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)

```
Had similar problem: "after training and saving embeddings in **binary** format using gensim, how I load them to torchtext?"

I just saved the file to txt format and then follow the superb [tutorial](https://github.com/bentrevett/pytorch-sentiment-analysis/blob/master/C%20-%20Loading%2C%20Saving%20and%20Freezing%20Embeddings.ipynb) of loading custom word embeddings.

```
def convert_bin_emb_txt(out_path,emb_file):
    txt_name = basename(emb_file).split(".")[0] +".txt"
    emb_txt_file = os.path.join(out_path,txt_name)
    emb_model = KeyedVectors.load_word2vec_format(emb_file,binary=True)
    emb_model.save_word2vec_format(emb_txt_file,binary=False)
    return emb_txt_file

emb_txt_file = convert_bin_emb_txt(out_path,emb_bin_file)
custom_embeddings = vocab.Vectors(name=emb_txt_file,
                                  cache='custom_embeddings',
                                  unk_init=torch.Tensor.normal_)

TEXT.build_vocab(train_data,
                 max_size=MAX_VOCAB_SIZE,
                 vectors=custom_embeddings,
                 unk_init=torch.Tensor.normal_)

```

tested for: PyTorch: 1.2.0 and TorchText: 0.4.0.

I added this answer because with the accepted answer I was not sure how to follow the linked [tutorial](https://github.com/bentrevett/pytorch-sentiment-analysis/blob/master/C%20-%20Loading%2C%20Saving%20and%20Freezing%20Embeddings.ipynb) and initialize all words not in the embeddings using the normal distribution and how to make the vectors and equal to zero.