facebookresearch/maskrcnn-benchmark

Half/mixed-precision is not faster

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#1,062 opened on 2019年8月27日

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help wanted

説明

❓ Questions and Help

As written in https://github.com/facebookresearch/maskrcnn-benchmark/issues/807#issuecomment-500112612 , DTYPE "float16" does not make training faster( and takes the same amount of memory) when tested with e2e_mask_rcnn_R_50_FPN_1x.yaml. Is this normal or am I doing something wrong? I also want to know if I can make inference faster by FP16(I tried model.half() in test_net, but doesn't work).
My environment is RTX Titan, cudnn 7.6, cuda10.0, driver version Driver Version: 418.67, pytorch-1.2.0, ubuntu 18.04.

コントリビューターガイド

技術スタック
pythonpytorch
領域
machine learningperformance
Issue 種別
performance
難度新しい貢献者にとっての実装難度です。1 は非常に小さな変更、5 は専門的な作業です。
3
推定時間経験ある貢献者が調査、実装、テスト、pull request 準備にかけるおおよその時間範囲です。
1-3 hours
活動状況Issue が今どれだけ取り組みやすいかを示します。新しい、活発、古い、ブロック中、またはメンテナー入力待ちなどです。
stale
明確さ期待される変更、受け入れ条件、次の手順がどれだけ明確かを示します。
needs investigation
前提条件
PyTorch basicsmixed precision concepts
初心者向け度初回貢献者にどれだけ取り組みやすいかを 1-100 で推定したスコアです。
30
調査方針
The issue is a user question about why half precision is not faster in maskrcnn benchmark. To help, one should investigate the use of automatic mixed precision (AMP) in PyTorch, check if the model and training script are properly configured for FP16 (e.g., using torch.cuda.amp or apex), and verify that the GPU supports fast FP16 operations (e.g., RTX Titan). Additionally, check for any known issues in the repository related to mixed precision, such as the linked comment in issue #807. The user also tried model.half() for inference without success, so explore proper FP16 inference workflows.