convert batch size to float before torch.std in params reporter #4828
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Summary: ### Description This diff converts the batch size to a float before calculating the standard deviation in the `params_reporter` module. This change is made to ensure accurate calculations, as `torch.std` expects a floating-point input. ### Changes - **Modified code in `bench_params_reporter.py`** - Changed the line `torch.std(torch.tensor([b for bs in batch_size_per_feature_per_rank for b in bs]))` to `torch.std(torch.tensor([b for bs in batch_size_per_feature_per_rank for b in bs])).float()`. ### Reason The `torch.std` function requires a floating-point tensor to calculate the standard deviation. By converting the batch size to a float, we ensure that the calculation is performed correctly. Differential Revision: D81809491
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This pull request was exported from Phabricator. Differential Revision: D81809491 |
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Summary:
Description
This diff converts the batch size to a float before calculating the standard deviation in the
params_reportermodule. This change is made to ensure accurate calculations, astorch.stdexpects a floating-point input.Changes
bench_params_reporter.pytorch.std(torch.tensor([b for bs in batch_size_per_feature_per_rank for b in bs]))totorch.std(torch.tensor([b for bs in batch_size_per_feature_per_rank for b in bs])).float().Reason
The
torch.stdfunction requires a floating-point tensor to calculate the standard deviation. By converting the batch size to a float, we ensure that the calculation is performed correctly.Differential Revision: D81809491