Fix diff sharing in FlattenLayer

[mike-shvets]

Make FlattenLayer behave like ReshapeLayer, setting diff
pointer of the top blob to diff pointer of the bottom blob
in Reshape, insead of sharing the other way around in Backward.

This prevents breaking the backpropagation in case previous
layer shares its top blob diff (aka this layer's bottom blob diff).

Below is the case where backpropagation would break in current master.
RecurrentLayer (LSTMLayer) shares top blob diff with its unrolled net, but
the following flatten layer breaks this connection during backward.

import numpy as np
import tempfile
import caffe
from caffe import layers as L
from caffe import params as P

TESTCASE = 'flatten'

spec = caffe.NetSpec()
spec.inp =  L.Input(shape=dict(dim=[2, 3, 4]))
spec.cont = L.Input(shape=dict(dim=[2, 3]))
spec.out = L.Input(shape=dict(dim=[2 * 3, 5]))
recurrent_param = dict(
    num_output=5,
    weight_filler=dict(type='xavier'),
    bias_filler=dict(type='constant', value=0.),
)
spec.lstm = L.LSTM(spec.inp, spec.cont, recurrent_param=recurrent_param)
if TESTCASE == 'flatten':
    spec.flat = L.Flatten(spec.lstm, flatten_param=dict(axis=0, end_axis=1))
else:
    # do the same with Reshape
    spec.flat = L.Reshape(spec.lstm, reshape_param=dict(shape=dict(dim=[-1, 5])))
spec.loss = L.EuclideanLoss(spec.flat, spec.out,  loss_weight=1.)

model_name = None
with tempfile.NamedTemporaryFile(delete=False) as f:
    f.write(str(spec.to_proto()))
    model_name = f.name
net = caffe.Net(model_name, caffe.TRAIN)

net.blobs['inp'].data[...] = np.random.rand(2, 3, 4)
net.blobs['cont'].data[...] = np.array([[0., 0., 0.], [1., 1., 1.]])
net.blobs['out'].data[...] = np.random.rand(2 * 3, 5)

net.clear_param_diffs()
net.forward()
net.backward()

# lstm params gradients
# prints all zeros in current master for 'flatten' test case
print(net.params['lstm'][0].diff)