GPT-2 language model decoding method
I am wondering what is the official decoding method when evaluating the language model? The  doc says `run_gpt2.py` implement the beam-search. While to me, it seems it's still greedy search with sampling. 
`run_gpt2` has top-K which is better than beam-search for high-entropy tasks like open-domain generation. The coming release example (currently on the xlnet branch to be merged with master on Monday) will have top-K and Nucleus sampling (see Holtzman et al. http://arxiv.org/abs/1904.09751)<|||||>Hi,
Is it possible to include beam search decoding in ```run_generation.py``` ?<|||||>hope that beam search appears in run_generation.py<|||||>We'll add it.
cc @rlouf <|||||>@thomwolf I see that run_generation.py has disappeared and beam_search does not exist anymore, nor in transformers/generate. Where could we find the implementation of batch beam_search in this repo ?<|||||>You can’t... so far. We are reworking the API for greedy decoding and sampling, and will work on beam search afterwards.