tensorflow-haskell/tensorflow/src/TensorFlow/Build.hs

377 lines
13 KiB
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 GeneralizedNewtypeDeriving #-}
{-# LANGUAGE LambdaCase #-}
{-# LANGUAGE Rank2Types #-}
{-# LANGUAGE OverloadedStrings #-}
module TensorFlow.Build
( -- * Graph node types
ControlNode(..)
, Unique
-- * Ops
, explicitName
, implicitName
, opDef
, opDefWithName
, opName
, opType
, opAttr
, opInputs
, opControlInputs
-- * The Build monad
, GraphState
, render
, renderNodeName
, renderedNodeDefs
, BuildT
, Build
, addInitializer
, hoistBuildT
, evalBuildT
, runBuildT
, asGraphDef
, addGraphDef
, flushInitializers
, flushNodeBuffer
-- * Creating and looking up Ops
, getOrAddOp
, addNewOp
, renderOutput
-- * Modifying all nodes in a Build action
, colocateWith
, withStateLens
, withDevice
, withNameScope
, withNodeDependencies
-- * Internal Summary related bits.
, addSummary
, SummaryTensor
, collectAllSummaries
) where
import Control.Monad.IO.Class (MonadIO(..))
import Control.Monad.Trans.Class (MonadTrans(..))
import Control.Monad.Trans.State.Strict(StateT(..), mapStateT, evalStateT)
import Data.ByteString (ByteString)
import Data.Default (def)
import Data.Functor.Identity (Identity(..))
import qualified Data.Map.Strict as Map
import Data.Monoid ((<>))
import qualified Data.Set as Set
import Data.Set (Set)
import Data.String (IsString(..))
import Data.Text (Text)
import qualified Data.Text as Text
import Lens.Family2 (Lens', (.~), (^.), (&))
import Lens.Family2.State.Strict (MonadState, use, uses, (.=), (<>=), (%=))
import Lens.Family2.Unchecked (lens)
import Proto.Tensorflow.Core.Framework.Graph
( GraphDef
, node
)
import Proto.Tensorflow.Core.Framework.NodeDef
( NodeDef
, attr
, input
, device
, name
, op
)
import TensorFlow.Orphans ()
import TensorFlow.Output
import TensorFlow.Tensor
newtype Unique = Unique Int
deriving (Eq, Ord, Enum)
--------------
implicitName :: PendingNodeName
implicitName = ImplicitName
explicitName :: Text -> PendingNodeName
explicitName = ExplicitName
newtype Scope = Scope {unScope :: Text}
deriving (Eq, Ord, IsString)
instance Show Scope where
show = show . unScope
opDef :: OpType -> OpDef
opDef = opDefWithName ImplicitName
opDefWithName :: PendingNodeName -> OpType -> OpDef
opDefWithName n t = OpDef
{ _opName = n
, _opType = t
, _opAttrs = Map.empty
, _opInputs = []
, _opControlInputs = []
}
-- | Synonym for the tensors that return serialized Summary proto.
type SummaryTensor = Tensor Value ByteString
data GraphState = GraphState
{ _renderedNodes :: !(Map.Map PendingNode NodeDef)
-- ^ Nodes which have been rendered. Keeps track of the unique ID we
-- assign each implicitly-named node. Also prevents us from adding the
-- same node (implicit or explicit) more than once to the nodeBuffer.
, _renderedNodeDefs :: !(Map.Map NodeName NodeDef)
-- ^ The NodeDefs of nodes which have been rendered. Used by the
-- Gradient module to inspect the node graph.
, _nodeBuffer :: [NodeDef]
-- ^ A list of nodes that should be passed to TensorFlow during
-- the next call to Session.extend (TF_ExtendGraph).
, _nextUnique :: !Unique
-- ^ Unique ID for the next node
-- TODO(judahjacobson): watch for clashes between auto and user names.
, _defaultDevice :: !(Maybe Device)
, _currentScope :: [Scope]
, _defaultControlInputs :: !(Set NodeName)
, _initializationNodes :: [NodeName]
-- ^ The nodes to run next time a TF.run is issued, typically
-- variable initializers.
, _summaries :: [SummaryTensor]
-- ^ The tensors for summary
}
-- | A node definition without its final name. Used as a key in the
-- "renderedNodes" map.
-- The NodeDef contained inside has an empty "name" field.
data PendingNode = PendingNode [Scope] !PendingNodeName !NodeDef
deriving (Eq, Ord)
-- Returns an _incomplete_ NodeDef. The name is fixed by addNewOpFromPending.
pendingNodeDef :: PendingNode -> NodeDef
pendingNodeDef (PendingNode _ _ n) = n
initGraphState :: GraphState
initGraphState =
GraphState Map.empty Map.empty [] (Unique 0) Nothing [] Set.empty [] []
renderedNodes :: Lens' GraphState (Map.Map PendingNode NodeDef)
renderedNodes = lens _renderedNodes (\g x -> g { _renderedNodes = x })
renderedNodeDefs :: Lens' GraphState (Map.Map NodeName NodeDef)
renderedNodeDefs = lens _renderedNodeDefs (\g x -> g { _renderedNodeDefs = x })
nodeBuffer :: Lens' GraphState [NodeDef]
nodeBuffer = lens _nodeBuffer (\g x -> g { _nodeBuffer = x })
nextUnique :: Lens' GraphState Unique
nextUnique = lens _nextUnique (\g x -> g { _nextUnique = x })
defaultDevice :: Lens' GraphState (Maybe Device)
defaultDevice = lens _defaultDevice (\g x -> g { _defaultDevice = x })
currentScope :: Lens' GraphState [Scope]
currentScope = lens _currentScope (\g x -> g { _currentScope = x })
defaultControlInputs :: Lens' GraphState (Set NodeName)
defaultControlInputs = lens _defaultControlInputs
(\g x -> g { _defaultControlInputs = x })
initializationNodes :: Lens' GraphState [NodeName]
initializationNodes = lens _initializationNodes (\g x -> g { _initializationNodes = x })
summaries :: Lens' GraphState [SummaryTensor]
summaries = lens _summaries (\g x -> g { _summaries = x })
-- | An action for building nodes in a TensorFlow graph.
-- Used to manage build state internally as part of the @Session@ monad.
newtype BuildT m a = BuildT (StateT GraphState m a)
deriving (Functor, Applicative, Monad, MonadIO, MonadTrans,
MonadState GraphState)
-- | An action for building nodes in a TensorFlow graph.
type Build = BuildT Identity
-- | This is Control.Monad.Morph.hoist sans the dependency.
hoistBuildT :: (forall a . m a -> n a) -> BuildT m b -> BuildT n b
hoistBuildT f (BuildT m) = BuildT $ mapStateT f m
runBuildT :: BuildT m a -> m (a, GraphState)
runBuildT (BuildT f) = runStateT f initGraphState
evalBuildT :: Monad m => BuildT m a -> m a
evalBuildT (BuildT f) = evalStateT f initGraphState
-- | Get all the NodeDefs that have accumulated so far, and clear that buffer.
flushNodeBuffer :: Monad m => BuildT m [NodeDef]
flushNodeBuffer = do
ns <- use nodeBuffer
nodeBuffer .= []
return ns
-- | Get all the initializers that have accumulated so far, and clear
-- that buffer.
flushInitializers :: Monad m => BuildT m [NodeName]
flushInitializers = do
ns <- use initializationNodes
initializationNodes .= []
return ns
-- | Registers the given node to be executed before the next
-- 'TensorFlow.Session.run'.
addInitializer :: ControlNode -> Build ()
addInitializer (ControlNode o) = do
i <- getOrAddOp o
initializationNodes %= (i:)
-- | Produce a GraphDef proto representation of the nodes that are rendered in
-- the given 'Build' action.
asGraphDef :: Build a -> GraphDef
asGraphDef b = def & node .~ gs ^. nodeBuffer
where
gs = snd $ runIdentity $ runBuildT b
-- TODO: check against existing nodes for conflicts?
addGraphDef :: GraphDef -> Build ()
addGraphDef g = nodeBuffer <>= g ^. node
-- | Render the given op if it hasn't been rendered already, and return its
-- name.
getOrAddOp :: Op -> Build NodeName
getOrAddOp o = NodeName . (^. name) <$> resolveOp o
resolveOp :: Op -> Build NodeDef
resolveOp (Rendered n) = return n
resolveOp (Unrendered o) = do
pending <- getPendingNode o
uses renderedNodes (Map.lookup pending) >>= \case
Just n -> return n
Nothing -> addNewOpFromPending pending
-- | Add a new node for a given 'OpDef'. This is used for making "stateful" ops
-- which are not safe to dedup (e.g, "variable" and "assign").
addNewOp :: OpDef -> Build NodeDef
addNewOp o = getPendingNode o >>= addNewOpFromPending
addNewOpFromPending :: PendingNode -> Build NodeDef
addNewOpFromPending pending = do
nodeName <- renderPendingNode pending
let nodeDef = pendingNodeDef pending & name .~ unNodeName nodeName
nodeBuffer %= (nodeDef :)
renderedNodes %= Map.insert pending nodeDef
renderedNodeDefs %= Map.insert nodeName nodeDef
return nodeDef
-- | Get the pending node corresponding to an OpDef, which may or may not have
-- been rendered before. Implicitly renders all of this node's inputs.
getPendingNode :: OpDef -> Build PendingNode
getPendingNode o = do
-- An empty string in the proto field means that no specific
-- device is specified.
dev <- maybe "" deviceName <$> use defaultDevice
inputs <- mapM getInput (o ^. opInputs)
scope <- use currentScope
controls <- use defaultControlInputs
let controlInputs
= map getDep (o ^. opControlInputs ++ Set.toList controls)
return $ PendingNode scope (o ^. opName)
$ def & op .~ (unOpType (o ^. opType) :: Text)
& attr .~ _opAttrs o
& input .~ (inputs ++ controlInputs)
& device .~ dev
where
getInput (Output (OutputIx k) subOp)
= (<> ":" <> Text.pack (show k)) . unNodeName <$> getOrAddOp subOp
getDep = ("^" <>) . unNodeName
-- | Pick a name for a pending node. If it has an explicit name, just use that;
-- if the name is implicit, assign a new unique name based on the op type.
renderPendingNode :: PendingNode -> Build NodeName
renderPendingNode (PendingNode scope pendingName nodeDef)
= NodeName . (scopePrefix <>) <$> getName
where
scopePrefix = Text.concat $ fmap ((<> "/") . unScope) scope
getName = case pendingName of
ExplicitName n -> return n
ImplicitName -> do
u@(Unique k) <- use nextUnique
nextUnique .= succ u
return $ nodeDef ^. op <> "_" <> Text.pack (show k)
-- | Render an 'Output' and return a string representation for the TensorFlow
-- foreign APIs.
renderOutput :: Output -> Build Text
renderOutput (Output (OutputIx i) o) = do
n <- getOrAddOp o
return $ unNodeName n <> Text.pack (":" ++ show i)
-- | Modify some part of the state, run an action, and restore the state
-- after that action is done.
withStateLens :: MonadState s m => Lens' s a -> (a -> a) -> m b -> m b
withStateLens accessor f act = do
old <- use accessor
accessor %= f
result <- act
accessor .= old
return result
-- | Set a device for all nodes rendered in the given 'Build' action
-- (unless further overridden by another use of withDevice).
withDevice :: Maybe Device -> Build a -> Build a
withDevice d = withStateLens defaultDevice (const d)
-- | Places all nodes rendered in the given 'Build' action on the same
-- device as the given Tensor (see also 'withDevice'). Make sure that
-- the action has side effects of rendering the desired tensors. A pure
-- return would not have the desired effect.
colocateWith :: forall a v b . Tensor v b -> Build a -> Build a
colocateWith t x = do
d <- Device . (^. device) <$> resolveOp (t ^. tensorOutput . outputOp)
withDevice (Just d) x
-- | Prepend a scope to all nodes rendered in the given 'Build' action.
withNameScope :: Text -> Build a -> Build a
withNameScope s = withStateLens currentScope (Scope s :)
-- | Add control inputs to all nodes rendered in the given 'Build' action.
withNodeDependencies :: Set NodeName -> Build a -> Build a
withNodeDependencies nodes = withStateLens defaultControlInputs (<> nodes)
-- | Render a 'Tensor', fixing its name, scope, device and control inputs from
-- the 'Build' context. Also renders any dependencies of the 'Tensor' that
-- weren't already rendered.
--
-- This operation is idempotent; @render >=> render === render@. However,
-- rendering a (previously un-rendered) 'Tensor' in two different contexts
-- may result in two different 'Tensor's.
render :: Tensor v a -> Build (Tensor v a)
render = tensorOutput $ outputOp $ fmap Rendered . resolveOp
-- | Render a 'Tensor' and get its node's name.
renderNodeName :: Tensor v a -> Build NodeName
renderNodeName t = getOrAddOp (t ^. tensorOutput . outputOp)
-- | Records the given summary action in Build for retrieval with
-- 'collectAllSummaries'. The summary op is required to produce a
-- Summary protocol buffer in string form. For safety, use the
-- pre-composed functions: Logging.scalarSummary and
-- Logging.histogramSummary.
addSummary :: SummaryTensor -> Build ()
addSummary t = summaries %= (t :)
-- | Retrieves the summary ops collected thus far. Typically this only
-- happens once, but if 'TensorFlow.Session.buildWithSummary' is used
-- repeatedly, the values accumulate.
collectAllSummaries :: Monad m => BuildT m [SummaryTensor]
collectAllSummaries = use summaries