Zha0q1 opened a new issue #19265:
URL: https://github.com/apache/incubator-mxnet/issues/19265
A customer is experiencing seg fault when feeding in a large input to MKL
LSTM. I have reduced the code to this:
```
import mxnet as mx
from mxnet import gluon, nd, autograd
from mxnet.gluon import nn, rnn, Trainer
hidden_size = 30
num_embed = 100
vocab_size = 13028#len(vocab.token_to_idx.keys())
inp = nd.random.uniform(0, vocab_size, (16758,500))
print(inp)
context = mx.cpu()
model = nn.Sequential()
model.add(nn.Embedding(vocab_size, num_embed), # Embedding layer
rnn.LSTM(hidden_size, num_layers=1,bidirectional=True), #
Recurrent layer ,bidirectional=True
nn.Dense(3)) # Output layer
model.collect_params().initialize(mx.init.Xavier(), ctx=context)
val_predictions = model(inp)
nd.waitall()
print(val_predictions)
```
I think this is some sort of out of memory issue because if we shrink the
input (first dim of `inp`) then there will not be a seg fault, but still, shall
we add some error message here so that users will be notified to reduce the
input size?
I also noticed the same input will run fine with `export
MXNET_USE_MKLDNN_RNN=0` but that is 3x slower than the mkldnn implementation
@PatricZhao
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