Introduce a MonadBuild class, and remove buildAnd. (#83)

This change adds a class that both `Build` and `Session` are instances of:

    class MonadBuild m where
        build :: Build a -> m a

All stateful ops (generated and manually written) now have a signature that returns
an instance of `MonadBuild` (rather than just `Build`).  For example:

    assign_ :: (MonadBuild m, TensorType t)
            => Tensor Ref t -> Tensor v t -> m (Tensor Ref t)

This lets us remove a bunch of spurious calls to `build` in user code.  It also
lets us replace the pattern `buildAnd run foo` with the simpler pattern `foo >>= run`
(or `run =<< foo`, which is sometimes nicer when foo is a complicated expression).

I went ahead and deleted `buildAnd` altogether since it seems to lead to
confusion; in particular a few tests had `buildAnd run . pure` which is
actually equivalent to just `run`.

README.md

@@ -45,13 +45,13 @@ fit xData yData = TF.runSession $ do
     let x = TF.vector xData
         y = TF.vector yData
     -- Create scalar variables for slope and intercept.
-    w <- TF.build (TF.initializedVariable 0)
+    w <- TF.initializedVariable 0
-    b <- TF.build (TF.initializedVariable 0)
+    b <- TF.initializedVariable 0
     -- Define the loss function.
     let yHat = (x `TF.mul` w) `TF.add` b
         loss = TF.square (yHat `TF.sub` y)
     -- Optimize with gradient descent.
-    trainStep <- TF.build (gradientDescent 0.001 loss [w, b])
+    trainStep <- gradientDescent 0.001 loss [w, b]
     replicateM_ 1000 (TF.run trainStep)
     -- Return the learned parameters.
     (TF.Scalar w', TF.Scalar b') <- TF.run (w, b)
@@ -60,7 +60,7 @@ fit xData yData = TF.runSession $ do
 gradientDescent :: Float
                 -> TF.Tensor TF.Value Float
                 -> [TF.Tensor TF.Ref Float]
-                -> TF.Build TF.ControlNode
+                -> TF.Session TF.ControlNode
 gradientDescent alpha loss params = do
     let applyGrad param grad =
             TF.assign param (param `TF.sub` (TF.scalar alpha `TF.mul` grad))

tensorflow-mnist/tests/ParseTest.hs

@@ -49,7 +49,7 @@ import TensorFlow.Tensor
     )
 import TensorFlow.Ops
 import TensorFlow.Session
-    (runSession, run, run_, runWithFeeds, build, buildAnd)
+    (runSession, run, run_, runWithFeeds, build)
 import TensorFlow.Types (TensorDataType(..), Shape(..), unScalar)
 import Test.Framework (Test)
 import Test.Framework.Providers.HUnit (testCase)
@@ -108,7 +108,7 @@ testGraphDefExec :: Test
 testGraphDefExec = testCase "testGraphDefExec" $ do
     let graphDef = asGraphDef $ render $ scalar (5 :: Float) * 10
     runSession $ do
-        build $ addGraphDef graphDef
+        addGraphDef graphDef
         x <- run $ tensorFromName ValueKind "Mul_2"
         liftIO $ (50 :: Float) @=? unScalar x
 
@@ -147,7 +147,7 @@ testMNISTExec = testCase "testMNISTExec" $ do
         wtsCkptPath <- liftIO wtsCkpt
         biasCkptPath <- liftIO biasCkpt
         -- Run those restoring nodes on the graph in the current session.
-        buildAnd run_ $ (sequence :: Monad m => [m a] -> m [a])
+        run_ =<< (sequence :: Monad m => [m a] -> m [a])
                         [ restore wtsCkptPath wts
                         , restoreFromName biasCkptPath "bias" bias
                         ]

tensorflow-nn/src/TensorFlow/NN.hs

@@ -23,7 +23,7 @@ module TensorFlow.NN
 import Prelude hiding           ( log
                                 , exp
                                 )
-import TensorFlow.Build         ( Build
+import TensorFlow.Build         ( MonadBuild
                                 , render
                                 , withNameScope
                                 )
@@ -71,10 +71,10 @@ import TensorFlow.Ops           ( zerosLike
 --
 --  `logits` and `targets` must have the same type and shape.
 sigmoidCrossEntropyWithLogits
-  :: (OneOf '[Float, Double] a, TensorType a, Num a)
+  :: (MonadBuild m, OneOf '[Float, Double] a, TensorType a, Num a)
      => Tensor Value a          -- ^ __logits__
      -> Tensor Value a          -- ^ __targets__
-     -> Build (Tensor Value a)
+     -> m (Tensor Value a)
 sigmoidCrossEntropyWithLogits logits targets = do
     logits' <- render logits
     targets' <- render targets

tensorflow-nn/tests/NNTest.hs

@@ -22,7 +22,6 @@ import           TensorFlow.Test                    (assertAllClose)
 import           Test.Framework (Test)
 import           Test.Framework.Providers.HUnit     (testCase)
 import qualified Data.Vector                        as V
-import qualified TensorFlow.Build                   as TF
 import qualified TensorFlow.Gradient                as TF
 import qualified TensorFlow.Nodes                   as TF
 import qualified TensorFlow.NN                      as TF
@@ -97,8 +96,8 @@ testGradientAtZero = testCase "testGradientAtZero" $ do
 
     assertAllClose (head r) (V.fromList [0.5, -0.5])
 
-run :: TF.Fetchable t a => TF.Build t -> IO a
+run :: TF.Fetchable t a => TF.Session t -> IO a
-run = TF.runSession . TF.buildAnd TF.run
+run = TF.runSession . (>>= TF.run)
 
 main :: IO ()
 main = googleTest [ testGradientAtZero

tensorflow-opgen/src/TensorFlow/OpGen.hs

@@ -220,9 +220,12 @@ renderHaskellAttrName :: Attr a -> Doc
 renderHaskellAttrName = renderHaskellName . attrName
 
 functionBody :: ParsedOp -> Doc
-functionBody pOp = buildFunction <+> parens (hang 0 (stack buildOpParts))
+functionBody pOp = maybeLift <+> buildFunction <+> parens (hang 0 (stack buildOpParts))
                         </> indent indentation (sep tensorArgs)
   where
+    maybeLift
+        | parsedOpIsMonadic pOp = "build $"
+        | otherwise = ""
     buildFunction
         | null outputListsSizes = "buildOp"
         | otherwise = "buildListOp" <+>
@@ -277,13 +280,18 @@ typeSig pOp = constraints
                                 ++ [outputs])
   where
     constraints
-        | null (inferredTypeAttrs pOp) = empty
+        | null classConstraints = empty
-        | otherwise = "forall" <+> sep typeParams <+> "." <+> classConstraints <+> "=>"
+        | otherwise = "forall" <+> sep typeParams <+> "." <+> tuple classConstraints <+> "=>"
     typeParams = [strictText v | k <- parsedInputs pOp ++ parsedOutputs pOp,
                   Just (ArgTensorEither v) <- [argKind $ parsedArgCase k]]
                 ++ [renderHaskellAttrName n | n <- inferredTypeAttrs pOp]
-    classConstraints = tuple $ map tensorArgConstraint
+                ++ if parsedOpIsMonadic pOp then ["m'"] else []
-                    $ inferredTypeAttrs pOp
+    -- Use m' as the type parameter to avoid clashing with an attribute name.
+    monadConstraint
+        | parsedOpIsMonadic pOp = ["MonadBuild m'"]
+        | otherwise = []
+    classConstraints = monadConstraint ++ map tensorArgConstraint
+                                                    (inferredTypeAttrs pOp)
     signatureFold = folddoc (\x y -> x </> "->" <+> y)
     attrInput a = renderAttrType (attrInfo a) <+> hang 0 ("-- ^" <+> attrComment a)
     renderAttrType (AttrSingle a) = renderAttrBaseType a
@@ -304,7 +312,7 @@ typeSig pOp = constraints
         [a] -> wrapOutput (tensorArg a) <+> "-- ^" <+> argComment a
         as -> wrapOutput (tuple (map tensorArg as)) <+/> resultComment as
     wrapOutput o
-        | parsedOpIsMonadic pOp = "Build" <+> parens o
+        | parsedOpIsMonadic pOp = "m'" <+> parens o
         | otherwise = o
         
 -- | Render an op input or output.

tensorflow-ops/src/TensorFlow/EmbeddingOps.hs

@@ -24,7 +24,7 @@ module TensorFlow.EmbeddingOps where
 
 import Control.Monad (zipWithM)
 import Data.Int (Int32, Int64)
-import TensorFlow.Build (Build, colocateWith, render)
+import TensorFlow.Build (MonadBuild, colocateWith, render)
 import TensorFlow.Ops (shape, vector)  -- Also Num instance for Tensor
 import TensorFlow.Tensor (Tensor, Value)
 import TensorFlow.Types (OneOf, TensorType)
@@ -44,8 +44,9 @@ import qualified TensorFlow.GenOps.Core as CoreOps
 --
 -- The results of the lookup are concatenated into a dense
 -- tensor. The returned tensor has shape `shape(ids) + shape(params)[1:]`.
-embeddingLookup :: forall a b v .
+embeddingLookup :: forall a b v m .
-                   ( TensorType a
+                   ( MonadBuild m
+                   , TensorType a
                    , OneOf '[Int64, Int32] b
                    , Num b
                    )
@@ -58,7 +59,7 @@ embeddingLookup :: forall a b v .
                 -- containing the ids to be looked up in `params`.
                 -- The ids are required to have fewer than 2^31
                 -- entries.
-                -> Build (Tensor Value a)
+                -> m (Tensor Value a)
                 -- ^ A dense tensor with shape `shape(ids) + shape(params)[1:]`.
 embeddingLookup [p0] ids = colocateWith p0 (render $ CoreOps.gather p0 ids)
 embeddingLookup params@(p0 : _) ids = do

tensorflow-ops/src/TensorFlow/Gradient.hs

@@ -56,7 +56,9 @@ import qualified Data.Text as Text
 
 import qualified TensorFlow.GenOps.Core as CoreOps
 import TensorFlow.Build
-    ( Build
+    ( MonadBuild
+    , Build
+    , build
     , render
     , renderNodeName
     , renderedNodeDefs
@@ -111,7 +113,8 @@ type GradientCompatible a =
 
 
 -- | Gradient of @y@ w.r.t. each element of @xs@.
-gradients :: forall a v1 v2 . ( Num (Tensor v1 a)
+gradients :: forall a v1 v2 m . (MonadBuild m
+                              , Num (Tensor v1 a)
                                 -- TODO(gnezdo): remove indirect constraint.
                                 -- It's a wart inherited from Num instance.
                               , v1 ~ Value
@@ -119,8 +122,8 @@ gradients :: forall a v1 v2 . ( Num (Tensor v1 a)
                               )
           => Tensor v1 a  -- ^ The output of the graph.
           -> [Tensor v2 a]  -- ^ Tensors for which gradients are computed.
-          -> Build [Tensor Value a]
+          -> m [Tensor Value a]
-gradients y xs = do
+gradients y xs = build $ do
     -- The gradients are computed using "reverse accumulation", similarly to
     -- what is described here:
     -- https://en.wikipedia.org/wiki/Automatic_differentiation#The_chain_rule.2C_forward_and_reverse_accumulation

tensorflow-ops/src/TensorFlow/Ops.hs

@@ -155,20 +155,20 @@ matTranspose :: forall a v . TensorType a
              => Tensor v a -> Tensor Value a
 matTranspose = flip CoreOps.transpose (vector [1, 0 :: Int32])
 
-placeholder :: forall a . TensorType a => Shape -> Build (Tensor Value a)
+placeholder :: forall a m . (MonadBuild m, TensorType a) => Shape -> m (Tensor Value a)
 placeholder shape' =
-    buildOp $ opDef "Placeholder"
+    build $ buildOp $ opDef "Placeholder"
             & opAttr "dtype" .~ tensorType (undefined :: a)
             & opAttr "shape" .~ shape'
 
 -- | Creates a variable initialized to the given value.
 -- Initialization happens next time session runs.
-initializedVariable :: forall a . TensorType a
+initializedVariable :: forall a m . (MonadBuild m, TensorType a)
-                    => Tensor Value a -> Build (Tensor Ref a)
+                    => Tensor Value a -> m (Tensor Ref a)
 initializedVariable initializer = do
     v <- CoreOps.variable []  -- The shape is not known initially.
     (i :: Tensor Ref a) <-
-        buildOp (opDef "Assign"
+        build $ buildOp (opDef "Assign"
                  & opAttr "T" .~ tensorType (undefined :: a)
                  & opAttr "use_locking" .~ True
                  & opAttr "validate_shape" .~ False
@@ -179,32 +179,32 @@ initializedVariable initializer = do
 
 -- | Creates a zero-initialized variable with the given shape.
 zeroInitializedVariable
-  :: (TensorType a, Num a) =>
+  :: (MonadBuild m, TensorType a, Num a) =>
-     TensorFlow.Types.Shape -> Build (Tensor TensorFlow.Tensor.Ref a)
+     TensorFlow.Types.Shape -> m (Tensor TensorFlow.Tensor.Ref a)
 zeroInitializedVariable = initializedVariable . zeros
 
 -- TODO: Support heterogeneous list of tensors.
-save :: forall a v . TensorType a
+save :: forall a m v . (MonadBuild m, TensorType a)
         => ByteString     -- ^ File path.
         -> [Tensor v a]  -- ^ Tensors to save.
-        -> Build ControlNode
+        -> m ControlNode
 save path xs = do
     let toByteStringTensor = scalar . encodeUtf8 . unNodeName
-    names <- mapM (fmap toByteStringTensor . renderNodeName) xs
+    names <- mapM (fmap toByteStringTensor . build . renderNodeName) xs
     let types = replicate (length xs) (tensorType (undefined :: a))
     let saveOp = buildOp $ opDef "Save"
                          & opAttr "T" .~ types
-    saveOp (scalar path) (CoreOps.pack names) xs
+    build $ saveOp (scalar path) (CoreOps.pack names) xs
 
 -- | Restore a tensor's value from a checkpoint file.
 --
 -- This version allows restoring from a checkpoint file that uses a different
 -- tensor name than the variable.
-restoreFromName :: forall a . TensorType a
+restoreFromName :: forall a m . (MonadBuild m, TensorType a)
                 => ByteString    -- ^ File path.
                 -> ByteString    -- ^ Tensor name override.
                 -> Tensor Ref a  -- ^ Tensor to restore.
-                -> Build ControlNode
+                -> m ControlNode
 restoreFromName path name x = do
     let restoreOp = buildOp $ opDef "Restore"
                             & opAttr "dt" .~ tensorType (undefined :: a)
@@ -212,12 +212,12 @@ restoreFromName path name x = do
                 (restoreOp (scalar path) (scalar name) :: Tensor Value a)
 
 -- | Restore a tensor's value from a checkpoint file.
-restore :: forall a . TensorType a
+restore :: forall a m . (MonadBuild m, TensorType a)
         => ByteString    -- ^ File path.
         -> Tensor Ref a  -- ^ Tensor to restore.
-        -> Build ControlNode
+        -> m ControlNode
 restore path x = do
-    name <- encodeUtf8 . unNodeName <$> renderNodeName x
+    name <- encodeUtf8 . unNodeName <$> build (renderNodeName x)
     restoreFromName path name x
 
 -- | Create a constant tensor.
@@ -264,12 +264,13 @@ scalar :: forall a . TensorType a => a -> Tensor Value a
 scalar x = constant [] [x]
 
 -- Random tensor from the unit normal distribution with bounded values.
-truncatedNormal :: forall a v . TensorType a
+truncatedNormal :: forall a m v . (MonadBuild m, TensorType a)
                 => Tensor v Int64  -- ^ Shape.
-                -> Build (Tensor Value a)
+                -> m (Tensor Value a)
-truncatedNormal = buildOp $ opDef "TruncatedNormal"
+truncatedNormal
+    = build . buildOp (opDef "TruncatedNormal"
                         & opAttr "dtype" .~ tensorType (undefined :: a)
-                          & opAttr "T" .~ tensorType (undefined :: Int64)
+                        & opAttr "T" .~ tensorType (undefined :: Int64))
 
 zeros :: forall a . (Num a, TensorType a) => Shape -> Tensor Value a
 zeros (Shape shape') = CoreOps.fill (vector $ map fromIntegral shape') (scalar 0)

tensorflow-ops/tests/BuildTest.hs

@@ -19,7 +19,6 @@
 module Main where
 
 import Control.Monad.IO.Class (liftIO)
-import Data.Functor.Identity (runIdentity)
 import Lens.Family2 ((^.))
 import Data.List (sort)
 import Proto.Tensorflow.Core.Framework.Graph
@@ -35,7 +34,6 @@ import TensorFlow.Build
     , asGraphDef
     , evalBuildT
     , flushNodeBuffer
-    , hoistBuildT
     , render
     , withDevice
     , colocateWith
@@ -53,9 +51,7 @@ import TensorFlow.Ops
 import TensorFlow.Output (Device(..))
 import TensorFlow.Tensor (Tensor, Value, Ref)
 import TensorFlow.Session
-    ( build
+    ( run
-    , buildAnd
-    , run
     , runSession
     , run_
     )
@@ -82,7 +78,7 @@ testNamedDeRef = testCase "testNamedDeRef" $ do
                     assign v 5
     -- TODO: Implement TensorFlow get_variable and test it.
     runSession $ do
-      out <- buildAnd run graph
+      out <- graph >>= run
       liftIO $ 5 @=? (unScalar out :: Float)
 
 -- | Test that "run" will render and extend any pure ops that haven't already
@@ -96,7 +92,7 @@ testPureRender = testCase "testPureRender" $ runSession $ do
 testInitializedVariable :: Test
 testInitializedVariable =
     testCase "testInitializedVariable" $ runSession $ do
-        (formula, reset) <- build $ do
+        (formula, reset) <- do
             v <- initializedVariable 42
             r <- assign v 24
             return (1 `add` v, r)
@@ -109,7 +105,7 @@ testInitializedVariable =
 testInitializedVariableShape :: Test
 testInitializedVariableShape =
     testCase "testInitializedVariableShape" $ runSession $ do
-        vector <- build $ initializedVariable (constant [1] [42 :: Float])
+        vector <- initializedVariable (constant [1] [42 :: Float])
         result <- run vector
         liftIO $ [42] @=? (result :: V.Vector Float)
 
@@ -132,23 +128,19 @@ testNamedAndScoped = testCase "testNamedAndScoped" $ do
     "RefIdentity" @=? (nodeDef ^. op)
     "foo1/bar1" @=? (nodeDef ^. name)
 
--- | Lift a Build action into a context for HUnit to run.
-liftBuild :: Build a -> BuildT IO a
-liftBuild = hoistBuildT (return . runIdentity)
-
 -- | Flush the node buffer and sort the nodes by name (for more stable tests).
 flushed :: Ord a => (NodeDef -> a) -> BuildT IO [a]
-flushed field = sort . map field <$> liftBuild flushNodeBuffer
+flushed field = sort . map field <$> flushNodeBuffer
 
 -- | Test the interaction of rendering, CSE and scoping.
 testRenderDedup :: Test
 testRenderDedup = testCase "testRenderDedup" $ evalBuildT $ do
-   liftBuild renderNodes
+   renderNodes
    names <- flushed (^. name)
    liftIO $ ["Const_1", "Variable_0", "Variable_2"] @=? names
    -- Render the nodes in a different scope, which should cause them
    -- to be distinct from the previous ones.
-   liftBuild $ withNameScope "foo" renderNodes
+   withNameScope "foo" renderNodes
    scopedNames <- flushed (^. name)
    liftIO $ ["foo/Const_4", "foo/Variable_3", "foo/Variable_5"] @=? scopedNames
   where
@@ -165,7 +157,7 @@ testRenderDedup = testCase "testRenderDedup" $ evalBuildT $ do
 -- | Test the interaction of rendering, CSE and scoping.
 testDeviceColocation :: Test
 testDeviceColocation = testCase "testDeviceColocation" $ evalBuildT $ do
-   liftBuild renderNodes
+   renderNodes
    devices <- flushed (\x -> (x ^. name, x ^. device))
    liftIO $ [ ("Add_2","dev0")
             , ("Const_1","dev0")

tensorflow-ops/tests/DataFlowOpsTest.hs

@@ -45,7 +45,7 @@ testDynamicPartitionStitchInverse (StitchExample numParts values partitions) =
        restitch = CoreOps.dynamicStitch restitchIndices splitParts
     in monadicIO $ run $ do
        fromIntegral numParts @=? length splitParts
-       valuesOut <- TF.runSession $ TF.buildAnd TF.run $ return restitch
+       valuesOut <- TF.runSession $ TF.run restitch
        V.fromList values @=? valuesOut
 
 data StitchExample a = StitchExample Int64 [a] [Int32]

tensorflow-ops/tests/EmbeddingOpsTest.hs

@@ -39,11 +39,6 @@ import qualified TensorFlow.Tensor as TF
 import qualified TensorFlow.Types as TF
 import qualified TensorFlow.Gradient as TF
 import qualified TensorFlow.Build as TF
-import qualified TensorFlow.Nodes as TF
-
-
-buildAndRun :: TF.Fetchable t a => TF.Build t -> IO a
-buildAndRun = TF.runSession . TF.buildAnd TF.run
 
 
 -- | Tries to perform a simple embedding lookup, with two partitions.
@@ -61,9 +56,9 @@ testEmbeddingLookupHasRightShapeWithPartition =
     let ids       = TF.constant (TF.Shape [1, 2]) idValues
     let op        = embeddingLookup embedding ids
 
-    (values, shape) <- buildAndRun $ do
+    (values, shape) <- TF.runSession $ do
         vs <- op
-        return (vs, TF.shape vs)
+        TF.run (vs, TF.shape vs)
 
     -- This is the shape that is returned in the equiv. Python.
     shape  @=? V.fromList [1, 2, 3]
@@ -87,9 +82,9 @@ testEmbeddingLookupHasRightShape =
     let ids       = TF.constant (TF.Shape [1, 2]) idValues
     let op        = embeddingLookup [embedding] ids
 
-    (values, shape) <- buildAndRun $ do
+    (values, shape) <- TF.runSession $ do
         vs <- op
-        return (vs, TF.shape vs)
+        TF.run (vs, TF.shape vs)
 
     -- This is the shape that is returned in the equiv. Python.
     shape  @=? V.fromList [1, 2, 3]
@@ -106,7 +101,6 @@ testEmbeddingLookupGradients = testCase "testEmbeddingLookupGradients" $ do
     let shape = TF.Shape [2]
 
     gs <- TF.runSession $ do
-        grads <- TF.build $ do
             let embShape      = TF.Shape [2, 1]
             let embeddingInit = [1, 20 ::Float]
             let idValues      = [1, 1 :: Int32]
@@ -121,9 +115,9 @@ testEmbeddingLookupGradients = testCase "testEmbeddingLookupGradients" $ do
                 loss    = TF.mean twoNorm (TF.scalar (0 :: Int32))
 
             grad <- fmap head (TF.gradients loss [embedding])
-            return $ \xs -> TF.runWithFeeds [TF.feed x xs] grad
+            TF.runWithFeeds
-
+                [TF.feed x $ TF.encodeTensorData shape xVals]
-        grads (TF.encodeTensorData shape xVals :: TF.TensorData Float)
+                grad
     -- Gradients should be zero (or close)
     assertAllClose gs (V.fromList ([0, 0 :: Float]))
 
@@ -148,7 +142,7 @@ testEmbeddingLookupUndoesSplit
         shapedValues = TF.constant shape values
     in monadicIO $ run $ do
        (shapeOut, got, want :: V.Vector a) <-
-           TF.runSession $ TF.buildAnd TF.run $ do
+           TF.runSession $ TF.run =<< do
                embeddings <- embeddingLookup modShardedValues indicesVector
                return (TF.cast (TF.shape embeddings), embeddings, directs)
        -- Checks the explicitly documented invariant of embeddingLookup.

tensorflow-ops/tests/GradientTest.hs

@@ -13,6 +13,7 @@
 -- limitations under the License.
 
 {-# LANGUAGE OverloadedStrings #-}
+{-# LANGUAGE NoMonomorphismRestriction #-}
 {-# LANGUAGE ScopedTypeVariables #-}
 
 import Data.Int (Int32)
@@ -40,7 +41,7 @@ testGradientSimple = testCase "testGradientSimple" $ do
         y = x*x + b
         grads = TF.gradients y [x, b]
     -- Assert that the gradients are right.
-    [dx, db] <- TF.runSession $ TF.buildAnd TF.run grads
+    [dx, db] <- TF.runSession $ grads >>= TF.run
     6 @=? TF.unScalar dx
     1 @=? TF.unScalar db
     -- Assert that the graph has the expected ops.
@@ -91,7 +92,7 @@ testGradientDisconnected = testCase "testGradientDisconnected" $ do
         b = TF.scalar (4 :: Float)
         grads = TF.gradients x [x, b]
     -- Assert that the gradients are right.
-    [dx, db] <- TF.runSession $ TF.buildAnd TF.run grads
+    [dx, db] <- TF.runSession $ grads >>= TF.run
     1 @=? TF.unScalar dx
     0 @=? TF.unScalar db
     -- Assert that the graph has the expected ops.
@@ -113,11 +114,11 @@ testGradientDisconnected = testCase "testGradientDisconnected" $ do
 -- Test that identical "stateful" ops work with createGraph.
 testCreateGraphStateful :: Test
 testCreateGraphStateful = testCase "testCreateGraphStateful" $ do
-    [dx, dy] <- TF.runSession $ TF.buildAnd TF.run $ do
+    [dx, dy] <- TF.runSession $ do
         let shape = TF.constant (TF.Shape [1]) [1]
         x :: TF.Tensor TF.Value Float <- TF.truncatedNormal shape
         y :: TF.Tensor TF.Value Float <- TF.truncatedNormal shape
-        TF.gradients (x + y*3) [x, y]
+        TF.gradients (x + y*3) [x, y] >>= TF.run
     -- If this test fails, it will likely be caused by an exception within
     -- `TF.gradients`. These asserts are extra.
     1 @=? TF.unScalar dx
@@ -127,11 +128,11 @@ testCreateGraphStateful = testCase "testCreateGraphStateful" $ do
 -- Test that name scopes work with createGraph.
 testCreateGraphNameScopes :: Test
 testCreateGraphNameScopes = testCase "testCreateGraphNameScopes" $ do
-    [dx] <- TF.runSession $ TF.buildAnd TF.run $ do
+    [dx] <- TF.runSession $ do
         let shape = TF.constant (TF.Shape [1]) [1]
         x :: TF.Tensor TF.Value Float <-
             TF.withNameScope "foo" (TF.truncatedNormal shape)
-        TF.gradients x [x]
+        TF.gradients x [x] >>= TF.run
     -- If this test fails, it will likely be caused by an exception within
     -- `TF.gradients`. This assert is extra.
     1 @=? TF.unScalar dx
@@ -140,20 +141,20 @@ testCreateGraphNameScopes = testCase "testCreateGraphNameScopes" $ do
 -- Test that createGraph can handle graphs with diamond shapes.
 testDiamond :: Test
 testDiamond = testCase "testDiamond" $ do
-    [dx] <- TF.runSession $ TF.buildAnd TF.run $ do
+    [dx] <- TF.runSession $ do
         let x = TF.vector [1]
             y = x*x
             z = y*y
-        TF.gradients z [x]
+        TF.gradients z [x] >>= TF.run
     (4 :: Float) @=? TF.unScalar dx
 
 
 testMaxGradient :: Test
 testMaxGradient = testCase "testMaxGradient" $ do
-    [dx] <- TF.runSession $ TF.buildAnd TF.run $ do
+    [dx] <- TF.runSession $ do
         let x = TF.vector [1, 2, 3, 0, 1 :: Float]
             y = TF.max x (0 :: TF.Tensor TF.Value Int32)
-        TF.gradients y [x]
+        TF.gradients y [x] >>= TF.run
     V.fromList [0, 0, 1, 0, 0 :: Float] @=? dx
 
 

tensorflow-ops/tests/OpsTest.hs

@@ -41,7 +41,7 @@ testSize = testCase "testSize" $ do
     TF.Scalar (2 * 3 :: Int32) @=? x
 
 eval :: TF.Fetchable t a => t -> IO a
-eval = TF.runSession . TF.buildAnd TF.run . return
+eval = TF.runSession . TF.run
 
 -- | Confirms that the original example from Python code works.
 testReducedShape :: Test
@@ -54,16 +54,16 @@ testSaveRestore :: Test
 testSaveRestore = testCase "testSaveRestore" $
     withSystemTempDirectory "" $ \dirPath -> do
         let path = B8.pack $ dirPath ++ "/checkpoint"
-            var :: TF.Build (TF.Tensor TF.Ref Float)
+            var :: TF.MonadBuild m => m (TF.Tensor TF.Ref Float)
             var = TF.render =<<
                   TF.named "a" <$> TF.zeroInitializedVariable (TF.Shape [])
         TF.runSession $ do
-            v <- TF.build var
+            v <- var
-            TF.buildAnd TF.run_ $ TF.assign v 134
+            TF.assign v 134 >>= TF.run_
-            TF.buildAnd TF.run_ $ TF.save path [v]
+            TF.save path [v] >>= TF.run_
         result <- TF.runSession $ do
-            v <- TF.build var
+            v <- var
-            TF.buildAnd TF.run_ $ TF.restore path v
+            TF.restore path v >>= TF.run_
             TF.run v
         liftIO $ TF.Scalar 134 @=? result
 

tensorflow-ops/tests/RegressionTest.hs

@@ -25,13 +25,13 @@ fit xData yData = TF.runSession $ do
     let x = TF.vector xData
         y = TF.vector yData
     -- Create scalar variables for slope and intercept.
-    w <- TF.build (TF.initializedVariable 0)
+    w <- TF.initializedVariable 0
-    b <- TF.build (TF.initializedVariable 0)
+    b <- TF.initializedVariable 0
     -- Define the loss function.
     let yHat = (x `TF.mul` w) `TF.add` b
         loss = TF.square (yHat `TF.sub` y)
     -- Optimize with gradient descent.
-    trainStep <- TF.build (gradientDescent 0.001 loss [w, b])
+    trainStep <- gradientDescent 0.001 loss [w, b]
     replicateM_ 1000 (TF.run trainStep)
     -- Return the learned parameters.
     (TF.Scalar w', TF.Scalar b') <- TF.run (w, b)
@@ -40,7 +40,7 @@ fit xData yData = TF.runSession $ do
 gradientDescent :: Float
                 -> TF.Tensor TF.Value Float
                 -> [TF.Tensor TF.Ref Float]
-                -> TF.Build TF.ControlNode
+                -> TF.Session TF.ControlNode
 gradientDescent alpha loss params = do
     let applyGrad param grad =
             TF.assign param (param `TF.sub` (TF.scalar alpha `TF.mul` grad))

tensorflow-ops/tests/TracingTest.hs

@@ -35,7 +35,7 @@ testTracing = do
     loggedValue <- newEmptyMVar
     TF.runSessionWithOptions
         (def & TF.sessionTracer .~ putMVar loggedValue)
-        (TF.buildAnd TF.run_ (pure (TF.scalar (0 :: Float))))
+        (TF.run_ (TF.scalar (0 :: Float)))
     tryReadMVar loggedValue >>=
         maybe (assertFailure "Logging never happened") expectedFormat
   where expectedFormat x =

tensorflow-queue/src/TensorFlow/Queue.hs

@@ -24,10 +24,11 @@ import Data.ByteString (ByteString)
 import Data.Int (Int64)
 import Data.Proxy (Proxy(..))
 import Lens.Family2 ((.~), (&))
-import TensorFlow.Build (ControlNode, Build, addInitializer, opAttr, opDef)
+import TensorFlow.Build (ControlNode, MonadBuild, build, addInitializer, opAttr, opDef)
 import TensorFlow.BuildOp (buildOp)
 import TensorFlow.ControlFlow (group)
-import TensorFlow.Tensor (Ref, Tensor, TensorList)
+import qualified TensorFlow.GenOps.Core as CoreOps
+import TensorFlow.Tensor (Ref, Value, Tensor, TensorList)
 import TensorFlow.Types (TensorTypes, fromTensorTypes)
 
 -- | A queue carrying tuples.
@@ -36,36 +37,30 @@ data Queue (as :: [*]) = Queue { handle :: Handle }
 type Handle = Tensor Ref ByteString
 
 -- | Adds the given values to the queue.
-enqueue :: forall as v . TensorTypes as
+enqueue :: forall as v m . (MonadBuild m, TensorTypes as)
            => Queue as
            -> TensorList v as
-           -> Build ControlNode
+           -> m ControlNode
-enqueue q =
+enqueue = CoreOps.queueEnqueue . handle
-    buildOp (opDef "QueueEnqueue"
-             & opAttr "Tcomponents" .~ fromTensorTypes (Proxy :: Proxy as))
-    (handle q)
 
 -- | Retrieves the values from the queue.
-dequeue :: forall as . TensorTypes as
+dequeue :: forall as m . (MonadBuild m, TensorTypes as)
            => Queue as
-           -> Build (TensorList Ref as)
+           -> m (TensorList Value as)
            -- ^ Dequeued tensors. They are coupled in a sense
            -- that values appear together, even if they are
            -- not consumed together.
-dequeue q =
+dequeue = CoreOps.queueDequeue . handle
-    buildOp (opDef "QueueDequeue"
-             & opAttr "component_types" .~ fromTensorTypes (Proxy :: Proxy as))
-    (handle q)
 
 -- | Creates a new queue with the given capacity and shared name.
-makeQueue :: forall as . TensorTypes as
+makeQueue :: forall as m . (MonadBuild m, TensorTypes as)
               => Int64  -- ^ The upper bound on the number of elements in
                         --  this queue. Negative numbers mean no limit.
               -> ByteString -- ^ If non-empty, this queue will be shared
                             -- under the given name across multiple sessions.
-              -> Build (Queue as)
+              -> m (Queue as)
 makeQueue capacity sharedName = do
-    q <- buildOp (opDef "FIFOQueue"
+    q <- build $ buildOp (opDef "FIFOQueue"
                      & opAttr "component_types" .~ fromTensorTypes (Proxy :: Proxy as)
                      & opAttr "shared_name" .~ sharedName
                      & opAttr "capacity" .~ capacity

tensorflow-queue/tensorflow-queue.cabal

@@ -39,6 +39,7 @@ Test-Suite QueueTest
                , lens-family
                , google-shim
                , tensorflow
+               , tensorflow-core-ops
                , tensorflow-ops
                , tensorflow-queue
                , test-framework

tensorflow-queue/tests/QueueTest.hs

@@ -27,7 +27,6 @@ import TensorFlow.Queue
 import TensorFlow.Session
     ( asyncProdNodes
     , build
-    , buildAnd
     , run
     , runSession
     , run_
@@ -41,12 +40,12 @@ import qualified Data.ByteString as BS
 testBasic :: Test
 testBasic = testCase "testBasic" $ runSession $ do
     q :: Queue [Int64, BS.ByteString] <- build $ makeQueue 1 ""
-    buildAnd run_ $ enqueue q $ 42 :/ scalar "Hi" :/ Nil
+    run_ =<< enqueue q (42 :/ scalar "Hi" :/ Nil)
-    x <- buildAnd run (dequeue q)
+    x <- run =<< dequeue q
     liftIO $ (Scalar 42 /:/ Scalar "Hi" /:/ Nil) @=? x
 
-    buildAnd run_ $ enqueue q $ 56 :/ scalar "Bar" :/ Nil
+    run_ =<< enqueue q (56 :/ scalar "Bar" :/ Nil)
-    y <- buildAnd run (dequeue q)
+    y <- run =<< dequeue q
     -- Note: we use explicit "Scalar" here to specify the type that was
     -- fetched.  Equivalently we could write
     -- 56 /:/ "Bar" /:/ Nil :: List [Scalar Int64, Scalar BS.ByteString]
@@ -74,7 +73,7 @@ testPump = testCase "testPump" $ runSession $ do
 
 testAsync :: Test
 testAsync = testCase "testAsync" $ runSession $ do
-    (deq, pump) <- build $ do
+    (deq, pump) <- do
         q :: Queue [Int64, BS.ByteString] <- makeQueue 2 ""
         (,) <$> dequeue q
             <*> enqueue q (10 :/ scalar "Async" :/ Nil)

tensorflow/src/TensorFlow/Build.hs

@@ -37,6 +37,7 @@ module TensorFlow.Build
     , renderedNodeDefs
     , BuildT
     , Build
+    , MonadBuild(..)
     , addInitializer
     , hoistBuildT
     , evalBuildT
@@ -212,9 +213,16 @@ runBuildT (BuildT f) = runStateT f initGraphState
 evalBuildT :: Monad m => BuildT m a -> m a
 evalBuildT (BuildT f) = evalStateT f initGraphState
 
+-- | Lift a 'Build' action into a monad, including any explicit op renderings.
+class Monad m => MonadBuild m where
+    build :: Build a -> m a
+
+instance Monad m => MonadBuild (BuildT m) where
+    build = hoistBuildT $ return . runIdentity
+
 -- | Get all the NodeDefs that have accumulated so far, and clear that buffer.
-flushNodeBuffer :: Monad m => BuildT m [NodeDef]
+flushNodeBuffer :: MonadBuild m => m [NodeDef]
-flushNodeBuffer = do
+flushNodeBuffer = build $ do
     ns <- use nodeBuffer
     nodeBuffer .= []
     return ns
@@ -229,8 +237,8 @@ flushInitializers = do
 
 -- | Registers the given node to be executed before the next
 -- 'TensorFlow.Session.run'.
-addInitializer :: ControlNode -> Build ()
+addInitializer :: MonadBuild m => ControlNode -> m ()
-addInitializer (ControlNode o) = do
+addInitializer (ControlNode o) = build $ do
     i <- getOrAddOp o
     initializationNodes %= (i:)
 
@@ -242,8 +250,8 @@ asGraphDef b = def & node .~ gs ^. nodeBuffer
     gs = snd $ runIdentity $ runBuildT b
 
 -- TODO: check against existing nodes for conflicts?
-addGraphDef :: GraphDef -> Build ()
+addGraphDef :: MonadBuild m => GraphDef -> m ()
-addGraphDef g = nodeBuffer <>= g ^. node
+addGraphDef g = build $ nodeBuffer <>= g ^. node
 
 -- | Render the given op if it hasn't been rendered already, and return its
 -- name.
@@ -318,34 +326,34 @@ renderOutput (Output (OutputIx i) o) = do
 
 -- | Modify some part of the state, run an action, and restore the state
 -- after that action is done.
-withStateLens :: MonadState s m => Lens' s a -> (a -> a) -> m b -> m b
+withStateLens :: MonadBuild m => Lens' GraphState a -> (a -> a) -> m b -> m b
 withStateLens accessor f act = do
-    old <- use accessor
+    old <- build $ use accessor
-    accessor %= f
+    build $ accessor %= f
     result <- act
-    accessor .= old
+    build $ accessor .= old
     return result
 
 -- | Set a device for all nodes rendered in the given 'Build' action
 -- (unless further overridden by another use of withDevice).
-withDevice :: Maybe Device -> Build a -> Build a
+withDevice :: MonadBuild m => Maybe Device -> m a -> m a
 withDevice d = withStateLens defaultDevice (const d)
 
 -- | Places all nodes rendered in the given 'Build' action on the same
 -- device as the given Tensor (see also 'withDevice'). Make sure that
 -- the action has side effects of rendering the desired tensors. A pure
 -- return would not have the desired effect.
-colocateWith :: forall a v b . Tensor v b -> Build a -> Build a
+colocateWith :: MonadBuild m => forall a v b . Tensor v b -> m a -> m a
 colocateWith t x = do
-    d <- Device . (^. device) <$> resolveOp (t ^. tensorOutput . outputOp)
+    d <- build $ Device . (^. device) <$> resolveOp (t ^. tensorOutput . outputOp)
     withDevice (Just d) x
 
 -- | Prepend a scope to all nodes rendered in the given 'Build' action.
-withNameScope :: Text -> Build a -> Build a
+withNameScope :: MonadBuild m => Text -> m a -> m a
 withNameScope s = withStateLens currentScope (Scope s :)
 
 -- | Add control inputs to all nodes rendered in the given 'Build' action.
-withNodeDependencies :: Set NodeName -> Build a -> Build a
+withNodeDependencies :: MonadBuild m => Set NodeName -> m a -> m a
 withNodeDependencies nodes = withStateLens defaultControlInputs (<> nodes)
 
 -- | Render a 'Tensor', fixing its name, scope, device and control inputs from
@@ -355,8 +363,8 @@ withNodeDependencies nodes = withStateLens defaultControlInputs (<> nodes)
 -- This operation is idempotent; @render >=> render === render@.  However,
 -- rendering a (previously un-rendered) 'Tensor' in two different contexts
 -- may result in two different 'Tensor's.
-render :: Tensor v a -> Build (Tensor v a)
+render :: MonadBuild m => Tensor v a -> m (Tensor v a)
-render = tensorOutput $ outputOp $ fmap Rendered . resolveOp
+render = build . tensorOutput (outputOp $ fmap Rendered . resolveOp)
 
 -- | Render a 'Tensor' and get its node's name.
 renderNodeName :: Tensor v a -> Build NodeName

tensorflow/src/TensorFlow/ControlFlow.hs

@@ -40,9 +40,9 @@ import TensorFlow.Types
 
 -- | Modify a 'Build' action, such that all new ops rendered in it will depend
 -- on the nodes in the first argument.
-withControlDependencies :: Nodes t => t -> Build a -> Build a
+withControlDependencies :: (MonadBuild m, Nodes t) => t -> m a -> m a
 withControlDependencies deps act = do
-    nodes <- getNodes deps
+    nodes <- build $ getNodes deps
     withNodeDependencies nodes act
 
 -- TODO(judahjacobson): Reimplement withDependencies.
@@ -51,9 +51,9 @@ withControlDependencies deps act = do
 --
 -- When this op finishes, all ops in the input @n@ have finished.  This op has
 -- no output.
-group :: Nodes t => t -> Build ControlNode
+group :: (MonadBuild m, Nodes t) => t -> m ControlNode
 group deps = do
-    nodes <- Set.toList <$> getNodes deps
+    nodes <- build $ Set.toList <$> getNodes deps
     -- TODO: slicker way
     return $ buildOp $ opDef "NoOp" & opControlInputs .~ nodes
 

tensorflow/src/TensorFlow/Core.hs

@@ -31,8 +31,7 @@ module TensorFlow.Core
     , runSession
     , runSessionWithOptions
       -- ** Building graphs
-    , build
+    , MonadBuild(..)
-    , buildAnd
       -- ** Running graphs
     , Fetchable
     , Nodes

tensorflow/src/TensorFlow/Session.hs

@@ -26,8 +26,7 @@ module TensorFlow.Session (
     sessionTracer,
     runSession,
     runSessionWithOptions,
-    build,
+    MonadBuild(..),
-    buildAnd,
     extend,
     addGraphDef,
     run,
@@ -44,7 +43,6 @@ import Control.Monad.Trans.Class (lift)
 import Control.Monad.Trans.Reader (ReaderT(..), ask, asks)
 import Data.ByteString (ByteString)
 import Data.Default (Default, def)
-import Data.Functor.Identity (runIdentity)
 import Data.Monoid ((<>))
 import Data.ProtoLens (showMessage)
 import Data.Set (Set)
@@ -124,10 +122,8 @@ runSessionWithOptions options (Session m) =
             FFI.setSessionTarget (options ^. sessionTarget) opt
             FFI.setSessionConfig (options ^. sessionConfig) opt
 
--- | Lift a 'Build' action into a 'Session', including any explicit op
+instance MonadBuild Session where
--- renderings.
+    build = Session . lift . build
-build :: Build a -> Session a
-build = Session . lift . hoistBuildT (return . runIdentity)
 
 -- | Add all pending rendered nodes to the TensorFlow graph and runs
 -- any pending initializers.
@@ -147,13 +143,6 @@ extend = do
     unless (null initializers) $
         void $ liftIO $ FFI.run session [] [] (toNodeNames initializers)
 
--- | Helper combinator for doing something with the result of a 'Build' action.
--- Example usage:
---
--- > buildAnd run :: Fetchable t a => Build t -> Session a
-buildAnd :: (a -> Session b) -> Build a -> Session b
-buildAnd f m = build m >>= f
-
 -- | Run a subgraph 't', rendering any dependent nodes that aren't already
 -- rendered, and fetch the corresponding values for 'a'.
 run :: Fetchable t a => t -> Session a