File size: 9,531 Bytes
d647964 | 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 | """Formal Table 3 training: 20,000 Adam updates, batch64 per constraint."""
import argparse
import csv
import fcntl
import json
import random
import resource
import sys
import time
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
import numpy as np
import torch
from common import (ROOT, atomic_json, atomic_save, build_model, evaluate, load_checkpoint,
load_config, load_validation, loss_terms, make_sobol_pools,
require_compute_node, resolved, sample_batches, seed_everything,
selection_score, sha256)
def train(config, seed, resume=None):
allocation = require_compute_node()
if seed not in config["training"]["seeds"]:
raise ValueError("Seed is not in the formal experiment list")
tcfg = config["training"]
seed_everything(seed, tcfg["num_threads"])
out = resolved(config, "results") / f"seed_{seed}"
weights = resolved(config, "weights") / f"seed_{seed}"
out.mkdir(parents=True, exist_ok=True)
weights.mkdir(parents=True, exist_ok=True)
if (out/"history.csv").exists() and resume is None:
raise FileExistsError("Existing training history; use --resume to preserve run provenance")
model = build_model(config)
pools = make_sobol_pools(config, seed)
rng = np.random.default_rng(seed+100000)
xval, yval, valids = load_validation(config)
np.save(out/"validation_indices.npy", valids)
optimizer = torch.optim.Adam(model.parameters(), lr=tcfg["learning_rate"],
betas=tuple(tcfg["betas"]), eps=tcfg["epsilon"],
weight_decay=tcfg["weight_decay"])
sched = tcfg["scheduler"]
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode="min", factor=sched["factor"],
patience=sched["patience"], threshold=sched["threshold"])
start_epoch, best_score, best_epoch = 1, float("inf"), None
accumulated_seconds = 0.0
if resume:
previous = load_checkpoint(resume, model)
if previous["config"] != config or previous["seed"] != seed:
raise ValueError("Resume config/seed differs from checkpoint")
optimizer.load_state_dict(previous["optimizer_state_dict"])
scheduler.load_state_dict(previous["scheduler_state_dict"])
start_epoch = previous["epoch"]+1
best_score, best_epoch = previous["best_score"], previous["best_epoch"]
torch.set_rng_state(previous["torch_rng"])
random.setstate(previous["python_rng"])
rng.bit_generator.state = previous["batch_rng"]
accumulated_seconds = previous["elapsed_seconds"]
provenance = {"config": config, "seed": seed, "allocation": allocation,
"reference": str(resolved(config, "reference")),
"reference_sha256": sha256(resolved(config, "reference")),
"parameter_count": sum(p.numel() for p in model.parameters()),
"torch_version": torch.__version__, "epoch_semantics": "one Adam step, 64 points per constraint",
"reference_used_for_gradient_training": False,
"validation_indices": str(out/"validation_indices.npy")}
atomic_json(out/"run_manifest.json", provenance)
print(f"START seed={seed} allocation={json.dumps(allocation)} parameters=923 epochs=20000 batch=64", flush=True)
columns = ["epoch", "total", "physics", "lid", "walls", "initial", "lr", "gradient_norm",
"step_seconds", "elapsed_seconds", "val_u_l2", "val_v_l2", "val_p_l2", "val_mean_l2"]
mode = "a" if resume else "w"
start = time.monotonic()
with open(out/"history.csv", mode, newline="", buffering=1) as history:
writer = csv.DictWriter(history, fieldnames=columns)
if not resume: writer.writeheader()
for epoch in range(start_epoch, tcfg["epochs"]+1):
tick = time.monotonic()
model.train()
optimizer.zero_grad(set_to_none=True)
terms = loss_terms(model, sample_batches(pools, tcfg["batch_size"], rng), config)
total = terms["total"]
if not torch.isfinite(total): raise FloatingPointError(f"Nonfinite loss at {epoch}")
total.backward()
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), tcfg["gradient_clip"], error_if_nonfinite=True)
optimizer.step()
values = {k: float(v.detach()) for k, v in terms.items()}
scheduler.step(values["total"])
step_seconds = time.monotonic()-tick
elapsed = accumulated_seconds+time.monotonic()-start
row = {"epoch": epoch, **values, "lr": optimizer.param_groups[0]["lr"],
"gradient_norm": float(grad_norm), "step_seconds": step_seconds, "elapsed_seconds": elapsed}
metrics = None
if epoch == 1 or epoch % config["validation"]["every"] == 0 or epoch == tcfg["epochs"]:
metrics = evaluate(model, xval, yval, config["validation"]["batch_size"])
score = selection_score(metrics)
row.update({f"val_{k}_l2": metrics[k]["relative_l2"] for k in ("u", "v", "p")})
row["val_mean_l2"] = score
if score < best_score:
best_score, best_epoch = score, epoch
is_best = True
else: is_best = False
state = {"model_state_dict": model.state_dict(), "config": config, "seed": seed,
"epoch": epoch, "validation_metrics": metrics, "selection_score": score,
"best_score": best_score, "best_epoch": best_epoch,
"optimizer_state_dict": optimizer.state_dict(), "scheduler_state_dict": scheduler.state_dict(),
"torch_rng": torch.get_rng_state(), "python_rng": random.getstate(),
"batch_rng": rng.bit_generator.state, "elapsed_seconds": elapsed,
"allocation": allocation, "loss": values}
if is_best:
atomic_save(weights/"best_model.pt", state)
# Publish the validation-best checkpoint at the requested root path
# throughout concurrent training, with one writer at a time.
root_weights = resolved(config, "weights")
root_meta = resolved(config, "results")/"best_checkpoint_selection.json"
with open(root_weights/".best_model.lock", "a") as lock:
try:
fcntl.flock(lock, fcntl.LOCK_EX | fcntl.LOCK_NB)
except BlockingIOError:
# Another seed is publishing; per-seed checkpoint remains authoritative.
pass
else:
incumbent = json.loads(root_meta.read_text()) if root_meta.exists() else None
if incumbent is None or score < incumbent["selection_score"]:
atomic_save(root_weights/"best_model.pt", state)
atomic_json(root_meta, {"seed": seed, "epoch": epoch, "selection_score": score,
"metrics": metrics, "checkpoint": str(root_weights/"best_model.pt")})
fcntl.flock(lock, fcntl.LOCK_UN)
if epoch % tcfg["checkpoint_every"] == 0 or epoch == tcfg["epochs"]:
atomic_save(weights/"last_model.pt", state)
atomic_json(out/"progress.json", {"status": "training", "epoch": epoch,
"epochs": tcfg["epochs"], "loss": values, "validation_metrics": metrics,
"best_score": best_score, "best_epoch": best_epoch, "allocation": allocation})
writer.writerow(row)
line = f"seed={seed} epoch={epoch}/20000 loss={values['total']:.8e} phy={values['physics']:.6e} lid={values['lid']:.6e} walls={values['walls']:.6e} ic={values['initial']:.6e} lr={row['lr']:.6e}"
if metrics is not None:
line += " " + " ".join(f"{k}_relL2={metrics[k]['relative_l2']:.8e} ({metrics[k]['relative_l2_percent']:.5f}%)" for k in ("u", "v", "p"))
print(line, flush=True)
elapsed = accumulated_seconds + time.monotonic()-start
atomic_json(out/"training_summary.json", {"status": "completed", "completed_epochs": tcfg["epochs"],
"seed": seed, "best_epoch": best_epoch, "best_validation_mean_l2": best_score,
"last_loss": values, "elapsed_seconds": elapsed,
"peak_rss_mb": resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024,
"best_checkpoint": str(weights/"best_model.pt"), "last_checkpoint": str(weights/"last_model.pt"),
"allocation": allocation})
atomic_json(out/"progress.json", {"status": "completed", "epoch": tcfg["epochs"], "best_epoch": best_epoch})
print(f"TRAINING_COMPLETED seed={seed} epochs=20000 best_epoch={best_epoch}", flush=True)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--config", default=str(ROOT/"config/config.yaml"))
parser.add_argument("--seed", type=int, required=True)
parser.add_argument("--resume", type=Path)
args = parser.parse_args()
train(load_config(args.config), args.seed, args.resume)
|