File size: 6,173 Bytes
e8edb9d | 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 | """Training loop for DeepPTR with KL warmup and early stopping."""
from __future__ import annotations
import math
from dataclasses import dataclass, field
import torch
from torch import nn
from torch.utils.data import DataLoader
from ._model import DeepPTR
@dataclass
class TrainHistory:
"""Stores per-epoch training metrics."""
train_loss: list[float] = field(default_factory=list)
val_loss: list[float] = field(default_factory=list)
train_recon: list[float] = field(default_factory=list)
val_recon: list[float] = field(default_factory=list)
train_kl: list[float] = field(default_factory=list)
val_kl: list[float] = field(default_factory=list)
kl_weight: list[float] = field(default_factory=list)
lr: list[float] = field(default_factory=list)
class Trainer:
"""Train a :class:`DeepPTR` model.
Parameters
----------
model
A :class:`DeepPTR` instance.
lr
Initial learning rate for Adam.
weight_decay
L2 regularization.
max_epochs
Maximum training epochs.
kl_warmup_epochs
Number of epochs for linear KL annealing (0→1).
patience
Early-stopping patience (starts counting after warmup).
max_grad_norm
Gradient clipping threshold.
device
``"cuda"`` or ``"cpu"``.
"""
def __init__(
self,
model: DeepPTR,
lr: float = 1e-3,
weight_decay: float = 1e-6,
max_epochs: int = 400,
kl_warmup_epochs: int = 50,
patience: int = 30,
max_grad_norm: float = 5.0,
device: str | None = None,
) -> None:
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
self.device = torch.device(device)
self.model = model.to(self.device)
self.max_epochs = max_epochs
self.kl_warmup_epochs = kl_warmup_epochs
self.patience = patience
self.max_grad_norm = max_grad_norm
self.optimizer = torch.optim.Adam(
model.parameters(), lr=lr, weight_decay=weight_decay
)
self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
self.optimizer, mode="min", factor=0.5, patience=10, min_lr=1e-6
)
self.history = TrainHistory()
def _kl_weight(self, epoch: int) -> float:
if self.kl_warmup_epochs <= 0:
return 1.0
return min(1.0, epoch / self.kl_warmup_epochs)
def _run_epoch(
self, loader: DataLoader, kl_w: float, train: bool = True
) -> tuple[float, float, float]:
self.model.train(train)
total_loss = 0.0
total_recon = 0.0
total_kl = 0.0
n_batches = 0
ctx = torch.enable_grad() if train else torch.no_grad()
with ctx:
for s, u, l_s, l_u in loader:
s = s.to(self.device)
u = u.to(self.device)
l_s = l_s.to(self.device)
l_u = l_u.to(self.device)
out = self.model(s, u, l_s, l_u, kl_weight=kl_w)
loss = out["loss"]
if train:
self.optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(
self.model.parameters(), self.max_grad_norm
)
self.optimizer.step()
total_loss += loss.item()
total_recon += out["recon_loss"].item()
total_kl += out["kl_loss"].item()
n_batches += 1
return (
total_loss / max(n_batches, 1),
total_recon / max(n_batches, 1),
total_kl / max(n_batches, 1),
)
def fit(
self,
train_dl: DataLoader,
val_dl: DataLoader,
verbose: bool = True,
) -> TrainHistory:
"""Run the full training loop.
Parameters
----------
train_dl, val_dl
Training and validation DataLoaders.
verbose
Print progress every 10 epochs.
Returns
-------
TrainHistory
"""
best_val = math.inf
wait = 0
best_state = None
for epoch in range(1, self.max_epochs + 1):
kl_w = self._kl_weight(epoch)
tr_loss, tr_recon, tr_kl = self._run_epoch(train_dl, kl_w, train=True)
vl_loss, vl_recon, vl_kl = self._run_epoch(val_dl, kl_w, train=False)
self.scheduler.step(vl_loss)
cur_lr = self.optimizer.param_groups[0]["lr"]
self.history.train_loss.append(tr_loss)
self.history.val_loss.append(vl_loss)
self.history.train_recon.append(tr_recon)
self.history.val_recon.append(vl_recon)
self.history.train_kl.append(tr_kl)
self.history.val_kl.append(vl_kl)
self.history.kl_weight.append(kl_w)
self.history.lr.append(cur_lr)
if verbose and (epoch % 10 == 0 or epoch == 1):
print(
f"Epoch {epoch:4d} | "
f"train {tr_loss:.2f} (recon {tr_recon:.2f}, kl {tr_kl:.2f}) | "
f"val {vl_loss:.2f} | kl_w {kl_w:.3f} | lr {cur_lr:.1e}"
)
# Early stopping (only after warmup)
if epoch >= self.kl_warmup_epochs:
if vl_loss < best_val:
best_val = vl_loss
wait = 0
best_state = {
k: v.cpu().clone()
for k, v in self.model.state_dict().items()
}
else:
wait += 1
if wait >= self.patience:
if verbose:
print(
f"Early stopping at epoch {epoch} "
f"(best val={best_val:.2f})"
)
break
# Restore best model
if best_state is not None:
self.model.load_state_dict(best_state)
self.model.to(self.device)
return self.history
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