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implement BertTensorizerImpl and XLMTensorizerImpl #1193
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…criptable
Summary:
BERTTensorizerBaseImpl to reimplement BERTTensorizerBase to be TorchScriptable
Over design:
PyText Tensorizer (for example: RoBERTaTensorizer) will delegate the numberize and tensorize logic to Scripted Tensorizer Implementation (for example: RoBERTaTensorizerImpl)
This requires to reimplement numberize() and tensorize() logic in Torchscriptable, but good news is that we already have such implementation in pytext/torchscript/tensorizer, we just need to make minor change.
On the PyText Tensorizer side, it will delegate numberize and tensorize logic to tensorizer_impl.
```
def numberize(self, row: Dict) -> Tuple[Any, ...]:
per_sentence_tokens = [
self.tokenizer.tokenize(row[column]) for column in self.columns
]
return self.tensorizer_impl.numberize(per_sentence_tokens)
def tensorize(self, batch) -> Tuple[torch.Tensor, ...]:
tokens, segment_labels, seq_lens, positions = zip(*batch)
return self.tensorizer_impl.tensorize(
tokens, segment_labels, seq_lens, positions
)
```
Differential Revision: D18651538
fbshipit-source-id: b3eb3b63227b950343f8ee46f0091ec78f0d3298
Summary: Implementation of BertTensorizerImpl and XLMTensorizerImpl Differential Revision: D18693249 fbshipit-source-id: 77ee47eebe9c46d2f4d6f4c7b38bf0d3caa77de7
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This pull request was exported from Phabricator. Differential Revision: D18693249 |
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This pull request has been merged in 3a5e5b3. |
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Summary: Implementation of BertTensorizerImpl and XLMTensorizerImpl
Differential Revision: D18693249