ghc-9.14.1: GHC/CmmToAsm/CFG.hs
{-# LANGUAGE DataKinds #-}
{-# LANGUAGE GeneralizedNewtypeDeriving #-}
{-# LANGUAGE Rank2Types #-}
{-# LANGUAGE ScopedTypeVariables #-}
{-# LANGUAGE TypeFamilies #-}
{-# LANGUAGE FlexibleContexts #-}
{-# LANGUAGE TupleSections #-}
--
-- Copyright (c) 2018 Andreas Klebinger
--
module GHC.CmmToAsm.CFG
( CFG, CfgEdge(..), EdgeInfo(..), EdgeWeight(..)
, TransitionSource(..)
--Modify the CFG
, addWeightEdge, addEdge
, delEdge
, addNodesBetween, shortcutWeightMap
, reverseEdges, filterEdges
, addImmediateSuccessor
, mkWeightInfo, adjustEdgeWeight, setEdgeWeight
--Query the CFG
, infoEdgeList, edgeList
, getSuccessorEdges, getSuccessors
, getSuccEdgesSorted
, getEdgeInfo
, getCfgNodes, hasNode
-- Loop Information
, loopMembers, loopLevels, loopInfo
--Construction/Misc
, getCfg, getCfgProc, pprEdgeWeights, sanityCheckCfg
--Find backedges and update their weight
, optimizeCFG
, mkGlobalWeights
)
where
import GHC.Prelude
import GHC.Platform
import GHC.Cmm.BlockId
import GHC.Cmm as Cmm
import GHC.Cmm.Switch
import GHC.Cmm.Dataflow.Label
import GHC.Cmm.Dataflow.Block
import qualified GHC.Cmm.Dataflow.Graph as G
import GHC.Utils.Misc
import GHC.Data.Graph.Directed
import GHC.Data.Maybe
import GHC.Types.Unique
import qualified GHC.CmmToAsm.CFG.Dominators as Dom
import GHC.CmmToAsm.CFG.Weight
import GHC.Data.Word64Map.Strict (Word64Map)
import GHC.Data.Word64Set (Word64Set)
import Data.IntMap.Strict (IntMap)
import Data.IntSet (IntSet)
import qualified Data.IntMap.Strict as IM
import qualified GHC.Data.Word64Map.Strict as WM
import qualified Data.Map as M
import qualified Data.IntSet as IS
import qualified GHC.Data.Word64Set as WS
import qualified Data.Set as S
import Data.Tree
import Data.Bifunctor
import GHC.Utils.Outputable
import GHC.Utils.Panic
-- DEBUGGING ONLY
--import GHC.Cmm.DebugBlock
--import GHC.Data.OrdList
--import GHC.Cmm.DebugBlock.Trace
import Data.List (sort, nub, partition)
import Data.STRef.Strict
import Control.Monad.ST
import Data.Array.MArray
import Data.Array.ST
import Data.Array.IArray
import Data.Array.Unsafe (unsafeFreeze)
import Data.Array.Base (unsafeRead, unsafeWrite)
import Control.Monad
import GHC.Data.UnionFind
import Data.Word
type Prob = Double
type Edge = (BlockId, BlockId)
type Edges = [Edge]
newtype EdgeWeight
= EdgeWeight { weightToDouble :: Double }
deriving (Eq,Ord,Enum,Num,Real,Fractional)
instance Outputable EdgeWeight where
ppr (EdgeWeight w) = doublePrec 5 w
type EdgeInfoMap edgeInfo = LabelMap (LabelMap edgeInfo)
-- | A control flow graph where edges have been annotated with a weight.
-- Implemented as IntMap (IntMap \<edgeData>)
-- We must uphold the invariant that for each edge A -> B we must have:
-- A entry B in the outer map.
-- A entry B in the map we get when looking up A.
-- Maintaining this invariant is useful as any failed lookup now indicates
-- an actual error in code which might go unnoticed for a while
-- otherwise.
type CFG = EdgeInfoMap EdgeInfo
data CfgEdge
= CfgEdge
{ edgeFrom :: !BlockId
, edgeTo :: !BlockId
, edgeInfo :: !EdgeInfo
}
-- | Careful! Since we assume there is at most one edge from A to B
-- the Eq instance does not consider weight.
instance Eq CfgEdge where
(==) (CfgEdge from1 to1 _) (CfgEdge from2 to2 _)
= from1 == from2 && to1 == to2
-- | Edges are sorted ascending pointwise by weight, source and destination
instance Ord CfgEdge where
compare (CfgEdge from1 to1 (EdgeInfo {edgeWeight = weight1}))
(CfgEdge from2 to2 (EdgeInfo {edgeWeight = weight2}))
| weight1 < weight2 || weight1 == weight2 && from1 < from2 ||
weight1 == weight2 && from1 == from2 && to1 < to2
= LT
| from1 == from2 && to1 == to2 && weight1 == weight2
= EQ
| otherwise
= GT
instance Outputable CfgEdge where
ppr (CfgEdge from1 to1 edgeInfo)
= parens (ppr from1 <+> text "-(" <> ppr edgeInfo <> text ")->" <+> ppr to1)
-- | Can we trace back a edge to a specific Cmm Node
-- or has it been introduced during assembly codegen. We use this to maintain
-- some information which would otherwise be lost during the
-- Cmm \<-> asm transition.
-- See also Note [Inverting conditions]
data TransitionSource
= CmmSource { trans_cmmNode :: (CmmNode O C)
, trans_info :: BranchInfo }
| AsmCodeGen
deriving (Eq)
data BranchInfo = NoInfo -- ^ Unknown, but not heap or stack check.
| HeapStackCheck -- ^ Heap or stack check
deriving Eq
instance Outputable BranchInfo where
ppr NoInfo = text "regular"
ppr HeapStackCheck = text "heap/stack"
isHeapOrStackCheck :: TransitionSource -> Bool
isHeapOrStackCheck (CmmSource { trans_info = HeapStackCheck}) = True
isHeapOrStackCheck _ = False
-- | Information about edges
data EdgeInfo
= EdgeInfo
{ transitionSource :: !TransitionSource
, edgeWeight :: !EdgeWeight
} deriving (Eq)
instance Outputable EdgeInfo where
ppr edgeInfo = text "weight:" <+> ppr (edgeWeight edgeInfo)
-- | Convenience function, generate edge info based
-- on weight not originating from cmm.
mkWeightInfo :: EdgeWeight -> EdgeInfo
mkWeightInfo = EdgeInfo AsmCodeGen
-- | Adjust the weight between the blocks using the given function.
-- If there is no such edge returns the original map.
adjustEdgeWeight :: CFG -> (EdgeWeight -> EdgeWeight)
-> BlockId -> BlockId -> CFG
adjustEdgeWeight cfg f from to
| Just info <- getEdgeInfo from to cfg
, !weight <- edgeWeight info
, !newWeight <- f weight
= addEdge from to (info { edgeWeight = newWeight}) cfg
| otherwise = cfg
-- | Set the weight between the blocks to the given weight.
-- If there is no such edge returns the original map.
setEdgeWeight :: CFG -> EdgeWeight
-> BlockId -> BlockId -> CFG
setEdgeWeight cfg !weight from to
| Just info <- getEdgeInfo from to cfg
= addEdge from to (info { edgeWeight = weight}) cfg
| otherwise = cfg
getCfgNodes :: CFG -> [BlockId]
getCfgNodes m =
mapKeys m
-- | Is this block part of this graph?
hasNode :: CFG -> BlockId -> Bool
hasNode m node =
-- Check the invariant that each node must exist in the first map or not at all.
assert (found || not (any (mapMember node) m))
found
where
found = mapMember node m
-- | Check if the nodes in the cfg and the set of blocks are the same.
-- In a case of a mismatch we panic and show the difference.
sanityCheckCfg :: CFG -> LabelSet -> SDoc -> Bool
sanityCheckCfg m blockSet msg
| blockSet == cfgNodes
= True
| otherwise =
pprPanic "Block list and cfg nodes don't match" (
text "difference:" <+> ppr diff $$
text "blocks:" <+> ppr blockSet $$
text "cfg:" <+> pprEdgeWeights m $$
msg )
False
where
cfgNodes = setFromList $ getCfgNodes m :: LabelSet
diff = (setUnion cfgNodes blockSet) `setDifference` (setIntersection cfgNodes blockSet) :: LabelSet
-- | Filter the CFG with a custom function f.
-- Parameters are `f from to edgeInfo`
filterEdges :: (BlockId -> BlockId -> EdgeInfo -> Bool) -> CFG -> CFG
filterEdges f cfg =
mapMapWithKey filterSources cfg
where
filterSources from m =
mapFilterWithKey (\to w -> f from to w) m
{- Note [Updating the CFG during shortcutting]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
See Note [What is shortcutting] in the control flow optimization
code (GHC.Cmm.ContFlowOpt) for a slightly more in depth explanation on shortcutting.
In the native backend we shortcut jumps at the assembly level. ("GHC.CmmToAsm")
This means we remove blocks containing only one jump from the code
and instead redirecting all jumps targeting this block to the deleted
blocks jump target.
However we want to have an accurate representation of control
flow in the CFG. So we add/remove edges accordingly to account
for the eliminated blocks and new edges.
If we shortcut A -> B -> C to A -> C:
* We delete edges A -> B and B -> C
* Replacing them with the edge A -> C
We also try to preserve jump weights while doing so.
Note that:
* The edge B -> C can't have interesting weights since
the block B consists of a single unconditional jump without branching.
* We delete the edge A -> B and add the edge A -> C.
* The edge A -> B can be one of many edges originating from A so likely
has edge weights we want to preserve.
For this reason we simply store the edge info from the original A -> B
edge and apply this information to the new edge A -> C.
Sometimes we have a scenario where jump target C is not represented by an
BlockId but an immediate value. I'm only aware of this happening without
tables next to code currently.
Then we go from A ---> B - -> IMM to A - -> IMM where the dashed arrows
are not stored in the CFG.
In that case we simply delete the edge A -> B.
In terms of implementation the native backend first builds a mapping
from blocks suitable for shortcutting to their jump targets.
Then it redirects all jump instructions to these blocks using the
built up mapping.
This function (shortcutWeightMap) takes the same mapping and
applies the mapping to the CFG in the way laid out above.
-}
shortcutWeightMap :: LabelMap (Maybe BlockId) -> CFG -> CFG
shortcutWeightMap cuts cfg
| mapNull cuts = cfg
| otherwise = normalised_cfg
where
-- First take the cuts map and collapse any shortcuts, for example
-- if the cuts map has A -> B and B -> C then we want to rewrite
-- A -> C and B -> C directly.
normalised_cuts_st :: forall s . ST s (LabelMap (Maybe BlockId))
normalised_cuts_st = do
(null :: Point s (Maybe BlockId)) <- fresh Nothing
let cuts_list = mapToList cuts
-- Create a unification variable for each of the nodes in a rewrite
cuts_vars <- traverse (\p -> (p,) <$> fresh (Just p)) (concatMap (\(a, b) -> [a] ++ maybe [] (:[]) b) cuts_list)
let cuts_map = mapFromList cuts_vars :: LabelMap (Point s (Maybe BlockId))
-- Then unify according to the rewrites in the cuts map
mapM_ (\(from, to) -> expectJust (mapLookup from cuts_map)
`union` expectJust (maybe (Just null) (flip mapLookup cuts_map) to) ) cuts_list
-- Then recover the unique representative, which is the result of following
-- the chain to the end.
mapM find cuts_map
normalised_cuts = runST normalised_cuts_st
cuts_domain :: LabelSet
cuts_domain = setFromList $ mapKeys cuts
-- The CFG is shortcutted using the normalised cuts map
normalised_cfg :: CFG
normalised_cfg = mapFoldlWithKey update_edge mapEmpty cfg
update_edge :: CFG -> Label -> LabelMap EdgeInfo -> CFG
update_edge new_map from edge_map
-- If the from edge is in the cuts map then delete the edge
| setMember from cuts_domain = new_map
-- Otherwise we are keeping the edge, but might have shortcutted some of
-- the target nodes.
| otherwise = mapInsert from (mapFoldlWithKey update_from_edge mapEmpty edge_map) new_map
update_from_edge :: LabelMap a -> Label -> a -> LabelMap a
update_from_edge new_map to_edge edge_info
-- Edge is in the normalised cuts
| Just new_edge <- mapLookup to_edge normalised_cuts =
case new_edge of
-- The result was Nothing, so edge is deleted
Nothing -> new_map
-- The new target for the edge, write it with the old edge_info.
Just new_to -> mapInsert new_to edge_info new_map
-- Node wasn't in the cuts map, so just add it back
| otherwise = mapInsert to_edge edge_info new_map
-- | Sometimes we insert a block which should unconditionally be executed
-- after a given block. This function updates the CFG for these cases.
-- So we get A -> B => A -> A' -> B
-- \ \
-- -> C => -> C
--
addImmediateSuccessor :: Weights -> BlockId -> BlockId -> CFG -> CFG
addImmediateSuccessor weights node follower cfg
= updateEdges . addWeightEdge node follower weight $ cfg
where
weight = fromIntegral (uncondWeight weights)
targets = getSuccessorEdges cfg node
successors = map fst targets :: [BlockId]
updateEdges = addNewSuccs . remOldSuccs
remOldSuccs m = foldl' (flip (delEdge node)) m successors
addNewSuccs m =
foldl' (\m' (t,info) -> addEdge follower t info m') m targets
-- | Adds a new edge, overwrites existing edges if present
addEdge :: BlockId -> BlockId -> EdgeInfo -> CFG -> CFG
addEdge from to info cfg =
mapAlter addFromToEdge from $
mapAlter addDestNode to cfg
where
-- Simply insert the edge into the edge list.
addFromToEdge Nothing = Just $ mapSingleton to info
addFromToEdge (Just wm) = Just $ mapInsert to info wm
-- We must add the destination node explicitly
addDestNode Nothing = Just $ mapEmpty
addDestNode n@(Just _) = n
-- | Adds a edge with the given weight to the cfg
-- If there already existed an edge it is overwritten.
-- `addWeightEdge from to weight cfg`
addWeightEdge :: BlockId -> BlockId -> EdgeWeight -> CFG -> CFG
addWeightEdge from to weight cfg =
addEdge from to (mkWeightInfo weight) cfg
delEdge :: BlockId -> BlockId -> CFG -> CFG
delEdge from to m =
mapAdjust (mapDelete to) from m
-- | Destinations from bid ordered by weight (descending)
getSuccEdgesSorted :: CFG -> BlockId -> [(BlockId,EdgeInfo)]
getSuccEdgesSorted m bid =
let destMap = mapFindWithDefault mapEmpty bid m
cfgEdges = mapToList destMap
sortedEdges = sortWith (negate . edgeWeight . snd) cfgEdges
in --pprTrace "getSuccEdgesSorted" (ppr bid <+> text "map:" <+> ppr m)
sortedEdges
-- | Get successors of a given node with edge weights.
getSuccessorEdges :: HasDebugCallStack => CFG -> BlockId -> [(BlockId,EdgeInfo)]
getSuccessorEdges m bid = maybe lookupError mapToList (mapLookup bid m)
where
lookupError = pprPanic "getSuccessorEdges: Block does not exist" $
ppr bid <+> pprEdgeWeights m
getEdgeInfo :: BlockId -> BlockId -> CFG -> Maybe EdgeInfo
getEdgeInfo from to m
| Just wm <- mapLookup from m
, Just info <- mapLookup to wm
= Just $! info
| otherwise
= Nothing
getEdgeWeight :: CFG -> BlockId -> BlockId -> EdgeWeight
getEdgeWeight cfg from to = edgeWeight $ expectJust $ getEdgeInfo from to cfg
getTransitionSource :: BlockId -> BlockId -> CFG -> TransitionSource
getTransitionSource from to cfg = transitionSource $ expectJust $ getEdgeInfo from to cfg
reverseEdges :: CFG -> CFG
reverseEdges cfg = mapFoldlWithKey (\cfg from toMap -> go (addNode cfg from) from toMap) mapEmpty cfg
where
-- We must preserve nodes without outgoing edges!
addNode :: CFG -> BlockId -> CFG
addNode cfg b = mapInsertWith mapUnion b mapEmpty cfg
go :: CFG -> BlockId -> (LabelMap EdgeInfo) -> CFG
go cfg from toMap = mapFoldlWithKey (\cfg to info -> addEdge to from info cfg) cfg toMap :: CFG
-- | Returns a unordered list of all edges with info
infoEdgeList :: CFG -> [CfgEdge]
infoEdgeList m =
go (mapToList m) []
where
-- We avoid foldMap to avoid thunk buildup
go :: [(BlockId,LabelMap EdgeInfo)] -> [CfgEdge] -> [CfgEdge]
go [] acc = acc
go ((from,toMap):xs) acc
= go' xs from (mapToList toMap) acc
go' :: [(BlockId,LabelMap EdgeInfo)] -> BlockId -> [(BlockId,EdgeInfo)] -> [CfgEdge] -> [CfgEdge]
go' froms _ [] acc = go froms acc
go' froms from ((to,info):tos) acc
= go' froms from tos (CfgEdge from to info : acc)
-- | Returns a unordered list of all edges without weights
edgeList :: CFG -> [Edge]
edgeList m =
go (mapToList m) []
where
-- We avoid foldMap to avoid thunk buildup
go :: [(BlockId,LabelMap EdgeInfo)] -> [Edge] -> [Edge]
go [] acc = acc
go ((from,toMap):xs) acc
= go' xs from (mapKeys toMap) acc
go' :: [(BlockId,LabelMap EdgeInfo)] -> BlockId -> [BlockId] -> [Edge] -> [Edge]
go' froms _ [] acc = go froms acc
go' froms from (to:tos) acc
= go' froms from tos ((from,to) : acc)
-- | Get successors of a given node without edge weights.
getSuccessors :: HasDebugCallStack => CFG -> BlockId -> [BlockId]
getSuccessors m bid
| Just wm <- mapLookup bid m
= mapKeys wm
| otherwise = lookupError
where
lookupError = pprPanic "getSuccessors: Block does not exist" $
ppr bid <+> pprEdgeWeights m
pprEdgeWeights :: CFG -> SDoc
pprEdgeWeights m =
let edges = sort $ infoEdgeList m :: [CfgEdge]
printEdge (CfgEdge from to (EdgeInfo { edgeWeight = weight }))
= text "\t" <> ppr from <+> text "->" <+> ppr to <>
text "[label=\"" <> ppr weight <> text "\",weight=\"" <>
ppr weight <> text "\"];\n"
--for the case that there are no edges from/to this node.
--This should rarely happen but it can save a lot of time
--to immediately see it when it does.
printNode node
= text "\t" <> ppr node <> text ";\n"
getEdgeNodes (CfgEdge from to _) = [from,to]
edgeNodes = setFromList $ concatMap getEdgeNodes edges :: LabelSet
nodes = filter (\n -> (not . setMember n) edgeNodes) . mapKeys $ mapFilter null m
in
text "digraph {\n" <>
(foldl' (<>) empty (map printEdge edges)) <>
(foldl' (<>) empty (map printNode nodes)) <>
text "}\n"
{-# INLINE updateEdgeWeight #-} --Allows eliminating the tuple when possible
-- | Invariant: The edge **must** exist already in the graph.
updateEdgeWeight :: (EdgeWeight -> EdgeWeight) -> Edge -> CFG -> CFG
updateEdgeWeight f (from, to) cfg
| Just oldInfo <- getEdgeInfo from to cfg
= let !oldWeight = edgeWeight oldInfo
!newWeight = f oldWeight
in addEdge from to (oldInfo {edgeWeight = newWeight}) cfg
| otherwise
= panic "Trying to update invalid edge"
-- from to oldWeight => newWeight
mapWeights :: (BlockId -> BlockId -> EdgeWeight -> EdgeWeight) -> CFG -> CFG
mapWeights f cfg =
foldl' (\cfg (CfgEdge from to info) ->
let oldWeight = edgeWeight info
newWeight = f from to oldWeight
in addEdge from to (info {edgeWeight = newWeight}) cfg)
cfg (infoEdgeList cfg)
-- | Insert a block in the control flow between two other blocks.
-- We pass a list of tuples (A,B,C) where
-- * A -> C: Old edge
-- * A -> B -> C : New Arc, where B is the new block.
-- It's possible that a block has two jumps to the same block
-- in the assembly code. However we still only store a single edge for
-- these cases.
-- We assign the old edge info to the edge A -> B and assign B -> C the
-- weight of an unconditional jump.
addNodesBetween :: Weights -> CFG -> [(BlockId,BlockId,BlockId)] -> CFG
addNodesBetween weights m updates =
foldl' updateWeight m .
weightUpdates $ updates
where
weight = fromIntegral (uncondWeight weights)
-- We might add two blocks for different jumps along a single
-- edge. So we end up with edges: A -> B -> C , A -> D -> C
-- in this case after applying the first update the weight for A -> C
-- is no longer available. So we calculate future weights before updates.
weightUpdates = map getWeight
getWeight :: (BlockId,BlockId,BlockId) -> (BlockId,BlockId,BlockId,EdgeInfo)
getWeight (from,between,old)
| Just edgeInfo <- getEdgeInfo from old m
= (from,between,old,edgeInfo)
| otherwise
= pprPanic "Can't find weight for edge that should have one" (
text "triple" <+> ppr (from,between,old) $$
text "updates" <+> ppr updates $$
text "cfg:" <+> pprEdgeWeights m )
updateWeight :: CFG -> (BlockId,BlockId,BlockId,EdgeInfo) -> CFG
updateWeight m (from,between,old,edgeInfo)
= addEdge from between edgeInfo .
addWeightEdge between old weight .
delEdge from old $ m
{-
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~ Note [CFG Edge Weights] ~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Edge weights assigned do not currently represent a specific
cost model and rather just a ranking of which blocks should
be placed next to each other given their connection type in
the CFG.
This is especially relevant if we whenever two blocks will
jump to the same target.
A B
\ /
C
Should A or B be placed in front of C? The block layout algorithm
decides this based on which edge (A,C)/(B,C) is heavier. So we
make a educated guess on which branch should be preferred.
We rank edges in this order:
* Unconditional Control Transfer - They will always
transfer control to their target. Unless there is a info table
we can turn the jump into a fallthrough as well.
We use 20k as default, so it's easy to spot if values have been
modified but unlikely that we run into issues with overflow.
* If branches (likely) - We assume branches marked as likely
are taken more than 80% of the time.
By ranking them below unconditional jumps we make sure we
prefer the unconditional if there is a conditional and
unconditional edge towards a block.
* If branches (regular) - The false branch can potentially be turned
into a fallthrough so we prefer it slightly over the true branch.
* Unlikely branches - These can be assumed to be taken less than 20%
of the time. So we given them one of the lowest priorities.
* Switches - Switches at this level are implemented as jump tables
so have a larger number of successors. So without more information
we can only say that each individual successor is unlikely to be
jumped to and we rank them accordingly.
* Calls - We currently ignore calls completely:
* By the time we return from a call there is a good chance
that the address we return to has already been evicted from
cache eliminating a main advantage sequential placement brings.
* Calls always require a info table in front of their return
address. This reduces the chance that we return to the same
cache line further.
-}
-- | Generate weights for a Cmm proc based on some simple heuristics.
getCfgProc :: Platform -> Weights -> GenCmmDecl d h CmmGraph -> CFG
getCfgProc _ _ (CmmData {}) = mapEmpty
getCfgProc platform weights (CmmProc _info _lab _live graph) = getCfg platform weights graph
getCfg :: Platform -> Weights -> CmmGraph -> CFG
getCfg platform weights graph =
foldl' insertEdge edgelessCfg $ concatMap getBlockEdges blocks
where
Weights
{ uncondWeight = uncondWeight
, condBranchWeight = condBranchWeight
, switchWeight = switchWeight
, callWeight = callWeight
, likelyCondWeight = likelyCondWeight
, unlikelyCondWeight = unlikelyCondWeight
-- Last two are used in other places
--, infoTablePenalty = infoTablePenalty
--, backEdgeBonus = backEdgeBonus
} = weights
-- Explicitly add all nodes to the cfg to ensure they are part of the
-- CFG.
edgelessCfg = mapFromList $ zip (map G.entryLabel blocks) (repeat mapEmpty)
insertEdge :: CFG -> ((BlockId,BlockId),EdgeInfo) -> CFG
insertEdge m ((from,to),weight) =
mapAlter f from m
where
f :: Maybe (LabelMap EdgeInfo) -> Maybe (LabelMap EdgeInfo)
f Nothing = Just $ mapSingleton to weight
f (Just destMap) = Just $ mapInsert to weight destMap
getBlockEdges :: CmmBlock -> [((BlockId,BlockId),EdgeInfo)]
getBlockEdges block =
case branch of
CmmBranch dest -> [mkEdge dest uncondWeight]
CmmCondBranch cond t f l
| l == Nothing ->
[mkEdge f condBranchWeight, mkEdge t condBranchWeight]
| l == Just True ->
[mkEdge f unlikelyCondWeight, mkEdge t likelyCondWeight]
| l == Just False ->
[mkEdge f likelyCondWeight, mkEdge t unlikelyCondWeight]
where
mkEdgeInfo = -- pprTrace "Info" (ppr branchInfo <+> ppr cond)
EdgeInfo (CmmSource branch branchInfo) . fromIntegral
mkEdge target weight = ((bid,target), mkEdgeInfo weight)
branchInfo =
foldRegsUsed
(panic "GHC.CmmToAsm.CFG.getCfg: foldRegsUsed")
(\info r -> if r == SpLim || r == HpLim || r == BaseReg
then HeapStackCheck else info)
NoInfo cond
(CmmSwitch _e ids) ->
let switchTargets = switchTargetsToList ids
--Compiler performance hack - for very wide switches don't
--consider targets for layout.
adjustedWeight =
if (length switchTargets > 10) then -1 else switchWeight
in map (\x -> mkEdge x adjustedWeight) switchTargets
(CmmCall { cml_cont = Just cont}) -> [mkEdge cont callWeight]
(CmmForeignCall {Cmm.succ = cont}) -> [mkEdge cont callWeight]
(CmmCall { cml_cont = Nothing }) -> []
other ->
panic "Foo" $
assertPpr False (text "Unknown successor cause:" <>
(pdoc platform branch <+> text "=>" <> pdoc platform (G.successors other))) $
map (\x -> ((bid,x),mkEdgeInfo 0)) $ G.successors other
where
bid = G.entryLabel block
mkEdgeInfo = EdgeInfo (CmmSource branch NoInfo) . fromIntegral
mkEdge target weight = ((bid,target), mkEdgeInfo weight)
branch = lastNode block :: CmmNode O C
blocks = revPostorder graph :: [CmmBlock]
--Find back edges by BFS
findBackEdges :: HasDebugCallStack => BlockId -> CFG -> Edges
findBackEdges root cfg =
--pprTraceIt "Backedges:" $
map fst .
filter (\x -> snd x == Backward) $ typedEdges
where
edges = edgeList cfg :: [(BlockId,BlockId)]
getSuccs = getSuccessors cfg :: BlockId -> [BlockId]
typedEdges =
classifyEdges root getSuccs edges :: [((BlockId,BlockId),EdgeType)]
optimizeCFG :: Bool -> Weights -> RawCmmDecl -> CFG -> CFG
optimizeCFG _ _ (CmmData {}) cfg = cfg
optimizeCFG doStaticPred weights proc@(CmmProc _info _lab _live graph) cfg =
(if doStaticPred then staticPredCfg (g_entry graph) else id) $
optHsPatterns weights proc $ cfg
-- | Modify branch weights based on educated guess on
-- patterns GHC tends to produce and how they affect
-- performance.
--
-- Most importantly we penalize jumps across info tables.
optHsPatterns :: Weights -> RawCmmDecl -> CFG -> CFG
optHsPatterns _ (CmmData {}) cfg = cfg
optHsPatterns weights (CmmProc info _lab _live graph) cfg =
{-# SCC optHsPatterns #-}
-- pprTrace "Initial:" (pprEdgeWeights cfg) $
-- pprTrace "Initial:" (ppr $ mkGlobalWeights (g_entry graph) cfg) $
-- pprTrace "LoopInfo:" (ppr $ loopInfo cfg (g_entry graph)) $
favourFewerPreds .
penalizeInfoTables info .
increaseBackEdgeWeight (g_entry graph) $ cfg
where
-- Increase the weight of all backedges in the CFG
-- this helps to make loop jumpbacks the heaviest edges
increaseBackEdgeWeight :: BlockId -> CFG -> CFG
increaseBackEdgeWeight root cfg =
let backedges = findBackEdges root cfg
update weight
--Keep irrelevant edges irrelevant
| weight <= 0 = 0
| otherwise
= weight + fromIntegral (backEdgeBonus weights)
in foldl' (\cfg edge -> updateEdgeWeight update edge cfg)
cfg backedges
-- Since we cant fall through info tables we penalize these.
penalizeInfoTables :: LabelMap a -> CFG -> CFG
penalizeInfoTables info cfg =
mapWeights fupdate cfg
where
fupdate :: BlockId -> BlockId -> EdgeWeight -> EdgeWeight
fupdate _ to weight
| mapMember to info
= weight - (fromIntegral $ infoTablePenalty weights)
| otherwise = weight
-- If a block has two successors, favour the one with fewer
-- predecessors and/or the one allowing fall through.
favourFewerPreds :: CFG -> CFG
favourFewerPreds cfg =
let
revCfg =
reverseEdges $ filterEdges
(\_from -> fallthroughTarget) cfg
predCount n = length $ getSuccessorEdges revCfg n
nodes = getCfgNodes cfg
modifiers :: Int -> Int -> (EdgeWeight, EdgeWeight)
modifiers preds1 preds2
| preds1 < preds2 = ( 1,-1)
| preds1 == preds2 = ( 0, 0)
| otherwise = (-1, 1)
update :: CFG -> BlockId -> CFG
update cfg node
| [(s1,e1),(s2,e2)] <- getSuccessorEdges cfg node
, !w1 <- edgeWeight e1
, !w2 <- edgeWeight e2
--Only change the weights if there isn't already a ordering.
, w1 == w2
, (mod1,mod2) <- modifiers (predCount s1) (predCount s2)
= (\cfg' ->
(adjustEdgeWeight cfg' (+mod2) node s2))
(adjustEdgeWeight cfg (+mod1) node s1)
| otherwise
= cfg
in foldl' update cfg nodes
where
fallthroughTarget :: BlockId -> EdgeInfo -> Bool
fallthroughTarget to (EdgeInfo source _weight)
| mapMember to info = False
| AsmCodeGen <- source = True
| CmmSource { trans_cmmNode = CmmBranch {} } <- source = True
| CmmSource { trans_cmmNode = CmmCondBranch {} } <- source = True
| otherwise = False
-- | Convert block-local branch weights to global weights.
staticPredCfg :: BlockId -> CFG -> CFG
staticPredCfg entry cfg = cfg'
where
(_, globalEdgeWeights) = {-# SCC mkGlobalWeights #-}
mkGlobalWeights entry cfg
cfg' = {-# SCC rewriteEdges #-}
mapFoldlWithKey
(\cfg from m ->
mapFoldlWithKey
(\cfg to w -> setEdgeWeight cfg (EdgeWeight w) from to )
cfg m )
cfg
globalEdgeWeights
-- | Determine loop membership of blocks based on SCC analysis
-- This is faster but only gives yes/no answers.
loopMembers :: HasDebugCallStack => CFG -> LabelMap Bool
loopMembers cfg =
foldl' (flip setLevel) mapEmpty sccs
where
mkNode :: BlockId -> Node BlockId BlockId
mkNode bid = DigraphNode bid bid (getSuccessors cfg bid)
nodes = map mkNode (getCfgNodes cfg)
sccs = stronglyConnCompFromEdgedVerticesOrd nodes
setLevel :: SCC BlockId -> LabelMap Bool -> LabelMap Bool
setLevel (AcyclicSCC bid) m = mapInsert bid False m
setLevel (CyclicSCC bids) m = foldl' (\m k -> mapInsert k True m) m bids
loopLevels :: CFG -> BlockId -> LabelMap Int
loopLevels cfg root = liLevels loopInfos
where
loopInfos = loopInfo cfg root
data LoopInfo = LoopInfo
{ liBackEdges :: [(Edge)] -- ^ List of back edges
, liLevels :: LabelMap Int -- ^ BlockId -> LoopLevel mapping
, liLoops :: [(Edge, LabelSet)] -- ^ (backEdge, loopBody), body includes header
}
instance Outputable LoopInfo where
ppr (LoopInfo _ _lvls loops) =
text "Loops:(backEdge, bodyNodes)" $$
(vcat $ map ppr loops)
{- Note [Determining the loop body]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Starting with the knowledge that:
* head dominates the loop
* `tail` -> `head` is a backedge
We can determine all nodes by:
* Deleting the loop head from the graph.
* Collect all blocks which are reachable from the `tail`.
We do so by performing bfs from the tail node towards the head.
-}
-- | Determine loop membership of blocks based on Dominator analysis.
-- This is slower but gives loop levels instead of just loop membership.
-- However it only detects natural loops. Irreducible control flow is not
-- recognized even if it loops. But that is rare enough that we don't have
-- to care about that special case.
loopInfo :: HasDebugCallStack => CFG -> BlockId -> LoopInfo
loopInfo cfg root = LoopInfo { liBackEdges = backEdges
, liLevels = mapFromList loopCounts
, liLoops = loopBodies }
where
revCfg = reverseEdges cfg
graph = -- pprTrace "CFG - loopInfo" (pprEdgeWeights cfg) $
fmap (setFromList . mapKeys ) cfg :: LabelMap LabelSet
--TODO - This should be a no op: Export constructors? Use unsafeCoerce? ...
rooted = ( fromBlockId root
, toWord64Map $ fmap toWord64Set graph) :: (Word64, Word64Map Word64Set)
tree = fmap toBlockId $ Dom.domTree rooted :: Tree BlockId
-- Map from Nodes to their dominators
domMap :: LabelMap LabelSet
domMap = mkDomMap tree
edges = edgeList cfg :: [(BlockId, BlockId)]
-- We can't recompute nodes from edges, there might be blocks not connected via edges.
nodes = getCfgNodes cfg :: [BlockId]
-- identify back edges
isBackEdge (from,to)
| Just doms <- mapLookup from domMap
, setMember to doms
= True
| otherwise = False
-- See Note [Determining the loop body]
-- Get the loop body associated with a back edge.
findBody edge@(tail, head)
= ( edge, setInsert head $ go (setSingleton tail) (setSingleton tail) )
where
-- See Note [Determining the loop body]
go :: LabelSet -> LabelSet -> LabelSet
go found current
| setNull current = found
| otherwise = go (setUnion newSuccessors found)
newSuccessors
where
-- Really predecessors, since we use the reversed cfg.
newSuccessors = setFilter (\n -> not $ setMember n found) successors :: LabelSet
successors = setDelete head $ setUnions $ map
(\x -> if x == head then setEmpty else setFromList (getSuccessors revCfg x))
(setElems current) :: LabelSet
backEdges = filter isBackEdge edges
loopBodies = map findBody backEdges :: [(Edge, LabelSet)]
-- Block b is part of n loop bodies => loop nest level of n
loopCounts =
let bodies = map (first snd) loopBodies -- [(Header, Body)]
loopCount n = length $ nub . map fst . filter (setMember n . snd) $ bodies
in map (\n -> (n, loopCount n)) $ nodes :: [(BlockId, Int)]
toWord64Set :: LabelSet -> Word64Set
toWord64Set s = WS.fromList . map fromBlockId . setElems $ s
toWord64Map :: LabelMap a -> Word64Map a
toWord64Map m = WM.fromList $ map (\(x,y) -> (fromBlockId x,y)) $ mapToList m
mkDomMap :: Tree BlockId -> LabelMap LabelSet
mkDomMap root = mapFromList $ go setEmpty root
where
go :: LabelSet -> Tree BlockId -> [(Label,LabelSet)]
go parents (Node lbl [])
= [(lbl, parents)]
go parents (Node _ leaves)
= let nodes = map rootLabel leaves
entries = map (\x -> (x,parents)) nodes
in entries ++ concatMap
(\n -> go (setInsert (rootLabel n) parents) n)
leaves
fromBlockId :: BlockId -> Word64
fromBlockId = getKey . getUnique
toBlockId :: Word64 -> BlockId
toBlockId = mkBlockId . mkUniqueGrimily
-- We make the CFG a Hoopl Graph, so we can reuse revPostOrder.
newtype BlockNode (e :: Extensibility) (x :: Extensibility) = BN (BlockId,[BlockId])
instance G.NonLocal (BlockNode) where
entryLabel (BN (lbl,_)) = lbl
successors (BN (_,succs)) = succs
revPostorderFrom :: HasDebugCallStack => CFG -> BlockId -> [BlockId]
revPostorderFrom cfg root =
map fromNode $ G.revPostorderFrom hooplGraph root
where
nodes = getCfgNodes cfg
hooplGraph = foldl' (\m n -> mapInsert n (toNode n) m) mapEmpty nodes
fromNode :: BlockNode C C -> BlockId
fromNode (BN x) = fst x
toNode :: BlockId -> BlockNode C C
toNode bid =
BN (bid,getSuccessors cfg $ bid)
-- | We take in a CFG which has on its edges weights which are
-- relative only to other edges originating from the same node.
--
-- We return a CFG for which each edge represents a GLOBAL weight.
-- This means edge weights are comparable across the whole graph.
--
-- For irreducible control flow results might be imprecise, otherwise they
-- are reliable.
--
-- The algorithm is based on the Paper
-- "Static Branch Prediction and Program Profile Analysis" by Y Wu, JR Larus
-- The only big change is that we go over the nodes in the body of loops in
-- reverse post order. Which is required for diamond control flow to work probably.
--
-- We also apply a few prediction heuristics (based on the same paper)
--
-- The returned result represents frequences.
-- For blocks it's the expected number of executions and
-- for edges is the number of traversals.
{-# NOINLINE mkGlobalWeights #-}
{-# SCC mkGlobalWeights #-}
mkGlobalWeights :: HasDebugCallStack => BlockId -> CFG -> (LabelMap Double, LabelMap (LabelMap Double))
mkGlobalWeights root localCfg
| null localCfg = panic "Error - Empty CFG"
| otherwise
= (blockFreqs', edgeFreqs')
where
-- Calculate fixpoints
(blockFreqs, edgeFreqs) = calcFreqs nodeProbs backEdges' bodies' revOrder'
blockFreqs' = mapFromList $ map (first fromVertex) (assocs blockFreqs) :: LabelMap Double
edgeFreqs' = fmap fromVertexMap $ fromVertexMap edgeFreqs
fromVertexMap :: IM.IntMap x -> LabelMap x
fromVertexMap m = mapFromList . map (first fromVertex) $ IM.toList m
revOrder = revPostorderFrom localCfg root :: [BlockId]
loopResults@(LoopInfo backedges _levels bodies) = loopInfo localCfg root
revOrder' = map toVertex revOrder
backEdges' = map (bimap toVertex toVertex) backedges
bodies' = map calcBody bodies
estimatedCfg = staticBranchPrediction root loopResults localCfg
-- Normalize the weights to probabilities and apply heuristics
nodeProbs = cfgEdgeProbabilities estimatedCfg toVertex
-- By mapping vertices to numbers in reverse post order we can bring any subset into reverse post
-- order simply by sorting.
-- TODO: The sort is redundant if we can guarantee that setElems returns elements ascending
calcBody (backedge, blocks) =
(toVertex $ snd backedge, sort . map toVertex $ (setElems blocks))
vertexMapping = mapFromList $ zip revOrder [0..] :: LabelMap Int
blockMapping = listArray (0,mapSize vertexMapping - 1) revOrder :: Array Int BlockId
-- Map from blockId to indices starting at zero
toVertex :: BlockId -> Int
toVertex blockId = expectJust $ mapLookup blockId vertexMapping
-- Map from indices starting at zero to blockIds
fromVertex :: Int -> BlockId
fromVertex vertex = blockMapping ! vertex
{- Note [Static Branch Prediction]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The work here has been based on the paper
"Static Branch Prediction and Program Profile Analysis" by Y Wu, JR Larus.
The primary differences are that if we branch on the result of a heap
check we do not apply any of the heuristics.
The reason is simple: They look like loops in the control flow graph
but are usually never entered, and if at most once.
Currently implemented is a heuristic to predict that we do not exit
loops (lehPredicts) and one to predict that backedges are more likely
than any other edge.
The back edge case is special as it supersedes any other heuristic if it
applies.
Do NOT rely solely on nofib results for benchmarking this. I recommend at least
comparing megaparsec and container benchmarks. Nofib does not seem to have
many instances of "loopy" Cmm where these make a difference.
TODO:
* The paper containers more benchmarks which should be implemented.
* If we turn the likelihood on if/else branches into a probability
instead of true/false we could implement this as a Cmm pass.
+ The complete Cmm code still exists and can be accessed by the heuristics
+ There is no chance of register allocation/codegen inserting branches/blocks
+ making the TransitionSource info wrong.
+ potential to use this information in CmmPasses.
- Requires refactoring of all the code relying on the binary nature of likelihood.
- Requires refactoring `loopInfo` to work on both, Cmm Graphs and the backend CFG.
-}
-- | Combination of target node id and information about the branch
-- we are looking at.
type TargetNodeInfo = (BlockId, EdgeInfo)
-- | Update branch weights based on certain heuristics.
-- See Note [Static Branch Prediction]
-- TODO: This should be combined with optimizeCFG
{-# SCC staticBranchPrediction #-}
staticBranchPrediction :: BlockId -> LoopInfo -> CFG -> CFG
staticBranchPrediction _root (LoopInfo l_backEdges loopLevels l_loops) cfg =
-- pprTrace "staticEstimatesOn" (ppr (cfg)) $
foldl' update cfg nodes
where
nodes = getCfgNodes cfg
backedges = S.fromList $ l_backEdges
-- Loops keyed by their back edge
loops = M.fromList $ l_loops :: M.Map Edge LabelSet
loopHeads = S.fromList $ map snd $ M.keys loops
update :: CFG -> BlockId -> CFG
update cfg node
-- No successors, nothing to do.
| null successors = cfg
-- Mix of backedges and others:
-- Always predict the backedges.
| not (null m) && length m < length successors
-- Heap/Stack checks "loop", but only once.
-- So we simply exclude any case involving them.
, not $ any (isHeapOrStackCheck . transitionSource . snd) successors
= let loopChance = repeat $! pred_LBH / (fromIntegral $ length m)
exitChance = repeat $! (1 - pred_LBH) / fromIntegral (length not_m)
updates = zip (map fst m) loopChance ++ zip (map fst not_m) exitChance
in -- pprTrace "mix" (ppr (node,successors)) $
foldl' (\cfg (to,weight) -> setEdgeWeight cfg weight node to) cfg updates
-- For (regular) non-binary branches we keep the weights from the STG -> Cmm translation.
| length successors /= 2
= cfg
-- Only backedges - no need to adjust
| length m > 0
= cfg
-- A regular binary branch, we can plug addition predictors in here.
| [(s1,s1_info),(s2,s2_info)] <- successors
, not $ any (isHeapOrStackCheck . transitionSource . snd) successors
= -- Normalize weights to total of 1
let !w1 = max (edgeWeight s1_info) (0)
!w2 = max (edgeWeight s2_info) (0)
-- Of both weights are <= 0 we set both to 0.5
normalizeWeight w = if w1 + w2 == 0 then 0.5 else w/(w1+w2)
!cfg' = setEdgeWeight cfg (normalizeWeight w1) node s1
!cfg'' = setEdgeWeight cfg' (normalizeWeight w2) node s2
-- Figure out which heuristics apply to these successors
heuristics = map ($ ((s1,s1_info),(s2,s2_info)))
[lehPredicts, phPredicts, ohPredicts, ghPredicts, lhhPredicts, chPredicts
, shPredicts, rhPredicts]
-- Apply result of a heuristic. Argument is the likelihood
-- predicted for s1.
applyHeuristic :: CFG -> Maybe Prob -> CFG
applyHeuristic cfg Nothing = cfg
applyHeuristic cfg (Just (s1_pred :: Double))
| s1_old == 0 || s2_old == 0 ||
isHeapOrStackCheck (transitionSource s1_info) ||
isHeapOrStackCheck (transitionSource s2_info)
= cfg
| otherwise =
let -- Predictions from heuristic
s1_prob = EdgeWeight s1_pred :: EdgeWeight
s2_prob = 1.0 - s1_prob
-- Update
d = (s1_old * s1_prob) + (s2_old * s2_prob) :: EdgeWeight
s1_prob' = s1_old * s1_prob / d
!s2_prob' = s2_old * s2_prob / d
!cfg_s1 = setEdgeWeight cfg s1_prob' node s1
in -- pprTrace "Applying heuristic!" (ppr (node,s1,s2) $$ ppr (s1_prob', s2_prob')) $
setEdgeWeight cfg_s1 s2_prob' node s2
where
-- Old weights
s1_old = getEdgeWeight cfg node s1
s2_old = getEdgeWeight cfg node s2
in
-- pprTraceIt "RegularCfgResult" $
foldl' applyHeuristic cfg'' heuristics
-- Branch on heap/stack check
| otherwise = cfg
where
-- Chance that loops are taken.
pred_LBH = 0.875
-- successors
successors = getSuccessorEdges cfg node
-- backedges
(m,not_m) = partition (\succ -> S.member (node, fst succ) backedges) successors
-- Heuristics return nothing if they don't say anything about this branch
-- or Just (prob_s1) where prob_s1 is the likelihood for s1 to be the
-- taken branch. s1 is the branch in the true case.
-- Loop exit heuristic.
-- We are unlikely to leave a loop unless it's to enter another one.
pred_LEH = 0.75
-- If and only if no successor is a loopheader,
-- then we will likely not exit the current loop body.
lehPredicts :: (TargetNodeInfo,TargetNodeInfo) -> Maybe Prob
lehPredicts ((s1,_s1_info),(s2,_s2_info))
| S.member s1 loopHeads || S.member s2 loopHeads
= Nothing
| otherwise
= --pprTrace "lehPredict:" (ppr $ compare s1Level s2Level) $
case compare s1Level s2Level of
EQ -> Nothing
LT -> Just (1-pred_LEH) --s1 exits to a shallower loop level (exits loop)
GT -> Just (pred_LEH) --s1 exits to a deeper loop level
where
s1Level = mapLookup s1 loopLevels
s2Level = mapLookup s2 loopLevels
-- Comparing to a constant is unlikely to be equal.
ohPredicts (s1,_s2)
| CmmSource { trans_cmmNode = src1 } <- getTransitionSource node (fst s1) cfg
, CmmCondBranch cond ltrue _lfalse likely <- src1
, likely == Nothing
, CmmMachOp mop args <- cond
, MO_Eq {} <- mop
, not (null [x | x@CmmLit{} <- args])
= if fst s1 == ltrue then Just 0.3 else Just 0.7
| otherwise
= Nothing
-- TODO: These are all the other heuristics from the paper.
-- Not all will apply, for now we just stub them out as Nothing.
phPredicts = const Nothing
ghPredicts = const Nothing
lhhPredicts = const Nothing
chPredicts = const Nothing
shPredicts = const Nothing
rhPredicts = const Nothing
-- We normalize all edge weights as probabilities between 0 and 1.
-- Ignoring rounding errors all outgoing edges sum up to 1.
cfgEdgeProbabilities :: CFG -> (BlockId -> Int) -> IM.IntMap (IM.IntMap Prob)
cfgEdgeProbabilities cfg toVertex
= mapFoldlWithKey foldEdges IM.empty cfg
where
foldEdges = (\m from toMap -> IM.insert (toVertex from) (normalize toMap) m)
normalize :: (LabelMap EdgeInfo) -> (IM.IntMap Prob)
normalize weightMap
| edgeCount <= 1 = mapFoldlWithKey (\m k _ -> IM.insert (toVertex k) 1.0 m) IM.empty weightMap
| otherwise = mapFoldlWithKey (\m k _ -> IM.insert (toVertex k) (normalWeight k) m) IM.empty weightMap
where
edgeCount = mapSize weightMap
-- Negative weights are generally allowed but are mapped to zero.
-- We then check if there is at least one non-zero edge and if not
-- assign uniform weights to all branches.
minWeight = 0 :: Prob
weightMap' = fmap (\w -> max (weightToDouble . edgeWeight $ w) minWeight) weightMap
totalWeight = sum weightMap'
normalWeight :: BlockId -> Prob
normalWeight bid
| totalWeight == 0
= 1.0 / fromIntegral edgeCount
| Just w <- mapLookup bid weightMap'
= w/totalWeight
| otherwise = panic "impossible"
-- This is the fixpoint algorithm from
-- "Static Branch Prediction and Program Profile Analysis" by Y Wu, JR Larus
-- The adaption to Haskell is my own.
calcFreqs :: IM.IntMap (IM.IntMap Prob) -> [(Int,Int)] -> [(Int, [Int])] -> [Int]
-> (Array Int Double, IM.IntMap (IM.IntMap Prob))
calcFreqs graph backEdges loops revPostOrder = runST $ do
visitedNodes <- newArray (0,nodeCount-1) False :: ST s (STUArray s Int Bool)
blockFreqs <- newArray (0,nodeCount-1) 0.0 :: ST s (STUArray s Int Double)
edgeProbs <- newSTRef graph
edgeBackProbs <- newSTRef graph
-- let traceArray a = do
-- vs <- forM [0..nodeCount-1] $ \i -> readArray a i >>= (\v -> return (i,v))
-- trace ("array: " ++ show vs) $ return ()
let -- See #1600, we need to inline or unboxing makes perf worse.
-- {-# INLINE getFreq #-}
{-# INLINE visited #-}
visited b = unsafeRead visitedNodes b
getFreq b = unsafeRead blockFreqs b
-- setFreq :: forall s. Int -> Double -> ST s ()
setFreq b f = unsafeWrite blockFreqs b f
-- setVisited :: forall s. Node -> ST s ()
setVisited b = unsafeWrite visitedNodes b True
-- Frequency/probability that edge is taken.
getProb' arr b1 b2 = readSTRef arr >>=
(\graph ->
return .
fromMaybe (error "getFreq 1") .
IM.lookup b2 .
fromMaybe (error "getFreq 2") $
(IM.lookup b1 graph)
)
setProb' arr b1 b2 prob = do
g <- readSTRef arr
let !m = fromMaybe (error "Foo") $ IM.lookup b1 g
!m' = IM.insert b2 prob m
writeSTRef arr $! (IM.insert b1 m' g)
getEdgeFreq b1 b2 = getProb' edgeProbs b1 b2
setEdgeFreq b1 b2 = setProb' edgeProbs b1 b2
getProb b1 b2 = fromMaybe (error "getProb") $ do
m' <- IM.lookup b1 graph
IM.lookup b2 m'
getBackProb b1 b2 = getProb' edgeBackProbs b1 b2
setBackProb b1 b2 = setProb' edgeBackProbs b1 b2
let -- calcOutFreqs :: Node -> ST s ()
calcOutFreqs bhead block = do
!f <- getFreq block
forM (successors block) $ \bi -> do
let !prob = getProb block bi
let !succFreq = f * prob
setEdgeFreq block bi succFreq
-- traceM $ "SetOut: " ++ show (block, bi, f, prob, succFreq)
when (bi == bhead) $ setBackProb block bi succFreq
let propFreq block head = do
-- traceM ("prop:" ++ show (block,head))
-- traceShowM block
!v <- visited block
if v then
return () --Dont look at nodes twice
else if block == head then
setFreq block 1.0 -- Loop header frequency is always 1
else do
let preds = IS.elems $ predecessors block
irreducible <- (fmap or) $ forM preds $ \bp -> do
!bp_visited <- visited bp
let bp_backedge = isBackEdge bp block
return (not bp_visited && not bp_backedge)
if irreducible
then return () -- Rare we don't care
else do
setFreq block 0
!cycleProb <- sum <$> (forM preds $ \pred -> do
if isBackEdge pred block
then
getBackProb pred block
else do
!f <- getFreq block
!prob <- getEdgeFreq pred block
setFreq block $! f + prob
return 0)
-- traceM $ "cycleProb:" ++ show cycleProb
let limit = 1 - 1/512 -- Paper uses 1 - epsilon, but this works.
-- determines how large likelyhoods in loops can grow.
!cycleProb <- return $ min cycleProb limit -- <- return $ if cycleProb > limit then limit else cycleProb
-- traceM $ "cycleProb:" ++ show cycleProb
!f <- getFreq block
setFreq block (f / (1.0 - cycleProb))
setVisited block
calcOutFreqs head block
-- Loops, by nesting, inner to outer
forM_ loops $ \(head, body) -> do
forM_ [0 .. nodeCount - 1] (\i -> unsafeWrite visitedNodes i True) -- Mark all nodes as visited.
forM_ body (\i -> unsafeWrite visitedNodes i False) -- Mark all blocks reachable from head as not visited
forM_ body $ \block -> propFreq block head
-- After dealing with all loops, deal with non-looping parts of the CFG
forM_ [0 .. nodeCount - 1] (\i -> unsafeWrite visitedNodes i False) -- Everything in revPostOrder is reachable
forM_ revPostOrder $ \block -> propFreq block (head revPostOrder)
-- trace ("Final freqs:") $ return ()
-- let freqString = pprFreqs freqs
-- trace (unlines freqString) $ return ()
-- trace (pprFre) $ return ()
graph' <- readSTRef edgeProbs
freqs' <- unsafeFreeze blockFreqs
return (freqs', graph')
where
-- How can these lookups fail? Consider the CFG [A -> B]
predecessors :: Int -> IS.IntSet
predecessors b = fromMaybe IS.empty $ IM.lookup b revGraph
successors :: Int -> [Int]
successors b = fromMaybe (lookupError "succ" b graph)$ IM.keys <$> IM.lookup b graph
lookupError s b g = pprPanic ("Lookup error " ++ s) $
( text "node" <+> ppr b $$
text "graph" <+>
vcat (map (\(k,m) -> ppr (k,m :: IM.IntMap Double)) $ IM.toList g)
)
nodeCount = IM.foldl' (\count toMap -> IM.foldlWithKey' countTargets (count + 1) toMap) 0 graph
where
countTargets = (\count k _ -> countNode k + count )
countNode n = if IM.member n graph then 0 else 1
isBackEdge from to = S.member (from,to) backEdgeSet
backEdgeSet = S.fromList backEdges
revGraph :: IntMap IntSet
revGraph = IM.foldlWithKey' (\m from toMap -> addEdges m from toMap) IM.empty graph
where
addEdges m0 from toMap = IM.foldlWithKey' (\m k _ -> addEdge m from k) m0 toMap
addEdge m0 from to = IM.insertWith IS.union to (IS.singleton from) m0