Is it possible to create a FIFO queue with pyTorch?
I need to create a fixed length Tensor in pyTorch that acts like a FIFO queue.

I have this fuction to do it:

def push_to_tensor(tensor, x):
    tensor[:-1] = tensor[1:]
    tensor[-1] = x
    return tensor


For example, I have:

tensor = Tensor([1,2,3,4])

&gt;&gt; tensor([ 1.,  2.,  3.,  4.])


then using the function will give:

push_to_tensor(tensor, 5)

&gt;&gt; tensor([ 2.,  3.,  4.,  5.])


However, I was wondering:


Does pyTorch have a native method for doing this?
If not, is there a more clever way of doing it?


I implemented another FIFO queue:

def push_to_tensor_alternative(tensor, x):
    return torch.cat((tensor[1:], Tensor([x])))


The functionality is the same, but then I checked their performance in speed:

# Small Tensor
tensor = Tensor([1,2,3,4])

%timeit push_to_tensor(tensor, 5)
&gt;&gt; 30.9 µs ± 1.26 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

%timeit push_to_tensor_alternative(tensor, 5)
&gt;&gt; 22.1 µs ± 2.25 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

# Larger Tensor
tensor = torch.arange(10000)

%timeit push_to_tensor(tensor, 5)
&gt;&gt; 57.7 µs ± 4.88 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

%timeit push_to_tensor_alternative(tensor, 5)
&gt;&gt; 28.9 µs ± 570 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)


Seems like this push_to_tensor_alternative which uses torch.cat (instead of shifting all items to the left) is faster.
