All tests in test_nnet.py pass with CUDA.
Only fp32 tests in test_nnet.py pass with OpenCL. GpuFromHost doesn't work with fp16 or fp64.
Larger work item size doesn't improve performance.
Add 2 local_barrier(), it's strange that AMD card doesn't need these local_barrier(), but they are necessary for NVIDIA cards.

max work item size on my ocl device(not too old) is 256

outdim -> ndim leftover and the same to is_flat

Wrap Op params for theano.tensor.nnet.corr.BaseCorrMM and sub-classes.

Fix the opt crash regression introduced by gh-5950.

Add Cudnn version to Theano cache key to the handle_type and _RNNSplitParams. Theano was trying to reload wrong cmodule and crash. For order of code, I needed to move much code around, but that moved coded didn't changed

Wrap Op params for theano.tensor.nnet.corr3d.BaseCorr3dMM and subclasses.

Add support for cudnn v6 dilated convolution.

 - direction (enum list)
 - dH, dW, dD: int64
 - dilH, dilW, dilD: int64
 - padH, padW, padD: int64

This influences all sub-classes:
 - theano.tensor.nnet.corr3d.Corr3dMM
 - theano.tensor.nnet.corr3d.Corr3dMM_gradWeights
 - theano.tensor.nnet.corr3d.Corr3dMM_gradInputs

Test for fix in libgpuarray, plus fix in batched_dot opt

Fix jenkins tests

Give a good error when people use device=gpu

Update nnet.txt

Fixes mismatch between the formula in implemented function in the docs

Partially factorize GpuKernelBase.get_params() and configure it to use ParamsType.

remove theano/compat/six.py

Allow c_support_code() to support list of strings and update ParamsType

Closes: #5761

Update ParamsType.c_code_cache_version().