exception guarding cuda extension import #71
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JiushengChen
approved these changes
Dec 15, 2020
| @@ -459,7 +501,13 @@ def _generate(self, | |||
| bsz = input_size[0] | |||
| src_len = input_size[1] | |||
| beam_size = self.beam_size | |||
| self.no_repeat_ngram_op = NGramRepeatBlock() | |||
| cuda_ngram_op_import = True | |||
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initialize no_repeat_ngram_op as None. In case of import exception, just pass. When no_repeat_ngram_op is None, use cpu code. Otherwise, use gpu code. So we don't need to create new var cuda_ngram_op_import.
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Could we do this kind of checking (which kind of ops (e.g., cpu v.s. gpu) to use) inside the ops implementation? So that we do not need to do the similar check twice for fairseq and transformers. It will be easier for us to maintain and change the code in the future.
| from fastseq.ops.ngram_repeat_block import NGramRepeatBlock | ||
| self.no_repeat_ngram_op = NGramRepeatBlock() | ||
| except: | ||
| cuda_ngram_op_import = False |
| from fastseq.ops.ngram_repeat_block import NGramRepeatBlock | ||
| self.no_repeat_ngram_op = NGramRepeatBlock() | ||
| except: | ||
| self.cuda_ngram_op_import = False |
yuyan2do
approved these changes
Dec 18, 2020
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This PR fixes the error in cuda extension import due to torch version change/ gpu change.
If import results in exception, cpu implementation for Ngram blocking is used.