Add Automatic Mixed Precision option for training and evaluation. #199
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Cadene
approved these changes
May 20, 2024
| @@ -10,6 +10,9 @@ hydra: | |||
| name: default | |||
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| device: cuda # cpu | |||
| # `use_amp` determines whether to use Automatic Mixed Precision (AMP) for training and evaluation. With AMP, | |||
| # automatic gradient scaling is used. | |||
| use_amp: false | |||
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Should we set it to true by default?
Suggested change
| use_amp: false | |
| use_amp: true |
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Maybe in a second PR once all our models on the hub are fp16 checkpoints?
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Yes I agree we should do it next. Good suggestion.
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| pin_memory=cfg.device != "cpu", | ||
| pin_memory=device.type != "cpu", |
Cadene
approved these changes
May 20, 2024
HalvardBariller
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May 21, 2024
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What this does
How it was tested
CI += end-to-end testing for training and eval with AMP.
How to checkout & try? (for the reviewer)
Try training with use_amp = false/true. I tried this with ACT/Aloha and saw a reduction in training time and memory usage.
Try evaluating with AMP:
For me, it took 61 seconds without AMP and 48 seconds with AMP.
Finally, I trained ACT/aloha_sim_transfer_cube_human with the same recipe as https://huggingface.co/lerobot/act_aloha_sim_transfer_cube_human but only to 25k iters. I evaluated 50 episodes and matched the the success rate of the baseline at 30k iters: ie ~56% (wandb run here, but I used the wrong simulation env during training https://wandb.ai/alexander-soare/lerobot/runs/vrrckq4p?nw=nwuseralexandersoare)