[GitHub] [incubator-mxnet] anko-intel commented on a diff in pull request #21046: [master] Node elimination graph pass

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

anko-intel commented on code in PR #21046:
URL: https://github.com/apache/incubator-mxnet/pull/21046#discussion_r902548998


##########
tests/python/dnnl/subgraphs/test_amp_subgraph.py:
##########
@@ -172,6 +173,7 @@ def test_amp_transformers():
 
 @mx.util.use_np
 def test_amp_concat():
+  os.environ["MXNET_NODE_ELIMINATION"] = "0"

Review Comment:
   To avoid disabling the lemination pass you can modyfy the test to the following one:
   
   ```
   @mx.util.use_np
   def test_amp_concat():
   
     class TestNet(nn.HybridBlock):
       def __init__(self):
         super(TestNet, self).__init__()
         self.fc1 = nn.Dense(16)
         self.fc2 = nn.Dense(16)
         self.fc2.share_parameters(self.fc1.collect_params())
   
       def forward(self, x):
         xx =  mx.nd.elemwise_add(x.as_nd_ndarray(), x.as_nd_ndarray()).as_np_ndarray() 
         x1 = self.fc1(xx)
         x2 = self.fc2(x)
         x = mx.np.concat((x1, x2), axis=1)
         return x
   
     net = TestNet()
     net.initialize()
   
     data_example = mx.np.random.uniform(-1, 1, (1, 16))
   
     exp_data = mx.symbol.Variable('data')
     exp_amp_data = mx.symbol.amp_cast(exp_data, dtype=AMP_DTYPE)
     exp_amp_data1 = exp_amp_data + exp_amp_data
     exp_weight = mx.symbol.Variable('weight')
     exp_bias = mx.symbol.Variable('bias')
     exp_fc1 = mx.symbol.FullyConnected(exp_amp_data1, exp_weight, exp_bias, num_hidden=1)
     exp_fc = mx.symbol.FullyConnected(exp_amp_data, exp_weight, exp_bias, num_hidden=1)
              
     exp_sym = mx.symbol.Concat(exp_fc1, exp_fc)
     exp_sym = mx.symbol.amp_cast(exp_sym, dtype='float32')
     exp_sym = exp_sym.get_backend_symbol(SG_PASS_NAME)
     check_amp_fuse(net, [data_example], exp_sym)
   
     amp_weight = mx.symbol.amp_cast(exp_weight, dtype=AMP_DTYPE)
     amp_bias = mx.symbol.amp_cast(exp_bias, dtype=AMP_DTYPE)
     exp_data1 = exp_data + exp_data
     exp_amp_data1 = mx.symbol.amp_cast(exp_data1, dtype=AMP_DTYPE)
     exp_fc1 = mx.symbol.FullyConnected(exp_amp_data1, amp_weight, amp_bias, num_hidden=1)
     exp_fc = mx.symbol.FullyConnected(exp_data, exp_weight, exp_bias, num_hidden=1)
     exp_sym = mx.symbol.Concat(exp_fc1, exp_fc)
     exp_sym = exp_sym.get_backend_symbol(SG_PASS_NAME)
     check_amp_fuse(net, [data_example], exp_sym, ['sg_onednn_fully_connected_1'])
   ```
   
   and line 72:
   `net.optimize_for(*data_example, backend=SG_PASS_NAME)`



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