X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=dagnn.git;a=blobdiff_plain;f=dagnn.lua;h=b82398c0de429bd19875776fa465222a84504bbe;hp=c6d54ad5a057d72c4029ee6c84b87a6e03a30e7d;hb=HEAD;hpb=0a630b54355382dfa68c0f3d51729bad0b4c58e6

diff --git a/dagnn.lua b/dagnn.lua
index c6d54ad..b82398c 100755
--- a/dagnn.lua
+++ b/dagnn.lua
@@ -26,7 +26,22 @@ local DAG, parent = torch.class('nn.DAG', 'nn.Container')
 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
+-- (i.e. same keys) as second parameter to f when available; return
+-- the results from f, organized in a similarly nested table.
+function DAG:nestedApply(f, t, args)
+   if torch.type(t) == 'table' then
+      local result = {}
+      for k, s in pairs(t) do
+         result[k] = self:nestedApply(f, s, args and args[k])
+      end
+      return result
+   else
+      return f(t, args)
+   end
 end
 
 function DAG:createNode(nnm)
@@ -40,9 +55,87 @@ function DAG:createNode(nnm)
    end
 end
 
--- The main use should be to add an edge between two modules, but it
--- can also add a full sequence of modules
-function DAG:addEdge(...)
+function DAG:putInOrder()
+   if self.sorted then
+      return
+   end
+
+   local distance = {}
+   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
+            if distance[nnma] and (not distance[nnmb] or distance[nnmb] < distance[nnma] + 1) then
+               distance[nnmb] = distance[nnma] + 1
+               nc = nc + 1
+            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 })
+   end
+
+   table.sort(self.sorted, function(a, b) return a.distance < b.distance 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
+      for k = 1, #gradInputSucc do
+         node.gradOutput = self:nestedAccTensor(node.gradOutput, gradInputSucc[k], k == 1)
+      end
+   end
+end
+
+----------------------------------------------------------------------
+
+-- Connect a sequence of modules
+function DAG:connect(...)
    self.sorted = nil
    local prev
    for _, nnm in pairs({...}) do
@@ -55,19 +148,8 @@ function DAG:addEdge(...)
    end
 end
 
--- Apply f on t recursively; use the corresponding element from args
--- (i.e. same keys) as second parameter to f when available; return
--- the results from f, organized in a similarly nested table.
-function DAG:nestedApply(f, t, args)
-   if torch.type(t) == 'table' then
-      local result = {}
-      for k, s in pairs(t) do
-         result[k] = self:nestedApply(f, s, args and args[k])
-      end
-      return result
-   else
-      return f(t, args)
-   end
+function DAG:setLabel(nnm, label)
+   self.node[nnm].label = label
 end
 
 function DAG:setInput(i)
@@ -75,12 +157,8 @@ function DAG:setInput(i)
    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
    )
@@ -91,70 +169,81 @@ function DAG:setOutput(o)
    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
    )
 end
 
-function DAG:putInOrder()
-   if self.sorted then
-      return
+function DAG:print()
+   self:putInOrder()
+
+   for i, d in ipairs(self.sorted) do
+      local decoration = ''
+      if self.node[d].label then
+         decoration = ' [' .. self.node[d].label .. ']'
+      end
+      print('#' .. i .. ' -> ' .. torch.type(d) .. decoration)
    end
+end
 
-   local distance = {}
-   self:nestedApply(function(m) distance[m] = 1 end, self.inputModules)
+----------------------------------------------------------------------
 
-   local nc
-   repeat
-      nc = 0
-      for nnma, node in pairs(self.node) do
-         for _, nnmb in pairs(node.succ) do
-            if distance[nnma] and (not distance[nnmb] or distance[nnmb] < distance[nnma] + 1) then
-               distance[nnmb] = distance[nnma] + 1
-               nc = nc + 1
-            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
-   until nc == 0
-
-   self.sorted = { }
-   for m, d in pairs(distance) do
-      table.insert(self.sorted, { distance = d, nnm = m })
    end
 
-   table.sort(self.sorted, function(a, b) return a.distance < b.distance end)
+   file:write('digraph {\n')
 
-   for i, a in ipairs(self.sorted) do self.sorted[i] = a.nnm end
-end
+   file:write('\n')
 
-function DAG:computeGradOutput(gradInputSucc)
-   local gi
-   if #gradInputSucc == 1 then
-      gi = gradInputSucc[1] -- we avoid a clone()
-   elseif #gradInputSucc > 1 then
-      for k = 1, #gradInputSucc do
-         if gi then
-            gi:add(gradInputSucc[k])
-         else
-            gi = gradInputSucc[k]:clone()
+   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=\"' .. (self.node[nnmb].label or torch.type(nnmb)) .. '\"]'
+            .. '\n'
+      )
+
+      for i, nnma in pairs(node.pred) do
+         local decoration = ''
+         if #node.pred > 1 then
+            -- decoration = ' [headlabel=\"' .. i .. '\"]'
+            decoration = ' [label=\"' .. i .. '\"]'
          end
+         file:write(
+            '  '
+               .. self.node[nnma].index
+               .. ' -> '
+               .. self.node[nnmb].index
+               .. decoration
+               .. '\n'
+         )
       end
+
+      file:write('\n')
    end
-   return gi
-end
 
-function DAG:print()
-   self:putInOrder()
+   file:write('}\n')
 
-   for i, d in ipairs(self.sorted) do
-      print('#' .. i .. ' -> ' .. torch.type(d))
-   end
 end
 
 ----------------------------------------------------------------------
@@ -164,9 +253,9 @@ function DAG:updateOutput(input)
 
    self:nestedApply(
       function(nnm, i)
-         self.node[nnm].input = i
-         -- nnm:updateOutput(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
@@ -174,19 +263,19 @@ function DAG:updateOutput(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
-         -- nnm:updateOutput(i)
-         self:rethrowErrors(nnm, self.node[nnm].index, 'updateOutput', i)
+         self:rethrowErrors(nnm, node.index, 'updateOutput', i)
       end
    end
 
@@ -199,12 +288,13 @@ function DAG:updateOutput(input)
 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)
-         -- nnm:updateGradInput(self.node[nnm].input, go)
-         self:rethrowErrors(nnm, self.node[nnm].index, 'updateGradInput', self.node[nnm].input, go)
+         local node = self.node[nnm]
+         node.gradOutput = go
+         self:rethrowErrors(nnm, node.index, 'updateGradInput', node.input, go)
       end,
       self.outputModules, gradOutput
    )
@@ -221,75 +311,59 @@ function DAG:updateGradInput(input, gradOutput)
    for k = #self.sorted, 1, -1 do
       local nnm = self.sorted[k]
       local node = self.node[nnm]
-      local pred, gradInputSucc = node.pred, node.gradInputSucc
+      local pred = node.pred
 
-      if #gradInputSucc > 0 then
-         node.gradOutput = self:computeGradOutput(gradInputSucc)
-         -- nnm:updateGradInput(node.input, node.gradOutput)
-         self:rethrowErrors(nnm, self.node[nnm].index, 'updateGradInput', node.input, node.gradOutput)
+      if #node.gradInputSucc > 0 then
+         self:updateGradOutput(node)
+         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 structure change before a DAG:accGradParameters.')
+
+   self:nestedApply(
+      function(nnm, go) self.node[nnm].gradOutput = go end,
+      self.outputModules, gradOutput
+   )
 
-   assert(self.sorted, 'there has been a DAG structure change before a DAG:accGradParameters')
+   self:nestedApply(
+      function(nnm, i) self.node[nnm].input = i end,
+      self.inputModules, input
+   )
 
    for k = 1, #self.modules do
       local nnm = self.modules[k]
       local node = self.node[nnm]
-      -- nnm:accGradParameters(node.input, node.gradOutput, scale)
-      self:rethrowErrors(nnm, k, 'accGradParameters', node.input, self:computeGradOutput(node.gradInputSucc), scale)
+      self:rethrowErrors(nnm, k, 'accGradParameters', node.input, node.gradOutput, scale)
    end
 end
 
-----------------------------------------------------------------------
-
-function DAG:dot(filename)
-   local file = (filename and io.open(filename, 'w')) or io.stdout
-
-   file:write('digraph {\n')
-
-   file:write('\n')
-
-   for nnma, node in pairs(self.node) do
-      file:write(
-         '  '
-            .. node.index
-            .. ' [shape=box,label=\"' .. torch.type(nnma) .. '\"]'
-            .. '\n'
-      )
-
-      for _, nnmb in pairs(node.succ) do
-         file:write(
-            '  '
-               .. node.index
-               .. ' -> '
-               .. self.node[nnmb].index
-               .. '\n'
-         )
-      end
-
-      file:write('\n')
+function DAG:clearState()
+   self.sorted = nil
+   for _, node in pairs(self.node) do
+      node.input = nil
+      node.gradInputSucc = nil
+      node.gradOutput = nil
    end
-
-   file:write('}\n')
-
+   return parent.clearState(self)
 end