[GitHub] Fiend1213 opened a new issue #13976: Problem of exporting FP16 SyncBN model.

[GitBox]

Fiend1213 opened a new issue #13976: Problem of exporting FP16 SyncBN model.
URL: https://github.com/apache/incubator-mxnet/issues/13976
 
 
   ## Description
   This is a problem when exporting fp16 model containing SyncBN.
   
   ```
   import mxnet as mx
   
   mx.random.seed(42)
   
   def data_xform(data):
       """Move channel axis to the beginning, cast to float32, and normalize to 
[0, 1]."""
       return mx.nd.moveaxis(data, 2, 0).astype('float32') / 255
   
   train_data = 
mx.gluon.data.vision.MNIST(train=True).transform_first(data_xform)
   val_data = 
mx.gluon.data.vision.MNIST(train=False).transform_first(data_xform)
   
   batch_size = 2000
   train_loader = mx.gluon.data.DataLoader(train_data, shuffle=True, 
batch_size=batch_size)
   val_loader = mx.gluon.data.DataLoader(val_data, shuffle=False, 
batch_size=batch_size)
   
   net = mx.gluon.nn.HybridSequential()
   with net.name_scope():
       net.add(mx.gluon.nn.Conv2D(64, (3, 3)))
       net.add(mx.gluon.contrib.nn.SyncBatchNorm())
       net.add(mx.gluon.nn.Conv2D(64, (3, 3)))
       net.add(mx.gluon.contrib.nn.SyncBatchNorm())
       net.add(mx.gluon.nn.Dense(128))
       net.add(mx.gluon.nn.Dense(10))
       net.hybridize()
       net.cast('float16')
   print('finish build the network')
   
   ctx =  [mx.gpu(int(id)) for id in [0,1,2,3,4,5,6,7]]
   net.initialize(mx.init.Normal(0.01), ctx=ctx)
   print('finish initializing')
   
   trainer = mx.gluon.Trainer(
       params=net.collect_params(),
       optimizer='sgd',
       optimizer_params={'learning_rate': 0.04, 'multi_precision':True},
   )
   
   loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss()
   metric = mx.metric.Accuracy()
   
   num_epochs = 10
   for epoch in range(num_epochs):
       print('start training')
       for inputs, labels in train_loader:
           data = mx.gluon.utils.split_and_load(inputs, ctx_list=ctx, 
batch_axis=0)
           label = mx.gluon.utils.split_and_load(labels, ctx_list=ctx, 
batch_axis=0)
           losses = []
           with mx.autograd.record():
               for X, Y in zip(data, label):
                   outputs = net(X.astype('float16'))
                   loss = loss_function(outputs.astype('float32'), Y)
                   losses.append(loss)
   
           for l in losses:
               l.backward()
           trainer.step(batch_size=inputs.shape[0])
   
       net.export('mnist_syncbn_fp16.params')
   ```
   
   ## Error Message:
   ```
   File "/home/ubuntu/Workspace/incubator-mxnet/python/mxnet/gluon/block.py", 
line 900, in export
       ndarray.save('%s-%04d.params'%(path, epoch), arg_dict)
     File 
"/home/ubuntu/Workspace/incubator-mxnet/python/mxnet/ndarray/utils.py", line 
273, in save
       keys))
     File "/home/ubuntu/Workspace/incubator-mxnet/python/mxnet/base.py", line 
255, in check_call
       raise MXNetError(py_str(_LIB.MXGetLastError()))
   mxnet.base.MXNetError: [18:43:24] include/mxnet/././tensor_blob.h:203: Check 
failed: mshadow::DataType<DType>::kFlag == type_flag_ TBlob.get_with_shape: 
data type do not match specified type.Expected: 2 v.s. given 0
   ```

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