kohillyang commented on issue #19264:
URL:
https://github.com/apache/incubator-mxnet/issues/19264#issuecomment-703104893
There is a simpler reproduce case:
```python
import os
os.environ["DMLC_LOG_STACK_TRACE_DEPTH"]="10"
# os.environ["MXNET_USE_FUSION"]="0"
import mxnet as mx
import mxnet.gluon as gluon
class HighResolutionModule(gluon.nn.HybridBlock):
def __init__(self):
super(HighResolutionModule, self).__init__()
self.relu = mx.gluon.nn.Activation("relu")
self.fff = mx.gluon.nn.Conv2D(in_channels=64, channels=64,
kernel_size=3, padding=1)
self.fff1 = mx.gluon.nn.Conv2D(in_channels=64, channels=64,
kernel_size=3, padding=1, strides=1)
def hybrid_forward(self, F, x0, x1):
x = [x0, x1]
print(x)
y0 = (x[0] + self.fff(x[1])).relu()
y1 = (self.fff1(x[0]) + x[1]).relu()
return y0 + y1
if __name__ == '__main__':
ctx = mx.gpu()
model = HighResolutionModule()
model.initialize()
model.collect_params().reset_ctx(ctx)
model.hybridize()
y_hat = model(mx.nd.random.randn(1, 64, 56, 56, ctx=ctx),
mx.nd.random.randn(1, 64, 56, 56, ctx=ctx))
print(y_hat.shape)
```
The input is a tuple instead of a list, so this bug is not caused by using a
list as inputs. And if removing relu in the above codes, the program can exit
with a segmentation fault。 Furthermore, If set env "MXNET_USE_FUSION" to 0, the
program can exit normally. Since as far as I know, MXNET_USE_FUSION would fuse
relu into the last layer through an in-place operation.
I think this bug is caused by the fusion process.
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