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--[[ Module to perform matrix multiplication on two minibatch inputs,
producing a minibatch.
]]
local MM, parent = torch.class('nn.MM', 'nn.Module')
--[[ The constructor takes two optional arguments, specifying whether or not transpose
any of the input matrices before perfoming the multiplication.
]]
function MM:__init(transA, transB)
parent.__init(self)
self.transA = transA or false
self.transB = transB or false
self.gradInput = {torch.Tensor(), torch.Tensor()}
end
function MM:updateOutput(input)
assert(#input == 2, 'input must be a pair of minibatch matrices')
local a, b = table.unpack(input)
assert(a:nDimension() == 2 or a:nDimension() == 3, 'input tensors must be 2D or 3D')
if a:nDimension() == 2 then
assert(b:nDimension() == 2, 'second input tensor must be 2D')
if self.transA then a = a:t() end
if self.transB then b = b:t() end
assert(a:size(2) == b:size(1), 'matrix sizes do not match')
self.output:resize(a:size(1), b:size(2))
self.output:mm(a, b)
else
assert(b:nDimension() == 3, 'second input tensor must be 3D')
assert(a:size(1) == b:size(1), 'inputs must contain the same number of minibatches')
if self.transA then a = a:transpose(2, 3) end
if self.transB then b = b:transpose(2, 3) end
assert(a:size(3) == b:size(2), 'matrix sizes do not match')
self.output:resize(a:size(1), a:size(2), b:size(3))
self.output:bmm(a, b)
end
return self.output
end
function MM:updateGradInput(input, gradOutput)
self.gradInput[1] = self.gradInput[1] or input[1].new()
self.gradInput[2] = self.gradInput[2] or input[2].new()
assert(#input == 2, 'input must be a pair of tensors')
local a, b = table.unpack(input)
self.gradInput[1]:resizeAs(a)
self.gradInput[2]:resizeAs(b)
assert(gradOutput:nDimension() == 2 or gradOutput:nDimension() == 3, 'arguments must be a 2D or 3D Tensor')
local h_dim, w_dim, f
if gradOutput:nDimension() == 2 then
assert(a:nDimension() == 2, 'first input tensor must be 2D')
assert(b:nDimension() == 2, 'second input tensor must be 2D')
h_dim, w_dim = 1, 2
f = "mm"
else
assert(a:nDimension() == 3, 'first input tensor must be 3D')
assert(b:nDimension() == 3, 'second input tensor must be 3D')
h_dim, w_dim = 2, 3
f = "bmm"
end
if self.transA == self.transB then
a = a:transpose(h_dim, w_dim)
b = b:transpose(h_dim, w_dim)
end
if self.transA then
self.gradInput[1][f](self.gradInput[1], b, gradOutput:transpose(h_dim, w_dim))
else
self.gradInput[1][f](self.gradInput[1], gradOutput, b)
end
if self.transB then
self.gradInput[2][f](self.gradInput[2], gradOutput:transpose(h_dim, w_dim), a)
else
self.gradInput[2][f](self.gradInput[2], a, gradOutput)
end
return self.gradInput
end
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