waytrue17 opened a new issue #20691:
URL: https://github.com/apache/incubator-mxnet/issues/20691
## Description
When a model contains `concat`, `slice_axis` and `broadcast_lesser_equal`,
it fails the backward pass with segmentation fault. It only failed with
non-hybridized model. Tested on MXNet 1.8.0
### Error Message
```
Thread 1 "python" received signal SIGSEGV, Segmentation fault.
0x00007ffdd132e401 in mxnet::imperative::SetDependency(nnvm::NodeAttrs
const&, mxnet::Context const&, std::vector<mxnet::NDArray*,
std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::NDArray*,
std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::engine::Var*,
std::allocator<mxnet::engine::Var*> >*, std::vector<mxnet::engine::Var*,
std::allocator<mxnet::engine::Var*> >*, std::vector<mxnet::Resource,
std::allocator<mxnet::Resource> >*, std::vector<unsigned int,
std::allocator<unsigned int> >*, mxnet::DispatchMode) ()
from /home/ubuntu/workspace/mxnet/python/mxnet/../../build/libmxnet.so
(gdb) bt
#0 0x00007ffdd132e401 in mxnet::imperative::SetDependency(nnvm::NodeAttrs
const&, mxnet::Context const&, std::vector<mxnet::NDArray*,
std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::NDArray*,
std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::engine::Var*,
std::allocator<mxnet::engine::Var*> >*, std::vector<mxnet::engine::Var*,
std::allocator<mxnet::engine::Var*> >*, std::vector<mxnet::Resource,
std::allocator<mxnet::Resource> >*, std::vector<unsigned int,
std::allocator<unsigned int> >*, mxnet::DispatchMode) ()
at /home/ubuntu/workspace/mxnet/python/mxnet/../../build/libmxnet.so
#1 0x00007ffdd1317050 in mxnet::Imperative::InvokeOp(mxnet::Context const&,
nnvm::NodeAttrs const&, std::vector<mxnet::NDArray*,
std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::NDArray*,
std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::OpReqType,
std::allocator<mxnet::OpReqType> > const&, mxnet::DispatchMode,
mxnet::OpStatePtr) ()
at /home/ubuntu/workspace/mxnet/python/mxnet/../../build/libmxnet.so
#2 0x00007ffdd132fae7 in std::_Function_handler<void (mxnet::OpStatePtr
const&), mxnet::imperative::RunGraph(bool, nnvm::IndexedGraph const&,
std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, unsigned
long, unsigned long, std::vector<mxnet::OpReqType,
std::allocator<mxnet::OpReqType> >&&, std::vector<unsigned int,
std::allocator<unsigned int> >&&, std::vector<mxnet::OpStatePtr,
std::allocator<mxnet::OpStatePtr> >*, std::vector<mxnet::DispatchMode,
std::allocator<mxnet::DispatchMode> > const&, bool, std::vector<mxnet::TShape,
std::allocator<mxnet::TShape> >*, std::function<void (char const*, char const*,
void*)> const&, bool)::{lambda(mxnet::OpStatePtr
const&)#1}>::_M_invoke(std::_Any_data const&, mxnet::OpStatePtr const&) ()
at /home/ubuntu/workspace/mxnet/python/mxnet/../../build/libmxnet.so
#3 0x00007ffdd132fe62 in (anonymous
namespace)::InvokeOperator(nnvm::IndexedGraph const&, int, bool,
std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&,
mxnet::Context, std::vector<mxnet::OpStatePtr,
std::allocator<mxnet::OpStatePtr> >*, std::vector<mxnet::NDArray*,
std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::NDArray*,
std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::OpReqType,
std::allocator<mxnet::OpReqType> >*, std::vector<unsigned int,
std::allocator<unsigned int> >*, std::function<void (mxnet::OpStatePtr
const&)>) () at
/home/ubuntu/workspace/mxnet/python/mxnet/../../build/libmxnet.so
#4 0x00007ffdd1331227 in mxnet::imperative::RunGraph(bool,
nnvm::IndexedGraph const&, std::vector<mxnet::NDArray*,
std::allocator<mxnet::NDArray*> > const&, unsigned long, unsigned long,
std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> >&&,
std::vector<unsigned int, std::allocator<unsigned int> >&&,
std::vector<mxnet::OpStatePtr, std::allocator<mxnet::OpStatePtr> >*,
std::vector<mxnet::DispatchMode, std::allocator<mxnet::DispatchMode> > const&,
bool, std::vector<mxnet::TShape, std::allocator<mxnet::TShape> >*,
std::function<void (char const*, char const*, void*)> const&, bool) () at
/home/ubuntu/workspace/mxnet/python/mxnet/../../build/libmxnet.so
#5 0x00007ffdd131ebd3 in
mxnet::Imperative::Backward(std::vector<mxnet::NDArray*,
std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::NDArray*,
std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::NDArray*,
std::allocator<mxnet::NDArray*> > const&, bool, bool, bool) () at
/home/ubuntu/workspace/mxnet/python/mxnet/../../build/libmxnet.so
#6 0x00007ffdd11a1e3f in MXAutogradBackwardEx () at
/home/ubuntu/workspace/mxnet/python/mxnet/../../build/libmxnet.so
```
## To Reproduce
```
import mxnet as mx
from mxnet.gluon import HybridBlock
class _TestBlock(HybridBlock):
def __init__(self):
super(_TestBlock, self).__init__()
def hybrid_forward(self, F, x, y):
x = F.concat(x, x, dim=-1)
x = F.slice_axis(x, axis=-1, begin=0, end=-1)
return F.broadcast_lesser_equal(x, y)
if __name__ == '__main__':
block = _TestBlock()
# block.hybridize() # hybridize works
x = mx.nd.ones([32, 500, 4])
y = mx.nd.ones([32, 500, 1])
x.attach_grad()
y.attach_grad()
with mx.autograd.record():
result = block(x, y)
# block.export('partitioned')
result.backward()
print(result)
```
### Steps to reproduce
(Paste the commands you ran that produced the error.)
1. pip3 install mxnet==1.8.0.post0
2. run the script
## What have you tried to solve it?
## Environment
***We recommend using our script for collecting the diagnostic information
with the following command***
`curl --retry 10 -s
https://raw.githubusercontent.com/apache/incubator-mxnet/master/tools/diagnose.py
| python3`
<details>
<summary>Environment Information</summary>
```
# Paste the diagnose.py command output here
```
</details>
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