The conv2d operation doesn't support int64 (th_sampled) and it looks like
it doesn't fail gracefully with a sensible error message when the GpuCorrMM
op is used.
If you cast th_sampled to float32 it should work fine. You'll also need to
cast kernel.
On Tuesday, July 18, 2017 at 2:07:36 AM UTC-7, David Anderson wrote:
>
> Hi there!
>
> I'm implementing a convolutional operation and I'm getting an unexpected
> error when I try perform a convolution on a Binomial sampled tensor.
>
> The error is:
> RuntimeError: GpuCorrMM forward encountered an error running gemm: 5
>
> The error can be re-created with the following code (At least on my
> machine it can):
>
> import numpy as np
> import theano as th
> from theano import tensor as T
> from theano.tensor.shared_randomstreams import RandomStreams
>
> rng = np.random.RandomState()
> theano_rng = RandomStreams(rng.randint(2 ** 30))
>
> th_input = T.tensor4()
> th_filter = T.tensor4()
>
> th_sampled = theano_rng.binomial(size=th_input.shape, n=1, p=th_input)
> th_output = T.nnet.conv2d(th_sampled, th_filter)
>
> op = th.function(
> inputs=[th_input, th_filter],
> outputs=th_output
> )
>
> input_sample = np.random.rand(1, 1, 28, 28)
> kernel = np.random.rand(1, 1, 6, 6)
>
> op(input_sample, kernel)
>
>
> Interestingly, the error is NOT shown for other distribution samples, like
> theano_rng.normal(),
> which has type RandomFunction{normal}.1 instead
> of RandomFunction{binomial}.1
>
> For what its worth, my THEANO_FLAGS are as follows:
> floatX=float64,device=cuda,nvcc.flags=-D_FORCE_INLINES,exception_verbosity
> =high
>
> The rest of the stack trace is as follows:
> Traceback (most recent call last):
> File "tmp2.py", line 23, in <module>
> op(input_sample, kernel)
> File
> "/home/dave/miniconda2/lib/python2.7/site-packages/theano/compile/function_module.py",
>
> line 898, in __call__
> storage_map=getattr(self.fn, 'storage_map', None))
> File
> "/home/dave/miniconda2/lib/python2.7/site-packages/theano/gof/link.py",
> line 325, in raise_with_op
> reraise(exc_type, exc_value, exc_trace)
> File
> "/home/dave/miniconda2/lib/python2.7/site-packages/theano/compile/function_module.py",
>
> line 884, in __call__
> self.fn() if output_subset is None else\
> RuntimeError: GpuCorrMM forward encountered an error running gemm: 5
> Apply node that caused the error: GpuCorrMM{valid, (1, 1), (1,
> 1)}(GpuContiguous.0, GpuContiguous.0)
> Toposort index: 11
> Inputs types: [GpuArrayType<None>(int64, (False, False, False, False)),
> GpuArrayType<None>(float64, (False, False, False, False))]
> Inputs shapes: [(1, 1, 28, 28), (1, 1, 6, 6)]
> Inputs strides: [(6272, 6272, 224, 8), (288, 288, 48, 8)]
> Inputs values: ['not shown', 'not shown']
> Inputs type_num: [7, 12]
> Outputs clients: [[HostFromGpu(gpuarray)(GpuCorrMM{valid, (1, 1), (1,
> 1)}.0)]]
>
> Debugprint of the apply node:
> GpuCorrMM{valid, (1, 1), (1, 1)} [id A] <GpuArrayType<None>(int64, (False,
> False, False, False))> ''
> |GpuContiguous [id B] <GpuArrayType<None>(int64, (False, False, False,
> False))> ''
> | |GpuFromHost<None> [id C] <GpuArrayType<None>(int64, (False, False,
> False, False))> ''
> | |RandomFunction{binomial}.1 [id D] <TensorType(int64, 4D)> ''
> | |<RandomStateType> [id E] <RandomStateType>
> | |MakeVector{dtype='int64'} [id F] <TensorType(int64, vector)> ''
> | | |Shape_i{0} [id G] <TensorType(int64, scalar)> ''
> | | | |<TensorType(float64, 4D)> [id H] <TensorType(float64, 4D)>
> | | |Shape_i{1} [id I] <TensorType(int64, scalar)> ''
> | | | |<TensorType(float64, 4D)> [id H] <TensorType(float64, 4D)>
> | | |Shape_i{2} [id J] <TensorType(int64, scalar)> ''
> | | | |<TensorType(float64, 4D)> [id H] <TensorType(float64, 4D)>
> | | |Shape_i{3} [id K] <TensorType(int64, scalar)> ''
> | | |<TensorType(float64, 4D)> [id H] <TensorType(float64, 4D)>
> | |TensorConstant{1} [id L] <TensorType(int8, scalar)>
> | |<TensorType(float64, 4D)> [id H] <TensorType(float64, 4D)>
> |GpuContiguous [id M] <GpuArrayType<None>(float64, (False, False, False,
> False))> ''
> |GpuFromHost<None> [id N] <GpuArrayType<None>(float64, (False, False,
> False, False))> ''
> |Subtensor{::, ::, ::int64, ::int64} [id O] <TensorType(float64, 4D)>
> ''
> |<TensorType(float64, 4D)> [id P] <TensorType(float64, 4D)>
> |Constant{-1} [id Q] <int64>
> |Constant{-1} [id Q] <int64>
>
> Storage map footprint:
> - GpuContiguous.0, Shape: (1, 1, 28, 28), ElemSize: 8 Byte(s), TotalSize:
> 6272 Byte(s)
> - <TensorType(float64, 4D)>, Input, Shape: (1, 1, 28, 28), ElemSize: 8
> Byte(s), TotalSize: 6272 Byte(s)
> - GpuContiguous.0, Shape: (1, 1, 6, 6), ElemSize: 8 Byte(s), TotalSize:
> 288 Byte(s)
> - <TensorType(float64, 4D)>, Input, Shape: (1, 1, 6, 6), ElemSize: 8
> Byte(s), TotalSize: 288 Byte(s)
> - Constant{-1}, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
> - TensorConstant{1}, Shape: (), ElemSize: 1 Byte(s), TotalSize: 1.0
> Byte(s)
> TotalSize: 13129.0 Byte(s) 0.000 GB
> TotalSize inputs: 6569.0 Byte(s) 0.000 GB
>
> Am I doing something wrong here? Any idea how I might get around this? It
> works if i split up the code into two functions, one that does the sampling
> and returns out the tensor, and then one that takes in this result and does
> the convolution. But it'd be really stupid to pass the value back out to
> the CPU RAM from the GPU RAM just to get around this...
>
> Any advice would be hugely appreciated!
>
> Cheers,
> Dave
>
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