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Updates - #533
Updates#533
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Looks good. What's the reasoning around the precision used? In pvnet-summation it was locked to |
Ah yeah, bit more precision than medium but gets same tensor core speedup on A6000s, maybe the safer default. Happy to change to medium to match the summation if you prefer. |
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So would that mean it would be better for the summation model to use |
Not sure, but I suppose yeah - speed won't be a bottleneck anyway right. May be worth updating also. |
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Yeh it sounds like it would. It feels like we shouldn't have different standards for the two libraries |
Set float32 matmul precision to high for performance.
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Couple comments added - also made the relevant update for summation: openclimatefix/pvnet-summation#48 |
Enable TF32 tensor cores and make validation bf16-safe
run.py: addtorch.set_float32_matmul_precision("high")— lets fp32 matmuls / convs use tensor cores on Ampere+ GPUs. Free speedup, negligible precision cost.lightning_module.py:y_hat.float().cpu().numpy()in val storage - fixes a crash when training withprecision=bf16-mixed(numpy has no bfloat16). No-op under fp32.Tested on A6000 (DE model): GPU fully utilised, no change to training behaviour.