centers_y = (idx_y - 4.5) * grid_scale - - centers = torch.stack( - (centers_x, centers_y), - dim=1, - ).to(device=device, dtype=torch.float32) - - if generator is None: - noise = torch.randn(batch_size, 2, device=device) * std - else: - noise = torch.randn(batch_size, 2, device=device, generator=generator) * std - - return centers + noise - - -# ========================= -# Metrics -# ========================= - -@torch.no_grad() -def mode_coverage( - generator: nn.Module, - prior: ParticlePrior, - device: torch.device, - n_eval: int = 20000, - std: float = 0.03, - min_count: int = 10, -) -> Tuple[int, float]: - """ - Coverage metrics over the full particle cloud: - - modes: number of grid centers with >= min_count "high quality" samples - (within 3 sigma of the center), - - hq_frac: fraction of samples that are high quality. - """ - generator.eval() - idx = torch.randint(0, prior.num_particles, (n_eval,), device=device) - fake = generator(prior.z[idx]) - coords = torch.arange(10, device=device, dtype=torch.float32) - 4.5 - cx, cy = torch.meshgrid(coords, coords, indexing="ij") - centers = torch.stack([cx.flatten(), cy.flatten()], dim=1) - dists = torch.cdist(fake, centers) - mind, nearest = dists.min(dim=1) - hq = mind <= 3 * std - counts = torch.bincount(nearest[hq], minlength=100) - generator.train() - return int((counts >= min_count).sum().item()), hq.float().mean().item() - +from lib.toy_models import ( # noqa: E402 + SimpleMLPGenerator, SimpleMLPDiscriminator, sample_100gaussians, mode_coverage, +) # ========================= # Visualization @@ -302,6 +156,7 @@ def train( snapshot_interval: int = 500, seed: int = 1234, device_str: str = None, + prior_kind: str = "particles", ): # Device / seeds if device_str is not None: @@ -321,9 +176,14 @@ def train( viz_gen = torch.Generator() train_gen.manual_seed(seed) viz_gen.manual_seed(seed + 1) + latent_gen = torch.Generator(device=device).manual_seed(seed + 2) + penalty_gen = torch.Generator(device=device).manual_seed(seed + 3) + eval_gen = torch.Generator(device=device).manual_seed(seed + 999) # Models - prior = ParticlePrior(num_particles=num_particles, z_dim=z_dim).to(device) + prior_kind = canonical_prior_kind(prior_kind) + learnable_prior = prior_kind == "particles" + prior = make_prior(prior_kind, num_particles=num_particles, z_dim=z_dim).to(device) G = SimpleMLPGenerator(z_dim=z_dim).to(device) D = SimpleMLPDiscriminator(in_dim=2, fourier=fourier).to(device) @@ -349,10 +209,9 @@ def train( lr=lr, betas=(beta1, 0.999), ) - opt_prior = torch.optim.Adam( - prior.parameters(), - lr=lr * 10.0, # Particles need higher mobility - betas=(beta1, 0.999), + opt_prior = ( + torch.optim.Adam(prior.parameters(), lr=lr * 10.0, betas=(beta1, 0.999)) + if learnable_prior else None ) opt_D = torch.optim.Adam( D.parameters(), @@ -380,9 +239,10 @@ def train( ) total_steps = epochs * steps_per_epoch + all_opts = tuple(opt for opt in (opt_G, opt_D, opt_prior) if opt is not None) base_lrs = { id(opt): [g["lr"] for g in opt.param_groups] - for opt in (opt_G, opt_D, opt_prior) + for opt in all_opts } global_step = 0 @@ -397,7 +257,7 @@ def train( scale = lr_floor + (1.0 - lr_floor) * 0.5 * ( 1.0 + math.cos(math.pi * frac) ) - for opt in (opt_G, opt_D, opt_prior): + for opt in all_opts: for group, base in zip(opt.param_groups, base_lrs[id(opt)]): group["lr"] = base * scale @@ -413,7 +273,7 @@ def train( generator=train_gen, ) with torch.no_grad(): - z_fake, _ = prior.sample(batch_size) + z_fake, _ = prior.sample(batch_size, generator=latent_gen) x_fake = G(z_fake) real_logits = D(x_real) @@ -425,7 +285,9 @@ def train( # Fourier D coexist with full mode coverage: it caps D's # steepness where the data is. The regularizer recomputes its own # graph internally, so neither batch needs requires_grad here. - pen, _ = regularizer.penalty(D, x_real, x_fake, global_step) + pen, _ = regularizer.penalty( + D, x_real, x_fake, global_step, generator=penalty_gen, + ) loss_d = loss_d + pen opt_D.zero_grad() @@ -438,7 +300,7 @@ def train( D.eval() G.train() - z_fake, idx = prior.sample(batch_size) + z_fake, idx = prior.sample(batch_size, generator=latent_gen) x_fake = G(z_fake) fake_logits = D(x_fake) @@ -454,20 +316,20 @@ def train( else: loss_gan = gan_loss.g_loss(fake_logits) - with torch.no_grad(): + ep_z = loss_gan.new_zeros(()) + if learnable_prior: unique_idx = torch.unique(idx) - - # VICReg-like regularization on the current batch - ep_z = vic_reg(prior.z[unique_idx]) - + ep_z = vic_reg(prior.z[unique_idx]) loss_g = loss_gan + lambda_ep * ep_z opt_G.zero_grad() - opt_prior.zero_grad() + if opt_prior is not None: + opt_prior.zero_grad() loss_g.backward() opt_G.step() - opt_prior.step() + if opt_prior is not None: + opt_prior.step() # EMA update with torch.no_grad(): @@ -480,7 +342,10 @@ def train( # Logging / snapshots # ------------------------- if global_step % log_interval == 0: - modes, hq_frac = mode_coverage(ema_G, ema_prior, device) + eval_gen.manual_seed(seed + 999) + modes, hq_frac = mode_coverage( + ema_G, ema_prior, device, sample_generator=eval_gen, + ) print( f"[epoch {epoch:04d} step {global_step:06d}] " f"D: {loss_d.item():.4f} " @@ -513,10 +378,11 @@ def train( return prior, G, D -def main() -> None: +def main(default_prior="particles", default_out_dir="100gaussians_samples") -> None: parser = argparse.ArgumentParser( - description="100 Gaussians toy problem with ParticlePrior + EP regularizer.", + description="100 Gaussians: matched learned-table and Gaussian prior controls.", ) + parser.add_argument("--prior", choices=PRIOR_KINDS, default=default_prior) parser.add_argument("--epochs", type=int, default=7) parser.add_argument("--steps_per_epoch", type=int, default=1000) parser.add_argument("--batch_size", type=int, default=256) @@ -573,7 +439,7 @@ def main() -> None: default="rp", choices=["vanilla", "rp", "ra"], ) - parser.add_argument("--out_dir", type=str, default="100gaussians_samples") + parser.add_argument("--out_dir", type=str, default=default_out_dir) parser.add_argument("--log_interval", type=int, default=100) parser.add_argument("--snapshot_interval", type=int, default=500) parser.add_argument("--seed", type=int, default=1234) @@ -618,6 +484,7 @@ def main() -> None: snapshot_interval=args.snapshot_interval, seed=args.seed, device_str=args.device, + prior_kind=args.prior, ) diff --git a/examples/100gaussians_no_particle_prior.py b/examples/100gaussians_no_particle_prior.py index c7d98db..df9782e 100644 --- a/examples/100gaussians_no_particle_prior.py +++ b/examples/100gaussians_no_particle_prior.py @@ -1,437 +1,27 @@ #!/usr/bin/env python -""" -100 gaussians with no particle prior. Unstable. +"""Fresh-Gaussian control using exactly the particle example's training recipe. + +All options are shared with 100gaussians.py. Only the default prior and output +folder differ. For the finite frozen-table control, pass --prior frozen_gaussian. """ -import argparse -import sys +from functools import partial +from importlib import import_module from pathlib import Path -from typing import Tuple - -import torch -import torch.nn as nn -import matplotlib.pyplot as plt +import sys -# Allow `python examples/100gaussians_no_particle_prior.py` from anywhere. _REPO_ROOT = Path(__file__).resolve().parents[1] if str(_REPO_ROOT) not in sys.path: sys.path.insert(0, str(_REPO_ROOT)) -from lib.particle_prior import ParticlePrior # noqa: E402 - after the sys.path shim -from lib.gan_loss import GANLoss # noqa: E402 -from lib.vicreg_loss import VICRegLikeLoss # noqa: E402 - - -# ========================= -# Simple MLP G / D -# ========================= - -class SimpleMLPGenerator(nn.Module): - """ - Very small MLP generator: z -> x in R^2. - - Strong enough for the toy problem but still minimal and CPU-friendly. - """ - - def __init__( - self, - z_dim: int = 2, - hidden_dim: int = 128, - n_hidden: int = 3, - out_dim: int = 2, - ) -> None: - super().__init__() - layers = [] - in_dim = z_dim - for _ in range(n_hidden): - layers.append(nn.Linear(in_dim, hidden_dim)) - layers.append(nn.LeakyReLU(0.2, inplace=True)) - in_dim = hidden_dim - layers.append(nn.Linear(in_dim, out_dim)) - self.net = nn.Sequential(*layers) - - def forward(self, z: torch.Tensor) -> torch.Tensor: - return self.net(z) - - -class SimpleMLPDiscriminator(nn.Module): - """ - Simple MLP discriminator: x in R^2 -> scalar score. - """ - - def __init__( - self, - in_dim: int = 2, - hidden_dim: int = 128, - n_hidden: int = 3, - ) -> None: - super().__init__() - layers = [] - dim = in_dim - for _ in range(n_hidden): - layers.append(nn.Linear(dim, hidden_dim)) - layers.append(nn.LeakyReLU(0.2, inplace=True)) - dim = hidden_dim - layers.append(nn.Linear(dim, 1)) - self.net = nn.Sequential(*layers) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - # Return shape (B,) for convenience. - return self.net(x).squeeze(-1) - - -# ========================= -# 100 Gaussians dataset -# ========================= - -def sample_100gaussians( - batch_size: int, - device: torch.device, - *, - generator: torch.Generator = None, - grid_scale: float = 1.0, - std: float = 0.03, -) -> torch.Tensor: - """ - Sample from a 100-Gaussian mixture: - - Centers on a 10x10 grid at coordinates: - { -4.5, -3.5, ..., 4.5 } * grid_scale - - Isotropic Gaussian noise with `std`. - - This is intentionally dense and low-variance to stress-test mode coverage. - """ - if batch_size <= 0: - raise ValueError(f"batch_size must be positive, got {batch_size}") - - if generator is None: - idx_x = torch.randint(0, 10, (batch_size,), device=device) - idx_y = torch.randint(0, 10, (batch_size,), device=device) - else: - idx_x = torch.randint(0, 10, (batch_size,), device=device, generator=generator) - idx_y = torch.randint(0, 10, (batch_size,), device=device, generator=generator) - - # Map indices 0..9 to coordinates -4.5..4.5 - centers_x = (idx_x - 4.5) * grid_scale - centers_y = (idx_y - 4.5) * grid_scale - - centers = torch.stack( - (centers_x, centers_y), - dim=1, - ).to(device=device, dtype=torch.float32) - - if generator is None: - noise = torch.randn(batch_size, 2, device=device) * std - else: - noise = torch.randn(batch_size, 2, device=device, generator=generator) * std - - return centers + noise - - -# ========================= -# Visualization -# ========================= - -def save_fake_scatter( - generator: nn.Module, - prior: ParticlePrior, - device: torch.device, - filename: str, - real_samples: torch.Tensor, - n_fake: int = 4096, - xlim: Tuple[float, float] = (-6.0, 6.0), - ylim: Tuple[float, float] = (-6.0, 6.0), -) -> None: - """ - Save a scatter plot comparing: - - fixed real samples from the 100-Gaussian mixture, - - fake samples from a fixed subset of particles (fixed_first_n=True). - - This keeps the visual trajectory consistent across training, which is - ideal for making a video. - """ - generator.eval() - prior.eval() - - with torch.no_grad(): - z_fake, _ = prior.sample(n_fake, fixed_first_n=True) - z_fake = z_fake.to(device) - z_fake = torch.randn_like(z_fake) - fake = generator(z_fake).cpu() - - real = real_samples.cpu() - - fig, ax = plt.subplots(figsize=(5, 5)) - ax.scatter(real[:, 0], real[:, 1], s=4, alpha=0.2, label="real") - ax.scatter(fake[:, 0], fake[:, 1], s=4, alpha=0.8, label="fake") - ax.set_xlim(*xlim) - ax.set_ylim(*ylim) - ax.set_aspect("equal", "box") - ax.legend(loc="upper right") - ax.set_xlabel("x") - ax.set_ylabel("y") - ax.set_title("100 Gaussians: real vs. model samples") - fig.tight_layout() - - out_path = Path(filename) - out_path.parent.mkdir(parents=True, exist_ok=True) - fig.savefig(out_path, dpi=150) - plt.close(fig) - - generator.train() - prior.train() - - -# ========================= -# Training -# ========================= - -def train( - epochs: int = 100, - steps_per_epoch: int = 1000, - batch_size: int = 256, - z_dim: int = 2, - num_particles: int = 20_000, - lr: float = 1e-3, - beta1: float = 0.5, - lambda_ep: float = 1.0, - loss_type: str = "hinge", - out_dir: str = "100gaussians_samples", - log_interval: int = 100, - snapshot_interval: int = 500, - seed: int = 1234, - device_str: str = None, -): - # Device / seeds - if device_str is not None: - device = torch.device(device_str) - else: - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - - torch.manual_seed(seed) - if device.type == "cuda": - torch.cuda.manual_seed_all(seed) - - if device.type == "cuda": - train_gen = torch.Generator(device=device) - viz_gen = torch.Generator(device=device) - else: - train_gen = torch.Generator() - viz_gen = torch.Generator() - train_gen.manual_seed(seed) - viz_gen.manual_seed(seed + 1) - - # Models - prior = ParticlePrior(num_particles=num_particles, z_dim=z_dim).to(device) - G = SimpleMLPGenerator(z_dim=z_dim).to(device) - D = SimpleMLPDiscriminator(in_dim=2).to(device) - - # Light xavier init for all linear layers. - for m in list(G.modules()) + list(D.modules()): - if isinstance(m, nn.Linear): - nn.init.xavier_uniform_(m.weight) - if m.bias is not None: - nn.init.zeros_(m.bias) - - vic_reg = VICRegLikeLoss() - gan_loss = GANLoss(loss_type=loss_type) - - opt_G = torch.optim.Adam( - G.parameters(), - lr=lr, - betas=(beta1, 0.999), - ) - opt_prior = torch.optim.Adam( - prior.parameters(), - lr=lr * 10.0, # Particles need higher mobility - betas=(beta1, 0.999), - ) - opt_D = torch.optim.Adam( - D.parameters(), - lr=lr, - betas=(beta1, 0.999), - ) - - out_path = Path(out_dir) - out_path.mkdir(parents=True, exist_ok=True) - - # Fixed real samples for visualization (same throughout training). - real_viz = sample_100gaussians( - batch_size=8192, - device=device, - generator=viz_gen, - ) - - # Initial snapshot (untrained model). - save_fake_scatter( - G, - prior, - device, - str(out_path / f"samples_step_{0:06d}.png"), - real_samples=real_viz, - ) - - warmup_steps = 5000 - global_step = 0 - step = 0 - for epoch in range(epochs): - for _ in range(steps_per_epoch): - step += 1 - # ------------------------- - # 1) Discriminator step - # ------------------------- - D.train() - G.eval() - - x_real = sample_100gaussians( - batch_size=batch_size, - device=device, - generator=train_gen, - ) - with torch.no_grad(): - z_fake, _ = prior.sample(batch_size) - z_fake = z_fake.to(device) - z_fake = torch.randn_like(z_fake) - x_fake = G(z_fake) - - real_logits = D(x_real) - fake_logits = D(x_fake) - - loss_d = gan_loss.d_loss(real_logits, fake_logits) - - opt_D.zero_grad() - loss_d.backward() - opt_D.step() - - # ------------------------- - # 2) Generator + prior step - # ------------------------- - D.eval() - G.train() - - z_fake, idx = prior.sample(batch_size) - z_fake = z_fake.to(device) - z_fake = torch.randn_like(z_fake) - x_fake = G(z_fake) - fake_logits = D(x_fake) - - loss_gan = gan_loss.g_loss(fake_logits) - - with torch.no_grad(): - unique_idx = torch.unique(idx) - - # VICReg-like regularization on the current batch - ep_z = vic_reg(prior.z[unique_idx]) - - if step < warmup_steps: - # Linear ramp from 0.0 to lambda_ep - current_lambda_ep = lambda_ep * (step / warmup_steps) - else: - current_lambda_ep = lambda_ep - - loss_g = loss_gan + current_lambda_ep * ep_z - - opt_G.zero_grad() - opt_prior.zero_grad() - loss_g.backward() - - opt_G.step() - opt_prior.step() - - # ------------------------- - # Logging / snapshots - # ------------------------- - if global_step % log_interval == 0: - print( - f"[epoch {epoch:04d} step {global_step:06d}] " - f"D: {loss_d.item():.4f} " - f"G_gan: {loss_gan.item():.4f} " - f"EP(z): {ep_z.item():.4f} " - f"G_total: {loss_g.item():.4f}" - ) - - if global_step % snapshot_interval == 0 and global_step > 0: - save_fake_scatter( - G, - prior, - device, - str(out_path / f"samples_step_{global_step:06d}.png"), - real_samples=real_viz, - ) - - global_step += 1 - - # End-of-epoch snapshot + checkpoint - save_fake_scatter( - G, - prior, - device, - str(out_path / f"samples_epoch_{epoch:04d}.png"), - real_samples=real_viz, - ) - - torch.save( - { - "epoch": epoch, - "step": global_step, - "prior_state_dict": prior.state_dict(), - "G_state_dict": G.state_dict(), - "D_state_dict": D.state_dict(), - "opt_G_state_dict": opt_G.state_dict(), - "opt_prior_state_dict": opt_prior.state_dict(), - "opt_D_state_dict": opt_D.state_dict(), - }, - out_path / "model_latest.pt", - ) - - return prior, G, D - +_benchmark = import_module("examples.100gaussians") +train = partial(_benchmark.train, prior_kind="fresh_gaussian", + out_dir="100gaussians_no_particles_samples") -def main() -> None: - parser = argparse.ArgumentParser( - description="100 Gaussians toy problem with ParticlePrior + EP regularizer.", - ) - parser.add_argument("--epochs", type=int, default=100) - parser.add_argument("--steps_per_epoch", type=int, default=1000) - parser.add_argument("--batch_size", type=int, default=256) - parser.add_argument("--z_dim", type=int, default=2) - parser.add_argument("--num_particles", type=int, default=20_000) - parser.add_argument("--lr", type=float, default=1e-4) - parser.add_argument("--beta1", type=float, default=0.5) - parser.add_argument("--lambda_ep", type=float, default=1.0) - parser.add_argument( - "--loss_type", - type=str, - default="hinge", - choices=["hinge", "wasserstein", "logistic"], - ) - parser.add_argument("--out_dir", type=str, default="100gaussians_samples") - parser.add_argument("--log_interval", type=int, default=100) - parser.add_argument("--snapshot_interval", type=int, default=500) - parser.add_argument("--seed", type=int, default=1234) - parser.add_argument( - "--device", - type=str, - default=None, - help="Optional device string, e.g. 'cpu' or 'cuda:0'. Defaults to CUDA if available.", - ) - args = parser.parse_args() - train( - epochs=args.epochs, - steps_per_epoch=args.steps_per_epoch, - batch_size=args.batch_size, - z_dim=args.z_dim, - num_particles=args.num_particles, - lr=args.lr, - beta1=args.beta1, - lambda_ep=args.lambda_ep, - loss_type=args.loss_type, - out_dir=args.out_dir, - log_interval=args.log_interval, - snapshot_interval=args.snapshot_interval, - seed=args.seed, - device_str=args.device, - ) +def main(): + _benchmark.main(default_prior="fresh_gaussian", + default_out_dir="100gaussians_no_particles_samples") if __name__ == "__main__": diff --git a/experiments/compare_priors.py b/experiments/compare_priors.py new file mode 100644 index 0000000..273f86e --- /dev/null +++ b/experiments/compare_priors.py @@ -0,0 +1,120 @@ +#!/usr/bin/env python +"""Generate or run a matched three-way prior comparison on unused study seeds. + +python experiments/compare_priors.py --study-dir runs/prior_comparison --run --device cuda:0 + +Omit --run to generate only. Generation writes one YAML config per run plus a manifest. --run runs +those configs sequentially and writes comparison.json. It does not reuse or +overwrite completed runs; choose a new study directory for a fresh execution. +""" + +import argparse +import hashlib +from importlib.metadata import version +import json +from pathlib import Path +import subprocess +import sys + +import yaml +import torch + +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +from experiments.train_arm import DEFAULTS # noqa: E402 + +PRIORS = ("particles", "frozen_gaussian", "fresh_gaussian") +SEEDS = (23001, 23002, 23003) + + +def generate(study_dir: Path, seeds=SEEDS, total_steps=7000): + """Only prior, output path and the explicitly paired seed vary across runs.""" + if len(set(seeds)) != len(seeds) or not seeds: + raise ValueError("Provide distinct seeds and at least one seed") + if total_steps <= 0: + raise ValueError("total_steps must be positive") + study_dir = study_dir.resolve() + if (study_dir / "manifest.json").exists(): + raise ValueError("Study manifest already exists; choose a new study directory") + (study_dir / "configs").mkdir(parents=True, exist_ok=True) + recipe = dict(DEFAULTS, arm="b_cap", coeff=1.0, lr=6e-4, + total_steps=total_steps, spectral=False) + entries = [] + for seed in seeds: + for prior in PRIORS: + name = f"{prior}_seed{seed}" + out_dir = study_dir / "runs" / name + config_path = study_dir / "configs" / f"{name}.yaml" + cfg = dict(recipe, prior=prior, seed=int(seed), out_dir=str(out_dir)) + config_path.write_text(yaml.safe_dump(cfg, sort_keys=True)) + entries.append(dict(prior=prior, seed=int(seed), config=str(config_path), + config_sha256=hashlib.sha256(config_path.read_bytes()).hexdigest(), + out_dir=str(out_dir))) + revision = subprocess.run(["git", "rev-parse", "HEAD"], cwd=ROOT, + check=True, text=True, capture_output=True).stdout.strip() + dirty = bool(subprocess.run(["git", "status", "--porcelain"], cwd=ROOT, + check=True, text=True, capture_output=True).stdout.strip()) + runtime = dict(python=sys.version, torch_cuda=torch.version.cuda, + packages={name: version(name) for name in + ("torch", "numpy", "matplotlib", "PyYAML", "POT")}) + manifest = dict(schema_version=1, source_revision=revision, source_dirty=dirty, + runtime=runtime, + rng_scheme="separate_data_latent_penalty_v1", seeds=list(seeds), + priors=list(PRIORS), runs=entries) + (study_dir / "manifest.json").write_text(json.dumps(manifest, indent=2) + "\n") + return manifest + + +def collect(manifest): + rows = [] + for run in manifest["runs"]: + config_bytes = Path(run["config"]).read_bytes() + if hashlib.sha256(config_bytes).hexdigest() != run["config_sha256"]: + raise ValueError(f"Config changed since generation: {run['config']}") + expected = yaml.safe_load(config_bytes) + summary = json.loads((Path(run["out_dir"]) / "summary.json").read_text()) + if (summary["config"] != expected or expected["prior"] != run["prior"] + or expected["seed"] != run["seed"] or expected["out_dir"] != run["out_dir"]): + raise ValueError(f"Summary/config mismatch for {run['out_dir']}") + rows.append(dict(prior=run["prior"], seed=run["seed"], + final=summary["final"], collapse_events=summary["collapse_events"])) + return rows + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--study-dir", type=Path, default=Path("runs/prior_comparison")) + parser.add_argument("--seeds", nargs="+", type=int, default=list(SEEDS)) + parser.add_argument("--total-steps", type=int, default=7000) + parser.add_argument("--device", default=None) + parser.add_argument("--run", action="store_true") + parser.add_argument("--collect", action="store_true", help="Collect an existing manifest's results") + args = parser.parse_args() + if args.collect: + manifest = json.loads((args.study_dir / "manifest.json").read_text()) + else: + # A manifest defines the original run settings and source revision. + # Never replace it with different seeds or settings after execution. + if (args.study_dir / "manifest.json").exists(): + parser.error("Study manifest already exists; use a new --study-dir or --collect") + manifest = generate(args.study_dir, args.seeds, args.total_steps) + print(f"Generated {len(manifest['runs'])} configs in {args.study_dir}") + if args.run: + for run in manifest["runs"]: + if (Path(run["out_dir"]) / "summary.json").exists(): + parser.error(f"Run already completed: {run['out_dir']}") + command = [sys.executable, str(ROOT / "experiments/train_arm.py"), + "--config", run["config"]] + if args.device: + command += ["--device", args.device] + subprocess.run(command, cwd=ROOT, check=True) + if args.run or args.collect: + path = args.study_dir / "comparison.json" + path.write_text(json.dumps(collect(manifest), indent=2) + "\n") + print(f"Wrote {path}") + + +if __name__ == "__main__": + main() diff --git a/experiments/train_arm.py b/experiments/train_arm.py index 0b064c0..33cd232 100644 --- a/experiments/train_arm.py +++ b/experiments/train_arm.py @@ -9,9 +9,10 @@ pairing, logistic), Fourier-2 discriminator, ParticlePrior + VICReg, Adam with beta1=0, prior LR x10, D LR x1.5, delayed cosine anneal, EMA(0.995) on G *and* the prior — with exactly one thing swapped out: the inline R1+R2 block becomes a -`lib.grad_regularizers.GradRegularizer`, selected by config. Everything else is -byte-identical so that any difference between runs is attributable to the -penalty. +`lib.grad_regularizers.GradRegularizer`, selected by config. The recipe is held +fixed within a comparison. Training now uses independent +data, latent, penalty, and evaluation RNG streams; historical runs using the +shared global RNG are not expected to reproduce bit for bit. On top of the example it adds the instrumentation an arm comparison needs: @@ -62,7 +63,7 @@ if str(_REPO_ROOT) not in sys.path: sys.path.insert(0, str(_REPO_ROOT)) -from lib.particle_prior import ParticlePrior +from lib.particle_prior import ParticlePrior, canonical_prior_kind, make_prior from lib.gan_loss import GANLoss from lib.vicreg_loss import VICRegLikeLoss from lib.grad_regularizers import GradRegularizer, grad_norm_stats @@ -81,6 +82,7 @@ mode_recall_and_hists, per_mode_moments, per_mode_core_ratio, + per_mode_covariance_ratios, ) @@ -101,8 +103,8 @@ "lr_mult": 1.0, "spectral": True, "out_dir": "runs/arm", - # Follow-up knobs. Every default reproduces the original recipe exactly, so - # a config written before they existed trains an identical trajectory. + # Follow-up knobs retain the historical hyperparameter defaults. RNG + # isolation is versioned in the summary and changes historical trajectories. "loss_type": "logistic", # any lib.gan_loss kernel, e.g. 'wasserstein' "optimizer": "adam", # 'adam' or 'oadam' (lib.oadam.OptimisticAdam) "norm": "l2", # gradient norm the penalty sees: l2 / l1 / linf @@ -113,11 +115,9 @@ "curriculum_arm2": "none", # second arm, or 'none' for no curriculum "curriculum_coeff2": 0.0, # its coefficient "curriculum_switch_frac": 0.6, # switch at this fraction of total_steps - # The repo's control condition: 'particles' is the learnable cloud every - # existing config trains; 'gaussian' freezes it into a fixed N(0, I) draw, - # which drops the prior optimizer and the VICReg term and leaves G alone to - # warp a rigid prior onto the data (examples/100gaussians_no_particle_prior.py). - "prior": "particles", # 'particles' or 'gaussian' + # 'gaussian' remains a compatibility alias for the frozen finite table. + # 'fresh_gaussian' draws new N(0, I) noise for training and evaluation. + "prior": "particles", # particles / frozen_gaussian / fresh_gaussian } # Held fixed across every arm: the winning recipe from docs/convergence-tips.md. @@ -282,12 +282,10 @@ def train( pre-hook script. It is called as `frame_callback(step, ema_G, ema_prior)` at step 0, at every - step that is a multiple of `frame_interval`, and at the final step. Because - plotting code (in particular `lib.toy_models.mode_coverage`) draws on the - *default* RNG -- the same stream the training loop's `prior.sample()` draws - from -- the global CPU and CUDA RNG states are snapshotted before the call - and restored after it. A callback therefore cannot shift the trajectory no - matter how much randomness it consumes. + step that is a multiple of `frame_interval`, and at the final step. Global + CPU and CUDA RNG states are preserved around the callback. Training owns + separate generators for data, latent samples and penalty interpolation; + diagnostics and frame rendering therefore cannot shift those streams. """ seed = int(cfg["seed"]) total_steps = int(cfg["total_steps"]) @@ -302,8 +300,13 @@ def train( train_gen = torch.Generator(device=device) if gen_device else torch.Generator() viz_gen = torch.Generator(device=device) if gen_device else torch.Generator() eval_gen = torch.Generator(device=device) if gen_device else torch.Generator() + eval_data_gen = torch.Generator(device=device) if gen_device else torch.Generator() + latent_gen = torch.Generator(device=device) if gen_device else torch.Generator() + penalty_gen = torch.Generator(device=device) if gen_device else torch.Generator() train_gen.manual_seed(seed) viz_gen.manual_seed(seed + 1) + latent_gen.manual_seed(seed + 2) + penalty_gen.manual_seed(seed + 3) out_path = Path(cfg["out_dir"]) out_path.mkdir(parents=True, exist_ok=True) @@ -313,12 +316,10 @@ def train( # ------------------------- # Models / optimizers # ------------------------- - prior_kind = str(cfg["prior"]).lower() - if prior_kind not in ("particles", "gaussian"): - raise ValueError(f"Unknown prior: {cfg['prior']} (expected 'particles' or 'gaussian')") + prior_kind = canonical_prior_kind(cfg["prior"]) learnable_prior = prior_kind == "particles" - prior = ParticlePrior( - num_particles=NUM_PARTICLES, z_dim=Z_DIM, learnable=learnable_prior + prior = make_prior( + prior_kind, num_particles=NUM_PARTICLES, z_dim=Z_DIM, ).to(device) G = SimpleMLPGenerator(z_dim=Z_DIM).to(device) D = SimpleMLPDiscriminator(in_dim=2, fourier=FOURIER).to(device) @@ -433,32 +434,31 @@ def active_regularizer(step: int) -> GradRegularizer: def run_eval(step: int, d_val: float, g_val: float, pen_val: float) -> Dict: """One eval row: sliced W1 + coverage on the EMA model, grad norms on live D.""" - # Re-seeded every time so the same particles and the same reals are used + # Re-seeded every time so the same latent samples and reals are used # at every eval: sampling noise would otherwise swamp the oscillation # signal we are trying to measure. eval_gen.manual_seed(seed + 999) + eval_data_gen.manual_seed(seed + 1999) ema_G.eval() with torch.no_grad(): - idx = torch.randint( - 0, ema_prior.num_particles, (EVAL_N,), device=device, generator=eval_gen - ) - fake = ema_G(ema_prior.z[idx]) + z_eval, _ = ema_prior.sample(EVAL_N, generator=eval_gen) + fake = ema_G(z_eval) real = sample_100gaussians( - batch_size=EVAL_N, device=device, generator=eval_gen + batch_size=EVAL_N, device=device, generator=eval_data_gen ) ema_G.train() w1 = sliced_w1(fake, real, n_proj=128, seed=seed + 7) - modes, hq = mode_coverage(ema_G, ema_prior, device) + modes, hq = mode_coverage(ema_G, ema_prior, device, sample_generator=eval_gen) with torch.no_grad(): real_small = sample_100gaussians( - batch_size=GRAD_STATS_N, device=device, generator=eval_gen + batch_size=GRAD_STATS_N, device=device, generator=eval_data_gen ) z_small, _ = prior.sample(GRAD_STATS_N, generator=eval_gen) fake_small = G(z_small) - gstats = grad_norm_stats(D, real_small, fake_small.detach()) + gstats = grad_norm_stats(D, real_small, fake_small.detach(), generator=eval_data_gen) events = update_collapse(collapse_state, modes) @@ -533,7 +533,7 @@ def emit_frame(step: int) -> None: batch_size=BATCH_SIZE, device=device, generator=train_gen ) with torch.no_grad(): - z_fake, _ = prior.sample(BATCH_SIZE) + z_fake, _ = prior.sample(BATCH_SIZE, generator=latent_gen) x_fake = G(z_fake) # The regularizer recomputes its own graph internally, so neither batch @@ -543,7 +543,7 @@ def emit_frame(step: int) -> None: # the switch the series is two different penalties end to end, not one # comparable quantity. pen, pstats = active_regularizer(global_step).penalty( - D, x_real, x_fake, global_step + D, x_real, x_fake, global_step, generator=penalty_gen, ) loss_d = loss_d_gan + pen @@ -559,7 +559,7 @@ def emit_frame(step: int) -> None: D.eval() G.train() - z_fake, idx = prior.sample(BATCH_SIZE) + z_fake, idx = prior.sample(BATCH_SIZE, generator=latent_gen) fake_logits = D(G(z_fake)) with torch.no_grad(): @@ -574,8 +574,7 @@ def emit_frame(step: int) -> None: ep_z = vic_reg(prior.z[unique_idx]) loss_g = loss_gan + LAMBDA_EP * ep_z else: - # Nothing to spread out: a frozen cloud is already an exact N(0, I) - # draw, and VICReg on it would be a constant with no gradient. + # Gaussian controls have no learned prior parameters to regularize. loss_g = loss_gan opt_G.zero_grad() @@ -617,14 +616,13 @@ def emit_frame(step: int) -> None: # Final metrics # ------------------------- eval_gen.manual_seed(seed + 4242) + eval_data_gen.manual_seed(seed + 5242) ema_G.eval() with torch.no_grad(): - idx = torch.randint( - 0, ema_prior.num_particles, (FINAL_N,), device=device, generator=eval_gen - ) - fake_final = ema_G(ema_prior.z[idx]) + z_final, _ = ema_prior.sample(FINAL_N, generator=eval_gen) + fake_final = ema_G(z_final) real_final = sample_100gaussians( - batch_size=FINAL_N, device=device, generator=eval_gen + batch_size=FINAL_N, device=device, generator=eval_data_gen ) ema_G.train() @@ -632,7 +630,9 @@ def emit_frame(step: int) -> None: balance = mode_recall_and_hists(fake_final) nll = mixture_nll(fake_final) w1_exact, w2_exact = exact_w1_w2(fake_final, real_final, max_points=4096, seed=seed) - modes_final, hq_final = mode_coverage(ema_G, ema_prior, device) + modes_final, hq_final = mode_coverage( + ema_G, ema_prior, device, sample_generator=eval_gen, + ) # Per-mode shape audit, on the *same* 100k EMA samples the metrics above # used: coverage says the modes were found, this says whether each blob has @@ -652,7 +652,13 @@ def emit_frame(step: int) -> None: # Spectral analysis # ------------------------- spectral: Dict = {} - if bool(cfg["spectral"]): + if bool(cfg["spectral"]) and not learnable_prior: + spectral = { + "status": "unsupported", + "reason": "The local-game probe differentiates the learned particle table; " + "Gaussian controls require a D/G-only diagnostic.", + } + elif bool(cfg["spectral"]): try: spec_gen = torch.Generator(device=device) if gen_device else torch.Generator() spec_gen.manual_seed(seed + 31337) @@ -708,6 +714,7 @@ def emit_frame(step: int) -> None: ) torch.save( { + "prior_kind": prior_kind, "G": G.state_dict(), "D": D.state_dict(), "prior": prior.state_dict(), @@ -719,12 +726,18 @@ def emit_frame(step: int) -> None: summary = { "config": dict(cfg), + "sampling": { + "prior": prior_kind, + "reference_particles": NUM_PARTICLES, + "rng_scheme": "separate_data_latent_penalty_v1", + }, "final": { "w1_exact": float(w1_exact), "w2_exact": float(w2_exact), "w1_sliced": float(w1_sliced), "modes": int(modes_final), "hq": float(hq_final), + "unique_samples": int(torch.unique(fake_final, dim=0).shape[0]), "mode_recall": float(balance["mode_recall"]), "hist_kl": float(balance["hist_kl"]), "hist_js": float(balance["hist_js"]), @@ -737,6 +750,9 @@ def emit_frame(step: int) -> None: # that separates the two (results/metric_recon.md; it is the core # ratio the leaderboard's variance-compression guard reads). **per_mode_core_ratio(fake_final, min_count=AUDIT_MIN_COUNT, std=DATA_STD), + **per_mode_covariance_ratios( + fake_final, min_count=AUDIT_MIN_COUNT, data_std=DATA_STD, + ), }, "mid": { "step": int(mid_row["step"]), diff --git a/lib/grad_regularizers.py b/lib/grad_regularizers.py index 27cf77a..25ae2b0 100644 --- a/lib/grad_regularizers.py +++ b/lib/grad_regularizers.py @@ -152,6 +152,7 @@ def penalty( x_real: torch.Tensor, x_fake: torch.Tensor, step: int, + generator: torch.Generator = None, ) -> Tuple[torch.Tensor, Dict]: """ Compute the penalty term to add to the discriminator loss. @@ -176,7 +177,7 @@ def penalty( center = self.center(step) if self.arm in ("e_interp", "g_interp_cap"): - pen = coeff_eff * self._interp_term(D, x_real, x_fake, center) + pen = coeff_eff * self._interp_term(D, x_real, x_fake, center, generator) else: n_r = self._grad_norm(D, x_real, squared=(self.arm == "a_r1r2")) n_f = self._grad_norm(D, x_fake, squared=(self.arm == "a_r1r2")) @@ -234,10 +235,12 @@ def _interp_term( x_real: torch.Tensor, x_fake: torch.Tensor, center: float, + generator: torch.Generator = None, ) -> torch.Tensor: """E[(||grad D(x_i)|| - c)^2] on per-sample real/fake interpolates.""" eps = torch.rand( - x_real.shape[0], 1, device=x_real.device, dtype=x_real.dtype + (x_real.shape[0],) + (1,) * (x_real.ndim - 1), + device=x_real.device, dtype=x_real.dtype, generator=generator, ) x_i = eps * x_real.detach() + (1.0 - eps) * x_fake.detach() n_i = self._grad_norm(D, x_i, squared=False) @@ -279,6 +282,7 @@ def grad_norm_stats( D: torch.nn.Module, x_real: torch.Tensor, x_fake: torch.Tensor, + generator: torch.Generator = None, ) -> Dict[str, float]: """ Measurement-only summary of D's gradient-norm field (no create_graph, so @@ -293,7 +297,8 @@ def grad_norm_stats( """ with torch.enable_grad(): eps = torch.rand( - x_real.shape[0], 1, device=x_real.device, dtype=x_real.dtype + (x_real.shape[0],) + (1,) * (x_real.ndim - 1), + device=x_real.device, dtype=x_real.dtype, generator=generator, ) x_interp = eps * x_real.detach() + (1.0 - eps) * x_fake.detach() diff --git a/lib/particle_prior.py b/lib/particle_prior.py index 94f8f1b..2ad143f 100644 --- a/lib/particle_prior.py +++ b/lib/particle_prior.py @@ -14,7 +14,7 @@ class ParticlePrior(nn.Module): - """ + r""" Learnable latent particle cloud. This module holds a parameter matrix z \in R^{M x D} where each row is a @@ -35,7 +35,8 @@ class ParticlePrior(nn.Module): prior, ... = accelerator.prepare(prior, ...) # later in the training loop - z, idx = accelerator.unwrap_model(prior).sample(batch_size) + idx = accelerator.unwrap_model(prior).sample_indices(batch_size) + z = prior(idx) # Keep the DDP forward path so gradients synchronize. """ def __init__( @@ -170,3 +171,46 @@ def sample( idx = self.sample_indices(batch_size, generator=generator) z_batch = self.z[idx] return z_batch, idx + + +class FreshGaussianPrior(ParticlePrior): + """Fresh Gaussian noise, with a fixed reference batch for snapshots only. + + Ordinary ``sample`` calls draw from N(0, init_std**2), independently of the + reference buffer ``z``. ``fixed_first_n=True`` instead selects that buffer + for reproducible plots. The buffer also keeps initialization RNG consumption + identical across prior controls. This prior has no trainable parameters; + ordinary samples return ``None`` for indices because they are not table rows. + """ + + def __init__(self, num_particles=100_000, z_dim=256, init_std=1.0, + device=None, dtype=None): + super().__init__(num_particles, z_dim, init_std, device, dtype, learnable=False) + self.init_std = float(init_std) + + def sample(self, batch_size, generator=None, *, fixed_first_n=False, offset=0): + if fixed_first_n: + return super().sample(batch_size, generator, fixed_first_n=True, offset=offset) + if batch_size <= 0: + raise ValueError(f"batch_size must be positive, got {batch_size}") + z = torch.randn(batch_size, self.z_dim, device=self.z.device, + dtype=self.z.dtype, generator=generator) * self.init_std + return z, None + + +PRIOR_KINDS = ("particles", "frozen_gaussian", "fresh_gaussian", "gaussian") + + +def canonical_prior_kind(kind: str) -> str: + """Keep historical ``gaussian`` configs as frozen finite-table controls.""" + kind = str(kind).lower() + if kind not in PRIOR_KINDS: + raise ValueError(f"Unknown prior: {kind!r} (expected one of {PRIOR_KINDS})") + return "frozen_gaussian" if kind == "gaussian" else kind + + +def make_prior(kind: str, **kwargs) -> ParticlePrior: + kind = canonical_prior_kind(kind) + if kind == "fresh_gaussian": + return FreshGaussianPrior(**kwargs) + return ParticlePrior(**kwargs, learnable=(kind == "particles")) diff --git a/lib/toy_models.py b/lib/toy_models.py index a180fd5..98842ba 100644 --- a/lib/toy_models.py +++ b/lib/toy_models.py @@ -1,9 +1,8 @@ """ Toy models and data for the 2D Gaussian-grid benchmark. -These are lifted verbatim from `examples/100gaussians.py` so that experiment -scripts can import the exact same generator, discriminator, dataset sampler and -coverage metric without depending on (or perturbing) the example itself. +The example and experiment trainer import the same generator, discriminator, +dataset sampler and coverage metric from this module. """ from typing import Tuple @@ -145,16 +144,18 @@ def mode_coverage( n_eval: int = 20000, std: float = 0.03, min_count: int = 10, + sample_generator: torch.Generator = None, ) -> Tuple[int, float]: """ - Coverage metrics over the full particle cloud: + Coverage metrics on samples from the selected prior: - modes: number of grid centers with >= min_count "high quality" samples (within 3 sigma of the center), - hq_frac: fraction of samples that are high quality. """ + was_training = generator.training generator.eval() - idx = torch.randint(0, prior.num_particles, (n_eval,), device=device) - fake = generator(prior.z[idx]) + z, _ = prior.sample(n_eval, generator=sample_generator) + fake = generator(z) coords = torch.arange(10, device=device, dtype=torch.float32) - 4.5 cx, cy = torch.meshgrid(coords, coords, indexing="ij") centers = torch.stack([cx.flatten(), cy.flatten()], dim=1) @@ -162,5 +163,5 @@ def mode_coverage( mind, nearest = dists.min(dim=1) hq = mind <= 3 * std counts = torch.bincount(nearest[hq], minlength=100) - generator.train() + generator.train(was_training) return int((counts >= min_count).sum().item()), hq.float().mean().item() diff --git a/tests/test_prior_controls.py b/tests/test_prior_controls.py new file mode 100644 index 0000000..c844d93 --- /dev/null +++ b/tests/test_prior_controls.py @@ -0,0 +1,166 @@ +"""Bounded CPU checks of prior semantics and actual training reproducibility.""" + +from functools import partial +from importlib import import_module +import json +from pathlib import Path +import sys +from unittest.mock import patch + +import numpy as np +import pytest +import torch + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +from experiments import train_arm +from experiments.compare_priors import collect, generate +from lib.particle_prior import FreshGaussianPrior, make_prior +from lib.toy_models import SimpleMLPGenerator, mode_coverage + + +@pytest.fixture(autouse=True) +def cpu_threads(): + old = torch.get_num_threads() + torch.set_num_threads(1) + yield + torch.set_num_threads(old) + + +def test_prior_controls_preserve_initialization_and_alias(): + draws, weights = [], [] + for kind in ("particles", "frozen_gaussian", "gaussian", "fresh_gaussian"): + torch.manual_seed(71) + prior = make_prior(kind, num_particles=8, z_dim=4) + draws.append(prior.z.detach().clone()) + weights.append(next(SimpleMLPGenerator().parameters()).detach().clone()) + assert bool(list(prior.parameters())) == (kind == "particles") + for draw, weight in zip(draws[1:], weights[1:]): + assert torch.equal(draws[0], draw) + assert torch.equal(weights[0], weight) + + +def test_fresh_noise_ignores_reference_buffer_and_has_no_particle_indices(): + prior = FreshGaussianPrior(num_particles=8, z_dim=4) + reference, _ = prior.sample(8, fixed_first_n=True) + assert torch.equal(reference, prior.z) + prior.z.fill_(float("nan")) + global_state = torch.get_rng_state() + draw, idx = prior.sample(64, generator=torch.Generator().manual_seed(9)) + repeat, _ = prior.sample(64, generator=torch.Generator().manual_seed(9)) + assert idx is None + assert torch.isfinite(draw).all() + assert torch.equal(draw, repeat) + assert draw.unique(dim=0).shape[0] == 64 + assert torch.equal(torch.get_rng_state(), global_state) + + +def test_coverage_uses_fresh_samples_and_restores_model_mode(): + class Recorder(torch.nn.Module): + def forward(self, z): + self.seen = z.detach().clone() + return z + + prior = FreshGaussianPrior(num_particles=8, z_dim=2) + prior.z.fill_(float("nan")) + model = Recorder().eval() + mode_coverage(model, prior, torch.device("cpu"), n_eval=64, + sample_generator=torch.Generator().manual_seed(4)) + assert torch.isfinite(model.seen).all() + assert not model.training + + +@pytest.mark.parametrize("kind", ["particles", "frozen_gaussian", "fresh_gaussian"]) +def test_eval_interval_does_not_change_actual_training(tmp_path, kind): + """Exercise all updates, diagnostics, final sampling and checkpoint writes. + + Use an interpolation penalty as well: its random draws must remain isolated + from evaluation and from latent draws. Only batch/cloud sizes and plotting + cost are reduced; losses, models, optimizers and final metrics are real. + """ + snapshots = [] + for interval in (1, 4): + out = tmp_path / str(interval) + cfg = dict(train_arm.DEFAULTS, total_steps=4, eval_interval=interval, + prior=kind, arm="e_interp", coeff=0.02, + spectral=(kind != "particles"), out_dir=str(out)) + with patch.multiple(train_arm, BATCH_SIZE=32, EVAL_N=64, GRAD_STATS_N=16, + FINAL_N=128, NUM_PARTICLES=256), \ + patch.object(train_arm, "save_fake_scatter"), \ + patch.object(train_arm, "mode_coverage", partial(mode_coverage, n_eval=128)): + summary = train_arm.train(cfg, torch.device("cpu")) + state = torch.load(out / "ckpt.pt", weights_only=True) + samples = np.load(out / "final_samples.npy") + assert summary["final"]["unique_samples"] == np.unique(samples, axis=0).shape[0] + if kind != "particles": + assert summary["spectral"]["status"] == "unsupported" + snapshots.append((state, samples, summary)) + first, second = snapshots + for module in ("G", "D", "prior", "ema_G", "ema_prior"): + for name, tensor in first[0][module].items(): + assert torch.equal(tensor, second[0][module][name]), (module, name) + np.testing.assert_array_equal(first[1], second[1]) + assert first[2]["final"]["w1_exact"] == second[2]["final"]["w1_exact"] + + +def test_examples_share_the_same_training_function(): + particles = import_module("examples.100gaussians") + gaussian = import_module("examples.100gaussians_no_particle_prior") + assert gaussian.train.func is particles.train + assert gaussian.train.keywords == { + "prior_kind": "fresh_gaussian", "out_dir": "100gaussians_no_particles_samples", + } + + +@pytest.mark.parametrize("kind", ["particles", "frozen_gaussian", "fresh_gaussian"]) +def test_shared_example_executes_each_prior(tmp_path, kind): + example = import_module("examples.100gaussians") + with patch.object(example, "save_fake_scatter"), \ + patch.object(example, "mode_coverage", partial(mode_coverage, n_eval=128)): + prior, generator, discriminator = example.train( + epochs=1, steps_per_epoch=2, batch_size=32, num_particles=128, + prior_kind=kind, out_dir=str(tmp_path), device_str="cpu", + ) + assert bool(list(prior.parameters())) == (kind == "particles") + assert all(torch.isfinite(p).all() for p in generator.parameters()) + assert all(torch.isfinite(p).all() for p in discriminator.parameters()) + + +def test_comparison_configs_are_paired_and_recipe_is_fixed(tmp_path): + import yaml + + manifest = generate(tmp_path, seeds=(23001, 23002), total_steps=7) + recipes = [] + for run in manifest["runs"]: + cfg = yaml.safe_load(Path(run["config"]).read_text()) + assert cfg.pop("prior") == run["prior"] + assert cfg.pop("seed") == run["seed"] + cfg.pop("out_dir") + recipes.append(cfg) + assert len(recipes) == 6 + assert all(recipe == recipes[0] for recipe in recipes) + assert recipes[0]["arm"] == "b_cap" + assert recipes[0]["lr"] == 6e-4 + with pytest.raises(ValueError, match="already exists"): + generate(tmp_path, seeds=(23003,), total_steps=10) + assert manifest["runtime"]["packages"]["torch"] + + +def test_comparison_rejects_mismatched_results(tmp_path): + import yaml + + manifest = generate(tmp_path, seeds=(23001,), total_steps=7) + for run in manifest["runs"]: + config = yaml.safe_load(Path(run["config"]).read_text()) + out = Path(run["out_dir"]) + out.mkdir(parents=True) + (out / "summary.json").write_text(json.dumps({ + "config": config, "final": {"hq": 0.5}, "collapse_events": 0, + })) + assert len(collect(manifest)) == 3 + summary_path = Path(manifest["runs"][0]["out_dir"]) / "summary.json" + summary = json.loads(summary_path.read_text()) + summary["config"]["seed"] = 1234 + summary_path.write_text(json.dumps(summary)) + with pytest.raises(ValueError, match="mismatch"): + collect(manifest) From 4bad3d3afc94766b4f0f93e319d6d7b01c1df2f8 Mon Sep 17 00:00:00 2001 From: Martyn Garcia Date: Fri, 4 Sep 2026 21:19:02 -0600 Subject: [PATCH 4/4] Report nine matched prior experiments with shape and transport caveats --- README.md | 2 + reports/prior-comparison/README.md | 115 + reports/prior-comparison/curves.png | Bin 0 -> 302698 bytes reports/prior-comparison/data.json | 12963 ++++++++++++++++++++++++++ reports/prior-comparison/plot.py | 56 + 5 files changed, 13136 insertions(+) create mode 100644 reports/prior-comparison/README.md create mode 100644 reports/prior-comparison/curves.png create mode 100644 reports/prior-comparison/data.json create mode 100644 reports/prior-comparison/plot.py diff --git a/README.md b/README.md index 0723ca6..40af00f 100644 --- a/README.md +++ b/README.md @@ -32,6 +32,8 @@ python experiments/compare_priors.py --study-dir runs/prior_comparison --run --d This runs learned particles, a frozen Gaussian table, and fresh Gaussian noise on paired seeds 23001–23003. It records configs, source revision, final samples, coverage, transport distances, and per-mode radial and covariance shape diagnostics. See [prior controls and interpretation](docs/prior-controls.md) and [reproducing the project](docs/reproducing.md). +The completed [nine-run matched comparison](reports/prior-comparison/README.md) reached 100/100 high-quality modes on every learned-prior seed, with a mean high-quality fraction of 98.6%, versus 8.1% for the frozen table and 6.4% for fresh Gaussian noise. This establishes a concentration advantage under this recipe. The report also shows remaining tail and covariance distortion, finite output support, and transport-metric tradeoffs; it does not establish complete Gaussian calibration or a general guarantee against collapse. + ## How It Works 1. **Particle Prior**: Instead of sampling z ~ N(0, I), we maintain a set of learnable latent vectors (particles). During training, we sample from this discrete set. diff --git a/reports/prior-comparison/README.md b/reports/prior-comparison/README.md new file mode 100644 index 0000000..036dd07 --- /dev/null +++ b/reports/prior-comparison/README.md @@ -0,0 +1,115 @@ +# Matched prior comparison: 100 Gaussians + +All three particle runs reached 100 high-quality modes after 7,000 steps, with +mean HQ **98.61%**. Frozen and fresh Gaussian controls produced much broader +samples under this same recipe. This supports better concentration near target +modes and high-quality coverage in this experiment; it does not establish full +Gaussian calibration or a guarantee against collapse. + +Particle core width averaged 0.857 times the target, but mean within-mode +covariance eigenvalue ratios were **2.09 / 42.37**, and **0.838%** of samples +were more than 10σ from their nearest center. Those unequal, inflated second +moments remain a substantial shape error. The finite particle table also +produced only about **19,869 distinct outputs** among 100,000 draws. + +## Final results + +Every run is included. “Modes” counts centers receiving at least ten samples +within 3σ; “recall” counts sufficiently populated nearest-center regions without +a distance cutoff. The Gaussian controls often have recall 1.0 despite low HQ, +so their broad output should not all be described as mode collapse. + +| Prior | Seed | Modes | HQ (%) | Recall | Core | Cov min / max | Tail (%) | W1 | W2 | Unique | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| Particles | 23001 | 100 | 98.85 | 0.99 | 0.883 | 0.71 / 33.10 | 0.619 | 0.259 | 0.536 | 19,874 | +| Particles | 23002 | 100 | 98.04 | 1.00 | 0.868 | 1.46 / 54.32 | 1.184 | 0.328 | 0.612 | 19,860 | +| Particles | 23003 | 100 | 98.94 | 0.99 | 0.818 | 4.08 / 39.70 | 0.710 | 0.369 | 0.658 | 19,873 | +| Frozen Gaussian | 23001 | 45 | 8.48 | 1.00 | 9.689 | 58.75 / 91.56 | 58.088 | 0.371 | 0.443 | 19,874 | +| Frozen Gaussian | 23002 | 45 | 8.14 | 1.00 | 9.759 | 59.10 / 90.94 | 57.874 | 0.375 | 0.447 | 19,860 | +| Frozen Gaussian | 23003 | 41 | 7.58 | 1.00 | 9.796 | 60.38 / 91.98 | 59.011 | 0.379 | 0.446 | 19,873 | +| Fresh Gaussian | 23001 | 26 | 4.82 | 0.90 | 10.254 | 71.58 / 103.98 | 64.387 | 0.543 | 0.636 | 100,000 | +| Fresh Gaussian | 23002 | 34 | 5.95 | 1.00 | 10.192 | 66.60 / 92.91 | 62.140 | 0.414 | 0.479 | 100,000 | +| Fresh Gaussian | 23003 | 37 | 8.53 | 1.00 | 9.963 | 64.61 / 90.97 | 59.657 | 0.361 | 0.423 | 100,000 | + +Means ± **sample standard deviations** across three seeds (`ddof=1`): + +| Metric | Particles | Frozen Gaussian | Fresh Gaussian | +|---|---:|---:|---:| +| Modes | 100.0 ± 0.0 | 43.7 ± 2.3 | 32.3 ± 5.7 | +| HQ (%) | 98.61 ± 0.50 | 8.07 ± 0.45 | 6.44 ± 1.90 | +| Recall | 0.993 ± 0.006 | 1.000 ± 0.000 | 0.967 ± 0.058 | +| Core ratio | 0.857 ± 0.034 | 9.748 ± 0.054 | 10.136 ± 0.153 | +| Covariance min ratio | 2.09 ± 1.77 | 59.41 ± 0.86 | 67.60 ± 3.59 | +| Covariance max ratio | 42.37 ± 10.86 | 91.49 ± 0.53 | 95.95 ± 7.02 | +| Tail (%) | 0.838 ± 0.303 | 58.324 ± 0.604 | 62.061 ± 2.366 | +| Unique outputs | 19869.0 ± 7.8 | 19869.0 ± 7.8 | 100000.0 ± 0.0 | +| Exact W1 | 0.319 ± 0.056 | 0.375 ± 0.004 | 0.439 ± 0.094 | +| Exact W2 | 0.602 ± 0.062 | 0.445 ± 0.002 | 0.513 ± 0.110 | +| Sliced W1 | 0.157 ± 0.020 | 0.105 ± 0.008 | 0.132 ± 0.044 | + +Transport metrics do not uniformly favor particles: frozen Gaussian has lower +mean W2 and sliced W1, while particles have lower mean exact W1. Coverage, tails, +shape, and transport need to be read together. Three seeds provide descriptive +replication, not significance or equivalence evidence. This recipe came from +particle experiments; the Gaussian controls were not separately tuned. The +learned table adds 80,000 trainable scalars, so total capacity is not matched. + +## Training curves + +![All nine training runs](curves.png) + +Each color is a prior and each line style a seed. Coverage and HQ use 20,000 +samples per checkpoint; periodic sliced W1 uses 4,096 samples and 128 projections. +Final sliced W1 in the table uses 100,000 samples and 512 projections, so it is +not the final plotted value. + +## Protocol and provenance + +The runs used clean source revision +[`f8b59bb647976b421afccdcb64fa2da583ddf992`](https://github.com/255BITS/ParticleGAN/commit/f8b59bb647976b421afccdcb64fa2da583ddf992), +paired seeds 23001–23003, and GPU 0, an NVIDIA RTX A6000, with three runs +concurrent. All nine processes exited successfully. Runtime: Python 3.12.13, +PyTorch 2.14.0 (CUDA build 13.0), NumPy 2.5.2, Matplotlib 3.11.1, PyYAML 6.0.3, +and POT 0.9.7.post1. This is a new comparison, separate from the historical GIFs +and regularizer-study results. + +All arms share a 10×10 grid mixture with σ=0.03, three hidden layers of width +128 in G and D, four latent dimensions, Fourier-2 D, batch size 256, Rp logistic +loss, and the sample-point `b_cap` penalty (coefficient 1, κ=1, L2 norm). Adam +uses β=(0, 0.999), generator LR 6e-4, and discriminator LR ×1.5. LR remains full +for 60% of the run, then follows a cosine to a 5% floor; G uses EMA 0.995. +Particles use a 20,000-row learned table, prior LR ×10, VICReg weight 1, and +EMA of the table. Frozen Gaussian uses the same initial finite table without +learning; fresh Gaussian draws new N(0,I) latents. Both controls omit prior +optimization and VICReg. Training data, latent draws, and penalty draws use +separate RNG streams; real evaluation samples are also independent of latent +sampling, with the same real draws across paired priors. Spectral analysis was +disabled for this comparison. + +## Metric definitions and artifacts + +- **Modes / HQ:** on a separate 20,000-sample EMA draw, assign the nearest + grid center. HQ means Euclidean distance ≤3σ=0.09; modes require ≥10 HQ + samples per center. The target is 100 modes. +- **Recall:** fraction of centers assigned ≥500 of the 100,000 final samples + (half the expected count under uniform mode mass), regardless of distance. +- **Core / covariance:** use the 100,000 final samples, grouped by nearest + center, keeping modes with ≥50 samples. Core is the mean per-mode median + radius around its coordinate-wise median, divided by σ√(2 ln 2). Cov min/max + are the mean smallest/largest population covariance eigenvalues divided by + σ². All 100 modes qualified in every run. Core is robust to tails; covariance + is tail-sensitive. Neither alone establishes Gaussian shape. +- **Tail / unique:** percentage farther than 10σ=0.3 from the nearest center; + number of exactly distinct output rows in the 100,000-sample final array. +- **W1 / W2:** exact empirical transport solutions on 4,096-point subsamples + of the final real and fake clouds, using Euclidean / squared-Euclidean costs + (square root for W2). They are not exact population distances. + +[data.json](data.json) contains portable configs, provenance, execution status, +all final metrics, aggregates, and all checkpoint metric rows; no checkpoints, +raw sample arrays, or machine-specific paths are included. Initial undefined +losses are stored as JSON `null`. Original config hashes are retained as source +identifiers. Rebuild the figure with `python reports/prior-comparison/plot.py`. +Reproduce the training protocol using [the comparison entrypoint](../../experiments/compare_priors.py) +and [prior-control instructions](../../docs/prior-controls.md); exact source and +dependency versions above identify these reported runs. diff --git a/reports/prior-comparison/curves.png b/reports/prior-comparison/curves.png new file mode 100644 index 0000000000000000000000000000000000000000..2bbb2c3a8fa4c656b61383a79b0d2d1e57c70eea GIT binary patch literal 302698 zcmeFZhgVhC`YwzyYGP1g7Zogk(m{$yH5Pi4-a$n`KzfsIV(dtj-c)**-oc21bm`KS zzNt!;>i0~|xxahAaqoX{93$tDE!lhRwdQ=^r@iyxWd+F{+YfE0qN3U%Ep-LIig)?dT1v}~it2C%`Dc^Nw-iQc{uqNBbw&vZ|+z1195_-6ATkI*m3&7CTMLO;47L>eEzkQ{!{~ z_dmxvX6wd(`rlu{&#y{I>s-YNaj?I}V>=PkB7J-{;&$ z!7|+e4(+FHZ*2+#*)+oXXa3oCv~GNW%JQPlw%e2Sopbh%Lebw2th|1wRkl2GI9pz9 zsmD%B?+E$i+j~l~i@sliZ#bQ>qg<)WLe@%MMa9MNGZxCBJVp@>qp$D&Hr^cfn7ZQS ziW@#WSvxnmwkEvy?%lhTx=>#K*zvX$69=1~f!X2O5vLO8Ib-z$Rl}D3WfmUgKW*85 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matplotlib.pyplot as plt +from matplotlib.lines import Line2D + + +def main(): + directory = Path(__file__).resolve().parent + data = json.loads((directory / "data.json").read_text()) + colors = {"particles": "#047857", "frozen_gaussian": "#2563eb", "fresh_gaussian": "#b45309"} + labels = {"particles": "Learned particles", "frozen_gaussian": "Frozen Gaussian table", + "fresh_gaussian": "Fresh Gaussian noise"} + styles = dict(zip(data["seeds"], ("-", "--", ":"))) + panels = (("modes", "Modes with ≥10 high-quality samples", 1), + ("hq", "High-quality samples (%)", 100), + ("sliced_w1", "Sliced W1 (log scale)", 1)) + with plt.rc_context({"font.size": 10, "axes.spines.top": False, "axes.spines.right": False}): + figure, axes = plt.subplots(1, 3, figsize=(15, 4.6)) + for run in data["runs"]: + rows = run["metrics"] + for axis, (key, title, scale) in zip(axes, panels): + axis.plot([row["step"] for row in rows], [scale * row[key] for row in rows], + color=colors[run["prior"]], linestyle=styles[run["seed"]], + linewidth=1.6, alpha=0.9) + for axis, (_, title, _) in zip(axes, panels): + axis.set_title(title, fontsize=11) + axis.set_xlabel("Training step") + axis.set_xlim(0, data["common_config"]["total_steps"]) + axis.axvline(4200, color="#64748b", linewidth=1, alpha=0.5) + axis.grid(alpha=0.2) + axes[0].set_ylim(0, 103) + axes[1].set_ylim(0, 103) + axes[2].set_yscale("log") + figure.suptitle("Matched prior comparison · 7,000 steps · all three seeds", fontsize=14, y=0.995) + prior_handles = [Line2D([], [], color=colors[k], linewidth=2, label=labels[k]) for k in colors] + seed_handles = [Line2D([], [], color="#475569", linestyle=style, label=f"Seed {seed}") + for seed, style in styles.items()] + figure.legend(handles=prior_handles + seed_handles, ncol=6, loc="lower center", + bbox_to_anchor=(0.5, 0.005), frameon=False, fontsize=9) + figure.text(0.5, 0.09, + "EMA read-out every 100 steps; vertical line marks the start of learning-rate annealing.", + ha="center", fontsize=9, color="#475569") + figure.tight_layout(rect=(0, 0.13, 1, 0.94)) + figure.savefig(directory / "curves.png", dpi=180, facecolor="white") + plt.close(figure) + + +if __name__ == "__main__": + main()