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Merge pull request #22 from tensorflow/embedding-lookup-fix
Embedding lookup fix
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commit
9e005e3af7
2 changed files with 83 additions and 18 deletions
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@ -23,9 +23,8 @@ module TensorFlow.EmbeddingOps where
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import Control.Monad (zipWithM)
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import Data.Int (Int32, Int64)
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import Data.List (genericLength)
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import TensorFlow.Build (Build, colocateWith, render)
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import TensorFlow.Ops () -- Num instance for Tensor
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import TensorFlow.Ops (scalar, shape, vector) -- Also Num instance for Tensor
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import TensorFlow.Tensor (Tensor, Value)
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import TensorFlow.Types (OneOf, TensorType)
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import qualified TensorFlow.GenOps.Core as CoreOps
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@ -56,21 +55,34 @@ embeddingLookup :: forall a b v .
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-> Tensor Value b
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-- ^ A `Tensor` with type `int32` or `int64`
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-- containing the ids to be looked up in `params`.
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-- The ids are required to be flat on entry and have
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-- fewer than 2^31 entries.
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-- The ids are required to have fewer than 2^31
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-- entries.
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-> Build (Tensor Value a)
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-- ^ A dense tensor with shape `shape(ids) + shape(params)[1:]`.
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embeddingLookup params ids =
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CoreOps.dynamicStitch pindices <$> partitionedResult
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where np = genericLength params
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pAssignments = CoreOps.cast (ids `CoreOps.mod` np)
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newIds = ids `CoreOps.div` np
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originalIndices = CoreOps.range 0 (CoreOps.size ids) 1
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-- Partition list of ids based on assignments into np separate lists
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gatherIds = CoreOps.dynamicPartition np newIds pAssignments
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-- Similarly, partition the original indices.
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pindices = CoreOps.dynamicPartition np originalIndices pAssignments
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-- Do np separate lookups, finding embeddings for plist[p] in params[p]
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partitionedResult = zipWithM
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(\p g -> colocateWith p $ render $ CoreOps.gather p g)
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params gatherIds
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embeddingLookup [p0] ids = colocateWith p0 (render $ CoreOps.gather p0 ids)
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embeddingLookup params@(p0 : _) ids = do
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-- Do np separate lookups, finding embeddings for plist[p] in params[p]
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partitionedResult <- zipWithM
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(\p g -> colocateWith p $ render $ CoreOps.gather p g)
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params gatherIds
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let unshapedResult = CoreOps.dynamicStitch pindices partitionedResult
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-- Shape restoration is not as optimal as it would be with client
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-- side shape tracking.
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paramShape <- colocateWith p0 (render (shape p0))
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let finalShape = CoreOps.concat 0 [shape ids, tailShape]
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tailShape = CoreOps.slice paramShape (singleton 1) (singleton (-1))
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render $ CoreOps.reshape unshapedResult finalShape
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where
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-- Avoids genericLength here which would be evaluated by TF.
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np = fromIntegral (length params)
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flatIds = CoreOps.reshape ids (singleton (-1))
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pAssignments = CoreOps.cast (flatIds `CoreOps.mod` np)
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newIds = flatIds `CoreOps.div` np
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originalIndices = CoreOps.range 0 (CoreOps.size flatIds) 1
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-- Partition list of ids based on assignments into np separate lists
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gatherIds = CoreOps.dynamicPartition np newIds pAssignments
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-- Similarly, partition the original indices.
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pindices = CoreOps.dynamicPartition np originalIndices pAssignments
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singleton i = vector [i :: Int32]
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embeddingLookup [] _ = error "embeddingLookup requires params to be non empty"
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@ -24,6 +24,7 @@ import Google.Test (googleTest)
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import TensorFlow.EmbeddingOps (embeddingLookup)
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import Test.Framework.Providers.QuickCheck2 (testProperty)
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import Test.HUnit ((@=?))
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import Test.Framework.Providers.HUnit (testCase)
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import Test.QuickCheck (Arbitrary(..), Property, choose, vectorOf)
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import Test.QuickCheck.Monadic (monadicIO, run)
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@ -34,6 +35,56 @@ import qualified TensorFlow.Session as TF
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import qualified TensorFlow.Tensor as TF
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import qualified TensorFlow.Types as TF
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buildAndRun = TF.runSession . TF.buildAnd TF.run
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-- | Tries to perform a simple embedding lookup, with two partitions.
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testEmbeddingLookupHasRightShapeWithPartition = testCase "testEmbeddingLookupHasRightShapeWithPartition" $ do
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let shape = TF.Shape [1, 3] -- Consider a 3-dim embedding of two items.
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let embedding1 = [ 1, 1, 1 ] :: [Int32]
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let embedding2 = [ 0, 0, 0 ] :: [Int32]
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let embedding = [ TF.constant shape embedding1
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, TF.constant shape embedding2
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]
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let idValues = [0, 1] :: [Int32]
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let ids = TF.constant (TF.Shape [1, 2]) idValues
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let op = embeddingLookup embedding ids
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(values, shape) <- buildAndRun $ do
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vs <- op
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return (vs, TF.shape vs)
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-- This is the shape that is returned in the equiv. Python.
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shape @=? V.fromList [ 1, 2, 3 ]
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-- "[0, 1]" should pull out the resulting vector.
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values @=? V.fromList [ 1, 1, 1, 0, 0, 0 ]
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-- | Tries to perform a simple embedding lookup, with only a single partition.
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testEmbeddingLookupHasRightShape = testCase "testEmbeddingLookupHasRightShape" $ do
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let shape = TF.Shape [2, 3] -- Consider a 3-dim embedding of two items.
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let embeddingInit = [ 1, 1, 1
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, 0, 0, 0 ] :: [Int32]
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let embedding = TF.constant shape embeddingInit
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let idValues = [0, 1] :: [Int32]
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let ids = TF.constant (TF.Shape [1, 2]) idValues
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let op = embeddingLookup [embedding] ids
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(values, shape) <- buildAndRun $ do
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vs <- op
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return (vs, TF.shape vs)
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-- This is the shape that is returned in the equiv. Python.
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shape @=? V.fromList [ 1, 2, 3 ]
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-- "[0, 1]" should pull out the resulting vector.
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values @=? V.fromList [ 1, 1, 1, 0, 0, 0 ]
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-- Verifies that direct gather is the same as dynamic split into
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-- partitions, followed by embedding lookup.
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testEmbeddingLookupUndoesSplit :: forall a. (TF.TensorType a, Show a, Eq a)
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@ -85,4 +136,6 @@ main :: IO ()
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main = googleTest
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[ testProperty "EmbeddingLookupUndoesSplit"
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(testEmbeddingLookupUndoesSplit :: LookupExample Double -> Property)
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, testEmbeddingLookupHasRightShape
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, testEmbeddingLookupHasRightShapeWithPartition
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]
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