Comments (2)
yygle
commented on August 10, 2024
抱歉之前论文看得不仔细，总体的FLOPs确实是比bert-base小很多，我自己测试是在C++引擎上推理，在medium-small模型上测试的速度慢于6层的albert，因为我自己测试medium模型应该是1GFLOPs左右，但是6层albert在11GFLOPs的级别，我对此感到困惑，望解答！我的CPU（Intel(R) Xeon(R) CPU E5-2678 v3 @ 2.50GHz）
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yygle
commented on August 10, 2024
我想我找到问题的原因了，主要计算花在了embedding的操作上，mixed-attention确实是比self-attention要快的
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Related Issues (20)
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- 请问有pytorch版本发布吗？ HOT 1
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- The exact English pretraining data and Chinese pretraining data that are exact same to the BERT paper's pretraining data.
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- 预测性能 HOT 4
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