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Cast to >=float32 tensor when passing scalar to self.log #19046

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merged 7 commits into from
Nov 24, 2023
Merged

Cast to >=float32 tensor when passing scalar to self.log #19046

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2951ba2
fix: __to_tensor_high_precision
MF-FOOM Nov 10, 2023
0277e7c
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Nov 21, 2023
1c24b86
fix: self._get_default_high_precision_dtype()
MF-FOOM Nov 21, 2023
b9894c9
fix: make __get_default_high_precision_dtype a staticmethod
MF-FOOM Nov 21, 2023
7957008
refactor: __to_tensor_high_precision -> __to_tensor
MF-FOOM Nov 22, 2023
2d19b05
refactor: import _get_default_dtype from logger connector
MF-FOOM Nov 23, 2023
3ee07db
chlog
awaelchli Nov 24, 2023
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3 changes: 3 additions & 0 deletions src/lightning/pytorch/CHANGELOG.md
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Expand Up @@ -52,6 +52,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
- Fixed an issue where Metric instances from `torchmetrics` wouldn't get moved to the device when using FSDP ([#18954](https://github.com/Lightning-AI/lightning/issues/18954))


- Fixed the tensor conversion in `self.log` to respect the default dtype ([#19046](https://github.com/Lightning-AI/lightning/issues/19046))


## [2.1.0] - 2023-10-11

### Added
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7 changes: 6 additions & 1 deletion src/lightning/pytorch/core/module.py
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Expand Up @@ -61,6 +61,7 @@
from lightning.pytorch.loggers import Logger
from lightning.pytorch.trainer import call
from lightning.pytorch.trainer.connectors.logger_connector.fx_validator import _FxValidator
from lightning.pytorch.trainer.connectors.logger_connector.result import _get_default_dtype
from lightning.pytorch.utilities import GradClipAlgorithmType
from lightning.pytorch.utilities.exceptions import MisconfigurationException
from lightning.pytorch.utilities.imports import _TORCHMETRICS_GREATER_EQUAL_0_9_1
Expand Down Expand Up @@ -622,7 +623,11 @@ def __check_allowed(v: Any, name: str, value: Any) -> None:
raise ValueError(f"`self.log({name}, {value})` was called, but `{type(v).__name__}` values cannot be logged")

def __to_tensor(self, value: Union[Tensor, numbers.Number], name: str) -> Tensor:
value = value.clone().detach() if isinstance(value, Tensor) else torch.tensor(value, device=self.device)
value = (
value.clone().detach()
if isinstance(value, Tensor)
else torch.tensor(value, device=self.device, dtype=_get_default_dtype())
)
if not torch.numel(value) == 1:
raise ValueError(
f"`self.log({name}, {value})` was called, but the tensor must have a single element."
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