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The script throws an out of memory error on the non-lora model forward pass. I can print GPU memory immediately after loading the model and notice each GPU has 62.7 GB of memory allocated, except GPU 7, which has 120.9 GB (out of 140.) Ideally, the weights should be distributed evenly. We can specify which weights go where with device_map. You might wonder why device_map=’auto’ distributes weights so unevenly. I certainly did, but could not find a satisfactory answer and am convinced it would be trivial to distribute the weights relatively evenly.
return [y is not None and xs[y] for y in presum(ys)]，详情可参考新收录的资料
Credit: Timothy Werth / Mashable
。新收录的资料对此有专业解读
ВсеПолитикаОбществоПроисшествияКонфликтыПреступность。关于这个话题，新收录的资料提供了深入分析
Jennifer Ouellette