From a3b33c633a6854561e23cd1164b3f2f069c2e600 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Thu, 4 Jun 2026 15:16:35 -0400 Subject: [PATCH 01/34] port: drive low-pass + keep_const to poly Ouroboros Hand-port from PR #4 (arneodo-parameterization). Adds drive_lowpass_ms (default 0.0) and keep_const (default False) to the polynomial Ouroboros only, with matching zero-phase Gaussian _lowpass / _lowpass_weights helpers applied to omega, gamma, and the kernel weights inside forward and get_funcs. Skips the Arneodo class entirely. model/kernels.py: gate the (0,0) constant-term zero on fullPolyModule.keep_const. train/train.py: persist parameterization / drive_lowpass_ms / keep_const in the checkpoint and read them back in load_model. Verified end-to-end (forward + get_funcs, both keep_const settings) on a tiny n_layers=2 poly model with drive_lowpass_ms=1.0. Co-Authored-By: Claude Opus 4.7 --- model/kernels.py | 12 ++++++--- model/model.py | 63 ++++++++++++++++++++++++++++++++++++++++++++---- train/train.py | 8 +++++- 3 files changed, 74 insertions(+), 9 deletions(-) diff --git a/model/kernels.py b/model/kernels.py index 781763f..2ae1bb2 100644 --- a/model/kernels.py +++ b/model/kernels.py @@ -62,6 +62,9 @@ def __init__(self, nTerms, device, x_dim, z_dim, lam=0.01, activation=lambda x: self.weights = nn.Linear(self.n, (self.poly_dim + 1) ** 2).to(self.device) self.powers = torch.arange(0, self.poly_dim + 1, device=self.device) self.lam = lam + # if True, keep the constant (0,0) term (the "alpha"-like forcing y^0*ydot^0). + # The linear (1,0)/(0,1) terms are always zeroed (omega/gamma handle those). + self.keep_const = False def forward( self, x: torch.FloatTensor, z: torch.FloatTensor @@ -85,7 +88,8 @@ def forward( weights = weights.view(B, L, self.poly_dim + 1, self.poly_dim + 1) ### constant term - weights[:, :, 0, 0] = weights[:, :, 0, 0] * 0 + if not self.keep_const: + weights[:, :, 0, 0] = weights[:, :, 0, 0] * 0 ### y, ydot terms weights[:, :, 1, 0] = weights[:, :, 1, 0] * 0 weights[:, :, 0, 1] = weights[:, :, 0, 1] * 0 @@ -124,7 +128,8 @@ def forward_given_weights( B, L, d = x.shape weights = weights.view(B, L, self.poly_dim + 1, self.poly_dim + 1) ### constant term - weights[:, :, 0, 0] = weights[:, :, 0, 0] * 0 + if not self.keep_const: + weights[:, :, 0, 0] = weights[:, :, 0, 0] * 0 ### y, ydot terms weights[:, :, 1, 0] = weights[:, :, 1, 0] * 0 weights[:, :, 0, 1] = weights[:, :, 0, 1] * 0 @@ -163,7 +168,8 @@ def forward_given_weights_numpy( weights = np.reshape(weights, (B, L, self.poly_dim + 1, self.poly_dim + 1)) # constant term - weights[:, :, 0, 0] = weights[:, :, 0, 0] * 0 + if not self.keep_const: + weights[:, :, 0, 0] = weights[:, :, 0, 0] * 0 ### y, ydot terms weights[:, :, 1, 0] = weights[:, :, 1, 0] * 0 weights[:, :, 0, 1] = weights[:, :, 0, 1] * 0 diff --git a/model/model.py b/model/model.py index c4d1d43..dbe2d7a 100644 --- a/model/model.py +++ b/model/model.py @@ -1,5 +1,6 @@ import torch from torch import nn +import torch.nn.functional as F from mambapy.mamba import Mamba, MambaConfig from model.model_utils import smooth from typing import Tuple, Union @@ -35,6 +36,8 @@ def __init__( device: str = "cuda", tau: float = 1 / 10000, smooth_len: float = 0.001, + drive_lowpass_ms: float = 0.0, + keep_const: bool = False, ): super().__init__() @@ -79,10 +82,49 @@ def __init__( self.tau = tau self.smooth_len = smooth_len + # if > 0, low-pass the drives omega(t), gamma(t), and the kernel weights w(t) with a + # zero-phase Gaussian (sigma = drive_lowpass_ms) -- "low-pass in the loop". Keeps the + # drives from re-encoding the audio carrier and improves autonomous behavior. + self.drive_lowpass_ms = drive_lowpass_ms self.kernel = kernel self.kernel.tau = self.tau + # if True, ADD the constant (0,0) "alpha"-like forcing term (y^0*ydot^0) to the kernel + # instead of zeroing it; it is low-passed at drive_lowpass_ms like the other drives. + self.keep_const = keep_const + if keep_const: + self.kernel.keep_const = True + # zero-init the (0,0) output of the kernel weight head so the alpha forcing starts at + # 0 and is learned gently (otherwise the random constant disrupts early training). + with torch.no_grad(): + self.kernel.weights.weight[0].zero_() + self.kernel.weights.bias[0].zero_() self.names = [r"$\omega$", r"$\gamma$", "weighted kernels", "states"] + def _lowpass(self, x: torch.FloatTensor, dt: float, lp_ms: float = None) -> torch.FloatTensor: + """centered zero-phase Gaussian low-pass along time of a (B, L, C) control series. + lp_ms overrides the timescale (defaults to self.drive_lowpass_ms). The kernel radius is + capped at L-1 so very slow (large-sigma) low-passes work on short segments (reduce to + ~a segment-wide average).""" + ms = self.drive_lowpass_ms if lp_ms is None else lp_ms + sigma = (ms / 1e3) / dt # samples + if sigma <= 0: + return x + L = x.shape[1] + radius = max(1, min(int(round(3 * sigma)), L - 1)) + t = torch.arange(-radius, radius + 1, device=x.device, dtype=x.dtype) + kern = torch.exp(-0.5 * (t / sigma) ** 2) + kern = kern / kern.sum() + C = x.shape[-1] + k = kern.view(1, 1, -1).expand(C, 1, -1) + xc = F.pad(x.transpose(1, 2), (radius, radius), mode="reflect") + return F.conv1d(xc, k, groups=C).transpose(1, 2) + + def _lowpass_weights(self, weights: torch.FloatTensor, dt: float) -> torch.FloatTensor: + """low-pass the polynomial kernel weights (B, L, P, P) along time.""" + B, L, P, P2 = weights.shape + w = self._lowpass(weights.reshape(B, L, P * P2), dt) + return w.reshape(B, L, P, P2) + def forward( self, x: torch.FloatTensor, @@ -131,11 +173,18 @@ def forward( omegaControl ).abs() # Since we take omega^2 anyway, we take the absolute value to prevent things from switching around too much gamma = self.gamma_net(gammaControl) - if smoothing: + weighted_kernels, weights = self.kernel(z, kernelControl) + if self.drive_lowpass_ms > 0: + # low-pass the drives in the loop, then recompute the nonlinearity from the + # low-passed weights so yhat is consistent with the (slow) drives + omega = self._lowpass(omega, dt) + gamma = self._lowpass(gamma, dt) + weights = self._lowpass_weights(weights, dt) + weighted_kernels = self.kernel.forward_given_weights(z, weights) + elif smoothing: # smooth our model functions, if we choose to do so. I do not. omega = smooth(omega, smooth_len) gamma = smooth(gamma, smooth_len) - weighted_kernels, weights = self.kernel(z, kernelControl) z1 = z[:, :, :1] z2 = z[:, :, 1:] @@ -208,13 +257,17 @@ def get_funcs( omega = self.omega_net(omegaControl).abs() gamma = self.gamma_net(gammaControl) + weighted_kernels, weights = self.kernel(z, kernelControl) - if smoothing: + if self.drive_lowpass_ms > 0: + omega = self._lowpass(omega, dt) + gamma = self._lowpass(gamma, dt) + weights = self._lowpass_weights(weights, dt) + weighted_kernels = self.kernel.forward_given_weights(z, weights) + elif smoothing: omega = smooth(omega.abs(), smooth_len) gamma = smooth(gamma, smooth_len) - weighted_kernels, weights = self.kernel(z, kernelControl) - return ( omega, gamma, diff --git a/train/train.py b/train/train.py index ef2f768..57704ed 100644 --- a/train/train.py +++ b/train/train.py @@ -64,10 +64,14 @@ def save_model( "d_state": d_state, "d_conv": d_conv, "expand_factor": expand_factor, + # parameterization tag for forward-compat (always "poly" on this branch) + "parameterization": getattr(model, "parameterization", "poly"), + "drive_lowpass_ms": getattr(model, "drive_lowpass_ms", 0.0), + "keep_const": getattr(model, "keep_const", False), } try: sd["n_kernel"] = model.kernel.nTerms - except KeyError: + except (KeyError, AttributeError): pass torch.save(sd, location) @@ -132,6 +136,8 @@ def load_model( tau=sd["tau"], smooth_len=sd["smooth_len"], kernel=kernel, + drive_lowpass_ms=sd.get("drive_lowpass_ms", 0.0), + keep_const=sd.get("keep_const", False), ) except: print("no kernel in savefile!") From 11677b5f7cab0912bac7f74441772a08f5cbaa9c Mon Sep 17 00:00:00 2001 From: John Pearson Date: Thu, 4 Jun 2026 15:18:28 -0400 Subject: [PATCH 02/34] port: autonomy_score + integrate_poly_autonomous + generate_autonomous Hand-port from PR #4 (arneodo-parameterization). Adds the closed-loop polynomial Ouroboros integrator, the autonomy-based validation metric (spec_corr - amp_pen - pitch_pen, plus bounded_frac), and the deployed generate-with-amplitude-rescale helper. Stripped the is_poly branch and integrate_model_autonomous reference -- this PR is poly-only. Verified end-to-end on a random-init poly model over a 50 ms segment: integration runs, autonomy_score returns finite breakdown, generate_autonomous returns bounded waveform. Co-Authored-By: Claude Opus 4.7 --- train/eval.py | 224 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 224 insertions(+) diff --git a/train/eval.py b/train/eval.py index 07b590a..55b1e54 100644 --- a/train/eval.py +++ b/train/eval.py @@ -10,6 +10,7 @@ from torchdiffeq import odeint_adjoint from scipy.interpolate import make_interp_spline +from scipy.signal import welch """ tools for evaluating model performance. covers both regular evaluation and model integration @@ -360,3 +361,226 @@ def pad_with_nan(array: np.ndarray, target_len: int) -> np.ndarray: return array +def integrate_poly_autonomous( + model: torch.nn.Module, + audio: np.ndarray, + dt: float, + method: str = "rk4", + detrend: bool = True, + noise_sd: float = 0.0, + seed: int = 0, + verbose: bool = True, +) -> np.ndarray: + """ + Fully autonomous (closed-loop) integration of a polynomial `Ouroboros`. + + Precomputes the drives omega(t), gamma(t) and the kernel weights w(t) from `audio` (these are + low-passed inside `get_funcs` if the model was trained with `drive_lowpass_ms`), then integrates + + dx/ds = x' + dx'/ds = -omega(s)^2 x - gamma(s) x' - kernel(x, x'; w(s)) + + in the model's rescaled time s = t/tau, feeding the generated state (x, x') back into the + nonlinearity each step. Only the drives (from data) and the initial condition come from outside. + """ + + L = len(audio) + t_steps = np.arange(0, L * dt + dt / 2, dt)[:L] + s_steps = t_steps / model.tau + + audio_3d = audio[None, :, None] + dy = deriv_approx_dy(audio_3d) + audio_t = torch.from_numpy(audio_3d).to(torch.float32).to("cuda") + dy_t = torch.from_numpy(dy).to(torch.float32).to("cuda") + + with torch.no_grad(): + omega, gamma, _, weights, _ = model.get_funcs(audio_t, dy_t, dt) + omega = omega.detach().cpu().numpy().squeeze() + gamma = gamma.detach().cpu().numpy().squeeze() + weights = weights.detach().cpu().numpy() # (1, L, P, P) + _, _, P, P2 = weights.shape + w_flat = weights.reshape(L, P * P2) + + omega_interp = make_interp_spline(s_steps, omega) + gamma_interp = make_interp_spline(s_steps, gamma) + w_interp = make_interp_spline(s_steps, w_flat) # vector-valued spline over time + + x0 = float(audio[0]) + xp0 = (model.tau / dt) * float(dy[0, 0, 0]) + ic = torch.tensor([x0, xp0], dtype=torch.float32, device="cuda") + kernel = model.kernel + + if noise_sd > 0: + # stochastic forcing (Euler-Maruyama on the velocity) to sustain a noise-driven + # oscillation at the data amplitude. RK4 drift per sample (drives held constant over + # the 1-sample step, fine since they are low-passed), plus additive noise on x'. + rng = np.random.default_rng(seed) + x, xp = x0, xp0 + xs = [x] + ww = weights.reshape(L, 1, 1, P, P2) + for k in range(L - 1): + om, ga, wk = omega[k], gamma[k], ww[k] + + def f(xx, vv): + kern = float(kernel.forward_given_weights_numpy(np.array([[[xx, vv]]]), wk).squeeze()) + return vv, -(om**2) * xx - ga * vv - kern + + k1x, k1v = f(x, xp) + k2x, k2v = f(x + 0.5 * k1x, xp + 0.5 * k1v) + k3x, k3v = f(x + 0.5 * k2x, xp + 0.5 * k2v) + k4x, k4v = f(x + k3x, xp + k3v) + x = x + (k1x + 2 * k2x + 2 * k3x + k4x) / 6 + xp = xp + (k1v + 2 * k2v + 2 * k3v + k4v) / 6 + noise_sd * rng.standard_normal() + xs.append(x) + x_gen = np.array(xs) + return correct(x_gen) if detrend else x_gen + + def dz_hat(s, z): + if verbose: + print( + f"{(s - s_steps[0]) / (s_steps[-1] - s_steps[0]) * 100:0.3f}%,", + end="\r", + ) + s_np = s.detach().cpu().numpy() + om = float(omega_interp(s_np)) + ga = float(gamma_interp(s_np)) + w = w_interp(s_np).reshape(1, 1, P, P2) + x = float(z[0]) + xp = float(z[1]) + kern = float( + kernel.forward_given_weights_numpy(np.array([[[x, xp]]]), w).squeeze() + ) + dxp = -(om**2) * x - ga * xp - kern + return torch.tensor([xp, dxp], dtype=torch.float32, device=z.device) + + eval_times = torch.from_numpy(s_steps).to(ic.device) + with torch.no_grad(): + sol = odeint_adjoint( + dz_hat, ic, eval_times, adjoint_params=(), method=method, options=dict() + ).transpose(0, 1) + + x_gen = sol[0].detach().cpu().numpy().squeeze() + if detrend: + x_gen = correct(x_gen) + return x_gen + + +def autonomy_score( + model: torch.nn.Module, + segments: list, + dt: float, + fmax: float = 8000.0, + w_amp: float = 1.0, + w_pitch: float = 1.0, + diverge_score: float = -5.0, + method: str = "rk4", + rescale: bool = False, + cold_start: bool = False, +) -> tuple: + """ + Validation metric for AUTONOMOUS reconstruction quality (model-selection criterion). + + For each pre-windowed (sustained) vocalization segment, run the model's DETERMINISTIC + autonomous integration and score it against the target by phase-robust spectral (log-PSD) + correlation, minus log-ratio penalties on amplitude and pitch: + + score = spectral_corr(autonomous, target) + - w_amp * |log(std_auto / std_target)| + - w_pitch * |log(pitch_auto / pitch_target)| + + A divergent / collapsed rollout (non-finite or ~zero) gets `diverge_score`. Poly-only. + + If `rescale=True`, the autonomous output's RMS is matched to the target's before scoring (the + deployed recipe -- amplitude is an arbitrary overall-scale gauge fixed at generation by + `generate_autonomous`). This zeroes `amp_pen` for bounded rollouts, so selection then turns on + the genuinely-constrained quantities (spectral shape + pitch + boundedness). A divergent rollout + is still detected on the RAW output and gets `diverge_score` (collapse is not rescaled away). + + If `cold_start=True`, the caller is asserting that each `segments[i]` already includes a silence + lead-in. The integration IC is the first sample of the segment, which then equals near-silence + and exercises the ignition path -- the situation our real-time synthesis target actually faces. + Mechanically the metric is unchanged; this flag exists so callers can request the cold-start + scoring contract and so the chosen mode is recorded in the breakdown. Typically paired with + `rescale=False` (amplitude must contribute to the score, because a single shipped rescale + constant cannot fix voc-variable cold-start amplitude). + + returns + ----- + - mean score over segments + - per-segment scores (list) + - breakdown dict (mean spectral corr, amp penalty, pitch penalty, bounded fraction, + plus the `cold_start` and `rescale` flags for downstream logging) + """ + fs = 1.0 / dt + + def _logpsd(x): + f, P = welch(x - np.mean(x), fs=fs, nperseg=min(1024, len(x))) + m = f <= fmax + return np.log(P[m] + 1e-20) + + def _peak(x): + f, P = welch(x - np.mean(x), fs=fs, nperseg=min(1024, len(x))) + P[0] = 0 + return float(f[np.argmax(P)]) + + scores, specs, amps, pits, bounded = [], [], [], [], [] + for seg in segments: + seg = np.asarray(seg, dtype=np.float64) + tgt = correct(seg) + auto = integrate_poly_autonomous(model, seg, dt, method=method, noise_sd=0.0, + detrend=True, verbose=False) + n = min(len(tgt), len(auto)) + tgt_n, auto_n = tgt[:n], auto[:n] + if (not np.isfinite(auto_n).all()) or np.nanstd(auto_n) < 1e-9: + scores.append(diverge_score) + bounded.append(0.0) + specs.append(np.nan); amps.append(np.nan); pits.append(np.nan) + continue + bounded.append(1.0) + if rescale: # gauge-fix amplitude to the target RMS (the deployed recipe) + auto_n = auto_n * (np.nanstd(tgt_n) / (np.nanstd(auto_n) + 1e-12)) + sc = float(np.corrcoef(_logpsd(tgt_n), _logpsd(auto_n))[0, 1]) + amp = abs(np.log((np.nanstd(auto_n) + 1e-12) / (np.nanstd(tgt_n) + 1e-12))) + pit = abs(np.log((_peak(auto_n) + 1e-9) / (_peak(tgt_n) + 1e-9))) + scores.append(sc - w_amp * amp - w_pitch * pit) + specs.append(sc); amps.append(amp); pits.append(pit) + + breakdown = { + "spec_corr": float(np.nanmean(specs)) if len(specs) else float("nan"), + "amp_pen": float(np.nanmean(amps)) if len(amps) else float("nan"), + "pitch_pen": float(np.nanmean(pits)) if len(pits) else float("nan"), + "bounded_frac": float(np.mean(bounded)) if len(bounded) else 0.0, + "cold_start": bool(cold_start), + "rescale": bool(rescale), + } + return float(np.mean(scores)), scores, breakdown + + +def generate_autonomous( + model: torch.nn.Module, + audio: np.ndarray, + dt: float, + rescale: bool = True, + ref_rms: float = None, + method: str = "rk4", + detrend: bool = True, + verbose: bool = False, +) -> np.ndarray: + """ + DEPLOYED autonomous generation: closed-loop integration + amplitude rescaling. + + Autonomous amplitude is a poorly-constrained, marginal direction (the transverse Floquet + exponent is left free by on-orbit teacher-forced fitting), so the raw free-running amplitude + decays/grows/varies by seed. The overall scale is an arbitrary audio-unit gauge, so we fix it + at generation: run the deterministic closed-loop rollout, then (if `rescale`) match its RMS to + a reference -- `ref_rms` if given, else the input window's own (detrended) RMS for reconstruction. + + Poly-only. Returns the (rescaled) generated waveform; a divergent/collapsed rollout is returned + unscaled. + """ + auto = integrate_poly_autonomous(model, audio, dt, method=method, detrend=detrend, + noise_sd=0.0, verbose=verbose) + if rescale and np.isfinite(auto).all() and np.nanstd(auto) > 1e-9: + target = ref_rms if ref_rms is not None else float(np.nanstd(correct(np.asarray(audio, dtype=np.float64)))) + auto = auto * (target / (np.nanstd(auto) + 1e-12)) + return auto From 818f14bc33f4aa20f810bcf9a8ecd5c2dfd09d29 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Thu, 4 Jun 2026 15:19:37 -0400 Subject: [PATCH 03/34] port: int16-aware voc-window loaders + blk445 staging script scripts/stage_finch_blk445_syllC.py is a verbatim port from PR #4 that symlinks the Mooney/Muscimol blk445 directory layout (segs//syllable_C + /double_denoised) into ~/ouroboros_data/blk445_syllC/day{day}/ with the {stem}.wav / {stem}.txt convention the segmented-audio loader expects. Idempotent. Verified: 738/624/871 paired files staged for days 84/85/86 (empty annotations skipped, no missing wavs). examples/_voc_windows.py is a new module that hoists the int16-fixed load_voc_windows / load_voc_windows_coldstart helpers out of the legacy run_lambda_pipeline so the new spectral entry point can reuse them without dragging in the rest of that pipeline. Verified against day 85 (sr=44100, 3 coldstart segs OK, 3 mid-voc segs OK). Co-Authored-By: Claude Opus 4.7 --- examples/_voc_windows.py | 62 +++++++++++++++++++++ scripts/stage_finch_blk445_syllC.py | 83 +++++++++++++++++++++++++++++ 2 files changed, 145 insertions(+) create mode 100644 examples/_voc_windows.py create mode 100644 scripts/stage_finch_blk445_syllC.py diff --git a/examples/_voc_windows.py b/examples/_voc_windows.py new file mode 100644 index 0000000..61a95dd --- /dev/null +++ b/examples/_voc_windows.py @@ -0,0 +1,62 @@ +"""Held-out per-vocalization window loaders for autonomy evaluation. + +Both helpers carry the int16-normalize fix (divide by 32768 on int16 input) so val/test +inputs are on the same scale as the (float-converted) training windows. The pre-fix +loaders were the source of the real-finch autonomy bug noted in +docs/finch_blk445_syllC_arneodo (MEMORY: finch-blk445-syllC-arneodo). + +- `load_voc_windows`: legacy mid-voc window starting `start_offset_ms` after onset. + Used by the rescaled-autonomy selection mode. +- `load_voc_windows_coldstart`: silence lead-in + full vocalization. The IC at the + segment start is near-silence, so the autonomy_score exercises the cold-start + ignition path the deployed pipeline cares about. Used by `--cold-start-selection`. +""" + +import glob +import os + +import numpy as np +from scipy.io import wavfile + + +def load_voc_windows(data_dir, n_vocs, start_offset_ms, n): + """Held-out sustained vocalization windows (start `start_offset_ms` after onset).""" + segs = [] + sr = None + for wav in sorted(glob.glob(os.path.join(data_dir, "*.wav")))[:n_vocs]: + sr, af = wavfile.read(wav) + if af.dtype == np.int16: + af = af / -np.iinfo(af.dtype).min + af = af.astype(np.float64) + onoffs = np.atleast_2d(np.loadtxt(wav.replace(".wav", ".txt"))) + s = int(onoffs[0][0] * sr) + int(start_offset_ms / 1e3 * sr) + seg = af[s:s + n] + if len(seg) == n: + segs.append(seg) + return segs, sr + + +def load_voc_windows_coldstart(data_dir, n_vocs, silence_pad_samples): + """Held-out cold-start windows: `silence_pad_samples` lead-in + full vocalization. + + Matches the 50 ms lead-in convention (2000 samples at 40 kHz) used by the existing + finchsim/scan_seed_amp_coldstart pipeline. Per-voc lengths vary, so segments are + trimmed to the shortest common length so they stack uniformly. + """ + raw = [] + sr = None + for wav in sorted(glob.glob(os.path.join(data_dir, "*.wav")))[:n_vocs]: + sr, af = wavfile.read(wav) + if af.dtype == np.int16: + af = af / -np.iinfo(af.dtype).min + af = af.astype(np.float64) + onoffs = np.atleast_2d(np.loadtxt(wav.replace(".wav", ".txt"))) + on_i = int(round(onoffs[0][0] * sr)) + off_i = int(round(onoffs[0][1] * sr)) + start = max(0, on_i - silence_pad_samples) + raw.append(af[start:off_i]) + if not raw: + return [], sr + L = min(len(s) for s in raw) + segs = [s[:L] for s in raw] + return segs, sr diff --git a/scripts/stage_finch_blk445_syllC.py b/scripts/stage_finch_blk445_syllC.py new file mode 100644 index 0000000..f3ef25b --- /dev/null +++ b/scripts/stage_finch_blk445_syllC.py @@ -0,0 +1,83 @@ +"""Stage blk445 / syllable_C real-finch data for the Ouroboros training pipeline. + +The pipeline (data/load_data.py::get_segmented_audio + examples/run_lambda_pipeline.py) +expects each shard directory to contain paired `{stem}.wav` and `{stem}.txt` files +side by side. The real data lives in two split locations with mismatched suffixes: + + audio: ~/isilon/All_Staff/.../blk445/{day}/double_denoised/{stem}_cleaned.wav + annotations: ~/isilon/.../blk445/segs/{day}/syllable_C/{stem}.txt + +This script symlinks (no copies) non-empty annotations and their matching cleaned +wavs into ~/ouroboros_data/blk445_syllC/day{day}/ with `{stem}.wav` / `{stem}.txt` +filenames the pipeline can glob. Skips annotations whose corresponding wav is +missing. Idempotent. +""" + +import os +import sys + +DAYS = [84, 85, 86] +SRC_ROOT = os.path.expanduser( + "~/isilon/All_Staff/birds/mooney/muscimol/Microdialysis/Muscimol/blk445" +) +DST_ROOT = os.path.expanduser("~/ouroboros_data/blk445_syllC") + + +def stage_day(day): + seg_dir = os.path.join(SRC_ROOT, "segs", str(day), "syllable_C") + wav_dir = os.path.join(SRC_ROOT, str(day), "double_denoised") + dst_dir = os.path.join(DST_ROOT, f"day{day}") + os.makedirs(dst_dir, exist_ok=True) + + if not os.path.isdir(seg_dir): + print(f"day{day}: missing seg_dir {seg_dir}", file=sys.stderr) + return 0, 0 + if not os.path.isdir(wav_dir): + print(f"day{day}: missing wav_dir {wav_dir}", file=sys.stderr) + return 0, 0 + + staged = 0 + skipped_empty = 0 + skipped_no_wav = 0 + for fn in sorted(os.listdir(seg_dir)): + if not fn.endswith(".txt"): + continue + seg_path = os.path.join(seg_dir, fn) + if os.path.getsize(seg_path) == 0: + skipped_empty += 1 + continue + stem = fn[:-len(".txt")] + wav_src = os.path.join(wav_dir, f"{stem}_cleaned.wav") + if not os.path.isfile(wav_src): + skipped_no_wav += 1 + continue + + wav_link = os.path.join(dst_dir, f"{stem}.wav") + txt_link = os.path.join(dst_dir, f"{stem}.txt") + for link, target in [(wav_link, wav_src), (txt_link, seg_path)]: + if os.path.lexists(link): + if os.readlink(link) == target: + continue + os.remove(link) + os.symlink(target, link) + staged += 1 + + print( + f"day{day}: staged={staged} (skipped {skipped_empty} empty annotations, " + f"{skipped_no_wav} with missing wav) -> {dst_dir}", + flush=True, + ) + return staged, skipped_no_wav + + +def main(): + os.makedirs(DST_ROOT, exist_ok=True) + total_staged = 0 + for day in DAYS: + n, _ = stage_day(day) + total_staged += n + print(f"TOTAL staged pairs: {total_staged}", flush=True) + + +if __name__ == "__main__": + main() From 7013d112f43d862eb88646d375e3ca886763983d Mon Sep 17 00:00:00 2001 From: John Pearson Date: Thu, 4 Jun 2026 15:22:31 -0400 Subject: [PATCH 04/34] feat: edge-biased segment sampler with onset/offset/mid categories data/load_data.py: - ONSET=0, OFFSET=1, MID=2 category constants. - get_audio_training_edge_weighted: builds three pools per (wav, txt) pair (onset-aligned + silence_prefix_ms prefix; offset-aligned + silence_suffix_ms tail; non-overlapping mid-syllable windows inside [on+edge_ms, off-edge_ms]), then samples to max_segs with the requested category ratio. int16 normalize. - get_segmented_audio_edge_weighted: convenience wrapper that pairs the audio/seg directories like get_segmented_audio. data/data_utils.py: - aud_neur_ds now optionally carries per-example category labels; __getitem__ returns a 4-tuple (x, dxdt, d2x, category) when categories are set. - _stratified_split + get_loaders_edge: per-category stratified train/val/test split so val/test are never starved of ONSET examples even at low onset ratio. Verified on day 85 (sr=44100): 300 segs sampled with the (0.4, 0.4, 0.2) ratio gives exactly 120/120/60 counts; stratified split yields 240/30/30 with 4-element batches containing category tensors. Co-Authored-By: Claude Opus 4.7 --- data/data_utils.py | 81 +++++++++++++++++++++- data/load_data.py | 163 +++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 243 insertions(+), 1 deletion(-) diff --git a/data/data_utils.py b/data/data_utils.py index fea0c59..1fa001b 100644 --- a/data/data_utils.py +++ b/data/data_utils.py @@ -12,9 +12,14 @@ class aud_neur_ds(Dataset): """ think about how to change this to accept lists, rather than arrays (probably just map, but make sure this works with arrays still) + + If `categories` is passed (np.ndarray of int, shape (N,)), __getitem__ returns a + 4-tuple (x, dxdt, dx2dt2, category) instead of the legacy 3-tuple. Used by the + edge-biased sampler so the training step knows which examples are ONSET/OFFSET/MID + (and should get the cold-start noise IC for the spectral rollout). """ - def __init__(self, data, deriv_approx="nine-point", dxdt=[], dx2dt2=[]): + def __init__(self, data, deriv_approx="nine-point", dxdt=[], dx2dt2=[], categories=None): self.x = data if len(dxdt) > 0: self.dxdt = dxdt @@ -41,6 +46,13 @@ def __init__(self, data, deriv_approx="nine-point", dxdt=[], dx2dt2=[]): ) elif deriv_approx == "nine-point": self.dx2dt2 = deriv_approx_d2y(self.x) + # Optional per-example category labels (0=ONSET, 1=OFFSET, 2=MID) used by the + # edge-biased training loop to pick which examples get the cold-start noise IC. + if categories is not None: + assert len(categories) == self.x.shape[0], "categories must match data length" + self.categories = np.asarray(categories, dtype=np.int64) + else: + self.categories = None def __len__(self): @@ -58,6 +70,8 @@ def __getitem__(self, idx): torch.from_numpy(dx2dt2).type(torch.DoubleTensor), ) + if self.categories is not None: + return x, dxdt, dx2dt2, int(self.categories[idx]) return x, dxdt, dx2dt2 def interpolate_oversample(self, oversample_prop, dt): @@ -217,3 +231,68 @@ def get_loaders_interp( ) return dls + + +# --- Edge-biased loaders for spectral-rollout training ------------------------------- + +def _stratified_split(N, categories, test_size, seed): + """Per-category random split into (train_idx, test_idx); preserves the ratio.""" + rng = np.random.default_rng(seed) + train_idx, test_idx = [], [] + for cat in np.unique(categories): + idx = np.where(categories == cat)[0] + rng.shuffle(idx) + n_test = max(1, int(round(test_size * len(idx)))) if len(idx) > 1 else 0 + test_idx.append(idx[:n_test]) + train_idx.append(idx[n_test:]) + return np.concatenate(train_idx), np.concatenate(test_idx) + + +def get_loaders_edge( + data, + categories, + num_workers=4, + batch_size=8, + train_size=0.8, + cv=True, + seed=None, +): + """ + DataLoaders for the edge-biased segment sampler. `data` is (N, L, 1) and `categories` + is (N,) int (ONSET=0/OFFSET=1/MID=2). Returns a dict {train, [val,] test} of loaders + whose datasets carry the matching per-example category labels so the training step + can drive the cold-start IC selection. + + Stratified split: each category is split independently with the same train_size, so + val/test never have zero ONSET examples even when the ratio is small. + """ + data = np.asarray(data) + categories = np.asarray(categories, dtype=np.int64) + test_size = 1 - train_size + + train_i, holdout = _stratified_split(len(data), categories, test_size, seed) + dls = {} + if cv: + # Stratified 50/50 split of the held-out tail into val and test (in local coords, + # then mapped back to globals via the `holdout` index array). + val_local, test_local = _stratified_split( + len(holdout), categories[holdout], test_size=0.5, seed=seed + ) + val_idx = holdout[val_local] + test_idx = holdout[test_local] + dsVal = aud_neur_ds(data[val_idx], categories=categories[val_idx]) + dls["val"] = DataLoader( + dsVal, num_workers=num_workers, batch_size=batch_size, shuffle=False + ) + else: + test_idx = holdout + + dsTrain = aud_neur_ds(data[train_i], categories=categories[train_i]) + dsTest = aud_neur_ds(data[test_idx], categories=categories[test_idx]) + dls["train"] = DataLoader( + dsTrain, num_workers=num_workers, batch_size=batch_size, shuffle=True + ) + dls["test"] = DataLoader( + dsTest, num_workers=num_workers, batch_size=batch_size, shuffle=False + ) + return dls diff --git a/data/load_data.py b/data/load_data.py index a350e23..321d1e8 100644 --- a/data/load_data.py +++ b/data/load_data.py @@ -197,3 +197,166 @@ def get_segmented_audio( ) return audio_segments, sr + + +# --- Edge-biased segment sampler for spectral-rollout training ----------------------- + +ONSET = 0 +OFFSET = 1 +MID = 2 + + +def get_audio_training_edge_weighted( + audio_files: list[str], + seg_files: list[str], + *, + context_len: float, + edge_ms: float = 10.0, + silence_prefix_ms: float = 25.0, + silence_suffix_ms: float = 25.0, + ratio=(0.4, 0.4, 0.2), + max_segs: int = 5000, + seed: int = 0, + int16_norm: bool = True, +): + """ + Categorized segment sampler that heavily oversamples syllable onsets and offsets. + + For each (wav, txt) pair, three pools of fixed-length (context_len seconds) windows + are built: + - ONSET (cat=0): window starts at on - silence_prefix_ms (real pre-onset audio + forms the silence-noise prefix; first context_len samples). + - OFFSET (cat=1): window ends at off + silence_suffix_ms (real post-offset audio + forms the silence tail; last context_len samples). + - MID (cat=2): non-overlapping windows of length context_len lying strictly inside + [on + edge_ms, off - edge_ms]. + + The pools are then sampled to `max_segs` segments with category-counts proportional to + `ratio` (with replacement only if a pool is exhausted). Returns + (segments_list, categories_array, sr). + + The cold-start training step gets the cold-start noise IC only on ONSET examples + (categories == ONSET); OFFSET/MID examples use the data IC. See + train/spectral_rollout.py. + """ + rng = np.random.default_rng(seed) + pools = {ONSET: [], OFFSET: [], MID: []} + sr = None + + for a, s in zip(audio_files, seg_files): + if not (os.path.isfile(a) and os.path.isfile(s)): + continue + sr, aud = wavfile.read(a) + if aud.dtype == np.int16 and int16_norm: + aud = aud / -np.iinfo(aud.dtype).min + aud = aud.astype(np.float64) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + on_off = np.loadtxt(s, usecols=(0, 1)) + if on_off.size == 0: + continue + if on_off.ndim == 1: + on_off = on_off[None, :] + + L_seg = int(round(context_len * sr)) + pre = int(round(silence_prefix_ms / 1000.0 * sr)) + suff = int(round(silence_suffix_ms / 1000.0 * sr)) + edge = int(round(edge_ms / 1000.0 * sr)) + + for on_s, off_s in on_off: + on_i = int(round(on_s * sr)) + off_i = int(round(off_s * sr)) + + # ONSET: window starts pre samples before onset + start = on_i - pre + end = start + L_seg + if start >= 0 and end <= len(aud) and end > on_i: + pools[ONSET].append(aud[start:end]) + + # OFFSET: window ends suff samples after offset + end = off_i + suff + start = end - L_seg + if start >= 0 and end <= len(aud) and start < off_i: + pools[OFFSET].append(aud[start:end]) + + # MID: non-overlapping windows strictly inside (on+edge, off-edge) + mid_start = on_i + edge + mid_end = off_i - edge + if mid_end - mid_start >= L_seg: + for ms in range(mid_start, mid_end - L_seg + 1, L_seg): + pools[MID].append(aud[ms:ms + L_seg]) + + # If max_segs is 0 use the whole pool; otherwise sample to that target with the ratio. + pool_sizes = {c: len(pools[c]) for c in (ONSET, OFFSET, MID)} + if sum(pool_sizes.values()) == 0: + return [], np.array([], dtype=np.int64), sr + + if max_segs <= 0: + # Return everything, no resampling. + out_segs, out_cats = [], [] + for cat in (ONSET, OFFSET, MID): + for seg in pools[cat]: + out_segs.append(seg.reshape(-1, 1).astype(np.float32)) + out_cats.append(cat) + order = rng.permutation(len(out_segs)) + return ([out_segs[i] for i in order], + np.array([out_cats[i] for i in order], dtype=np.int64), + sr) + + ratio = np.asarray(ratio, dtype=np.float64) + ratio = ratio / ratio.sum() + counts = np.round(ratio * max_segs).astype(int) + counts[-1] = max_segs - counts[:-1].sum() # exact total + + out_segs, out_cats = [], [] + for cat, n in zip((ONSET, OFFSET, MID), counts): + pool = pools[cat] + if not pool or n <= 0: + continue + replace = n > len(pool) + idx = rng.choice(len(pool), size=n, replace=replace) + for i in idx: + out_segs.append(pool[i].reshape(-1, 1).astype(np.float32)) + out_cats.append(cat) + + order = rng.permutation(len(out_segs)) + return ([out_segs[i] for i in order], + np.array([out_cats[i] for i in order], dtype=np.int64), + sr) + + +def get_segmented_audio_edge_weighted( + audio_path: str, + seg_path: str, + *, + audio_id: str = ".wav", + context_len: float, + edge_ms: float = 10.0, + silence_prefix_ms: float = 25.0, + silence_suffix_ms: float = 25.0, + ratio=(0.4, 0.4, 0.2), + max_segs: int = 5000, + seed: int = 0, + shuffle_files: bool = True, +): + """Convenience wrapper: glob audio files from audio_path, pair with seg_path/*.txt, + then call get_audio_training_edge_weighted. Returns (segments, categories, sr). + """ + gen = np.random.default_rng(seed) + audio_files = glob.glob(os.path.join(audio_path, "*" + audio_id)) + if shuffle_files: + order = gen.permutation(len(audio_files)) + audio_files = [audio_files[o] for o in order] + audio_tags = [a.split("/")[-1].split(audio_id)[0] for a in audio_files] + seg_files = [os.path.join(seg_path, a + ".txt") for a in audio_tags] + return get_audio_training_edge_weighted( + audio_files, seg_files, + context_len=context_len, + edge_ms=edge_ms, + silence_prefix_ms=silence_prefix_ms, + silence_suffix_ms=silence_suffix_ms, + ratio=ratio, + max_segs=max_segs, + seed=seed, + ) From 33a0cdd87a03a2bc259b933b3dd5120b02972774 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Thu, 4 Jun 2026 15:22:50 -0400 Subject: [PATCH 05/34] port: multi-resolution STFT loss module (rollout_refine.py) Verbatim port of train/rollout_refine.py from PR #4. This PR uses mrstft_loss, stft_mag, gaussian_envelope, env_loss, and gather_windows as building blocks for the new spectral-rollout training step. The rollout_refine driver function itself is kept too (post-hoc fine-tune is still useful as a diagnostic) but is NOT the primary training loop for this PR. Verified mrstft_loss + env_loss imports and run on random (4, 2000) waveforms. Co-Authored-By: Claude Opus 4.7 --- train/rollout_refine.py | 173 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 173 insertions(+) create mode 100644 train/rollout_refine.py diff --git a/train/rollout_refine.py b/train/rollout_refine.py new file mode 100644 index 0000000..451de84 --- /dev/null +++ b/train/rollout_refine.py @@ -0,0 +1,173 @@ +""" +Rollout refinement objective for the polynomial Ouroboros -- a reusable training-pipeline component. + +Phase-invariant objective on the soft-saturated differentiable RK4 AUTONOMOUS rollout, so a +phase-drifted-but-correct-content free run is scored well and the optimizer fixes frequency content ++ loudness instead of collapsing amplitude (the failure mode of pointwise rollout MSE on a drifting +oscillator): + + L = lam_spec * L_mrstft + lam_env * L_env + lam_tf * L_tf + + - L_mrstft : multi-resolution STFT MAGNITUDE loss (spectral convergence + log-magnitude L1). + Magnitude discards phase -> tolerant of carrier drift; penalizes a wrong pitch/harmonic + stack that persists across frames. + - L_env : Gaussian-low-passed |x| envelope L1 (per-voc normalized). The envelope IS the + instantaneous amplitude, so REQUIRING IT TO MATCH pins the autonomous loudness/scale -- + but it must be weighted up (lam_env >> lam_spec) or the spectral term swamps it and the + global amplitude drifts free. lam_env~10 fixes amplitude across seeds without regressing + already-good models (validated 2026-05-28). + - L_tf : original one-step teacher-forced anchor (preserve the well-fit dynamics / R^2). + +Backprops through the rollout with a curriculum on the horizon; because the loss is phase-invariant +the horizon can be long. See examples/finetune_rollout_spectral_poly.py (single-model entry) and +examples/train_poly_rollout.py (integrated TF + refine trainer). +""" + +import glob +import os + +import numpy as np +import torch +import torch.nn.functional as F +from scipy.io import wavfile + +from utils import deriv_approx_dy, deriv_approx_d2y + +BX, BXP = 0.5, 1.0 # soft-saturation bounds (>> data/limit-cycle scale; only tame divergence) +DEFAULT_CONFIGS = ((256, 64), (512, 128), (1024, 256)) + + +def gather_windows(dirs, n, L, start_off_ms): + """held-out sustained vocalization windows of length L, starting start_off_ms after onset.""" + segs, sr = [], None + for d in dirs: + for wav in sorted(glob.glob(os.path.join(d, "*.wav"))): + sr, af = wavfile.read(wav) + af = af.astype(np.float64) + on = np.atleast_2d(np.loadtxt(wav.replace(".wav", ".txt")))[0][0] + s = int(on * sr) + int(start_off_ms / 1e3 * sr) + seg = af[s:s + L] + if len(seg) == L: + segs.append(seg) + if len(segs) >= n: + return np.stack(segs)[:, :, None], sr + return np.stack(segs)[:, :, None], sr + + +def stft_mag(x, n_fft, hop): + win = torch.hann_window(n_fft, device=x.device, dtype=x.dtype) + S = torch.stft(x, n_fft=n_fft, hop_length=hop, win_length=n_fft, window=win, + center=True, return_complex=True) + return S.abs() # (B, F, T) + + +def mrstft_loss(xg, tgt, configs=DEFAULT_CONFIGS, eps=1e-5): + """multi-resolution STFT magnitude loss: spectral convergence + log-magnitude L1.""" + total = 0.0 + for n_fft, hop in configs: + A = stft_mag(xg, n_fft, hop) + G = stft_mag(tgt, n_fft, hop) + sc = torch.norm(G - A, dim=(-2, -1)) / (torch.norm(G, dim=(-2, -1)) + 1e-8) + logm = (torch.log(G + eps) - torch.log(A + eps)).abs().mean(dim=(-2, -1)) + total = total + (sc + logm).mean() + return total / len(configs) + + +def gaussian_envelope(x, dt, env_ms): + """Gaussian low-pass of |x| (removes the carrier, keeps the loudness modulation). x: (B,H).""" + sigma = (env_ms / 1e3) / dt + radius = max(1, int(round(3 * sigma))) + t = torch.arange(-radius, radius + 1, device=x.device, dtype=x.dtype) + k = torch.exp(-0.5 * (t / sigma) ** 2) + k = k / k.sum() + xr = F.pad(x.abs().unsqueeze(1), (radius, radius), mode="reflect") + return F.conv1d(xr, k.view(1, 1, -1)).squeeze(1) # (B,H) + + +def env_loss(xg, tgt, dt, env_ms, eps=1e-6): + ea = gaussian_envelope(xg, dt, env_ms) + eg = gaussian_envelope(tgt, dt, env_ms) + return ((ea - eg).abs().mean(dim=1) / (eg.mean(dim=1) + eps)).mean() + + +def rollout_refine(model, X, dt, *, epochs=8, hmin=768, hmax=1500, batch_size=6, lr=1e-4, + lam_spec=1.0, lam_env=10.0, lam_tf=1.0, clip=5.0, env_ms=2.0, + configs=DEFAULT_CONFIGS, device="cuda", verbose=True): + """ + In-place rollout refinement of a trained polynomial Ouroboros. + + X: numpy (N, L, 1) sustained vocalization windows (L >= hmax). Modifies `model` in place. + Returns (history, opt): per-epoch loss history (list of dicts) and the Adam optimizer (so the + caller can persist it via train.train.save_model). + """ + assert hmax <= X.shape[1], "hmax must be <= window length L" + model.train() + D1 = deriv_approx_dy(X) + D2 = deriv_approx_d2y(X) + Xt = torch.tensor(X, dtype=torch.float32, device=device) + Dt = torch.tensor(D1, dtype=torch.float32, device=device) + D2t = torch.tensor(D2, dtype=torch.float32, device=device) + var_d2 = float(D2t.var()) + P = model.kernel.poly_dim + 1 + powers = torch.arange(P, device=device) + opt = torch.optim.Adam(model.parameters(), lr=lr) + N = Xt.shape[0] + H_sched = np.unique(np.round(np.geomspace(hmin, hmax, epochs)).astype(int)) + if verbose: + print(f"rollout refine: {N} windows L={X.shape[1]} H {hmin}->{hmax} | " + f"lam tf={lam_tf} spec={lam_spec} env={lam_env} env_ms={env_ms} epochs={epochs}", flush=True) + + history = [] + for epoch in range(epochs): + H = int(H_sched[min(epoch, len(H_sched) - 1)]) + perm = torch.randperm(N) + tot = {"spec": 0.0, "env": 0.0, "tf": 0.0} + nb = 0 + for i in range(0, N, batch_size): + idx = perm[i:i + batch_size] + x, dxd, d2b = Xt[idx], Dt[idx], D2t[idx] + z2 = (model.tau / dt) * dxd + omega, gamma, wk, weights, _ = model.get_funcs(x, dxd.clone(), dt) # differentiable + tf = -(omega ** 2) * x - gamma * z2 - wk + L_tf = ((tf - d2b) ** 2).mean() / var_d2 + + om, ga, w = omega[:, :, 0], gamma[:, :, 0], weights # w: (B,L,P,P) + + def f(xx, vv, k): + xpw = xx.unsqueeze(1) ** powers + xvw = vv.unsqueeze(1) ** powers + kern = torch.einsum("bp,bk,bpk->b", xpw, xvw, w[:, k]) + return vv, -(om[:, k] ** 2) * xx - ga[:, k] * vv - kern + + xc = x[:, 0, 0].detach() + xp = z2[:, 0, 0].detach() + xs = [xc] + for k in range(H - 1): + k1x, k1v = f(xc, xp, k) + k2x, k2v = f(xc + 0.5 * k1x, xp + 0.5 * k1v, k) + k3x, k3v = f(xc + 0.5 * k2x, xp + 0.5 * k2v, k) + k4x, k4v = f(xc + k3x, xp + k3v, k) + xc = xc + (k1x + 2 * k2x + 2 * k3x + k4x) / 6 + xp = xp + (k1v + 2 * k2v + 2 * k3v + k4v) / 6 + xc = BX * torch.tanh(xc / BX) + xp = BXP * torch.tanh(xp / BXP) + xs.append(xc) + xg = torch.stack(xs, dim=1) # (B,H) autonomous + tgt = x[:, :H, 0] # (B,H) target, same time span + + L_spec = mrstft_loss(xg, tgt, configs) + L_env = env_loss(xg, tgt, dt, env_ms) + loss = lam_spec * L_spec + lam_env * L_env + lam_tf * L_tf + opt.zero_grad() + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), clip) + opt.step() + tot["spec"] += float(L_spec); tot["env"] += float(L_env); tot["tf"] += float(L_tf) + nb += 1 + rec = {"epoch": epoch + 1, "H": H, "spec": tot["spec"] / nb, + "env": tot["env"] / nb, "tf": tot["tf"] / nb} + history.append(rec) + if verbose: + print(f"[refine ep {epoch + 1}/{epochs} H={H}] spec={rec['spec']:.4f} " + f"env={rec['env']:.4f} tf={rec['tf']:.4f}", flush=True) + return history, opt From a419b2709f666a5a82e76caa3af5f670e0b978a3 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Thu, 4 Jun 2026 15:32:56 -0400 Subject: [PATCH 06/34] feat: spectral-rollout training step + loss_mode='spectral_rollout' branch train/spectral_rollout.py (NEW): - teacher_forced_rollout: lifts the RK4 + soft-tanh kernel from train/rollout_refine.py into a standalone function. Drives encoded from the target via model.get_funcs (low-passed inside the model); state (x, x') autonomous starting from the data IC or, for examples whose ic_mask is True, from N(0, ic_noise_rms^2) -- the cold-start training mode for ONSET segments. - spectral_rollout_step: combines MRSTFT magnitude loss on the integrated waveform, a variance-normalized teacher-forced d2-MSE anchor, and an optional envelope L1 term. Auto-drops STFT configs with n_fft > H. - horizon_for_epoch: geom/linear/const curriculum so the rollout horizon ramps up across training. train/train.py: - loss_mode='mse_accel' (default, legacy) | 'spectral_rollout' kwarg, with the spectral path branching at the top of the inner loop -- skips model.forward entirely (spectral_rollout_step does its own get_funcs with a cloned dxdt), unpacks the 4-tuple batch from the edge-biased loader, builds ic_mask = (cats == ONSET), and logs per-component losses + the current horizon to TB. NaN total -> skip optimizer step + log counter, grad-clip 5.0. - Val loop now tolerates 4-tuple batches; in spectral mode it is skipped entirely (autonomy-based selection in train/model_cv handles validation). - Pre-existing bug fixed: total_loss was previously only defined inside the if-reg_weights block, which crashed unregularized runs. Smoke-tested end-to-end on day 85: 2 epochs, H=256, 64 segments, 4-tuple batches with ONSET ic_mask. Pipeline runs, gradients finite, no NaNs. autonomy_score returns the expected -5.0 divergence baseline for an undertrained model. Co-Authored-By: Claude Opus 4.7 --- train/spectral_rollout.py | 194 ++++++++++++++++++++++++++++++++++++++ train/train.py | 80 +++++++++++++++- 2 files changed, 271 insertions(+), 3 deletions(-) create mode 100644 train/spectral_rollout.py diff --git a/train/spectral_rollout.py b/train/spectral_rollout.py new file mode 100644 index 0000000..db94ac4 --- /dev/null +++ b/train/spectral_rollout.py @@ -0,0 +1,194 @@ +"""Spectral-rollout training step for the polynomial Ouroboros. + +Replaces the legacy MSE-on-d²y/ds² objective with a DDSP-style multi-resolution +STFT magnitude loss (arxiv:1910.11480) computed on a teacher-forced RK4 reconstruction +of the waveform: + + 1. Encode the drives (ω(t), γ(t), w(t)) from the target audio via `model.get_funcs` + (with optional low-pass filtering inside the model). + 2. Integrate the polynomial ODE forward in rescaled time s = t/τ with those drives + held open-loop, but the state (x, x') autonomous. Initial condition is the data + IC by default; for examples whose `ic_mask` is True, the IC is overridden with + low-amplitude Gaussian noise (the cold-start training mode). + 3. Compute MRSTFT magnitude loss between the integrated waveform and the target. + Optionally add a teacher-forced d²y/ds² MSE anchor (default on, weight 1.0) so + early-epoch training has a smooth gradient signal before the spectral basin + becomes informative, and an envelope L1 term (default off) for amplitude pinning. + +The RK4 inner loop and soft-tanh saturation (BX=0.5, BXP=1.0) match train/rollout_refine.py, +which already validated this kernel for post-hoc fine-tuning. The novelty here is using +it as the PRIMARY objective from scratch, with per-example cold-start IC selection +driven by the edge-biased sampler's category labels. +""" + +from typing import Optional + +import torch + +from train.rollout_refine import ( + DEFAULT_CONFIGS, + BX, + BXP, + mrstft_loss, + env_loss, +) + +# ONSET category from data.load_data (kept local to avoid the import cycle the data +# module would introduce through utils). +ONSET = 0 + + +def _filter_configs_for_horizon(configs, H): + """Drop STFT (n_fft, hop) configs whose n_fft > H so torch.stft does not pad + the rollout up to a longer effective window than we actually integrated.""" + keep = [(n_fft, hop) for (n_fft, hop) in configs if n_fft <= H] + if not keep: + # Smallest config still too big? Use a tiny one matched to H. + n_fft = max(16, H // 2) + keep = [(n_fft, max(1, n_fft // 4))] + return tuple(keep) + + +def teacher_forced_rollout( + model, + x: torch.Tensor, # (B, L, 1) target audio + dxdt: torch.Tensor, # (B, L, 1) numerical first derivative (per-sample) + dt: float, + *, + H: int, # rollout horizon in samples + ic_mask: Optional[torch.Tensor] = None, # (B,) bool; True => silence-noise IC + ic_noise_rms: float = 1e-3, + rng: Optional[torch.Generator] = None, +) -> torch.Tensor: # (B, H) + """RK4 H-step rollout of the poly Ouroboros, drives encoded from target audio. + + Drives (ω, γ, kernel weights w) come from `model.get_funcs(x, dxdt.clone(), dt)` + and are held OPEN-LOOP across the integration; the state (x, x') is updated + autonomously. Same soft-tanh saturation as train/rollout_refine.py:136-155 + (BX=0.5 on x, BXP=1.0 on x') so a random-init model can't blow up. + + `ic_mask` is a (B,) bool tensor. Where True, the initial (x, x') is replaced with + N(0, ic_noise_rms²) samples — this is the cold-start training mode for ONSET + examples. Where False (or if ic_mask is None), the IC is (x[:, 0, 0], z2[:, 0, 0]) + as in rollout_refine. + """ + B, L, _ = x.shape + assert H <= L, f"rollout horizon H={H} must be <= segment length L={L}" + + # Encode drives in a single forward of the Mamba encoders. + omega, gamma, _, weights, _ = model.get_funcs(x, dxdt.clone(), dt) # ω,γ:(B,L,1); w:(B,L,P,P) + z2 = (model.tau / dt) * dxdt # rescaled-time velocity (B, L, 1) + + om = omega[:, :, 0] + ga = gamma[:, :, 0] + w = weights + P = w.shape[-1] + powers = torch.arange(P, device=x.device) + + # IC -- (B,) tensors of x and x' at s=0. + xc = x[:, 0, 0].detach().clone() + xp = z2[:, 0, 0].detach().clone() + if ic_mask is not None and ic_mask.any(): + # NOTE: torch.randn supports a `generator=` arg even for CUDA when the + # generator was created on the same device. + noise_x = torch.randn(B, device=x.device, generator=rng) * ic_noise_rms + noise_xp = torch.randn(B, device=x.device, generator=rng) * ic_noise_rms + m = ic_mask.to(x.device).to(x.dtype) + xc = xc * (1 - m) + noise_x * m + xp = xp * (1 - m) + noise_xp * m + + def f(xx, vv, k): + xpw = xx.unsqueeze(1) ** powers # (B, P) + xvw = vv.unsqueeze(1) ** powers # (B, P) + kern = torch.einsum("bp,bk,bpk->b", xpw, xvw, w[:, k]) + return vv, -(om[:, k] ** 2) * xx - ga[:, k] * vv - kern + + xs = [xc] + for k in range(H - 1): + k1x, k1v = f(xc, xp, k) + k2x, k2v = f(xc + 0.5 * k1x, xp + 0.5 * k1v, k) + k3x, k3v = f(xc + 0.5 * k2x, xp + 0.5 * k2v, k) + k4x, k4v = f(xc + k3x, xp + k3v, k) + xc = xc + (k1x + 2 * k2x + 2 * k3x + k4x) / 6 + xp = xp + (k1v + 2 * k2v + 2 * k3v + k4v) / 6 + # soft-tanh saturation (>> data scale; only tames blow-up during training) + xc = BX * torch.tanh(xc / BX) + xp = BXP * torch.tanh(xp / BXP) + xs.append(xc) + return torch.stack(xs, dim=1) # (B, H) + + +def spectral_rollout_step( + model, + x: torch.Tensor, # (B, L, 1) target audio + dxdt: torch.Tensor, # (B, L, 1) + d2x: torch.Tensor, # (B, L, 1) rescaled-time target acceleration + dt: float, + *, + H: int, + configs=DEFAULT_CONFIGS, + lam_spec: float = 1.0, + lam_tf: float = 1.0, + lam_env: float = 0.0, + env_ms: float = 2.0, + ic_mask: Optional[torch.Tensor] = None, + ic_noise_rms: float = 1e-3, + rng: Optional[torch.Generator] = None, +) -> dict: + """One forward + loss for the spectral-rollout objective. + + Returns a dict {'spec', 'tf', 'env', 'total'} of scalar tensors. The caller is + responsible for backprop on 'total'. The TF anchor is the same variance-normalized + d²y/ds² MSE used in train/rollout_refine.py:132 — keeps the model in a learnable + basin in early epochs while the rollout horizon is short. + + `d2x` is expected to ALREADY be in rescaled-time units (i.e. multiplied by τ²/dt² + by the caller), matching the convention used by train.train.train() for the legacy + MSE-on-accel loss. The TF anchor compares it to the model-predicted rescaled + acceleration tf_d2 = -ω²·x - γ·z2 - weighted_kernels. + """ + # Encode drives once (model.get_funcs mutates dxdt in place; pass a clone). + omega, gamma, wk, weights, _ = model.get_funcs(x, dxdt.clone(), dt) + z2 = (model.tau / dt) * dxdt # rescaled velocity + tf_d2 = -(omega ** 2) * x - gamma * z2 - wk + # variance-normalized so the anchor scale is commensurable across vocs / runs + var_d2 = d2x.var().clamp_min(1e-12) + L_tf = ((tf_d2 - d2x) ** 2).mean() / var_d2 + + # Rollout + spectral loss. The rollout calls model.get_funcs again with a fresh + # clone (drives recomputed for the rollout-state forward). Could be unified with + # the TF computation above, but keeping them separate makes the per-component + # losses easy to reason about and matches rollout_refine's structure. + xg = teacher_forced_rollout( + model, x, dxdt, dt, H=H, + ic_mask=ic_mask, ic_noise_rms=ic_noise_rms, rng=rng, + ) + tgt = x[:, :H, 0] + + configs_H = _filter_configs_for_horizon(configs, H) + L_spec = mrstft_loss(xg, tgt, configs_H) + if lam_env > 0: + L_env = env_loss(xg, tgt, dt, env_ms) + else: + L_env = torch.zeros((), device=x.device, dtype=x.dtype) + + total = lam_spec * L_spec + lam_tf * L_tf + lam_env * L_env + return {"spec": L_spec, "tf": L_tf, "env": L_env, "total": total} + + +def horizon_for_epoch(epoch: int, n_epochs: int, H_min: int, H_max: int, + schedule: str = "geom") -> int: + """Curriculum on the rollout horizon: small H early (cheap, easy gradient), full H + by the end. 'geom' = geometric spacing; 'linear' = linear; 'const' = H_max throughout. + """ + if n_epochs <= 1 or schedule == "const": + return int(H_max) + t = epoch / max(1, n_epochs - 1) + if schedule == "linear": + H = H_min + t * (H_max - H_min) + elif schedule == "geom": + import math + H = H_min * (H_max / H_min) ** t + else: + raise ValueError(f"unknown schedule {schedule!r}") + return int(round(H)) diff --git a/train/train.py b/train/train.py index 57704ed..6006ca7 100644 --- a/train/train.py +++ b/train/train.py @@ -168,6 +168,18 @@ def train( start_epoch=0, model_info={}, save_freq=0, + loss_mode: str = "mse_accel", + # spectral-rollout knobs (only consulted when loss_mode == "spectral_rollout") + H_min: int = 512, + H_max: int = 2000, + H_schedule: str = "geom", + lam_spec: float = 1.0, + lam_tf: float = 1.0, + lam_env: float = 0.0, + env_ms: float = 2.0, + spec_configs=None, + ic_noise_rms: float = 1e-3, + grad_clip: float = 5.0, ) -> Tuple[ list[float], list[Tuple[int, float, float]], nn.Module, torch.optim.Optimizer ]: @@ -206,6 +218,21 @@ def train( train_losses, val_losses = [], [] + if loss_mode == "spectral_rollout": + from train.spectral_rollout import ( + spectral_rollout_step, + horizon_for_epoch, + DEFAULT_CONFIGS, + ONSET, + ) + if spec_configs is None: + spec_configs = DEFAULT_CONFIGS + print( + f"spectral_rollout mode: lam_spec={lam_spec} lam_tf={lam_tf} lam_env={lam_env} " + f"H={H_min}->{H_max} ({H_schedule}) ic_noise_rms={ic_noise_rms}", + flush=True, + ) + for epoch in tqdm(range(start_epoch, nEpochs), desc="training model"): model.train() @@ -213,7 +240,11 @@ def train( loaders["train"], start=epoch * len(loaders["train"]) ): optimizer.zero_grad() - x, dxdt, dx2dt2 = batch # each is bsz x seq len x 1 + if len(batch) == 4: + x, dxdt, dx2dt2, cats = batch # categories from edge-biased sampler + else: + x, dxdt, dx2dt2 = batch + cats = None bsz, _, n = x.shape x = x.to("cuda").to(torch.float32) @@ -222,6 +253,36 @@ def train( dx2dt2.to("cuda").to(torch.float32) / (dt**2) * model.tau**2 ) # rescale dx2, rather than model output + if loss_mode == "spectral_rollout": + # Skip model.forward entirely; spectral_rollout_step calls get_funcs + # internally with a cloned dxdt (forward and get_funcs mutate dxdt in place). + ic_mask = None + if cats is not None: + ic_mask = (cats == ONSET).to("cuda") + H = horizon_for_epoch(epoch, nEpochs, H_min, H_max, H_schedule) + out = spectral_rollout_step( + model, x, dxdt, dx2, dt, + H=H, configs=spec_configs, + lam_spec=lam_spec, lam_tf=lam_tf, + lam_env=lam_env, env_ms=env_ms, + ic_mask=ic_mask, ic_noise_rms=ic_noise_rms, + ) + total_loss = out["total"] + if not torch.isfinite(total_loss): + writer.add_scalar("Loss/nan_skip", 1.0, idx) + continue + total_loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip) + optimizer.step() + train_losses.append(float(out["spec"].item())) + writer.add_scalar("Loss/spec", float(out["spec"].item()), idx) + writer.add_scalar("Loss/tf", float(out["tf"].item()), idx) + if lam_env > 0: + writer.add_scalar("Loss/env", float(out["env"].item()), idx) + writer.add_scalar("Loss/total", float(total_loss.item()), idx) + writer.add_scalar("Train/H", float(H), idx) + continue + dx2hat, weights = model(x, dxdt, dt, smoothing) # state: B x L x SD yhat = dx2hat @@ -295,7 +356,10 @@ def px(x): train_loss = loss_fn(y, yhat[:, :L, :]) - #l = loss + # Default objective is the data loss; the polynomial parameterization adds a + # weight-complexity penalty when reg_weights=True. Previously total_loss was + # only defined inside the if-reg_weights block, which crashed unregularized runs. + total_loss = train_loss if reg_weights: B, L, P, P = weights.shape lam_mat = torch.arange( @@ -324,11 +388,21 @@ def px(x): vl = 0.0 vp = 0.0 + # In spectral_rollout mode the autonomy-based selection in train.model_cv + # handles validation; the MSE-accel val loop below is informative only for + # the legacy mode, so skip it (and skip scheduler stepping; the scheduler + # is also unused here -- model_cv does its own LR control). + if loss_mode == "spectral_rollout" or "val" not in loaders: + continue + for idx, batch in enumerate( loaders["val"], start=epoch * len(loaders["train"]) ): with torch.no_grad(): - x, dxdt, dx2dt2 = batch # each is bsz x seq len x 1 + if len(batch) == 4: + x, dxdt, dx2dt2, _ = batch + else: + x, dxdt, dx2dt2 = batch bsz, _, n = x.shape x = x.to("cuda").to(torch.float32) From 3a766c2aa57ec309817d2dee5ba0b952d4568c5b Mon Sep 17 00:00:00 2001 From: John Pearson Date: Thu, 4 Jun 2026 15:40:00 -0400 Subject: [PATCH 07/34] feat: model_seed_cv_spectral -- seed loop + cull + cold-start selection Adds a poly-only seed loop alongside the existing model_cv_lambdas. Fixes lambda (no kernel-weight CV in this PR) and varies only the random init, which is the dominant axis for autonomous-reconstruction quality. Each seed trains with loss_mode='spectral_rollout'; validation uses cold-start raw autonomy_score on held-out (silence lead-in + voc) windows. Features: - Resume-from-checkpoint per seed: a seed with a checkpoint past the target epoch is not retrained. - Optional seed culling (cull_frac, cull_keep): train all seeds to cull_frac * n_epochs, rank by val cold-start autonomy, finish only the top cull_keep. Roughly halves seed-search cost. - seed_cv.csv summary written to model_path. - Tie-breaker: bounded_frac (so a collapsed-but-lucky rollout doesn't beat a truly bounded one at the same val_autonomy). - Attaches _selected_seed / _selected_lambda / _selected_val_autonomy / _selected_test_autonomy / _selected_test_breakdown to the returned model for the caller's manifest. Verified end-to-end on day 85: 2 seeds, cull_frac=0.5, cull_keep=1. Both seeds train, cull ranking runs, top-1 resumes from its cull checkpoint, autonomy_score returns the expected -5.0 (divergence sentinel) for the undertrained pair, manifest fields populate, seed_cv.csv lands on disk. Co-Authored-By: Claude Opus 4.7 --- train/model_cv.py | 188 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 188 insertions(+) diff --git a/train/model_cv.py b/train/model_cv.py index 38f1f32..d7547e2 100644 --- a/train/model_cv.py +++ b/train/model_cv.py @@ -9,6 +9,7 @@ from torch.optim.lr_scheduler import ReduceLROnPlateau import os import glob +import gc import pandas as pd import numpy as np import matplotlib.pyplot as plt @@ -212,3 +213,190 @@ def model_cv_lambdas( data_df.to_csv(os.path.join(model_path, "cv_errs.csv")) return full_model_poly + + +# --- Seed loop + culling + cold-start autonomy selection --------------------------------- + +def model_seed_cv_spectral( + dls: dict, + dt: float, + val_vocs: list, + test_vocs: list, + *, + # model capacity + n_kernels: int = 15, + n_layers: int = 4, + d_state: int = 4, + d_conv: int = 4, + expand_factor: int = 4, + tau: float = None, # default 1/sr if None + smooth_len: float = 0.001, + drive_lowpass_ms: float = 1.0, + keep_const: bool = False, + lam: float = 1.068, # fixed kernel-weight lambda (no CV in this PR) + # training + n_epochs: int = 50, + lr: float = 1e-3, + n_seeds: int = 4, + cull_frac: float = 0.0, + cull_keep: int = 2, + save_freq: int = 5, + model_path: str = "", + # spectral loss + H_min: int = 512, + H_max: int = 2000, + H_schedule: str = "geom", + lam_spec: float = 1.0, + lam_tf: float = 1.0, + lam_env: float = 0.0, + env_ms: float = 2.0, + spec_configs=None, + ic_noise_rms: float = 1e-3, + grad_clip: float = 5.0, + # selection + rescale_autonomy: bool = False, + cold_start_autonomy: bool = True, +) -> torch.nn.Module: + """Seed loop for the spectral-rollout polynomial Ouroboros, with optional + culling and cold-start-raw autonomy selection. + + Trains `n_seeds` independent random inits at a fixed kernel-weight `lam`, all + with loss_mode='spectral_rollout' (see train/spectral_rollout.py). Each seed + integrates over the edge-biased training set; validation uses cold-start + autonomy (held-out vocs `val_vocs` already include the silence lead-in -- the + integration IC then equals near-silence and exercises the ignition path). + + If `cull_frac` in (0, 1) and `cull_keep < n_seeds`, train all seeds to + `cull_frac * n_epochs`, rank by val cold-start autonomy, and finish only the + top `cull_keep`. Roughly halves the seed-search cost (see + docs/autonomous_amplitude.md on main branch). + + Returns the best seed by val autonomy. Saves a `seed_cv.csv` summary and + sets `best._selected_seed`, `best._selected_test_autonomy`, + `best._selected_breakdown` for the caller's manifest. + """ + from train.eval import autonomy_score + + model_info = { + "n layers": n_layers, "d state": d_state, "d conv": d_conv, + "expand factor": expand_factor, + } + assert tau is not None, "model_seed_cv_spectral requires explicit tau (e.g. 1/sr)" + + def _build(seed): + torch.manual_seed(seed); np.random.seed(seed) + kernel = fullPolyModule(nTerms=n_kernels, device="cuda", x_dim=1, z_dim=2, + activation=lambda x: x, lam=float(lam)) + model = Ouroboros(d_data=1, n_layers=n_layers, d_state=d_state, d_conv=d_conv, + expand_factor=expand_factor, tau=tau, smooth_len=smooth_len, + kernel=kernel, drive_lowpass_ms=drive_lowpass_ms, + keep_const=keep_const) + opt = Adam(model.parameters(), lr=lr) + sched = ReduceLROnPlateau(opt, factor=0.5, patience=max(n_epochs // 25, 2), + min_lr=1e-10) + return model, opt, sched + + def _train_to(seed, target): + run_dir = os.path.join(model_path, f"seed{seed}") + os.makedirs(run_dir, exist_ok=True) + if glob.glob(os.path.join(run_dir, "*.tar")): + model, opt, sched, start_epoch = load_model(run_dir) + model.kernel.lam = float(lam) + else: + model, opt, sched = _build(seed) + start_epoch = 0 + if start_epoch < target: + train( + model, opt, + loss_fn=lambda y, yhat: sse(yhat, y, reduction="mean"), # unused in spectral mode + loaders=dls, scheduler=sched, + nEpochs=target, val_freq=max(1, target // 10), runDir=run_dir, + dt=dt, vis_freq=0, smoothing=False, reg_weights=False, + start_epoch=start_epoch, save_freq=save_freq, model_info=model_info, + loss_mode="spectral_rollout", + H_min=H_min, H_max=H_max, H_schedule=H_schedule, + lam_spec=lam_spec, lam_tf=lam_tf, lam_env=lam_env, env_ms=env_ms, + spec_configs=spec_configs, ic_noise_rms=ic_noise_rms, + grad_clip=grad_clip, + ) + save_model(model, opt, os.path.join(run_dir, f"checkpoint_{target}.tar"), + n_layers=n_layers, d_state=d_state, expand_factor=expand_factor, + d_conv=d_conv) + return model, run_dir + + def _autonomy(model, vocs): + model.eval() + with torch.no_grad(): + score, _, bd = autonomy_score(model, vocs, dt, + rescale=rescale_autonomy, + cold_start=cold_start_autonomy) + return score, bd + + records = [] + do_cull = (0.0 < cull_frac < 1.0) and cull_keep < n_seeds + if do_cull: + cull_epoch = max(1, int(round(cull_frac * n_epochs))) + metric_tag = ("cold-start-raw" if cold_start_autonomy + else ("rescaled" if rescale_autonomy else "raw")) + print(f"\n=== seed culling: train all {n_seeds} seeds to epoch {cull_epoch} " + f"({cull_frac:.0%} of {n_epochs}), then finish top {cull_keep} by " + f"{metric_tag} val autonomy ===", flush=True) + ranked = [] + for seed in range(n_seeds): + model, run_dir = _train_to(seed, cull_epoch) + va, bd = _autonomy(model, val_vocs) + ranked.append((va, seed, run_dir)) + print(f" [cull@{cull_epoch}] seed={seed}: val autonomy={va:+.4f} " + f"(spec={bd['spec_corr']:.2f} amp_pen={bd['amp_pen']:.2f} " + f"pitch_pen={bd['pitch_pen']:.2f} bounded={bd['bounded_frac']:.2f})", + flush=True) + del model; gc.collect(); torch.cuda.empty_cache() + ranked.sort(key=lambda r: (r[0] if np.isfinite(r[0]) else -np.inf), reverse=True) + keep = ranked[:cull_keep] + culled = ranked[cull_keep:] + print(" keep: " + ", ".join(f"seed{s}({a:+.3f})" for a, s, _ in keep), flush=True) + if culled: + print(" cull: " + ", ".join(f"seed{s}({a:+.3f})" for a, s, _ in culled), + flush=True) + for _, seed, _ in keep: + model, run_dir = _train_to(seed, n_epochs) + va, bd = _autonomy(model, val_vocs) + print(f" [final] seed={seed}: val autonomy={va:+.4f} " + f"(spec={bd['spec_corr']:.2f} amp_pen={bd['amp_pen']:.2f} " + f"pitch_pen={bd['pitch_pen']:.2f} bounded={bd['bounded_frac']:.2f})", + flush=True) + records.append({"seed": seed, "val_autonomy": va, "ckpt": run_dir, + **{f"val_{k}": v for k, v in bd.items()}}) + del model; gc.collect(); torch.cuda.empty_cache() + else: + for seed in range(n_seeds): + model, run_dir = _train_to(seed, n_epochs) + va, bd = _autonomy(model, val_vocs) + print(f"seed={seed}: val autonomy={va:+.4f} " + f"(spec={bd['spec_corr']:.2f} amp_pen={bd['amp_pen']:.2f} " + f"pitch_pen={bd['pitch_pen']:.2f} bounded={bd['bounded_frac']:.2f})", + flush=True) + records.append({"seed": seed, "val_autonomy": va, "ckpt": run_dir, + **{f"val_{k}": v for k, v in bd.items()}}) + del model; gc.collect(); torch.cuda.empty_cache() + + df = pd.DataFrame(records) + df.to_csv(os.path.join(model_path, "seed_cv.csv"), index=False) + # tie-breaker: max bounded_frac among the top val_autonomy (so collapsed-but-lucky + # rollouts don't beat truly bounded ones if they happen to land at the same score) + best_row = df.sort_values(["val_autonomy", "val_bounded_frac"]).iloc[-1] + best_ckpt = best_row["ckpt"] + print(f"\n=== seed selection: best seed={int(best_row['seed'])} " + f"val_autonomy={best_row['val_autonomy']:+.4f} ===", flush=True) + + best_model, _, _, _ = load_model(best_ckpt) + best_model.eval() + test_score, test_bd = _autonomy(best_model, test_vocs) if test_vocs else (float("nan"), {}) + print(f"BEST seed={int(best_row['seed'])}: test autonomy={test_score:+.4f}", + flush=True) + best_model._selected_seed = int(best_row["seed"]) + best_model._selected_lambda = float(lam) + best_model._selected_val_autonomy = float(best_row["val_autonomy"]) + best_model._selected_test_autonomy = float(test_score) + best_model._selected_test_breakdown = dict(test_bd) + return best_model From 17d7187adad16e8135e009ec55862fac5fdbe549 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Thu, 4 Jun 2026 15:47:08 -0400 Subject: [PATCH 08/34] feat: blk445 spectral training entry point examples/train_poly_spectral_blk445.py: production entry-point for the spectral-rollout polynomial Ouroboros on a single (bird, syllable, day). Pipeline: - file-level holdout of WAV stems inside --data-dir (shuffle deterministic by --seed): last --test-frac for test, next --val-frac for val, rest train. - training set via get_audio_training_edge_weighted (onset/offset/mid pools, configurable ratio). - held-out val/test cold-start vocs (silence-pad lead-in + voc) via inline _coldstart_from_files helper (parallel to examples/_voc_windows.py but file-list based for the single-directory holdout). - model_seed_cv_spectral with full CLI surface for the spectral loss (lam-spec/lam-tf/lam-env, MRSTFT configs, H curriculum, ic-noise-rms, grad-clip) + seed culling. - deployed (rescaled) generation of the first test voc -> WAV. - selected_model.json manifest with selected seed/lambda, val/test cold-start raw scores, deployed rescaled score, all loss + sampler hparams, sr. Verified end-to-end on day 85 (2 seeds x 1 cull + 1 finish epoch, H=256, lr=1e-4): file-level split 500/62/62, sampler 26/26/12 (ONSET/OFFSET/MID), both seeds run, cull ranks them, top seed finishes, bounded_frac=1.0 (soft- tanh holds), manifest + WAV + seed_cv.csv all land on disk. Co-Authored-By: Claude Opus 4.7 --- examples/train_poly_spectral_blk445.py | 278 +++++++++++++++++++++++++ 1 file changed, 278 insertions(+) create mode 100644 examples/train_poly_spectral_blk445.py diff --git a/examples/train_poly_spectral_blk445.py b/examples/train_poly_spectral_blk445.py new file mode 100644 index 0000000..c10383e --- /dev/null +++ b/examples/train_poly_spectral_blk445.py @@ -0,0 +1,278 @@ +"""Train the polynomial Ouroboros on a single (bird, syllable, day) with the +DDSP-style spectral-rollout loss and a cold-start initial condition. + +Pipeline: + 1. File-level holdout. Glob WAV stems in --data-dir, split into train/val/test + by stem (e.g. last 10% of stems for test, the next 10% for val, the rest + for training). + 2. Build the edge-biased training set with `get_audio_training_edge_weighted` + (heavily oversamples onset/offset windows, with a small mid-syllable share + for steady-state coverage). Per-segment category labels drive the + cold-start IC choice in the spectral training step. + 3. Load held-out cold-start val/test vocs (silence lead-in + voc). + 4. Run `model_seed_cv_spectral` (seed loop + optional culling + cold-start + raw autonomy selection). + 5. Save the deployed (rescaled) autonomous reconstruction of the first test + voc + a manifest with the selection breakdown. + +Example: + python -m examples.train_poly_spectral_blk445 \\ + --data-dir ~/ouroboros_data/blk445_syllC/day85 --out-dir poly_spectral_day85 \\ + --n-seeds 4 --n-epochs 50 --cull-frac 0.4 --cull-keep 2 \\ + --context-len 0.05 --silence-prefix-ms 25 --silence-suffix-ms 25 \\ + --H-min 512 --H-max 2000 --lam-spec 1.0 --lam-tf 1.0 --ic-noise-rms 1e-3 +""" + +import argparse +import glob +import json +import os +import warnings + +import numpy as np +from scipy.io import wavfile + +from data.load_data import get_audio_training_edge_weighted +from data.data_utils import get_loaders_edge +from train.model_cv import model_seed_cv_spectral +from train.eval import autonomy_score, generate_autonomous + + +def _stems(data_dir, audio_id="_cleaned.wav"): + # Files in the staged dir end with `.wav` (symlinks point at *_cleaned.wav); + # but legacy callers may have used the raw _cleaned suffix. Try both. + wavs = sorted(glob.glob(os.path.join(data_dir, "*.wav"))) + return wavs + + +def _file_level_split(wavs, val_frac=0.1, test_frac=0.1, seed=1234): + """Deterministic per-stem split (no overlap of recordings across splits).""" + rng = np.random.default_rng(seed) + idx = np.arange(len(wavs)) + rng.shuffle(idx) + n_test = max(1, int(round(test_frac * len(wavs)))) + n_val = max(1, int(round(val_frac * len(wavs)))) + test_i = idx[:n_test] + val_i = idx[n_test:n_test + n_val] + train_i = idx[n_test + n_val:] + return ([wavs[i] for i in train_i], + [wavs[i] for i in val_i], + [wavs[i] for i in test_i]) + + +def _coldstart_from_files(wav_files, silence_pad_samples, n_vocs): + """Held-out cold-start vocs from an explicit list of WAV files (parallel to + examples/_voc_windows.load_voc_windows_coldstart, but file-list-based so we can + do file-level train/val/test holdout inside a single data directory).""" + raw = [] + sr = None + for wav in wav_files[:n_vocs]: + sr, af = wavfile.read(wav) + if af.dtype == np.int16: + af = af / -np.iinfo(af.dtype).min + af = af.astype(np.float64) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + onoffs = np.atleast_2d(np.loadtxt(wav.replace(".wav", ".txt"))) + on_i = int(round(onoffs[0][0] * sr)) + off_i = int(round(onoffs[0][1] * sr)) + start = max(0, on_i - silence_pad_samples) + raw.append(af[start:off_i]) + if not raw: + return [], sr + L = min(len(s) for s in raw) + segs = [s[:L] for s in raw] + return segs, sr + + +def main(): + p = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + # data + p.add_argument("--data-dir", required=True, + help="staged single-syllable directory containing {stem}.wav + {stem}.txt " + "pairs (run scripts/stage_finch_blk445_syllC.py first).") + p.add_argument("--out-dir", default="poly_spectral") + p.add_argument("--val-frac", type=float, default=0.1) + p.add_argument("--test-frac", type=float, default=0.1) + p.add_argument("--n-val-vocs", type=int, default=8) + p.add_argument("--n-test-vocs", type=int, default=8) + p.add_argument("--silence-pad-samples", type=int, default=2000, + help="cold-start lead-in (samples). 2000 matches the existing finchsim convention.") + # sampler + p.add_argument("--context-len", type=float, default=0.05, + help="training segment length in seconds (50 ms ~ 2205 samples @ 44.1 kHz).") + p.add_argument("--edge-ms", type=float, default=10.0, + help="mid-syllable windows lie strictly inside [on+edge_ms, off-edge_ms].") + p.add_argument("--silence-prefix-ms", type=float, default=25.0, + help="pre-onset audio in ONSET windows (cold-start training lead-in).") + p.add_argument("--silence-suffix-ms", type=float, default=25.0, + help="post-offset audio in OFFSET windows.") + p.add_argument("--ratio", default="0.4,0.4,0.2", + help="sampling ratio ONSET,OFFSET,MID (comma-separated, summed to 1).") + p.add_argument("--max-segs", type=int, default=6000) + p.add_argument("--batch-size", type=int, default=8) + p.add_argument("--n-jobs", type=int, default=4) + # model + p.add_argument("--n-kernels", type=int, default=15) + p.add_argument("--n-layers", type=int, default=3) + p.add_argument("--d-state", type=int, default=4) + p.add_argument("--d-conv", type=int, default=4) + p.add_argument("--expand-factor", type=int, default=10) + p.add_argument("--drive-lowpass-ms", type=float, default=1.0) + p.add_argument("--keep-const", action=argparse.BooleanOptionalAction, default=True) + p.add_argument("--lam", type=float, default=1.068, + help="fixed kernel-weight lambda (no CV in this PR).") + # loss + p.add_argument("--lam-spec", type=float, default=1.0) + p.add_argument("--lam-tf", type=float, default=1.0, + help="variance-normalized acceleration MSE anchor. Default-on so early " + "epochs have a smooth gradient signal before the spectral basin is informative.") + p.add_argument("--lam-env", type=float, default=0.0, + help="Gaussian-envelope L1 amplitude pin. 0 disables.") + p.add_argument("--env-ms", type=float, default=2.0) + p.add_argument("--spec-configs", default="256,64;512,128;1024,256", + help="MRSTFT (n_fft,hop) configs, semicolon-separated.") + p.add_argument("--H-min", type=int, default=512) + p.add_argument("--H-max", type=int, default=2000) + p.add_argument("--H-schedule", choices=["geom", "linear", "const"], default="geom") + p.add_argument("--ic-noise-rms", type=float, default=1e-3, + help="cold-start initial-condition noise RMS (training only).") + p.add_argument("--grad-clip", type=float, default=5.0) + # run / selection + p.add_argument("--n-epochs", type=int, default=50) + p.add_argument("--lr", type=float, default=1e-3) + p.add_argument("--n-seeds", type=int, default=4) + p.add_argument("--cull-frac", type=float, default=0.0) + p.add_argument("--cull-keep", type=int, default=2) + p.add_argument("--save-freq", type=int, default=5) + p.add_argument("--seed", type=int, default=1234) + args = p.parse_args() + + data_dir = os.path.expanduser(args.data_dir) + out_dir = os.path.abspath(os.path.expanduser(args.out_dir)) + os.makedirs(out_dir, exist_ok=True) + + wavs = _stems(data_dir) + if not wavs: + raise SystemExit(f"no WAV files found under {data_dir}; " + "run scripts/stage_finch_blk445_syllC.py first.") + train_wavs, val_wavs, test_wavs = _file_level_split( + wavs, val_frac=args.val_frac, test_frac=args.test_frac, seed=args.seed + ) + train_txts = [w.replace(".wav", ".txt") for w in train_wavs] + print(f"file-level split: train={len(train_wavs)} val={len(val_wavs)} test={len(test_wavs)}", + flush=True) + + ratio = tuple(float(r) for r in args.ratio.split(",")) + assert len(ratio) == 3, "--ratio must be ONSET,OFFSET,MID" + spec_configs = tuple( + tuple(int(v) for v in c.split(",")) for c in args.spec_configs.split(";") + ) + + segs, cats, sr = get_audio_training_edge_weighted( + train_wavs, train_txts, + context_len=args.context_len, + edge_ms=args.edge_ms, + silence_prefix_ms=args.silence_prefix_ms, + silence_suffix_ms=args.silence_suffix_ms, + ratio=ratio, + max_segs=args.max_segs, + seed=args.seed, + ) + if not segs: + raise SystemExit("edge-biased sampler returned 0 segments; check the data layout.") + data = np.stack(segs, axis=0).astype(np.float64) + import collections + counts = collections.Counter(cats.tolist()) + print(f"sampler: {len(segs)} segs (ONSET={counts[0]}, OFFSET={counts[1]}, MID={counts[2]}) " + f"L_seg={data.shape[1]} sr={sr}", flush=True) + + dt = 1.0 / sr + dls = get_loaders_edge( + data, cats, num_workers=args.n_jobs, batch_size=args.batch_size, + train_size=1.0 - args.val_frac - args.test_frac, # split inside the sampler pool + cv=False, seed=args.seed, + ) + # `cv=False` because we use FILE-LEVEL holdout for val/test (cleaner than splitting + # inside the sampler pool, which would mix recordings across the split). + + val_vocs, _ = _coldstart_from_files(val_wavs, args.silence_pad_samples, args.n_val_vocs) + test_vocs, _ = _coldstart_from_files(test_wavs, args.silence_pad_samples, args.n_test_vocs) + voc_L = len(val_vocs[0]) if val_vocs else 0 + print(f"val_vocs={len(val_vocs)} test_vocs={len(test_vocs)} (cold-start L={voc_L})", + flush=True) + + best_model = model_seed_cv_spectral( + dls=dls, dt=dt, val_vocs=val_vocs, test_vocs=test_vocs, + n_kernels=args.n_kernels, n_layers=args.n_layers, + d_state=args.d_state, d_conv=args.d_conv, expand_factor=args.expand_factor, + tau=dt, drive_lowpass_ms=args.drive_lowpass_ms, keep_const=args.keep_const, + lam=args.lam, + n_epochs=args.n_epochs, lr=args.lr, n_seeds=args.n_seeds, + cull_frac=args.cull_frac, cull_keep=args.cull_keep, + save_freq=args.save_freq, model_path=out_dir, + H_min=args.H_min, H_max=args.H_max, H_schedule=args.H_schedule, + lam_spec=args.lam_spec, lam_tf=args.lam_tf, lam_env=args.lam_env, env_ms=args.env_ms, + spec_configs=spec_configs, ic_noise_rms=args.ic_noise_rms, grad_clip=args.grad_clip, + cold_start_autonomy=True, rescale_autonomy=False, + ) + + # Deployed generation: rescaled autonomous reconstruction of the first test voc. + manifest = { + "data_dir": data_dir, + "n_train_files": len(train_wavs), + "n_val_files": len(val_wavs), + "n_test_files": len(test_wavs), + "selected_seed": int(getattr(best_model, "_selected_seed", -1)), + "selected_lambda": float(getattr(best_model, "_selected_lambda", args.lam)), + "selected_val_autonomy": float(getattr(best_model, "_selected_val_autonomy", float("nan"))), + "selected_test_autonomy": float(getattr(best_model, "_selected_test_autonomy", float("nan"))), + "selected_test_breakdown": dict(getattr(best_model, "_selected_test_breakdown", {})), + "context_len_s": args.context_len, + "silence_prefix_ms": args.silence_prefix_ms, + "silence_suffix_ms": args.silence_suffix_ms, + "ratio": list(ratio), + "max_segs": args.max_segs, + "drive_lowpass_ms": args.drive_lowpass_ms, + "keep_const": bool(args.keep_const), + "lam_spec": args.lam_spec, + "lam_tf": args.lam_tf, + "lam_env": args.lam_env, + "env_ms": args.env_ms, + "spec_configs": [list(c) for c in spec_configs], + "H_min": args.H_min, + "H_max": args.H_max, + "H_schedule": args.H_schedule, + "ic_noise_rms": args.ic_noise_rms, + "sr": int(sr), + } + if test_vocs: + # Cold-start RAW score (the no-rescale number selection used; the metric the + # deployment target actually has to clear) is already in selected_test_breakdown. + # ALSO report the deployed (rescale=True) number so the manifest captures both. + deployed_score, _, deployed_bd = autonomy_score( + best_model, test_vocs, dt, rescale=True, cold_start=True, + ) + manifest["rescaled_test_autonomy"] = float(deployed_score) + manifest["rescaled_test_breakdown"] = { + k: (float(v) if not isinstance(v, bool) else bool(v)) + for k, v in deployed_bd.items() + } + recon = generate_autonomous(best_model, test_vocs[0], dt, rescale=True) + if np.isfinite(recon).all() and np.abs(recon).max() > 0: + wav = (recon / (np.abs(recon).max() + 1e-12) * 0.95 * 32767).astype(np.int16) + wavfile.write(os.path.join(out_dir, "selected_autonomous_recon.wav"), + int(round(1 / dt)), wav) + print(f"wrote {out_dir}/selected_autonomous_recon.wav", flush=True) + else: + print("WARNING: recon non-finite or zero; skipped WAV write.", flush=True) + + with open(os.path.join(out_dir, "selected_model.json"), "w") as f: + json.dump(manifest, f, indent=2) + print(f"wrote {out_dir}/selected_model.json", flush=True) + print("PIPELINE DONE", flush=True) + + +if __name__ == "__main__": + main() From 68ebcc453cddd24d570dfba61dc4da2e3d67ace6 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Thu, 4 Jun 2026 15:47:19 -0400 Subject: [PATCH 09/34] docs: spectral_rollout_blk445 recipe Recipe + smoke-test invocation + loss math + acceptance bar for the new spectral-rollout training pipeline. Includes a comparison table vs the legacy run_lambda_pipeline so the differences (loss, sampler, IC, holdout, selection) are easy to scan, and a list of known follow-ups (multi-day holdout, causal Mamba, generic per-syllable autosegmenter) explicitly called out as out of scope. Co-Authored-By: Claude Opus 4.7 --- docs/spectral_rollout_blk445.md | 159 ++++++++++++++++++++++++++++++++ 1 file changed, 159 insertions(+) create mode 100644 docs/spectral_rollout_blk445.md diff --git a/docs/spectral_rollout_blk445.md b/docs/spectral_rollout_blk445.md new file mode 100644 index 0000000..b44bbab --- /dev/null +++ b/docs/spectral_rollout_blk445.md @@ -0,0 +1,159 @@ +# Spectral-rollout polynomial Ouroboros on blk445 syllable_C + +DDSP-style multi-resolution STFT magnitude loss (arxiv:1910.11480) on a +teacher-forced RK4 reconstruction of the polynomial Ouroboros, with cold-start +training on syllable onset/offset windows. + +## Quick start + +```bash +# 1. Stage the data (idempotent symlinks; uses ~/isilon/.../blk445). +python scripts/stage_finch_blk445_syllC.py +# day84/day85/day86 are staged under ~/ouroboros_data/blk445_syllC/. + +# 2. Train (single day, single syllable). +python -m examples.train_poly_spectral_blk445 \ + --data-dir ~/ouroboros_data/blk445_syllC/day85 \ + --out-dir poly_spectral_day85 \ + --n-seeds 4 --n-epochs 50 \ + --cull-frac 0.4 --cull-keep 2 \ + --context-len 0.05 --silence-prefix-ms 25 --silence-suffix-ms 25 \ + --ratio 0.4,0.4,0.2 --max-segs 6000 \ + --H-min 512 --H-max 2000 --H-schedule geom \ + --lam-spec 1.0 --lam-tf 1.0 --ic-noise-rms 1e-3 +``` + +Outputs land in `poly_spectral_day85/`: + +- `seed{0..n}/checkpoint_*.tar` per-seed checkpoints +- `seed_cv.csv` selection table (one row per seed, val autonomy + breakdown) +- `selected_model.json` manifest with the selection breakdown, all loss hparams, + test cold-start raw and rescaled autonomy scores +- `selected_autonomous_recon.wav` deployed (rescaled) autonomous reconstruction + of the first test voc + +## What's different from the legacy pipeline + +| Knob | Legacy (run_lambda_pipeline) | This script | +|------|------------------------------|-------------| +| Loss | MSE on d²y/ds² (acceleration) | MRSTFT magnitude on TF rollout + variance-normalized d² MSE anchor | +| Training segments | uniform context_len chunks across each voc | onset-aligned + offset-aligned + mid-syllable, sampled in ratio | +| Initial condition | data IC throughout | data IC on OFFSET/MID; low-amp Gaussian noise on ONSET (cold-start training) | +| λ (kernel-weight) | swept (7-point grid) | fixed at 1.068 (λ is irrelevant for autonomy; see docs/autonomous_amplitude.md) | +| Selection | rescaled or cold-start-raw autonomy | cold-start-raw autonomy (`rescale=False`) only | +| Holdout | shard-level (multi-directory) | file-level inside a single day directory | + +## Loss math (per batch) + +`spectral_rollout_step(model, x, dxdt, d2x, dt, H, ...)` computes: + +1. **Drives**: `omega, gamma, weighted_kernels, weights = model.get_funcs(x, dxdt.clone(), dt)`. + The Mamba encoders see the full (B, L, 1) target; drives are low-passed + inside the model when `drive_lowpass_ms > 0`. + +2. **Teacher-forced acceleration anchor** (variance-normalized so the scale is + stable across vocs; matches `train/rollout_refine.py:132`): + + tf_d2 = -omega² · x - gamma · z2 - weighted_kernels + L_tf = mean((tf_d2 - d2x)²) / var(d2x) + +3. **Open-drive autonomous rollout** (RK4 with soft-tanh saturation at BX=0.5, + BXP=1.0 — same kernel as `rollout_refine`). Initial condition is the data IC + except for examples where `ic_mask == True` (ONSET category), which get + `(x0, xp0) ~ N(0, ic_noise_rms²)`. State (x, x') updates autonomously; drives + come from the encoded target, held open-loop for the whole H-sample horizon. + +4. **Multi-resolution STFT magnitude loss** (DDSP): + + L_spec = mrstft_loss(x_rollout, x[:, :H, 0], configs) + + over `(n_fft, hop)` configs (default `(256,64);(512,128);(1024,256)`). + Per config: spectral convergence + log-magnitude L1, summed and averaged. + +5. **Optional envelope L1** (off by default; turn on with `--lam-env > 0` if the + rollout amplitude drifts): + + L_env = env_loss(x_rollout, target, dt, env_ms) + +6. **Total**: `lam_spec * L_spec + lam_tf * L_tf + lam_env * L_env`. + +Curriculum: `H` ramps from `H_min` to `H_max` over training (geometric by +default). Short horizons early give a learnable signal at random init; the +soft-tanh tames blowups while the model is still figuring out the dynamics. + +NaN total → skip optimizer step (counter logged to TB as `Loss/nan_skip`). +Grad-clip at `--grad-clip` (default 5.0). + +## Edge-biased segment sampler + +`data.load_data.get_audio_training_edge_weighted` builds three per-(wav, txt) +pools and samples to `max_segs` with the requested ratio: + +- **ONSET** (cat=0): window starts at `on - silence_prefix_ms`, length `context_len`. + The first `silence_prefix_ms` of audio is pre-onset (silence-near-silence). +- **OFFSET** (cat=1): window ends at `off + silence_suffix_ms`. Last + `silence_suffix_ms` of audio is post-offset. +- **MID** (cat=2): non-overlapping windows lying strictly inside + `[on + edge_ms, off - edge_ms]`. + +Per-example category labels travel through the dataset to the training loop, +which uses them to set `ic_mask = (cats == ONSET)` for the cold-start IC. + +## Validation / selection + +`model_seed_cv_spectral` trains `n_seeds` seeds at fixed `--lam`, optionally +culls (`--cull-frac > 0`: train all to `cull_frac * n_epochs`, rank by val +cold-start autonomy, finish only top `--cull-keep`), and selects the best +seed by val `autonomy_score(..., rescale=False, cold_start=True)`. + +The held-out vocs are file-level holdouts inside `--data-dir`: shuffle the +list of WAV stems with `--seed`, take the last `--test-frac` for test, the +next `--val-frac` for val, the rest for training. Each held-out voc is loaded +as `silence_pad_samples` lead-in + full vocalization (so the integration IC +at sample 0 is near-silence — the situation finchsim's real-time synthesis +target faces). + +Tie-breaker on val autonomy: higher `bounded_frac` (so a +collapsed-but-lucky rollout doesn't beat a truly bounded one at the same +score). + +## Smoke-test recipe (verify the pipeline runs) + +```bash +python -m examples.train_poly_spectral_blk445 \ + --data-dir ~/ouroboros_data/blk445_syllC/day85 \ + --out-dir /tmp/spec_smoke \ + --val-frac 0.1 --test-frac 0.1 --n-val-vocs 2 --n-test-vocs 2 \ + --context-len 0.05 --max-segs 64 --batch-size 8 --n-jobs 0 \ + --n-kernels 3 --n-layers 2 --d-state 2 --expand-factor 2 \ + --n-epochs 2 --lr 1e-4 --n-seeds 2 \ + --cull-frac 0.5 --cull-keep 1 \ + --H-min 256 --H-max 256 --H-schedule const --save-freq 2 +``` + +Should run in ~1 minute on a single GPU. Acceptance: `seed_cv.csv` + manifest +land in the out dir; no NaN crashes; `Loss/nan_skip` counter not heavily +incremented in TensorBoard. + +For real training results (~6 h on one GPU), use the default `--n-epochs 50`, +`--n-seeds 4`, `--cull-frac 0.4 --cull-keep 2`, full model size. + +## Acceptance bar (full run) + +On the validation set: + +- `bounded_frac >= 0.9` by epoch 10 +- `spec_corr >= 0.5` by end of training +- `coldstart_raw_test_autonomy > 0` (above the divergence baseline of -5.0) + +Listen to `selected_autonomous_recon.wav`: should be syllable-like with a +recognizable onset (no DC click, no immediate collapse to silence). + +## Known follow-ups (out of scope for this PR) + +- Multi-day train/val/test holdout (combine days 84/85/86). +- Causal / streaming Mamba in `get_funcs` (current bidirectional encoder lets + drives "see the future" — fine for research demonstration, but a train / + inference gap for real-time synthesis). +- Per-bird or per-syllable autosegmenter so the recipe can run on bird names / + syllable names other than blk445 / syllable_C without code edits. From 4aed0d439ab0ce07e6f64cbc321d2ba895e69aa1 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Thu, 4 Jun 2026 16:01:15 -0400 Subject: [PATCH 10/34] gitignore: poly_spectral_*/ training output dirs Co-Authored-By: Claude Opus 4.7 --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index a3c3180..aa6aaa9 100644 --- a/.gitignore +++ b/.gitignore @@ -161,3 +161,6 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ + +# Training outputs from this PR's pipeline (checkpoints, plots, manifests, TB logs). +poly_spectral_*/ From e9ad1ab84b1ee71b587ca1335703a0981edea349 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Thu, 4 Jun 2026 19:30:10 -0400 Subject: [PATCH 11/34] fix: move periodic save_model out of the val block In spectral_rollout mode the val loop is skipped (`continue` at top of the val block) because cold-start autonomy_score handles validation in model_seed_cv_spectral. The periodic save_model was nested INSIDE the val block, so save_freq was a no-op in spectral mode: only the final checkpoint written by _train_to() landed on disk. Move the periodic checkpoint OUTSIDE the val block, gated on save_freq > 0. Both legacy MSE mode and spectral mode now write checkpoints at the configured cadence. (This does not affect the in-flight day85 run, which is already going without intermediate checkpoints. Future runs pick up the fix.) Co-Authored-By: Claude Opus 4.7 --- train/train.py | 22 ++++++++++++---------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/train/train.py b/train/train.py index 6006ca7..9ec466b 100644 --- a/train/train.py +++ b/train/train.py @@ -516,15 +516,17 @@ def px(x): writer.add_scalar("Loss/validation", vl / len(loaders["val"]), idx) writer.add_scalar("Penalty/validation", vp / len(loaders["val"]), idx) - if epoch % save_freq == 0: - save_model( - model, - optimizer, - location=os.path.join(runDir, f"checkpoint_{epoch}.tar"), - n_layers=model_info["n layers"], - d_state=model_info["d state"], - d_conv=model_info["d conv"], - expand_factor=model_info["expand factor"], - ) + # Periodic checkpoint -- OUTSIDE the val block so spectral_rollout mode (which + # skips the val loop) still saves. Triggered by save_freq>0 only. + if save_freq > 0 and (epoch % save_freq) == 0: + save_model( + model, + optimizer, + location=os.path.join(runDir, f"checkpoint_{epoch}.tar"), + n_layers=model_info["n layers"], + d_state=model_info["d state"], + d_conv=model_info["d conv"], + expand_factor=model_info["expand factor"], + ) writer.close() return train_losses, val_losses, model, optimizer From 63f1a227f57af0582dbd4c6a7293bb6b1542df09 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Thu, 4 Jun 2026 23:19:30 -0400 Subject: [PATCH 12/34] fix: spectral val-block continue was skipping save_model too Round 2 of the spectral-mode save-checkpoint fix. The previous fix moved save_model OUT of the val block, but the spectral-mode early-exit inside the val block was still a `continue`. Because the val block sits at the epoch-loop level, that `continue` jumps to the NEXT epoch -- skipping the (now sibling) save_model block too. So periodic checkpoints still never landed in spectral mode. Restructure the val block to gate its entry on loss_mode != spectral and 'val' in loaders (instead of entering and bailing with continue). The save_model block at the bottom of the epoch loop now always runs. (Observed live: a 4h spectral run produced ZERO checkpoints; the bug was masked by the smoke test, which used --cull-frac > 0, so save_model fired from _train_to() after each `target`-epoch milestone.) Co-Authored-By: Claude Opus 4.7 --- train/train.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/train/train.py b/train/train.py index 9ec466b..609d93c 100644 --- a/train/train.py +++ b/train/train.py @@ -383,18 +383,20 @@ def px(x): if reg_weights: writer.add_scalar("Penalty/train", penalty.item(), idx) - if epoch % val_freq == 0: + # The MSE-accel val loop runs only for the legacy mode (and only if a val loader + # was provided). In spectral_rollout mode the autonomy-based selection in + # train.model_cv handles validation, so we skip the body here -- but we MUST + # fall through to the save_model block below, so do NOT use `continue` (it + # would skip the rest of this epoch iteration including save_model). + if ( + epoch % val_freq == 0 + and loss_mode != "spectral_rollout" + and "val" in loaders + ): model.eval() vl = 0.0 vp = 0.0 - # In spectral_rollout mode the autonomy-based selection in train.model_cv - # handles validation; the MSE-accel val loop below is informative only for - # the legacy mode, so skip it (and skip scheduler stepping; the scheduler - # is also unused here -- model_cv does its own LR control). - if loss_mode == "spectral_rollout" or "val" not in loaders: - continue - for idx, batch in enumerate( loaders["val"], start=epoch * len(loaders["train"]) ): From b5538637fd0044c02f4b8c0d7794a750b50d8c84 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Thu, 4 Jun 2026 23:42:57 -0400 Subject: [PATCH 13/34] feat: spec_warmup_epochs + precomputed tf_var for stable spectral training Two follow-up fixes for the spectral-rollout training step, both motivated by an end-to-end smoke run on day85 that exposed early-epoch instability: 1. Precompute Var(d2x) over the training set ONCE before the epoch loop (rollout_refine.py:110 convention), pass it to spectral_rollout_step as tf_var. Per-batch d2x.var() is unstable when the batch is dominated by silence (ONSET segments), and dividing the TF anchor by a near-zero normaliser blows it up. Cache the precomputed value with a 1e-6 floor. 2. Add spec_warmup_epochs (default 5): linearly ramp lam_spec from 0 to its target over the first N epochs. Random-init Mamba produces saturated rollouts whose MRSTFT against quiet / onset targets is enormous; without warmup the spectral term swamps the TF anchor and the model fails to enter a learnable basin. Threading: - train/train.py: tf_var precomputed in the spectral_rollout setup block, spec_warmup_epochs ramps lam_spec_t each step, logs Loss/total and Train/lam_spec_t to TensorBoard. - train/spectral_rollout.py: spectral_rollout_step accepts tf_var (uses it instead of per-batch variance when provided). - train/model_cv.py: model_seed_cv_spectral accepts spec_warmup_epochs and forwards it to train(). - examples/train_poly_spectral_blk445.py: --spec-warmup-epochs CLI flag, persisted in selected_model.json manifest. - docs/spectral_rollout_blk445.md: short section explaining when to set spec_warmup_epochs to 0 (resuming a checkpoint past warmup). Smoke run (1 seed, 2 epochs, 64 segs, n_layers=2, H=256) on day85: PIPELINE DONE, manifest + WAV written, test breakdown bounded_frac=1.0 -- the soft-tanh saturation + cold-start noise IC integrate cleanly even at random init. (autonomy still -11 at this microscopic budget; real numbers need the full 50-epoch / 4-seed run.) Co-Authored-By: Claude Opus 4.7 --- docs/spectral_rollout_blk445.md | 9 +++++++++ examples/train_poly_spectral_blk445.py | 6 ++++++ train/model_cv.py | 2 ++ train/spectral_rollout.py | 14 ++++++++++--- train/train.py | 28 ++++++++++++++++++++++++-- 5 files changed, 54 insertions(+), 5 deletions(-) diff --git a/docs/spectral_rollout_blk445.md b/docs/spectral_rollout_blk445.md index b44bbab..dc19d28 100644 --- a/docs/spectral_rollout_blk445.md +++ b/docs/spectral_rollout_blk445.md @@ -157,3 +157,12 @@ recognizable onset (no DC click, no immediate collapse to silence). inference gap for real-time synthesis). - Per-bird or per-syllable autosegmenter so the recipe can run on bird names / syllable names other than blk445 / syllable_C without code edits. + +## spec_warmup_epochs + +`--spec-warmup-epochs N` linearly ramps `lam_spec` from 0 → its target over the +first N epochs (default 5). Needed at random init because the rollout against a +quiet or onset target produces enormous MRSTFT values; without warmup the +spectral term swamps the TF anchor and the model fails to enter a learnable +basin. Set to 0 to disable when resuming a partly-trained checkpoint that is +already past the warmup. diff --git a/examples/train_poly_spectral_blk445.py b/examples/train_poly_spectral_blk445.py index c10383e..33e9856 100644 --- a/examples/train_poly_spectral_blk445.py +++ b/examples/train_poly_spectral_blk445.py @@ -136,6 +136,10 @@ def main(): p.add_argument("--H-min", type=int, default=512) p.add_argument("--H-max", type=int, default=2000) p.add_argument("--H-schedule", choices=["geom", "linear", "const"], default="geom") + p.add_argument("--spec-warmup-epochs", type=int, default=5, + help="linearly ramp lam_spec from 0 to its target over this many epochs. " + "0 disables the warmup. Needed at random init because the spectral " + "term against quiet/onset targets is enormous and would swamp the TF anchor.") p.add_argument("--ic-noise-rms", type=float, default=1e-3, help="cold-start initial-condition noise RMS (training only).") p.add_argument("--grad-clip", type=float, default=5.0) @@ -214,6 +218,7 @@ def main(): save_freq=args.save_freq, model_path=out_dir, H_min=args.H_min, H_max=args.H_max, H_schedule=args.H_schedule, lam_spec=args.lam_spec, lam_tf=args.lam_tf, lam_env=args.lam_env, env_ms=args.env_ms, + spec_warmup_epochs=args.spec_warmup_epochs, spec_configs=spec_configs, ic_noise_rms=args.ic_noise_rms, grad_clip=args.grad_clip, cold_start_autonomy=True, rescale_autonomy=False, ) @@ -244,6 +249,7 @@ def main(): "H_min": args.H_min, "H_max": args.H_max, "H_schedule": args.H_schedule, + "spec_warmup_epochs": args.spec_warmup_epochs, "ic_noise_rms": args.ic_noise_rms, "sr": int(sr), } diff --git a/train/model_cv.py b/train/model_cv.py index d7547e2..6f99128 100644 --- a/train/model_cv.py +++ b/train/model_cv.py @@ -250,6 +250,7 @@ def model_seed_cv_spectral( lam_tf: float = 1.0, lam_env: float = 0.0, env_ms: float = 2.0, + spec_warmup_epochs: int = 5, spec_configs=None, ic_noise_rms: float = 1e-3, grad_clip: float = 5.0, @@ -316,6 +317,7 @@ def _train_to(seed, target): loss_mode="spectral_rollout", H_min=H_min, H_max=H_max, H_schedule=H_schedule, lam_spec=lam_spec, lam_tf=lam_tf, lam_env=lam_env, env_ms=env_ms, + spec_warmup_epochs=spec_warmup_epochs, spec_configs=spec_configs, ic_noise_rms=ic_noise_rms, grad_clip=grad_clip, ) diff --git a/train/spectral_rollout.py b/train/spectral_rollout.py index db94ac4..3f5d1f7 100644 --- a/train/spectral_rollout.py +++ b/train/spectral_rollout.py @@ -131,6 +131,7 @@ def spectral_rollout_step( lam_tf: float = 1.0, lam_env: float = 0.0, env_ms: float = 2.0, + tf_var: Optional[float] = None, # precomputed Var(d2x) over the dataset; matches rollout_refine.py:110 ic_mask: Optional[torch.Tensor] = None, ic_noise_rms: float = 1e-3, rng: Optional[torch.Generator] = None, @@ -151,9 +152,16 @@ def spectral_rollout_step( omega, gamma, wk, weights, _ = model.get_funcs(x, dxdt.clone(), dt) z2 = (model.tau / dt) * dxdt # rescaled velocity tf_d2 = -(omega ** 2) * x - gamma * z2 - wk - # variance-normalized so the anchor scale is commensurable across vocs / runs - var_d2 = d2x.var().clamp_min(1e-12) - L_tf = ((tf_d2 - d2x) ** 2).mean() / var_d2 + # Variance-normalized so the anchor scale is commensurable across vocs / runs. + # tf_var should be the variance computed ONCE over the whole training set (see + # rollout_refine.py:110) -- per-batch variance is unstable when the batch is mostly + # silence (ONSET segments) and can drive the loss to explode. We clamp at a floor + # to be extra safe. + if tf_var is None: + v = float(d2x.detach().var().clamp_min(1e-3).item()) + else: + v = max(float(tf_var), 1e-6) + L_tf = ((tf_d2 - d2x) ** 2).mean() / v # Rollout + spectral loss. The rollout calls model.get_funcs again with a fresh # clone (drives recomputed for the rollout-state forward). Could be unified with diff --git a/train/train.py b/train/train.py index 609d93c..6d65d4b 100644 --- a/train/train.py +++ b/train/train.py @@ -177,6 +177,7 @@ def train( lam_tf: float = 1.0, lam_env: float = 0.0, env_ms: float = 2.0, + spec_warmup_epochs: int = 5, # linearly ramp lam_spec 0 -> lam_spec over these epochs spec_configs=None, ic_noise_rms: float = 1e-3, grad_clip: float = 5.0, @@ -227,9 +228,22 @@ def train( ) if spec_configs is None: spec_configs = DEFAULT_CONFIGS + # Precompute Var(d2x) over the whole training set once, in the same + # rescaled-time units used in the inner loop. Matches rollout_refine.py:110 + # and prevents the per-batch variance from blowing up the TF anchor on + # batches dominated by silence (ONSET segments). + tf_var_running = 0.0 + n_seen = 0 + with torch.no_grad(): + for batch in loaders["train"]: + d2 = batch[2] # (B, L, 1) + d2 = d2.to("cuda").to(torch.float32) / (dt ** 2) * model.tau ** 2 + tf_var_running += float(d2.var().item()) * d2.shape[0] + n_seen += d2.shape[0] + tf_var = max(tf_var_running / max(1, n_seen), 1e-6) print( f"spectral_rollout mode: lam_spec={lam_spec} lam_tf={lam_tf} lam_env={lam_env} " - f"H={H_min}->{H_max} ({H_schedule}) ic_noise_rms={ic_noise_rms}", + f"H={H_min}->{H_max} ({H_schedule}) ic_noise_rms={ic_noise_rms} tf_var={tf_var:.4g}", flush=True, ) @@ -260,11 +274,20 @@ def train( if cats is not None: ic_mask = (cats == ONSET).to("cuda") H = horizon_for_epoch(epoch, nEpochs, H_min, H_max, H_schedule) + # Linearly ramp the spectral term so the random-init Mamba can first move + # into the TF basin (where drives become meaningful) before the spectral + # loss -- which is enormous when the rollout is saturated against quiet + # targets -- starts pulling on params. + if spec_warmup_epochs > 0 and epoch < spec_warmup_epochs: + lam_spec_t = lam_spec * (epoch / float(spec_warmup_epochs)) + else: + lam_spec_t = lam_spec out = spectral_rollout_step( model, x, dxdt, dx2, dt, H=H, configs=spec_configs, - lam_spec=lam_spec, lam_tf=lam_tf, + lam_spec=lam_spec_t, lam_tf=lam_tf, lam_env=lam_env, env_ms=env_ms, + tf_var=tf_var, ic_mask=ic_mask, ic_noise_rms=ic_noise_rms, ) total_loss = out["total"] @@ -281,6 +304,7 @@ def train( writer.add_scalar("Loss/env", float(out["env"].item()), idx) writer.add_scalar("Loss/total", float(total_loss.item()), idx) writer.add_scalar("Train/H", float(H), idx) + writer.add_scalar("Train/lam_spec_t", float(lam_spec_t), idx) continue dx2hat, weights = model(x, dxdt, dt, smoothing) # state: B x L x SD From c0ac15c4efd586678ee4d38402fe010cdadb4182 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Thu, 4 Jun 2026 23:49:00 -0400 Subject: [PATCH 14/34] feat: --max-saved retains all per-epoch checkpoints for long runs The save_model 5-keep policy evicts older checkpoints once a 6th is written. For a 50-epoch run at --save-freq 1 that means only checkpoints 46-50 survive, which loses the ability to revisit / resume from any earlier epoch (and the single-seed 50-epoch monitoring run we're about to kick off wants to keep all of them so the user can identify when the model actually converged). Threaded max_saved through train() (default 5, backward-compat), through model_seed_cv_spectral, and added --max-saved to the entry point (default 60 -- enough to retain every epoch of a 50-epoch run). Verified end-to-end with --save-freq 1 --max-saved 10: both checkpoint_0 and checkpoint_1 are retained on disk after training (vs. checkpoint_1 only with the prior default). Co-Authored-By: Claude Opus 4.7 --- examples/train_poly_spectral_blk445.py | 5 ++++- train/model_cv.py | 6 ++++-- train/train.py | 2 ++ 3 files changed, 10 insertions(+), 3 deletions(-) diff --git a/examples/train_poly_spectral_blk445.py b/examples/train_poly_spectral_blk445.py index 33e9856..d56f0d2 100644 --- a/examples/train_poly_spectral_blk445.py +++ b/examples/train_poly_spectral_blk445.py @@ -150,6 +150,9 @@ def main(): p.add_argument("--cull-frac", type=float, default=0.0) p.add_argument("--cull-keep", type=int, default=2) p.add_argument("--save-freq", type=int, default=5) + p.add_argument("--max-saved", type=int, default=60, + help="how many per-epoch checkpoints to retain on disk before evicting the " + "oldest. Default 60 keeps every checkpoint of a 50-epoch run at save-freq=1.") p.add_argument("--seed", type=int, default=1234) args = p.parse_args() @@ -215,7 +218,7 @@ def main(): lam=args.lam, n_epochs=args.n_epochs, lr=args.lr, n_seeds=args.n_seeds, cull_frac=args.cull_frac, cull_keep=args.cull_keep, - save_freq=args.save_freq, model_path=out_dir, + save_freq=args.save_freq, max_saved=args.max_saved, model_path=out_dir, H_min=args.H_min, H_max=args.H_max, H_schedule=args.H_schedule, lam_spec=args.lam_spec, lam_tf=args.lam_tf, lam_env=args.lam_env, env_ms=args.env_ms, spec_warmup_epochs=args.spec_warmup_epochs, diff --git a/train/model_cv.py b/train/model_cv.py index 6f99128..cb76dbf 100644 --- a/train/model_cv.py +++ b/train/model_cv.py @@ -241,6 +241,7 @@ def model_seed_cv_spectral( cull_frac: float = 0.0, cull_keep: int = 2, save_freq: int = 5, + max_saved: int = 5, model_path: str = "", # spectral loss H_min: int = 512, @@ -313,7 +314,8 @@ def _train_to(seed, target): loaders=dls, scheduler=sched, nEpochs=target, val_freq=max(1, target // 10), runDir=run_dir, dt=dt, vis_freq=0, smoothing=False, reg_weights=False, - start_epoch=start_epoch, save_freq=save_freq, model_info=model_info, + start_epoch=start_epoch, save_freq=save_freq, max_saved=max_saved, + model_info=model_info, loss_mode="spectral_rollout", H_min=H_min, H_max=H_max, H_schedule=H_schedule, lam_spec=lam_spec, lam_tf=lam_tf, lam_env=lam_env, env_ms=env_ms, @@ -323,7 +325,7 @@ def _train_to(seed, target): ) save_model(model, opt, os.path.join(run_dir, f"checkpoint_{target}.tar"), n_layers=n_layers, d_state=d_state, expand_factor=expand_factor, - d_conv=d_conv) + d_conv=d_conv, max_saved=max_saved) return model, run_dir def _autonomy(model, vocs): diff --git a/train/train.py b/train/train.py index 6d65d4b..6e3d6f3 100644 --- a/train/train.py +++ b/train/train.py @@ -168,6 +168,7 @@ def train( start_epoch=0, model_info={}, save_freq=0, + max_saved: int = 5, loss_mode: str = "mse_accel", # spectral-rollout knobs (only consulted when loss_mode == "spectral_rollout") H_min: int = 512, @@ -553,6 +554,7 @@ def px(x): d_state=model_info["d state"], d_conv=model_info["d conv"], expand_factor=model_info["expand factor"], + max_saved=max_saved, ) writer.close() return train_losses, val_losses, model, optimizer From ce570f26a5d1b22ff51f5f09fbaba3c82de143a6 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Fri, 5 Jun 2026 08:46:10 -0400 Subject: [PATCH 15/34] fix: monitor latest-checkpoint sort + add spectrogram plot script scripts/monitor_spectral_diagnose.py: latest_checkpoint() sorted glob results lexicographically, so once the epoch counter went into double digits checkpoint_9.tar sorted AFTER checkpoint_15.tar and the monitor silently stopped emitting NEW_CKPT / PLATEAU events on every ckpt past epoch 9. Sort by parsed epoch number instead. scripts/plot_blk445_specgram.py: new helper that loads a checkpoint, picks the same held-out cold-start val voc autonomy_score evaluates (rng seed 1234, voc-idx CLI flag), runs integrate_poly_autonomous (or generate_autonomous with --rescale), and writes a 2x2 figure (target waveform + spectrogram on top, autonomous on bottom). Saves to /specgram_ckpt_voc.png. Co-Authored-By: Claude Opus 4.7 --- scripts/monitor_spectral_diagnose.py | 300 +++++++++++++++++++++++++++ scripts/plot_blk445_specgram.py | 175 ++++++++++++++++ 2 files changed, 475 insertions(+) create mode 100644 scripts/monitor_spectral_diagnose.py create mode 100644 scripts/plot_blk445_specgram.py diff --git a/scripts/monitor_spectral_diagnose.py b/scripts/monitor_spectral_diagnose.py new file mode 100644 index 0000000..2540894 --- /dev/null +++ b/scripts/monitor_spectral_diagnose.py @@ -0,0 +1,300 @@ +"""One-shot diagnostic for the live spectral-rollout training run. + +Reads the TB events file, checks for a new checkpoint, runs cold-start +autonomy_score on the latest checkpoint if it hasn't been scored yet, and emits +a single line to stdout if there is something actionable to report. + +Signals (priority order): +- DEAD: training process gone. +- NAN_SPIKE: nan_skip counter jumped by > 50 since last poll. +- LOSS_SPIKE: spec_recent5 / spec_prev5 > 2.0. +- NEW_CKPT: a fresh checkpoint was found; ran autonomy_score on it. +- PLATEAU: val_autonomy hasn't improved by > 0.02 across the last 2 checkpoints. + Falls back to "best_spec hasn't improved in 8 polls" before any + checkpoint exists. +- HEARTBEAT: nothing actionable, but every HEARTBEAT_EVERY polls. +- UNCHANGED: not printed; monitor silent. + +The autonomy check is done in a SUBPROCESS so the main monitor process never holds +a CUDA context (avoids contention with the live training run -- subprocess will +queue waiting for a GPU slot for ~30-60s during a forward of the autonomous +integrator, finish, and release). +""" + +import glob +import json +import os +import subprocess +import sys + +import numpy as np + + +STATE_PATH = "/home/pearson/.claude/jobs/712703d8/tmp/spectral_monitor/state.json" +SEED_DIR = "/home/pearson/code/ouroboros-spectral/poly_spectral_day85/seed0" +HEARTBEAT_EVERY = 4 # heartbeat every 4 polls = 1 hour +PLATEAU_NO_IMPROVE_POLLS = 8 # 8 polls = 2 hours with no new spec best +PLATEAU_IMPROVE_MARGIN = 0.02 # "new best" requires beating prior best by 2% +AUTONOMY_PLATEAU_CKPTS = 2 # 2 consecutive ckpts with no autonomy improvement +AUTONOMY_IMPROVE_MARGIN = 0.02 # required autonomy improvement per ckpt + + +def load_state(): + if os.path.exists(STATE_PATH): + with open(STATE_PATH) as f: + return json.load(f) + return {"last_nan_skips": 0, "poll_count": 0, "best_spec_recent5": None, + "best_poll": 0, "last_ckpt_epoch": -1, "last_val_autonomy": None, + "best_val_autonomy": None, "ckpts_since_best_autonomy": 0, + "last_plateau_emit_poll": 0, "history": []} + + +def save_state(s): + with open(STATE_PATH, "w") as f: + json.dump(s, f, indent=2) + + +def process_alive(): + out = subprocess.run( + ["pgrep", "-f", "train_poly_spectral_blk445"], + capture_output=True, text=True, + ).stdout.strip() + return bool(out) + + +def latest_event_file(): + evs = sorted(glob.glob(os.path.join(SEED_DIR, "events.out.tfevents.*"))) + return evs[-1] if evs else None + + +def read_loss(ev_path): + from tensorboard.backend.event_processing.event_accumulator import EventAccumulator + ea = EventAccumulator(ev_path, size_guidance={"scalars": 0}) + ea.Reload() + tags = ea.Tags().get("scalars", []) + out = {} + for t in ("Loss/spec", "Loss/tf", "Train/H"): + if t in tags: + sc = ea.Scalars(t) + out[t] = np.array([s.value for s in sc]) + nan = ea.Scalars("Loss/nan_skip") if "Loss/nan_skip" in tags else [] + out["nan_skips"] = int(sum(s.value for s in nan)) + return out + + +def window_means(vals, w=100, n_windows=20): + if len(vals) < w * n_windows: + n_windows = len(vals) // w + if n_windows == 0: + return np.array([]) + means = [] + for i in range(n_windows): + end = len(vals) - i * w + start = end - w + if start < 0: + break + means.append(vals[start:end].mean()) + return np.array(list(reversed(means))) + + +def latest_checkpoint(): + # Sort by parsed epoch number, NOT lexicographically -- checkpoint_9.tar would + # otherwise sort after checkpoint_10.tar and silently mask all double-digit ckpts. + ckpts = glob.glob(os.path.join(SEED_DIR, "checkpoint_*.tar")) + if not ckpts: + return None, -1 + def _ep(p): + return int(os.path.basename(p).split("_")[1].split(".")[0]) + ckpts.sort(key=_ep) + latest = ckpts[-1] + return latest, _ep(latest) + + +AUTONOMY_SNIPPET = r""" +import sys, os, glob, json, warnings +import numpy as np +sys.path.insert(0, '/home/pearson/code/ouroboros-spectral') +import torch +from scipy.io import wavfile +from train.train import load_model +from train.eval import autonomy_score + +DATA = os.path.expanduser('~/ouroboros_data/blk445_syllC/day85') +SR = 44100 +DT = 1.0 / SR +N_VAL = 8 + +wavs = sorted(glob.glob(os.path.join(DATA, '*.wav'))) +rng = np.random.default_rng(1234) +idx = np.arange(len(wavs)) +rng.shuffle(idx) +n_test = max(1, int(round(0.1 * len(wavs)))) +n_val = max(1, int(round(0.1 * len(wavs)))) +val_i = idx[n_test:n_test + n_val] +val_wavs = [wavs[i] for i in val_i] + +raw, sr = [], None +for wav in val_wavs[:N_VAL]: + sr, af = wavfile.read(wav) + if af.dtype == np.int16: + af = af / -np.iinfo(af.dtype).min + af = af.astype(np.float64) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + onoffs = np.atleast_2d(np.loadtxt(wav.replace('.wav', '.txt'))) + on_i = int(round(onoffs[0][0] * sr)) + off_i = int(round(onoffs[0][1] * sr)) + raw.append(af[max(0, on_i - 2000):off_i]) +L = min(len(s) for s in raw) +val_vocs = [s[:L] for s in raw] + +ckpt_dir = os.path.dirname(sys.argv[1]) +model, _, _, _ = load_model(ckpt_dir) +model.eval() +with torch.no_grad(): + score, _, bd = autonomy_score(model, val_vocs, DT, rescale=False, cold_start=True) +print(json.dumps({'autonomy': float(score), 'breakdown': {k: (float(v) if not isinstance(v, bool) else bool(v)) for k, v in bd.items()}})) +""" + + +def run_autonomy_on_checkpoint(ckpt_path): + r = subprocess.run( + ["/home/pearson/code/ouroboros/.venv/bin/python", "-c", AUTONOMY_SNIPPET, ckpt_path], + capture_output=True, text=True, timeout=900, + ) + if r.returncode != 0: + last_err = r.stderr.strip().splitlines()[-1] if r.stderr else "unknown" + return None, {"error": last_err} + try: + out = json.loads(r.stdout.strip().splitlines()[-1]) + return out["autonomy"], out["breakdown"] + except Exception as e: + return None, {"error": f"parse: {e}; stdout='{r.stdout[:200]}'"} + + +def emit(parts): + status = parts.pop("status") + fields = [f"status={status}"] + [f"{k}={v}" for k, v in parts.items()] + print(" ".join(fields), flush=True) + + +def main(): + state = load_state() + state["poll_count"] = state.get("poll_count", 0) + 1 + + alive = process_alive() + ev_path = latest_event_file() + if ev_path is None: + emit({"status": "DEAD", "reason": "no_events_file"}) + save_state(state); return + + loss = read_loss(ev_path) + spec = loss.get("Loss/spec", np.array([])) + tf = loss.get("Loss/tf", np.array([])) + H_arr = loss.get("Train/H", np.array([])) + nan_skips = loss["nan_skips"] + cur_H = int(H_arr[-1]) if len(H_arr) else -1 + epoch_est = int(len(spec) // 750) + + spec_w = window_means(spec, w=100, n_windows=20) + spec_recent5 = float(spec_w[-5:].mean()) if len(spec_w) >= 5 else float("nan") + spec_prev5 = float(spec_w[-10:-5].mean()) if len(spec_w) >= 10 else float("nan") + spec_ratio = spec_recent5 / spec_prev5 if (spec_prev5 and not np.isnan(spec_prev5)) else float("nan") + tf_w = window_means(tf, w=100, n_windows=5) + tf_recent5 = float(tf_w.mean()) if len(tf_w) else float("nan") + + # Best-spec tracking with margin + if not np.isnan(spec_recent5): + prev_best = state.get("best_spec_recent5") + if prev_best is None or spec_recent5 < prev_best * (1 - PLATEAU_IMPROVE_MARGIN): + state["best_spec_recent5"] = spec_recent5 + state["best_poll"] = state["poll_count"] + elif prev_best is None or spec_recent5 < prev_best: + state["best_spec_recent5"] = spec_recent5 + + best_spec_for_fmt = state.get("best_spec_recent5") + if best_spec_for_fmt is None: + best_spec_for_fmt = float("nan") + base = { + "poll": state["poll_count"], "epoch": epoch_est, "H": cur_H, + "spec_recent5": f"{spec_recent5:.3g}", + "spec_ratio": f"{spec_ratio:.3f}", + "tf_recent5": f"{tf_recent5:.3f}", + "nan_skips": nan_skips, + "best_spec": f"{best_spec_for_fmt:.3g}", + "polls_since_best": state["poll_count"] - state.get("best_poll", 0), + } + + # 1. Process death + if not alive: + emit({"status": "DEAD", **base}) + save_state(state); return + + # 2. NaN spike + nan_delta = nan_skips - state.get("last_nan_skips", 0) + state["last_nan_skips"] = nan_skips + if nan_delta > 50: + emit({"status": "NAN_SPIKE", "nan_delta": nan_delta, **base}) + save_state(state); return + + # 3. Loss explosion + if not np.isnan(spec_ratio) and spec_ratio > 2.0: + emit({"status": "LOSS_SPIKE", **base}) + save_state(state); return + + # 4. New checkpoint -> run autonomy_score + ckpt_path, ckpt_epoch = latest_checkpoint() + if ckpt_path is not None and ckpt_epoch > state.get("last_ckpt_epoch", -1): + score, bd = run_autonomy_on_checkpoint(ckpt_path) + if score is None: + emit({"status": "CKPT_ERR", "ckpt_epoch": ckpt_epoch, + "err": bd.get("error", "?"), **base}) + save_state(state); return + + prev_val = state.get("last_val_autonomy") + prev_best_val = state.get("best_val_autonomy") + if prev_best_val is None or score > prev_best_val + AUTONOMY_IMPROVE_MARGIN: + state["best_val_autonomy"] = score + state["ckpts_since_best_autonomy"] = 0 + else: + state["ckpts_since_best_autonomy"] = state.get("ckpts_since_best_autonomy", 0) + 1 + state["last_val_autonomy"] = score + state["last_ckpt_epoch"] = ckpt_epoch + + autonomy_line = { + "ckpt_epoch": ckpt_epoch, + "val_autonomy": f"{score:+.3f}", + "val_spec_corr": f"{bd.get('spec_corr', float('nan')):+.3f}", + "val_amp_pen": f"{bd.get('amp_pen', float('nan')):.2f}", + "val_pitch_pen": f"{bd.get('pitch_pen', float('nan')):.2f}", + "val_bounded": f"{bd.get('bounded_frac', float('nan')):.2f}", + "best_val_autonomy": f"{state['best_val_autonomy']:+.3f}", + "ckpts_since_best": state["ckpts_since_best_autonomy"], + } + status = "PLATEAU" if state["ckpts_since_best_autonomy"] >= AUTONOMY_PLATEAU_CKPTS else "NEW_CKPT" + emit({"status": status, **autonomy_line, **base}) + save_state(state); return + + # 5. Loss-side plateau (before any ckpt exists, or as a fallback) + polls_since_best = state["poll_count"] - state.get("best_poll", 0) + if polls_since_best >= PLATEAU_NO_IMPROVE_POLLS: + last_emit = state.get("last_plateau_emit_poll", 0) + if state["poll_count"] - last_emit >= PLATEAU_NO_IMPROVE_POLLS: + emit({"status": "PLATEAU_LOSS", **base}) + state["last_plateau_emit_poll"] = state["poll_count"] + save_state(state); return + + # 6. Heartbeat + if state["poll_count"] % HEARTBEAT_EVERY == 0: + emit({"status": "HEARTBEAT", **base}) + + state["history"].append({"poll": state["poll_count"], "epoch": epoch_est, + "spec_recent5": spec_recent5, + "tf_recent5": tf_recent5}) + if len(state["history"]) > 200: + state["history"] = state["history"][-200:] + save_state(state) + + +if __name__ == "__main__": + main() diff --git a/scripts/plot_blk445_specgram.py b/scripts/plot_blk445_specgram.py new file mode 100644 index 0000000..5974401 --- /dev/null +++ b/scripts/plot_blk445_specgram.py @@ -0,0 +1,175 @@ +"""Spectrogram comparison for the blk445 spectral-rollout run. + +Loads a checkpoint, picks a held-out cold-start val voc (silence lead-in + full +syllable), runs the closed-loop polynomial-Ouroboros integrator, and produces +a 2-row figure: target on top, autonomous reconstruction on bottom. Each row +shows waveform (left) and spectrogram (right). + +The val voc selection mirrors `scripts/monitor_spectral_diagnose.py` so the +plotted voc is one autonomy_score actually evaluated. + +usage: + python scripts/plot_blk445_specgram.py [--checkpoint ] [--voc-idx 0] +""" + +import argparse +import glob +import os +import sys +import warnings + +import numpy as np +import torch +from scipy.io import wavfile +from scipy.signal import spectrogram +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) +from train.train import load_model +from train.eval import integrate_poly_autonomous, generate_autonomous, correct # noqa: E402 + +DATA = os.path.expanduser("~/ouroboros_data/blk445_syllC/day85") +SEED_DIR = "/home/pearson/code/ouroboros-spectral/poly_spectral_day85/seed0" + + +def load_cold_start_vocs(data_dir, n_max=8, silence_pad_samples=2000): + """Match the holdout selection used in scripts/monitor_spectral_diagnose.py.""" + wavs = sorted(glob.glob(os.path.join(data_dir, "*.wav"))) + rng = np.random.default_rng(1234) + idx = np.arange(len(wavs)) + rng.shuffle(idx) + n_test = max(1, int(round(0.1 * len(wavs)))) + n_val = max(1, int(round(0.1 * len(wavs)))) + val_i = idx[n_test:n_test + n_val] + val_wavs = [wavs[i] for i in val_i] + + raw, sr = [], None + for wav in val_wavs[:n_max]: + sr, af = wavfile.read(wav) + if af.dtype == np.int16: + af = af / -np.iinfo(af.dtype).min + af = af.astype(np.float64) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + onoffs = np.atleast_2d(np.loadtxt(wav.replace(".wav", ".txt"))) + on_i = int(round(onoffs[0][0] * sr)) + off_i = int(round(onoffs[0][1] * sr)) + raw.append(af[max(0, on_i - silence_pad_samples):off_i]) + L = min(len(s) for s in raw) + return [s[:L] for s in raw], int(sr), val_wavs[:n_max] + + +def main(): + p = argparse.ArgumentParser() + p.add_argument("--checkpoint", default=None, + help="Specific checkpoint .tar. Default: most recent in SEED_DIR.") + p.add_argument("--seed-dir", default=SEED_DIR) + p.add_argument("--voc-idx", type=int, default=0, + help="Which held-out val voc to plot (0..7).") + p.add_argument("--out", default=None, + help="Output PNG path. Default: /specgram_ckpt_voc.png") + p.add_argument("--n-fft", type=int, default=512) + p.add_argument("--hop", type=int, default=128) + p.add_argument("--fmax", type=float, default=8000.0) + p.add_argument("--rescale", action="store_true", + help="Use generate_autonomous (rescaled to target RMS) instead of raw integrator.") + args = p.parse_args() + + seed_dir = args.seed_dir + if args.checkpoint is None: + ckpts = sorted(glob.glob(os.path.join(seed_dir, "checkpoint_*.tar")), + key=lambda s: int(os.path.basename(s).split("_")[1].split(".")[0])) + if not ckpts: + raise SystemExit(f"no checkpoints under {seed_dir}") + ckpt_path = ckpts[-1] + else: + ckpt_path = args.checkpoint + ckpt_epoch = int(os.path.basename(ckpt_path).split("_")[1].split(".")[0]) + + model, _, _, _ = load_model(seed_dir if args.checkpoint is None else os.path.dirname(ckpt_path)) + # load_model picks the most recent — if we asked for a specific one, reload manually. + if args.checkpoint is not None: + sd = torch.load(ckpt_path, weights_only=False, map_location="cuda") + model.load_state_dict(sd["ouroboros"]) + model.eval() + + vocs, sr, val_wavs = load_cold_start_vocs(DATA) + if not (0 <= args.voc_idx < len(vocs)): + raise SystemExit(f"voc-idx {args.voc_idx} out of range [0, {len(vocs)})") + target = vocs[args.voc_idx] + src_path = val_wavs[args.voc_idx] + dt = 1.0 / sr + + print(f"checkpoint: {ckpt_path}", flush=True) + print(f"voc {args.voc_idx} source: {src_path}", flush=True) + print(f"voc length: {len(target)} samples ({len(target) * dt * 1000:.1f} ms)", flush=True) + + with torch.no_grad(): + if args.rescale: + auto = generate_autonomous(model, target, dt, rescale=True, verbose=False) + else: + auto = integrate_poly_autonomous(model, target, dt, verbose=False) + auto = np.asarray(auto, dtype=np.float64) + auto_clean = correct(auto) + tgt_clean = correct(np.asarray(target, dtype=np.float64)) + print(f"target std: {np.nanstd(tgt_clean):.4f}, auto std: {np.nanstd(auto_clean):.4f}", flush=True) + print(f"auto finite frac: {np.isfinite(auto).mean():.3f}", flush=True) + + # Spectrograms + def _spec(x): + f, t, S = spectrogram(x, fs=sr, nperseg=args.n_fft, noverlap=args.n_fft - args.hop, + window="hann", detrend=False, scaling="spectrum", mode="magnitude") + m = f <= args.fmax + return f[m], t, np.log10(S[m] + 1e-8) + + f_t, t_t, S_t = _spec(tgt_clean) + f_a, t_a, S_a = _spec(auto_clean) + vmin = min(S_t.min(), S_a.min()) + vmax = max(S_t.max(), S_a.max()) + + times = np.arange(len(tgt_clean)) * dt * 1000 # ms + + fig, axes = plt.subplots(2, 2, figsize=(14, 6), + gridspec_kw={"width_ratios": [1, 2]}) + # Row 0: target + axes[0, 0].plot(times, tgt_clean, lw=0.5, color="tab:orange") + axes[0, 0].set_ylabel("target") + axes[0, 0].set_title("waveform") + axes[0, 0].set_xlim(times[0], times[-1]) + im0 = axes[0, 1].pcolormesh(t_t * 1000, f_t, S_t, vmin=vmin, vmax=vmax, + shading="auto", cmap="viridis") + axes[0, 1].set_title("spectrogram (log10 magnitude)") + axes[0, 1].set_ylabel("Hz") + fig.colorbar(im0, ax=axes[0, 1], pad=0.01) + + # Row 1: autonomous + color_a = "tab:blue" if not args.rescale else "tab:green" + label_a = "autonomous (raw)" if not args.rescale else "autonomous (rescaled)" + axes[1, 0].plot(times, auto_clean, lw=0.5, color=color_a) + axes[1, 0].set_ylabel(label_a) + axes[1, 0].set_xlabel("ms") + axes[1, 0].set_xlim(times[0], times[-1]) + im1 = axes[1, 1].pcolormesh(t_a * 1000, f_a, S_a, vmin=vmin, vmax=vmax, + shading="auto", cmap="viridis") + axes[1, 1].set_xlabel("ms") + axes[1, 1].set_ylabel("Hz") + fig.colorbar(im1, ax=axes[1, 1], pad=0.01) + + fig.suptitle(f"blk445 syllable_C (checkpoint {ckpt_epoch}, val voc {args.voc_idx})", + fontsize=11) + plt.tight_layout() + + if args.out is None: + suffix = "_rescaled" if args.rescale else "" + out = os.path.join(seed_dir, f"specgram_ckpt{ckpt_epoch}_voc{args.voc_idx}{suffix}.png") + else: + out = args.out + fig.savefig(out, dpi=140, bbox_inches="tight") + plt.close(fig) + print(f"wrote {out}", flush=True) + + +if __name__ == "__main__": + main() From d56fabb2f798da4703305b35e84eeabdf920bf7c Mon Sep 17 00:00:00 2001 From: John Pearson Date: Fri, 5 Jun 2026 16:36:28 -0400 Subject: [PATCH 16/34] plot_blk445_specgram: share waveform y-axis between target and autonomous The autonomous waveform now inherits the target's ylim so the reconstruction is read on the target's amplitude scale. For rescaled plots this makes the match easy to judge; for raw plots it visually clips a saturated rollout / flattens a collapsed one, which is itself informative. Co-Authored-By: Claude Opus 4.7 --- scripts/plot_blk445_specgram.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/scripts/plot_blk445_specgram.py b/scripts/plot_blk445_specgram.py index 5974401..501f49d 100644 --- a/scripts/plot_blk445_specgram.py +++ b/scripts/plot_blk445_specgram.py @@ -144,13 +144,17 @@ def _spec(x): axes[0, 1].set_ylabel("Hz") fig.colorbar(im0, ax=axes[0, 1], pad=0.01) - # Row 1: autonomous + # Row 1: autonomous. Share the waveform y-axis with the target so the + # reconstruction is judged on the target's amplitude scale (rescaled mode); + # in raw mode this will visually clip a saturated rollout / hide a collapsed + # one, which is itself informative. color_a = "tab:blue" if not args.rescale else "tab:green" label_a = "autonomous (raw)" if not args.rescale else "autonomous (rescaled)" axes[1, 0].plot(times, auto_clean, lw=0.5, color=color_a) axes[1, 0].set_ylabel(label_a) axes[1, 0].set_xlabel("ms") axes[1, 0].set_xlim(times[0], times[-1]) + axes[1, 0].set_ylim(axes[0, 0].get_ylim()) im1 = axes[1, 1].pcolormesh(t_a * 1000, f_a, S_a, vmin=vmin, vmax=vmax, shading="auto", cmap="viridis") axes[1, 1].set_xlabel("ms") From 16d0d3285591c6385057f88f42a4ce54e620cd46 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Fri, 5 Jun 2026 18:55:48 -0400 Subject: [PATCH 17/34] monitor diagnose CLI + plot_loss_panels auto-smoothing scripts/monitor_spectral_diagnose.py: --seed-dir, --state-path, --train-pattern CLI flags so a second monitor instance can watch a parallel run dir without copying the script. Defaults preserve the legacy single-run behavior. scripts/plot_loss_panels.py: --smooth defaults to 'auto', picking max(1, min(5, shortest_run_epochs // 3)) so a short overlay (e.g. env-run with 3 epochs vs live-run with 27) keeps multiple points instead of dissolving under valid-mode convolve. Integer override still works. Co-Authored-By: Claude Opus 4.7 --- scripts/monitor_spectral_diagnose.py | 31 ++++- scripts/plot_loss_panels.py | 168 +++++++++++++++++++++++++++ 2 files changed, 196 insertions(+), 3 deletions(-) create mode 100644 scripts/plot_loss_panels.py diff --git a/scripts/monitor_spectral_diagnose.py b/scripts/monitor_spectral_diagnose.py index 2540894..0764e05 100644 --- a/scripts/monitor_spectral_diagnose.py +++ b/scripts/monitor_spectral_diagnose.py @@ -23,6 +23,7 @@ import glob import json +import argparse import os import subprocess import sys @@ -30,8 +31,15 @@ import numpy as np -STATE_PATH = "/home/pearson/.claude/jobs/712703d8/tmp/spectral_monitor/state.json" -SEED_DIR = "/home/pearson/code/ouroboros-spectral/poly_spectral_day85/seed0" +_DEFAULT_STATE = "/home/pearson/.claude/jobs/712703d8/tmp/spectral_monitor/state.json" +_DEFAULT_SEED = "/home/pearson/code/ouroboros-spectral/poly_spectral_day85/seed0" +_DEFAULT_TRAIN_PATTERN = "train_poly_spectral_blk445" + +# These three module-level names are populated at __main__ time from CLI args so the +# rest of the script can keep referring to them as constants. +STATE_PATH = _DEFAULT_STATE +SEED_DIR = _DEFAULT_SEED +TRAIN_PATTERN = _DEFAULT_TRAIN_PATTERN HEARTBEAT_EVERY = 4 # heartbeat every 4 polls = 1 hour PLATEAU_NO_IMPROVE_POLLS = 8 # 8 polls = 2 hours with no new spec best PLATEAU_IMPROVE_MARGIN = 0.02 # "new best" requires beating prior best by 2% @@ -55,8 +63,12 @@ def save_state(s): def process_alive(): + # The CLI lets the user disambiguate two concurrent runs by --train-pattern -- + # the live run matches on its --out-dir basename, the env run matches on a + # different basename. Falls back to the legacy "train_poly_spectral_blk445" + # pattern if the user didn't override (single-run case). out = subprocess.run( - ["pgrep", "-f", "train_poly_spectral_blk445"], + ["pgrep", "-f", TRAIN_PATTERN], capture_output=True, text=True, ).stdout.strip() return bool(out) @@ -297,4 +309,17 @@ def main(): if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--seed-dir", default=_DEFAULT_SEED, + help="run/seed directory to watch (default: live run).") + parser.add_argument("--state-path", default=_DEFAULT_STATE, + help="JSON file to persist monitor state across polls.") + parser.add_argument("--train-pattern", default=_DEFAULT_TRAIN_PATTERN, + help="pgrep -f pattern that identifies the training process. " + "Distinguish concurrent runs by their out-dir basename.") + args = parser.parse_args() + SEED_DIR = args.seed_dir + STATE_PATH = args.state_path + TRAIN_PATTERN = args.train_pattern + os.makedirs(os.path.dirname(STATE_PATH), exist_ok=True) main() diff --git a/scripts/plot_loss_panels.py b/scripts/plot_loss_panels.py new file mode 100644 index 0000000..75b4363 --- /dev/null +++ b/scripts/plot_loss_panels.py @@ -0,0 +1,168 @@ +"""Multi-panel loss + autonomy plot for one or two spectral-rollout runs. + +Reads the TB events file under each --run-dir/seed0 and aggregates Loss/spec, +Loss/tf, Loss/env, Loss/total per epoch (mean over batches). Optionally reads +the monitor state.json next to each run for the per-checkpoint cold-start +autonomy + breakdown and overlays them. + +usage: + python scripts/plot_loss_panels.py \ + --run live:/home/pearson/code/ouroboros-spectral/poly_spectral_day85 \ + :/home/pearson/.claude/jobs/712703d8/tmp/spectral_monitor/state.json \ + --run env:/home/pearson/code/ouroboros-spectral/poly_spectral_day85_env \ + :/home/pearson/.claude/jobs/712703d8/tmp/spectral_monitor_env/state.json \ + --out /tmp/spectral_loss_panels.png + +(each --run is "label:run_dir[:state_path]"; state_path is optional) +""" + +import argparse +import glob +import json +import os +import sys + +import numpy as np +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from tensorboard.backend.event_processing.event_accumulator import EventAccumulator + + +def parse_run_arg(s): + parts = s.split(":") + if len(parts) == 2: + label, run_dir = parts + state_path = None + elif len(parts) == 3: + label, run_dir, state_path = parts + else: + raise argparse.ArgumentTypeError( + f"--run expects 'label:run_dir' or 'label:run_dir:state_path', got {s!r}" + ) + return {"label": label, "run_dir": run_dir, "state_path": state_path} + + +def read_run(run_dir): + """Return per-batch scalars dict + an inferred batches-per-epoch.""" + seed_dir = os.path.join(run_dir, "seed0") + evs = sorted(glob.glob(os.path.join(seed_dir, "events.out.tfevents.*"))) + if not evs: + return None, None + ea = EventAccumulator(evs[-1], size_guidance={"scalars": 0}) + ea.Reload() + tags = ea.Tags().get("scalars", []) + out = {} + for t in ("Loss/spec", "Loss/tf", "Loss/env", "Loss/total", "Train/H", + "Train/lam_spec_t", "Loss/nan_skip"): + if t in tags: + sc = ea.Scalars(t) + out[t] = np.array([s.value for s in sc]) + # Infer batches-per-epoch from the train pipeline config: 6000 segs * 0.8 (train split) / 8 batch = 600. + # If the run's tags reveal more we could overwrite; for now hard-code as the entry script does. + return out, 600 + + +def per_epoch_means(values, batches_per_epoch): + """Group batch-indexed scalars into per-epoch means.""" + n = len(values) + n_eps = n // batches_per_epoch + if n_eps == 0: + return np.array([]) + truncated = values[:n_eps * batches_per_epoch] + return truncated.reshape(n_eps, batches_per_epoch).mean(axis=1) + + +def read_ckpt_autonomy(state_path): + """Pull per-ckpt autonomy + breakdown from monitor state.json's history (if present).""" + if not state_path or not os.path.exists(state_path): + return {} + with open(state_path) as f: + state = json.load(f) + # state["history"] is only loss snapshots, not ckpt scores. The monitor doesn't currently + # persist per-ckpt autonomy; surface what we have via state's last_* fields if no ckpt log. + # For now we attempt to re-derive a per-ckpt timeline by checking what the diagnose script + # has recorded in last_val_autonomy / best_val_autonomy snapshots over time -- the file + # is overwritten each poll so we only have the most recent. Return empty if no history. + return {} + + +def main(): + p = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + p.add_argument("--run", action="append", type=parse_run_arg, required=True, + help="label:run_dir[:state_path]; repeat for multiple runs to overlay") + p.add_argument("--out", default="/tmp/spectral_loss_panels.png") + p.add_argument("--smooth", default="auto", + help="rolling-window smoothing in epochs. 'auto' (default) picks " + "max(1, min(5, shortest_run_epochs // 3)) so a short overlay " + "doesn't disappear under valid-mode convolve. Integer overrides.") + args = p.parse_args() + + runs = [] + for r in args.run: + scalars, bpe = read_run(r["run_dir"]) + if scalars is None: + print(f"warn: no TB events in {r['run_dir']}/seed0", file=sys.stderr) + continue + per_epoch = {t: per_epoch_means(v, bpe) for t, v in scalars.items()} + runs.append({"label": r["label"], "run_dir": r["run_dir"], + "scalars": scalars, "per_epoch": per_epoch}) + + if not runs: + raise SystemExit("no runs with data") + + # Auto-pick the smoothing window so the shortest run keeps multiple points. + if isinstance(args.smooth, str) and args.smooth.lower() == "auto": + per_run_n_eps = [] + for r in runs: + ns = [len(v) for v in r["per_epoch"].values() if len(v) > 0] + if ns: + per_run_n_eps.append(min(ns)) + smooth = max(1, min(5, (min(per_run_n_eps) // 3) if per_run_n_eps else 1)) + else: + smooth = max(1, int(args.smooth)) + print(f"smoothing window = {smooth} epoch(s)") + + panels = [ + ("Loss/spec", "spec (MRSTFT)", True), + ("Loss/tf", "tf anchor", False), + ("Loss/env", "env L1", False), + ("Loss/total", "total", True), + ("Train/H", "rollout H (samples)", False), + ("Train/lam_spec_t", "lam_spec_t (warmup)", False), + ] + n = len(panels) + fig, axes = plt.subplots(n, 1, figsize=(11, 2.0 * n), sharex=True) + + colors = {"live": "tab:blue", "env": "tab:orange"} + for ax, (tag, title, logy) in zip(axes, panels): + for r in runs: + v = r["per_epoch"].get(tag) + if v is None or len(v) == 0: + continue + if smooth > 1 and len(v) >= smooth: + # simple moving average + vs = np.convolve(v, np.ones(smooth) / smooth, mode="valid") + xs = np.arange(len(vs)) + (smooth - 1) + else: + vs = v + xs = np.arange(len(vs)) + c = colors.get(r["label"], None) + ax.plot(xs, vs, label=r["label"], color=c, lw=1.4) + ax.set_ylabel(title) + if logy: + ax.set_yscale("log") + ax.grid(True, alpha=0.3) + ax.legend(loc="upper right", fontsize=8) + axes[-1].set_xlabel("epoch") + + fig.suptitle("spectral-rollout training: per-epoch losses", fontsize=12, y=1.005) + plt.tight_layout() + fig.savefig(args.out, dpi=140, bbox_inches="tight") + plt.close(fig) + print(f"wrote {args.out}") + + +if __name__ == "__main__": + main() From 3de020ddd78119803d0d1993c28df137842b3696 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Fri, 5 Jun 2026 19:59:29 -0400 Subject: [PATCH 18/34] plot_loss_panels: dashed warmup boundary per run Adds a faint dashed vertical line at the first epoch where Train/lam_spec_t reaches its max (detected from the TB stream). Drawn in the run's color on every panel so readers know that pre-boundary total-loss values are weighted- ramp artifacts of spec_warmup, not signal. Co-Authored-By: Claude Opus 4.7 --- scripts/plot_loss_panels.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/scripts/plot_loss_panels.py b/scripts/plot_loss_panels.py index 75b4363..354a62b 100644 --- a/scripts/plot_loss_panels.py +++ b/scripts/plot_loss_panels.py @@ -136,6 +136,23 @@ def main(): fig, axes = plt.subplots(n, 1, figsize=(11, 2.0 * n), sharex=True) colors = {"live": "tab:blue", "env": "tab:orange"} + + # Detect the spec_warmup boundary per run -- the first epoch where lam_spec_t hits + # its max (i.e., the warmup ramp ends). Drawn as a faint vertical line per run so + # readers know that pre-boundary total-loss values are weighted-mix artifacts of + # the ramp, not signal. + warmup_boundaries = [] + for r in runs: + lam = r["per_epoch"].get("Train/lam_spec_t") + if lam is None or len(lam) == 0: + continue + lam_max = float(np.max(lam)) + if lam_max <= 0: + continue + # epoch index where the ramp first reaches (within 1%) its max + idx = int(np.argmax(lam >= lam_max * 0.99)) + warmup_boundaries.append((r["label"], idx)) + for ax, (tag, title, logy) in zip(axes, panels): for r in runs: v = r["per_epoch"].get(tag) @@ -150,6 +167,10 @@ def main(): xs = np.arange(len(vs)) c = colors.get(r["label"], None) ax.plot(xs, vs, label=r["label"], color=c, lw=1.4) + # warmup boundary marker per run + for label, b in warmup_boundaries: + c = colors.get(label, None) + ax.axvline(b, color=c, alpha=0.25, lw=1.0, ls="--", zorder=0) ax.set_ylabel(title) if logy: ax.set_yscale("log") From 3524155d96beb479249e970b7eb9d75a57d2e21c Mon Sep 17 00:00:00 2001 From: John Pearson Date: Fri, 5 Jun 2026 20:56:36 -0400 Subject: [PATCH 19/34] perf: drop duplicate Mamba forward in spectral_rollout_step spectral_rollout_step and teacher_forced_rollout each called model.get_funcs (all three Mamba encoders) on identical (x, dxdt, dt) every training step -- once for the teacher-forced anchor, once for the rollout. The drives are deterministic in their inputs, so the second pass was pure waste. Add a `drives` parameter to teacher_forced_rollout and pass the (omega, gamma, weights, z2) already encoded for the TF anchor. Backprop through the single shared forward sums the TF and spectral gradients exactly as the two separate forwards did, so this is gradient-equivalent -- it just halves the encoder forwards per spectral-rollout step. Co-Authored-By: Claude Opus 4.8 (1M context) --- train/spectral_rollout.py | 22 +++++++++++++++------- 1 file changed, 15 insertions(+), 7 deletions(-) diff --git a/train/spectral_rollout.py b/train/spectral_rollout.py index 3f5d1f7..06b205b 100644 --- a/train/spectral_rollout.py +++ b/train/spectral_rollout.py @@ -59,6 +59,7 @@ def teacher_forced_rollout( ic_mask: Optional[torch.Tensor] = None, # (B,) bool; True => silence-noise IC ic_noise_rms: float = 1e-3, rng: Optional[torch.Generator] = None, + drives: Optional[tuple] = None, # precomputed (omega, gamma, weights, z2); skips get_funcs ) -> torch.Tensor: # (B, H) """RK4 H-step rollout of the poly Ouroboros, drives encoded from target audio. @@ -75,9 +76,15 @@ def teacher_forced_rollout( B, L, _ = x.shape assert H <= L, f"rollout horizon H={H} must be <= segment length L={L}" - # Encode drives in a single forward of the Mamba encoders. - omega, gamma, _, weights, _ = model.get_funcs(x, dxdt.clone(), dt) # ω,γ:(B,L,1); w:(B,L,P,P) - z2 = (model.tau / dt) * dxdt # rescaled-time velocity (B, L, 1) + # Encode drives via the Mamba encoders. When the caller already ran get_funcs + # (e.g. spectral_rollout_step computes the TF anchor from the same drives), it + # passes them in so we don't run a redundant Mamba forward pass — the drives are + # deterministic in (x, dxdt, dt), so reusing them is gradient-equivalent. + if drives is None: + omega, gamma, _, weights, _ = model.get_funcs(x, dxdt.clone(), dt) # ω,γ:(B,L,1); w:(B,L,P,P) + z2 = (model.tau / dt) * dxdt # rescaled-time velocity (B, L, 1) + else: + omega, gamma, weights, z2 = drives om = omega[:, :, 0] ga = gamma[:, :, 0] @@ -163,13 +170,14 @@ def spectral_rollout_step( v = max(float(tf_var), 1e-6) L_tf = ((tf_d2 - d2x) ** 2).mean() / v - # Rollout + spectral loss. The rollout calls model.get_funcs again with a fresh - # clone (drives recomputed for the rollout-state forward). Could be unified with - # the TF computation above, but keeping them separate makes the per-component - # losses easy to reason about and matches rollout_refine's structure. + # Rollout + spectral loss. Reuse the drives (ω, γ, w) and rescaled velocity z2 + # already encoded above for the TF anchor instead of re-running the Mamba encoders + # inside the rollout — they are deterministic in (x, dxdt, dt), so backprop through + # the single shared forward sums the TF and spectral gradients exactly as before. xg = teacher_forced_rollout( model, x, dxdt, dt, H=H, ic_mask=ic_mask, ic_noise_rms=ic_noise_rms, rng=rng, + drives=(omega, gamma, weights, z2), ) tgt = x[:, :H, 0] From 0a654abdc3b30932117bb8dc3c779dd3543c841a Mon Sep 17 00:00:00 2001 From: John Pearson Date: Fri, 5 Jun 2026 20:56:54 -0400 Subject: [PATCH 20/34] perf: speed up spectral-rollout training loop hot path Four correctness-preserving speedups to the per-step training path: - RK4 rollout: hoist per-substep constants out of the 4x-called inner f() -- square omega once over the horizon and slice ga[:,k]/w[:,k] once per step instead of re-slicing/re-squaring on every RK4 substep; reuse the kernel's cached powers vector instead of allocating a fresh arange each rollout. Applied in both spectral_rollout.py and rollout_refine.py. - Dataloader: store batches as float32 instead of float64 (halves H2D bytes) and add pin_memory=True + non_blocking=True host->device transfers so copies overlap compute. - Logging: collapse 5 per-step .item() device->host syncs (one a duplicate of out["spec"]) into a single torch.stack(...).tolist(). - MRSTFT: lru_cache the STFT Hann window (depends only on n_fft, was rebuilt ~6x per step). Co-Authored-By: Claude Opus 4.8 (1M context) --- data/data_utils.py | 36 ++++++++++++++++++++++++------------ train/rollout_refine.py | 28 ++++++++++++++++++++-------- train/spectral_rollout.py | 23 +++++++++++++++-------- train/train.py | 31 +++++++++++++++++++------------ 4 files changed, 78 insertions(+), 40 deletions(-) diff --git a/data/data_utils.py b/data/data_utils.py index 1fa001b..1c3f4cb 100644 --- a/data/data_utils.py +++ b/data/data_utils.py @@ -64,10 +64,13 @@ def __getitem__(self, idx): dxdt = self.dxdt[idx] dx2dt2 = self.dx2dt2[idx] + # Store batches as float32 -- the model runs in float32, so float64 here just + # doubled the host->device transfer bytes and forced a post-copy GPU cast. The + # derivative numpy arrays remain float64; only the per-item tensor is downcast. x, dxdt, dx2dt2 = ( - torch.from_numpy(x).type(torch.DoubleTensor), - torch.from_numpy(dxdt).type(torch.DoubleTensor), - torch.from_numpy(dx2dt2).type(torch.DoubleTensor), + torch.from_numpy(x).type(torch.FloatTensor), + torch.from_numpy(dxdt).type(torch.FloatTensor), + torch.from_numpy(dx2dt2).type(torch.FloatTensor), ) if self.categories is not None: @@ -139,17 +142,20 @@ def get_loaders( if oversample_prop > 1: dsVal.interpolate_oversample(oversample_prop=oversample_prop, dt=dt) dls["val"] = DataLoader( - dsVal, num_workers=num_workers, batch_size=batch_size, shuffle=False + dsVal, num_workers=num_workers, batch_size=batch_size, shuffle=False, + pin_memory=True, ) dsTrain, dsTest = aud_neur_ds(X_train), aud_neur_ds(X_test) if oversample_prop > 1: dsTrain.interpolate_oversample(oversample_prop=oversample_prop, dt=dt) dsTest.interpolate_oversample(oversample_prop=oversample_prop, dt=dt) dls["train"] = DataLoader( - dsTrain, num_workers=num_workers, batch_size=batch_size, shuffle=True + dsTrain, num_workers=num_workers, batch_size=batch_size, shuffle=True, + pin_memory=True, ) dls["test"] = DataLoader( - dsTest, num_workers=num_workers, batch_size=batch_size, shuffle=False + dsTest, num_workers=num_workers, batch_size=batch_size, shuffle=False, + pin_memory=True, ) return dls @@ -214,7 +220,8 @@ def get_loaders_interp( if oversample_prop > 1: dsVal.interpolate_oversample(oversample_prop=oversample_prop, dt=dt) dls["val"] = DataLoader( - dsVal, num_workers=num_workers, batch_size=batch_size, shuffle=False + dsVal, num_workers=num_workers, batch_size=batch_size, shuffle=False, + pin_memory=True, ) t_test = np.tile(t[None, :], (X_test.shape[0], 1)) @@ -224,10 +231,12 @@ def get_loaders_interp( dsTrain.interpolate_oversample(oversample_prop=oversample_prop, dt=dt) dsTest.interpolate_oversample(oversample_prop=oversample_prop, dt=dt) dls["train"] = DataLoader( - dsTrain, num_workers=num_workers, batch_size=batch_size, shuffle=True + dsTrain, num_workers=num_workers, batch_size=batch_size, shuffle=True, + pin_memory=True, ) dls["test"] = DataLoader( - dsTest, num_workers=num_workers, batch_size=batch_size, shuffle=False + dsTest, num_workers=num_workers, batch_size=batch_size, shuffle=False, + pin_memory=True, ) return dls @@ -282,7 +291,8 @@ def get_loaders_edge( test_idx = holdout[test_local] dsVal = aud_neur_ds(data[val_idx], categories=categories[val_idx]) dls["val"] = DataLoader( - dsVal, num_workers=num_workers, batch_size=batch_size, shuffle=False + dsVal, num_workers=num_workers, batch_size=batch_size, shuffle=False, + pin_memory=True, ) else: test_idx = holdout @@ -290,9 +300,11 @@ def get_loaders_edge( dsTrain = aud_neur_ds(data[train_i], categories=categories[train_i]) dsTest = aud_neur_ds(data[test_idx], categories=categories[test_idx]) dls["train"] = DataLoader( - dsTrain, num_workers=num_workers, batch_size=batch_size, shuffle=True + dsTrain, num_workers=num_workers, batch_size=batch_size, shuffle=True, + pin_memory=True, ) dls["test"] = DataLoader( - dsTest, num_workers=num_workers, batch_size=batch_size, shuffle=False + dsTest, num_workers=num_workers, batch_size=batch_size, shuffle=False, + pin_memory=True, ) return dls diff --git a/train/rollout_refine.py b/train/rollout_refine.py index 451de84..1ed5e48 100644 --- a/train/rollout_refine.py +++ b/train/rollout_refine.py @@ -25,6 +25,7 @@ import glob import os +from functools import lru_cache import numpy as np import torch @@ -54,8 +55,16 @@ def gather_windows(dirs, n, L, start_off_ms): return np.stack(segs)[:, :, None], sr +@lru_cache(maxsize=None) +def _hann_window(n_fft, device, dtype): + """Cache STFT analysis windows -- they depend only on n_fft (constant across all + steps/epochs), so the per-call torch.hann_window alloc (~6×/step in mrstft_loss) is + pure overhead.""" + return torch.hann_window(n_fft, device=device, dtype=dtype) + + def stft_mag(x, n_fft, hop): - win = torch.hann_window(n_fft, device=x.device, dtype=x.dtype) + win = _hann_window(n_fft, x.device, x.dtype) S = torch.stft(x, n_fft=n_fft, hop_length=hop, win_length=n_fft, window=win, center=True, return_complex=True) return S.abs() # (B, F, T) @@ -132,21 +141,24 @@ def rollout_refine(model, X, dt, *, epochs=8, hmin=768, hmax=1500, batch_size=6, L_tf = ((tf - d2b) ** 2).mean() / var_d2 om, ga, w = omega[:, :, 0], gamma[:, :, 0], weights # w: (B,L,P,P) + om2 = om ** 2 # square omega once over the horizon (was re-squared every substep) - def f(xx, vv, k): + def f(xx, vv, om2k, gak, w_k): xpw = xx.unsqueeze(1) ** powers xvw = vv.unsqueeze(1) ** powers - kern = torch.einsum("bp,bk,bpk->b", xpw, xvw, w[:, k]) - return vv, -(om[:, k] ** 2) * xx - ga[:, k] * vv - kern + kern = torch.einsum("bp,bk,bpk->b", xpw, xvw, w_k) + return vv, -om2k * xx - gak * vv - kern xc = x[:, 0, 0].detach() xp = z2[:, 0, 0].detach() xs = [xc] for k in range(H - 1): - k1x, k1v = f(xc, xp, k) - k2x, k2v = f(xc + 0.5 * k1x, xp + 0.5 * k1v, k) - k3x, k3v = f(xc + 0.5 * k2x, xp + 0.5 * k2v, k) - k4x, k4v = f(xc + k3x, xp + k3v, k) + # slice step-k drives once; the 4 RK4 substeps reuse them (was 4× re-sliced) + om2k, gak, w_k = om2[:, k], ga[:, k], w[:, k] + k1x, k1v = f(xc, xp, om2k, gak, w_k) + k2x, k2v = f(xc + 0.5 * k1x, xp + 0.5 * k1v, om2k, gak, w_k) + k3x, k3v = f(xc + 0.5 * k2x, xp + 0.5 * k2v, om2k, gak, w_k) + k4x, k4v = f(xc + k3x, xp + k3v, om2k, gak, w_k) xc = xc + (k1x + 2 * k2x + 2 * k3x + k4x) / 6 xp = xp + (k1v + 2 * k2v + 2 * k3v + k4v) / 6 xc = BX * torch.tanh(xc / BX) diff --git a/train/spectral_rollout.py b/train/spectral_rollout.py index 06b205b..c6d240e 100644 --- a/train/spectral_rollout.py +++ b/train/spectral_rollout.py @@ -90,7 +90,11 @@ def teacher_forced_rollout( ga = gamma[:, :, 0] w = weights P = w.shape[-1] - powers = torch.arange(P, device=x.device) + # reuse the kernel's cached powers vector instead of allocating a fresh arange each + # rollout, and square omega once over the whole horizon (it was re-squared on every + # RK4 substep below). + powers = model.kernel.powers[:P] + om2 = om ** 2 # IC -- (B,) tensors of x and x' at s=0. xc = x[:, 0, 0].detach().clone() @@ -104,18 +108,21 @@ def teacher_forced_rollout( xc = xc * (1 - m) + noise_x * m xp = xp * (1 - m) + noise_xp * m - def f(xx, vv, k): + def f(xx, vv, om2k, gak, w_k): xpw = xx.unsqueeze(1) ** powers # (B, P) xvw = vv.unsqueeze(1) ** powers # (B, P) - kern = torch.einsum("bp,bk,bpk->b", xpw, xvw, w[:, k]) - return vv, -(om[:, k] ** 2) * xx - ga[:, k] * vv - kern + kern = torch.einsum("bp,bk,bpk->b", xpw, xvw, w_k) + return vv, -om2k * xx - gak * vv - kern xs = [xc] for k in range(H - 1): - k1x, k1v = f(xc, xp, k) - k2x, k2v = f(xc + 0.5 * k1x, xp + 0.5 * k1v, k) - k3x, k3v = f(xc + 0.5 * k2x, xp + 0.5 * k2v, k) - k4x, k4v = f(xc + k3x, xp + k3v, k) + # slice the step-k drives once; the 4 RK4 substeps below all reuse them + # (previously om[:,k]**2, ga[:,k], w[:,k] were recomputed/re-sliced 4× per step). + om2k, gak, w_k = om2[:, k], ga[:, k], w[:, k] + k1x, k1v = f(xc, xp, om2k, gak, w_k) + k2x, k2v = f(xc + 0.5 * k1x, xp + 0.5 * k1v, om2k, gak, w_k) + k3x, k3v = f(xc + 0.5 * k2x, xp + 0.5 * k2v, om2k, gak, w_k) + k4x, k4v = f(xc + k3x, xp + k3v, om2k, gak, w_k) xc = xc + (k1x + 2 * k2x + 2 * k3x + k4x) / 6 xp = xp + (k1v + 2 * k2v + 2 * k3v + k4v) / 6 # soft-tanh saturation (>> data scale; only tames blow-up during training) diff --git a/train/train.py b/train/train.py index 6e3d6f3..844014c 100644 --- a/train/train.py +++ b/train/train.py @@ -238,7 +238,7 @@ def train( with torch.no_grad(): for batch in loaders["train"]: d2 = batch[2] # (B, L, 1) - d2 = d2.to("cuda").to(torch.float32) / (dt ** 2) * model.tau ** 2 + d2 = d2.to("cuda", non_blocking=True).to(torch.float32) / (dt ** 2) * model.tau ** 2 tf_var_running += float(d2.var().item()) * d2.shape[0] n_seen += d2.shape[0] tf_var = max(tf_var_running / max(1, n_seen), 1e-6) @@ -262,10 +262,10 @@ def train( cats = None bsz, _, n = x.shape - x = x.to("cuda").to(torch.float32) - dxdt = dxdt.to("cuda").to(torch.float32) + x = x.to("cuda", non_blocking=True).to(torch.float32) + dxdt = dxdt.to("cuda", non_blocking=True).to(torch.float32) dx2 = ( - dx2dt2.to("cuda").to(torch.float32) / (dt**2) * model.tau**2 + dx2dt2.to("cuda", non_blocking=True).to(torch.float32) / (dt**2) * model.tau**2 ) # rescale dx2, rather than model output if loss_mode == "spectral_rollout": @@ -298,12 +298,19 @@ def train( total_loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip) optimizer.step() - train_losses.append(float(out["spec"].item())) - writer.add_scalar("Loss/spec", float(out["spec"].item()), idx) - writer.add_scalar("Loss/tf", float(out["tf"].item()), idx) + # One device->host sync for all scalar logging instead of 5 separate + # .item() calls (one of which was a duplicate of out["spec"]): stack the + # loss tensors and transfer once. out["env"] is always a tensor (zeros + # when lam_env==0), so the stack is well-formed. + spec_v, tf_v, env_v, total_v = torch.stack( + [out["spec"], out["tf"], out["env"], total_loss] + ).tolist() + train_losses.append(spec_v) + writer.add_scalar("Loss/spec", spec_v, idx) + writer.add_scalar("Loss/tf", tf_v, idx) if lam_env > 0: - writer.add_scalar("Loss/env", float(out["env"].item()), idx) - writer.add_scalar("Loss/total", float(total_loss.item()), idx) + writer.add_scalar("Loss/env", env_v, idx) + writer.add_scalar("Loss/total", total_v, idx) writer.add_scalar("Train/H", float(H), idx) writer.add_scalar("Train/lam_spec_t", float(lam_spec_t), idx) continue @@ -432,9 +439,9 @@ def px(x): x, dxdt, dx2dt2 = batch bsz, _, n = x.shape - x = x.to("cuda").to(torch.float32) - dxdt = dxdt.to("cuda").to(torch.float32) - dx2 = dx2dt2.to("cuda").to(torch.float32) / (dt**2) * model.tau**2 + x = x.to("cuda", non_blocking=True).to(torch.float32) + dxdt = dxdt.to("cuda", non_blocking=True).to(torch.float32) + dx2 = dx2dt2.to("cuda", non_blocking=True).to(torch.float32) / (dt**2) * model.tau**2 dx2hat, weights = model(x, dxdt, dt, smoothing) From 5d5617447651e098565d07a48bf485cde852fc87 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Fri, 5 Jun 2026 21:01:43 -0400 Subject: [PATCH 21/34] feat: env_warmup_epochs (mirror of spec_warmup_epochs) train/train.py: --env-warmup-epochs (default 0) linearly ramps lam_env from 0 to its target over the first N epochs, mirroring spec_warmup_epochs. Needed when running large lam_env (1e4+) so the random-init env gradient doesn't blow up params before the TF anchor has stabilized. Logs Train/lam_env_t to TB so the ramp is visible. train/model_cv.py: env_warmup_epochs threaded through model_seed_cv_spectral. examples/train_poly_spectral_blk445.py: --env-warmup-epochs CLI flag, persisted in selected_model.json manifest. Co-Authored-By: Claude Opus 4.7 --- examples/train_poly_spectral_blk445.py | 6 ++++++ train/model_cv.py | 2 ++ train/train.py | 8 +++++++- 3 files changed, 15 insertions(+), 1 deletion(-) diff --git a/examples/train_poly_spectral_blk445.py b/examples/train_poly_spectral_blk445.py index d56f0d2..d113ba8 100644 --- a/examples/train_poly_spectral_blk445.py +++ b/examples/train_poly_spectral_blk445.py @@ -140,6 +140,10 @@ def main(): help="linearly ramp lam_spec from 0 to its target over this many epochs. " "0 disables the warmup. Needed at random init because the spectral " "term against quiet/onset targets is enormous and would swamp the TF anchor.") + p.add_argument("--env-warmup-epochs", type=int, default=0, + help="linearly ramp lam_env from 0 to its target over this many epochs. " + "0 disables the warmup. Set to 5+ when using a large --lam-env (e.g. 1e4+) " + "so the random-init env gradient doesn't blow up params before TF stabilizes.") p.add_argument("--ic-noise-rms", type=float, default=1e-3, help="cold-start initial-condition noise RMS (training only).") p.add_argument("--grad-clip", type=float, default=5.0) @@ -222,6 +226,7 @@ def main(): H_min=args.H_min, H_max=args.H_max, H_schedule=args.H_schedule, lam_spec=args.lam_spec, lam_tf=args.lam_tf, lam_env=args.lam_env, env_ms=args.env_ms, spec_warmup_epochs=args.spec_warmup_epochs, + env_warmup_epochs=args.env_warmup_epochs, spec_configs=spec_configs, ic_noise_rms=args.ic_noise_rms, grad_clip=args.grad_clip, cold_start_autonomy=True, rescale_autonomy=False, ) @@ -253,6 +258,7 @@ def main(): "H_max": args.H_max, "H_schedule": args.H_schedule, "spec_warmup_epochs": args.spec_warmup_epochs, + "env_warmup_epochs": args.env_warmup_epochs, "ic_noise_rms": args.ic_noise_rms, "sr": int(sr), } diff --git a/train/model_cv.py b/train/model_cv.py index cb76dbf..c8a72a2 100644 --- a/train/model_cv.py +++ b/train/model_cv.py @@ -252,6 +252,7 @@ def model_seed_cv_spectral( lam_env: float = 0.0, env_ms: float = 2.0, spec_warmup_epochs: int = 5, + env_warmup_epochs: int = 0, spec_configs=None, ic_noise_rms: float = 1e-3, grad_clip: float = 5.0, @@ -320,6 +321,7 @@ def _train_to(seed, target): H_min=H_min, H_max=H_max, H_schedule=H_schedule, lam_spec=lam_spec, lam_tf=lam_tf, lam_env=lam_env, env_ms=env_ms, spec_warmup_epochs=spec_warmup_epochs, + env_warmup_epochs=env_warmup_epochs, spec_configs=spec_configs, ic_noise_rms=ic_noise_rms, grad_clip=grad_clip, ) diff --git a/train/train.py b/train/train.py index 844014c..329127f 100644 --- a/train/train.py +++ b/train/train.py @@ -179,6 +179,7 @@ def train( lam_env: float = 0.0, env_ms: float = 2.0, spec_warmup_epochs: int = 5, # linearly ramp lam_spec 0 -> lam_spec over these epochs + env_warmup_epochs: int = 0, # linearly ramp lam_env 0 -> lam_env over these epochs spec_configs=None, ic_noise_rms: float = 1e-3, grad_clip: float = 5.0, @@ -283,11 +284,15 @@ def train( lam_spec_t = lam_spec * (epoch / float(spec_warmup_epochs)) else: lam_spec_t = lam_spec + if env_warmup_epochs > 0 and epoch < env_warmup_epochs: + lam_env_t = lam_env * (epoch / float(env_warmup_epochs)) + else: + lam_env_t = lam_env out = spectral_rollout_step( model, x, dxdt, dx2, dt, H=H, configs=spec_configs, lam_spec=lam_spec_t, lam_tf=lam_tf, - lam_env=lam_env, env_ms=env_ms, + lam_env=lam_env_t, env_ms=env_ms, tf_var=tf_var, ic_mask=ic_mask, ic_noise_rms=ic_noise_rms, ) @@ -313,6 +318,7 @@ def train( writer.add_scalar("Loss/total", total_v, idx) writer.add_scalar("Train/H", float(H), idx) writer.add_scalar("Train/lam_spec_t", float(lam_spec_t), idx) + writer.add_scalar("Train/lam_env_t", float(lam_env_t), idx) continue dx2hat, weights = model(x, dxdt, dt, smoothing) # state: B x L x SD From dbbb84f8138164732df1ed8dd6b5709ccf5ad413 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Fri, 5 Jun 2026 21:54:12 -0400 Subject: [PATCH 22/34] plot_loss_panels: env1e4 in tab:green env1e4 was falling through to matplotlib's default color cycle, which starts at tab:blue and collided with live. Add an explicit entry so the three concurrent runs are distinguishable. Co-Authored-By: Claude Opus 4.7 --- scripts/plot_loss_panels.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/scripts/plot_loss_panels.py b/scripts/plot_loss_panels.py index 354a62b..64a71ed 100644 --- a/scripts/plot_loss_panels.py +++ b/scripts/plot_loss_panels.py @@ -135,7 +135,14 @@ def main(): n = len(panels) fig, axes = plt.subplots(n, 1, figsize=(11, 2.0 * n), sharex=True) - colors = {"live": "tab:blue", "env": "tab:orange"} + # Stable, explicit color per known run label so the three concurrent runs are + # distinguishable. Unknown labels fall back to matplotlib's default cycle, which + # starts at tab:blue and would collide with `live`. + colors = { + "live": "tab:blue", + "env": "tab:orange", + "env1e4": "tab:green", + } # Detect the spec_warmup boundary per run -- the first epoch where lam_spec_t hits # its max (i.e., the warmup ramp ends). Drawn as a faint vertical line per run so From 7ce85fe0b9e1e9f206b9b95a6b550abbed8b27cf Mon Sep 17 00:00:00 2001 From: John Pearson Date: Fri, 5 Jun 2026 22:03:02 -0400 Subject: [PATCH 23/34] build: pull torch from the cu126 index so it runs on Pascal GPUs The torch index pin used bare top-level `[sources]`/`[[index]]` tables, which uv does not recognize (the schema is `[tool.uv.sources]` / `[[tool.uv.index]]`). So the directive was silently ignored and the lock resolved torch 2.8.0 from PyPI -- the +cu128 build, whose binaries start at sm_70 and fail on Pascal cards (e.g. the GTX 1080 Ti, sm_61) with "no kernel image is available for execution on the device". Move the config under `[tool.uv.*]` and relock so torch resolves to 2.8.0+cu126 (ships sm_61 kernels) on Linux, CPU wheel elsewhere. Drop the unused torchvision source. Requires uv >= 0.4.23 for named indexes. Verified: a fresh `uv sync` yields torch 2.8.0+cu126 and runs a CUDA matmul on the 1080 Ti. Co-Authored-By: Claude Opus 4.8 (1M context) --- pyproject.toml | 14 +- uv.lock | 2871 ++++++++++++++++++++++++------------------------ 2 files changed, 1471 insertions(+), 1414 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 7137383..41b2547 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,22 +23,22 @@ dependencies = [ ] -[sources] +# torch index selection. The default PyPI torch 2.8.0 linux wheel is the +cu128 build, +# whose binaries start at sm_70 and fail on Pascal GPUs (e.g. GTX 1080 Ti, sm_61) with +# "no kernel image available". Pull the +cu126 build on Linux instead -- it ships sm_61 +# kernels. Off-Linux falls back to the CPU wheel. Requires uv >= 0.4.23 (named indexes). +[tool.uv.sources] torch = [ { index = "pytorch-cpu", marker = "sys_platform != 'linux'" }, { index = "pytorch-cu126", marker = "sys_platform == 'linux'" }, ] -torchvision = [ - { index = "pytorch-cpu", marker = "sys_platform != 'linux'" }, - { index = "pytorch-cu126", marker = "sys_platform == 'linux'" }, -] -[[index]] +[[tool.uv.index]] name = "pytorch-cpu" url = "https://download.pytorch.org/whl/cpu" explicit = true -[[index]] +[[tool.uv.index]] name = "pytorch-cu126" url = "https://download.pytorch.org/whl/cu126" explicit = true diff --git a/uv.lock b/uv.lock index 34a3854..3de5674 100644 --- a/uv.lock +++ b/uv.lock @@ -1,120 +1,129 @@ version = 1 +revision = 3 requires-python = ">=3.10" resolution-markers = [ - "python_full_version < '3.11'", - "python_full_version == '3.11.*'", - "python_full_version == '3.12.*'", - "python_full_version >= '3.13'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux'", + 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(cudagraph/compile) + pow2 horizon buckets MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The autonomous RK4 rollout is a sequential Python loop over the horizon H on tiny tensors, so it is launch-overhead-bound (~3.2 s/step eager at H=2000 on a contended 1080 Ti). Add a `rollout_backend` switch threaded through train() -> spectral_rollout_step() -> teacher_forced_rollout(): - 'eager' : the existing Python loop (default; unchanged behavior). - 'cudagraph' : torch.cuda.make_graphed_callables captures a fwd+bwd CUDA graph and replays it (~3.4x measured at H=400). Works on Pascal+ (no Triton). Falls back to eager if capture fails (e.g. OOM), cached so it does not re-attempt every step. - 'compile' : torch.compile(mode='reduce-overhead'); needs CUDA capability >= 7.0 for the Triton backend, else auto-falls back to 'cudagraph'. The RK4 core is factored into `_rk4_core_factory(H, powers)` so all three backends share one implementation; graphs/compiled fns are cached per (H, batch, dtype). Graphs require static shapes (one capture per distinct H), so add a 'pow2' horizon schedule: factor-of-2 buckets from H_min to H_max (final capped at H_max), spread evenly across the same n_epochs ramp -- e.g. 512->1024->2000 over the run, just 3 distinct horizons. train() warns if a graph backend is paired with a non-bucketed schedule. Verified on the GTX 1080 Ti (cu126): eager matches the original loop bit-for-bit (fwd + all grads, max|Δ|=0), cudagraph matches eager (fwd + grads, max|Δ|=0), and 'compile' correctly falls back to cudagraph on sm_61. Default stays 'eager', so training behavior is unchanged unless opted in. Co-Authored-By: Claude Opus 4.8 (1M context) --- train/spectral_rollout.py | 171 +++++++++++++++++++++++++++++++------- train/train.py | 12 ++- 2 files changed, 151 insertions(+), 32 deletions(-) diff --git a/train/spectral_rollout.py b/train/spectral_rollout.py index c6d240e..c2c8850 100644 --- a/train/spectral_rollout.py +++ b/train/spectral_rollout.py @@ -21,6 +21,7 @@ driven by the edge-biased sampler's category labels. """ +import warnings from typing import Optional import torch @@ -49,6 +50,112 @@ def _filter_configs_for_horizon(configs, H): return tuple(keep) +# --- RK4 rollout backends ------------------------------------------------------------ +# The autonomous RK4 integration is a sequential Python loop over the horizon H on tiny +# (B,)/(B,P) tensors, so it is launch-overhead-bound, not FLOP-bound. CUDA graphs capture +# the launch sequence once and replay it, cutting that overhead (~3x measured at H=400 on +# a GTX 1080 Ti). torch.compile(reduce-overhead) does the same plus operator fusion, but +# its Triton backend needs CUDA capability >= 7.0, so on Pascal it falls back to cudagraph. +# +# Backends ('rollout_backend'): +# 'eager' -- plain Python loop (default; always correct, no capture). +# 'cudagraph' -- torch.cuda.make_graphed_callables (fwd+bwd graph); works on Pascal+. +# 'compile' -- torch.compile(mode='reduce-overhead'); needs sm>=70, else -> cudagraph. +# +# Graphs require static shapes, so one graph is captured per (H, batch, dtype). Use the +# 'pow2' horizon schedule (horizon_for_epoch) to keep the number of distinct H small. +_ROLLOUT_CACHE = {} +_ROLLOUT_WARNED = set() + + +def _warn_once(key, msg): + if key not in _ROLLOUT_WARNED: + _ROLLOUT_WARNED.add(key) + warnings.warn(msg) + + +def _rk4_core_factory(H, powers): + """Build the pure RK4 integrator for a fixed horizon H, closing over H and the + (constant) `powers` vector so the returned callable takes only the per-step tensors + (om2, ga, w, xc, xp) -- the form torch.compile / make_graphed_callables require.""" + def core(om2, ga, w, xc, xp): + xs = [xc] + for k in range(H - 1): + om2k, gak, w_k = om2[:, k], ga[:, k], w[:, k] + + def f(xx, vv): + xpw = xx.unsqueeze(1) ** powers # (B, P) + xvw = vv.unsqueeze(1) ** powers # (B, P) + kern = torch.einsum("bp,bk,bpk->b", xpw, xvw, w_k) + return vv, -om2k * xx - gak * vv - kern + + k1x, k1v = f(xc, xp) + k2x, k2v = f(xc + 0.5 * k1x, xp + 0.5 * k1v) + k3x, k3v = f(xc + 0.5 * k2x, xp + 0.5 * k2v) + k4x, k4v = f(xc + k3x, xp + k3v) + xc = xc + (k1x + 2 * k2x + 2 * k3x + k4x) / 6 + xp = xp + (k1v + 2 * k2v + 2 * k3v + k4v) / 6 + # soft-tanh saturation (>> data scale; only tames blow-up during training) + xc = BX * torch.tanh(xc / BX) + xp = BXP * torch.tanh(xp / BXP) + xs.append(xc) + return torch.stack(xs, dim=1) # (B, H) + return core + + +def _make_graphed(H, powers, om2, ga, w, xc, xp): + """Capture a fwd+bwd CUDA graph of the RK4 core for these shapes, or fall back to the + eager core if capture fails (e.g. OOM under a busy GPU). The eager fallback is cached + so we don't re-attempt capture every step.""" + core = _rk4_core_factory(H, powers) + try: + sample = ( + om2.detach().clone().requires_grad_(om2.requires_grad), + ga.detach().clone().requires_grad_(ga.requires_grad), + w.detach().clone().requires_grad_(w.requires_grad), + xc.detach().clone(), + xp.detach().clone(), + ) + return torch.cuda.make_graphed_callables(core, sample) + except Exception as e: # OOM during capture, unsupported op, etc. + _warn_once(("graph_fail", H), + f"CUDA-graph capture failed for H={H} " + f"({type(e).__name__}: {e}); using eager rollout for this horizon.") + return core + + +def _run_rk4(backend, om2, ga, w, xc, xp, powers, H): + """Execute the RK4 rollout under the requested backend. om2/ga/w are (B, >=H[, P, P]); + xc/xp are (B,). Returns (B, H).""" + if backend == "eager" or not om2.is_cuda: + return _rk4_core_factory(H, powers)(om2, ga, w, xc, xp) + + if backend == "compile": + cap = torch.cuda.get_device_capability(om2.device) + if cap[0] < 7: + _warn_once("compile_cap", + f"rollout_backend='compile' needs CUDA capability >= 7.0 for the " + f"Triton backend; this GPU is sm_{cap[0]}{cap[1]} -> using " + f"'cudagraph' instead.") + return _run_rk4("cudagraph", om2, ga, w, xc, xp, powers, H) + key = ("compile", H, int(powers.numel())) + fn = _ROLLOUT_CACHE.get(key) + if fn is None: + fn = torch.compile(_rk4_core_factory(H, powers), mode="reduce-overhead") + _ROLLOUT_CACHE[key] = fn + return fn(om2, ga, w, xc, xp) + + if backend == "cudagraph": + key = ("graph", H, int(xc.shape[0]), om2.dtype, int(powers.numel())) + fn = _ROLLOUT_CACHE.get(key) + if fn is None: + fn = _make_graphed(H, powers, om2, ga, w, xc, xp) + _ROLLOUT_CACHE[key] = fn + return fn(om2, ga, w, xc, xp) + + raise ValueError(f"unknown rollout_backend {backend!r}") + + def teacher_forced_rollout( model, x: torch.Tensor, # (B, L, 1) target audio @@ -60,6 +167,7 @@ def teacher_forced_rollout( ic_noise_rms: float = 1e-3, rng: Optional[torch.Generator] = None, drives: Optional[tuple] = None, # precomputed (omega, gamma, weights, z2); skips get_funcs + rollout_backend: str = "eager", # 'eager' | 'cudagraph' | 'compile' (see _run_rk4) ) -> torch.Tensor: # (B, H) """RK4 H-step rollout of the poly Ouroboros, drives encoded from target audio. @@ -86,15 +194,14 @@ def teacher_forced_rollout( else: omega, gamma, weights, z2 = drives - om = omega[:, :, 0] - ga = gamma[:, :, 0] - w = weights - P = w.shape[-1] + P = weights.shape[-1] # reuse the kernel's cached powers vector instead of allocating a fresh arange each - # rollout, and square omega once over the whole horizon (it was re-squared on every - # RK4 substep below). + # rollout, and square omega once over the horizon (it was re-squared every RK4 substep). + # Slice the drives to the horizon so the graphed/compiled shapes depend only on H. powers = model.kernel.powers[:P] - om2 = om ** 2 + om2 = omega[:, :H, 0] ** 2 # (B, H) + ga_h = gamma[:, :H, 0] # (B, H) + w_h = weights[:, :H] # (B, H, P, P) # IC -- (B,) tensors of x and x' at s=0. xc = x[:, 0, 0].detach().clone() @@ -108,28 +215,7 @@ def teacher_forced_rollout( xc = xc * (1 - m) + noise_x * m xp = xp * (1 - m) + noise_xp * m - def f(xx, vv, om2k, gak, w_k): - xpw = xx.unsqueeze(1) ** powers # (B, P) - xvw = vv.unsqueeze(1) ** powers # (B, P) - kern = torch.einsum("bp,bk,bpk->b", xpw, xvw, w_k) - return vv, -om2k * xx - gak * vv - kern - - xs = [xc] - for k in range(H - 1): - # slice the step-k drives once; the 4 RK4 substeps below all reuse them - # (previously om[:,k]**2, ga[:,k], w[:,k] were recomputed/re-sliced 4× per step). - om2k, gak, w_k = om2[:, k], ga[:, k], w[:, k] - k1x, k1v = f(xc, xp, om2k, gak, w_k) - k2x, k2v = f(xc + 0.5 * k1x, xp + 0.5 * k1v, om2k, gak, w_k) - k3x, k3v = f(xc + 0.5 * k2x, xp + 0.5 * k2v, om2k, gak, w_k) - k4x, k4v = f(xc + k3x, xp + k3v, om2k, gak, w_k) - xc = xc + (k1x + 2 * k2x + 2 * k3x + k4x) / 6 - xp = xp + (k1v + 2 * k2v + 2 * k3v + k4v) / 6 - # soft-tanh saturation (>> data scale; only tames blow-up during training) - xc = BX * torch.tanh(xc / BX) - xp = BXP * torch.tanh(xp / BXP) - xs.append(xc) - return torch.stack(xs, dim=1) # (B, H) + return _run_rk4(rollout_backend, om2, ga_h, w_h, xc, xp, powers, H) def spectral_rollout_step( @@ -149,6 +235,7 @@ def spectral_rollout_step( ic_mask: Optional[torch.Tensor] = None, ic_noise_rms: float = 1e-3, rng: Optional[torch.Generator] = None, + rollout_backend: str = "eager", ) -> dict: """One forward + loss for the spectral-rollout objective. @@ -185,6 +272,7 @@ def spectral_rollout_step( model, x, dxdt, dt, H=H, ic_mask=ic_mask, ic_noise_rms=ic_noise_rms, rng=rng, drives=(omega, gamma, weights, z2), + rollout_backend=rollout_backend, ) tgt = x[:, :H, 0] @@ -199,18 +287,39 @@ def spectral_rollout_step( return {"spec": L_spec, "tf": L_tf, "env": L_env, "total": total} +def pow2_horizon_buckets(H_min: int, H_max: int) -> list: + """Horizon buckets in factor-of-2 jumps from H_min, with the final bucket capped at + exactly H_max: e.g. H_min=512, H_max=2000 -> [512, 1024, 2000]. Keeping the set of + distinct horizons small bounds the number of CUDA graphs the graphed/compiled rollout + backends capture (one per H).""" + hs = [] + h = int(H_min) + while h < int(H_max): + hs.append(h) + h *= 2 + if not hs or hs[-1] != int(H_max): + hs.append(int(H_max)) + return hs + + def horizon_for_epoch(epoch: int, n_epochs: int, H_min: int, H_max: int, schedule: str = "geom") -> int: """Curriculum on the rollout horizon: small H early (cheap, easy gradient), full H - by the end. 'geom' = geometric spacing; 'linear' = linear; 'const' = H_max throughout. + by the end. 'geom' = geometric spacing; 'linear' = linear; 'const' = H_max throughout; + 'pow2' = factor-of-2 buckets (pow2_horizon_buckets) spread evenly across the same + n_epochs ramp -- coarse steps so the graphed backends capture only a few graphs. """ if n_epochs <= 1 or schedule == "const": return int(H_max) + if schedule == "pow2": + buckets = pow2_horizon_buckets(H_min, H_max) + # even division of the ramp across buckets; reaches the last bucket by the final epoch + idx = min(int(epoch * len(buckets) / n_epochs), len(buckets) - 1) + return int(buckets[idx]) t = epoch / max(1, n_epochs - 1) if schedule == "linear": H = H_min + t * (H_max - H_min) elif schedule == "geom": - import math H = H_min * (H_max / H_min) ** t else: raise ValueError(f"unknown schedule {schedule!r}") diff --git a/train/train.py b/train/train.py index 329127f..b775f28 100644 --- a/train/train.py +++ b/train/train.py @@ -183,6 +183,7 @@ def train( spec_configs=None, ic_noise_rms: float = 1e-3, grad_clip: float = 5.0, + rollout_backend: str = "eager", # RK4 backend: 'eager' | 'cudagraph' | 'compile' ) -> Tuple[ list[float], list[Tuple[int, float, float]], nn.Module, torch.optim.Optimizer ]: @@ -245,9 +246,17 @@ def train( tf_var = max(tf_var_running / max(1, n_seen), 1e-6) print( f"spectral_rollout mode: lam_spec={lam_spec} lam_tf={lam_tf} lam_env={lam_env} " - f"H={H_min}->{H_max} ({H_schedule}) ic_noise_rms={ic_noise_rms} tf_var={tf_var:.4g}", + f"H={H_min}->{H_max} ({H_schedule}) ic_noise_rms={ic_noise_rms} tf_var={tf_var:.4g} " + f"rollout_backend={rollout_backend}", flush=True, ) + if rollout_backend != "eager" and H_schedule not in ("pow2", "const"): + print( + f" WARNING: rollout_backend={rollout_backend!r} captures one CUDA graph " + f"per distinct H; schedule {H_schedule!r} yields many. Use H_schedule='pow2' " + f"to bucket horizons in factor-of-2 steps.", + flush=True, + ) for epoch in tqdm(range(start_epoch, nEpochs), desc="training model"): model.train() @@ -295,6 +304,7 @@ def train( lam_env=lam_env_t, env_ms=env_ms, tf_var=tf_var, ic_mask=ic_mask, ic_noise_rms=ic_noise_rms, + rollout_backend=rollout_backend, ) total_loss = out["total"] if not torch.isfinite(total_loss): From 28ef0d453eb42d42af0144d277e124f30d303a75 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Fri, 5 Jun 2026 22:39:15 -0400 Subject: [PATCH 25/34] config: H_max=2048 (clean pow2 buckets) + expose rollout_backend/pow2 via CLI Bump the spectral-rollout H_max default 2000 -> 2048 so the pow2 horizon schedule yields clean factor-of-2 buckets [512, 1024, 2048] with no capped final bucket (2048 < segment length L, which is context-len 0.05s + 25ms silence pre/suffix). Updated in train.train, train.model_cv, the example CLI default, and the docstring. Thread `rollout_backend` from the example CLI through model_seed_cv_spectral to train(), and add 'pow2' to the --H-schedule choices so the bucketed schedule (needed by the graphed backends) is selectable. Default stays 'eager'; recorded in the run manifest. NOTE: a contended benchmark on the shared GTX 1080 Ti (5 concurrent seed jobs, GPU at 100% util) showed no cudagraph speedup -- CUDA-graph capture OOM'd and fell back to eager, and a saturated GPU hides the launch overhead graphs remove anyway. cudagraph is expected to help only on an underutilized/single-process GPU, so it stays opt-in. Co-Authored-By: Claude Opus 4.8 (1M context) --- examples/train_poly_spectral_blk445.py | 12 +++++++++--- train/model_cv.py | 5 +++-- train/spectral_rollout.py | 2 +- train/train.py | 2 +- 4 files changed, 14 insertions(+), 7 deletions(-) diff --git a/examples/train_poly_spectral_blk445.py b/examples/train_poly_spectral_blk445.py index d113ba8..2e4cf33 100644 --- a/examples/train_poly_spectral_blk445.py +++ b/examples/train_poly_spectral_blk445.py @@ -20,7 +20,7 @@ --data-dir ~/ouroboros_data/blk445_syllC/day85 --out-dir poly_spectral_day85 \\ --n-seeds 4 --n-epochs 50 --cull-frac 0.4 --cull-keep 2 \\ --context-len 0.05 --silence-prefix-ms 25 --silence-suffix-ms 25 \\ - --H-min 512 --H-max 2000 --lam-spec 1.0 --lam-tf 1.0 --ic-noise-rms 1e-3 + --H-min 512 --H-max 2048 --lam-spec 1.0 --lam-tf 1.0 --ic-noise-rms 1e-3 """ import argparse @@ -134,8 +134,12 @@ def main(): p.add_argument("--spec-configs", default="256,64;512,128;1024,256", help="MRSTFT (n_fft,hop) configs, semicolon-separated.") p.add_argument("--H-min", type=int, default=512) - p.add_argument("--H-max", type=int, default=2000) - p.add_argument("--H-schedule", choices=["geom", "linear", "const"], default="geom") + p.add_argument("--H-max", type=int, default=2048) + p.add_argument("--H-schedule", choices=["geom", "linear", "const", "pow2"], + default="geom") + p.add_argument("--rollout-backend", choices=["eager", "cudagraph", "compile"], + default="eager", + help="RK4 rollout backend; 'cudagraph'/'compile' want --H-schedule pow2.") p.add_argument("--spec-warmup-epochs", type=int, default=5, help="linearly ramp lam_spec from 0 to its target over this many epochs. " "0 disables the warmup. Needed at random init because the spectral " @@ -228,6 +232,7 @@ def main(): spec_warmup_epochs=args.spec_warmup_epochs, env_warmup_epochs=args.env_warmup_epochs, spec_configs=spec_configs, ic_noise_rms=args.ic_noise_rms, grad_clip=args.grad_clip, + rollout_backend=args.rollout_backend, cold_start_autonomy=True, rescale_autonomy=False, ) @@ -257,6 +262,7 @@ def main(): "H_min": args.H_min, "H_max": args.H_max, "H_schedule": args.H_schedule, + "rollout_backend": args.rollout_backend, "spec_warmup_epochs": args.spec_warmup_epochs, "env_warmup_epochs": args.env_warmup_epochs, "ic_noise_rms": args.ic_noise_rms, diff --git a/train/model_cv.py b/train/model_cv.py index c8a72a2..227ccd4 100644 --- a/train/model_cv.py +++ b/train/model_cv.py @@ -245,7 +245,7 @@ def model_seed_cv_spectral( model_path: str = "", # spectral loss H_min: int = 512, - H_max: int = 2000, + H_max: int = 2048, H_schedule: str = "geom", lam_spec: float = 1.0, lam_tf: float = 1.0, @@ -256,6 +256,7 @@ def model_seed_cv_spectral( spec_configs=None, ic_noise_rms: float = 1e-3, grad_clip: float = 5.0, + rollout_backend: str = "eager", # selection rescale_autonomy: bool = False, cold_start_autonomy: bool = True, @@ -323,7 +324,7 @@ def _train_to(seed, target): spec_warmup_epochs=spec_warmup_epochs, env_warmup_epochs=env_warmup_epochs, spec_configs=spec_configs, ic_noise_rms=ic_noise_rms, - grad_clip=grad_clip, + grad_clip=grad_clip, rollout_backend=rollout_backend, ) save_model(model, opt, os.path.join(run_dir, f"checkpoint_{target}.tar"), n_layers=n_layers, d_state=d_state, expand_factor=expand_factor, diff --git a/train/spectral_rollout.py b/train/spectral_rollout.py index c2c8850..b59af08 100644 --- a/train/spectral_rollout.py +++ b/train/spectral_rollout.py @@ -289,7 +289,7 @@ def spectral_rollout_step( def pow2_horizon_buckets(H_min: int, H_max: int) -> list: """Horizon buckets in factor-of-2 jumps from H_min, with the final bucket capped at - exactly H_max: e.g. H_min=512, H_max=2000 -> [512, 1024, 2000]. Keeping the set of + exactly H_max: e.g. H_min=512, H_max=2048 -> [512, 1024, 2048]. Keeping the set of distinct horizons small bounds the number of CUDA graphs the graphed/compiled rollout backends capture (one per H).""" hs = [] diff --git a/train/train.py b/train/train.py index b775f28..cb40951 100644 --- a/train/train.py +++ b/train/train.py @@ -172,7 +172,7 @@ def train( loss_mode: str = "mse_accel", # spectral-rollout knobs (only consulted when loss_mode == "spectral_rollout") H_min: int = 512, - H_max: int = 2000, + H_max: int = 2048, H_schedule: str = "geom", lam_spec: float = 1.0, lam_tf: float = 1.0, From 6fbaded7b62d2863efede1f30e8b0be1911d1e8c Mon Sep 17 00:00:00 2001 From: John Pearson Date: Fri, 5 Jun 2026 23:34:46 -0400 Subject: [PATCH 26/34] feat: scan-based RK4 rollout core (rollout_backend='scan'), experimental MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Express the autonomous RK4 rollout as a scan (the jax.lax.scan idiom): a single-step combine_fn `_rk4_step(carry=(xc,xp), x=(om2k,gak,w_k)) -> ((xc',xp'), xc')`, driven by `_scan_rollout`. Two drivers: - use_hop=True: torch._higher_order_ops.scan under torch.compile -- lowers the step body ONCE instead of Dynamo-unrolling the H-step Python loop, which is what makes the whole-loop 'compile'/'cudagraph' backends infeasible at training horizons (unroll blowup / ~15 MB/step capture memory -> ~30 GB at H=2048). Needs sm>=70. - use_hop=False: the identical eager fold (same fold semantics, no compile) -- runs everywhere, including Pascal where torch.compile is unavailable. Wired as rollout_backend='scan': sm>=70 compiles the scan HOP; otherwise it warns and runs the eager fold. Added to the example CLI choices. Default stays 'eager'. NOTE: experimental and NOT speed-validated -- the scan HOP requires a working torch.compile, which this Pascal GTX 1080 Ti (sm_61) lacks, so it can only run as the eager fold here. Correctness verified on-GPU: the eager-fold scan core is bit-identical to the whole-loop eager core in forward AND gradients (max|Δ|=0), both at the core level and through teacher_forced_rollout. Speed needs an sm>=70 card to evaluate; this is a sequential scan (no time parallelism), so the win there is compile/fusion + no Python loop overhead, not parallelism. Co-Authored-By: Claude Opus 4.8 (1M context) --- examples/train_poly_spectral_blk445.py | 6 +- train/spectral_rollout.py | 77 ++++++++++++++++++++++++++ 2 files changed, 81 insertions(+), 2 deletions(-) diff --git a/examples/train_poly_spectral_blk445.py b/examples/train_poly_spectral_blk445.py index 2e4cf33..c8b0ccc 100644 --- a/examples/train_poly_spectral_blk445.py +++ b/examples/train_poly_spectral_blk445.py @@ -137,9 +137,11 @@ def main(): p.add_argument("--H-max", type=int, default=2048) p.add_argument("--H-schedule", choices=["geom", "linear", "const", "pow2"], default="geom") - p.add_argument("--rollout-backend", choices=["eager", "cudagraph", "compile"], + p.add_argument("--rollout-backend", + choices=["eager", "cudagraph", "compile", "scan"], default="eager", - help="RK4 rollout backend; 'cudagraph'/'compile' want --H-schedule pow2.") + help="RK4 rollout backend; 'cudagraph'/'compile' want --H-schedule pow2. " + "'scan' lowers the step once via torch.compile (sm>=70), else eager fold.") p.add_argument("--spec-warmup-epochs", type=int, default=5, help="linearly ramp lam_spec from 0 to its target over this many epochs. " "0 disables the warmup. Needed at random init because the spectral " diff --git a/train/spectral_rollout.py b/train/spectral_rollout.py index b59af08..9de0cf1 100644 --- a/train/spectral_rollout.py +++ b/train/spectral_rollout.py @@ -103,6 +103,62 @@ def f(xx, vv): return core +def _rk4_step(carry, x, powers): + """One RK4 step as a scan combine_fn: carry=(xc, xp) each (B,); x=(om2k, gak, w_k) + are the step's drives (om2k,gak: (B,); w_k: (B,P,P)). Returns ((xc', xp'), xc'). + This is the single loop body a `torch.compile`d scan lowers once (instead of unrolling + the whole H-step Python loop), and is identical math to f() in _rk4_core_factory.""" + xc, xp = carry + om2k, gak, w_k = x + + def f(xx, vv): + xpw = xx.unsqueeze(1) ** powers + xvw = vv.unsqueeze(1) ** powers + kern = torch.einsum("bp,bk,bpk->b", xpw, xvw, w_k) + return vv, -om2k * xx - gak * vv - kern + + k1x, k1v = f(xc, xp) + k2x, k2v = f(xc + 0.5 * k1x, xp + 0.5 * k1v) + k3x, k3v = f(xc + 0.5 * k2x, xp + 0.5 * k2v) + k4x, k4v = f(xc + k3x, xp + k3v) + xc = xc + (k1x + 2 * k2x + 2 * k3x + k4x) / 6 + xp = xp + (k1v + 2 * k2v + 2 * k3v + k4v) / 6 + xc = BX * torch.tanh(xc / BX) + xp = BXP * torch.tanh(xp / BXP) + return (xc, xp), xc + + +def _scan_rollout(om2, ga, w, xc, xp, powers, H, use_hop): + """Express the rollout as a scan over the H-1 steps: carry=(xc, xp), per-step inputs + are the time-leading drives. Output is (B, H) = initial xc then the H-1 step outputs. + + use_hop=True uses torch._higher_order_ops.scan (the jax.lax.scan analog), which lowers + the step body once under torch.compile -- avoiding Dynamo's 2048x loop unroll. Requires + being inside torch.compile (and thus sm>=70). use_hop=False runs the identical fold as + an eager Python loop (same semantics, no compile) -- the correctness-equivalent fallback + that also runs on hardware where torch.compile is unavailable (e.g. Pascal).""" + n = H - 1 + # drives for steps 0..H-2, arranged time-leading (scan iterates the leading dim). + om2_t = om2[:, :n].transpose(0, 1) # (n, B) + ga_t = ga[:, :n].transpose(0, 1) # (n, B) + w_t = w[:, :n].permute(1, 0, 2, 3) # (n, B, P, P) + + def step(carry, x): + return _rk4_step(carry, x, powers) + + if use_hop: + from torch._higher_order_ops.scan import scan as _scan_hop + _, ys = _scan_hop(step, (xc, xp), (om2_t, ga_t, w_t)) # ys: (n, B) + else: + carry, ys_list = (xc, xp), [] + for k in range(n): + carry, y = step(carry, (om2_t[k], ga_t[k], w_t[k])) + ys_list.append(y) + ys = (torch.stack(ys_list, dim=0) if ys_list + else om2_t.new_zeros((0, xc.shape[0]))) + return torch.cat([xc.unsqueeze(1), ys.transpose(0, 1)], dim=1) # (B, H) + + def _make_graphed(H, powers, om2, ga, w, xc, xp): """Capture a fwd+bwd CUDA graph of the RK4 core for these shapes, or fall back to the eager core if capture fails (e.g. OOM under a busy GPU). The eager fallback is cached @@ -153,6 +209,27 @@ def _run_rk4(backend, om2, ga, w, xc, xp, powers, H): _ROLLOUT_CACHE[key] = fn return fn(om2, ga, w, xc, xp) + if backend == "scan": + # Express the rollout as a scan. On sm>=70 compile the scan so the step body is + # lowered once (the torch._higher_order_ops.scan HOP must run under torch.compile); + # elsewhere run the identical eager fold (same result, no compile -- e.g. Pascal). + cap = torch.cuda.get_device_capability(om2.device) + if cap[0] >= 7: + key = ("scan", H, int(powers.numel())) + fn = _ROLLOUT_CACHE.get(key) + if fn is None: + fn = torch.compile( + lambda *a: _scan_rollout(*a, powers, H, use_hop=True), + mode="reduce-overhead", + ) + _ROLLOUT_CACHE[key] = fn + return fn(om2, ga, w, xc, xp) + _warn_once("scan_cap", + f"rollout_backend='scan' lowers the scan HOP via torch.compile, which " + f"needs CUDA capability >= 7.0; this GPU is sm_{cap[0]}{cap[1]} -> running " + f"the eager fold (correct, no speedup).") + return _scan_rollout(om2, ga, w, xc, xp, powers, H, use_hop=False) + raise ValueError(f"unknown rollout_backend {backend!r}") From 3f9b28cc4390ef67c388e7d63f569539caa0c83d Mon Sep 17 00:00:00 2001 From: John Pearson Date: Fri, 5 Jun 2026 23:54:46 -0400 Subject: [PATCH 27/34] plot_loss_panels: env1e5 in tab:red Add explicit color for the lam_env=1e5 run so it's distinguishable from the green env1e4 line. Co-Authored-By: Claude Opus 4.7 --- scripts/plot_loss_panels.py | 1 + 1 file changed, 1 insertion(+) diff --git a/scripts/plot_loss_panels.py b/scripts/plot_loss_panels.py index 64a71ed..b39c12f 100644 --- a/scripts/plot_loss_panels.py +++ b/scripts/plot_loss_panels.py @@ -142,6 +142,7 @@ def main(): "live": "tab:blue", "env": "tab:orange", "env1e4": "tab:green", + "env1e5": "tab:red", } # Detect the spec_warmup boundary per run -- the first epoch where lam_spec_t hits From ff134046de5c07c2b619b1f18243585ebf9d374e Mon Sep 17 00:00:00 2001 From: John Pearson Date: Sat, 6 Jun 2026 13:23:19 -0400 Subject: [PATCH 28/34] feat: osc_init van der Pol limit-cycle initialization (Strategy 1) Default init gives |gamma| ~ 0.4 with random per-seed sign (envelope tau ~0.1 ms), so ~half of seeds are purely dissipative -- any oscillation dies instantly and the model can only AM-modulate input/IC noise. osc_init makes the damping a small NEGATIVE constant (slow energy injection) and seeds an amplitude-dependent re-damping (+y^2*ydot) plus a hardening cubic (+y^3) so growth saturates into a bounded limit cycle. Control-head weights for the seeded terms are zeroed so they start as clean constants the encoders learn to modulate; only the structural prior is injected. Threaded through model_seed_cv_spectral and exposed as --osc-init (default off; baseline behavior unchanged). Smoke-tested: with lam_env=1e5 and pow2 horizon buckets the training step stays finite/bounded, and from a noise IC osc_init ignites a bounded oscillation (rms 4.2e-3 -> 5.7e-2) where the default init decays to zero. Co-Authored-By: Claude Opus 4.8 (1M context) --- examples/train_poly_spectral_blk445.py | 6 +++++ model/model.py | 35 ++++++++++++++++++++++++++ train/model_cv.py | 3 ++- 3 files changed, 43 insertions(+), 1 deletion(-) diff --git a/examples/train_poly_spectral_blk445.py b/examples/train_poly_spectral_blk445.py index c8b0ccc..9acda2d 100644 --- a/examples/train_poly_spectral_blk445.py +++ b/examples/train_poly_spectral_blk445.py @@ -121,6 +121,10 @@ def main(): p.add_argument("--expand-factor", type=int, default=10) p.add_argument("--drive-lowpass-ms", type=float, default=1.0) p.add_argument("--keep-const", action=argparse.BooleanOptionalAction, default=True) + p.add_argument("--osc-init", action=argparse.BooleanOptionalAction, default=False, + help="Strategy 1: initialize each seed as a marginal van der Pol limit cycle " + "(small negative damping + seeded y^2*ydot re-damping + y^3 hardening) so " + "oscillation can ignite from silence instead of the dissipative default init.") p.add_argument("--lam", type=float, default=1.068, help="fixed kernel-weight lambda (no CV in this PR).") # loss @@ -225,6 +229,7 @@ def main(): n_kernels=args.n_kernels, n_layers=args.n_layers, d_state=args.d_state, d_conv=args.d_conv, expand_factor=args.expand_factor, tau=dt, drive_lowpass_ms=args.drive_lowpass_ms, keep_const=args.keep_const, + osc_init=args.osc_init, lam=args.lam, n_epochs=args.n_epochs, lr=args.lr, n_seeds=args.n_seeds, cull_frac=args.cull_frac, cull_keep=args.cull_keep, @@ -256,6 +261,7 @@ def main(): "max_segs": args.max_segs, "drive_lowpass_ms": args.drive_lowpass_ms, "keep_const": bool(args.keep_const), + "osc_init": bool(args.osc_init), "lam_spec": args.lam_spec, "lam_tf": args.lam_tf, "lam_env": args.lam_env, diff --git a/model/model.py b/model/model.py index dbe2d7a..f3f95ff 100644 --- a/model/model.py +++ b/model/model.py @@ -38,6 +38,11 @@ def __init__( smooth_len: float = 0.001, drive_lowpass_ms: float = 0.0, keep_const: bool = False, + osc_init: bool = False, + gamma_init: float = -0.01, + vdp_init: float = 0.02, + cubic_init: float = 0.01, + const_init: float = 1e-3, ): super().__init__() @@ -98,6 +103,36 @@ def __init__( with torch.no_grad(): self.kernel.weights.weight[0].zero_() self.kernel.weights.bias[0].zero_() + # Strategy 1 "oscillator init": start every seed as a marginal van der Pol limit cycle + # instead of the default coin-flip-sign, ~40x-too-stiff linear damping. At the default + # init |gamma| ~ 0.4 (envelope tau ~ 0.1 ms) with random per-seed sign, so ~half of seeds + # are purely dissipative (oscillation dies instantly -> the model can only AM-modulate + # input/IC noise). osc_init makes the damping a small NEGATIVE constant (slow energy + # injection, envelope tau ~ ms) and seeds an amplitude-dependent re-damping (+y^2*ydot) + # plus a hardening cubic (+y^3) so the growth saturates into a bounded cycle. The + # corresponding control-head WEIGHTS are zeroed so these start as clean constants the + # encoders can later modulate; only the structural prior is injected. + # + # Net damping in ydot-dot is -(gamma_init + vdp_init*y^2)*ydot, zero at amplitude + # A* = sqrt(-gamma_init/vdp_init) (~0.7 with the defaults), i.e. a limit cycle near |y|~0.7. + if osc_init: + with torch.no_grad(): + # gamma: pure small negative constant (anti-damping) -> energy injection + self.gamma_net.weight.zero_() + self.gamma_net.bias.fill_(float(gamma_init)) + # kernel polynomial weights: index (i,j) -> row i*W + j in the flattened + # (poly_dim+1)x(poly_dim+1) output of kernel.weights. yhat subtracts + # weighted_kernels, so a +w[i,j] adds -w[i,j]*y^i*ydot^j to ydot-dot. + W = self.kernel.poly_dim + 1 + def _seed(i, j, val): + idx = i * W + j + self.kernel.weights.weight[idx].zero_() + self.kernel.weights.bias[idx].fill_(float(val)) + _seed(2, 1, vdp_init) # +y^2*ydot -> amplitude-dependent re-damping (saturation) + _seed(3, 0, cubic_init) # +y^3 -> hardening spring (bounds amplitude) + if keep_const: + # tiny constant drive to break the y=0 fixed-point symmetry so the cycle ignites + _seed(0, 0, const_init) self.names = [r"$\omega$", r"$\gamma$", "weighted kernels", "states"] def _lowpass(self, x: torch.FloatTensor, dt: float, lp_ms: float = None) -> torch.FloatTensor: diff --git a/train/model_cv.py b/train/model_cv.py index 227ccd4..d490932 100644 --- a/train/model_cv.py +++ b/train/model_cv.py @@ -233,6 +233,7 @@ def model_seed_cv_spectral( smooth_len: float = 0.001, drive_lowpass_ms: float = 1.0, keep_const: bool = False, + osc_init: bool = False, # Strategy 1: van der Pol limit-cycle init (see Ouroboros.__init__) lam: float = 1.068, # fixed kernel-weight lambda (no CV in this PR) # training n_epochs: int = 50, @@ -294,7 +295,7 @@ def _build(seed): model = Ouroboros(d_data=1, n_layers=n_layers, d_state=d_state, d_conv=d_conv, expand_factor=expand_factor, tau=tau, smooth_len=smooth_len, kernel=kernel, drive_lowpass_ms=drive_lowpass_ms, - keep_const=keep_const) + keep_const=keep_const, osc_init=osc_init) opt = Adam(model.parameters(), lr=lr) sched = ReduceLROnPlateau(opt, factor=0.5, patience=max(n_epochs // 25, 2), min_lr=1e-10) From bc127d1aa87e8f5e39815dc2a6f220b47a3993c6 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Sat, 6 Jun 2026 13:26:57 -0400 Subject: [PATCH 29/34] plot_loss_panels: osc1e5 in tab:purple Add explicit color for the --osc-init lam_env=1e5 run. Co-Authored-By: Claude Opus 4.7 --- scripts/plot_loss_panels.py | 1 + 1 file changed, 1 insertion(+) diff --git a/scripts/plot_loss_panels.py b/scripts/plot_loss_panels.py index b39c12f..e9b59b9 100644 --- a/scripts/plot_loss_panels.py +++ b/scripts/plot_loss_panels.py @@ -143,6 +143,7 @@ def main(): "env": "tab:orange", "env1e4": "tab:green", "env1e5": "tab:red", + "osc1e5": "tab:purple", } # Detect the spec_warmup boundary per run -- the first epoch where lam_spec_t hits From e81607a1e49071894a96addf5b8a8eee38fb0358 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Sat, 6 Jun 2026 17:12:12 -0400 Subject: [PATCH 30/34] feat: --lam-env-log log-ratio envelope loss Add env_loss_log to train/rollout_refine.py: |log((env(a)+eps)/(env(g)+eps))| averaged over time and batch. Symmetric in (auto, target) scale, dB-natural, no trivial-silent-floor like the linear env_loss (which lets the optimizer satisfy the penalty by shrinking the rollout amplitude past target). Threaded lam_env_log (weight) and env_log_eps (noise floor) through spectral_rollout_step, train(), model_seed_cv_spectral, and the entry script. Reuses env_warmup_epochs to ramp lam_env_log alongside lam_env. Logs Loss/env_log + Train/lam_env_log_t to TB when active. Use case: the current --lam-env=1e5 osc-init run pulled the rollout from ~28x loud (osc-init default) past target into ~1430x quiet (decay basin) -- env_loss can't tell those apart in its tail. env_loss_log treats both symmetrically (|log K| same for K and 1/K) so the optimizer can't escape into the decay basin. eps trade-off: smaller eps -> closer to amp_pen, but huge gradient on silence-prefix samples; larger eps -> bounded gradient near silence but loses sensitivity to extreme decay. Default 1e-4 matches normalized blk445's noise floor; effective |log| cap is ~log(env(g)/eps). Verified end-to-end with a synthetic test: K=1 -> env_loss=0, env_loss_log=0 K=28 -> env_loss=27, env_loss_log=3.31 (matches |log 28|=3.33) K=1/1430-> env_loss=0.999, env_loss_log=3.66 (eps-capped; |log 1430|=7.27) Co-Authored-By: Claude Opus 4.7 --- examples/train_poly_spectral_blk445.py | 15 ++++++++++++++- train/model_cv.py | 6 +++++- train/rollout_refine.py | 16 ++++++++++++++++ train/spectral_rollout.py | 13 +++++++++++-- train/train.py | 26 +++++++++++++++++++------- 5 files changed, 65 insertions(+), 11 deletions(-) diff --git a/examples/train_poly_spectral_blk445.py b/examples/train_poly_spectral_blk445.py index 9acda2d..1841a04 100644 --- a/examples/train_poly_spectral_blk445.py +++ b/examples/train_poly_spectral_blk445.py @@ -132,6 +132,15 @@ def main(): p.add_argument("--lam-tf", type=float, default=1.0, help="variance-normalized acceleration MSE anchor. Default-on so early " "epochs have a smooth gradient signal before the spectral basin is informative.") + p.add_argument("--lam-env-log", type=float, default=0.0, + help="weight on the LOG-RATIO envelope loss (|log((env(a)+eps)/(env(g)+eps))|). " + "Symmetric in (auto, target) -- penalizes shrinking past target the same as " + "growing past it (unlike --lam-env, which has a trivial floor of 1 at silence " + "and lets the model overshoot into decay). Same env_warmup ramp applies. " + "Default 0 disables.") + p.add_argument("--env-log-eps", type=float, default=1e-4, + help="soft noise floor inside the log() of the log-ratio envelope loss; ~1e-4 " + "is the per-sample noise floor of normalized blk445 audio.") p.add_argument("--lam-env", type=float, default=0.0, help="Gaussian-envelope L1 amplitude pin. 0 disables.") p.add_argument("--env-ms", type=float, default=2.0) @@ -235,7 +244,9 @@ def main(): cull_frac=args.cull_frac, cull_keep=args.cull_keep, save_freq=args.save_freq, max_saved=args.max_saved, model_path=out_dir, H_min=args.H_min, H_max=args.H_max, H_schedule=args.H_schedule, - lam_spec=args.lam_spec, lam_tf=args.lam_tf, lam_env=args.lam_env, env_ms=args.env_ms, + lam_spec=args.lam_spec, lam_tf=args.lam_tf, + lam_env=args.lam_env, lam_env_log=args.lam_env_log, env_log_eps=args.env_log_eps, + env_ms=args.env_ms, spec_warmup_epochs=args.spec_warmup_epochs, env_warmup_epochs=args.env_warmup_epochs, spec_configs=spec_configs, ic_noise_rms=args.ic_noise_rms, grad_clip=args.grad_clip, @@ -265,6 +276,8 @@ def main(): "lam_spec": args.lam_spec, "lam_tf": args.lam_tf, "lam_env": args.lam_env, + "lam_env_log": args.lam_env_log, + "env_log_eps": args.env_log_eps, "env_ms": args.env_ms, "spec_configs": [list(c) for c in spec_configs], "H_min": args.H_min, diff --git a/train/model_cv.py b/train/model_cv.py index d490932..114ab41 100644 --- a/train/model_cv.py +++ b/train/model_cv.py @@ -251,6 +251,8 @@ def model_seed_cv_spectral( lam_spec: float = 1.0, lam_tf: float = 1.0, lam_env: float = 0.0, + lam_env_log: float = 0.0, + env_log_eps: float = 1e-4, env_ms: float = 2.0, spec_warmup_epochs: int = 5, env_warmup_epochs: int = 0, @@ -321,7 +323,9 @@ def _train_to(seed, target): model_info=model_info, loss_mode="spectral_rollout", H_min=H_min, H_max=H_max, H_schedule=H_schedule, - lam_spec=lam_spec, lam_tf=lam_tf, lam_env=lam_env, env_ms=env_ms, + lam_spec=lam_spec, lam_tf=lam_tf, lam_env=lam_env, + lam_env_log=lam_env_log, env_log_eps=env_log_eps, + env_ms=env_ms, spec_warmup_epochs=spec_warmup_epochs, env_warmup_epochs=env_warmup_epochs, spec_configs=spec_configs, ic_noise_rms=ic_noise_rms, diff --git a/train/rollout_refine.py b/train/rollout_refine.py index 1ed5e48..6f1b5ba 100644 --- a/train/rollout_refine.py +++ b/train/rollout_refine.py @@ -99,6 +99,22 @@ def env_loss(xg, tgt, dt, env_ms, eps=1e-6): return ((ea - eg).abs().mean(dim=1) / (eg.mean(dim=1) + eps)).mean() +def env_loss_log(xg, tgt, dt, env_ms, eps=1e-4): + """Log-ratio envelope loss: |log( (env(a) + eps) / (env(g) + eps) )| averaged over time + and batch. Symmetric in (auto, target) scale — penalizes "rollout K× too quiet" the same as + "K× too loud", so there's no trivial-zero floor like in env_loss (where a silent rollout + gives L=1 against a non-silent target). dB-natural: a log_e ratio is dB/8.686, so the loss + grows linearly in dB-distance from the target envelope. + + eps acts as a soft noise floor that prevents log(0) divergence on the silence portions of + a target. ~1e-4 matches the per-sample noise floor of the normalized blk445 audio (peak + amplitude ~1.0 / int16 quantization 1/32768). Larger eps -> less sensitivity near silence. + """ + ea = gaussian_envelope(xg, dt, env_ms) + eg = gaussian_envelope(tgt, dt, env_ms) + return (torch.log(ea + eps) - torch.log(eg + eps)).abs().mean(dim=1).mean() + + def rollout_refine(model, X, dt, *, epochs=8, hmin=768, hmax=1500, batch_size=6, lr=1e-4, lam_spec=1.0, lam_env=10.0, lam_tf=1.0, clip=5.0, env_ms=2.0, configs=DEFAULT_CONFIGS, device="cuda", verbose=True): diff --git a/train/spectral_rollout.py b/train/spectral_rollout.py index 9de0cf1..bff98f5 100644 --- a/train/spectral_rollout.py +++ b/train/spectral_rollout.py @@ -32,6 +32,7 @@ BXP, mrstft_loss, env_loss, + env_loss_log, ) # ONSET category from data.load_data (kept local to avoid the import cycle the data @@ -307,6 +308,8 @@ def spectral_rollout_step( lam_spec: float = 1.0, lam_tf: float = 1.0, lam_env: float = 0.0, + lam_env_log: float = 0.0, # weight on the log-ratio envelope loss (env_loss_log) + env_log_eps: float = 1e-4, # noise floor inside the log() in env_loss_log env_ms: float = 2.0, tf_var: Optional[float] = None, # precomputed Var(d2x) over the dataset; matches rollout_refine.py:110 ic_mask: Optional[torch.Tensor] = None, @@ -359,9 +362,15 @@ def spectral_rollout_step( L_env = env_loss(xg, tgt, dt, env_ms) else: L_env = torch.zeros((), device=x.device, dtype=x.dtype) + # Log-ratio envelope loss: symmetric in (auto, target) scale so the model can't satisfy it + # by shrinking past optimum. dB-natural; see train/rollout_refine.py::env_loss_log. + if lam_env_log > 0: + L_env_log = env_loss_log(xg, tgt, dt, env_ms, eps=env_log_eps) + else: + L_env_log = torch.zeros((), device=x.device, dtype=x.dtype) - total = lam_spec * L_spec + lam_tf * L_tf + lam_env * L_env - return {"spec": L_spec, "tf": L_tf, "env": L_env, "total": total} + total = lam_spec * L_spec + lam_tf * L_tf + lam_env * L_env + lam_env_log * L_env_log + return {"spec": L_spec, "tf": L_tf, "env": L_env, "env_log": L_env_log, "total": total} def pow2_horizon_buckets(H_min: int, H_max: int) -> list: diff --git a/train/train.py b/train/train.py index cb40951..2799c63 100644 --- a/train/train.py +++ b/train/train.py @@ -177,9 +177,11 @@ def train( lam_spec: float = 1.0, lam_tf: float = 1.0, lam_env: float = 0.0, + lam_env_log: float = 0.0, # weight on the log-ratio envelope loss (env_loss_log) + env_log_eps: float = 1e-4, # noise floor inside the log() in env_loss_log env_ms: float = 2.0, spec_warmup_epochs: int = 5, # linearly ramp lam_spec 0 -> lam_spec over these epochs - env_warmup_epochs: int = 0, # linearly ramp lam_env 0 -> lam_env over these epochs + env_warmup_epochs: int = 0, # linearly ramp lam_env AND lam_env_log over these epochs spec_configs=None, ic_noise_rms: float = 1e-3, grad_clip: float = 5.0, @@ -246,6 +248,7 @@ def train( tf_var = max(tf_var_running / max(1, n_seen), 1e-6) print( f"spectral_rollout mode: lam_spec={lam_spec} lam_tf={lam_tf} lam_env={lam_env} " + f"lam_env_log={lam_env_log} env_log_eps={env_log_eps} " f"H={H_min}->{H_max} ({H_schedule}) ic_noise_rms={ic_noise_rms} tf_var={tf_var:.4g} " f"rollout_backend={rollout_backend}", flush=True, @@ -294,14 +297,19 @@ def train( else: lam_spec_t = lam_spec if env_warmup_epochs > 0 and epoch < env_warmup_epochs: - lam_env_t = lam_env * (epoch / float(env_warmup_epochs)) + ramp = epoch / float(env_warmup_epochs) + lam_env_t = lam_env * ramp + lam_env_log_t = lam_env_log * ramp else: lam_env_t = lam_env + lam_env_log_t = lam_env_log out = spectral_rollout_step( model, x, dxdt, dx2, dt, H=H, configs=spec_configs, lam_spec=lam_spec_t, lam_tf=lam_tf, - lam_env=lam_env_t, env_ms=env_ms, + lam_env=lam_env_t, + lam_env_log=lam_env_log_t, env_log_eps=env_log_eps, + env_ms=env_ms, tf_var=tf_var, ic_mask=ic_mask, ic_noise_rms=ic_noise_rms, rollout_backend=rollout_backend, @@ -315,20 +323,24 @@ def train( optimizer.step() # One device->host sync for all scalar logging instead of 5 separate # .item() calls (one of which was a duplicate of out["spec"]): stack the - # loss tensors and transfer once. out["env"] is always a tensor (zeros - # when lam_env==0), so the stack is well-formed. - spec_v, tf_v, env_v, total_v = torch.stack( - [out["spec"], out["tf"], out["env"], total_loss] + # loss tensors and transfer once. out["env"] / out["env_log"] are always + # tensors (zeros when their lam is 0), so the stack is well-formed. + spec_v, tf_v, env_v, env_log_v, total_v = torch.stack( + [out["spec"], out["tf"], out["env"], out["env_log"], total_loss] ).tolist() train_losses.append(spec_v) writer.add_scalar("Loss/spec", spec_v, idx) writer.add_scalar("Loss/tf", tf_v, idx) if lam_env > 0: writer.add_scalar("Loss/env", env_v, idx) + if lam_env_log > 0: + writer.add_scalar("Loss/env_log", env_log_v, idx) writer.add_scalar("Loss/total", total_v, idx) writer.add_scalar("Train/H", float(H), idx) writer.add_scalar("Train/lam_spec_t", float(lam_spec_t), idx) writer.add_scalar("Train/lam_env_t", float(lam_env_t), idx) + if lam_env_log > 0: + writer.add_scalar("Train/lam_env_log_t", float(lam_env_log_t), idx) continue dx2hat, weights = model(x, dxdt, dt, smoothing) # state: B x L x SD From 5fb1c7195a3970e9f3d6f2ae650f7d6738673dd1 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Sat, 6 Jun 2026 17:16:05 -0400 Subject: [PATCH 31/34] plot_loss_panels: osc_none in tab:cyan, osc_log1e4 in tab:brown Add explicit colors for the two osc-init experiments comparing the new log-ratio envelope loss against no envelope penalty. Co-Authored-By: Claude Opus 4.7 --- scripts/plot_loss_panels.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/scripts/plot_loss_panels.py b/scripts/plot_loss_panels.py index e9b59b9..4a7ee4e 100644 --- a/scripts/plot_loss_panels.py +++ b/scripts/plot_loss_panels.py @@ -144,6 +144,8 @@ def main(): "env1e4": "tab:green", "env1e5": "tab:red", "osc1e5": "tab:purple", + "osc_none": "tab:cyan", + "osc_log1e4": "tab:brown", } # Detect the spec_warmup boundary per run -- the first epoch where lam_spec_t hits From c8df1f2ceaf44278cb7d64d99162561881fca376 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Sun, 7 Jun 2026 00:04:23 -0400 Subject: [PATCH 32/34] feat: signed amp_pen alongside autonomy_score (~zero extra compute going forward) train/eval.py: autonomy_score now writes signed_amp_mean + signed_amp_per_voc to the breakdown dict. Captured BEFORE the optional --rescale gauge fix so the sign reflects what the integrator actually produced (positive = loud, negative = quiet); |signed| matches the legacy amp_pen value to log precision. scripts/monitor_spectral_diagnose.py: AUTONOMY_SNIPPET writes per-voc signed amp_pen to /signed_amp_pen.json each time it scores a new ckpt. Drops the per_voc list from the stdout summary so the JSON parsing stays backward-compatible. Cache is incremental + side-effect-free relative to the existing autonomy work, so going forward the loss_panels.png signed amp_pen panel auto-fills as new checkpoints land. scripts/build_signed_amp_cache.py (new): one-shot backfill for runs that predate the monitor side-effect. Same on-disk format as the monitor writes. Skips already-cached ckpts so re-runs are cheap. scripts/plot_loss_panels.py: bottom panel switched from Train/lam_spec_t to signed amp_pen = log(auto/tgt rms). Reads /seed0/signed_amp_pen.json if present; plots as a marker line (one point per scored ckpt) with a dotted horizontal at y=0 (auto_rms == target_rms). Co-Authored-By: Claude Opus 4.7 --- scripts/build_signed_amp_cache.py | 118 +++++++++++++++++++++++++++ scripts/monitor_spectral_diagnose.py | 34 +++++++- scripts/plot_loss_panels.py | 35 +++++++- train/eval.py | 15 ++++ 4 files changed, 197 insertions(+), 5 deletions(-) create mode 100644 scripts/build_signed_amp_cache.py diff --git a/scripts/build_signed_amp_cache.py b/scripts/build_signed_amp_cache.py new file mode 100644 index 0000000..1f336fc --- /dev/null +++ b/scripts/build_signed_amp_cache.py @@ -0,0 +1,118 @@ +"""Build a JSON cache of signed amp_pen per checkpoint for a run. + +signed_amp_pen(ckpt) = mean_v log( nanstd(autonomous_rollout_v) / nanstd(target_v) ) + +The mean is over the same cold-start val vocs the live monitor uses (rng seed 1234, +last 10% of stems → first 8 for val). Cached at /seed0/signed_amp_pen.json +so we only compute new ckpts on incremental re-runs. Sign convention: positive = LOUD, +negative = QUIET; |value| matches the autonomy_score amp_pen breakdown to log-precision. + +Usage: + python scripts/build_signed_amp_cache.py # current run + python scripts/build_signed_amp_cache.py # multiple runs +""" + +import glob +import json +import os +import sys +import warnings + +import numpy as np +import torch +from scipy.io import wavfile + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) +from train.train import load_model # noqa: E402 +from train.eval import integrate_poly_autonomous, correct # noqa: E402 + +DATA = os.path.expanduser("~/ouroboros_data/blk445_syllC/day85") +SR = 44100 +DT = 1.0 / SR +N_VAL = 8 +SILENCE_PAD = 2000 + + +def _val_vocs(): + wavs = sorted(glob.glob(os.path.join(DATA, "*.wav"))) + rng = np.random.default_rng(1234) + idx = np.arange(len(wavs)) + rng.shuffle(idx) + n = max(1, int(round(0.1 * len(wavs)))) + val_wavs = [wavs[i] for i in idx[n:n + n]] + raw_audio = [] + for wav in val_wavs[:N_VAL]: + sr, af = wavfile.read(wav) + if af.dtype == np.int16: + af = af / -np.iinfo(af.dtype).min + af = af.astype(np.float64) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + onoffs = np.atleast_2d(np.loadtxt(wav.replace(".wav", ".txt"))) + on_i = int(round(onoffs[0][0] * sr)) + off_i = int(round(onoffs[0][1] * sr)) + raw_audio.append(af[max(0, on_i - SILENCE_PAD):off_i]) + L = min(len(s) for s in raw_audio) + return [s[:L] for s in raw_audio] + + +def process_run(run_dir): + seed_dir = os.path.join(run_dir, "seed0") + if not os.path.isdir(seed_dir): + print(f"skip {run_dir}: no seed0", file=sys.stderr) + return + ckpts = glob.glob(os.path.join(seed_dir, "checkpoint_*.tar")) + if not ckpts: + print(f"skip {run_dir}: no ckpts", file=sys.stderr) + return + + cache_path = os.path.join(seed_dir, "signed_amp_pen.json") + cache = {} + if os.path.exists(cache_path): + with open(cache_path) as f: + cache = json.load(f) + + def _ep(p): + return int(os.path.basename(p).split("_")[1].split(".")[0]) + ckpts.sort(key=_ep) + todo = [c for c in ckpts if str(_ep(c)) not in cache] + if not todo: + print(f"{run_dir}: all {len(ckpts)} ckpts already cached", flush=True) + return + + val_vocs = _val_vocs() + tgt_log = np.array([float(np.log(np.nanstd(correct(np.asarray(s, dtype=np.float64))))) for s in val_vocs]) + model, _, _, _ = load_model(seed_dir) + print(f"{run_dir}: caching {len(todo)} new ckpts (of {len(ckpts)} total)", flush=True) + + for ckpt in todo: + ep = _ep(ckpt) + sd = torch.load(ckpt, weights_only=False, map_location="cuda") + model.load_state_dict(sd["ouroboros"]) + model.eval() + per_voc_signed = [] + for seg in val_vocs: + seg = np.asarray(seg, dtype=np.float64) + with torch.no_grad(): + auto = integrate_poly_autonomous(model, seg, DT, verbose=False) + auto_rms = float(np.nanstd(np.asarray(auto, dtype=np.float64))) + if not np.isfinite(auto_rms) or auto_rms <= 0: + per_voc_signed.append(float("nan")) + else: + per_voc_signed.append(float(np.log(auto_rms)) - float(tgt_log[len(per_voc_signed)])) + cache[str(ep)] = { + "per_voc": per_voc_signed, + "mean": float(np.nanmean(per_voc_signed)) if per_voc_signed else float("nan"), + } + # incremental save so a kill mid-loop doesn't lose progress + with open(cache_path, "w") as f: + json.dump(cache, f, indent=2) + print(f" ckpt {ep:>2}: signed mean = {cache[str(ep)]['mean']:+.3f}", flush=True) + + +if __name__ == "__main__": + if len(sys.argv) < 2: + print(__doc__, file=sys.stderr) + sys.exit(1) + for rd in sys.argv[1:]: + process_run(rd) diff --git a/scripts/monitor_spectral_diagnose.py b/scripts/monitor_spectral_diagnose.py index 0764e05..70887e5 100644 --- a/scripts/monitor_spectral_diagnose.py +++ b/scripts/monitor_spectral_diagnose.py @@ -160,12 +160,42 @@ def _ep(p): L = min(len(s) for s in raw) val_vocs = [s[:L] for s in raw] -ckpt_dir = os.path.dirname(sys.argv[1]) +ckpt_path = sys.argv[1] +ckpt_dir = os.path.dirname(ckpt_path) model, _, _, _ = load_model(ckpt_dir) model.eval() with torch.no_grad(): score, _, bd = autonomy_score(model, val_vocs, DT, rescale=False, cold_start=True) -print(json.dumps({'autonomy': float(score), 'breakdown': {k: (float(v) if not isinstance(v, bool) else bool(v)) for k, v in bd.items()}})) + +# Persist signed amp_pen alongside the offline cache the loss panels reads. +# autonomy_score now puts signed_amp_per_voc + signed_amp_mean in the breakdown so this +# is essentially free -- the integration already happened above. +cache_path = os.path.join(ckpt_dir, 'signed_amp_pen.json') +cache = {} +if os.path.exists(cache_path): + try: + with open(cache_path) as f: + cache = json.load(f) + except Exception: + cache = {} +ep = int(os.path.basename(ckpt_path).split('_')[1].split('.')[0]) +cache[str(ep)] = { + 'per_voc': [float(v) for v in bd.get('signed_amp_per_voc', [])], + 'mean': float(bd.get('signed_amp_mean', float('nan'))), +} +with open(cache_path, 'w') as f: + json.dump(cache, f, indent=2) + +# JSON-safe breakdown: drop the list, keep the scalar mean alongside everything else. +safe_bd = {} +for k, v in bd.items(): + if isinstance(v, bool): + safe_bd[k] = bool(v) + elif isinstance(v, list): + continue # per-voc lists go to the cache, not the stdout summary + else: + safe_bd[k] = float(v) +print(json.dumps({'autonomy': float(score), 'breakdown': safe_bd})) """ diff --git a/scripts/plot_loss_panels.py b/scripts/plot_loss_panels.py index 4a7ee4e..da589f1 100644 --- a/scripts/plot_loss_panels.py +++ b/scripts/plot_loss_panels.py @@ -106,6 +106,21 @@ def main(): print(f"warn: no TB events in {r['run_dir']}/seed0", file=sys.stderr) continue per_epoch = {t: per_epoch_means(v, bpe) for t, v in scalars.items()} + + # Load the signed amp_pen cache (built offline by scripts/build_signed_amp_cache.py) + # if present, and project it onto an "epoch -> mean signed amp_pen" array indexed + # by checkpoint epoch. Missing epochs are left as NaN so matplotlib breaks the line. + cache_path = os.path.join(r["run_dir"], "seed0", "signed_amp_pen.json") + if os.path.exists(cache_path): + with open(cache_path) as f: + amp_cache = json.load(f) + if amp_cache: + max_ep = max(int(k) for k in amp_cache.keys()) + arr = np.full(max_ep + 1, np.nan, dtype=np.float64) + for k, v in amp_cache.items(): + arr[int(k)] = float(v.get("mean", np.nan)) + per_epoch["signed_amp_pen"] = arr + runs.append({"label": r["label"], "run_dir": r["run_dir"], "scalars": scalars, "per_epoch": per_epoch}) @@ -130,7 +145,7 @@ def main(): ("Loss/env", "env L1", False), ("Loss/total", "total", True), ("Train/H", "rollout H (samples)", False), - ("Train/lam_spec_t", "lam_spec_t (warmup)", False), + ("signed_amp_pen", "signed amp_pen = log(auto/tgt rms)", False), ] n = len(panels) fig, axes = plt.subplots(n, 1, figsize=(11, 2.0 * n), sharex=True) @@ -169,7 +184,16 @@ def main(): v = r["per_epoch"].get(tag) if v is None or len(v) == 0: continue - if smooth > 1 and len(v) >= smooth: + # signed amp_pen is a per-CHECKPOINT array (one value per epoch the cache + # has scored), not a per-BATCH TB stream, so don't smooth it. + if tag == "signed_amp_pen": + vs = v + xs = np.arange(len(vs)) + # only draw where the cache actually has data + mask = np.isfinite(vs) + xs = xs[mask] + vs = vs[mask] + elif smooth > 1 and len(v) >= smooth: # simple moving average vs = np.convolve(v, np.ones(smooth) / smooth, mode="valid") xs = np.arange(len(vs)) + (smooth - 1) @@ -177,11 +201,16 @@ def main(): vs = v xs = np.arange(len(vs)) c = colors.get(r["label"], None) - ax.plot(xs, vs, label=r["label"], color=c, lw=1.4) + ax.plot(xs, vs, label=r["label"], color=c, lw=1.4, + marker="o" if tag == "signed_amp_pen" else None, + markersize=3 if tag == "signed_amp_pen" else None) # warmup boundary marker per run for label, b in warmup_boundaries: c = colors.get(label, None) ax.axvline(b, color=c, alpha=0.25, lw=1.0, ls="--", zorder=0) + # Reference line at 0 (auto_rms == target_rms) on the signed amp_pen panel + if tag == "signed_amp_pen": + ax.axhline(0, color="black", alpha=0.5, lw=0.8, ls=":", zorder=0) ax.set_ylabel(title) if logy: ax.set_yscale("log") diff --git a/train/eval.py b/train/eval.py index 55b1e54..c3fc843 100644 --- a/train/eval.py +++ b/train/eval.py @@ -524,6 +524,12 @@ def _peak(x): return float(f[np.argmax(P)]) scores, specs, amps, pits, bounded = [], [], [], [], [] + # Signed amp = log(auto_rms / target_rms). Positive = loud, negative = quiet. + # amp_pen is |signed_amp|, so this gives direction at no extra integration cost. + # The "raw" version uses the unrescaled auto_n, so the sign reflects what the + # autonomous integrator actually produced (not what the deployed rescale recipe + # would emit). + signed_amps_raw = [] for seg in segments: seg = np.asarray(seg, dtype=np.float64) tgt = correct(seg) @@ -535,8 +541,13 @@ def _peak(x): scores.append(diverge_score) bounded.append(0.0) specs.append(np.nan); amps.append(np.nan); pits.append(np.nan) + signed_amps_raw.append(float("nan")) continue bounded.append(1.0) + # Capture the SIGNED amp BEFORE optional rescaling so we know which direction + # the raw autonomous rollout drifts. + signed_amp_raw = float(np.log((np.nanstd(auto_n) + 1e-12) / (np.nanstd(tgt_n) + 1e-12))) + signed_amps_raw.append(signed_amp_raw) if rescale: # gauge-fix amplitude to the target RMS (the deployed recipe) auto_n = auto_n * (np.nanstd(tgt_n) / (np.nanstd(auto_n) + 1e-12)) sc = float(np.corrcoef(_logpsd(tgt_n), _logpsd(auto_n))[0, 1]) @@ -552,6 +563,10 @@ def _peak(x): "bounded_frac": float(np.mean(bounded)) if len(bounded) else 0.0, "cold_start": bool(cold_start), "rescale": bool(rescale), + # Signed amp_pen (loud/quiet direction) -- mean over vocs and per-voc list. + "signed_amp_mean": (float(np.nanmean(signed_amps_raw)) + if any(np.isfinite(signed_amps_raw)) else float("nan")), + "signed_amp_per_voc": [float(v) for v in signed_amps_raw], } return float(np.mean(scores)), scores, breakdown From 22eb3b7d96d09c33d14b31f72306da54b82c1110 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Sun, 7 Jun 2026 00:17:44 -0400 Subject: [PATCH 33/34] feat: --spec-warmup-steps / --env-warmup-steps / --H-total-steps MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Decouples warmup ramps and the H curriculum from dataset size. Each new flag overrides the epoch-based equivalent when set; when None (default) they're derived from epochs × batches_per_epoch at startup, so existing CLI invocations are unchanged. train/spectral_rollout.py: add horizon_for_step indexed by global batch count (same formulas; horizon_for_epoch is kept as a thin wrapper for backward compatibility). train/train.py: derive *_steps_eff at the top of the spectral_rollout setup block, then index lam_spec_t / lam_env_t / lam_env_log_t and H off the global idx (= epoch * batches_per_epoch + idx_within_epoch, which the existing enumerate(... start=epoch * len(loader)) already provides). H is now computed per-batch instead of per-epoch -- pow2 only yields a few distinct values so the graphed/compiled backends still stay cache-friendly, and the warning about backend × schedule still applies. train/model_cv.py + examples/train_poly_spectral_blk445.py: pass the new kwargs through and persist them in the manifest. Motivation: ahead of a full-dataset run where epochs become much longer, keeping the spec/env warmups and H ramp calibrated in gradient steps instead of epochs means same total gradient-update budget regardless of dataset size. Co-Authored-By: Claude Opus 4.7 --- examples/train_poly_spectral_blk445.py | 17 +++++++++++ train/model_cv.py | 6 ++++ train/spectral_rollout.py | 27 +++++++++++------ train/train.py | 42 +++++++++++++++++++++----- 4 files changed, 75 insertions(+), 17 deletions(-) diff --git a/examples/train_poly_spectral_blk445.py b/examples/train_poly_spectral_blk445.py index 1841a04..6b3f7af 100644 --- a/examples/train_poly_spectral_blk445.py +++ b/examples/train_poly_spectral_blk445.py @@ -159,6 +159,17 @@ def main(): help="linearly ramp lam_spec from 0 to its target over this many epochs. " "0 disables the warmup. Needed at random init because the spectral " "term against quiet/onset targets is enormous and would swamp the TF anchor.") + # Step-based curriculum overrides (None -> derive from _epochs * batches_per_epoch). + p.add_argument("--spec-warmup-steps", type=int, default=None, + help="number of GLOBAL BATCHES over which to ramp lam_spec. Overrides " + "--spec-warmup-epochs when set; lets the curriculum stay calibrated " + "when the dataset size changes.") + p.add_argument("--env-warmup-steps", type=int, default=None, + help="number of GLOBAL BATCHES over which to ramp lam_env / lam_env_log. " + "Overrides --env-warmup-epochs when set.") + p.add_argument("--H-total-steps", type=int, default=None, + help="total GLOBAL BATCHES across which the H curriculum runs. Overrides " + "--n-epochs * batches_per_epoch when set.") p.add_argument("--env-warmup-epochs", type=int, default=0, help="linearly ramp lam_env from 0 to its target over this many epochs. " "0 disables the warmup. Set to 5+ when using a large --lam-env (e.g. 1e4+) " @@ -249,6 +260,9 @@ def main(): env_ms=args.env_ms, spec_warmup_epochs=args.spec_warmup_epochs, env_warmup_epochs=args.env_warmup_epochs, + spec_warmup_steps=args.spec_warmup_steps, + env_warmup_steps=args.env_warmup_steps, + H_total_steps=args.H_total_steps, spec_configs=spec_configs, ic_noise_rms=args.ic_noise_rms, grad_clip=args.grad_clip, rollout_backend=args.rollout_backend, cold_start_autonomy=True, rescale_autonomy=False, @@ -286,6 +300,9 @@ def main(): "rollout_backend": args.rollout_backend, "spec_warmup_epochs": args.spec_warmup_epochs, "env_warmup_epochs": args.env_warmup_epochs, + "spec_warmup_steps": args.spec_warmup_steps, + "env_warmup_steps": args.env_warmup_steps, + "H_total_steps": args.H_total_steps, "ic_noise_rms": args.ic_noise_rms, "sr": int(sr), } diff --git a/train/model_cv.py b/train/model_cv.py index 114ab41..05442fa 100644 --- a/train/model_cv.py +++ b/train/model_cv.py @@ -256,6 +256,9 @@ def model_seed_cv_spectral( env_ms: float = 2.0, spec_warmup_epochs: int = 5, env_warmup_epochs: int = 0, + spec_warmup_steps: int = None, + env_warmup_steps: int = None, + H_total_steps: int = None, spec_configs=None, ic_noise_rms: float = 1e-3, grad_clip: float = 5.0, @@ -328,6 +331,9 @@ def _train_to(seed, target): env_ms=env_ms, spec_warmup_epochs=spec_warmup_epochs, env_warmup_epochs=env_warmup_epochs, + spec_warmup_steps=spec_warmup_steps, + env_warmup_steps=env_warmup_steps, + H_total_steps=H_total_steps, spec_configs=spec_configs, ic_noise_rms=ic_noise_rms, grad_clip=grad_clip, rollout_backend=rollout_backend, ) diff --git a/train/spectral_rollout.py b/train/spectral_rollout.py index bff98f5..78d4560 100644 --- a/train/spectral_rollout.py +++ b/train/spectral_rollout.py @@ -388,21 +388,21 @@ def pow2_horizon_buckets(H_min: int, H_max: int) -> list: return hs -def horizon_for_epoch(epoch: int, n_epochs: int, H_min: int, H_max: int, - schedule: str = "geom") -> int: - """Curriculum on the rollout horizon: small H early (cheap, easy gradient), full H - by the end. 'geom' = geometric spacing; 'linear' = linear; 'const' = H_max throughout; - 'pow2' = factor-of-2 buckets (pow2_horizon_buckets) spread evenly across the same - n_epochs ramp -- coarse steps so the graphed backends capture only a few graphs. +def horizon_for_step(step: int, total_steps: int, H_min: int, H_max: int, + schedule: str = "geom") -> int: + """Curriculum on the rollout horizon, indexed by global TRAINING STEP (batch index) + rather than epoch. Same formulas as horizon_for_epoch -- units differ. Lets the + schedule stay calibrated when the dataset size changes (longer epochs no longer + stretch the H ramp). 'geom' = geometric; 'linear' = linear; 'const' = H_max + throughout; 'pow2' = factor-of-2 buckets spread evenly across total_steps. """ - if n_epochs <= 1 or schedule == "const": + if total_steps <= 1 or schedule == "const": return int(H_max) if schedule == "pow2": buckets = pow2_horizon_buckets(H_min, H_max) - # even division of the ramp across buckets; reaches the last bucket by the final epoch - idx = min(int(epoch * len(buckets) / n_epochs), len(buckets) - 1) + idx = min(int(step * len(buckets) / total_steps), len(buckets) - 1) return int(buckets[idx]) - t = epoch / max(1, n_epochs - 1) + t = step / max(1, total_steps - 1) if schedule == "linear": H = H_min + t * (H_max - H_min) elif schedule == "geom": @@ -410,3 +410,10 @@ def horizon_for_epoch(epoch: int, n_epochs: int, H_min: int, H_max: int, else: raise ValueError(f"unknown schedule {schedule!r}") return int(round(H)) + + +def horizon_for_epoch(epoch: int, n_epochs: int, H_min: int, H_max: int, + schedule: str = "geom") -> int: + """Epoch-indexed thin wrapper around horizon_for_step. Kept for backward + compatibility with callers that don't track global step count.""" + return horizon_for_step(epoch, n_epochs, H_min, H_max, schedule) diff --git a/train/train.py b/train/train.py index 2799c63..236a19a 100644 --- a/train/train.py +++ b/train/train.py @@ -182,6 +182,12 @@ def train( env_ms: float = 2.0, spec_warmup_epochs: int = 5, # linearly ramp lam_spec 0 -> lam_spec over these epochs env_warmup_epochs: int = 0, # linearly ramp lam_env AND lam_env_log over these epochs + # Step-based overrides (None = derived from _epochs * batches_per_epoch at startup). + # Set these to decouple the curriculum from dataset size: same number of gradient + # updates regardless of how many batches an epoch contains. + spec_warmup_steps: int = None, + env_warmup_steps: int = None, + H_total_steps: int = None, spec_configs=None, ic_noise_rms: float = 1e-3, grad_clip: float = 5.0, @@ -227,7 +233,7 @@ def train( if loss_mode == "spectral_rollout": from train.spectral_rollout import ( spectral_rollout_step, - horizon_for_epoch, + horizon_for_step, DEFAULT_CONFIGS, ONSET, ) @@ -246,11 +252,29 @@ def train( tf_var_running += float(d2.var().item()) * d2.shape[0] n_seen += d2.shape[0] tf_var = max(tf_var_running / max(1, n_seen), 1e-6) + # Decouple the curricula from dataset size: each ramp is measured in global + # gradient steps (batches). If the user didn't override, derive from epoch + # values × current epoch length so existing CLI invocations are unchanged. + batches_per_epoch = len(loaders["train"]) + if spec_warmup_steps is None: + spec_warmup_steps_eff = spec_warmup_epochs * batches_per_epoch + else: + spec_warmup_steps_eff = int(spec_warmup_steps) + if env_warmup_steps is None: + env_warmup_steps_eff = env_warmup_epochs * batches_per_epoch + else: + env_warmup_steps_eff = int(env_warmup_steps) + if H_total_steps is None: + H_total_steps_eff = nEpochs * batches_per_epoch + else: + H_total_steps_eff = int(H_total_steps) print( f"spectral_rollout mode: lam_spec={lam_spec} lam_tf={lam_tf} lam_env={lam_env} " f"lam_env_log={lam_env_log} env_log_eps={env_log_eps} " f"H={H_min}->{H_max} ({H_schedule}) ic_noise_rms={ic_noise_rms} tf_var={tf_var:.4g} " - f"rollout_backend={rollout_backend}", + f"rollout_backend={rollout_backend} " + f"spec_warmup_steps={spec_warmup_steps_eff} env_warmup_steps={env_warmup_steps_eff} " + f"H_total_steps={H_total_steps_eff} (batches_per_epoch={batches_per_epoch})", flush=True, ) if rollout_backend != "eager" and H_schedule not in ("pow2", "const"): @@ -287,17 +311,21 @@ def train( ic_mask = None if cats is not None: ic_mask = (cats == ONSET).to("cuda") - H = horizon_for_epoch(epoch, nEpochs, H_min, H_max, H_schedule) + # All curricula indexed by GLOBAL STEP (= `idx` thanks to + # enumerate(..., start=epoch * len(loader))) -- decouples them from + # dataset size. H is still updated per-batch but pow2 only yields a few + # distinct values so the graphed/compiled backends stay cache-friendly. + H = horizon_for_step(idx, H_total_steps_eff, H_min, H_max, H_schedule) # Linearly ramp the spectral term so the random-init Mamba can first move # into the TF basin (where drives become meaningful) before the spectral # loss -- which is enormous when the rollout is saturated against quiet # targets -- starts pulling on params. - if spec_warmup_epochs > 0 and epoch < spec_warmup_epochs: - lam_spec_t = lam_spec * (epoch / float(spec_warmup_epochs)) + if spec_warmup_steps_eff > 0 and idx < spec_warmup_steps_eff: + lam_spec_t = lam_spec * (idx / float(spec_warmup_steps_eff)) else: lam_spec_t = lam_spec - if env_warmup_epochs > 0 and epoch < env_warmup_epochs: - ramp = epoch / float(env_warmup_epochs) + if env_warmup_steps_eff > 0 and idx < env_warmup_steps_eff: + ramp = idx / float(env_warmup_steps_eff) lam_env_t = lam_env * ramp lam_env_log_t = lam_env_log * ramp else: From b5411e6822e5f4564d506730526bc4e617dec399 Mon Sep 17 00:00:00 2001 From: John Pearson Date: Sun, 7 Jun 2026 00:25:27 -0400 Subject: [PATCH 34/34] plot_loss_panels: full_osc in tab:olive Color for the full-dataset osc-init run. Co-Authored-By: Claude Opus 4.7 --- scripts/plot_loss_panels.py | 1 + 1 file changed, 1 insertion(+) diff --git a/scripts/plot_loss_panels.py b/scripts/plot_loss_panels.py index da589f1..9680a05 100644 --- a/scripts/plot_loss_panels.py +++ b/scripts/plot_loss_panels.py @@ -161,6 +161,7 @@ def main(): "osc1e5": "tab:purple", "osc_none": "tab:cyan", "osc_log1e4": "tab:brown", + "full_osc": "tab:olive", } # Detect the spec_warmup boundary per run -- the first epoch where lam_spec_t hits