[Numpy-discussion] Dot/inner products with broadcasting?

Oscar Villellas oscar.villellas@continuum...
Wed Mar 20 12:14:41 CDT 2013


Reproduced it. I will take a look at it. That error comes direct from
BLAS and shouldn't be happening.

I will also look why inner1d is not performing well. Note: inner1d is
implemented with calls to BLAS (dot).

I will get back to you later :)

On Wed, Mar 20, 2013 at 4:10 PM, Jaakko Luttinen
<jaakko.luttinen@aalto.fi> wrote:
> Well, thanks to seberg, I finally noticed that there is a dot product
> function in this new module numpy.core.gufuncs_linalg, it was just named
> differently (matrix_multiply instead of dot).
>
> However, I may have found a bug in it:
>
> import numpy.core.gufuncs_linalg as gula
> A = np.arange(2*2).reshape((2,2))
> B = np.arange(2*1).reshape((2,1))
> gula.matrix_multiply(A, B)
> ----
> ValueError: On entry to DGEMM parameter number 10 had an illegal value
>
> -Jaakko
>
> On 03/20/2013 03:33 PM, Jaakko Luttinen wrote:
>> I tried using this inner1d as an alternative to dot because it uses
>> broadcasting. However, I found something surprising: Not only is inner1d
>> much much slower than dot, it is also slower than einsum which is much
>> more general:
>>
>> In [68]: import numpy as np
>>
>> In [69]: import numpy.core.gufuncs_linalg as gula
>>
>> In [70]: K = np.random.randn(1000,1000)
>>
>> In [71]: %timeit gula.inner1d(K[:,np.newaxis,:],
>> np.swapaxes(K,-1,-2)[np.newaxis,:,:])
>> 1 loops, best of 3: 6.05 s per loop
>>
>> In [72]: %timeit np.dot(K,K)
>> 1 loops, best of 3: 392 ms per loop
>>
>> In [73]: %timeit np.einsum('ik,kj->ij', K, K)
>> 1 loops, best of 3: 1.24 s per loop
>>
>> Why is it so? I thought that the performance of inner1d would be
>> somewhere in between dot and einsum, probably closer to dot. Now I don't
>> see any reason to use inner1d instead of einsum..
>>
>> -Jaakko
>>
>> On 03/15/2013 04:22 PM, Oscar Villellas wrote:
>>> In fact, there is already an inner1d implemented in
>>> numpy.core.umath_tests.inner1d
>>>
>>> from numpy.core.umath_tests import inner1d
>>>
>>> It should do the trick :)
>>>
>>> On Thu, Mar 14, 2013 at 12:54 PM, Jaakko Luttinen
>>> <jaakko.luttinen@aalto.fi> wrote:
>>>> Answering to myself, this pull request seems to implement an inner
>>>> product with broadcasting (inner1d) and many other useful functions:
>>>> https://github.com/numpy/numpy/pull/2954/
>>>> -J
>>>>
>>>> On 03/13/2013 04:21 PM, Jaakko Luttinen wrote:
>>>>> Hi!
>>>>>
>>>>> How can I compute dot product (or similar multiply&sum operations)
>>>>> efficiently so that broadcasting is utilized?
>>>>> For multi-dimensional arrays, NumPy's inner and dot functions do not
>>>>> match the leading axes and use broadcasting, but instead the result has
>>>>> first the leading axes of the first input array and then the leading
>>>>> axes of the second input array.
>>>>>
>>>>> For instance, I would like to compute the following inner-product:
>>>>> np.sum(A*B, axis=-1)
>>>>>
>>>>> But numpy.inner gives:
>>>>> A = np.random.randn(2,3,4)
>>>>> B = np.random.randn(3,4)
>>>>> np.inner(A,B).shape
>>>>> # -> (2, 3, 3) instead of (2, 3)
>>>>>
>>>>> Similarly for dot product, I would like to compute for instance:
>>>>> np.sum(A[...,:,:,np.newaxis]*B[...,np.newaxis,:,:], axis=-2)
>>>>>
>>>>> But numpy.dot gives:
>>>>> In [12]: A = np.random.randn(2,3,4); B = np.random.randn(2,4,5)
>>>>> In [13]: np.dot(A,B).shape
>>>>> # -> (2, 3, 2, 5) instead of (2, 3, 5)
>>>>>
>>>>> I could use einsum for these operations, but I'm not sure whether that's
>>>>> as efficient as using some BLAS-supported(?) dot products.
>>>>>
>>>>> I couldn't find any function which could perform this kind of
>>>>> operations. NumPy's functions seem to either flatten the input arrays
>>>>> (vdot, outer) or just use the axes of the input arrays separately (dot,
>>>>> inner, tensordot).
>>>>>
>>>>> Any help?
>>>>>
>>>>> Best regards,
>>>>> Jaakko
>>>>> _______________________________________________
>>>>> NumPy-Discussion mailing list
>>>>> NumPy-Discussion@scipy.org
>>>>> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>>>>>
>>>>
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