##Enviroment:
python2.7/windows7_64bit
mxnet-1.2.0
Nvidia Driver Version 397.31
## Error Message:
[15:41:28] G:\deeplearn\mxnet\dmlc-core\include\dmlc/logging.h:308: [15:41:28]
g:\deeplearn\mxnet\mshadow\mshadow\./stream_gpu-inl.h:62: Check failed: e ==
cudaSuccess CUDA: unknown error
[15:41:28] G:\deeplearn\mxnet\dmlc-core\include\dmlc/logging.h:308: [15:41:28]
g:\deeplearn\mxnet\src\engine\./threaded_engine.h:370: [15:41:28]
g:\deeplearn\mxnet\mshadow\mshadow\./stream_gpu-inl.h:62: Check failed: e ==
cudaSuccess CUDA: unknown error
## Minimum reproducible example
```python
import mxnet as mx
import numpy as np
ctx=mx.gpu(0)
alphabet_size=3000
in_var = mx.sym.Variable('data')
labels_var = mx.sym.Variable('label')
ctc = mx.sym.contrib.ctc_loss(in_var, labels_var)
loss = mx.symbol.MakeLoss(ctc)
arg_shapes,_,_ = loss.infer_shape(data=(6,2,alphabet_size), label=(2,3))
arg_array = [mx.nd.normal(shape=shape, ctx=ctx) for shape in arg_shapes]
exe = loss.bind(ctx=ctx, args=arg_array)
exe.forward(is_train=True)
exe.backward()
outTest = exe.outputs[0]
print '%s'%(outTest.asnumpy())
```
when `alphabet_size=200` the code works fine, when `alphabet_size=3000` (for
chinese ocr task) the code crashes.
[ Full content available at:
https://github.com/apache/incubator-mxnet/issues/12493 ]
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