[Cython] gsoc: array expressions
Dag Sverre Seljebotn
d.s.seljebotn at astro.uio.no
Mon May 21 13:14:26 CEST 2012
On 05/21/2012 12:56 PM, mark florisson wrote:
> On 21 May 2012 11:34, Dag Sverre Seljebotn<d.s.seljebotn at astro.uio.no> wrote:
>> On 05/20/2012 04:03 PM, mark florisson wrote:
>>> For my gsoc we already have some simple initial ideas, i.e.
>>> elementwise vector expressions (a + b with a and b arrays with
>>> arbitrary rank), I don't think these need any discussion. However,
>>> there are a lot of things that haven't been formally discussed on the
>>> mailing list, so here goes.
>>> Frédéric, I am CCing you since you expressed interest on the numpy
>>> mailing list, and I think your insights as a Theano developer can be
>>> very helpful in this discussion.
>>> User Interface
>>> Besides simple array expressions for dense arrays I would like a
>>> mechanism for "custom ufuncs", although to a different extent to what
>>> Numpy or Numba provide. There are several ways in which we could want
>>> them, e.g. as typed functions (cdef, or external C) functions, as
>>> lambas or Python functions in the same module, or as general objects
>>> (e.g. functions Cython doesn't know about).
>>> To achieve maximum efficiency it will likely be good to allow sharing
>>> these functions in .pxd files. We have 'cdef inline' functions, but I
>>> would prefer annotated def functions where the parameters are
>>> specialized on demand, e.g.
>>> def add(a, b): # elemental functions can have any number of arguments
>>> and operate on any compatible dtype
>>> return a + b
>>> When calling cdef functions or elemental functions with memoryview
>>> arguments, the arguments perform a (broadcasted) elementwise
>>> operation. Alternatively, we can have a parallel.elementwise function
>>> which maps the function elementwise, which would also work for object
>>> callables. I prefer the former, since I think it will read much
>>> Secondly, we can have a reduce function (and maybe a scan function),
>>> that reduce (respectively scan) in a specified axis or number of axes.
>>> parallel.reduce(add, a, b, axis=(0, 2))
>>> where the default for axis is "all axes". As for the default value,
>>> this could be perhaps optionally provided to the elemental decorator.
>>> Otherwise, the reducer will have to get the default values from each
>>> dimension that is reduced in, and then skip those values when
>>> reducing. (Of course, the reducer function must be associate and
>>> commutative). Also, a lambda could be passed in instead of an
>> Only associative, right?
>> Sounds good to me.
> Ah, I guess, because we can reduce thead-local results manually in a
> specified (elementwise) order (I was thinking of generating OpenMP
> annotated loops, that can be enabled/disabled at the C level, with an
> 'if' clause with a sensible lower bound of iterations required).
>>> elementwise or typed cdef function.
>>> Finally, we would have a parallel.nditer/ndenumerate/nditerate
>>> function, which would iterate over N memoryviews, and provide a
>>> sensible memory access pattern (like numpy.nditer). I'm not sure if it
>>> should provide only the indices, or also the values. e.g. an inplace
>>> elementwise add would read as follows:
>>> for i, j, k in parallel.nditerate(A, B):
>>> A[i, j, k] += B[i, j, k]
>> I think this sounds good; I guess don't see a particular reason for
>> "ndenumerate", I think code like the above is clearer.
>> It's perhaps worth at least thinking about how to support "for idx in ...",
>> "A[idx, Ellipsis] = ...", i.e. arbitrary number of dimensions. Not in
>> first iteration though.
> Yeah, definitely.
>> Putting it in "parallel" is nice because prange already have out-of-order
>> semantics.... But of course, there are performance benefits even within a
>> single thread because of the out-of-order aspect. This should at least be a
>> big NOTE box in the documentation.
>>> Frédéric, feel free to correct me at any point here :)
>>> As for the implementation, I think it will be a good idea to at least
>>> reuse (optionally through command line flags) Theano's optimization
>>> pipeline. I think it would be reasonably easy to build a Theano
>>> expression graph (after fusing the expressions in Cython first), run
>>> the Theano optimizations on that and map back to a Cython AST.
>>> Optionally, we could store a pickled graph representation (or even
>>> compiled theano function?), and provide it as an optional
>>> specialization at runtime (but mapping back correctly to memoryviews
>>> where needed, etc). As Numba matures, a numba runtime specialization
>>> could optionally be provided.
>> Can you enlighten us a bit about what Theano's optimizations involve? You
>> mention doing the iteration specializations yourself below, and also the
>> Is it just "scalar" optimizations of the form "x**3 -> x * x * x" and
>> numeric stabilization like "log(1 + x) -> log1p(x)" that would be provided
>> by Theano?
> Yes, it does those kind of things, and it also eliminates common
> subexpressions, and it transforms certain expressions to BLAS/LAPACK
> functionality. I'm not sure we want that specifically. I'm thinking it
> might be more fruitful to start off with a theano-only specialization,
> and implement low-level code generation in Theano, and use that from
> Cython by either directly dumping in the code, or deferring that to
> Theano. At this point I'm not entirely sure.
Still, if this is all Theano provides, I question structuring the
project around reusing Theano. It's the sort of things that are
nice-to-have but not fundamental (like memory access patterns).
Put another way, it sounds like Theano could easily be made an optional
Another question is of course whether it is better to work on Theano to
implement tiling etc. for the CPU (and even compile all the
specializations and select between them).
You could perhaps even have Theano use PEP 3118 rather than NumPy too.
I guess I should subscribe to the Theano list.
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