[GitHub] [incubator-mxnet] kohillyang edited a comment on issue #19264: Same Network can hybridize on CPU but can not hybridize on GPU.

[GitBox <gi...@apache.org>]

kohillyang edited a comment 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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