In pytorch, I have multiple (scale of hundred thousand) 300 dim vectors (which I think I should upload in a matrix), I want to sort them by their cosine similarity with another vector and extract the top-1000. I want to avoid for loop as it is time consuming. I was looking for an efficient solution.
You can use torch.nn.functional.cosine_similarity function for computing cosine similarity. And torch.argsort to extract top 1000.
Here is an example:
x = torch.rand(10000,300)
y = torch.rand(1,300)
dist = F.cosine_similarity(x,y)
index_sorted = torch.argsort(dist)
top_1000 = index_sorted[:1000]
Please note the shape of y, don't forget to reshape before calling similarity function. Also note that argsort simply returns the indexes of closest vectors. To access those vectors themselves, just write x[top_1000], which will return a matrix shaped (1000,300).