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Merged
nbereux merged 1 commit into from
Jun 24, 2026
Merged

save effective time #69

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2 changes: 2 additions & 0 deletions rbms/io.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@ def save_model(
num_updates: int,
time: float,
learning_rate: Tensor,
effective_time: np.ndarray,
flags: list[str] = [],
) -> None:
"""Save the current state of the model.
Expand Down Expand Up @@ -48,6 +49,7 @@ def save_model(
checkpoint["numpy_rng_arg3"] = np.random.get_state()[3]
checkpoint["numpy_rng_arg4"] = np.random.get_state()[4]
checkpoint["time"] = time
checkpoint["effective_time"] = effective_time
checkpoint["learning_rate"] = learning_rate.cpu().numpy()
# Update the parallel chains to resume training
if "parallel_chains" in f.keys():
Expand Down
6 changes: 3 additions & 3 deletions rbms/training/pcd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,7 @@ def train(
pbar.set_description(f"Training {params.name}")

start = time.perf_counter()

effective_time = np.zeros(len(optimizer))
for idx in range(curr_update + 1, num_updates + 1):
batch = train_dataset.batch(batch_size)
data, weights = batch["data"], batch["weights"]
Expand Down Expand Up @@ -69,7 +69,8 @@ def train(

params.post_grad_update()
sampler.post_grad_update(params=params)

learning_rates = np.asarray([opt.param_groups[0]["lr"] for opt in optimizer])
effective_time += learning_rates
# Get flags for save
flags = []
flags = params.save_flags(flags)
Expand All @@ -81,7 +82,6 @@ def train(
names_params = (
list(params.named_parameters().keys()) if len(optimizer) > 1 else ["all"]
)
learning_rates = np.asarray([opt.param_groups[0]["lr"] for opt in optimizer])

metrics = {}
metrics = sampler.get_metrics_display(
Expand Down
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