In Pin Documentation for INS_IsProcedureCall(INS ins), it is given that
true if ins is a procedure call. This filters out call instructions that are (ab)used for other purposes
On the other hand, there is INS_IsCall(INS ins) which will return true for all the call instructions.
My question is, how exactly both these functions differ? More precisely, what kind of call instructions will be returned true by INS_IsCall(INS ins) but not by INS_IsProcedureCall(INS ins)?
Any examples would be greatly appreciated.
A call could be a real procedure call (using INS_IsProcedureCall) or an instruction pointer materialization (using INS_IsPcMaterialization).
Basically only those calls that are not true by INS_IsPcMaterialization function will be true by INS_IsProcedureCall.
Related
Background
While playing around with dialyzer, typespecs and currying, I was able to create an example of a false positive in dialyzer.
For the purposes of this MWE, I am using diallyxir (versions included) because it makes my life easier. The author of dialyxir confirmed this was not a problem on their side, so that possibility is excluded for now.
Environment
$ elixir -v
Erlang/OTP 24 [erts-12.2.1] [source] [64-bit] [smp:12:12] [ds:12:12:10] [async-threads:1] [jit]
Elixir 1.13.2 (compiled with Erlang/OTP 24)
Which version of Dialyxir are you using? (cat mix.lock | grep dialyxir):
"dialyxir": {:hex, :dialyxir, "1.1.0", "c5aab0d6e71e5522e77beff7ba9e08f8e02bad90dfbeffae60eaf0cb47e29488", [:mix], [{:erlex, ">= 0.2.6", [hex: :erlex, repo: "hexpm", optional: false]}], "hexpm", "07ea8e49c45f15264ebe6d5b93799d4dd56a44036cf42d0ad9c960bc266c0b9a"},
"erlex": {:hex, :erlex, "0.2.6", "c7987d15e899c7a2f34f5420d2a2ea0d659682c06ac607572df55a43753aa12e", [:mix], [], "hexpm", "2ed2e25711feb44d52b17d2780eabf998452f6efda104877a3881c2f8c0c0c75"},
Current behavior
Given the following code sample:
defmodule PracticingCurrying do
#spec greater_than(integer()) :: (integer() -> String.t())
def greater_than(min) do
fn number -> number > min end
end
end
Which clearly has a wrong typing, I get a success message:
$ mix dialyzer
Compiling 1 file (.ex)
Generated grokking_fp app
Finding suitable PLTs
Checking PLT...
[:compiler, :currying, :elixir, :gradient, :gradualizer, :kernel, :logger, :stdlib, :syntax_tools]
Looking up modules in dialyxir_erlang-24.2.1_elixir-1.13.2_deps-dev.plt
Finding applications for dialyxir_erlang-24.2.1_elixir-1.13.2_deps-dev.plt
Finding modules for dialyxir_erlang-24.2.1_elixir-1.13.2_deps-dev.plt
Checking 518 modules in dialyxir_erlang-24.2.1_elixir-1.13.2_deps-dev.plt
Adding 44 modules to dialyxir_erlang-24.2.1_elixir-1.13.2_deps-dev.plt
done in 0m24.18s
No :ignore_warnings opt specified in mix.exs and default does not exist.
Starting Dialyzer
[
check_plt: false,
init_plt: '/home/user/Workplace/fl4m3/grokking_fp/_build/dev/dialyxir_erlang-24.2.1_elixir-1.13.2_deps-dev.plt',
files: ['/home/user/Workplace/fl4m3/grokking_fp/_build/dev/lib/grokking_fp/ebin/Elixir.ImmutableValues.beam',
'/home/user/Workplace/fl4m3/grokking_fp/_build/dev/lib/grokking_fp/ebin/Elixir.PracticingCurrying.beam',
'/home/user/Workplace/fl4m3/grokking_fp/_build/dev/lib/grokking_fp/ebin/Elixir.TipCalculator.beam'],
warnings: [:unknown]
]
Total errors: 0, Skipped: 0, Unnecessary Skips: 0
done in 0m1.02s
done (passed successfully)
Expected behavior
I expected dialyzer to tell me the correct spec is #spec greater_than(integer()) :: (integer() -> bool()).
As a side note (and comparison, if you will) gradient does pick up the error.
I know that comparing these tools is like comparing oranges and apples, but I think it is still worth mentioning.
Questions
Is dialyzer not intended to catch this type of error?
If it should catch the error, what can possibly be failing? (is it my example that is incorrect, or something inside dialyzer?)
I personally find it hard to believe this could be a bug in Dialyzer, the tool has been used rather extensively by a lot of people for me to be the first to discover this error. However, I cannot explain what is happening.
Help is appreciated.
Dialyzer is pretty optimistic in its analysis and ignores some categories of errors.
This article provides some advanced explanations about its approach and limitations.
In the particular case of anonymous functions, dialyzer seems to perform a very minimal check
when they are being declared: it will ignore both the types of its arguments and return type, e.g.
the following doesn't lead any error even if is clearly wrong:
# no error
#spec add(integer()) :: (String.t() -> String.t())
def add(x) do
fn y -> x + y end
end
It will however point out a mismatch in arity, e.g.
# invalid_contract
# The #spec for the function does not match the success typing of the function.
#spec add2(integer()) :: (integer(), integer() -> integer())
def add2(x) do
fn y -> x + y end
end
Dialyzer might be able to detect a type conflict when trying to use the anonymous function,
but this isn't guaranteed (see article above), and the error message might not be helpful:
# Function main/0 has no local return.
def main do
positive? = greater_than(0)
positive?.(2)
end
We don't know what is the problem exactly, not even the line causing the error. But at least we know there is one and can debug it.
In the following example, the error is a bit more informative (using :lists.map/2 instead of Enum.map/2 because
dialyzer doesn't understand the enumerable protocol):
# Function main2/0 has no local return.
def main2 do
positive? = greater_than(0)
# The function call will not succeed.
# :lists.map(_positive? :: (integer() -> none()), [-2 | 0 | 1, ...])
# will never return since the success typing arguments are
# ((_ -> any()), [any()])
:lists.map(positive?, [1, 0, -2])
end
This tells us that dialyzer inferred the return type of greater_than/1 to be (integer() -> none()).
none is described in the article above as:
This is a special type that means that no term or type is valid.
Usually, when Dialyzer boils down the possible return values of a function to none(), it means the function should crash.
It is synonymous with "this stuff won't work."
So dialyzer knows that this function cannot be called successfully, but doesn't consider it to be a type clash until actually called, so it will allow the declaration (in the same way you can perfectly create a function that just raises).
Disclaimer: I couldn't find an official explanation regarding how dialyzer handles anonymous
functions in detail, so the explanations above are based on my observations and interpretation
Usually the multiple dispatch in julia is straightforward if one of the parameters in a function changes data type, for example Float64 vs Complex{Float64}. How can I implement multiple dispatch if the parameter is an integer, and I want two functions, one for even and other for odd values?
You may be able to solve this with a #generated function: https://docs.julialang.org/en/v1/manual/metaprogramming/#Generated-functions-1
But the simplest solution is to use an ordinary branch in your code:
function foo(x::MyType{N}) where {N}
if isodd(N)
return _oddfoo(x)
else
return _evenfoo(x)
end
end
This may seem as a defeat for the type system, but if N is known at compile-time, the compiler will actually select only the correct branch, and you will get static dispatch to the correct function, without loss of performance.
This is idiomatic, and as far as I know the recommended solution in most cases.
I expect that with type dispatch you ultimately still are calling after a check on odd versus even, so the most economical of code, without a run-time penatly, is going to be having the caller check the argument and call the proper function.
If you nevertheless have to be type based, for some reason unrelated to run-time efficiency, here is an example of such:
abstract type HasParity end
struct Odd <: HasParity
i::Int64
Odd(i::Integer) = new(isodd(i) ? i : error("not odd"))
end
struct Even <: HasParity
i::Int64
Even(i::Integer) = new(iseven(i) ? i : error("not even"))
end
parity(i) = return iseven(i) ? Even(i) : Odd(i)
foo(i::Odd) = println("$i is odd.")
foo(i::Even) = println("$i is even.")
for n in 1:4
k::HasParity = parity(n)
foo(k)
end
So here's other option which I think is cleaner and more multiple dispatch oriented (given by a coworker). Let's think N is the natural number to be checked and I want two functions that do different stuff depending if N is even or odd. Thus
boolN = rem(N,2) == 0
(...)
function f1(::Val{true}, ...)
(...)
end
function f1(::Val{false}, ...)
(...)
end
and to call the function just do
f1(Val(boolN))
As #logankilpatrick pointed out the dispatch system is type based. What you are dispatching on, though, is well established pattern known as a trait.
Essentially your code looks like
myfunc(num) = iseven(num) ? _even_func(num) : _odd_func(num)
I am used to implement a recursive function that checks if a given list L is written in a reverse-order:
orderIsReverse(L):-
[X|Q]=L,
[XP|_]=Q,
(X<XP -> false; orderIsReverse(Q)),
true.
However after compiling the code and prompting orderIsReverse([3,2,1]) within SWI Prolog, I get false returned.
What's wrong with the code?
You need to handle the case when the input list is empty (and also when it contains one single element, as you need two for a comparison).
orderIsReverse([X1,X2|L]):-
X1 > X2, orderIsReverse([X2|L]).
orderIsReverse([_]).
orderIsReverse([]).
Update: fixed the logic.
This is probably a newbie question... but is it possible to show the definition of a (user defined) function? While debugging/optimizing it is convenient to quickly see how a certain function was programmed.
Thanks in advance.
You can use the #edit macro, which is supposed to take you to the definition of a method, similarly to how the #which macro which shows the file and line # where that particular method was defined, for example:
julia> #which push!(CDFBuf(),"foo")
push!{T<:CDF.CDFBuf}(buff::T, x) at /d/base/DA/DA.jl:105
julia> #which search("foobar","foo")
search(s::AbstractString, t::AbstractString) at strings/search.jl:146
Note that methods that are part of Julia will show a path relative to the julia source directory "base".
While this is not an automatic feature available with Julia in general (as pointed out by Stefan), if you add docstrings when you define your initial function, you can always use the help?> prompt to query this docstring. For example
julia> """mytestfunction(a::Int, b)""" function mytestfunction(a::Int, b)
return true
This attaches the docstring "mytestfunction(a::Int, b)" to the function mytestfunction(a::Int, b). Once this is defined, you can then use the Julia help prompt (by typing ? at the REPL), to query this documentation.
help?> mytestfunction
mytestfunction(a::Int, b)
In Julia, is there any way to get the name of a passed to a function?
x = 10
function myfunc(a)
# do something here
end
assert(myfunc(x) == "x")
Do I need to use macros or is there a native method that provides introspection?
You can grab the variable name with a macro:
julia> macro mymacro(arg)
string(arg)
end
julia> #mymacro(x)
"x"
julia> #assert(#mymacro(x) == "x")
but as others have said, I'm not sure why you'd need that.
Macros operate on the AST (code tree) during compile time, and the x is passed into the macro as the Symbol :x. You can turn a Symbol into a string and vice versa. Macros replace code with code, so the #mymacro(x) is simply pulled out and replaced with string(:x).
Ok, contradicting myself: technically this is possible in a very hacky way, under one (fairly limiting) condition: the function name must have only one method signature. The idea is very similar the answers to such questions for Python. Before the demo, I must emphasize that these are internal compiler details and are subject to change. Briefly:
julia> function foo(x)
bt = backtrace()
fobj = eval(current_module(), symbol(Profile.lookup(bt[3]).func))
Base.arg_decl_parts(fobj.env.defs)[2][1][1]
end
foo (generic function with 1 method)
julia> foo(1)
"x"
Let me re-emphasize that this is a bad idea, and should not be used for anything! (well, except for backtrace display). This is basically "stupid compiler tricks", but I'm showing it because it can be kind of educational to play with these objects, and the explanation does lead to a more useful answer to the clarifying comment by #ejang.
Explanation:
bt = backtrace() generates a ... backtrace ... from the current position. bt is an array of pointers, where each pointer is the address of a frame in the current call stack.
Profile.lookup(bt[3]) returns a LineInfo object with the function name (and several other details about each frame). Note that bt[1] and bt[2] are in the backtrace-generation function itself, so we need to go further up the stack to get the caller.
Profile.lookup(...).func returns the function name (the symbol :foo)
eval(current_module(), Profile.lookup(...)) returns the function object associated with the name :foo in the current_module(). If we modify the definition of function foo to return fobj, then note the equivalence to the foo object in the REPL:
julia> function foo(x)
bt = backtrace()
fobj = eval(current_module(), symbol(Profile.lookup(bt[3]).func))
end
foo (generic function with 1 method)
julia> foo(1) == foo
true
fobj.env.defs returns the first Method entry from the MethodTable for foo/fobj
Base.decl_arg_parts is a helper function (defined in methodshow.jl) that extracts argument information from a given Method.
the rest of the indexing drills down to the name of the argument.
Regarding the restriction that the function have only one method signature, the reason is that multiple signatures will all be listed (see defs.next) in the MethodTable. As far as I know there is no currently exposed interface to get the specific method associated with a given frame address. (as an exercise for the advanced reader: one way to do this would be to modify the address lookup functionality in jl_getFunctionInfo to also return the mangled function name, which could then be re-associated with the specific method invocation; however, I don't think we currently store a reverse mapping from mangled name -> Method).
Note also that (1) backtraces are slow (2) there is no notion of "function-local" eval in Julia, so even if one has the variable name, I believe it would be impossible to actually access the variable (and the compiler may completely elide local variables, unused or otherwise, put them in a register, etc.)
As for the IDE-style introspection use mentioned in the comments: foo.env.defs as shown above is one place to start for "object introspection". From the debugging side, Gallium.jl can inspect DWARF local variable info in a given frame. Finally, JuliaParser.jl is a pure-Julia implementation of the Julia parser that is actively used in several IDEs to introspect code blocks at a high level.
Another method is to use the function's vinfo. Here is an example:
function test(argx::Int64)
vinfo = code_lowered(test,(Int64,))
string(vinfo[1].args[1][1])
end
test (generic function with 1 method)
julia> test(10)
"argx"
The above depends on knowing the signature of the function, but this is a non-issue if it is coded within the function itself (otherwise some macro magic could be needed).