[GitHub] [incubator-mxnet] feevos edited a comment on pull request #18403: Gluon.probability

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

feevos edited a comment on pull request #18403:
URL: https://github.com/apache/incubator-mxnet/pull/18403#issuecomment-786459914


   Hi, thank you VERY much for your contribution!!!!!
   
   I have noticed an issue, and am not sure if this is a bug or not (so as to submit a report). Currently,  in the return statement of forward I cannot have a model hybridized and return an object of a distribution. For example, in the VAE code from the mystery_demo, if I want to return the predictions as well as the distribution that I used to get the reconstruction, I get an error when my model is hybridized. 
   
   Example 
   
   ```python
   import mxnet as mx
   mx.npx.set_np()
   
   from mxnet.gluon import HybridBlock
   import mxnet.gluon.probability as mgp
   
   class SimpleNet(HybridBlock):
       def __init__(self, **kwargs):
           super().__init__(**kwargs)        
           
       
       def forward(self,input):
           mu=0.0
           scale=1.
           return input, mgp.Normal(mu,scale)
   
   net = SimpleNet()
   net.initialize()
   net.hybridize() # <=========== if hybridized code exits with error
   
   xx = mx.np.random.rand(3,5)
   out = net(xx)
   ```
   
   error: 
   
   ```python
   ---------------------------------------------------------------------------
   ValueError                                Traceback (most recent call last)
   <ipython-input-6-9e229665d8a2> in <module>
   ----> 1 out = net(xx)
   
   ~/Software/incubator-mxnet/python/mxnet/gluon/block.py in __call__(self, x, *args)
      1645 
      1646             with x.ctx:
   -> 1647                 return self._call_cached_op(x, *args)
      1648 
      1649     def forward(self, x, *args):
   
   ~/Software/incubator-mxnet/python/mxnet/gluon/block.py in _call_cached_op(self, *args)
      1263     def _call_cached_op(self, *args):
      1264         if self._cached_op is None:
   -> 1265             self._build_cache(*args)
      1266 
      1267         if self._first_forward and self._partition_if_dynamic:
   
   ~/Software/incubator-mxnet/python/mxnet/gluon/block.py in _build_cache(self, update_graph, *args)
      1136 
      1137     def _build_cache(self, *args, update_graph=True):
   -> 1138         data, out = self._get_graph(*args)
      1139         data_names = {data.name: i for i, data in enumerate(data)}
      1140         params = {p.var().name: p for p in self.collect_params().values()}
   
   ~/Software/incubator-mxnet/python/mxnet/gluon/block.py in _get_graph(self, *args)
      1132                 return self._get_graph_v1(*args)
      1133             else:  # Gluon 2 based on deferred compute mode
   -> 1134                 return self._get_graph_v2(*args)
      1135         return self._cached_graph
      1136 
   
   ~/Software/incubator-mxnet/python/mxnet/gluon/block.py in _get_graph_v2(self, *args)
      1122             with autograd.pause(), dc.context():
      1123                 out = super().__call__(*args)
   -> 1124             flatten_out, self._out_format = _flatten(out, "output")
      1125             symbol_outputs = dc.get_symbol(flatten_out, sym_cls=type(symbol_inputs[0]))
      1126             self._cached_graph = symbol_inputs, symbol_outputs
   
   ~/Software/incubator-mxnet/python/mxnet/gluon/block.py in _flatten(args, inout_str)
       151     fmts = []
       152     for i in args:
   --> 153         arg, fmt = _flatten(i, inout_str)
       154         flat.extend(arg)
       155         fmts.append(fmt)
   
   ~/Software/incubator-mxnet/python/mxnet/gluon/block.py in _flatten(args, inout_str)
       144 
       145     if not isinstance(args, (list, tuple)):
   --> 146         raise ValueError("When hybridized, the input of HybridBlock {}"
       147                          " must be (nested) list of Symbol"
       148                          " or NDArray, "
   
   ValueError: When hybridized, the input of HybridBlock output must be (nested) list of Symbol or NDArray, but got Normal(loc: size (), scale: size (), F: mxnet.ndarray, event_dim: 0) of type <class 'mxnet.gluon.probability.distributions.normal.Normal'>
   
   ```
   
   Regards


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