Simplify Machine Translation demo by using Trainer API #10895
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nickyfantasy
requested review from
jetfuel,
jacquesqiao,
daming-lu and
sidgoyal78
| context = encoder(is_sparse) | ||
| translation_ids, translation_scores = decoder_decode(context, is_sparse) | ||
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| exe = Executor(place) |
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We should move away from using executor, right?
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Yeah, I think we should not expose executor. Probably we can write decode_main() similar to the train() method above?
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The decode and train in this example are not compatible with each other, we cannot provide the save model from train and use it in infer
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We cannot use trainer.train either because during decode, it is not using optimizer or backward pass, it is doing a beam search
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Yeah, and I think the GPU implementation of beam search is still missing?
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| src_word_data = to_lodtensor(map(lambda x: x[0], data), place) | ||
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| result_ids, result_scores = exe.run( |
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I don't see any Inferencer. We should use the high level api.
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Or will there be any sub-sequence PR to add the infer part?
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Discussed with Nicky and Jeff. We could add some simple test to translate a sample sentence later.
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I will talk to Longfei to see if there is any solution to not expose executor, will try to update in next PR
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