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Enable CUDA mixed precision in NeuralNet #15

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Enable CUDA mixed precision in NeuralNet #15

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13 changes: 11 additions & 2 deletions include/neural_net/neural_net.py
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Original file line number Diff line number Diff line change
Expand Up @@ -211,6 +211,8 @@ def __init__(self,
"mps" if torch.backends.mps.is_available() else "cpu")
self.num_workers = num_workers
gsim_logger.info(f"Using {self.device_type} device")
if self.device_type == 'cuda':
torch.set_float32_matmul_precision('high')
if nn_folder is None:
gsim_logger.warning("* " * 50)
gsim_logger.warning(
Expand Down Expand Up @@ -378,7 +380,12 @@ def _get_loss(self, data: tuple[InputType, TargetType],
input_batch = self._move_to_device(input_batch)
targets_batch = self._move_to_device(targets_batch)

output_batch = self(input_batch)
with torch.autocast('cuda', dtype=torch.bfloat16, enabled=self.device_type == 'cuda'):
output_batch = self(input_batch)
# Loss computed in fp32 regardless of autocast — avoids underflow/NaN
# for MAPE/MAE when output is bf16.
if isinstance(output_batch, torch.Tensor):
output_batch = output_batch.float()
loss = f_loss(output_batch, targets_batch)

if isinstance(targets_batch, torch.Tensor):
Expand Down Expand Up @@ -641,7 +648,9 @@ def make_output(l_out, output_class):

# Run the forward pass
input_batch = self._move_to_device(input_batch)
output_batch = self._move_to_cpu(self(input_batch))
with torch.autocast('cuda', dtype=torch.bfloat16, enabled=self.device_type == 'cuda'):
output_batch = self(input_batch)
output_batch = self._move_to_cpu(output_batch)
if unnormalize and self.normalizer is not None:
output_batch = self.normalizer.unnormalize_output_batch(
output_batch)
Expand Down
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