That does not seem to work. So I have this:
a = T.fmatrix()
ctx = pygpu.init(theano.config.device)
theano.gpuarray.reg_context("mine", ctx)
a_gpu = theano.gpuarray.GpuArrayType(a.dtype, a.broadcastable, "mine")
f2 = theano.function([a_gpu], a + T.constant(2), givens={a: a_gpu})
return f1, f2
However, Theano complains about:
TypeError: Unknown parameter type: <class
'theano.gpuarray.type.GpuArrayType'>
If instead of the [a_gpu] I have [a] it complains that the givens is
overwriting an input:
RuntimeError: You are trying to replace variable '<TensorType(float32,
matrix)>' through the `givens` parameter, but this variable is an input to
your function. Replacing inputs is currently forbidden because it has no
effect. One way to modify an input `x` to a function evaluating f(x) is to
define a new input `y` and use `theano.function([y], f(x), givens={x:
g(y)})`. Another solution consists in using `theano.clone`, e.g. like this:
`theano.function([x], theano.clone(f(x), replace={x: g(x)}))`.
On Tuesday, 9 May 2017 15:19:10 UTC+1, Adam Becker wrote:
>
> In the main graph, replace the input variables with type:
> theano.gpuarray.GpuArrayType (Can be done using givens parameter of
> theano.function). Then, feed pygpu.gpuarray.GpuArray object directly to
> the compiled function. pygpu.gpuarray.asarray can be used to move numpy
> array to GPU.
>
> On Tuesday, May 9, 2017 at 5:01:42 PM UTC+8, Alexander Botev wrote:
>>
>> Actually one thing I've just realized is that to do this consistently I
>> need to have access to the underlying Theano pygpu Context. Is there anyway
>> to get that?
>>
>> On Tuesday, 9 May 2017 09:53:02 UTC+1, Alexander Botev wrote:
>>>
>>> So recently I was wondering if there is any way that after compiling a
>>> theano function, rather than taking numpy arrays / native lists / native
>>> numbers it can accept as an input something like a libgpuarray or anything
>>> else that lives on the GPU. However, I know that in the computation graph
>>> usually when you compile it there is a Transfer Op if it is on the GPU. Is
>>> there a way to avoid that transfer?
>>>
>>>
>>>
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