[GitHub] [incubator-mxnet] arcadiaphy opened a new issue #17785: Inconsistency between HybridBlock and Block

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

arcadiaphy opened a new issue #17785: Inconsistency between HybridBlock and Block
URL: https://github.com/apache/incubator-mxnet/issues/17785
 
 
   Following #16279, another example of inconsistency between HybridBlock and Block:
   
   ```
   import mxnet as mx
   from mxnet.gluon import HybridBlock
   
   class Foo(HybridBlock):
       def hybrid_forward(self, F, a, b):
           return a + b
   
   
   b1 = Foo(prefix='non_hybrid')
   b2 = Foo(prefix='hybrid')
   b2.hybridize()
   
   print(b1(mx.nd.ones((10,)), mx.nd.ones((1,))))
   print(b2(mx.nd.ones((10,)), mx.nd.ones((1,))))
   ```
   
   MXNetError is triggered for non-hybridized case:
   
   ```
   MXNetError: MXNetError: Error in operator hybridized_plus0: [14:24:15] src/operator/numpy/linalg/../../tensor/../elemwise_op_common.h:135: Check failed: assign(&dattr, vec.at(i)): Incompatible attr in node hybridized_plus0 at 1-th input: expected [10], got [1]
   ```
   
   I think it's because for symbol, `elemwise_add` is used for `__add__`, while for ndarray broadcasting is considered in `__add__` considering input shape.
   
   Can we add broadcasting in symbol too and avoid the need to differentiate between `elemwise_add` and `broadcast_add`?
   
   Note that the same problem exists in subtract, multiply and divide operation, too.

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