From: Francois Fleuret <francois@fleuret.org>
Date: Sat, 3 Dec 2016 16:04:34 +0000 (+0100)
Subject: Initial commit
X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=21c3bd2eb990e3fa58aa36b0e8fcd8901de5569c;p=profiler-torch.git

Initial commit
---

21c3bd2eb990e3fa58aa36b0e8fcd8901de5569c
diff --git a/profiler.lua b/profiler.lua
new file mode 100755
index 0000000..91b0915
--- /dev/null
+++ b/profiler.lua
@@ -0,0 +1,50 @@
+
+require 'torch'
+require 'nn'
+require 'sys'
+
+profiler = {}
+
+function profiler.decor(model, functionsToDecorate)
+
+   local functionsToDecorate = functionsToDecorate or
+      {
+         'updateOutput',
+         'backward'
+      }
+
+   for _, name in pairs(functionsToDecorate) do
+      model.orig = model.orig or {}
+      model.timings = 0
+
+      if model[name] and not model.orig[name] then
+         model.orig[name] = model[name]
+         model[name] = function(self, ...)
+            local startTime = sys.clock()
+            local result = { self.orig[name](self, unpack({...})) }
+            local endTime = sys.clock()
+            self.timings = self.timings + endTime - startTime
+            return unpack(result)
+         end
+      end
+
+   end
+
+   if torch.isTypeOf(model, nn.Container) then
+      for _, m in ipairs(model.modules) do
+         profiler.decor(m, functionsToDecorate)
+      end
+   end
+
+end
+
+function profiler.print(model)
+   print('----------------------------------------------------------------------')
+   print(model)
+   print(string.format('TIMING %.02fs', model.timings))
+   if torch.isTypeOf(model, nn.Container) then
+      model:applyToModules(profiler.print)
+   end
+end
+
+return profiler
diff --git a/test-profiler.lua b/test-profiler.lua
new file mode 100755
index 0000000..b394a33
--- /dev/null
+++ b/test-profiler.lua
@@ -0,0 +1,25 @@
+#!/usr/bin/env luajit
+
+require 'torch'
+require 'nn'
+
+require 'profiler'
+
+local model = nn.Sequential()
+model:add(nn.Linear(1000, 1000))
+model:add(nn.ReLU())
+model:add(nn.Linear(1000, 100))
+
+profiler.decor(model)
+
+for k = 1, 10 do
+   local input = torch.Tensor(1000, 1000):uniform(-1, 1)
+   local target = torch.Tensor(input:size(1), 100):uniform()
+   local criterion = nn.MSECriterion()
+   local output = model:forward(input)
+   local loss = criterion:forward(output, target)
+   local dloss = criterion:backward(output, target)
+   model:backward(input, dloss)
+end
+
+profiler.print(model)