line 5 is supposed to be:
a_gpu = gpu_fmatrix()
On Thursday, May 11, 2017 at 12:09:14 AM UTC+8, Alexander Botev wrote:
>
> the host_to_gpu does not like it:
>
> AttributeError: 'GpuArrayType' object has no attribute 'type'
>>
>
>
> On Wednesday, 10 May 2017 16:43:55 UTC+1, Adam Becker wrote:
>>
>> Correct typo: T.fmatrix -> gpu_fmatrix on line 5. I'm in need of more
>> coffee ...
>>
>> On Wednesday, May 10, 2017 at 11:13:48 PM UTC+8, Alexander Botev wrote:
>>>
>>> Again with Theano "0.9.0.dev-ca213aa43e78ab3fb074a7c679907ea4d5412ed1" I
>>> get:
>>>
>>> Traceback (most recent call last):
>>>> File "tt.py", line 9, in <module>
>>>> a = theano.gpuarray.host_from_gpu(a_gpu)
>>>> File
>>>> "/share/apps/barber/system/lib/python3.6/site-packages/theano/gof/op.py",
>>>> line 615, in __call__
>>>> node = self.make_node(*inputs, **kwargs)
>>>> File
>>>> "/share/apps/barber/system/lib/python3.6/site-packages/theano/gpuarray/basic_ops.py",
>>>>
>>>> line 549, in make_node
>>>> raise TypeError(x)
>>>> TypeError: <TensorType(float32, matrix)>
>>>>
>>>
>>>
>>> On Wednesday, 10 May 2017 04:11:46 UTC+1, Adam Becker wrote:
>>>>
>>>> Hmm ... my bad. I thought givens would work.
>>>>
>>>> Anyway, this trick would work:
>>>>
>>>> import theano
>>>> from theano.gpuarray.basic_ops import infer_context_name
>>>>
>>>> gpu_fmatrix = theano.gpuarray.GpuArrayType(dtype='float32',
>>>> broadcastable=(False,False))
>>>> a_gpu = T.fmatrix()
>>>>
>>>> # insert transfer
>>>> a = theano.gpuarray.host_from_gpu(a_gpu)
>>>> # define graph as usual
>>>> b = a + 2.
>>>>
>>>> # compiles function, but takes GpdevuArray as input
>>>> fn = theano.function([a_gpu], b)
>>>> theano.printing.debugprint(fn)
>>>>
>>>> # compiles function that takes GpuArray as input/output
>>>> ctx_name = infer_context_name(a_gpu)
>>>> b_gpu = theano.gpuarray.as_gpuarray_variable(b, ctx_name)
>>>> fn2 = theano.function([a_gpu], b_gpu)
>>>> theano.printing.debugprint(fn2)
>>>>
>>>>
>>>> Console output:
>>>>
>>>> HostFromGpu(gpuarray) [id A] '' 1
>>>> |GpuElemwise{add,no_inplace} [id B] '' 0
>>>> |GpuArrayConstant{[[ 2.]]} [id C]
>>>> |<GpuArrayType<None>(float32, matrix)> [id D]
>>>>
>>>> GpuElemwise{add,no_inplace} [id A] '' 0
>>>> |GpuArrayConstant{[[ 2.]]} [id B]
>>>> |<GpuArrayType<None>(float32, matrix)> [id C]
>>>>
>>>> The above works because the optimizer can remove redundant GPU -> CPU
>>>> -> GPU transfers. The downside is the above approach doesn't work with
>>>> config optimizer=None
>>>>
>>>> On Wednesday, May 10, 2017 at 5:02:20 AM UTC+8, Alexander Botev wrote:
>>>>>
>>>>> 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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