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f170df9d13
In addition, you can now fetch TensorData directly. This might be useful in
scenarios where you feed the result of a computation back in, like RNN.
Before:
benchmarking feedFetch/4 byte
time 83.31 μs (81.88 μs .. 84.75 μs)
0.997 R² (0.994 R² .. 0.998 R²)
mean 87.32 μs (86.06 μs .. 88.83 μs)
std dev 4.580 μs (3.698 μs .. 5.567 μs)
variance introduced by outliers: 55% (severely inflated)
benchmarking feedFetch/4 KiB
time 114.9 μs (111.5 μs .. 118.2 μs)
0.996 R² (0.994 R² .. 0.998 R²)
mean 117.3 μs (116.2 μs .. 118.6 μs)
std dev 3.877 μs (3.058 μs .. 5.565 μs)
variance introduced by outliers: 31% (moderately inflated)
benchmarking feedFetch/4 MiB
time 109.0 ms (107.9 ms .. 110.7 ms)
1.000 R² (0.999 R² .. 1.000 R²)
mean 108.6 ms (108.2 ms .. 109.2 ms)
std dev 740.2 μs (353.2 μs .. 1.186 ms)
After:
benchmarking feedFetch/4 byte
time 82.92 μs (80.55 μs .. 85.24 μs)
0.996 R² (0.993 R² .. 0.998 R²)
mean 83.58 μs (82.34 μs .. 84.89 μs)
std dev 4.327 μs (3.664 μs .. 5.375 μs)
variance introduced by outliers: 54% (severely inflated)
benchmarking feedFetch/4 KiB
time 85.69 μs (83.81 μs .. 87.30 μs)
0.997 R² (0.996 R² .. 0.999 R²)
mean 86.99 μs (86.11 μs .. 88.15 μs)
std dev 3.608 μs (2.854 μs .. 5.273 μs)
variance introduced by outliers: 43% (moderately inflated)
benchmarking feedFetch/4 MiB
time 1.582 ms (1.509 ms .. 1.677 ms)
0.970 R² (0.936 R² .. 0.993 R²)
mean 1.645 ms (1.554 ms .. 1.981 ms)
std dev 490.6 μs (138.9 μs .. 1.067 ms)
variance introduced by outliers: 97% (severely inflated)
44 lines
1.7 KiB
Haskell
44 lines
1.7 KiB
Haskell
-- Disable full-laziness to keep ghc from optimizing most of the benchmark away.
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{-# OPTIONS_GHC -fno-full-laziness #-}
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import Control.DeepSeq (NFData(rnf))
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import Control.Exception (evaluate)
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import Control.Monad.IO.Class (liftIO)
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import Criterion.Main (defaultMain, bgroup, bench)
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import Criterion.Types (Benchmarkable(..))
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import qualified Data.Vector.Storable as S
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import qualified TensorFlow.Core as TF
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import qualified TensorFlow.Ops as TF
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-- | Create 'Benchmarkable' for 'TF.Session'.
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--
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-- The entire benchmark will be run in a single tensorflow session. The
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-- 'TF.Session' argument will be run once and then its result will be run N
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-- times.
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nfSession :: NFData b => TF.Session (a -> TF.Session b) -> a -> Benchmarkable
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nfSession init x = Benchmarkable $ \m -> TF.runSession $ do
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f <- init
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-- Can't use replicateM because n is Int64.
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let go n | n <= 0 = return ()
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| otherwise = f x >>= liftIO . evaluate . rnf >> go (n-1)
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go m
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-- | Benchmark feeding and fetching a vector.
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feedFetchBenchmark :: TF.Session (S.Vector Float -> TF.Session (S.Vector Float))
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feedFetchBenchmark = do
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input <- TF.build (TF.placeholder (TF.Shape [-1]))
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output <- TF.build (TF.render (TF.identity input))
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return $ \v -> do
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let shape = TF.Shape [fromIntegral (S.length v)]
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inputData = TF.encodeTensorData shape v
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feeds = [TF.feed input inputData]
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TF.runWithFeeds feeds output
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main :: IO ()
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main = defaultMain
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[ bgroup "feedFetch"
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[ bench "4 byte" $ nfSession feedFetchBenchmark (S.replicate 1 0)
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, bench "4 KiB" $ nfSession feedFetchBenchmark (S.replicate 1024 0)
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, bench "4 MiB" $ nfSession feedFetchBenchmark (S.replicate (1024^2) 0)
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]
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]
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