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When training a large model and using the adam optimizer, the optimizer parameters/status are often offloaded to the cpu memory, and then used to update the parameters on the cpu. At present, the Adam-CPU implemented by Paddle has poor performance, and I hope to provide higher performance. The Adam-CPU implementation improves the overall computing speed.
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When training a large model and using the adam optimizer, the optimizer parameters/status are often offloaded to the cpu memory, and then used to update the parameters on the cpu. At present, the Adam-CPU implemented by Paddle has poor performance, and I hope to provide higher performance. The Adam-CPU implementation improves the overall computing speed.
reference: Section 5.1 in the paper
https://arxiv.org/pdf/2101.06840.pdf
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