horde-ad-0.1.0.0: test/simplified/TestMnistCNNS.hs
{-# OPTIONS_GHC -fplugin GHC.TypeLits.KnownNat.Solver #-}
{-# OPTIONS_GHC -fplugin GHC.TypeLits.Normalise #-}
-- | Tests of "MnistCnnShaped2" convolutional neural network
-- using a few different optimization pipelines.
--
-- With the current CPU backend it's slow enough that it's hard to see
-- if it trains.
module TestMnistCNNS
( testTrees
) where
import Prelude
import Control.Monad (foldM, unless)
import GHC.TypeLits (KnownNat, type (<=))
import System.IO (hPutStrLn, stderr)
import System.Random
import Test.Tasty
import Test.Tasty.HUnit hiding (assert)
import Text.Printf
import Data.Array.Nested.Shaped.Shape
import HordeAd
import HordeAd.Core.Adaptor
import HordeAd.Core.AstEnv
import HordeAd.Core.AstFreshId
import HordeAd.Core.AstInterpret
import EqEpsilon
import MnistCnnShaped2 qualified
import MnistData
-- TODO: optimize enough that it can run for one full epoch in reasonable time
-- and then verify it trains down to ~20% validation error in a short enough
-- time to include such a training run in tests.
testTrees :: [TestTree]
testTrees = [ tensorADValMnistTestsCNNSA
, tensorADValMnistTestsCNNSI
, tensorADValMnistTestsCNNSO
]
type XParams kh kw c_out n_hidden r =
X (MnistCnnShaped2.ADCnnMnistParametersShaped
Concrete SizeMnistHeight SizeMnistWidth kh kw c_out n_hidden r)
-- POPL differentiation, straight via the ADVal instance of RankedTensor,
-- which side-steps vectorization.
mnistTestCaseCNNSA
:: forall kh kw r.
( 1 <= kh, 1 <= kw
, Differentiable r, GoodScalar r, PrintfArg r, AssertEqualUpToEpsilon r )
=> String
-> Int -> Int -> SNat kh -> SNat kw -> Int -> Int -> Int -> Int -> r
-> TestTree
mnistTestCaseCNNSA prefix epochs maxBatches kh@SNat kw@SNat c_outInt n_hiddenInt
miniBatchSizeInt totalBatchSize expected =
withSNat c_outInt $ \(_c_outSNat :: SNat c_out) ->
withSNat n_hiddenInt $ \(_n_hiddenSNat :: SNat n_hidden) ->
withSNat miniBatchSizeInt $ \(miniBatchSize :: SNat miniBatchSize) ->
let targetInit =
fst $ randomValue
@(Concrete (X (MnistCnnShaped2.ADCnnMnistParametersShaped
Concrete SizeMnistHeight SizeMnistWidth
kh kw c_out n_hidden r)))
0.4 (mkStdGen 44)
name = prefix ++ ": "
++ unwords [ show epochs, show maxBatches
, show (sNatValue kh), show (sNatValue kw)
, show c_outInt, show n_hiddenInt
, show miniBatchSizeInt
, show $ widthSTK $ knownSTK @(XParams kh kw c_out n_hidden r)
, show (tsize knownSTK targetInit) ]
ftest :: KnownNat batch_size
=> MnistDataBatchS batch_size r
-> Concrete (XParams kh kw c_out n_hidden r) -> r
ftest @batch_size mnistData pars =
MnistCnnShaped2.convMnistTestS kh kw (SNat @c_out) (SNat @n_hidden)
(SNat @batch_size) mnistData (fromTarget @Concrete pars)
in testCase name $ do
hPutStrLn stderr $
printf "\n%s: Epochs to run/max batches per epoch: %d/%d"
prefix epochs maxBatches
trainData <- map mkMnistDataS
<$> loadMnistData trainGlyphsPath trainLabelsPath
testData <- map mkMnistDataS . take (totalBatchSize * maxBatches)
<$> loadMnistData testGlyphsPath testLabelsPath
withSNat (totalBatchSize * maxBatches) $ \(SNat @lenTestData) -> do
let testDataS = mkMnistDataBatchS testData
f :: MnistDataBatchS miniBatchSize r
-> ADVal Concrete (XParams kh kw c_out n_hidden r)
-> ADVal Concrete (TKScalar r)
f (glyphR, labelR) adinputs =
MnistCnnShaped2.convMnistLossFusedS
kh kw (SNat @c_out) (SNat @n_hidden)
miniBatchSize (sconcrete glyphR, sconcrete labelR)
(fromTarget adinputs)
runBatch :: (Concrete (XParams kh kw c_out n_hidden r), StateAdam (XParams kh kw c_out n_hidden r))
-> (Int, [MnistDataS r])
-> IO (Concrete (XParams kh kw c_out n_hidden r), StateAdam (XParams kh kw c_out n_hidden r))
runBatch (!parameters, !stateAdam) (k, chunk) = do
let chunkS = map mkMnistDataBatchS
$ filter (\ch -> length ch == miniBatchSizeInt)
$ chunksOf miniBatchSizeInt chunk
res@(parameters2, _) =
sgdAdam f chunkS parameters stateAdam
trainScore = withSNat (length chunk) $ \(SNat @len) ->
ftest @len (mkMnistDataBatchS chunk) parameters2
testScore = ftest @lenTestData testDataS parameters2
lenChunk = length chunk
unless (n_hiddenInt < 10) $ do
hPutStrLn stderr $
printf "\n%s: (Batch %d with %d points)"
prefix k lenChunk
hPutStrLn stderr $
printf "%s: Training error: %.2f%%"
prefix ((1 - trainScore) * 100)
hPutStrLn stderr $
printf "%s: Validation error: %.2f%%"
prefix ((1 - testScore ) * 100)
return res
let runEpoch :: Int
-> (Concrete (XParams kh kw c_out n_hidden r), StateAdam (XParams kh kw c_out n_hidden r))
-> IO (Concrete (XParams kh kw c_out n_hidden r))
runEpoch n (params2, _) | n > epochs = return params2
runEpoch n paramsStateAdam@(!_, !_) = do
unless (n_hiddenInt < 10) $
hPutStrLn stderr $ printf "\n%s: [Epoch %d]" prefix n
let trainDataShuffled = shuffle (mkStdGen $ n + 5) trainData
chunks = take maxBatches
$ zip [1 ..]
$ chunksOf totalBatchSize trainDataShuffled
res <- foldM runBatch paramsStateAdam chunks
runEpoch (succ n) res
ftk = tftk @Concrete (knownSTK @(XParams kh kw c_out n_hidden r)) targetInit
res <- runEpoch 1 (targetInit, initialStateAdam ftk)
let testErrorFinal =
1 - ftest testDataS res
testErrorFinal @?~ expected
tensorADValMnistTestsCNNSA :: TestTree
tensorADValMnistTestsCNNSA = testGroup "CNNS ADVal MNIST tests"
[ mnistTestCaseCNNSA "CNNSA 1 epoch, 1 batch"
1 1 (SNat @4) (SNat @4) 8 16 1 1
(1 :: Double)
, mnistTestCaseCNNSA "CNNSA artificial 1 2 3 4 5"
1 1 (SNat @2) (SNat @3) 4 5 1 10
(1 :: Float)
, mnistTestCaseCNNSA "CNNSA artificial 5 4 3 2 1"
5 4 (SNat @3) (SNat @2) 1 1 1 1
(1 :: Double)
, mnistTestCaseCNNSA "CNNSA 1 epoch, 0 batch"
1 0 (SNat @4) (SNat @4) 16 64 16 50
(1.0 :: Float)
]
-- POPL differentiation, with Ast term defined and vectorized only once,
-- but differentiated anew in each gradient descent iteration.
mnistTestCaseCNNSI
:: forall kh kw r.
( 1 <= kh, 1 <= kw
, Differentiable r, GoodScalar r, PrintfArg r, AssertEqualUpToEpsilon r )
=> String
-> Int -> Int -> SNat kh -> SNat kw -> Int -> Int -> Int -> Int -> r
-> TestTree
mnistTestCaseCNNSI prefix epochs maxBatches kh@SNat kw@SNat c_outInt n_hiddenInt
miniBatchSizeInt totalBatchSize expected =
withSNat c_outInt $ \(_c_outSNat :: SNat c_out) ->
withSNat n_hiddenInt $ \(_n_hiddenSNat :: SNat n_hidden) ->
withSNat miniBatchSizeInt $ \(miniBatchSize :: SNat miniBatchSize) ->
let targetInit =
fst $ randomValue
@(Concrete (X (MnistCnnShaped2.ADCnnMnistParametersShaped
Concrete SizeMnistHeight SizeMnistWidth
kh kw c_out n_hidden r)))
0.4 (mkStdGen 44)
name = prefix ++ ": "
++ unwords [ show epochs, show maxBatches
, show (sNatValue kh), show (sNatValue kw)
, show c_outInt, show n_hiddenInt
, show miniBatchSizeInt
, show $ widthSTK $ knownSTK @(XParams kh kw c_out n_hidden r)
, show (tsize knownSTK targetInit) ]
ftest :: KnownNat batch_size
=> MnistDataBatchS batch_size r
-> Concrete (XParams kh kw c_out n_hidden r) -> r
ftest @batch_size mnistData pars =
MnistCnnShaped2.convMnistTestS kh kw (SNat @c_out) (SNat @n_hidden)
(SNat @batch_size) mnistData (fromTarget @Concrete pars)
in testCase name $ do
hPutStrLn stderr $
printf "\n%s: Epochs to run/max batches per epoch: %d/%d"
prefix epochs maxBatches
trainData <- map mkMnistDataS
<$> loadMnistData trainGlyphsPath trainLabelsPath
testData <- map mkMnistDataS . take (totalBatchSize * maxBatches)
<$> loadMnistData testGlyphsPath testLabelsPath
withSNat (totalBatchSize * maxBatches) $ \(SNat @lenTestData) -> do
let testDataS = mkMnistDataBatchS testData
ftk = tftk @Concrete (knownSTK @(XParams kh kw c_out n_hidden r)) targetInit
(_, _, var, varAst2) <- funToAstRevIO ftk
(varGlyph, astGlyph) <-
funToAstIO (FTKS (miniBatchSize
:$$ sizeMnistHeight
:$$ sizeMnistWidth
:$$ ZSS) FTKScalar) id
(varLabel, astLabel) <-
funToAstIO (FTKS (miniBatchSize
:$$ sizeMnistLabel
:$$ ZSS) FTKScalar) id
let ast :: AstTensor AstMethodLet FullSpan (TKScalar r)
ast = simplifyInline
$ MnistCnnShaped2.convMnistLossFusedS
kh kw (SNat @c_out) (SNat @n_hidden)
miniBatchSize (astGlyph, astLabel)
(fromTarget varAst2)
f :: MnistDataBatchS miniBatchSize r
-> ADVal Concrete (XParams kh kw c_out n_hidden r)
-> ADVal Concrete (TKScalar r)
f (glyph, label) varInputs =
let env = extendEnv var varInputs emptyEnv
envMnist = extendEnv varGlyph (sconcrete glyph)
$ extendEnv varLabel (sconcrete label) env
in interpretAstFull envMnist ast
runBatch :: (Concrete (XParams kh kw c_out n_hidden r), StateAdam (XParams kh kw c_out n_hidden r))
-> (Int, [MnistDataS r])
-> IO (Concrete (XParams kh kw c_out n_hidden r), StateAdam (XParams kh kw c_out n_hidden r))
runBatch (!parameters, !stateAdam) (k, chunk) = do
let chunkS = map mkMnistDataBatchS
$ filter (\ch -> length ch == miniBatchSizeInt)
$ chunksOf miniBatchSizeInt chunk
res@(parameters2, _) =
sgdAdam f chunkS parameters stateAdam
!trainScore = withSNat (length chunk) $ \(SNat @len) ->
ftest @len (mkMnistDataBatchS chunk) parameters2
!testScore = ftest @lenTestData testDataS parameters2
!lenChunk = length chunk
unless (n_hiddenInt < 10) $ do
hPutStrLn stderr $
printf "\n%s: (Batch %d with %d points)"
prefix k lenChunk
hPutStrLn stderr $
printf "%s: Training error: %.2f%%"
prefix ((1 - trainScore) * 100)
hPutStrLn stderr $
printf "%s: Validation error: %.2f%%"
prefix ((1 - testScore ) * 100)
return res
let runEpoch :: Int
-> (Concrete (XParams kh kw c_out n_hidden r), StateAdam (XParams kh kw c_out n_hidden r))
-> IO (Concrete (XParams kh kw c_out n_hidden r))
runEpoch n (params2, _) | n > epochs = return params2
runEpoch n paramsStateAdam@(!_, !_) = do
unless (n_hiddenInt < 10) $
hPutStrLn stderr $ printf "\n%s: [Epoch %d]" prefix n
let trainDataShuffled = shuffle (mkStdGen $ n + 5) trainData
chunks = take maxBatches
$ zip [1 ..]
$ chunksOf totalBatchSize trainDataShuffled
res <- foldM runBatch paramsStateAdam chunks
runEpoch (succ n) res
res <- runEpoch 1 (targetInit, initialStateAdam ftk)
let testErrorFinal =
1 - ftest testDataS res
testErrorFinal @?~ expected
tensorADValMnistTestsCNNSI :: TestTree
tensorADValMnistTestsCNNSI = testGroup "CNNS Intermediate MNIST tests"
[ mnistTestCaseCNNSI "CNNSI 1 epoch, 1 batch"
1 1 (SNat @4) (SNat @4) 8 16 1 1
(1 :: Double)
, mnistTestCaseCNNSI "CNNSI artificial 1 2 3 4 5"
1 1 (SNat @2) (SNat @3) 4 5 1 10
(1 :: Float)
, mnistTestCaseCNNSI "CNNSI artificial 5 4 3 2 1"
5 4 (SNat @3) (SNat @2) 1 1 1 1
(1 :: Double)
, mnistTestCaseCNNSI "CNNSI 1 epoch, 0 batch"
1 0 (SNat @4) (SNat @4) 16 64 16 50
(1.0 :: Float)
]
-- JAX differentiation, Ast term built and differentiated only once
-- and the result interpreted with different inputs in each gradient
-- descent iteration.
mnistTestCaseCNNSO
:: forall kh kw r.
( 1 <= kh, 1 <= kw
, Differentiable r, GoodScalar r
, PrintfArg r, AssertEqualUpToEpsilon r, ADTensorScalar r ~ r )
=> String
-> Int -> Int -> SNat kh -> SNat kw -> Int -> Int -> Int -> Int -> r
-> TestTree
mnistTestCaseCNNSO prefix epochs maxBatches kh@SNat kw@SNat c_outInt n_hiddenInt
miniBatchSizeInt totalBatchSize expected =
withSNat c_outInt $ \(_c_outSNat :: SNat c_out) ->
withSNat n_hiddenInt $ \(_n_hiddenSNat :: SNat n_hidden) ->
withSNat miniBatchSizeInt $ \(miniBatchSize :: SNat miniBatchSize) ->
let targetInit =
fst $ randomValue
@(Concrete (X (MnistCnnShaped2.ADCnnMnistParametersShaped
Concrete SizeMnistHeight SizeMnistWidth
kh kw c_out n_hidden r)))
0.4 (mkStdGen 44)
name = prefix ++ ": "
++ unwords [ show epochs, show maxBatches
, show (sNatValue kh), show (sNatValue kw)
, show c_outInt, show n_hiddenInt
, show miniBatchSizeInt
, show $ widthSTK
$ knownSTK @(XParams kh kw c_out n_hidden r)
, show (tsize knownSTK targetInit) ]
ftest :: KnownNat batch_size
=> MnistDataBatchS batch_size r
-> Concrete (XParams kh kw c_out n_hidden r) -> r
ftest @batch_size mnistData pars =
MnistCnnShaped2.convMnistTestS kh kw (SNat @c_out) (SNat @n_hidden)
(SNat @batch_size) mnistData (fromTarget @Concrete pars)
in testCase name $ do
hPutStrLn stderr $
printf "\n%s: Epochs to run/max batches per epoch: %d/%d"
prefix epochs maxBatches
trainData <- map mkMnistDataS
<$> loadMnistData trainGlyphsPath trainLabelsPath
testData <- map mkMnistDataS . take (totalBatchSize * maxBatches)
<$> loadMnistData testGlyphsPath testLabelsPath
withSNat (totalBatchSize * maxBatches) $ \(SNat @lenTestData) -> do
let testDataS = mkMnistDataBatchS testData
dataInit = case chunksOf miniBatchSizeInt testData of
d : _ -> let (dglyph, dlabel) = mkMnistDataBatchS d
in (sconcrete dglyph, sconcrete dlabel)
[] -> error "empty test data"
f :: ( MnistCnnShaped2.ADCnnMnistParametersShaped
(AstTensor AstMethodLet FullSpan)
SizeMnistHeight SizeMnistWidth
kh kw c_out n_hidden r
, ( AstTensor AstMethodLet FullSpan (TKS '[miniBatchSize, SizeMnistHeight, SizeMnistWidth] r)
, AstTensor AstMethodLet FullSpan (TKS '[miniBatchSize, SizeMnistLabel] r) ) )
-> AstTensor AstMethodLet FullSpan (TKScalar r)
f = \ (pars, (glyphR, labelR)) ->
MnistCnnShaped2.convMnistLossFusedS
kh kw (SNat @c_out) (SNat @n_hidden)
miniBatchSize (sprimalPart glyphR, sprimalPart labelR) pars
artRaw = gradArtifact f (fromTarget targetInit, dataInit)
art = simplifyArtifactGradient artRaw
go :: [MnistDataBatchS miniBatchSize r]
-> ( Concrete (XParams kh kw c_out n_hidden r)
, StateAdam (XParams kh kw c_out n_hidden r) )
-> ( Concrete (XParams kh kw c_out n_hidden r)
, StateAdam (XParams kh kw c_out n_hidden r) )
go [] (parameters, stateAdam) = (parameters, stateAdam)
go ((glyph, label) : rest) (!parameters, !stateAdam) =
let parametersAndInput =
tpair parameters (tpair (sconcrete glyph) (sconcrete label))
gradient =
tproject1 $ fst
$ revInterpretArtifact art parametersAndInput Nothing
in go rest (updateWithGradientAdam
@(XParams kh kw c_out n_hidden r)
defaultArgsAdam stateAdam knownSTK parameters
gradient)
runBatch :: ( Concrete (XParams kh kw c_out n_hidden r)
, StateAdam (XParams kh kw c_out n_hidden r) )
-> (Int, [MnistDataS r])
-> IO ( Concrete (XParams kh kw c_out n_hidden r)
, StateAdam (XParams kh kw c_out n_hidden r) )
runBatch (!parameters, !stateAdam) (k, chunk) = do
let chunkS = map mkMnistDataBatchS
$ filter (\ch -> length ch == miniBatchSizeInt)
$ chunksOf miniBatchSizeInt chunk
res@(parameters2, _) = go chunkS (parameters, stateAdam)
trainScore = withSNat (length chunk) $ \(SNat @len) ->
ftest @len (mkMnistDataBatchS chunk) parameters2
testScore = ftest @lenTestData testDataS parameters2
lenChunk = length chunk
unless (n_hiddenInt < 10) $ do
hPutStrLn stderr $
printf "\n%s: (Batch %d with %d points)"
prefix k lenChunk
hPutStrLn stderr $
printf "%s: Training error: %.2f%%"
prefix ((1 - trainScore) * 100)
hPutStrLn stderr $
printf "%s: Validation error: %.2f%%"
prefix ((1 - testScore ) * 100)
return res
let runEpoch :: Int
-> (Concrete (XParams kh kw c_out n_hidden r), StateAdam (XParams kh kw c_out n_hidden r))
-> IO (Concrete (XParams kh kw c_out n_hidden r))
runEpoch n (params2, _) | n > epochs = return params2
runEpoch n paramsStateAdam@(!_, !_) = do
unless (n_hiddenInt < 10) $
hPutStrLn stderr $ printf "\n%s: [Epoch %d]" prefix n
let trainDataShuffled = shuffle (mkStdGen $ n + 5) trainData
chunks = take maxBatches
$ zip [1 ..]
$ chunksOf totalBatchSize trainDataShuffled
res <- foldM runBatch paramsStateAdam chunks
runEpoch (succ n) res
ftk = tftk @Concrete (knownSTK @(XParams kh kw c_out n_hidden r)) targetInit
res <- runEpoch 1 (targetInit, initialStateAdam ftk)
let testErrorFinal =
1 - ftest testDataS res
assertEqualUpToEpsilon 1e-1 expected testErrorFinal
tensorADValMnistTestsCNNSO :: TestTree
tensorADValMnistTestsCNNSO = testGroup "CNNS Once MNIST tests"
[ mnistTestCaseCNNSO "CNNSO 1 epoch, 1 batch"
1 1 (SNat @4) (SNat @4) 8 16 1 1
(1 :: Double)
, mnistTestCaseCNNSO "CNNSO artificial 1 2 3 4 5"
1 1 (SNat @2) (SNat @3) 4 5 1 10
(1 :: Float)
, mnistTestCaseCNNSO "CNNSO artificial 5 4 3 2 1"
5 4 (SNat @3) (SNat @2) 1 1 1 1
(1 :: Double)
, mnistTestCaseCNNSO "CNNSO 1 epoch, 0 batch"
1 0 (SNat @4) (SNat @4) 16 64 16 50
(1.0 :: Float)
]