chrishkchris commented on pull request #733:
URL: https://github.com/apache/singa/pull/733#issuecomment-643791102
I took a look at your cudnn rnn function:
you transpose the input so that "inputs has shape of {sequence length, batch
size, feature size}"
Meanwhile, when I read the cudnn API
https://docs.nvidia.com/deeplearning/sdk/cudnn-api/index.html#cudnnRNNForwardInference
the description of x is:
_xDesc
Input. An array of seqLength fully packed tensor descriptors. Each
descriptor in the array should have three dimensions that describe the input
data format to one recurrent iteration (one descriptor per RNN time-step).
**The first dimension (batch size)** of the tensors may decrease from iteration
n to iteration n+1 but may not increase. Each tensor descriptor must have the
same second dimension (RNN input vector length, inputSize). The third dimension
of each tensor should be 1. Input data are expected to be arranged in the
column-major order so strides in xDesc should be set as follows:_
See the highlighted text in the above description. The first dimension is
batch size?
It is a bit confusing for me, so I am not sure what should be the input
shape. I suggest checking all those input output format if you don't have
further idea for debug.
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