为什么遍历 pytorch 张量如此缓慢（与 Numpy 相比）？

Question

I've been working with image transformations recently and came to a situation where I have a large array (shape of 100,000 x 3) where each row represents a point in 3D space like:

pnt = [x y z]

All I'm trying to do is iterating through each point and matrix multiplying each point with a matrix called T (shape = 3 X 3).

Test with Numpy:

def transform(pnt_cloud, T):
    
    i = 0
    for pnt in pnt_cloud:
        xyz_pnt = np.dot(T, pnt)
        
        if xyz_pnt[0] > 0:
            arr[i] = xyz_pnt[0]
            
        i += 1
           
    return arr

Calling the following code and calculating runtime (using %time) gives the output:

Out[190]: CPU times: user 670 ms, sys: 7.91 ms, total: 678 ms
Wall time: 674 ms

Test with Pytorch Tensor:

import torch

tensor_cld = torch.tensor(pnt_cloud)
tensor_T   = torch.tensor(T)

def transform(pnt_cloud, T):
    depth_array = torch.tensor(np.zeros(pnt_cloud.shape[0]))

    i = 0
    for pnt in pnt_cloud:
        xyz_pnt = torch.matmul(T, pnt)
        
        if xyz_pnt[0] > 0:
            depth_array[i] = xyz_pnt[0]
            
        i += 1
            
        
    return depth_array

Calling the following code and calculating runtime (using %time) gives the output:

Out[199]: CPU times: user 6.15 s, sys: 28.1 ms, total: 6.18 s
Wall time: 6.09 s

NOTE: Doing the same with torch.jit only reduces 2s

I would have thought that PyTorch tensor computations would be much faster due to the way PyTorch breaks its code down in the compiling stage. What am I missing here?

Would there be any faster way to do this other than using Numba?

Answer 1

Why are you using a for loop??
Why do you compute a 3x3 dot product and only uses the first element of the result??

You can do all the math in a single matmul :

with torch.no_grad():
  depth_array = torch.matmul(pnt_cloud, T[:1, :].T)  # nx3 dot 3x1 -> nx1
  # since you only want non negative results
  depth_array = torch.maximum(depth_array, 0)

Since you want to compare runtime to numpy, you should disable gradient accumulation .

Answer 2

For the speed, I got this reply from the PyTorch forums:

1. operations of 1-3 elements are generally rather expensive in PyTorch as the overhead of Tensor creation becomes significant (this includes setting single elements), I think this is the main thing here. This is also the reason why the JIT doesn't help a whole lot (it only takes away the Python overhead) and Numby shines (where eg the assignment to depth_array[i] is just memory write).

2. the matmul itself might differ in speed if you have different BLAS backends for it in PyTorch vs. NumPy.