PyTorch / Gensim - How do I load pre-trained word embeddings?
I want to load a pre-trained word2vec embedding with gensim into a PyTorch embedding layer.
How do I get the embedding weights loaded by gensim into the PyTorch embedding layer?

I just wanted to report my findings about loading a gensim embedding with PyTorch.




Solution for PyTorch 0.4.0 and newer:


From v0.4.0 there is a new function from_pretrained() which makes loading an embedding very comfortable.
Here is an example from the documentation.

import torch
import torch.nn as nn

# FloatTensor containing pretrained weights
weight = torch.FloatTensor([[1, 2.3, 3], [4, 5.1, 6.3]])
embedding = nn.Embedding.from_pretrained(weight)
# Get embeddings for index 1
input = torch.LongTensor([1])
embedding(input)


The weights from gensim can easily be obtained by:

import gensim
model = gensim.models.KeyedVectors.load_word2vec_format('path/to/file')
weights = torch.FloatTensor(model.vectors) # formerly syn0, which is soon deprecated


As noted by @Guglie: in newer gensim versions the weights can be obtained by model.wv:

weights = model.wv





Solution for PyTorch version 0.3.1 and older:


I'm using version 0.3.1 and from_pretrained() isn't available in this version.

Therefore I created my own from_pretrained so I can also use it with 0.3.1.

Code for from_pretrained for PyTorch versions 0.3.1 or lower:

def from_pretrained(embeddings, freeze=True):
    assert embeddings.dim() == 2, \
         'Embeddings parameter is expected to be 2-dimensional'
    rows, cols = embeddings.shape
    embedding = torch.nn.Embedding(num_embeddings=rows, embedding_dim=cols)
    embedding.weight = torch.nn.Parameter(embeddings)
    embedding.weight.requires_grad = not freeze
    return embedding


The embedding can be loaded then just like this:

embedding = from_pretrained(weights)


I hope this is helpful for someone.
