Basic cuDNN v3 support (update)

[shelhamer]

This is the same as #2737 except for

• Restoring the pooling layer fallback for max + argmax output when the layer is configured to have two tops. The padding is dropped since it is now supported by cuDNN pooling.
• Clearing the warnings for unused variables in cuDNNConvolutionLayer::Forward_gpu() now that algo and workspace are determined in Reshape().

Thanks @slayton58 for the integration.

[slayton58]

@shelhamer Regarding the test failure for Groups -- it seems like this->weight_offset_ for the cuDNN routines is getting set incorrectly / not set (either way, it's wrong!) This seems to have been introduced in 9d8206e

Setting it back explicitly to:
this->weight_offset_ = (this->num_output_ / this->group_) * (this->channels_ / this->group_) * kernel_h * kernel_w;

in CuDNNConvolutionLayer::Setup seems to fix the issue, not sure if there's a better way - let me know and I'll update the PR

[ronghanghu]

Great 👍

[shelhamer]

cuDNN v3 is not itself backward compatible with v2, so adopting v3 in this PR does deprecate v2. We plan to follow the latest cuDNN version in master but keep compatability as the cuDNN interface itself allows.

[ronghanghu]

@shelhamer @slayton58 I am a bit confused here... Why do we need to zero out the diff? It confuses me as parameter gradients should be accumulated.

[shelhamer]

This is definitely a bug -- thanks for the fix in #3254.