Adds temperature, top_k decoding, and top_p decoding to decode_seq2seq.py #44
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Phirefly9
commented
Dec 6, 2019
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This is ready for review. arguments were set so that results of the paper will not change by default when running eval. |
donglixp
reviewed
Dec 16, 2019
| @@ -1449,10 +1453,15 @@ def forward(self, input_ids, token_type_ids, position_ids, attention_mask, task_ | |||
| last_hidden = new_encoded_layers[-1][:, -1:, :] | |||
| prediction_scores, _ = self.cls( | |||
| last_hidden, None, task_idx=task_idx) | |||
| prediction_scores = prediction_scores[:, -1, :] / (self.temperature if self.temperature > 0 else 1.) | |||
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prediction_scores[:, -1, :] reduces the dimension from 3 to 2, while other places assume the dim=3 (such as prediction_scores[:, :, token_id].fill_(-10000.0)).
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good catch, I checked and prediction_scores only needed to be 3 dimensional for the not_predict_set if conditional, and is not used after that, so I modified the access to work properly with 2 dimensions.
unfortunately due to to way torch.multinomial works I cannot leave it as 3 dimensions, so had to make the modification that way
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This PR adds the capability to provide different token decoding strategies.
Pretty much a transplant of some code from https://github.com/huggingface/transformers/blob/master/examples/run_generation.py