classicsong opened a new issue #16560: It is easy to crash MXNet when tensor goes larger URL: https://github.com/apache/incubator-mxnet/issues/16560 ## Description When I use large tensor, it is easy to crash the MXNet kernel. Using following python code to reproduce: ``` >>> import mxnet.ndarray as nd >>> a = nd.random.randn(4, 256, 1, 100, 100) >>> b = nd.broadcast_axis(a, axis=2, size=256) >>> b.size 2621440000 >>> b.asnumpy() CRASH HERE ``` The error looks like an int32 overflow on shape.size. Any easy way to fix this out? The only way I found out is to compile MXNet with USE_INT64_TENSOR_SIZE = ON, which is slower than the default one. ## Environment info (Required) mxnet 1.5.1 (pip3 install) Package used (Python/R/Scala/Julia): Python ## Error Message: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.5/dist-packages/mxnet/ndarray/ndarray.py", line 1996, in asnumpy ctypes.c_size_t(data.size))) File "/usr/local/lib/python3.5/dist-packages/mxnet/base.py", line 253, in check_call raise MXNetError(py_str(_LIB.MXGetLastError())) mxnet.base.MXNetError: [07:26:09] include/mxnet/././tensor_blob.h:290: Check failed: this->shape_.Size() == static_cast<size_t>(shape.Size()) (2621440000 vs. 18446744072036024320) : TBlob.get_with_shape: new and old shape do not match total elements ```
---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
