[benchmarks] Default to bfloat16 (inference) and AMP (training) precision.
#6518
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- Pick data type based on `test`. - Create `cast_to_dtype` function.
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This was referenced Feb 12, 2024
ysiraichi
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frgossen
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I'm still running the benchmarks in order to grasp the regressions this PR introduces. |
frgossen
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Feb 15, 2024
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| FORCE_AMP_FOR_FP16_BF16_MODELS = { | |||
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Do you know how this is configured on PyTorch HUD? Maintaining a list like this feels prone to divergence.
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These lists are taken from the scripts in the PyTorch main repo. They are used for generating the PyTorch HUD results.
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I will leave a comment.
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Could you share the link to the scripts in the PyTorch main repo that are used to generate the HUD result?
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frgossen
approved these changes
Feb 15, 2024
| @@ -144,6 +144,18 @@ | |||
| "hf_T5_generate", | |||
| } | |||
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| FORCE_AMP_FOR_FP16_BF16_MODELS = { | |||
golechwierowicz
approved these changes
Feb 16, 2024
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The profiling results (in the following posts) were generated with the following command: python xla/benchmarks/experiment_runner.py \
--suite-name torchbench --accelerator cuda --dump-pytorch-profiles \
--xla PJRT --dynamo openxla --test eval --repeat 8 --iterations-per-run 1 \
-k BERT_pytorch |
BERT_pytorch (before) |
BERT_pytorch (after) |
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Thank you! Looks good to push forward. |
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Fix: #6483
This PR makes
bfloat16the default data-type for inference, and AMP the default execution mode for training. This follows the execution found in the PyTorch HUD.cc @miladm