A framework for Idris RTS.
- An abstraction of some intermediate representations(common abstract machine, aka CAM)
- Back end: Python AST
- Back end: Julia AST
- Persisting locations with Idris IRs, like DDecls.
- Python standard libraries
- Handy FFI
- Tail call elimination
- Back end: Python Bytecode
- Specializations for some primitive data types
- Incremental compilation
-
Build
git clone https://github.com/thautwarm/idris-cam && cd idris-cam stack build
-
Codegen
stack exec idris -- --codegen=cam <input idris files> -o <output cam file>
-
Install to
.local
stack install
After installing this, your
idris
command could usecam
back end viaidris --codegen cam
. -
Usage within
idris
command.idris f1.idr f2.idr -o out.cam --codegen cam -p cam
-
Run
.cam
files-
Idris-Python
You can check Idris-Python, and install it via pip will give you a command
run-cam
which accepts a filename of.cam
file and executes it in Python.Also, you can load
.cam
file in Python session, via load_cam function. -
Idris-Julia
Check cam-julia/test/runtests.jl.
It hasn't been published yet.
-
You can test the Idris package Cam
, generating test files for Python or Julia with
~/github/idris-cam | master> cd libs && python gen_test.py
Above command compiles idris-cam/libs/Test/Simple.idr
into .cam
file.
-- idris-cam/libs/Test/Simple.idr
module Test.Simple
import Cam.FFI
import Cam.OS.FileSystem
import Cam.IO
import Cam.Data.Collections
import Cam.Data.FCollections
import Cam.Data.Compat
import Data.Vect
import Data.HVect
%hide HVect.index
%hide Vect.index
%hide Vect.reverse
%access export
testSimple : FFI.IO ()
testSimple = do
-- In Julia, use "MLStyle" or other Julia module.
sklearn <- camImport $ TheModule "sklearn"
-- fprintln works for only foreign objects
fprintln sklearn
-- get property of module
external_mod <- camImportFrom sklearn "externals"
-- println works for all objects
println external_mod
file <- openFile "./text.txt" "r"
text <- readAllText file
closeFile file
fprintln $ text
println $ "test hvec: " show hvec
where
hvec : HVect [Double, Int]
hvec = reverse [1.0, 5]
Use idris-python
command from Idris-Python. Change module name from Test.Simple
to Main
is okay.
idris-python main.idr
~/github/idris-cam | master> cd cam-julia
~/github/idris-cam | master> julia
~/github/idris-cam | master> cd cam-julia
julia> ]
v1.1> activate .
(CamJulia) pkg> test
Testing CamJulia
Resolving package versions...
MLStyle
MLStyle.MatchCore.gen_match
啊,太懂了!太懂Idris Julia辣!
test hvec: [1.0, 5]
A .cam
file is simply a JSON file consist of the abstract instructions which could be loaded by Python and Julia and compiled conveniently.
For Julia, there's a @load_cam
macro:
macro load_cam(path)
aeson = CamJulia.ReadIR.load_aeson(path);
ir = CamJulia.ReadIR.aeson_to_ir(aeson)
x = CamJulia.CAM.ir_to_julia(ir)
esc(x)
end
@load_cam "./test.cam"
For Python, there is a load_cal
function:
def load_cam(path, session):
with open(path, 'r') as f:
js = json.load(f)
letrec: LetRec = aeson_to_ir(js)
return run_code(letrec, session)
The session
argument of Python's load_cam
indicates a symbol generator. Comparisons of symbols are O(1) thus allow us to
create efficient tagged unions(ADTs, data types). In Julia, Symbol
is an essential type.
Our handy FFI becomes feasible due to New Foreign Function Interface.
In Idris side, we have made a FFI implementation for Idris-CAM, and then you can declare foreign functions via such codes:
println : Unsafe -> FFI.IO Unsafe
println s = fcall (Unsafe -> FFI.IO Unsafe) (Builtin "println") s
You can check libs/Cam for more information, and I'll make Idris libraries for both Julia and Python sooner.
We have already Julia and Python back ends, and Python's is already available, so we use Python to demonstrate how to setup FFI.
Note that we referenced println
as builtin functions in previous Idris codes.
So we update runtime.py, add its implementation:
return {
'cam-rt.cmp': operator.eq,
'cam-rt.is': operator.is_,
...
'builtin-println': print,
...
}