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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/05/12 07:22:07 UTC
[GitHub] wkcn opened a new issue #10914: cudnn_softmax_activation error in
MXNet 1.2.0
wkcn opened a new issue #10914: cudnn_softmax_activation error in MXNet 1.2.0
URL: https://github.com/apache/incubator-mxnet/issues/10914
## Description
Hi.
It seems that SoftmaxActivation(cudnn version) has a problem in MXNet 1.2.0.
It will cause the error when using SoftmaxActivation.
In the issue(#9823), chinakook considers the bug produced after [PR#9677](https://github.com/apache/incubator-mxnet/pull/9677)
I found the function of `mx.nd.Softmax` and `mx.nd.SoftmaxActivation` seems to be the same. The difference is that `Softmax` uses pure CUDA, and `SoftmaxActivation` uses CUDNN.
Is it necessary to merge them?
## Environment info (Required)
```
----------Python Info----------
('Version :', '2.7.12')
('Compiler :', 'GCC 5.4.0 20160609')
('Build :', ('default', 'Dec 4 2017 14:50:18'))
('Arch :', ('64bit', 'ELF'))
------------Pip Info-----------
('Version :', '10.0.1')
('Directory :', '/usr/local/lib/python2.7/dist-packages/pip')
----------MXNet Info-----------
('Version :', '1.2.0')
('Directory :', '/usr/local/lib/python2.7/dist-packages/mxnet')
('Commit Hash :', '5088ca9a65641ddf905b60deae00fa6006f5e431')
----------System Info----------
('Platform :', 'Linux-4.13.9-coreos-x86_64-with-Ubuntu-16.04-xenial')
('system :', 'Linux')
('release :', '4.13.9-coreos')
('version :', '#1 SMP Thu Oct 26 03:21:00 UTC 2017')
```
Package used (Python/R/Scala/Julia):
Python
## Build info (Required if built from source)
Installed by pip:
pip install mxnet-cu80 --pre
## Error Message:
mxnet.base.MXNetError: [07:07:42] src/operator/nn/./cudnn/cudnn_softmax_activation-inl.h:154: Check failed: e == CUDNN_STATUS_SUCCESS (3 vs. 0) cuDNN: CUDNN_STATUS_BAD_PARAM
## Minimum reproducible example
```python
import mxnet as mx
ctx = mx.gpu(0)
a = mx.nd.array([[1,2,3]], ctx = ctx)
a.attach_grad()
with mx.autograd.record():
y = mx.nd.SoftmaxActivation(data = a)
y.backward()
mx.nd.waitall()
print (a.grad.asnumpy())
```
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