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2 years ago Hi @yeyinthtoon!
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Original implementation of read_txt_embeddings takes a lot of memory. For example, to load an embedding txt file that contains a vocab size of 2,000,000 with 300 embedding dimension, vectors list takes 643002,000,000=4.8 GB, np.concatenate takes 4.8 GB and torch.from_numpy takes 2.4 GB, totally it takes around 12 GB. Knowing vocab_size in advance and setting dtype of vector to np.float32, memory requirement can be reduced to around 2.4 GB instead of 12GB.