Question: 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?

Answer: 
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()`](https://pytorch.org/docs/stable/nn.html#torch.nn.Embedding.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*](https://radimrehurek.com/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`](https://radimrehurek.com/gensim/models/word2vec.html):

```
weights = model.wv

```

---

* Solution for PyTorch version `0.3.1` and older:
-----------------------------------------------

I'm using version `0.3.1` and [`from_pretrained()`](https://pytorch.org/docs/stable/nn.html#torch.nn.Embedding.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.
I had quite some problems in understanding the documentation myself and there aren't that many good examples around. Hopefully this example helps other people. It is a simple classifier, that takes the pretrained embeddings in the `matrix_embeddings`. By setting `requires_grad` to false we make sure that we are not changing them.

```
class InferClassifier(nn.Module):
  def __init__(self, input_dim, n_classes, matrix_embeddings):
    """initializes a 2 layer MLP for classification.
    There are no non-linearities in the original code, Katia instructed us 
    to use tanh instead"""

    super(InferClassifier, self).__init__()

    #dimensionalities
    self.input_dim = input_dim
    self.n_classes = n_classes
    self.hidden_dim = 512

    #embedding
    self.embeddings = nn.Embedding.from_pretrained(matrix_embeddings)
    self.embeddings.requires_grad = False

    #creates a MLP
    self.classifier = nn.Sequential(
            nn.Linear(self.input_dim, self.hidden_dim),
            nn.Tanh(), #not present in the original code.
            nn.Linear(self.hidden_dim, self.n_classes))

  def forward(self, sentence):
    """forward pass of the classifier
    I am not sure it is necessary to make this explicit."""

    #get the embeddings for the inputs
    u = self.embeddings(sentence)

    #forward to the classifier
    return self.classifier(x)

```

`sentence` is a vector with the indexes of `matrix_embeddings` instead of words.