Equipping your Library for Backprop
So you want your users to be able to use your numerical library with backprop, huh?
This page is specifically for library authors who want to allow their users to use their library operations and API with backprop. End-users of the backprop library should not have to worry about the contents of this page.
Equipping your library with backprop involves providing "backprop-aware"
versions of your library functions. In fact, it is possible to make a
library fully by providing only backprop versions of your functions, since
you can use a backprop-aware function as a normal function with evalBP
.
Alternatively, you can re-export all of your functions in a separate module with
"backprop-aware" versions.
Know Thy Types
The most significant effort will be in lifting your library's functions. If you have a function:
myFunc :: a -> b
Then its lifted version would have type:
myFunc :: Reifies s W => BVar s a -> BVar s b
That is, instead of a function directly taking an a
and returning a b
, it's
a function taking a BVar
containing an a
, and returning a BVar
containing
a b
.
Functions taking multiple arguments can be translated pretty straightforwardly:
func1 :: a -> b -> c
func1BP :: Reifies s W => BVar s a -> BVar s b -> BVar s c
And also functions returning multiple arguments:
func2 :: a -> ( b, c)
func2BP :: Reifies s W => BVar s a -> (BVar s b, BVar s c)
It is recommended (for ease of use with -XTypeApplications
) that Reifies s
W
be the final constraint in all code you write.
Note that almost all operations involving BVar
'd items require that the
contents have a Backprop
instance. Alternative API's to backprop that
require Num
instances instead (or explicitly specified addition functions)
are available in Numeric.Backprop.Num and Numeric.Backprop.Explicit.
The Easy Way
BVar
based functions are just normal functions, so they can be applied
normally and passed as first-class values. If possible, if you can utilize
functions that are already BVar
'd/lifted, then you can just define your API
in terms of those lifted functions. This is also how users are expected to
be able to use your library: just use the lifted functions you provide, in
order to make their own lifted functions using normal function application and
composition.
Lifting operations manually
However, if no lifted primitive functions are available, then you do have to do some legwork to provide information on gradient computation for your types. Ideally, you would only need to do this for some minimal set of your operations, and then define the rest of them in terms of the functions you have already lifted.
A full tutorial on lifting your library functions can be found
here. It describes the usage of the liftOp
and op
family of functions to fully lift your single-argument single-result and
multiple-argument single-result functions to be backpropagatable.
Returning multiple items
As an extension of the manual gradient tutorial, we can consider functions that return multiple items.
You can always return tuples inside BVar
s:
splitAt
:: (Backprop a, Reifies s W)
=> Int
-> BVar s [a]
-> BVar s ([a], [a])
splitAt n = liftOp1 . op1 $ \xs ->
let (ys, zs) = Data.List.splitAt n xs
in ((ys, zs), \(dys,dzs) -> dys ++ dzs)
-- assumes dys and dzs have the same lengths as ys and zs
This works as expected. However, it is recommended, for the benefit of your
users, that you return a tuple of BVar
s instead of a BVar
of tuples:
splitAt'
:: (Backprop a, Reifies s W)
=> Int
-> BVar s [a]
-> (BVar s [a], BVar s [a])
splitAt' n xs = (yszs ^^. _1, yszs ^^. _2)
where
yszs = liftOp1 (op1 $ \xs' ->
let (ys, zs) = Data.List.splitAt n xs'
in ((ys, zs), \(dys,dzs) -> dys ++ dzs)
) xs
using _1
and _2
from the microlens or lens packages. This
might also be cleaner if you take advantage of the T2
or T3
pattern
synonyms:
splitAt''
:: (Backprop a, Reifies s W)
=> Int
-> BVar s [a]
-> (BVar s [a], BVar s [a])
splitAt'' n xs = (ys, zs)
where
T2 ys zs = liftOp1 (op1 $ \xs' ->
let (ys, zs) = Data.List.splitAt n xs'
in ((ys, zs), \(dys,dzs) -> dys ++ dzs)
) xs
Isomorphisms
If your function witnesses an isomorphism, there are handy combinators for making this easy to write. This is especially useful in the case of data constructors:
newtype Foo = MkFoo { getFoo :: Double }
deriving Generic
instance Backprop Foo
mkFoo
:: Reifies s W
=> BVar s Double
-> BVar s Foo
mkFoo = isoVar MkFoo getFoo
data Bar = MkBar { bar1 :: Double, bar2 :: Float }
deriving Generic
instance Backprop Bar
mkBar
:: Reifies s W
=> BVar s Double
-> BVar s Float
-> BVar s Bar
mkBar = isoVar2 MkBar (\b -> (bar1 b, bar2 b))
Note also that if you have a newtype with one constructor (or any other two
Coercible
types), you can simply use coerceVar
:
mkFoo'
:: BVar s Double
-> BVar s Foo
mkFoo' = coerceVar -- requires no `Reifies s W` constraint
NoGrad
If you do decide to go to the extreme, and provide only a BVar-based
interface to your library (and no non-BVar based one), then you might have a
situation where you have a function where you cannot define the gradient --
maybe no gradient exists, or you haven't put in the time to write one. In this
case, you can use noGrad
and noGrad1
:
negateNoGrad
:: (Num a, Backprop a, Reifies s W)
=> BVar s a
-> BVar s a
negateNoGrad = liftOp1 (noGrad1 negate)
This function can still be used with evalBP
to get the correct answer. It
can even be used with gradBP
if the result is never used in the final answer.
However, if it is used in the final answer, then computing the gradient will throw a runtime exception.
Be sure to warn your users! Like any partial function, this is not recommended unless in extreme circumstances.
Monadic Operations
This should all work if your operations are all "pure". However, what about the cases where your operations have to be performed in some Applicative or Monadic context?
For example, what if add :: X -> X -> IO X
?
One option you can do is to newtype-wrap your operations, and then give those a backprop instance:
newtype IOX = IOX (IO X)
instance Backprop IOX where
zero (IOX x) = IOX (fmap zeroForX x)
-- or, depending on the type of `zeroForX`:
-- zero (IOX x) = IOX (zeroForX =<< x)
add (IOX x) (IOX y) = IOX $ do
x' <- x
y' <- y
addForX x' y'
one (IOX x) = IOX (fmap oneForX x)
And you can define your functions in terms of this:
addX
:: Reifies s W
=> BVar s IOX
-> BVar s IOX
-> BVar s IOX
addX = liftOp2 . op2 $ \(IOX x) (IOX y) ->
( IOX (do x' <- x; y' <- y; addForX x' y')
, \dzdy -> (dzdy, dzdy)
)
This should work fine as long as you never "branch" on any results of your
actions. You must not ever need to peek inside the results of the action in
order to decide what operations to do next. In other words, this works if
the operations you need to perform are all known and fixed before-hand, before
any actions are performed. So, this means no access to the Eq
or Ord
instances of BVars (unless your monad has Eq
or Ord
instances defined).
A newtype wrapper is provided to give you this behavior automatically -- it's
ABP
, from Numeric.Backprop and Numeric.Backprop.Class.
type IOX = ABP IO X
However, this will not work if you need to do things like compare contents, etc. to decide what operations to use.
At the moment, this is not supported. Please open an issue if this becomes an issue!
Supporting Data Types
Your library will probably have data types that you expect your users to use. To equip your data types for backpropagation, you can take a few steps.
Backprop Class
First of all, all of your library's types should have instances of the
Backprop
typeclass. This allows values of your type to be used in
backpropagatable functions. See the Backprop typeclass section of
this documentation for more information on writing a Backprop
instance for
your types.
In short:
If your type is a type with a single constructor whose fields are all instances of
Backprop
, you can just writeinstance Backprop MyType
, and the instance is generated automatically (as long as your type has aGeneric
instance)data MyType = MkMyType Double [Float] (R 10) (L 20 10) (V.Vector Double) deriving Generic instance Backprop MyType
If your type is an instance of
Num
, you can usezeroNum
,addNum
, andoneNum
to get free definitions of the typeclass methods.instance Backprop Double where zero = zeroNum add = addNum one = oneNum
If your type is made using a
Functor
instance, you can usezeroFunctor
andoneFunctor
:instance Backprop a => Backprop (V.Vector a) where zero = zeroFunctor add = undefined -- ?? one = oneFunctor
If your type has an
IsList
instance, you can useaddIsList
:instance Backprop a => Backprop (V.Vector a) where zero = zeroFunctor add = addIsList one = oneFunctor
For more details, see the aforementioned documentation or the actual typeclass haddock documentation.
Accessors
If you have product types, users should be able to access values inside BVar
s
of your data type. There are two main ways to provide access: the lens-based
interface and the higher-kinded-data-based interface.
The lens-based interface gives your users "getter" and "setter" functions for fields, and the higher-kinded-data-based interface lets your users pattern match on your data type's original constructor to get fields and construct values.
Lens-Based Interface
If you are defining a product type, like
data MyType = MT { _mtDouble :: Double
, _mtInt :: Int
, _mtDoubles :: [Double]
}
Users who have a BVar s MyType
can't normally access the fields inside,
because you can't directly pattern match normally, and the record accessors
are MyType -> Int
(unlifted). As a library maintainer, you can provide them
lenses to the fields, either generated automatically using the lens or
microlens-th packages:
-- requires -XTemplateHaskell
makeLenses ''MyType
or manually by hand:
mtInt' :: Functor f => (Int -> f Int) -> MyType -> f MyType
mtInt' f mt = (\i -> mt { _mtInt = i }) <$> f (_mtInt mt)
Now, users can use ^.
or view
from the lens or microlens packages
to retrieve your fields:
(^. mtDouble) :: MyType -> Double
And (^^.)
and viewVar
from backprop to retrieve fields from a BVar
:
(^^. mtDouble) :: BVar s MyType -> BVar s Double
They can also use set
or .~
to modify fields, and setVar
and .~~
to
modify and "set" fields in a BVar
:
set mtDouble :: Double -> MyType -> MyType
setVar mtDouble :: BVar s Double -> BVar s MyType -> BVar s MyType
Likewise, over
and %~
can be used to apply a function to the contents of a
field, and overVar
and %~~
can be used to apply backpropagatable functions
to over fields of a value in a BVar
.
Higher-Kinded Data Interface
The alternative "Higher-Kinded Data" technique, inspired by this
article, allows your users to directly pattern match on BVar
s of your
types to get their contents.
Doing this requires modifying the definition of your data types slightly.
Instead of MyType
above, we can make a type family that can be re-used for
all of your data types:
type family HKD f a where
HKD Identity a = a
HKD f a = f a
and define your data types in terms of this type family (remembering to derive
Generic
):
data MyType2' f = MT2 { mt2Double :: HKD f Double
, mt2Int :: HKD f Int
, mt2Doubles :: HKD f [Double]
}
deriving Generic
Now your original data type can be recovered with MyType2' Identity
, and can
be pattern matched directly in the same way as the original type (the
Identity
disappears):
type MyType2 = MyType2' Identity
deriving instance Show MyType2
instance Backprop MyType2
getMT2Double :: MyType2 -> Double
getMT2Double (MT2 d _ _) = d
But now, users can pattern match on a BVar s MyType2
to get BVar
s of the
contents, with splitBV
or the BV
pattern synonym:
getMT2DoubleBVar
:: Reifies s W
=> BVar s MyType2
-> BVar s Double
getMT2DoubleBVar (splitBV -> MT2 d _ _) = d
Under splitBV
, your users can pattern match on the MT2
constructor and get
the contents as BVar
s.
Note that HKD access through pattern matching is potentially less performant than access using lens (by about 10-20%).
Users can also use joinBV
(or the BV
pattern synonym in constructor mode)
to re-construct a BVar
of MyType2
in terms of BVar
s of its contents using
the MT2
constructor:
makeMyType2
:: Reifies s W
=> BVar s Double
-> BVar s Int
-> BVar s [Double]
-> BVar s MyType2
makeMyType2 d i ds = joinBV $ MT2 d i ds