class HorizontalStackBlock(object):
"""
Concatenates input matrices horizontally.
Parameters
----------
matrices : Matrix (GpuMatrix or CpuMatrix)
Input matrices that need to be concatenated.
device_id: int
Defines the device's id on which the computation will take place
"""
def __init__(self, *matrices, **kwargs):
# TODO(sergii): change hsplit to aditive_hsplit for propper gradients accumulation
self.context = Context(kwargs.get('device_id'))
device_id = self.context.device_id
self.matrices = []
self.dL_dmatrices = []
self.bpropagable = []
for matrix in matrices:
self.bpropagable.append(matrix.bpropagable)
if matrix.bpropagable:
matrix, dL_dmatrix = matrix.register_usage(device_id, device_id)
self.dL_dmatrices.append(dL_dmatrix)
else:
matrix = matrix.register_usage(device_id)
self.matrices.append(matrix)
ncols = [matrix.ncols for matrix in matrices]
ncols = sum([e for e in ncols[1:]], ncols[0])
dtype = matrices[0].dtype
bu_device_id = device_id if self.dL_dmatrices else None
output = Matrix.empty(matrices[0].nrows, ncols, dtype, device_id)
self.output = Connector(output, bu_device_id)
def fprop(self):
self.output.assign_hstack(self.context, self.matrices)
self.output.fprop()
def bprop(self):
if self.dL_dmatrices:
col_slices = []
ncols = [0]
for matrix, bpropagable in izip(self.matrices, self.bpropagable):
ncols.append(ncols[-1] + int(matrix.ncols))
if bpropagable:
col_slices.append((ncols[-2], ncols[-1]))
self.output.backward_matrix.hsplit(self.context, self.dL_dmatrices, col_slices)
