test case to show can't calculate grad for embedding (and associated fix) (#23)

* Fix for embedding gradient calculation

- Passes vectors instead of scalars to slice
- converts the numRows to a scalar
- add `toScalar` utility function
- minor change to test case so that it actually works

* added lib for testing helper functions

* add flatSlice function

stack.yaml

@@ -11,6 +11,7 @@ packages:
 - tensorflow-mnist-input-data
 - tensorflow-queue
 - tensorflow-nn
+- tensorflow-test
 
 extra-deps:
 # proto-lens is not yet in Stackage.

tensorflow-nn/tensorflow-nn.cabal

@@ -30,6 +30,7 @@ Test-Suite NNTest
                , QuickCheck
                , base
                , tensorflow
+               , tensorflow-test
                , tensorflow-ops
                , tensorflow-nn
                , google-shim

tensorflow-nn/tests/NNTest.hs

@@ -22,6 +22,7 @@ module Main where
 
 import           Data.Maybe                         (fromMaybe)
 import           Google.Test                        (googleTest)
+import           TensorFlow.Test                    (assertAllClose)
 import           Test.Framework.Providers.HUnit     (testCase)
 import           Test.HUnit                         ((@?))
 import           Test.HUnit.Lang                    (Assertion(..))
@@ -63,15 +64,6 @@ defInputs = Inputs {
     }
 
 
-assertAllClose :: V.Vector Float -> V.Vector Float -> Assertion
-assertAllClose xs ys = all (<= tol) (V.zipWith absDiff xs ys) @?
-    ("Difference > tolerance: \nxs: " ++ show xs ++ "\nys: " ++ show ys
-        ++ "\ntolerance: " ++ show tol)
-  where
-      absDiff x y = abs (x - y)
-      tol = 0.001 :: Float
-
-
 testLogisticOutput = testCase "testLogisticOutput" $ do
     let inputs     = defInputs
         vLogits    = TF.vector $ logits  inputs

tensorflow-ops/src/TensorFlow/Gradient.hs

@@ -77,6 +77,7 @@ import TensorFlow.Ops
     , shape
     , softmaxCrossEntropyWithLogits
     , sum
+    , scalarize
     , vector
     , zerosLike
     )
@@ -402,6 +403,21 @@ type GradientFunc a = NodeDef
 toT :: Output -> Tensor Value a
 toT = Tensor ValueKind
 
+
+-- | Wrapper around `TensorFlow.GenOps.Core.slice` that builds vectors from scalars for
+-- simple slicing operations.
+flatSlice :: forall v1 t i . (TensorType t)
+         => Tensor v1 t    -- ^ __input__
+         -> Int32          -- ^ __begin__: specifies the offset into the first dimension of
+                           -- 'input' to slice from.
+         -> Int32          -- ^ __size__: specifies the number of elements of the first dimension
+                           -- of 'input' to slice. If size is -1, all remaining elements in the dimension
+                           -- are included in the slice (i.e. this is equivalent to setting
+                           -- size = input.dim_size(0) - begin).
+         -> Tensor Value t -- ^ __output__
+flatSlice input begin size = CoreOps.slice input (vector [begin]) (vector [size])
+
+
 -- | The gradient function for an op type.
 --
 -- These implementations should match their python counterparts in:
@@ -430,10 +446,9 @@ opGrad "Gather" _ [toT -> x, toT -> indices] [dz] =
   where
     -- TODO(gnezdo): Use colocateWith but it requires Build monad.
     denseShape = shape (x :: Tensor Value a)
-    numRows = CoreOps.slice denseShape 0 (1 :: Tensor Value Int32)
+    numRows = scalarize $ flatSlice denseShape 0 1
-    valuesShape = CoreOps.concat 0 [
+    valuesShape = CoreOps.concat 0 [ allDimensions
-                                 allDimensions
+                                   , flatSlice denseShape 1 (-1)
-                               , CoreOps.slice denseShape 1 (-1 :: Tensor Value Int32)
                                    ]
     values = reshape dz valuesShape
     -- TODO(fmayle): This could be either Int32 or Int64.
@@ -628,7 +643,7 @@ opGrad "SparseSegmentSum" _ [toT -> x, toT -> y, toT -> t] [dz] =
     , Nothing
     , Nothing
     ]
-  where inputRows = CoreOps.slice (shape (x :: Tensor Value a)) (scalar (0 :: Int32)) (scalar 1)
+  where inputRows = flatSlice (shape (x :: Tensor Value a)) 0 1
 
 opGrad "LabelClasses" _ _ _ = [Nothing, Nothing]
 opGrad "LabelWeights" _ _ _ = [Nothing]

tensorflow-ops/src/TensorFlow/Ops.hs

@@ -101,6 +101,7 @@ module TensorFlow.Ops
     , vector
     , zeros
     , CoreOps.zerosLike
+    , scalarize
     ) where
 
 import Data.ByteString (ByteString)
@@ -256,6 +257,15 @@ constant (Shape shape') values
                       [def & TensorShape.size .~ x | x <- shape']
                 & tensorVal .~ values
 
+-- | Reshape a N-D tensor down to a scalar.
+-- 
+-- See `TensorFlow.GenOps.Core.reshape`.
+scalarize :: (TensorType a) => Tensor v a -> Tensor Value a
+scalarize t = CoreOps.reshape t (vector scalarShape)
+    where
+        scalarShape = [] :: [Int32]
+
+
 -- | Create a constant vector.
 vector :: TensorType a => [a] -> Tensor Value a
 vector xs = constant [fromIntegral $ length xs] xs

tensorflow-ops/tensorflow-ops.cabal

@@ -60,6 +60,7 @@ Test-Suite EmbeddingOpsTest
                , lens-family
                , google-shim
                , tensorflow
+               , tensorflow-test
                , tensorflow-core-ops
                , tensorflow-ops
                , tensorflow-proto

tensorflow-ops/tests/EmbeddingOpsTest.hs

@@ -23,10 +23,12 @@ import Data.List (genericLength)
 import Google.Test (googleTest)
 import TensorFlow.EmbeddingOps (embeddingLookup)
 import Test.Framework.Providers.QuickCheck2 (testProperty)
-import Test.HUnit ((@=?))
+import Test.HUnit.Lang (Assertion(..))
+import Test.HUnit ((@=?), (@?))
 import Test.Framework.Providers.HUnit (testCase)
 import Test.QuickCheck (Arbitrary(..), Property, choose, vectorOf)
 import Test.QuickCheck.Monadic (monadicIO, run)
+import TensorFlow.Test (assertAllClose)
 
 import qualified Data.Vector as V
 import qualified TensorFlow.GenOps.Core as CoreOps
@@ -34,21 +36,26 @@ import qualified TensorFlow.Ops as TF
 import qualified TensorFlow.Session as TF
 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.
-testEmbeddingLookupHasRightShapeWithPartition = testCase "testEmbeddingLookupHasRightShapeWithPartition" $ do
-    let shape       = TF.Shape [1, 3] -- Consider a 3-dim embedding of two items.
-    let embedding1  = [ 1, 1, 1 ] :: [Int32]
-    let embedding2  = [ 0, 0, 0 ] :: [Int32]
 
+-- | Tries to perform a simple embedding lookup, with two partitions.
+testEmbeddingLookupHasRightShapeWithPartition = 
+        testCase "testEmbeddingLookupHasRightShapeWithPartition" $ do
+    let shape       = TF.Shape [1, 3] -- Consider a 3-dim embedding of two items.
+    let embedding1  = [1, 1, 1 :: Int32]
+    let embedding2  = [0, 0, 0 :: Int32]
     let embedding   = [ TF.constant shape embedding1
                       , TF.constant shape embedding2
                       ]
 
-    let idValues  = [0, 1] :: [Int32]
+    let idValues  = [0, 1 :: Int32]
     let ids       = TF.constant (TF.Shape [1, 2]) idValues
     let op        = embeddingLookup embedding ids
 
@@ -57,20 +64,23 @@ testEmbeddingLookupHasRightShapeWithPartition = testCase "testEmbeddingLookupHas
         return (vs, TF.shape vs)
 
     -- This is the shape that is returned in the equiv. Python.
-    shape  @=? V.fromList [ 1, 2, 3 ]
+    shape  @=? V.fromList [1, 2, 3]
 
     -- "[0, 1]" should pull out the resulting vector.
-    values @=? V.fromList [ 1, 1, 1, 0, 0, 0 ]
+    values @=? V.fromList [1, 1, 1, 0, 0, 0]
 
 
 -- | Tries to perform a simple embedding lookup, with only a single partition.
-testEmbeddingLookupHasRightShape = testCase "testEmbeddingLookupHasRightShape" $ do
+testEmbeddingLookupHasRightShape = 
-    let shape         = TF.Shape [2, 3] -- Consider a 3-dim embedding of two items.
+        testCase "testEmbeddingLookupHasRightShape" $ do
+    -- Consider a 3-dim embedding of two items
+    let shape         = TF.Shape [2, 3]
     let embeddingInit = [ 1, 1, 1
-                        , 0, 0, 0 ] :: [Int32]
+                        , 0, 0, 0 :: Int32
+                        ]
 
     let embedding = TF.constant shape embeddingInit
-    let idValues  = [0, 1] :: [Int32]
+    let idValues  = [0, 1 :: Int32]
     let ids       = TF.constant (TF.Shape [1, 2]) idValues
     let op        = embeddingLookup [embedding] ids
 
@@ -79,10 +89,39 @@ testEmbeddingLookupHasRightShape = testCase "testEmbeddingLookupHasRightShape" $
         return (vs, TF.shape vs)
 
     -- This is the shape that is returned in the equiv. Python.
-    shape  @=? V.fromList [ 1, 2, 3 ]
+    shape  @=? V.fromList [1, 2, 3]
 
     -- "[0, 1]" should pull out the resulting vector.
-    values @=? V.fromList [ 1, 1, 1, 0, 0, 0 ]
+    values @=? V.fromList [1, 1, 1, 0, 0, 0]
+
+
+-- | Check that we can calculate gradients w.r.t embeddings.
+testEmbeddingLookupGradients = testCase "testEmbeddingLookupGradients" $ do
+    -- Agrees with "embedding", so gradient should be zero.
+    let xVals = V.fromList ([20, 20 :: Float])
+    let shape = TF.Shape [2]
+
+    gs <- TF.runSession $ do
+        grads <- TF.build $ do
+            let shape         = TF.Shape [2, 1] 
+            let embeddingInit = [1, 20 ::Float]
+            let idValues      = [1, 1 :: Int32]
+            let ids           = TF.constant (TF.Shape [1, 2]) idValues
+
+            x <- TF.placeholder (TF.Shape [2]) 
+            embedding <- TF.initializedVariable 
+                            =<< TF.render (TF.constant shape embeddingInit)
+            
+            op <- embeddingLookup [embedding] ids
+            let twoNorm = CoreOps.square $ TF.abs (op - x)
+                loss    = TF.mean twoNorm (TF.scalar (0 :: Int32))
+
+            grad <- fmap head (TF.gradients loss [embedding])
+            return $ \xs -> TF.runWithFeeds [TF.feed x xs] grad
+
+        grads (TF.encodeTensorData shape xVals :: TF.TensorData Float)
+    -- Gradients should be zero (or close)
+    assertAllClose gs (V.fromList ([0, 0 :: Float]))
 
 
 -- Verifies that direct gather is the same as dynamic split into
@@ -138,4 +177,5 @@ main = googleTest
          (testEmbeddingLookupUndoesSplit :: LookupExample Double -> Property)
        , testEmbeddingLookupHasRightShape
        , testEmbeddingLookupHasRightShapeWithPartition
+       , testEmbeddingLookupGradients
        ]

tensorflow-test/Setup.hs

@@ -0,0 +1,3 @@
+import Distribution.Simple
+
+main = defaultMain

tensorflow-test/src/TensorFlow/Test.hs

@@ -0,0 +1,34 @@
+-- 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 OverloadedStrings #-}
+
+module TensorFlow.Test
+    ( assertAllClose
+    ) where
+
+import qualified Data.Vector as V
+import Test.HUnit ((@?))
+import Test.HUnit.Lang (Assertion(..))
+
+-- | Compares that the vectors are element-by-element equal within the given
+-- tolerance. Raises an assertion and prints some information if not.
+assertAllClose :: V.Vector Float -> V.Vector Float -> Assertion
+assertAllClose xs ys = all (<= tol) (V.zipWith absDiff xs ys) @?
+    "Difference > tolerance: \nxs: " ++ show xs ++ "\nys: " ++ show ys
+        ++ "\ntolerance: " ++ show tol
+  where
+      absDiff x y = abs (x - y)
+      tol = 0.001 :: Float
+

tensorflow-test/tensorflow-test.cabal

@@ -0,0 +1,24 @@
+name:                tensorflow-test
+version:             0.1.0.0
+synopsis:            Some common functions for test suites.
+homepage:            https://github.com/tensorflow/haskell#readme
+license:             Apache
+author:              TensorFlow authors
+maintainer:          tensorflow-haskell@googlegroups.com
+copyright:           Google Inc.
+category:            Machine Learning
+build-type:          Simple
+cabal-version:       >=1.22
+
+library
+  hs-source-dirs:   src
+  exposed-modules: TensorFlow.Test
+  build-depends:  base >= 4.7 && < 5
+                , HUnit
+                , vector
+  default-language:    Haskell2010
+
+
+source-repository head
+  type:     git
+  location: https://github.com/tensorflow/haskell