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
+ assert(nl < #self.modules, 'Cycle detected in the graph.')
nc = 0
for nnma, node in pairs(self.node) do
for _, nnmb in pairs(node.succ) do
end
end
end
+ nl = nl + 1
until nc == 0
+ 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 })
for i, a in ipairs(self.sorted) do self.sorted[i] = a.nnm end
end
--- This accumulate x in a where they are both nested tables of
--- tensors. If first is true, set a = x.
+-- 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
- a = a or {}
+ local b = {}
for i in pairs(x) do
- a[i] = self:nestedAccTensor(a[i], x[i], first)
+ b[i] = self:nestedAccTensor(a[i], x[i], first)
end
+ a = b
else
if first then
if a then
end
end
+function DAG:setLabel(nnm, label)
+ self.node[nnm].label = label
+end
+
function DAG:setInput(i)
self.sorted = nil
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 cannot 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:putInOrder()
for i, d in ipairs(self.sorted) do
- print('#' .. i .. ' -> ' .. torch.type(d))
+ local decoration = ''
+ if self.node[d].label then
+ decoration = ' [' .. self.node[d].label .. ']'
+ end
+ print('#' .. i .. ' -> ' .. torch.type(d) .. decoration)
end
end
function DAG:saveDot(filename)
local file = (filename and io.open(filename, 'w')) or io.stdout
+ local function writeNestedCluster(prefix, list, indent)
+ local indent = indent or ''
+ if torch.type(list) == 'table' then
+ file:write(indent .. ' subgraph cluster_' .. prefix .. ' {\n');
+ for k, x in pairs(list) do
+ writeNestedCluster(prefix .. '_' .. k, x, ' ' .. indent)
+ end
+ file:write(indent .. ' }\n');
+ else
+ file:write(indent .. ' ' .. self.node[list].index .. ' [color=red]\n')
+ end
+ end
+
file:write('digraph {\n')
file:write('\n')
+ writeNestedCluster('input', self.inputModules)
+ writeNestedCluster('output', self.outputModules)
+
+ file:write('\n')
+
for nnmb, node in pairs(self.node) do
file:write(
' '
.. node.index
- .. ' [shape=box,label=\"' .. torch.type(nnmb) .. '\"]'
+ .. ' [shape=box,label=\"' .. (self.node[nnmb].label or torch.type(nnmb)) .. '\"]'
.. '\n'
)
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,
function DAG:clearState()
self.sorted = nil
for _, node in pairs(self.node) do
- node.gradInputSucc = nil
node.input = nil
+ node.gradInputSucc = nil
node.gradOutput = nil
end
return parent.clearState(self)
projects / dagnn.git / blobdiff