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Element Research rnn benchmark ++ #5

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merged 3 commits into from
Mar 30, 2016
Merged

Element Research rnn benchmark ++ #5

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cfb04b9
theano setup time
nicholas-leonard Mar 29, 2016
256dcd9
rnn speedup + setup time
nicholas-leonard Mar 29, 2016
5641e67
setup time + cutorch.sync
nicholas-leonard Mar 29, 2016
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6 changes: 4 additions & 2 deletions theano/rnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -231,7 +231,7 @@ def recurrence(x_t, c_tm1, h_tm1):
seq_length = opts.seq_length
batch_size = opts.batch_size


start = time.time()
# Data

n_samples = 100000
Expand Down Expand Up @@ -261,7 +261,9 @@ def recurrence(x_t, c_tm1, h_tm1):
print 'compiling...'
f_test = theano.function(inputs=[index], outputs=output, givens={x: x_values[index:index + batch_size]})
f_train = theano.function(inputs=[index], outputs=cost, updates=updates, givens={x: x_values[index:index + batch_size], y: y_values[index:index + batch_size]})

f_train(1)
print "Setup : compile + forward/backward x 1"
print "--- %s seconds" % time.time() - start

start = time.time()
for k, i in enumerate(xrange(0, n_samples, batch_size)):
Expand Down
24 changes: 16 additions & 8 deletions torch/rnn.lua
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Expand Up @@ -32,13 +32,12 @@ end
local xBatches = xValues:split(batchSize, 1)
local yBatches = yValues:split(batchSize, 1)

local a = torch.Timer()
local rnn
if networkType == 'rnn' then
rnn = nn.Sequential()
:add(nn.ParallelTable()
:add(nn.Linear(inputSize, hiddenSize))
:add(nn.Linear(hiddenSize, hiddenSize)))
:add(nn.CAddTable())
:add(nn.JoinTable(1,1))
:add(nn.Linear(inputSize+hiddenSize, hiddenSize))
:add(nn.Sigmoid())
rnn = nn.Recurrence(rnn, hiddenSize, 1)
elseif networkType == 'lstm' then
Expand All @@ -53,17 +52,25 @@ if cpu ~= true then
criterion:cuda()
end

local start = os.clock()
local input = xBatches[1]:split(inputSize, 2)
criterion:forward(rnn:forward(input), yBatches[1])
rnn:backward(input, criterion:backward(rnn.output, yBatches[1]))
if cpu ~= true then cutorch.synchronize() end
print("Setup : compile + forward/backward x 1")
print("--- " .. a:time().real .. " seconds ---")

a:reset()
for i = 1, #xBatches do
if (i % 100 == 0) then
print(i)
end
rnn:forward(xBatches[i]:split(inputSize, 2))
end
if cpu ~= true then cutorch.synchronize() end
print("Forward:")
print("--- " .. nSamples .. " samples in " .. (os.clock() - start) .. " seconds (" .. nSamples / (os.clock() - start) .. " samples/s) ---")
print("--- " .. nSamples .. " samples in " .. a:time().real .. " seconds (" .. nSamples / a:time().real .. " samples/s) ---")

start = os.clock()
a:reset()
for i = 1, #xBatches do
if (i % 100 == 0) then
print(i)
Expand All @@ -74,5 +81,6 @@ for i = 1, #xBatches do
rnn:backward(input, criterion:backward(rnn.output, yBatches[i]))
rnn:updateParameters(0.01)
end
if cpu ~= true then cutorch.synchronize() end
print("Forward + Backward:")
print("--- " .. nSamples .. " samples in " .. (os.clock() - start) .. " seconds (" .. nSamples / (os.clock() - start) .. " samples/s) ---")
print("--- " .. nSamples .. " samples in " .. a:time().real .. " seconds (" .. nSamples / a:time().real .. " samples/s) ---")