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31e2456 | 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 | """Training loop shared across all four models.
Differences across runs are entirely in the model registered under `config.model`.
"""
from __future__ import annotations
import json
import math
import os
import time
from dataclasses import dataclass, field
from pathlib import Path
import numpy as np
import torch
import yaml
from torch.utils.data import DataLoader
from .data import MIMICAlignedDataset, collate_with_dt, split_by_subject
from .ema import ema_tau
from .models import MODEL_REGISTRY, ModelConfig
from .monitor import CollapseMonitor, cross_modal_cosine, effective_rank
@dataclass
class TrainConfig:
run_name: str = "debug"
model: str = "F" # one of A, B, C, F
epochs: int = 100
batch_size: int = 64
lr: float = 1e-4
weight_decay: float = 0.04
warmup_epochs: int = 10
ema_start: float = 0.996
ema_end: float = 0.9999
ema_warmup_frac: float = 0.30
grad_clip: float = 1.0
log_every: int = 100
ckpt_every_epochs: int = 5
seed: int = 0
wandb_project: str = "physiojepa"
wandb_mode: str = "online"
wandb_entity: str | None = None
output_dir: str = "runs"
index_path: str = "cache/mimic_index.json"
shard_roots: list[str] = field(default_factory=list)
num_workers: int = 4
amp: bool = True
# controls for Δt sampling inside collate_fn
log_uniform_frac: float = 0.6
# window-level subsetting (for fast iteration / K2 gate runs)
subset_frac: float = 1.0
# ablation knobs forwarded to ModelConfig
pred_depth: int = 4
query_mode: str = "learned"
mask_ratio: float = 0.50
# precomputed mmap dataset (overrides shard_roots + index_path if set)
fast_cache_dir: str = ""
def load_yaml_config(path: str) -> TrainConfig:
with open(path, "r") as f:
d = yaml.safe_load(f)
return TrainConfig(**d)
class _Collator:
"""Top-level callable so DataLoader workers can serialize it across fork."""
def __init__(self, log_uniform_frac: float, seed: int):
self.log_uniform_frac = log_uniform_frac
self.seed = seed
self._rng = None
def __call__(self, items):
if self._rng is None:
self._rng = np.random.default_rng(self.seed + os.getpid())
return collate_with_dt(items, log_uniform_frac=self.log_uniform_frac, rng=self._rng)
def _build_dataloaders(cfg: TrainConfig) -> tuple[DataLoader, DataLoader, list[str]]:
if cfg.fast_cache_dir:
from .data_fast import MIMICFastDataset
cache_dir = Path(cfg.fast_cache_dir)
import json
meta = json.loads((cache_dir / "windows_meta.json").read_text())
subjects = sorted(set(meta["subjects"]))
train_subj, val_subj = split_by_subject(subjects, frac=0.9, seed=cfg.seed)
train_ds = MIMICFastDataset(cache_dir, subjects_allow=train_subj)
val_ds = MIMICFastDataset(cache_dir, subjects_allow=val_subj)
else:
shard_roots = [Path(p) for p in cfg.shard_roots]
ds_full = MIMICAlignedDataset(
shard_roots=shard_roots,
index_path=Path(cfg.index_path),
build_index=not Path(cfg.index_path).exists(),
)
subjects = sorted({r["subject_id"] for r in ds_full.index})
train_subj, val_subj = split_by_subject(subjects, frac=0.9, seed=cfg.seed)
train_ds = MIMICAlignedDataset(
shard_roots, Path(cfg.index_path), build_index=False, subjects_allow=train_subj,
subset_frac=cfg.subset_frac, subset_seed=cfg.seed,
)
val_ds = MIMICAlignedDataset(
shard_roots, Path(cfg.index_path), build_index=False, subjects_allow=val_subj,
)
collate = _Collator(cfg.log_uniform_frac, cfg.seed)
train_loader = DataLoader(
train_ds, batch_size=cfg.batch_size, shuffle=True,
num_workers=cfg.num_workers, collate_fn=collate, drop_last=True,
persistent_workers=cfg.num_workers > 0,
)
val_loader = DataLoader(
val_ds, batch_size=cfg.batch_size, shuffle=False,
num_workers=max(cfg.num_workers, 1), collate_fn=collate, drop_last=False,
)
return train_loader, val_loader, subjects
def _cosine_lr(step: int, total_steps: int, base: float, warmup_steps: int) -> float:
if step < warmup_steps:
return base * (step + 1) / max(1, warmup_steps)
progress = (step - warmup_steps) / max(1, total_steps - warmup_steps)
return 0.5 * base * (1 + math.cos(math.pi * progress))
def train(cfg: TrainConfig) -> dict:
import wandb
torch.manual_seed(cfg.seed)
np.random.seed(cfg.seed)
device = torch.device("cuda" if torch.cuda.is_available()
else ("mps" if torch.backends.mps.is_available() else "cpu"))
train_loader, val_loader, subjects = _build_dataloaders(cfg)
print(f"[trainer] device={device} n_train_windows={len(train_loader.dataset)} "
f"n_val_windows={len(val_loader.dataset)} subjects={len(subjects)}")
mcfg = ModelConfig(
pred_depth=cfg.pred_depth,
query_mode=cfg.query_mode,
mask_ratio=cfg.mask_ratio,
)
model = MODEL_REGISTRY[cfg.model](mcfg).to(device)
opt = torch.optim.AdamW(model.parameters(), lr=cfg.lr, weight_decay=cfg.weight_decay)
scaler = torch.amp.GradScaler(device.type) if cfg.amp and device.type == "cuda" else None
total_steps = cfg.epochs * len(train_loader)
warmup_steps = cfg.warmup_epochs * len(train_loader)
wandb.init(project=cfg.wandb_project, name=cfg.run_name, config=cfg.__dict__,
mode=cfg.wandb_mode, entity=cfg.wandb_entity)
monitor = CollapseMonitor()
step = 0
out_root = Path(cfg.output_dir) / cfg.run_name
out_root.mkdir(parents=True, exist_ok=True)
aborted = False
for epoch in range(cfg.epochs):
model.train(True)
for batch in train_loader:
# move to device
for k in ("ecg", "ppg", "dt_seconds", "ptt_ms"):
if k in batch and isinstance(batch[k], torch.Tensor):
batch[k] = batch[k].to(device)
# lr schedule
lr_now = _cosine_lr(step, total_steps, cfg.lr, warmup_steps)
for g in opt.param_groups:
g["lr"] = lr_now
opt.zero_grad(set_to_none=True)
if scaler is not None:
with torch.amp.autocast("cuda"):
out = model.step(batch)
scaler.scale(out["loss"]).backward()
scaler.unscale_(opt)
torch.nn.utils.clip_grad_norm_(model.parameters(), cfg.grad_clip)
scaler.step(opt)
scaler.update()
else:
out = model.step(batch)
out["loss"].backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), cfg.grad_clip)
opt.step()
# EMA update
tau = ema_tau(step, total_steps, cfg.ema_start, cfg.ema_end, cfg.ema_warmup_frac)
for online, tgt in model.targets():
tgt.update(online, tau)
if step % cfg.log_every == 0:
metrics = {
"step": step, "epoch": epoch, "lr": lr_now, "tau": tau,
"loss": float(out["loss"].detach().item()),
"L_cross": float(out.get("L_cross", torch.tensor(0.0)).item()),
"L_self": float(out.get("L_self", torch.tensor(0.0)).item()),
}
z_e = out.get("z_ecg")
if z_e is not None and z_e.shape[0] > 1:
metrics["ecg_latent_var"] = float(z_e.var(dim=0).mean().item())
metrics["ecg_eff_rank"] = effective_rank(z_e)
z_p_pred = out.get("z_pred")
z_p_tgt = out.get("z_ppg")
if z_p_pred is not None and z_p_tgt is not None and z_p_pred.shape[0] > 1:
cosine = cross_modal_cosine(z_p_pred, z_p_tgt)
metrics["cross_modal_cosine"] = cosine
if monitor.update(cosine):
print(f"[trainer] COLLAPSE DETECTED at step={step} cosine={cosine:.4f}")
aborted = True
wandb.log(metrics, step=step)
print(f"[step {step}] loss={metrics['loss']:.4f} "
f"L_cross={metrics['L_cross']:.4f} L_self={metrics['L_self']:.4f} "
f"tau={tau:.4f}")
step += 1
if aborted:
break
if aborted:
break
if (epoch + 1) % cfg.ckpt_every_epochs == 0 or epoch == cfg.epochs - 1:
ckpt = out_root / f"ckpt_epoch{epoch + 1:03d}.pt"
torch.save({"model": model.state_dict(), "cfg": cfg.__dict__, "epoch": epoch + 1,
"step": step}, ckpt)
print(f"[trainer] saved {ckpt}")
final_ckpt = out_root / "ckpt_final.pt"
torch.save({"model": model.state_dict(), "cfg": cfg.__dict__, "aborted": aborted,
"step": step}, final_ckpt)
wandb.finish()
return {"aborted": aborted, "final_step": step, "ckpt": str(final_ckpt)}
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