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RuntimeError: Expected to have finished reduction in the prior . . . The specific error "RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one" indicates that not all parameters are participating in the calculation of loss, which is why it can't complete the reduction operation
Expected to have finished reduction in the prior iteration before . . . I have modified the nlp_example to finetune an EncoderDecoder on translation data like this: if accelerator distributed_type == DistributedType TPU: src = tokenizer(batch[0], padding="max_length", max_length=128, return_tensors="pt") tgt = tokenizer(batch[1], padding="max_length", max_length=128, return_tensors="pt") else:
【异常错误】 Expected to have finished reduction in the prior iteration . . . RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one This error indicates that your module has parameters that were not used in producing loss You can enable unused parameter detection by passing the keyword argument `find_unused_parameters=True` to `torch nn parallel DistributedDataParallel`, and by
RuntimeError: Expected to have finished reduction in the prior . . . 🐛 Bug To Reproduce Epoch: 1, iter 0: loss = 10 099 0%| | 1 144967 [00:02<116:54:31, 2 90s it] Traceback (most recent call last): File "train py", line 99, in solver train () File " home yckj2453 nlp_space jd_multimodal_dialogue multi-moda