运行 run_example.sh时，会出现RuntimeError: "index_select_out_cuda_impl" not implemented for 'Float'

[Waterdrop-One]

Products to Synthons 0%| | 0/1 [00:00<?, ?it/s]/home/lzf/software/anaconda3/envs/seq_gr/lib/python3.7/site-packages/torchtext/data/field.py:359: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor). var = torch.tensor(arr, dtype=self.dtype, device=device) /home/lzf/programme/Retroprime/RetroPrime/retroprime/transformer_model/onmt/translate/translator.py:613: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor). return torch.tensor(a, requires_grad=False) 0%| | 0/1 [00:00<?, ?it/s] Traceback (most recent call last): File "retroprime/transformer_model/translate.py", line 53, in main(opt) File "retroprime/transformer_model/translate.py", line 34, in main attn_debug=opt.attn_debug) File "/home/lzf/programme/Retroprime/RetroPrime/retroprime/transformer_model/onmt/translate/translator.py", line 238, in translate batch_data = self.translate_batch(batch, data, fast=self.fast) File "/home/lzf/programme/Retroprime/RetroPrime/retroprime/transformer_model/onmt/translate/translator.py", line 375, in translate_batch return self._translate_batch(batch, data) File "/home/lzf/programme/Retroprime/RetroPrime/retroprime/transformer_model/onmt/translate/translator.py", line 712, in _translate_batch beam_attn.data[:, j, :memory_lengths[j]]) File "/home/lzf/programme/Retroprime/RetroPrime/retroprime/transformer_model/onmt/translate/beam.py", line 140, in advance self.attn.append(attn_out.index_select(0, prev_k)) RuntimeError: "index_select_out_cuda_impl" not implemented for 'Float'

[1991806559]

请问你这个问题后来解决了吗？

[woshi45]

This error may be caused by using an unsupported CUDA operation. Possible solutions are to make sure that your version of CUDA is compatible with the version of PyTorch you are using, and to check for other dependencies or configuration issues.