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51bb0b7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 | """Streamed, fully vectorised synthetic time-series generator (torch; runs on GPU).
Univariate bases (each switched on per series with its own probability, summed or
multiplied):
* KernelSynth-style GP components via random Fourier features: RBF (log-uniform
length-scale), rational-quadratic (scale-mixture of RBFs), periodic kernels at
frequency-typical periods with random harmonics, linear (trend), white noise;
* TSI-style trends: linear / quadratic / piecewise-linear with change points / damped;
* AR(1) / random walk (FFT convolution with phi^k kernels);
* heteroscedastic noise (log-GP volatility, GARCH-like clustering), student-t tails;
* level shifts, spikes / outliers;
* intermittent (Croston-like zero-inflated count) and bursty (Poisson bursts with
exponential decay) observability-style signals;
* non-negativity transforms, random offset / scale over many orders of magnitude.
Multivariatizers (Chronos-2 / CauKer style): latent univariate drivers mixed into each
row by a random sparse matrix through random nonlinearities with lead-lag delays, plus
idiosyncratic noise; optional covariate tasks where some rows are past-only or
known-future covariates (incl. 0/1 event / calendar indicators) and targets respond to
them through random lagged nonlinear effects.
Masks: iid missing points, contiguous gaps, late series starts (left padding).
`SynthStream(...).next_batch()` returns dict(values, observed, roles, s) ready for
`ModularTS.forward_train`.
"""
from __future__ import annotations
import math
import torch
from torch import Tensor
PERIOD_BANK = torch.tensor([4, 5, 7, 12, 24, 48, 52, 60, 96, 144, 168, 288, 336, 365, 672, 1008, 1440, 2016], dtype=torch.float32)
def _std(x: Tensor) -> Tensor:
return x.std(-1, keepdim=True).clamp_min(1e-6)
def _z(x: Tensor) -> Tensor:
return (x - x.mean(-1, keepdim=True)) / _std(x)
class SynthGen:
def __init__(self, T: int, device: str | torch.device = "cpu", seed: int = 0, n_rff: int = 32):
self.T = T
self.dev = torch.device(device)
self.g = torch.Generator(device=self.dev)
self.g.manual_seed(seed)
self.n_rff = n_rff
self.t = torch.arange(T, device=self.dev, dtype=torch.float32)
# -- random helpers
def U(self, *shape, lo=0.0, hi=1.0):
return lo + (hi - lo) * torch.rand(*shape, generator=self.g, device=self.dev)
def N(self, *shape):
return torch.randn(*shape, generator=self.g, device=self.dev)
def B(self, p, *shape):
return (self.U(*shape) < p).float()
def logU(self, *shape, lo, hi):
return torch.exp(self.U(*shape, lo=math.log(lo), hi=math.log(hi)))
# -- components (n, T)
def rff(self, n, lengthscale: Tensor) -> Tensor:
M = self.n_rff
w = self.N(n, M, 1) / lengthscale.view(n, 1, 1)
b = self.U(n, M, 1, hi=2 * math.pi)
a = self.N(n, M, 1)
return (a * torch.cos(w * self.t + b)).sum(1) * math.sqrt(2.0 / M)
def rbf(self, n):
return self.rff(n, self.logU(n, lo=self.T / 400, hi=self.T / 1.5))
def rq(self, n): # scale mixture: sum of RBFs at several length-scales
out = 0
for _ in range(3):
out = out + self.U(n, 1) * self.rff(n, self.logU(n, lo=self.T / 1000, hi=self.T / 4))
return out
def periodic(self, n):
k = len(PERIOD_BANK)
idx = torch.randint(0, k, (n,), generator=self.g, device=self.dev)
per = PERIOD_BANK.to(self.dev)[idx]
rnd = self.logU(n, lo=3.0, hi=max(4.0, self.T / 3))
per = torch.where(self.U(n) < 0.25, rnd, per)
per = per * self.U(n, lo=0.97, hi=1.03) * torch.where(self.U(n) < 0.1, 1.0 + 0.2 * self.N(n), torch.ones(n, device=self.dev))
H = 6
h = torch.arange(1, H + 1, device=self.dev, dtype=torch.float32).view(1, H, 1)
decay = self.U(n, 1, 1, lo=0.3, hi=1.5)
amp = self.N(n, H, 1) / h.pow(decay) * self.B(0.8, n, H, 1).clamp_min((h == 1).float())
ph = self.U(n, H, 1, hi=2 * math.pi)
sig = (amp * torch.sin(2 * math.pi * h * self.t / per.view(n, 1, 1) + ph)).sum(1)
# optional slowly varying amplitude (multiplicative modulation)
mod = 1 + 0.3 * self.B(0.3, n, 1) * torch.tanh(self.rbf(n))
return sig * mod
def trend(self, n):
tt = self.t / self.T
lin = self.N(n, 1) * tt
quad = self.N(n, 1) * 0.5 * tt**2 * self.B(0.3, n, 1)
# piecewise linear with up to 4 change points
K = 4
cps = self.U(n, K, 1)
slopes = self.N(n, K, 1) * self.B(0.5, n, K, 1)
pw = (slopes * torch.relu(tt.view(1, 1, -1) - cps)).sum(1)
damp = self.N(n, 1) * (1 - torch.exp(-tt / self.U(n, 1, lo=0.05, hi=0.5))) * self.B(0.2, n, 1)
return lin + quad + pw + damp
def ar(self, n):
phi = torch.where(self.U(n) < 0.3, torch.ones(n, device=self.dev), self.U(n, lo=0.3, hi=0.995))
e = self.N(n, self.T)
L = min(self.T, 1024)
k = phi.view(n, 1) ** torch.arange(L, device=self.dev, dtype=torch.float32)
nfft = 1 << (self.T + L - 1).bit_length()
y = torch.fft.irfft(torch.fft.rfft(e, nfft) * torch.fft.rfft(k, nfft), nfft)[:, : self.T]
rw = torch.cumsum(e, -1)
return torch.where((phi == 1).view(n, 1), rw, y)
def noise(self, n):
z = self.N(n, self.T)
# heavy tails: scale mixture of normals (generator-friendly stand-in for student-t)
heavy = self.B(0.3, n, 1)
tz = z * (1 + heavy * (self.N(n, self.T).abs() ** 2) * 0.5)
vol = torch.where(self.U(n, 1) < 0.25, torch.exp(0.7 * self.rbf(n).clamp(-3, 3)), torch.ones(n, 1, device=self.dev))
return tz * vol
def level_shifts(self, n):
K = 3
loc = self.U(n, K, 1) * self.T
mag = self.N(n, K, 1) * self.B(0.6, n, K, 1)
return (mag * (self.t.view(1, 1, -1) > loc).float()).sum(1)
def spikes(self, n):
p = self.logU(n, 1, lo=1e-3, hi=2e-2)
return (self.U(n, self.T) < p).float() * self.N(n, self.T) * self.U(n, 1, lo=3, hi=10)
def bursts(self, n):
rate = self.logU(n, 1, lo=1e-3, hi=2e-2)
ev = (self.U(n, self.T) < rate).float() * self.logU(n, self.T, lo=1.0, hi=20.0)
tau = self.logU(n, lo=2.0, hi=60.0)
L = min(self.T, 512)
k = torch.exp(-torch.arange(L, device=self.dev).float().view(1, -1) / tau.view(n, 1))
nfft = 1 << (self.T + L - 1).bit_length()
return torch.fft.irfft(torch.fft.rfft(ev, nfft) * torch.fft.rfft(k, nfft), nfft)[:, : self.T]
# -- univariate
def univariate(self, n) -> Tensor:
z = torch.zeros(n, self.T, device=self.dev)
on = lambda p: self.B(p, n, 1) # noqa: E731
w = lambda: self.logU(n, 1, lo=0.1, hi=2.0) # noqa: E731
trend = on(0.6) * w() * _z(self.trend(n))
seas = on(0.7) * w() * _z(self.periodic(n)) + on(0.25) * w() * _z(self.periodic(n))
smooth = on(0.5) * w() * _z(self.rbf(n)) + on(0.25) * w() * _z(self.rq(n))
arp = on(0.4) * w() * _z(self.ar(n))
sig = trend + seas + smooth + arp + z
mult = on(0.25)
sig = torch.where(mult > 0, (1 + 0.4 * torch.tanh(trend + smooth)) * seas + trend + arp, sig)
sig = sig + on(0.15) * w() * self.level_shifts(n)
noise_lvl = self.logU(n, 1, lo=0.01, hi=1.0)
sig = sig + noise_lvl * self.noise(n)
sig = sig + on(0.12) * self.spikes(n)
# intermittent / bursty regimes (non-negative count-like)
inter = on(0.10)
lam = torch.exp(0.8 * _z(sig).clamp(-4, 4) + self.U(n, 1, lo=-3, hi=1.5))
p0 = self.U(n, 1, lo=0.2, hi=0.9)
cnt = torch.poisson(lam, generator=self.g) * (self.U(n, self.T) > p0).float()
sig = torch.where(inter > 0, cnt, sig)
burst = on(0.07)
sig = torch.where(burst > 0, self.U(n, 1, lo=0, hi=2) + 0.1 * self.noise(n).abs() + self.bursts(n), sig)
# non-negativity transforms on some continuous series
nn_ = on(0.2) * (1 - inter) * (1 - burst)
sig = torch.where(nn_ > 0, torch.nn.functional.softplus(sig * self.U(n, 1, lo=0.5, hi=3)), sig)
return sig
def affine(self, x: Tensor) -> Tensor:
n = x.shape[0]
scale = self.logU(n, 1, lo=1e-2, hi=1e4)
off = self.N(n, 1) * scale * self.logU(n, 1, lo=0.01, hi=20.0) * self.B(0.6, n, 1)
sgn = torch.where(self.U(n, 1) < 0.5, -1.0, 1.0)
pos = (x.min(-1, keepdim=True).values >= 0).float() # keep count-like series non-negative
return torch.where(pos > 0, x * scale, sgn * x * scale + off)
# -- multivariate group batch
def group_batch(self, Bg: int, V: int, cov_prob: float = 0.35):
"""Returns values (Bg,V,T), roles (Bg,V)."""
T = self.T
R = Bg * V
if V == 1:
vals = self.affine(self.univariate(Bg)).view(Bg, 1, T)
return vals, torch.zeros(Bg, 1, dtype=torch.long, device=self.dev)
K = max(1, V // 2)
lat = _z(self.univariate(Bg * K)).view(Bg, K, T)
# lead-lag delays per (row, latent)
lag = torch.randint(0, 48, (Bg, V, K), generator=self.g, device=self.dev)
lag = lag * self.B(0.5, Bg, V, K).long()
idx = (self.t.long().view(1, 1, 1, T) - lag[..., None]).clamp_min(0)
shifted = torch.gather(lat[:, None].expand(Bg, V, K, T), 3, idx)
W = self.N(Bg, V, K, 1) * self.B(0.6, Bg, V, K, 1)
W[:, :, 0] = W[:, :, 0] + 0.5
mixed = (W * shifted).sum(2)
f = torch.randint(0, 5, (Bg, V, 1), generator=self.g, device=self.dev)
nl = torch.stack([mixed, torch.tanh(mixed), mixed.abs(), torch.relu(mixed), mixed + 0.3 * mixed**2], 0)
mixed = torch.gather(nl, 0, f[None].expand(1, Bg, V, T))[0]
own = _z(self.univariate(R)).view(Bg, V, T)
a = self.U(Bg, V, 1, lo=0.0, hi=1.0)
indep = self.B(0.15, Bg, 1, 1)
rows = torch.where(indep > 0, own, (1 - a) * _z(mixed) + a * own)
roles = torch.zeros(Bg, V, dtype=torch.long, device=self.dev)
# covariate tasks
cov_g = self.U(Bg) < cov_prob
if cov_g.any():
n_cov = torch.randint(1, V, (Bg,), generator=self.g, device=self.dev)
ridx = torch.arange(V, device=self.dev).view(1, V)
is_cov = (ridx >= (V - n_cov).view(Bg, 1)) & cov_g.view(Bg, 1)
known = is_cov & (self.U(Bg, V) < 0.6)
# some covariates are 0/1 event indicators (holiday / promo / calendar)
ev_rate = self.logU(Bg, V, 1, lo=0.005, hi=0.15)
per = PERIOD_BANK.to(self.dev)[torch.randint(0, len(PERIOD_BANK), (Bg, V, 1), generator=self.g, device=self.dev)]
cal = ((self.t.view(1, 1, T) % per) < torch.clamp(per * self.U(Bg, V, 1, lo=0.05, hi=0.3), min=1)).float()
evt = (self.U(Bg, V, T) < ev_rate).float()
ind = torch.where(self.U(Bg, V, 1) < 0.5, cal, evt)
binary = is_cov & (self.U(Bg, V) < 0.4)
cov_vals = torch.where(binary[..., None], ind, rows)
# target response to covariates: lagged nonlinear effects
beta = self.N(Bg, V, 1) * self.U(Bg, 1, 1, lo=0.5, hi=2.5)
clag = torch.randint(0, 8, (Bg, V, 1), generator=self.g, device=self.dev) * (~known[..., None]).long()
cidx = (self.t.long().view(1, 1, T) - clag).clamp_min(0)
cl = torch.gather(cov_vals, 2, cidx)
eff_in = torch.where(binary[..., None], cl, _z(cl))
eff = (beta * torch.where(self.U(Bg, V, 1) < 0.5, eff_in, torch.tanh(eff_in)) * is_cov[..., None].float()).sum(1, keepdim=True)
tgt_mask = (~is_cov)[..., None].float()
rows = torch.where(is_cov[..., None], cov_vals, rows + tgt_mask * eff * cov_g.view(Bg, 1, 1).float())
roles = torch.where(is_cov, torch.where(known, 2, 1), roles)
bin_rows = binary.view(R)
else:
bin_rows = torch.zeros(R, dtype=torch.bool, device=self.dev)
flat = rows.reshape(R, T)
aff = self.affine(flat)
flat = torch.where(bin_rows[:, None], flat, aff)
return flat.view(Bg, V, T), roles
def masks(self, vals: Tensor, roles: Tensor) -> Tensor:
Bg, V, T = vals.shape
obs = torch.ones(Bg, V, T, dtype=torch.bool, device=self.dev)
# iid missing
pm = self.U(Bg, V, 1, hi=0.15) * self.B(0.2, Bg, V, 1)
obs &= self.U(Bg, V, T) >= pm
# contiguous gaps
gap = self.B(0.15, Bg, V, 1) > 0
g0 = self.U(Bg, V, 1) * T
gl = self.logU(Bg, V, 1, lo=8, hi=T / 4)
in_gap = (self.t.view(1, 1, T) >= g0) & (self.t.view(1, 1, T) < g0 + gl)
obs &= ~(gap & in_gap)
# late start (left padding), per group
late = self.B(0.25, Bg, 1, 1) > 0
st = (self.U(Bg, 1, 1) * T * 0.8)
obs &= ~(late & (self.t.view(1, 1, T) < st))
return obs
class SynthStream:
"""Endless batches of roughly `tokens_per_batch` encoder patch tokens."""
V_CHOICES = (1, 2, 4, 8, 16)
V_PROBS = (0.45, 0.1, 0.15, 0.15, 0.15)
def __init__(self, T: int, tokens_per_batch: int, device="cpu", seed=0, patch=32, block=32, min_ctx=4, chunk=8):
self.gen = SynthGen(T, device, seed)
self.chunk = chunk # batches generated per call (amortises kernel-launch overhead on GPU)
self.cache: dict[int, list] = {}
self.T, self.patch, self.N = T, patch, T // patch
self.tok = tokens_per_batch
self.min_ctx = min_ctx
self.dev = torch.device(device)
self.rng = torch.Generator().manual_seed(seed + 1)
def next_batch(self, V: int | None = None) -> dict:
if V is None:
i = torch.multinomial(torch.tensor(self.V_PROBS), 1, generator=self.rng).item()
V = self.V_CHOICES[i]
Bg = max(1, self.tok // (V * self.N))
if not self.cache.get(V):
vals, roles = self.gen.group_batch(Bg * self.chunk, V)
obs = self.gen.masks(vals, roles)
vals = torch.nan_to_num(vals, nan=0.0, posinf=0.0, neginf=0.0)
s = torch.randint(self.min_ctx, self.N - 1, (Bg * self.chunk,), generator=self.gen.g, device=self.dev)
self.cache[V] = [
{"values": vals[i * Bg:(i + 1) * Bg], "observed": obs[i * Bg:(i + 1) * Bg],
"roles": roles[i * Bg:(i + 1) * Bg], "s": s[i * Bg:(i + 1) * Bg]}
for i in range(self.chunk)
]
return self.cache[V].pop()
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