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fix early stop #481

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fix early stop #481

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3690324
fix early stop
Tetracarbonylnickel Sep 2, 2025
6df3ddc
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Sep 2, 2025
fc062fd
linting
Tetracarbonylnickel Sep 2, 2025
a70a80b
Merge branch 'main' into early-stopping-fix
Tetracarbonylnickel Sep 3, 2025
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200 changes: 104 additions & 96 deletions apax/train/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,10 @@
log = logging.getLogger(__name__)


class EarlyStop(Exception):
pass


def fit(
state: TrainState,
train_ds: InMemoryDataset,
Expand Down Expand Up @@ -113,124 +117,128 @@ def fit(
epoch_pbar = trange(
start_epoch, n_epochs, desc="Epochs", ncols=100, disable=disable_pbar, leave=True
)
with (
ocp.CheckpointManager(
latest_dir.resolve(), options=options
) as latest_ckpt_manager,
ocp.CheckpointManager(best_dir.resolve(), options=options) as best_ckpt_manager,
):
for epoch in range(start_epoch, n_epochs):
epoch_start_time = time.time()
callbacks.on_epoch_begin(epoch=epoch + 1)

if ema_handler:
ema_handler.update(state.params, epoch)

epoch_loss.update({"train_loss": 0.0})
train_batch_metrics = Metrics.empty()

batch_pbar = trange(
0,
train_steps_per_epoch,
desc="Batches",
ncols=100,
mininterval=1.0,
disable=disable_batch_pbar,
leave=False,
)

for batch_idx in range(train_steps_per_epoch):
callbacks.on_train_batch_begin(batch=batch_idx)

batch = next(batch_train_ds)
(
(state, train_batch_metrics),
batch_loss,
) = train_step(
(state, train_batch_metrics),
batch,
)

epoch_loss["train_loss"] += jnp.mean(batch_loss)
callbacks.on_train_batch_end(batch=batch_idx)
batch_pbar.update()

epoch_loss["train_loss"] /= train_steps_per_epoch
epoch_loss["train_loss"] = float(epoch_loss["train_loss"])

epoch_metrics = {
f"train_{key}": float(val)
for key, val in train_batch_metrics.compute().items()
}

if ema_handler:
ema_handler.update(state.params, epoch)
val_params = ema_handler.ema_params
else:
val_params = state.params

if val_ds is not None:
epoch_loss.update({"val_loss": 0.0})
val_batch_metrics = Metrics.empty()
try:
with (
ocp.CheckpointManager(
latest_dir.resolve(), options=options
) as latest_ckpt_manager,
ocp.CheckpointManager(
best_dir.resolve(), options=options
) as best_ckpt_manager,
):
for epoch in range(start_epoch, n_epochs):
epoch_start_time = time.time()
callbacks.on_epoch_begin(epoch=epoch + 1)

if ema_handler:
ema_handler.update(state.params, epoch)

epoch_loss.update({"train_loss": 0.0})
train_batch_metrics = Metrics.empty()

batch_pbar = trange(
0,
val_steps_per_epoch,
train_steps_per_epoch,
desc="Batches",
ncols=100,
mininterval=1.0,
disable=disable_batch_pbar,
leave=False,
)
for batch_idx in range(val_steps_per_epoch):
batch = next(batch_val_ds)

batch_loss, val_batch_metrics = val_step(
val_params, batch, val_batch_metrics
for batch_idx in range(train_steps_per_epoch):
callbacks.on_train_batch_begin(batch=batch_idx)

batch = next(batch_train_ds)
(
(state, train_batch_metrics),
batch_loss,
) = train_step(
(state, train_batch_metrics),
batch,
)
epoch_loss["val_loss"] += batch_loss

epoch_loss["train_loss"] += jnp.mean(batch_loss)
callbacks.on_train_batch_end(batch=batch_idx)
batch_pbar.update()

epoch_loss["val_loss"] /= val_steps_per_epoch
epoch_loss["val_loss"] = float(epoch_loss["val_loss"])
epoch_loss["train_loss"] /= train_steps_per_epoch
epoch_loss["train_loss"] = float(epoch_loss["train_loss"])

epoch_metrics.update(
{
f"val_{key}": float(val)
for key, val in val_batch_metrics.compute().items()
}
)
epoch_metrics = {
f"train_{key}": float(val)
for key, val in train_batch_metrics.compute().items()
}

epoch_metrics.update({**epoch_loss})
epoch_end_time = time.time()
epoch_metrics.update({"epoch_time": epoch_end_time - epoch_start_time})
if ema_handler:
ema_handler.update(state.params, epoch)
val_params = ema_handler.ema_params
else:
val_params = state.params

if val_ds is not None:
epoch_loss.update({"val_loss": 0.0})
val_batch_metrics = Metrics.empty()

batch_pbar = trange(
0,
val_steps_per_epoch,
desc="Batches",
ncols=100,
mininterval=1.0,
disable=disable_batch_pbar,
leave=False,
)
for batch_idx in range(val_steps_per_epoch):
batch = next(batch_val_ds)

batch_loss, val_batch_metrics = val_step(
val_params, batch, val_batch_metrics
)
epoch_loss["val_loss"] += batch_loss
batch_pbar.update()

epoch_loss["val_loss"] /= val_steps_per_epoch
epoch_loss["val_loss"] = float(epoch_loss["val_loss"])

epoch_metrics.update(
{
f"val_{key}": float(val)
for key, val in val_batch_metrics.compute().items()
}
)

ckpt = {"model": state, "epoch": epoch}
if epoch % ckpt_interval == 0:
latest_ckpt_manager.save(epoch, args=ocp.args.StandardSave(ckpt))
epoch_metrics.update({**epoch_loss})
epoch_end_time = time.time()
epoch_metrics.update({"epoch_time": epoch_end_time - epoch_start_time})

if epoch_metrics["val_loss"] < best_loss:
best_ckpt_manager.save(epoch, args=ocp.args.StandardSave(ckpt))
if abs(epoch_metrics["val_loss"] - best_loss) < patience_min_delta:
early_stopping_counter += 1
ckpt = {"model": state, "epoch": epoch}
if epoch % ckpt_interval == 0:
latest_ckpt_manager.save(epoch, args=ocp.args.StandardSave(ckpt))

if epoch_metrics["val_loss"] < best_loss:
best_ckpt_manager.save(epoch, args=ocp.args.StandardSave(ckpt))
if abs(epoch_metrics["val_loss"] - best_loss) < patience_min_delta:
early_stopping_counter += 1
else:
early_stopping_counter = 0

best_loss = epoch_metrics["val_loss"]
else:
early_stopping_counter = 0
early_stopping_counter += 1

best_loss = epoch_metrics["val_loss"]
else:
early_stopping_counter += 1
callbacks.on_epoch_end(epoch=epoch, logs=epoch_metrics)

callbacks.on_epoch_end(epoch=epoch, logs=epoch_metrics)
epoch_pbar.set_postfix(val_loss=epoch_metrics["val_loss"])
epoch_pbar.update()

epoch_pbar.set_postfix(val_loss=epoch_metrics["val_loss"])
epoch_pbar.update()
if patience is not None and early_stopping_counter >= patience:
raise EarlyStop()
except EarlyStop:
log.info(
f"Early stopping patience exceeded. Stopping training after {epoch} epochs."
)

if patience is not None and early_stopping_counter >= patience:
log.info(
"Early stopping patience exceeded. Stopping training after"
f" {epoch} epochs."
)
break
epoch_pbar.close()
callbacks.on_train_end()

Expand Down