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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