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d62c614695
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.
103 lines
3.8 KiB
Haskell
103 lines
3.8 KiB
Haskell
-- Copyright 2016 TensorFlow authors.
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--
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-- Licensed under the Apache License, Version 2.0 (the "License");
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-- you may not use this file except in compliance with the License.
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-- You may obtain a copy of the License at
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--
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-- http://www.apache.org/licenses/LICENSE-2.0
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--
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-- Unless required by applicable law or agreed to in writing, software
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-- distributed under the License is distributed on an "AS IS" BASIS,
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-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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-- See the License for the specific language governing permissions and
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-- limitations under the License.
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{-# LANGUAGE OverloadedLists #-}
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{-# LANGUAGE OverloadedStrings #-}
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module Main where
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import Control.Monad.IO.Class (liftIO)
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import Data.Int (Int32, Int64)
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import Google.Test (googleTest)
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import Lens.Family2 ((.~))
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import System.IO.Temp (withSystemTempDirectory)
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import Test.Framework (Test)
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import Test.Framework.Providers.HUnit (testCase)
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import Test.HUnit ((@=?))
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import qualified Data.ByteString.Char8 as B8
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import qualified Data.Vector as V
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import qualified TensorFlow.Build as TF
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import qualified TensorFlow.Nodes as TF
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import qualified TensorFlow.Ops as TF
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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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-- | Test that one can easily determine number of elements in the tensor.
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testSize :: Test
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testSize = testCase "testSize" $ do
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x <- eval $ TF.size (TF.constant (TF.Shape [2, 3]) [0..5 :: Float])
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TF.Scalar (2 * 3 :: Int32) @=? x
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eval :: TF.Fetchable t a => t -> IO a
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eval = TF.runSession . TF.run
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-- | Confirms that the original example from Python code works.
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testReducedShape :: Test
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testReducedShape = testCase "testReducedShape" $ do
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x <- eval $ TF.reducedShape (TF.vector [2, 3, 5, 7 :: Int64])
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(TF.vector [1, 2 :: Int32])
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V.fromList [2, 1, 1, 7 :: Int32] @=? x
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testSaveRestore :: Test
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testSaveRestore = testCase "testSaveRestore" $
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withSystemTempDirectory "" $ \dirPath -> do
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let path = B8.pack $ dirPath ++ "/checkpoint"
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var :: TF.MonadBuild m => m (TF.Tensor TF.Ref Float)
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var = TF.zeroInitializedVariable' (TF.opName .~ "a")
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(TF.Shape [])
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TF.runSession $ do
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v <- var
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TF.assign v 134 >>= TF.run_
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TF.save path [v] >>= TF.run_
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result <- TF.runSession $ do
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v <- var
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TF.restore path v >>= TF.run_
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TF.run v
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liftIO $ TF.Scalar 134 @=? result
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-- | Test that 'placeholder' is not CSE'd.
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testPlaceholderCse :: Test
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testPlaceholderCse = testCase "testPlaceholderCse" $ TF.runSession $ do
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p1 <- TF.placeholder []
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p2 <- TF.placeholder []
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let enc :: Float -> TF.TensorData Float
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enc n = TF.encodeTensorData [] (V.fromList [n])
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result <- TF.runWithFeeds [TF.feed p1 (enc 2), TF.feed p2 (enc 3)]
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$ p1 `TF.add` p2
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liftIO $ result @=? TF.Scalar 5
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-- | Test that regular tensors can also be used for feeds, as long as they each
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-- have a different name.
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testScalarFeedCse :: Test
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testScalarFeedCse = testCase "testScalarFeedCse" $ TF.runSession $ do
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p1 <- TF.render $ TF.scalar' (TF.opName .~ "A") 0
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-- The second op is identical to the first other than its name; make sure
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-- we don't aggressively CSE them together and prevent feeding them
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-- separately.
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p2 <- TF.render $ TF.scalar' (TF.opName .~ "B") 0
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let enc :: Float -> TF.TensorData Float
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enc n = TF.encodeTensorData [] (V.fromList [n])
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result <- TF.runWithFeeds [TF.feed p1 (enc 2), TF.feed p2 (enc 3)]
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$ p1 `TF.add` p2
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liftIO $ result @=? TF.Scalar 5
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main :: IO ()
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main = googleTest [ testSaveRestore
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, testSize
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, testReducedShape
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, testPlaceholderCse
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, testScalarFeedCse
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]
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