tensorflow-haskell/tensorflow/src/TensorFlow/Nodes.hs

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-- Copyright 2016 TensorFlow authors.
--
-- Licensed under the Apache License, Version 2.0 (the "License");
-- you may not use this file except in compliance with the License.
-- You may obtain a copy of the License at
--
-- http://www.apache.org/licenses/LICENSE-2.0
--
-- Unless required by applicable law or agreed to in writing, software
-- distributed under the License is distributed on an "AS IS" BASIS,
-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-- See the License for the specific language governing permissions and
-- limitations under the License.
{-# LANGUAGE DataKinds #-}
Support fetching storable vectors + use them in benchmark (#50) 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)
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{-# LANGUAGE FlexibleContexts #-}
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{-# LANGUAGE FlexibleInstances #-}
{-# LANGUAGE MultiParamTypeClasses #-}
{-# LANGUAGE RankNTypes #-}
{-# LANGUAGE ScopedTypeVariables #-}
{-# LANGUAGE TypeFamilies #-}
{-# LANGUAGE TypeOperators #-}
{-# LANGUAGE UndecidableInstances #-} -- For Fetchable (TensorExpr a)
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module TensorFlow.Nodes where
import Control.Applicative (liftA2, liftA3)
import Data.Functor.Identity (Identity)
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import Data.Map.Strict (Map)
import Data.Set (Set)
import Data.Text (Text)
import qualified Data.Map.Strict as Map
import qualified Data.Set as Set
import TensorFlow.Build
import TensorFlow.Output
import TensorFlow.Tensor
import TensorFlow.Types
import qualified TensorFlow.Internal.FFI as FFI
-- | Types that contain ops which can be run.
class Nodes t where
getNodes :: t -> Build (Set NodeName)
-- | Types that tensor representations (e.g. 'Tensor', 'ControlNode') can be
-- fetched into.
--
-- Includes collections of tensors (e.g. tuples).
class Nodes t => Fetchable t a where
getFetch :: t -> Build (Fetch a)
-- | Fetch action. Keeps track of what needs to be fetched and how to decode
-- the fetched data.
data Fetch a = Fetch
{ -- | Nodes to fetch
fetches :: Set Text
-- | Function to create an 'a' from the fetched data.
, fetchRestore :: Map Text FFI.TensorData -> a
}
instance Functor Fetch where
fmap f (Fetch fetch restore) = Fetch fetch (f . restore)
instance Applicative Fetch where
pure x = Fetch Set.empty (const x)
Fetch fetch restore <*> Fetch fetch' restore' =
Fetch (fetch <> fetch') (restore <*> restore')
nodesUnion :: (Monoid b, Traversable t, Applicative f) => t (f b) -> f b
nodesUnion = fmap (foldMap id) . sequenceA
instance (Nodes t1, Nodes t2) => Nodes (t1, t2) where
getNodes (x, y) = nodesUnion [getNodes x, getNodes y]
instance (Nodes t1, Nodes t2, Nodes t3) => Nodes (t1, t2, t3) where
getNodes (x, y, z) = nodesUnion [getNodes x, getNodes y, getNodes z]
instance (Fetchable t1 a1, Fetchable t2 a2) => Fetchable (t1, t2) (a1, a2) where
getFetch (x, y) = liftA2 (,) <$> getFetch x <*> getFetch y
instance (Fetchable t1 a1, Fetchable t2 a2, Fetchable t3 a3)
=> Fetchable (t1, t2, t3) (a1, a2, a3) where
getFetch (x, y, z) =
liftA3 (,,) <$> getFetch x <*> getFetch y <*> getFetch z
instance Nodes t => Nodes [t] where
getNodes = nodesUnion . map getNodes
instance Fetchable t a => Fetchable [t] [a] where
getFetch ts = sequenceA <$> mapM getFetch ts
instance Nodes t => Nodes (Maybe t) where
getNodes = nodesUnion . fmap getNodes
instance Fetchable t a => Fetchable (Maybe t) (Maybe a) where
getFetch = fmap sequenceA . mapM getFetch
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instance Nodes ControlNode where
getNodes (ControlNode o) = pure $ Set.singleton o
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-- We use the constraint @(a ~ ())@ to help with type inference. For example,
-- if @t :: ControlNode@, then this constraint ensures that @run t :: Session
-- ()@. If we used @instance Fetchable ControlNode ()@ instead, then that
-- expression would be ambiguous without explicitly specifying the return type.
instance a ~ () => Fetchable ControlNode a where
getFetch _ = return $ pure ()
instance Nodes (ListOf f '[]) where
getNodes _ = return Set.empty
instance (Nodes (f a), Nodes (ListOf f as)) => Nodes (ListOf f (a ': as)) where
getNodes (x :/ xs) = liftA2 Set.union (getNodes x) (getNodes xs)
instance l ~ List '[] => Fetchable (ListOf f '[]) l where
getFetch _ = return $ pure Nil
instance (Fetchable (f t) a, Fetchable (ListOf f ts) (List as), i ~ Identity)
=> Fetchable (ListOf f (t ': ts)) (ListOf i (a ': as)) where
getFetch (x :/ xs) = liftA2 (\y ys -> y /:/ ys) <$> getFetch x <*> getFetch xs
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instance Nodes (Tensor v a) where
getNodes (Tensor o) = Set.singleton . outputNodeName <$> toBuild o
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fetchTensorVector :: forall a v . (TensorType a)
Support fetching storable vectors + use them in benchmark (#50) 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)
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=> Tensor v a -> Build (Fetch (TensorData a))
fetchTensorVector (Tensor o) = do
outputName <- encodeOutput <$> toBuild o
pure $ Fetch (Set.singleton outputName) $ \tensors ->
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let tensorData = tensors Map.! outputName
expectedType = tensorType (undefined :: a)
actualType = FFI.tensorDataType tensorData
badTypeError = error $ "Bad tensor type: expected "
++ show expectedType
++ ", got "
++ show actualType
in if expectedType /= actualType
then badTypeError
Support fetching storable vectors + use them in benchmark (#50) 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)
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else TensorData tensorData
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-- The constraint "a ~ a'" means that the input/output of fetch can constrain
-- the TensorType of each other.
Support fetching storable vectors + use them in benchmark (#50) 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)
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instance (TensorType a, a ~ a') => Fetchable (Tensor v a) (TensorData a') where
getFetch = fetchTensorVector
instance (TensorType a, TensorDataType s a, a ~ a') => Fetchable (Tensor v a) (s a') where
getFetch t = fmap decodeTensorData <$> fetchTensorVector t