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Manipulating Intermediate Represenation (IR)

Generated functions

Sometimes it is convenient to generate function once types of arguments are known. For example if we have function foo(args...), we can generate different body for different length of Tuple and types in args. Do we really need such thing, or it is just wish of curious programmer? Not really, as

  • we can deal with variability of args using normal control-flow logic if length(args) == 1 elseif ...
  • we can (automatically) generate (a possibly very large) set of functions foo specialized for each length of args (or combination of types of args) and let multiple dispatch to deal with this
  • we cannot deal with this situation with macros, because macros do not see types, only parsed AST, which is in this case always the same.

Generated functions allow to specialize the code for a given type of argumnets. They are like macros in the sense that they return expressions and not results. But unlike macros, the input is not expression or value of arguments, but their types (the arguments are of type Type). They are also called when compiler needs (which means at least once for each combination of arguments, but possibly more times due to code invalidation).

Let's look at an example

@generated function genplus(x, y)
  println("generating genplus(x, y)")
  @show (x, y, typeof(x), typeof(y))
  quote
    println("executing generated genplus(x, y)")
    @show (x, y, typeof(x), typeof(y))
    x + y
  end
end

and observe the output

julia> genplus(1.0, 1.0) == 1.0 + 1.0
generating genplus(x, y)
(x, y, typeof(x), typeof(y)) = (Float64, Float64, DataType, DataType)
executing generated genplus(x, y)
(x, y, typeof(x), typeof(y)) = (1.0, 1.0, Float64, Float64)
true

julia> genplus(1.0, 1.0) == 1.0 + 1.0
executing generated genplus(x, y)
(x, y, typeof(x), typeof(y)) = (1.0, 1.0, Float64, Float64)
true

julia> genplus(1, 1) == 1 + 1
generating genplus(x, y)
(x, y, typeof(x), typeof(y)) = (Int64, Int64, DataType, DataType)
executing generated genplus(x, y)
(x, y, typeof(x), typeof(y)) = (1, 1, Int64, Int64)
true

which shows that the body of genplus is called for each combination of types of parameters, but the generated code is called whenever genplus is called.

Generated functions has to be pure in the sense that they are not allowed to have side effects, for example modifying some global variables. Note that printing is not allowed in pure functions, as it modifies the global buffer. From the above example this rule does not seems to be enforced, but not obeying it can lead to unexpected errors mostly caused by not knowing when and how many times the functions will be called.

Finally, generated functions cannot call functions that has been defined after their definition.

julia> @generated function genplus(x, y)
         foo()
         :(x + y)
       endgenplus (generic function with 1 method)
julia> foo() = println("foo")foo (generic function with 1 method)
julia> genplus(1,1)ERROR: MethodError: no method matching foo()
The applicable method may be too new: running in world age 27664, while current world is 27665.

Closest candidates are:
  foo() (method too new to be called from this world context.)
   @ Main REPL[2]:1

Here, the applicable method is foo.

An example that explains everything.

Consider a version of map applicable to NamedTuples with permuted names. Recall the behavior of normal map, which works if the names are in the same order.

julia> x = (a = 1, b = 2, c = 3)(a = 1, b = 2, c = 3)
julia> y = (a = 4, b = 5, c = 6)(a = 4, b = 5, c = 6)
julia> map(+, x, y)(a = 5, b = 7, c = 9)

The same does not work with permuted names:

julia> x = (a = 1, b = 2, c = 3)(a = 1, b = 2, c = 3)
julia> y = (c = 6, b = 5, a = 4)(c = 6, b = 5, a = 4)
julia> map(+, x, y)ERROR: ArgumentError: Named tuple names do not match.

How to fix this? The usual approach would be to iterate over the keys in named tuples:

function permuted_map(f, x::NamedTuple{KX}, y::NamedTuple{KY}) where {KX, KY}
    ks = tuple(intersect(KX,KY)...)
    NamedTuple{ks}(map(k -> f(x[k], y[k]), ks))
end

But, can we do better? Recall that in NamedTuples, we exactly know the position of the arguments, hence we should be able to directly match the corresponding arguments without using get.

Since creation (and debugging) of generated functions is difficult, we start with a single-argument unrolled map.

julia> @generated function unrolled_map(f, x::NamedTuple{KX}) where {KX}
           vals = [:(f(getfield(x, $(QuoteNode(k))))) for k in KX]
           :(($(vals...),))
       endunrolled_map (generic function with 1 method)
julia> unrolled_map(e->e+1, x)ERROR: UndefVarError: `x` not defined in `Main`
Suggestion: check for spelling errors or missing imports.

We see that inserting a Symbol specifying the field in the NamedTuple is a bit tricky. It needs to be quoted, since $() which is needed to substitute k for its value "peels" one layer of the quoting. Compare this to

julia> vals = [:(f(getfield(x, $(k)))) for k in KX]ERROR: UndefVarError: `KX` not defined in `Main`
Suggestion: check for spelling errors or missing imports.

Since getting the field is awkward, we write syntactic sugar for that

_get(name, k) = :(getfield($(name), $(QuoteNode(k))))

with that, we proceed to a nicer two argument function which we have desired:

julia> @generated function unrolled_map(f, x::NamedTuple{KX}, y::NamedTuple{KY}) where {KX, KY}
           ks = tuple(intersect(KX,KY)...)
           _get(name, k) = :(getfield($(name), $(QuoteNode(k))))
           vals = [:(f($(_get(:x, k)), $(_get(:y, k)))) for k in ks]
           :(NamedTuple{$(ks)}(($(vals...),)))
       endunrolled_map (generic function with 1 method)

We can check that the unrolled_map unrolls the map and generates just needed operations

julia> @code_typed unrolled_map(+, x, y)CodeInfo(
1 ─ %1  = Main.getfield(x, :a)::Int64
│   %2  = Main.getfield(y, :a)::Int64
│   %3  = Base.add_int(%1, %2)::Int64
│   %4  = Main.getfield(x, :b)::Int64
│   %5  = Main.getfield(y, :b)::Int64
│   %6  = Base.add_int(%4, %5)::Int64
│   %7  = Main.getfield(x, :c)::Int64
│   %8  = Main.getfield(y, :c)::Int64
│   %9  = Base.add_int(%7, %8)::Int64
│   %10 = %new(@NamedTuple{a::Int64, b::Int64, c::Int64}, %3, %6, %9)::@NamedTuple{a::Int64, b::Int64, c::Int64}
└──       return %10
) => @NamedTuple{a::Int64, b::Int64, c::Int64}

and compare this to the code generated by the non-generated version permuted_map:

@code_typed permuted_map(+, x, y)

which is not shown here for the sake of conciseness.

For fun, we can create a version which replaces the Symbol arguments directly by position numbers

@generated function unrolled_map(f, x::NamedTuple{KX}, y::NamedTuple{KY}) where {KX, KY} 
    ks = tuple(intersect(KX,KY)...)
    _get(name, k, KS) = :(getfield($(name), $(findfirst(k .== KS))))
    vals = [:(f($(_get(:x, k, KX)), $(_get(:y, k, KY)))) for k in KX]
    :(NamedTuple{$(KX)}(($(vals...),)))
end

Optionally generated functions

Macro @generated is expanded to

julia> @macroexpand @generated function gentest(x)
           return :(x + x)
       end

:(function gentest(x)
      if $(Expr(:generated))
          return $(Expr(:copyast, :($(QuoteNode(:(x + x))))))
      else
          $(Expr(:meta, :generated_only))
          return
      end
  end)

which is a function with an if-condition, where the first branch $(Expr(:generated)) generates the expression :(x + x) and returns it. The other spits out an error saying that the function has only a generated version. This suggests the possibility (and reality) that one can implement two versions of the same function; A generated and a normal version. It is left up to the compiler to decide which one to use. It is entirely up to the author to ensure that both versions are the same. Which version will the compiler take? The last comment on 23168 (as of time of writing) states:

"Currently the @generated branch is always used. In the future, which branch is used will mostly depend on whether the JIT compiler is enabled and available, and if it's not available, then it will depend on how much we were able to compile before the compiler was taken away. So I think it will mostly be a concern for those that might need static compilation and JIT-less deployment."

Contextual dispatch / overdubbing

Imagine that under some circumstances (context), you would like to use alternative implementations of some functions. One of the most cited motivations for this is automatic differentiation, where you would like to take the code as-is and calculate gradients with respect to some variables. Other use cases of this approach are mentioned in Cassette.jl:

"Downstream applications for Cassette include dynamic code analysis (e.g. profiling, record and replay style debugging, etc.), JIT compilation to new hardware/software backends, automatic differentiation, interval constraint programming, automatic parallelization/rescheduling, automatic memoization, lightweight multistage programming, graph extraction, and more."

In theory, we can do all the above by directly modifying the code or introducing new types, but that may require a lot of coding and changing of foreign libraries.

The technique we desire is called contextual dispatch, which means that under some context, we invoke a different function. The library Casette.jl provides a high-level API for overdubbing, but it is interesting to see, how it works, as it shows, how we can "interact" with the lowered code before the code is typed.

Insertion of code

Imagine that julia has compiled some function. For example 

foo(x,y) = x * y + sin(x)

and observe its lowered SSA format

julia> @code_lowered foo(1.0, 1.0)
CodeInfo(
1 ─ %1 = x * y
│   %2 = Main.sin(x)
│   %3 = %1 + %2
└──      return %3
)

The lowered form is convenient, because on the left hand, there is always one variable and the right-hand side is simplified to have (mostly) a single call / expression. Moreover, in the lowered form, all control flow operations like if, for, while and exceptions are converted to Goto and GotoIfNot, which simplifies their handling.

Codeinfo

We can access the lowered form by

ci = @code_lowered foo(1.0, 1.0)

which returns an object of type CodeInfo containing many fields docs. To make the investigation slightly more interesting, we modify the function a bit to have local variables:

julia> function foo(x,y)
         z = x * y
         z + sin(x)
       endfoo (generic function with 2 methods)
julia> ci = @code_lowered foo(1.0, 1.0)CodeInfo(
1 ─ %1 = Main.:*
│        z = (%1)(x, y)
│   %3 = Main.:+
│   %4 = z
│   %5 = Main.sin
│   %6 = (%5)(x)
│   %7 = (%3)(%4, %6)
└──      return %7
)

The most important (and interesting) field is code:

julia> ci.code8-element Vector{Any}:
 :(Main.:*)
 :(_4 = (%1)(_2, _3))
 :(Main.:+)
 :(_4)
 :(Main.sin)
 :((%5)(_2))
 :((%3)(%4, %6))
 :(return %7)

It contains expressions corresponding to each line of the lowered form. You are free to access them (and modify them with care). Variables identified with underscore Int, for example _2, are slotted variables which are variables which have a name in the code, defined via input arguments or through an explicit assignment :(=). The names of slotted variables are stored in ci.slotnames and they are of type 

julia> typeof(ci.code[1].args[2].args[2])ERROR: type GlobalRef has no field args
julia> ci.slotnames[ci.code[1].args[2].args[2].id]ERROR: type GlobalRef has no field args
julia> ci.slotnames[ci.code[1].args[2].args[3].id]ERROR: type GlobalRef has no field args
julia> ci.slotnames[ci.code[1].args[1].id]ERROR: type GlobalRef has no field args

The remaining variables are identified by an integer with prefix %, where the number corresponds to the line (index in ci.code), in which the variable was created. For example the fourth line :(%2 + %3) adds the results of the second line :(_4) containing variable z and the third line :(Main.sin(_2)). The type of each slot variable is stored in slottypes, which provides some information about how the variable is used (see docs). Note that if you modify / introduce slot variables, the length of slotnames and slottypes has to match and it has to be equal to the maximum number of slotted variables.

CodeInfo also contains information about the source code. Each item of ci.code has an identifier in ci.codelocs which is an index into ci.linetable containing Core.LineInfoNode identifying lines in the source code (or in the REPL). Notice that ci.linetable is generally shorter then ci.codelocs, as one line of source code can be translated to multiple lines in lowered code.

The important feature of the lowered form is that we can freely edit (create new) CodeInfo and that generated functions can return a CodeInfo object instead of the AST. However, you need to explicitly write a return statement (see issue 25678).

Strategy for overdubbing

In overdubbing, our intention is to recursively dive into called function definitions and modify / change their code. In our example below, with which we will demonstrate the manual implementation (for educational purposes), our goal is to enclose each function call with statements that log the exection time. This means we would like to implement a simplified recording profiler. This functionality cannot be implemented by a macros, since macros do not allow us to dive into function definitions. For example, in our function foo, we would would not be able to dive into the definition of sin (not that this is a terribly good idea, but the point should be clear).

The overdubbing pattern works as follows.

  1. We define a @generated function overdub(f, args...) which takes as a first argument a function f and then its arguments.
  2. In the function overdub we retrieve the CodeInfo for f(args...), which is possible as we know types of the arguments at this time.
  3. We modify the the CodeInfo of f(args...) according to our liking. Importantly, we replace all function calls some_fun(some_args...) with overdub(some_fun, some_args...) which establishes the recursive pattern.
  4. Modify the arguments of the CodeInfo of f(args...) to match overdub(f, args..).
  5. Return the modified CodeInfo.

The profiler

The implementation of the simplified logging profiler is straightforward and looks as follows.

module LoggingProfiler
struct Calls
    stamps::Vector{Float64} # contains the time stamps
    event::Vector{Symbol}  # name of the function that is being recorded
    startstop::Vector{Symbol} # if the time stamp corresponds to start or to stop
    i::Ref{Int}
end

function Calls(n::Int)
    Calls(Vector{Float64}(undef, n+1), Vector{Symbol}(undef, n+1), Vector{Symbol}(undef, n+1), Ref{Int}(0))
end

function Base.show(io::IO, calls::Calls)
    offset = 0
    if calls.i[] >= length(calls.stamps)
        @warn "The recording buffer was too small, consider increasing it"
    end
    for i in 1:min(calls.i[], length(calls.stamps))
        offset -= calls.startstop[i] == :stop
        foreach(_ -> print(io, " "), 1:max(offset, 0))
        rel_time = calls.stamps[i] - calls.stamps[1]
        println(io, calls.event[i], ": ", rel_time)
        offset += calls.startstop[i] == :start
    end
end

global const to = Calls(100)

"""
    record_start(ev::Symbol)

    record the start of the event, the time stamp is recorded after all counters are 
    appropriately increased
"""
record_start(ev::Symbol) = record_start(to, ev)
function record_start(calls, ev::Symbol)
    n = calls.i[] = calls.i[] + 1
    n > length(calls.stamps) && return 
    calls.event[n] = ev
    calls.startstop[n] = :start
    calls.stamps[n] = time_ns()
end

"""
    record_end(ev::Symbol)

    record the end of the event, the time stamp is recorded before all counters are 
    appropriately increased
"""
record_end(ev::Symbol) = record_end(to, ev::Symbol)
function record_end(calls, ev::Symbol)
    t = time_ns()
    n = calls.i[] = calls.i[] + 1
    n > length(calls.stamps) && return 
    calls.event[n] = ev
    calls.startstop[n] = :stop
    calls.stamps[n] = t
end

reset!() = to.i[] = 0

function Base.resize!(calls::Calls, n::Integer)
  resize!(calls.stamps, n)
  resize!(calls.event, n)
  resize!(calls.startstop, n)
end


exportname(ex::GlobalRef) = QuoteNode(ex.name)
exportname(ex::Symbol) = QuoteNode(ex)
exportname(ex::Expr) = exportname(ex.args[1])
exportname(i::Int) = QuoteNode(Symbol("Int(",i,")"))

function overdubbable(ex::Expr)
    ex.head != :call && return(false)
    a = ex.args[1]
    a != GlobalRef && return(true)
    a.mod != Core
end 
overdubbable(ex) = false 


function timable(ex::Expr) 
    ex.head != :call && return(false)
    length(ex.args) < 2 && return(false)
    ex.args[1] isa Core.GlobalRef && return(true)
    ex.args[1] isa Symbol && return(true)
    return(false)
end
timable(ex) = false

export timable, exportname, overdubbable
end

The important functions are report_start and report_end which mark the beggining and end of the executed function. They differ mainly when time is recorded (on the end or on the start of the function call). The profiler has a fixed capacity to prevent garbage collection, which might be increased.

Let's now describe the individual parts of overdub before presenting it in its entirety. At first, we retrieve the codeinfo ci of the overdubbed function. For now, we will just assume we obtain it for example by

ci = @code_lowered foo(1.0, 1.0)

we initialize the new CodeInfo object by emptying some dummy function as 

dummy() = return
new_ci = code_lowered(dummy, Tuple{})[1]
empty!(new_ci.code)
empty!(new_ci.slotnames)
empty!(new_ci.linetable)
empty!(new_ci.codelocs)
new_ci
CodeInfo(
)

Then, we need to copy the slot variables from the ci codeinfo of foo to the new codeinfo. Additionally, we have to add the arguments of overdub(f, args...) since the compiler sees overdub(f, args...) and not foo(x,y):

julia> new_ci.slotnames = vcat([Symbol("#self#"), :f, :args], ci.slotnames[2:end])6-element Vector{Symbol}:
 Symbol("#self#")
 :f
 :args
 :x
 :y
 :z
julia> new_ci.slotflags = vcat([0x00, 0x00, 0x00], ci.slotflags[2:end])6-element Vector{UInt8}:
 0x00
 0x00
 0x00
 0x08
 0x08
 0x18

Above, we also filled the slotflags. Authors admit that names :f and :args in the above should be replaced by a gensymed name, but they do not anticipate this code to be used outside of this educative example where name-clashes might occur. We also copy information about the lines from the source code:

julia> foreach(s -> push!(new_ci.linetable, s), ci.linetable)

The most difficult part when rewriting CodeInfo objects is working with indexes, as the line numbers and left hand side variables are strictly ordered one by one and we need to properly change the indexes to reflect changes we made. We will therefore keep three lists

maps = (
    ssa = Dict{Int, Int}(),
    slots = Dict{Int, Any}(),
    goto = Dict{Int,Int}(),
)

where

  • slots maps slot variables in ci to those in new_ci
  • ssa maps indexes of left-hand side assignments in ci to new_ci
  • goto maps lines to which GotoNode and GotoIfNot point to variables in ci to new_ci (in our profiler example, we need to ensure to jump on the beggining of logging of executions)

Mapping of slots can be initialized in advance, as it is a static shift by 2 :

maps.slots[1] = Core.SlotNumber(1)
foreach(i -> maps.slots[i] = Core.SlotNumber(i + 2), 2:length(ci.slotnames))

and we can check the correctness by

@assert all(ci.slotnames[i] == new_ci.slotnames[maps.slots[i].id] for i in 1:length(ci.slotnames))  #test that

Equipped with that, we start rewriting the code of foo(x, y). We start by a small preample, where we assign values of args... to x, and y. For the sake of simplicity, we map the slotnames to either Core.SlotNumber or to Core.SSAValues which simplifies the rewriting logic a bit.

newci_no = 0
args = (Float64, Float64)
for i in 1:length(args)
    newci_no +=1
    push!(new_ci.code, Expr(:call, Base.getindex, Core.SlotNumber(3), i))
    maps.slots[i+1] = Core.SSAValue(newci_no)
    push!(new_ci.codelocs, ci.codelocs[1])
end

Now we come to the pinnacle of rewriting the body of foo(x,y) while inserting calls to the profiler:

for (ci_no, ex) in enumerate(ci.code)
    if timable(ex)
        fname = exportname(ex)
        push!(new_ci.code, Expr(:call, GlobalRef(LoggingProfiler, :record_start), fname))
        push!(new_ci.codelocs, ci.codelocs[ci_no])
        newci_no += 1
        maps.goto[ci_no] = newci_no
        ex = overdubbable(ex) ? Expr(:call, GlobalRef(Main, :overdub), ex.args...) : ex
        push!(new_ci.code, ex)
        push!(new_ci.codelocs, ci.codelocs[ci_no])
        newci_no += 1
        maps.ssa[ci_no] = newci_no
        push!(new_ci.code, Expr(:call, GlobalRef(LoggingProfiler, :record_end), fname))
        push!(new_ci.codelocs, ci.codelocs[ci_no])
        newci_no += 1
    else
        push!(new_ci.code, ex)
        push!(new_ci.codelocs, ci.codelocs[ci_no])
        newci_no += 1
        maps.ssa[ci_no] = newci_no
    end
end

which yields

julia> new_ci.code
15-element Vector{Any}:
 :((getindex)(_3, 1))
 :((getindex)(_3, 2))
 :(_4 = _2 * _3)
 :(_4)
 :(Main.LoggingProfiler.record_start(:sin))
 :(Main.overdub(Main.sin, _2))
 :(Main.LoggingProfiler.record_end(:sin))
 :(Main.LoggingProfiler.record_start(:+))
 :(Main.overdub(Main.:+, %2, %3))
 :(Main.LoggingProfiler.record_end(:+))
 :(return %4)

The important parts are:

  • Depending on the type of expressions (controlled by timable) we decide, if a function's execution time should be recorded.
  • fname = exportname(ex) obtains the name of the profiled function call.
  • push!(new_ci.code, Expr(:call, GlobalRef(LoggingProfiler, :record_start), fname)) records the start of the exection.
  • maps.goto[ci_ssa_no] = ssa_no updates the map from the code line number in ci to the one in new_ci.
  • maps.ssa[ci_ssa_no] = ssa_no updates the map from the SSA line number in ci to new_ci.
  • ex = overdubbable(ex) ? Expr(:call, GlobalRef(Main, :overdub), ex.args...) : ex modifies the function call (expression in general) to recurse the overdubbing.

Finally, we need to change the names of slot variables (Core.SlotNumber) and variables indexed by the SSA (Core.SSAValue).

for i in length(args)+1:length(new_ci.code)
    new_ci.code[i] = remap(new_ci.code[i], maps)
end

where remap is defined by the following block of code

remap(ex::Expr, maps) = Expr(ex.head, remap(ex.args, maps)...)
remap(args::AbstractArray, maps) = map(a -> remap(a, maps), args)
remap(c::Core.GotoNode, maps) = Core.GotoNode(maps.goto[c.label])
remap(c::Core.GotoIfNot, maps) = Core.GotoIfNot(remap(c.cond, maps), maps.goto[c.dest])
remap(r::Core.ReturnNode, maps) = Core.ReturnNode(remap(r.val, maps))
remap(a::Core.SlotNumber, maps) = maps.slots[a.id]
remap(a::Core.SSAValue, maps) = Core.SSAValue(maps.ssa[a.id])
remap(a::Core.NewvarNode, maps) = Core.NewvarNode(maps.slots[a.slot.id])
remap(a::GlobalRef, maps) = a
remap(a::QuoteNode, maps) = a
remap(ex, maps) = ex
Warn

Retrieving the code properly

Consider the following function:

function test(x::T) where T<:Union{Float64, Float32}
   x < T(pi)
end

julia> ci = @code_lowered test(1.0)
CodeInfo(
1 ─ %1 = ($(Expr(:static_parameter, 1)))(Main.pi)
│   %2 = x < %1
└──      return %2
)

the Expr(:static_parameter, 1) in the first line of code obtains the type parameter T of the function test. Since this information is not accessible in the CodeInfo, it might render our tooling useless. The needed hook is Base.Meta.partially_inline! which partially inlines this into the CodeInfo object. The code to retrieve the CodeInfo adapted from IRTools is a little involved:

function retrieve_code_info(sigtypes, world = Base.get_world_counter())
    S = Tuple{map(s -> Core.Compiler.has_free_typevars(s) ? typeof(s.parameters[1]) : s, sigtypes)...}
    _methods = Base._methods_by_ftype(S, -1, world)
    if isempty(_methods) 
        @info("method $(sigtypes) does not exist")
        return(nothing)
    end
    type_signature, raw_static_params, method = _methods[1]
    mi = Core.Compiler.specialize_method(method, type_signature, raw_static_params, false)
    ci = Base.isgenerated(mi) ? Core.Compiler.get_staged(mi) : Base.uncompressed_ast(method)
    Base.Meta.partially_inline!(ci.code, [], method.sig, Any[raw_static_params...], 0, 0, :propagate)
    ci
end

but

julia> ci = retrieve_code_info((typeof(test), Float64))
CodeInfo(
    @ REPL[5]:2 within `test'
1 ─ %1 = ($(QuoteNode(Float64)))(Main.pi)
│   %2 = x < %1
└──      return %2
)

it performs the needed inlining of Float64.

Implementing the profiler with IRTools

The above implementation of the profiler has shown, that rewriting IR manually is doable, but requires a lot of careful book-keeping. IRTools.jl makes our life much simpler, as they take away all the needed book-keeping and let us focus on what is important.

julia> using IRTools
julia> function foo(x, y)
          z =  x * y
          z + sin(y)
       end;
julia> ir = @code_ir foo(1.0, 1.0)1: (%1, %2, %3)
  %4 = Main.:*
  %5 = (%4)(%2, %3)
  %6 = Main.:+
  %7 = Main.sin
  %8 = (%7)(%3)
  %9 = (%6)(%5, %8)
  return %9

We can see that at first sight, the representation of the lowered code in IRTools is similar to that of CodeInfo. Some notable differences:

  • SlotNumber are converted to SSAValues
  • SSA form is divided into blocks by GotoNode and GotoIfNot in the parsed CodeInfo
  • SSAValues do not need to be ordered. The reordering is deffered to the moment when one converts IRTools.Inner.IR back to the CodeInfo.

Let's now use the IRTools to insert the timing statements into the code for foo:

using IRTools: xcall, insert!, insertafter!

ir = @code_ir foo(1.0, 1.0)
for (v, ex) in ir
    if timable(ex.expr)
        fname = exportname(ex.expr)
        insert!(ir, v, xcall(LoggingProfiler, :record_start, fname))
        insertafter!(ir, v, xcall(LoggingProfiler, :record_end, fname))
    end
end

julia> ir
1: (%1, %2, %3)
  %7 = Main.LoggingProfiler.record_start(:*)
  %4 = %2 * %3
  %8 = Main.LoggingProfiler.record_end(:*)
  %9 = Main.LoggingProfiler.record_start(:sin)
  %5 = Main.sin(%3)
  %10 = Main.LoggingProfiler.record_end(:sin)
  %11 = Main.LoggingProfiler.record_start(:+)
  %6 = %4 + %5
  %12 = Main.LoggingProfiler.record_end(:+)
  return %6

Observe that the statements are on the right places but they are not ordered. We can turn the ir object into an anonymous function

f = IRTools.func(ir)
LoggingProfiler.reset!()
f(nothing, 1.0, 1.0)
LoggingProfiler.to

where we can observe that our profiler is working as it should. But this is not yet our final goal. Originally, our goal was to recursivelly dive into the nested functions. IRTools offers a macro @dynamo, which is similar to @generated but simplifies our job by allowing to return the IRTools.Inner.IR object and it also taking care of properly renaming the arguments. With that we write

using IRTools: @dynamo
profile_fun(f::Core.IntrinsicFunction, args...) = f(args...)
profile_fun(f::Core.Builtin, args...) = f(args...)

@dynamo function profile_fun(f, args...)
    ir = IRTools.Inner.IR(f, args...)
    for (v, ex) in ir
        if timable(ex.expr)
            fname = exportname(ex.expr)
            insert!(ir, v, xcall(LoggingProfiler, :record_start, fname))
            insertafter!(ir, v, xcall(LoggingProfiler, :record_end, fname))
        end
    end
    for (x, st) in ir
        recursable(st.expr) || continue
        ir[x] = xcall(profile_fun, st.expr.args...)
    end
    return ir
end

where the first pass is as it was above and the ir[x] = xcall(profile_fun, st.expr.args...) ensures that the profiler will recursively call itself. recursable is a filter defined as below, which is used to prevent profiling itself (and possibly other things).

recursable(gr::GlobalRef) = gr.name ∉ [:profile_fun, :record_start, :record_end]
recursable(ex::Expr) = ex.head == :call && recursable(ex.args[1])
recursable(ex) = false

Additionally, the first two definitions of profile_fun for Core.IntrinsicFunction and for Core.Builtin prevent trying to dive into functions which do not have a Julia IR. And that's all. The full code is 

using IRTools
using IRTools: var, xcall, insert!, insertafter!, func, recurse!, @dynamo
include("loggingprofiler.jl")
LoggingProfiler.resize!(LoggingProfiler.to, 10000)

function timable(ex::Expr) 
    ex.head != :call && return(false)
    length(ex.args) < 2 && return(false)
    ex.args[1] isa Core.GlobalRef && return(true)
    ex.args[1] isa Symbol && return(true)
    return(false)
end
timable(ex) = false

function recursable_fun(ex::GlobalRef)
    ex.name ∈ (:profile_fun, :record_start, :record_end) && return(false)
    iswhite(recursable_list, ex) && return(true)
    isblack(recursable_list, ex) && return(false)
    return(isempty(recursable_list) ? true : false)
end

recursable_fun(ex::IRTools.Inner.Variable) = true

function recursable(ex::Expr) 
    ex.head != :call && return(false)
    isempty(ex.args) && return(false)
    recursable(ex.args[1])
end

recursable(ex) = false

exportname(ex::GlobalRef) = QuoteNode(ex.name)
exportname(ex::Symbol) = QuoteNode(ex)
exportname(ex::Expr) = exportname(ex.args[1])
exportname(i::Int) = QuoteNode(Symbol("Int(",i,")"))

profile_fun(f::Core.IntrinsicFunction, args...) = f(args...)
profile_fun(f::Core.Builtin, args...) = f(args...)

@dynamo function profile_fun(f, args...)
    ir = IRTools.Inner.IR(f, args...)
    for (v, ex) in ir
        if timable(ex.expr)
            fname = exportname(ex.expr)
            insert!(ir, v, xcall(LoggingProfiler, :record_start, fname))
            insertafter!(ir, v, xcall(LoggingProfiler, :record_end, fname))
        end
    end
    for (x, st) in ir
        recursable(st.expr) || continue
        ir[x] = xcall(profile_fun, st.expr.args...)
    end
    # recurse!(ir)
    return ir
end

macro record(ex)
    esc(Expr(:call, :profile_fun, ex.args...))
end

LoggingProfiler.reset!()
@record foo(1.0, 1.0)
LoggingProfiler.to

where you should notice the long time the first execution of @record foo(1.0, 1.0) takes. This is caused by the compiler specializing for every function into which we dive into. The second execution of @record foo(1.0, 1.0) is fast. It is also interesting to observe how the time of the compilation is logged by the profiler. The output of the profiler to is not shown here due to the length of the output.

Petite Zygote

IRTools.jl were created for Zygote.jl –- Julia's source-to-source AD system currently powering Flux.jl. An interesting aspect of Zygote was to recognize that TensorFlow is in its nutshell a compiler, PyTorch is an interpreter. So the idea was to let Julia's compiler compile the gradient and perform optimizations that are normally performed with normal code. Recall that a lot of research went into how to generate efficient code and it is reasonable to use this research. Zygote.jl provides mainly reversediff, but there was an experimental support for forwarddiff.

One of the questions when developing an AD engine is where and how to create a computation graph. Recall that in TensorFlow, you specify it through a domain specific language, in PyTorch it generated on the fly. Mike Innes' idea was use SSA form provided by the julia compiler. 

julia> @code_lowered foo(1.0, 1.0)
CodeInfo(
1 ─      z = x * y
│   %2 = z
│   %3 = Main.sin(y)
│   %4 = %2 + %3
└──      return %4
)

It is very easy to differentiate each line, as they correspond to single expressions (or function calls) and importantly, each variable is assigned exactly once. The strategy to use it for AD would as follows.

Strategy

We assume to have a set of AD rules (e.g. ChainRules), which for a given function returns its evaluation and pullback. If Zygote.jl is tasked with computing the gradient.

  1. If a rule exists for this function, directly return the rule.
  2. If not, deconstruct the function into a sequence of functions using CodeInfo / IR representation
  3. Replace statements by calls to obtain the evaluation of the statements and the pullback.
  4. Chain pullbacks in reverse order.
  5. Return the function evaluation and the chained pullback.

Simplified implementation

The following code is adapted from this example

using IRTools, ChainRules
using IRTools: @dynamo, IR, Pipe, finish, substitute, return!, block, blocks,
  returnvalue, arguments, isexpr, xcall, self, stmt

struct Pullback{S,T}
  data::T
end

Pullback{S}(data) where S = Pullback{S,typeof(data)}(data)

function primal(ir, T = Any)
  pr = Pipe(ir)
  calls = []
  ret = []
  for (v, st) in pr
    ex = st.expr
    if isexpr(ex, :call)
      t = insert!(pr, v, stmt(xcall(Main, :forward, ex.args...), line = st.line))
      pr[v] = xcall(:getindex, t, 1)
      J = push!(pr, xcall(:getindex, t, 2))
      push!(calls, v)
      push!(ret, J)
    end
  end
  pb = Expr(:call, Pullback{T}, xcall(:tuple, ret...))
  return!(pr, xcall(:tuple, returnvalue(block(ir, 1)), pb))
  return finish(pr), calls
end

@dynamo function forward(m...)
  ir = IR(m...)
  ir == nothing && return :(error("Non-differentiable function ", repr(args[1])))
  length(blocks(ir)) == 1 || error("control flow is not supported")
  return primal(ir, Tuple{m...})[1]
end

where

  • the generated function forward calls primal to perform AD manual chainrule
  • actual chainrule is performed in the for loop
  • every function call is replaced xcall(Main, :forward, ex.args...), which is the recursion we have observed above. stmt allows to insert information about lines in the source code).
  • the output of the forward is the value of the function, and pullback, the function calculating gradient with respect to its inputs.
  • pr[v] = xcall(:getindex, t, 1) fixes the output of the overwritten function call to be the output of forward(...)
  • the next line logs the pullback
  • Expr(:call, Pullback{T}, xcall(:tuple, ret...)) will serve to call generated function which will assemble the pullback in the right order

Let's now observe how the the IR of foo is transformed

ir = IR(typeof(foo), Float64, Float64)
julia> primal(ir)[1]
1: (%1, %2, %3)
  %4 = Main.forward(Main.:*, %2, %3)
  %5 = Base.getindex(%4, 1)
  %6 = Base.getindex(%4, 2)
  %7 = Main.forward(Main.sin, %3)
  %8 = Base.getindex(%7, 1)
  %9 = Base.getindex(%7, 2)
  %10 = Main.forward(Main.:+, %5, %8)
  %11 = Base.getindex(%10, 1)
  %12 = Base.getindex(%10, 2)
  %13 = Base.tuple(%6, %9, %12)
  %14 = (Pullback{Any, T} where T)(%13)
  %15 = Base.tuple(%11, %14)
  return %15
  • Every function call was transformed into the sequence of forward(...) and obtaining first and second item from the returned typle.
  • Line %14 constructs the Pullback, which (as will be seen shortly below) will allow to generate the pullback for the generated function
  • Line %15 generates the returned tuple, where the first item is the function value (computed at line %11) and pullback (constructed at libe %15).

We define few AD rules by specializing forward with calls from ChainRules

forward(::typeof(sin), x)    = ChainRules.rrule(sin, x)
forward(::typeof(*), x, y)   = ChainRules.rrule(*, x, y)
forward(::typeof(+), x, y)   = ChainRules.rrule(+, x, y)

Zygote implements this inside the generated function, such that whatever is added to ChainRules is automatically reflected. The process is not as trivial (see has_chain_rule) and for the brevity is not shown here.

We now obtain the value and the pullback of function foo as 

julia> v, pb = forward(foo, 1.0, 1.0);
  • The pullback contains in data field with individual jacobians that have been collected in ret in primal function.
pb.data[1]
pb.data[2]
pb.data[3]

The function for which the Jacobian has been created is stored in type parameter S of the Pullback type. The pullback for foo is generated in another generated function, as Pullback struct is a functor. This is an interesting design pattern, which allows us to return closure from a generated function.

Let's now investigate the code generating code for pullback.


_sum() = 0
_sum(x) = x
_sum(x...) = xcall(:+, x...)

function pullback(pr)
  ir = empty(pr)
  grads = Dict()
  grad(x) = _sum(get(grads, x, [])...)
  grad(x, x̄) = push!(get!(grads, x, []), x̄)
  grad(returnvalue(block(pr, 1)), IRTools.argument!(ir))
  data = push!(ir, xcall(:getfield, self, QuoteNode(:data)))
  _, pbs = primal(pr)
  pbs = Dict(pbs[i] => push!(ir, xcall(:getindex, data, i)) for i = 1:length(pbs))
  for v in reverse(keys(pr))
    ex = pr[v].expr
    isexpr(ex, :call) || continue
    Δs = push!(ir, Expr(:call, pbs[v], grad(v)))
    for (i, x) in enumerate(ex.args)
      grad(x, push!(ir, xcall(:getindex, Δs, i)))
    end
  end
  return!(ir, xcall(:tuple, [grad(x) for x in arguments(pr)]...))
end

@dynamo function (pb::Pullback{S})(Δ) where S
  return pullback(IR(S.parameters...))
end

Let's walk how the reverse is constructed for pr = IR(typeof(foo), Float64, Float64)

ir = empty(pr)
grads = Dict()
grad(x) = _sum(get(grads, x, [])...)
grad(x, x̄) = push!(get!(grads, x, []), x̄)

construct the empty ir for the constructed pullback, defines Dict where individual contributors of the gradient with respect to certain variable will be stored, and two function for pushing statements to to grads. The next statement

grad(returnvalue(block(pr, 1)), IRTools.argument!(ir))

pushes to grads statement that the gradient of the output of the primal pr is provided as an argument of the pullback IRTools.argument!(ir). 

data = push!(ir, xcall(:getfield, self, QuoteNode(:data)))
_, pbs = primal(pr)
pbs = Dict(pbs[i] => push!(ir, xcall(:getindex, data, i)) for i = 1:length(pbs))

sets data to the data field of the Pullback structure containing pullback functions. Then it create a dictionary pbs, where the output of each call in the primal (identified by the line) is mapped to the corresponding pullback, which is now a line in the IR representation. The IR so far looks as 

1: (%1)
  %2 = Base.getfield(IRTools.Inner.Self(), :data)
  %3 = Base.getindex(%2, 1)
  %4 = Base.getindex(%2, 2)
  %5 = Base.getindex(%2, 3)

and pbs contains 

julia> pbs
Dict{IRTools.Inner.Variable, IRTools.Inner.Variable} with 3 entries:
  %6 => %5
  %4 => %3
  %5 => %4

says that the pullback of a function producing variable at line %6 in the primal is stored at variable %5 in the contructed pullback. The real deal comes in the for loop 

for v in reverse(keys(pr))
  ex = pr[v].expr
  isexpr(ex, :call) || continue
  Δs = push!(ir, Expr(:call, pbs[v], grad(v)))
  for (i, x) in enumerate(ex.args)
    grad(x, push!(ir, xcall(:getindex, Δs, i)))
  end
end

which iterates the primal pr in the reverse order and for every call, it inserts statement to calls the appropriate pullback Δs = push!(ir, Expr(:call, pbs[v], grad(v))) and adds gradients with respect to the inputs to values accumulating corresponding gradient in the loop for (i, x) in enumerate(ex.args) ... The last line

return!(ir, xcall(:tuple, [grad(x) for x in arguments(pr)]...))

puts statements accumulating gradients with respect to individual variables to the ir.

The final generated IR code looks as

julia> pullback(IR(typeof(foo), Float64, Float64))
1: (%1)
  %2 = Base.getfield(IRTools.Inner.Self(), :data)
  %3 = Base.getindex(%2, 1)
  %4 = Base.getindex(%2, 2)
  %5 = Base.getindex(%2, 3)
  %6 = (%5)(%1)
  %7 = Base.getindex(%6, 1)
  %8 = Base.getindex(%6, 2)
  %9 = Base.getindex(%6, 3)
  %10 = (%4)(%9)
  %11 = Base.getindex(%10, 1)
  %12 = Base.getindex(%10, 2)
  %13 = (%3)(%8)
  %14 = Base.getindex(%13, 1)
  %15 = Base.getindex(%13, 2)
  %16 = Base.getindex(%13, 3)
  %17 = %12 + %16
  %18 = Base.tuple(0, %15, %17)
  return %18

and it calculates the gradient with respect to the input as

julia> pb(1.0)
(0, 1.0, 1.5403023058681398)

where the first item is gradient with parameters of the function itself.

Conclusion

The above examples served to demonstrate that @generated functions offers extremely powerful paradigm, especially if coupled with manipulation of intermediate representation. Within few lines of code, we have implemented reasonably powerful profiler and reverse AD engine. Importantly, it has been done without a single-purpose engine or tooling.