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tensorflow-haskell/tensorflow-ops/tests/BuildTest.hs
Judah Jacobson d62c614695 Distinguish between "rendered" and "unrendered" Tensors. (#88)
Distinguish between "rendered" and "unrendered" Tensors.

There are now three types of `Tensor`:

- `Tensor Value a`: rendered value
- `Tensor Ref a`: rendered reference
- `Tensor Build a` : unrendered value

The extra bookkeeping makes it easier to track (and enforce) which tensors are
rendered or not.  For examples where this has been confusing in the past, see

With this change, pure ops look similar to before, returning `Tensor Build`
instead of `Tensor Value`.  "Stateful" (monadic) ops are unchanged.  For
example:

    add :: OneOf [..] t => Tensor v'1 t -> Tensor v'2 t -> Tensor Build t
    assign :: (MonadBuild m, TensorType t)
           => Tensor Ref t -> Tensor v'2 t -> m (Tensor Ref t)

The `gradients` function now requires that the variables over which it's
differentiating are pre-rendered:

    gradients :: (..., Rendered v2) => Tensor v1 a -> [Tensor v2 a]
              -> m [Tensor Value a]

(`Rendered v2` means that `v2` is either a `Ref` or a `Value`.)

Additionally, the implementation of `gradients` now takes care to render every
intermediate value when performing the reverse accumulation.  I suspect this
fixes an exponential blowup for complicated expressions.
2017-04-06 15:10:33 -07:00

176 lines
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Haskell

-- 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 #-}
{-# LANGUAGE OverloadedLists #-}
{-# LANGUAGE ScopedTypeVariables #-}
module Main where
import Control.Monad.IO.Class (liftIO)
import Lens.Family2 ((^.), (.~))
import Data.List (sort)
import Proto.Tensorflow.Core.Framework.Graph
( node )
import Proto.Tensorflow.Core.Framework.NodeDef
( NodeDef
, device
, name
, op )
import TensorFlow.Build
( Build
, BuildT
, asGraphDef
, evalBuildT
, flushNodeBuffer
, withDevice
, withNameScope
, opName
)
import TensorFlow.Types (unScalar)
import TensorFlow.Ops
( add
, assign
, constant
, initializedVariable
, variable
, variable'
)
import TensorFlow.Output (Device(..))
import TensorFlow.Tensor
( colocateWith
, render
, Tensor
, Value
, Ref
)
import TensorFlow.Session
( run
, runSession
, run_
)
import Test.Framework (Test)
import Test.Framework.Providers.HUnit (testCase)
import Test.HUnit ((@=?))
import Google.Test (googleTest)
import qualified Data.Vector as V
-- | Test 'opName' behavior.
testOpName :: Test
testOpName = testCase "testOpName" $ do
let graph = variable' (opName .~ "foo") [] :: Build (Tensor Ref Float)
nodeDef :: NodeDef
nodeDef = head $ asGraphDef graph ^. node
"Variable" @=? (nodeDef ^. op)
"foo" @=? (nodeDef ^. name)
-- | Test that "run" will render and extend any pure ops that haven't already
-- been rendered.
testPureRender :: Test
testPureRender = testCase "testPureRender" $ runSession $ do
result <- run $ 2 `add` 2
liftIO $ 4 @=? (unScalar result :: Float)
-- | Test that "run" assigns any previously accumulated initializers.
testInitializedVariable :: Test
testInitializedVariable =
testCase "testInitializedVariable" $ runSession $ do
(formula, reset) <- do
v <- initializedVariable 42
r <- assign v 24
return (1 `add` v, r)
result <- run formula
liftIO $ 43 @=? (unScalar result :: Float)
run_ reset -- Updates v to a different value
rerunResult <- run formula
liftIO $ 25 @=? (unScalar rerunResult :: Float)
testInitializedVariableShape :: Test
testInitializedVariableShape =
testCase "testInitializedVariableShape" $ runSession $ do
vector <- initializedVariable (constant [1] [42 :: Float])
result <- run vector
liftIO $ [42] @=? (result :: V.Vector Float)
-- | Test nameScoped behavior.
testNameScoped :: Test
testNameScoped = testCase "testNameScoped" $ do
let graph = withNameScope "foo" $ variable [] :: Build (Tensor Ref Float)
nodeDef :: NodeDef
[nodeDef] = asGraphDef graph ^. node
"foo/Variable_0" @=? (nodeDef ^. name) -- TODO: Check prefix.
"Variable" @=? (nodeDef ^. op)
-- | Test combined opName and nameScoped behavior.
testNamedAndScoped :: Test
testNamedAndScoped = testCase "testNamedAndScoped" $ do
let graph :: Build (Tensor Ref Float)
graph = withNameScope "foo1" (variable' (opName .~ "bar1") [])
nodeDef :: NodeDef
nodeDef = head $ asGraphDef graph ^. node
"Variable" @=? (nodeDef ^. op)
"foo1/bar1" @=? (nodeDef ^. name)
-- | 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 <$> flushNodeBuffer
-- | Test the interaction of rendering, CSE and scoping.
testRenderDedup :: Test
testRenderDedup = testCase "testRenderDedup" $ evalBuildT $ do
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.
withNameScope "foo" renderNodes
scopedNames <- flushed (^. name)
liftIO $ ["foo/Const_4", "foo/Variable_3", "foo/Variable_5"] @=? scopedNames
where
renderNodes = do
-- A stateful op and a pure op.
_ :: Tensor Ref Float <- variable []
_ :: Tensor Value Float <- render 3
-- Another stateful op, and a pure op which should be
-- deduped with the previous one.
_ :: Tensor Ref Float <- variable []
_ :: Tensor Value Float <- render 3
return ()
-- | Test the interaction of rendering, CSE and scoping.
testDeviceColocation :: Test
testDeviceColocation = testCase "testDeviceColocation" $ evalBuildT $ do
renderNodes
devices <- flushed (\x -> (x ^. name, x ^. device))
liftIO $ [ ("Add_2","dev0")
, ("Const_1","dev0")
, ("Variable_0","dev0")] @=? devices
where
renderNodes = do
-- A stateful op and a pure op.
var :: Tensor Ref Float <- withDevice (Just $ Device "dev0") $ variable []
-- Uses render to cause the expression be added to the graph.
_ <- colocateWith var $ render $ 3 `add` var
return ()
main :: IO ()
main = googleTest [ testInitializedVariable
, testInitializedVariableShape
, testDeviceColocation
, testOpName
, testNameScoped
, testNamedAndScoped
, testPureRender
, testRenderDedup
]