[GitHub] [incubator-mxnet] bgawrych opened a new issue #19446: net.optimize_for doesn't work with numpy semantics

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

bgawrych opened a new issue #19446:
URL: https://github.com/apache/incubator-mxnet/issues/19446


   ## Description
   Forward pass after calling optimize_for with specific backend doesn't work. I'm not sure what this error mean, but found a way to overcome this (ugly way :))
   Problem occurs on master and 1.x branches
   
   ### Error Message
   ```
   Traceback (most recent call last):
     File "../d.py", line 23, in <module>
       print(net(a, b))
     File "/home/bgawrych/Desktop/mxnet/python/mxnet/gluon/block.py", line 1407, in __call__
       return super().__call__(x, *args)
     File "/home/bgawrych/Desktop/mxnet/python/mxnet/gluon/block.py", line 716, in __call__
       _check_all_np_ndarrays(out)
     File "/home/bgawrych/Desktop/mxnet/python/mxnet/gluon/utils.py", line 480, in _check_all_np_ndarrays
       raise TypeError("Block's output ndarrays/symbols must be of type `mxnet.numpy.ndarray`"
   TypeError: Block's output ndarrays/symbols must be of type `mxnet.numpy.ndarray` or `mxnet.symbol.numpy._Symbol`, while got output type <class 'mxnet.ndarray.ndarray.NDArray'>
   ```
   
   ## To Reproduce
   ```
   import mxnet as mx
   from mxnet.gluon import HybridBlock
   
   mx.npx.set_np()
   
   class TestBlock(HybridBlock):
       def __init__(self):
           super(TestBlock, self).__init__()
           self.d = mx.gluon.nn.Dense(1)
       def hybrid_forward(self, F, a, b, *args):
           res = self.d.hybrid_forward(F, a, b)
           return res
   
   a = mx.np.random.uniform(low=-1, high=1, size=(1,1))
   b = mx.np.random.uniform(low=-1, high=1, size=(1,1))
   
   net = TestBlock()
   net.initialize()
   net.hybridize()
   
   print(net(a, b))
   net.optimize_for(a, b, backend="MKLDNN")
   #print(net(a, b)) # <---- this line doesn't work now - we need to reload symbol with JSON
   inputs, sym = net._cached_graph
   sym = mx.sym.np._symbol.load_json(sym.tojson())
   x = mx.gluon.SymbolBlock(sym, [mx.sym.var('data0'), mx.sym.var('data1')], net.collect_params())
   
   print(x(a, b))
   ```
   
   ## What have you tried to solve it?
   
   1. Add `ConvertShapeAttrToNumPyCompatible(&g);` in `MXOptimizeForBackend`- doesn't help
   
   
   @samskalicky maybe you will be able to help
   


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[GitHub] [incubator-mxnet] samskalicky closed issue #19446: net.optimize_for doesn't work with numpy semantics

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

samskalicky closed issue #19446:
URL: https://github.com/apache/incubator-mxnet/issues/19446


   


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