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When I run this:
import numpy as np
class MyThing(object):
def __init__(self, shape):
self.shape = shape
def __len__(self):
return self.shape[0]
def __getitem__(self, i):
if not isinstance(i, tuple):
i = (i,)
if len(i) > len(self.shape):
raise IndexError("boo")
return MyThing(self.shape[len(i):])
def __rmul__(self, other):
print "RMUL"
return self
print np.float64(5)*MyThing((3,3))I get this:
RMUL
RMUL
RMUL
RMUL
RMUL
RMUL
RMUL
RMUL
RMUL
[[<__main__.MyThing object at 0x2298b90>
<__main__.MyThing object at 0x2298bd0>
<__main__.MyThing object at 0x2298c10>]
[<__main__.MyThing object at 0x2298c50>
<__main__.MyThing object at 0x2298c90>
<__main__.MyThing object at 0x2298cd0>]
[<__main__.MyThing object at 0x2298d10>
<__main__.MyThing object at 0x2298d50>
<__main__.MyThing object at 0x2298d90>]]
Is there a way to tell numpy, "no, don't worry about it, just call __rmul__ on the whole thing, instead of picking it apart?"
In my specific case, MyThing is an array-like object that lives on a GPU, and while it's possible (and not necessarily incorrect) to pick the array apart in this way, it's unexpected and has really terrible performance.
(sorry about the many edits)
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