Serialization.cpp fails on big endian machines.
This patch fixes the endian bugs and also makes the pytorch
model files portable across different endian architectures.
x86 generated model file can be read on s390 arch.

First problem, is serialization.cpp forgets to convert "size" value
of the storage elements to the native byte order.
torch.load throws an assertion as a result
(see the first stack trace below).

Second problem is when it reads the model from storage (doRead)
it decodes values to little endian which is the wrong order
on a big endian machine.  The decode should be
to THP_nativeByteOrder() instead
	(see the model dump below)

loaded_model = torch.load( opt.model_file, map_location=torch.device("cpu"))
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 422, in load
return _load(f, map_location, pickle_module, **pickle_load_args)
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 616, in _load
deserialized_objects[key]._set_from_file(f, offset, f_should_read_directly)
RuntimeError: storage has wrong size: expected 2305843009213693952 got 32
	(the very long number is actually 32 in the wrong endianness)

x86 (original)
>>> import torch
>>> torch.load('400f2k_best.model', map_location=torch.device("cpu"))
{'epoch': 24, 'model_type': 'emb_aec', 'classifier_model': OrderedDict([('model.0.weight', tensor([[ 2.4608e-01, -1.1174e-01, -1.0854e-01,  4.0124e-01, -1.5261e-02,
         -1.2206e-01,  1.3229e-01, -1.2615e-01, -5.2773e-01,  2.6333e-01,
         -3.1462e-03, -1.4902e-01,  9.8545e-02, -1.5789e-01, -2.2625e-01,
         -1.0776e-01, -9.0895e-02, -3.8530e-01,  9.1152e-01, -3.9720e-01,
         -8.5848e-01, -4.7837e-02, -1.5178e-01,  8.5023e-02,  1.5013e-01,
         -9.9294e-02, -2.7422e-01, -4.3986e-01, -4.4297e-01, -3.9570e-01,

s390x (wrong endianness)
>>> import torch
>>> torch.load( "400f2k_best.model", map_location=torch.device("cpu"))
{'epoch': 24, 'model_type': 'emb_aec', 'classifier_model': OrderedDict([('model.0.weight', tensor([[ 9.2780e+21, -9.7722e-11,  4.1350e+33,  7.782e+34,  4.2056e-31,
          9.0784e+18,  1.1846e-32,  3.3320e-32, -4.8288e-28, -7.2679e+12,
          1.5379e-16, -5.2604e+12, -4.7240e+17,  4.6092e-21, -1.8360e-20,
         -2.7712e-31,  1.4548e-16, -2.5089e-27,  7.9094e-10,  7.1977e+34,
          1.1930e+26,  8.4536e+15,  2.7757e+23, -5.8455e-10, -1.5611e+09,
         -1.1311e-23,  6.6451e+19, -2.0970e+20,  3.4878e-19, -1.0857e-12,
          7.8098e+22,  5.3998e-35],

detect erroneous usage if it ever arises.