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 had the same question except that I use [torchtext](https://github.com/pytorch/text) library with pytorch as it helps with padding, batching, and other things. This is what I've done to load pre-trained embeddings with torchtext 0.3.0 and to pass them to pytorch 0.4.1 (the pytorch part uses the method mentioned by [blue-phoenox](https://stackoverflow.com/a/49802495/401075)):

```
import torch
import torch.nn as nn
import torchtext.data as data
import torchtext.vocab as vocab

# use torchtext to define the dataset field containing text
text_field = data.Field(sequential=True)

# load your dataset using torchtext, e.g.
dataset = data.Dataset(examples=..., fields=[('text', text_field), ...])

# build vocabulary
text_field.build_vocab(dataset)

# I use embeddings created with
# model = gensim.models.Word2Vec(...)
# model.wv.save_word2vec_format(path_to_embeddings_file)

# load embeddings using torchtext
vectors = vocab.Vectors(path_to_embeddings_file) # file created by gensim
text_field.vocab.set_vectors(vectors.stoi, vectors.vectors, vectors.dim)

# when defining your network you can then use the method mentioned by blue-phoenox
embedding = nn.Embedding.from_pretrained(torch.FloatTensor(text_field.vocab.vectors))

# pass data to the layer
dataset_iter = data.Iterator(dataset, ...)
for batch in dataset_iter:
    ...
    embedding(batch.text)

```
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.