function DAG:__init()
parent.__init(self)
-- Nodes are indexed by the module they contain
- self.node = { }
+ self.node = {}
end
-- Apply f on t recursively; use the corresponding elements from args
end
local distance = {}
- self:nestedApply(function(m) distance[m] = 1 end, self.inputModules)
+ self:nestedApply(
+ function(m) distance[m] = 1 end,
+ self.inputModules
+ )
local nc
+ local nl = 0
repeat
nc = 0
for nnma, node in pairs(self.node) do
end
end
end
+ assert(nl < #self.modules, 'Cycle detected in the graph.')
+ nl = nl + 1
until nc == 0
- self.sorted = { }
+ for _, nnm in pairs(self.modules) do
+ assert(distance[nnm], 'Some modules are not connected to inputs')
+ end
+
+ self.sorted = {}
for m, d in pairs(distance) do
table.insert(self.sorted, { distance = d, nnm = m })
end
for i, a in ipairs(self.sorted) do self.sorted[i] = a.nnm end
end
+-- This accumulates x in a, where they are both nested tables of
+-- tensors with same structures / keys. If first is true, set a = x
+-- (in which case a can be nil) otherwise a = a + x. The behavior is
+-- undefined if a and x do not have the exact same structure.
+function DAG:nestedAccTensor(a, x, first)
+ if torch.type(x) == 'table' then
+ local b = {}
+ for i in pairs(x) do
+ b[i] = self:nestedAccTensor(a[i], x[i], first)
+ end
+ a = b
+ else
+ if first then
+ if a then
+ a:resizeAs(x):copy(x)
+ else
+ a = x:clone()
+ end
+ else
+ a:add(x)
+ end
+ end
+ return a
+end
+
function DAG:updateGradOutput(node)
local gradInputSucc = node.gradInputSucc
if #gradInputSucc == 1 then
node.gradOutput = gradInputSucc[1]
elseif #gradInputSucc > 1 then
- if node.gradOutput then
- node.gradOutput:resize(gradInputSucc[1]):copy(gradInputSucc[1])
- else
- node.gradOutput = gradInputSucc[1]:clone()
- end
- for k = 2, #gradInputSucc do
- node.gradOutput:add(gradInputSucc[k])
+ for k = 1, #gradInputSucc do
+ node.gradOutput = self:nestedAccTensor(node.gradOutput, gradInputSucc[k], k == 1)
end
end
end
self.inputModules = i
self:nestedApply(
function(nnm)
- if #self.node[nnm].succ == 0 then
- error('Input modules must have outgoing edges.')
- end
- if #self.node[nnm].pred > 0 then
- error('Input modules cannog have incoming edges.')
- end
+ assert(#self.node[nnm].succ > 0, 'Input modules must have outgoing edges.')
+ assert(#self.node[nnm].pred == 0, 'Input modules cannot have incoming edges.')
end,
self.inputModules
)
self.outputModules = o
self:nestedApply(
function(nnm)
- if #self.node[nnm].pred == 0 then
- error('Output module must have incoming edges.')
- end
- if #self.node[nnm].succ > 0 then
- error('Output module cannot have outgoing edges.')
- end
+ assert(#self.node[nnm].pred > 0, 'Output module must have incoming edges.')
+ assert(#self.node[nnm].succ == 0, 'Output module cannot have outgoing edges.')
end,
self.outputModules
)
self:nestedApply(
function(nnm, i)
- self.node[nnm].input = i
- self:rethrowErrors(nnm, self.node[nnm].index, 'updateOutput', i)
+ local node = self.node[nnm]
+ node.input = i
+ self:rethrowErrors(nnm, node.index, 'updateOutput', i)
end,
self.inputModules,
input
for _, nnm in ipairs(self.sorted) do
local node = self.node[nnm]
- if #node.pred > 0 then
+ local pred = node.pred
+ if #pred > 0 then
local i
- if #node.pred == 1 then
- i = node.pred[1].output
- elseif #node.pred > 1 then
+ if #pred == 1 then
+ i = pred[1].output
+ elseif #pred > 1 then
i = {}
- for k = 1, #node.pred do
- i[k] = node.pred[k].output
+ for k = 1, #pred do
+ i[k] = pred[k].output
end
end
node.input = i
- self:rethrowErrors(nnm, self.node[nnm].index, 'updateOutput', i)
+ self:rethrowErrors(nnm, node.index, 'updateOutput', i)
end
end
end
function DAG:updateGradInput(input, gradOutput)
- assert(self.sorted, 'There has been a DAG structure change before a DAG:updateGradInput')
+ assert(self.sorted, 'There has been a structure change before a DAG:updateGradInput')
self:nestedApply(
function(nnm, go)
local node = self.node[nnm]
node.gradOutput = go
- self:rethrowErrors(nnm, node.index, 'updateGradInput', self.node[nnm].input, go)
+ self:rethrowErrors(nnm, node.index, 'updateGradInput', node.input, go)
end,
self.outputModules, gradOutput
)
if #node.gradInputSucc > 0 then
self:updateGradOutput(node)
- self:rethrowErrors(nnm, self.node[nnm].index, 'updateGradInput', node.input, node.gradOutput)
+ self:rethrowErrors(nnm, node.index, 'updateGradInput', node.input, node.gradOutput)
end
-- We fill the gradInputSucc of our predecessors
if #pred == 1 then
table.insert(self.node[pred[1]].gradInputSucc, nnm.gradInput)
elseif #pred > 1 then
- if not torch.type(nnm.gradInput) == 'table' then
- error('Should have a table gradInput since it has multiple predecessors')
- end
+ assert(torch.type(nnm.gradInput) == 'table',
+ 'Should have a table gradInput since it has multiple predecessors')
for n = 1, #pred do
- table.insert(self.node[node.pred[n]].gradInputSucc, nnm.gradInput[n])
+ table.insert(self.node[pred[n]].gradInputSucc, nnm.gradInput[n])
end
end
end
- self.gradInput = self:nestedApply(function(m) return m.gradInput end, self.inputModules)
+ self.gradInput = self:nestedApply(
+ function(m) return m.gradInput end,
+ self.inputModules
+ )
return self.gradInput
end
function DAG:accGradParameters(input, gradOutput, scale)
- scale = scale or 1
-
- assert(self.sorted, 'There has been a DAG structure change before a DAG:accGradParameters')
+ assert(self.sorted, 'There has been a structure change before a DAG:accGradParameters')
self:nestedApply(
function(nnm, go) self.node[nnm].gradOutput = go end,
self:rethrowErrors(nnm, k, 'accGradParameters', node.input, node.gradOutput, scale)
end
end
+
+function DAG:clearState()
+ self.sorted = nil
+ for _, node in pairs(self.node) do
+ node.input = nil
+ node.gradInputSucc = nil
+ node.gradOutput = nil
+ end
+ return parent.clearState(self)
+end
projects / dagnn.git / blobdiff