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Support OpenNMT-py and Fairseq options --alignment_layer and --alignment_heads which specify how the multi-head attention is reduced and returned by the Transformer decoder
Support dynamic loading of CUDA libraries on Windows
Fixes and improvements
Fix division by zero when normalizing the score of an empty target
Fix error that was not raised when the input length is greater than the number of position encodings
Improve performance of random sampling on GPU for large values of sampling_topk or when sampling over the full vocabulary
Include transformer_align and transformer_wmt_en_de_big_align in the list of supported Fairseq architectures
Add a CUDA kernel to prepare the length mask to avoid moving back to the CPU