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:
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@@ -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 elimination 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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