Error if the gradient of tensor is None.

The gradient of the tensor may be None, if the tensor is forward, but do not backward.

For example, I'm using BERT to finetune a model with the last second enocded_layer. The last layer is calculated when forward, however, it's not gradient do not be calculated when backward.

The following is the error message.

File "/usr/lib/python3.7/site-packages/apex-0.1-py3.7-linux-x86_64.egg/apex/optimizers/fp16_optimizer.py", line 147, in step
    grads_groups_flat.append(_flatten_dense_tensors([p.grad for p in group]))
File "/usr/lib/python3.7/site-packages/torch/_utils.py", line 194, in _flatten_dense_tensors
    flat = torch.cat([t.contiguous().view(-1) for t in tensors], dim=0)
File "/usr/lib/python3.7/site-packages/torch/_utils.py", line 194, in <listcomp>
    flat = torch.cat([t.contiguous().view(-1) for t in tensors], dim=0)
AttributeError: 'NoneType' object has no attribute 'contiguous'

Start in apex/optimizers/fp16_optimizer.py at the step method and inspect how the gradients passed to _flatten_dense_tensors are collected. Reproduce the reported case where a tensor is used during forward but has no gradient during backward; done means the optimizer no longer fails on a None gradient.