inplace embedding

embedding.py

import torch
import torch.nn as nn

# an Embedding module containing 10 tensors of size 3
embedding = nn.Embedding(10, 3)
embedding.weight.requires_grad_(False)
# a batch of 4 indices
input = torch.LongTensor([1, 2, 4, 5])
output = embedding(input)

output_buffer = torch.empty(4, 3)
torch.index_select(embedding.weight, dim=0, index=input, out=output_buffer)

assert torch.allclose(output, output_buffer)