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17d5066 | 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 | """Hydra training entrypoint for Sheaf-ADMM (and the MPNN baseline).
One entrypoint for every task (maze / mnist / sudoku) and both model families
(``model_type=sheaf|mpnn``); the task and HPs come entirely from config.
python scripts/train.py +experiment=maze_sheaf
python scripts/train.py +experiment=sudoku_sheaf training.seed=123
python scripts/train.py +experiment=mnist_sheaf
Runs do not log by default. Set ``wandb.mode=online`` to enable Weights & Biases.
Importing ``sheaf_admm`` pins ``float32`` matmul precision to ``highest`` (the
paper setting) before any compilation.
"""
from __future__ import annotations
import os
import sys
from pathlib import Path
import hydra
import jax
import numpy as np
import wandb
from hydra.core.hydra_config import HydraConfig
from omegaconf import DictConfig, OmegaConf
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT / "src") not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT / "src"))
import sheaf_admm as _sheaf_admm # noqa: F401,E402 (sets matmul precision on import)
from repro_control.checkpoints import save_checkpoint_atomic # noqa: E402
from repro_control.configs import ( # noqa: E402
config_sha256,
load_json_config,
verify_before_optimizer,
)
from repro_control.hashing import atomic_write_json # noqa: E402
from repro_control.interventions import assert_common_bit_identical # noqa: E402
from sheaf_admm.data import ImageDataset, PuzzleDataset # noqa: E402
from sheaf_admm.models import model_config_from_dict # noqa: E402
from sheaf_admm.training import ( # noqa: E402
build_model,
create_train_state,
evaluate,
make_task,
make_train_step,
sample_k,
)
def _puzzle_batch(batch):
out = {"inputs": np.asarray(batch["inputs"]), "labels": np.asarray(batch["labels"])}
for key in ("height", "width"):
if key in batch:
out[key] = batch[key]
return out
def _train_batches(cfg: DictConfig, epoch: int):
"""Yield ``{inputs/images, labels}`` batches for one training epoch."""
d = cfg.data
if d.loader == "puzzle":
ds = PuzzleDataset(d.dir, d.train_split)
for _set, batch in ds.iter_train_batches(
cfg.training.batch_size, seed=cfg.training.seed + epoch
):
yield _puzzle_batch(batch)
else: # image
ds = ImageDataset(d.dir, d.train_split)
for batch in ds.iter_batches(
cfg.training.batch_size, shuffle=True, seed=cfg.training.seed + epoch
):
yield {"images": np.asarray(batch["images"]), "labels": np.asarray(batch["labels"])}
def _val_batches(cfg: DictConfig, split: str):
d = cfg.data
if d.loader == "puzzle":
ds = PuzzleDataset(d.dir, split)
for _set, batch in ds.iter_test_batches(cfg.training.batch_size):
yield _puzzle_batch(batch)
else:
ds = ImageDataset(d.dir, split)
for batch in ds.iter_batches(cfg.training.batch_size, shuffle=False):
yield {"images": np.asarray(batch["images"]), "labels": np.asarray(batch["labels"])}
@hydra.main(config_path="../configs", config_name="config", version_base=None)
def main(cfg: DictConfig) -> None:
sys.stdout.reconfigure(line_buffering=True) # flush per line so sbatch log-tailing works live
expected_path = os.environ.get("REPRO_EXPECTED_CONFIG_PATH", "")
expected_sha256 = os.environ.get("REPRO_EXPECTED_CONFIG_SHA256", "")
if bool(expected_path) != bool(expected_sha256):
raise RuntimeError("both registered config environment variables are required together")
if expected_path:
resolved = OmegaConf.to_container(cfg, resolve=True)
expected = load_json_config(Path(expected_path))
verify_before_optimizer(resolved, expected, expected_sha256=expected_sha256)
if config_sha256(expected) != expected_sha256:
raise RuntimeError("registered config file hash mismatch")
print(f"CONFIG_VERIFIED {expected_sha256}", flush=True)
print(OmegaConf.to_yaml(cfg))
t = cfg.training
run = wandb.init(
project=cfg.wandb.project,
entity=cfg.wandb.entity,
name=cfg.wandb.name,
group=cfg.wandb.group,
tags=list(cfg.wandb.tags),
mode=cfg.wandb.mode,
config=OmegaConf.to_container(cfg, resolve=True),
)
task = make_task(cfg.task, **OmegaConf.to_container(cfg.task_cfg, resolve=True))
model_cfg = model_config_from_dict(OmegaConf.to_container(cfg.model, resolve=True))
model = build_model(model_cfg, cfg.model_type)
graph_readout = model_cfg.mpnn_graph_readout
sample_fwd, _, _ = task.prepare(next(_train_batches(cfg, 0)))
state = create_train_state(
model,
sample_fwd,
model_type=cfg.model_type,
lr=t.lr,
weight_decay=t.weight_decay,
warmup_steps=t.warmup_steps,
grad_clip=t.grad_clip,
ema_decay=t.ema_decay,
k_init=t.K_train,
loss_window=t.loss_window,
seed=cfg.training.seed,
)
if cfg.model_type == "sheaf" and bool(cfg.model.get("rm_constant", False)):
control_model_config = OmegaConf.to_container(cfg.model, resolve=True)
control_model_config["rm_init"] = "soft_slice"
control_model_config["rm_constant"] = False
control_model = build_model(model_config_from_dict(control_model_config), "sheaf")
control_state = create_train_state(
control_model,
sample_fwd,
model_type="sheaf",
lr=t.lr,
weight_decay=t.weight_decay,
warmup_steps=t.warmup_steps,
grad_clip=t.grad_clip,
ema_decay=t.ema_decay,
k_init=t.K_train,
loss_window=t.loss_window,
seed=cfg.training.seed,
)
counters = {"step": 0, "training_seed": int(cfg.training.seed)}
assert_common_bit_identical(
control_state.params,
state.params,
control_state.opt_state,
state.opt_state,
counters,
counters,
)
parity_path = Path(HydraConfig.get().runtime.output_dir) / "identity-parity.json"
atomic_write_json(
parity_path,
{
"format": 1,
"common_parameter_leaves_bit_identical": True,
"common_optimizer_leaves_bit_identical": True,
"counters_bit_identical": True,
"identity_parameter_tree_has_restriction_map": False,
"checked_before_first_optimizer_step": True,
"outcomes": {},
},
)
print("IDENTITY_COMMON_PARITY_VERIFIED", flush=True)
run.summary["params"] = sum(x.size for x in jax.tree_util.tree_leaves(state.params))
print(f"[init] model_type={cfg.model_type} params={run.summary['params']:,}")
train_step = make_train_step(task, cfg.model_type, graph_readout)
rng = jax.random.PRNGKey(cfg.training.seed)
rng_np = np.random.default_rng(cfg.training.seed)
history: list[dict] = []
best: dict[str, float] = {}
step = 0
for epoch in range(t.epochs):
for batch in _train_batches(cfg, epoch):
fwd, targets, _ = task.prepare(batch)
rng, sub = jax.random.split(rng)
k = (
sample_k(rng_np, t.train_iters_dist, t.train_iters_min, t.K_train)
if cfg.model_type == "sheaf"
else t.mpnn_train_rounds
)
state, loss = train_step(state, fwd, targets, sub, n_iter=k, loss_window=t.loss_window)
step += 1
loss = float(loss)
run.log({"train/loss": loss, "train/k": k, "epoch": epoch}, step=step)
if t.exit_on_nan and not np.isfinite(loss):
print(f"[epoch {epoch}] non-finite loss — stopping (exit_on_nan).")
run.finish(exit_code=1)
return
if epoch % t.val_interval == 0 or epoch == t.epochs - 1:
k_eval = t.K_eval if cfg.model_type == "sheaf" else t.mpnn_eval_rounds
row = {"epoch": epoch, "loss": loss}
for split in cfg.data.val_splits:
m = evaluate(
state,
task,
_val_batches(cfg, split),
model_type=cfg.model_type,
graph_readout=graph_readout,
k_eval=k_eval,
)
row[split] = m
run.log({f"val/{split}/{kk}": vv for kk, vv in m.items()}, step=step)
for kk, vv in m.items(): # track best-so-far in the run summary
key = f"best/{split}/{kk}"
best[key] = max(best.get(key, vv), vv)
print(
f"[epoch {epoch}] loss={loss:.4f} {split}: "
+ " ".join(f"{kk}={vv * 100:.2f}%" for kk, vv in m.items())
)
history.append(row)
run.summary.update(best)
out = Path(HydraConfig.get().runtime.output_dir)
save_checkpoint_atomic(
out / "checkpoint.partial.pkl",
{
"params": jax.device_get(state.params),
"ema_params": jax.device_get(state.ema_params),
"optimizer_state": jax.device_get(state.opt_state),
"config": OmegaConf.to_container(cfg, resolve=True),
"next_epoch": epoch + 1,
"step": step,
"jax_rng": np.asarray(jax.device_get(rng)),
"numpy_rng_state": rng_np.bit_generator.state,
},
)
atomic_write_json(out / "history.partial.json", history)
out = Path(HydraConfig.get().runtime.output_dir)
save_checkpoint_atomic(
out / "checkpoint.pkl",
{
"params": jax.device_get(state.params),
"ema_params": jax.device_get(state.ema_params),
"optimizer_state": jax.device_get(state.opt_state),
"config": OmegaConf.to_container(cfg, resolve=True),
"seed": int(cfg.training.seed),
"final_epoch": int(t.epochs) - 1,
},
)
atomic_write_json(out / "history.json", history)
print(f"[done] saved checkpoint + history to {out}")
run.finish()
if __name__ == "__main__":
main()
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