fastnn version 1.0

1 files changed, 111 insertions, 0 deletions

diff --git a/fastnn/threads/test/test-threads-shared.lua b/fastnn/threads/test/test-threads-shared.lua
new file mode 100644
index 0000000..3d63851
--- /dev/null
+++ b/fastnn/threads/test/test-threads-shared.lua
@@ -0,0 +1,111 @@
+require 'torch'
+
+local threads = require 'threads'
+local status, tds = pcall(require, 'tds')
+tds = status and tds or nil
+
+local nthread = 4
+local njob = 10
+local msg = "hello from a satellite thread"
+
+threads.Threads.serialization('threads.sharedserialize')
+
+local x = {}
+local xh = tds and tds.hash() or {}
+local xs = {}
+local z = tds and tds.hash() or {}
+local D = 10
+local K = tds and 100000 or 100  -- good luck in non-shared (30M)
+for i=1,njob do
+   x[i] = torch.ones(D)
+   xh[i] = torch.ones(D)
+   xs[i] = torch.FloatStorage(D):fill(1)
+   for j=1,K do
+      z[(i-1)*K+j] = "blah" .. i .. j
+   end
+end
+collectgarbage()
+collectgarbage()
+
+print('GO')
+
+local pool = threads.Threads(
+   nthread,
+   function(threadIdx)
+      pcall(require, 'tds')
+      print('starting a new thread/state number:', threadIdx)
+      gmsg = msg -- we copy here an upvalue of the main thread
+   end
+)
+
+local jobdone = 0
+for i=1,njob do
+   pool:addjob(
+      function()
+         assert(x[i]:sum() == D)
+         assert(xh[i]:sum() == D)
+         assert(torch.FloatTensor(xs[i]):sum() == D)
+         for j=1,K do
+            assert(z[(i-1)*K+j] == "blah" .. i .. j)
+         end
+         x[i]:add(1)
+         xh[i]:add(1)
+         torch.FloatTensor(xs[i]):add(1)
+         print(string.format('%s -- thread ID is %x', gmsg, __threadid))
+         collectgarbage()
+         collectgarbage()
+         return __threadid
+      end,
+
+      function(id)
+         print(string.format("task %d finished (ran on thread ID %x)", i, id))
+         jobdone = jobdone + 1
+      end
+   )
+end
+
+for i=1,njob do
+   pool:addjob(
+      function()
+         collectgarbage()
+         collectgarbage()
+      end
+   )
+end
+
+pool:synchronize()
+
+print(string.format('%d jobs done', jobdone))
+
+pool:terminate()
+
+-- did we do the job in shared mode?
+for i=1,njob do
+   assert(x[i]:sum() == 2*D)
+   assert(xh[i]:sum() == 2*D)
+   assert(torch.FloatTensor(xs[i]):sum() == 2*D)
+end
+
+-- serialize and zero x
+local str = torch.serialize(x)
+local strh = torch.serialize(xh)
+local strs = torch.serialize(xs)
+for i=1,njob do
+   x[i]:zero()
+   xh[i]:zero()
+   xs[i]:fill(0)
+end
+
+-- dude, check that unserialized x does not point on x
+local y = torch.deserialize(str)
+local yh = torch.deserialize(strh)
+local ys = torch.deserialize(strs)
+for i=1,njob do
+   assert(y[i]:sum() == 2*D)
+   assert(yh[i]:sum() == 2*D)
+   assert(torch.FloatTensor(ys[i]):sum() == 2*D)
+end
+
+pool:terminate()
+
+print('PASSED')