I have cythonized the following file that uses numpy's matrix multiplication:
def cell(float[:, ::1] a, float[:, ::1] b):
c = a @ b
return c
However, when I call it with:
from matmul import cell
import numpy as np
a = np.zeros((1, 64), dtype=np.float32)
b = np.zeros((64, 64), dtype=np.float32)
c = cell(a, b)
I get the following error:
TypeError: unsupported operand type(s) for @: _memoryviewslice and _memoryviewslice
How can I perform matrix multiplication with Cython?
Context: the function "cell" is part of a code I wrote that performs a prediction by an LSTM network (I wrote it manually, without using PyTorch or Tensorflow, just NumPy). I need to speed up the code to be able to use the network in real-time.
If that's all you're doing there's literally no point in adding the types for the argument of cell - all you're doing is adding expensive type-checks for no reason. Cython can't make useful use of these types. Just leave a and b untyped.
If you do actually need to fix memoryviews operations with Numpy whole-array operations the easiest solution is to call np.asarray
def cell(float[:, ::1] a, float[:, ::1] b):
c = np.asarray(a) @ np.asarray(b)
return c
You aren't getting any benefit from Cython here - it's just calling into the Numpy matrix multiply code. So only do this where you need to mix it with some operations where you do benefit from Cython.