Ravi is a derivative/dialect of Lua 5.3 with limited optional static typing and an LLVM powered JIT compiler. The name Ravi comes from the Sanskrit word for the Sun. Interestingly a precursor to Lua was Sol which had support for static types; Sol means the Sun in Portugese.

Lua is perfect as a small embeddable dynamic language so why a derivative? Ravi extends Lua with static typing for greater performance under JIT compilation. However, the static typing is optional and therefore Lua programs are also valid Ravi programs.

There are other attempts to add static typing to Lua - e.g. Typed Lua but these efforts are mostly about adding static type checks in the language while leaving the VM unmodified. The Typed Lua effort is very similar to the approach taken by Typescript in the JavaScript world. The static typing is to aid programming in the large - the code is eventually translated to standard Lua and executed in the unmodified Lua VM.

My motivation is somewhat different - I want to enhance the VM to support more efficient operations when types are known. Type information can be exploited by JIT compilation technology to improve performance. At the same time, I want to keep the language safe and therefore usable by non-expert programmers.

Of course there is also the fantastic LuaJIT implementation. Ravi has a different goal compared to LuaJIT. Ravi prioritizes ease of maintenance and support, language safety, and compatibility with Lua 5.3, over maximum performance. For more detailed comparison please refer to the documentation links below.

• Optional static typing
• Type specific bytecodes to improve performance
• Compatibility with Lua 5.3 (see Compatibility section below)
• LLVM powered JIT compiler
• Additionally a libgccjit based alternative JIT compiler is also available
• LLVM bindings exposed in Lua

See Ravi Documentation. As more stuff is built I will keep updating the documentation so please revisit for latest information.

Also see the slides I presented at the Lua 2015 Workshop.

The LLVM JIT compiler is functional. The Lua and Ravi bytecodes currently implemented in LLVM are described in JIT Status page.

Ravi also provides an LLVM binding; this is still work in progress so please check documentation for the latest status.

There is also a libgccjit based JIT implementation but this implementation is lagging behind the LLVM based implementation. Further development of this is currently not planned.

Ravi allows you to annotate local variables and function parameters with static types. The supported types and the resulting behaviour are as follows:

integer

denotes an integral value of 64-bits.

number

denotes a double (floating point) value of 8 bytes.

integer[]

denotes an array of integers

number[]

denotes an array of numbers

table

a Lua table

Declaring the types of local variables and function parameters has following advantages.

• integer and number types are automatically initialized to zero
• Arithmetic operations on numeric types make use of type specific bytecodes which leads to more efficient JIT compilation
• Specialised operators to get/set from array types are implemented; this makes array access more efficient in JIT mode as the access can be inlined
• Declared tables allow specialized opcodes for table gets involving integer and short literal string keys; these opcodes result in more efficient JIT code
• Values assigned to typed variables are checked statically when possible; if the values are results from a function call then runtime type checking is performed
• The standard table operations on arrays are checked to ensure that the type is not subverted
• Even if a typed variable is captured in a closure its type must be respected
• When function parameters are decorated with types, Ravi performs an implicit coersion of those parameters to the required types. If the coersion fails a runtime error occurs.

The array types (number[] and integer[]) are specializations of Lua table with some additional special behaviour:

• Array types are not compatible with declared table variables, i.e. following is not allowed:

  local t: table = {}
  local t2: number[] = t  -- error!
  
  local t3: number[] = {}
  local t4: table = t3    -- error!
  

  But following is okay:

  local t5: number[] = {}
  local t6 = t5           -- t6 treated as table
  

  The reason for these restrictions is that declared table types generate optimized JIT code which assumes that the keys are integers or literal short strings. Similarly declared array types result in specialized JIT code that assume integer keys and numeric values. The generated JIT code would be incorrect if the types were not as expected.

• Indices >= 1 should be used when accessing array elements. Ravi arrays (and slices) have a hidden slot at index 0 for performance reasons, but this is not visible in pairs() or ipairs(), or when initializing an array using a literal initializer; only direct access via the [] operator can see this slot.

• Arrays must always be initialized:

  local t: number[] = {} -- okay
  local t2: number[]     -- error!
  

  This restriction is placed as otherwise the JIT code would need to insert tests to validate that the variable is not nil.

• An array will grow automatically (unless the array was created as fixed length using table.intarray() or table.numarray()) if the user sets the element just past the array length:

  local t: number[] = {} -- dynamic array
  t[1] = 4.2             -- okay, array grows by 1
  t[5] = 2.4             -- error! as attempt to set value
  

• It is an error to attempt to set an element that is beyond len+1 on dynamic arrays; for fixed length arrays attempting to set elements at positions greater than len will cause an error.

• The current used length of the array is recorded and returned by len operations

• The array only permits the right type of value to be assigned (this is also checked at runtime to allow compatibility with Lua)

• Accessing out of bounds elements will cause an error, except for setting the len+1 element on dynamic arrays

• It is possible to pass arrays to functions and return arrays from functions. Arrays passed to functions appear as Lua tables inside those functions if the parameters are untyped - however the tables will still be subject to restrictions as above. If the parameters are typed then the arrays will be recognized at compile time:

  local function f(a, b: integer[], c)
    -- Here a is dynamic type
    -- b is declared as integer[]
    -- c is also a dynamic type
    b[1] = a[1] -- Okay only if a is actually also integer[]
    b[1] = c[1] -- Will fail if c[1] cannot be converted to an integer
  end
  
  local a : integer[] = {1}
  local b : integer[] = {}
  local c = {1}
  
  f(a,b,c)        -- ok as c[1] is integer
  f(a,b, {'hi'})  -- error!
  

• Arrays returned from functions can be stored into appropriately typed local variables - there is validation that the types match:

  local t: number[] = f() -- type will be checked at runtime
  

• Operations on array types can be optimised to special bytecode and JIT only when the array type is statically known. Otherwise regular table access will be used subject to runtime checks.

• Array types ignore __index, __newindex and __len metamethods.

• Array types cannot be set as metatables for other values.

• pairs() and ipairs() work on arrays as normal

• There is no way to delete an array element.

• The array data is stored in contiguous memory just like native C arrays; morever the garbage collector does not scan the array data

A declared table (as shown below) has some additional nuances:

local t: table = {}

• Like array types, a variable of table type must be initialized

• Array types are not compatible with declared table variables, i.e. following is not allowed:

  local t: table = {}
  local t2: number[] = t -- error!
  

• When short string literals are used to access a table element, specialized bytecodes are generated that are more efficiently JIT compiled:

  local t: table = { name='dibyendu'}
  print(t.name) -- The GETTABLE opcode is specialized in this case
  

• As with array types, specialized bytecodes are generated when integer keys are used

Following library functions allow creation of array types of defined length.

table.intarray(num_elements, initial_value)

creates an integer array of specified size, and initializes with initial value. The return type is integer[]. The size of the array cannot be changed dynamically, i.e. it is fixed to the initial specified size. This allows slices to be created on such arrays.

table.numarray(num_elements, initial_value)

creates an number array of specified size, and initializes with initial value. The return type is number[]. The size of the array cannot be changed dynamically, i.e. it is fixed to the initial specified size. This allows slices to be created on such arrays.

Ravi does not support defining new types, or structured types based on tables. This creates some practical issues when dynamic types are mixed with static types. For example:

local t = { 1,2,3 }
local i: integer = t[1] -- generates an error

Above code generates an error as the compiler does not know that the value in t[1] is an integer. However often we as programmers know the type that is expected and it would be nice to be able to tell the compiler what the expected type of t[1] is above. To enable this Ravi supports type assertion operators. A type assertion is introduced by the '@' symbol, which must be followed by the type name. So we can rewrite the above example as:

local t = { 1,2,3 }
local i: integer = @integer( t[1] )

The type assertion operator is a unary operator and binds to the expression following the operator. We use the parenthesis above to enure that the type assertion is applied to t[1] rather than t. More examples are shown below:

local a: number[] = @number[] { 1,2,3 }
local t = { @number[] { 4,5,6 }, @integer[] { 6,7,8 } }
local a1: number[] = @number[]( t[1] )
local a2: integer[] = @integer[]( t[2] )

For a real example of how type assertions can be used, please have a look at the test program gaussian2.lua

Since release 0.6 Ravi supports array slices. An array slice allows a portion of a Ravi array to be treated as if it is an array - this allows efficient access to the underlying array elements. Following new functions are available:

table.slice(array, start_index, num_elements)

creates a slice from an existing fixed size array - allowing efficient access to the underlying array elements.

Slices access the memory of the underlying array; hence a slice can only be created on fixed size arrays (constructed by table.numarray() or table.intarray()). This ensures that the array memory cannot be reallocated while a slice is referring to it. Ravi does not track the slices that refer to arrays - slices get garbage collected as normal.

Slices cannot extend the array size for the same reasons above.

The type of a slice is the same as that of the underlying array - hence slices get the same optimized JIT operations for array access.

Each slice holds an internal reference to the underlying array to ensure that the garbage collector does not reclaim the array while there are slices pointing to it.

For an example use of slices please see the matmul1.ravi benchmark program in the repository. Note that this feature is highly experimental and not very well tested.

Example of code that works - you can copy this to the command line input:

function tryme()
  local i,j = 5,6
  return i,j
end
local i:integer, j:integer = tryme(); print(i+j)

When values from a function call are assigned to a typed variable, an implicit type coersion takes place. In above example an error would occur if the function returned values that could not converted to integers.

In the following example, the parameter j is defined as a number, hence it is an error to pass a value that cannot be converted to a number:

function tryme(j: number)
  for i=1,1000000000 do
    j = j+1
  end
  return j
end
print(tryme(0.0))

An example with arrays:

function tryme()
  local a : number[], j:number = {}
  for i=1,10 do
    a[i] = i
    j = j + a[i]
  end
  return j
end
print(tryme())

Another example using arrays. Here the function receives a parameter arr of type number[] - it would be an error to pass any other type to the function because only number[] types can be converted to number[] types:

function sum(arr: number[])
  local n: number = 0.0
  for i = 1,#arr do
    n = n + arr[i]
  end
  return n
end

print(sum(table.numarray(10, 2.0)))

The table.numarray(n, initial_value) creates a number[] of specified size and initializes the array with the given initial value.

To keep with Lua's dynamic nature Ravi uses a mix of compile type checking and runtime type checks. However due to the dynamic nature of Lua, compilation happens at runtime anyway so effectually all checks are at runtime.

The LLVM based JIT compiler is functional. There are two modes of JIT compilation.

auto mode

in this mode the compiler decides when to compile a Lua function. The current implementation is very simple - any Lua function call is checked to see if the bytecodes contained in it can be compiled. If this is true then the function is compiled provided either a) function has a fornum loop, or b) it is largish (greater than 150 bytecodes) or c) it is being executed many times (> 50). Because of the simplistic behaviour performance the benefit of JIT compilation is only available if the JIT compiled functions will be executed many times so that the cost of JIT compilation can be amortized.

manual mode

in this mode user must explicitly request compilation. This is the default mode. This mode is suitable for library developers who can pre compile the functions in library module table.

A JIT api is available with following functions:

ravi.jit([b])

returns enabled setting of JIT compiler; also enables/disables the JIT compiler; defaults to true

ravi.auto([b [, min_size [, min_executions]]])

returns setting of auto compilation and compilation thresholds; also sets the new settings if values are supplied; defaults are false, 150, 50.

ravi.compile(func_or_table[, options])

compiles a Lua function (or functions if a table is supplied) if possible, returns true if compilation was successful for at least one function. options is an optional table with compilation options - in particular omitArrayGetRangeCheck - which disables range checks in array get operations to improve performance in some cases. Note that at present if the first argument is a table of functions and has more than 100 functions then only the first 100 will be compiled. You can invoke compile() repeatedly on the table until it returns false. Each invocation leads to a new module being created; any functions already compiled are skipped.

ravi.iscompiled(func)

returns the JIT status of a function

ravi.dumplua(func)

dumps the Lua bytecode of the function

ravi.dumpir(func)

dumps the IR of the compiled function (only if function was compiled; only LLVM version)

ravi.dumpasm(func)

dumps the machine code using the currently set optimization level (only if function was compiled; only LLVM)

ravi.optlevel([n])

sets LLVM optimization level (0, 1, 2, 3); defaults to 2

ravi.sizelevel([n])

sets LLVM size level (0, 1, 2); defaults to 0

ravi.tracehook([b])

Enables support for line hooks via the debug api. Note that enabling this option will result in inefficient JIT as a call to a C function will be inserted at beginning of every Lua bytecode boundary; use this option only when you want to use the debug api to step through code line by line

For performance benchmarks please visit the Ravi Performance Benchmarks page.

To obtain the best possible performance, types must be annotated so that Ravi's JIT compiler can generate efficient code. Additionally function calls are expensive - as the JIT compiler cannot inline function calls, all function calls go via the Lua call protocol which has a large overhead. This is true for both Lua functions and C functions. For best performance avoid function calls inside loops.

Ravi should be able to run all Lua 5.3 programs in interpreted mode, but following should be noted:

• Ravi supports optional typing and enhanced types such as arrays (described above). Programs using these features cannot be run by standard Lua. However all types in Ravi can be passed to Lua functions; operations on Ravi arrays within Lua code will be subject to restrictions as described in the section above on arrays.
• Values crossing from Lua to Ravi will be subjected to typechecks should these values be assigned to typed variables.
• Upvalues cannot subvert the static typing of local variables (issue #26) when types are annotated.
• Certain Lua limits are reduced due to changed byte code structure. These are described below.

When JIT compilation is enabled there are following additional constraints:

• Ravi will only execute JITed code from the main Lua thread; any secondary threads (coroutines) execute in interpreter mode.
• In JITed code tailcalls are implemented as regular calls so unlike the interpreter VM which supports infinite tail recursion JIT compiled code only supports tail recursion to a depth of about 110 (issue #17)

• CMake

Ravi can be built with or without LLVM. Following versions of LLVM work with Ravi.

• LLVM 3.7 or 3.8 or 3.9
• LLVM 3.5 and 3.6 should also work but have not been recently tested

Unless otherwise noted the instructions below should work for LLVM 3.7 or later.

I built LLVM from source. I used the following sequence from the VS2015 command window:

cd \github\llvm
mkdir build
cd build
cmake -DCMAKE_INSTALL_PREFIX=c:\LLVM -DLLVM_TARGETS_TO_BUILD="X86" -G "Visual Studio 14 Win64" ..

I then opened the generated solution in VS2015 and performed a INSTALL build from there. Above will build the 64-bit version of LLVM libraries. To build a 32-bit version omit the Win64 parameter.

On Ubuntu I found that the official LLVM distributions don't work with CMake. The CMake config files appear to be broken. So I ended up downloading and building LLVM from source and that worked. The approach is similar to that described for MAC OS X below.

I am using Max OSX El Capitan. Pre-requisites are XCode 7.x and CMake. Ensure cmake is on the path. Assuming that LLVM source has been extracted to $HOME/llvm-3.7.0.src I follow these steps:

cd llvm-3.7.0.src
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=$HOME/LLVM -DLLVM_TARGETS_TO_BUILD="X86" ..
make install

I am developing Ravi using Visual Studio 2015 Community Edition on Windows 10 64bit, gcc on Unbuntu 64-bit, and clang/Xcode on MAC OS X. I was also able to successfully build a Ubuntu version on Windows 10 using the newly released Ubuntu/Linux sub-system for Windows 10.

Assuming that LLVM has been installed as described above, then on Windows I invoke the cmake config as follows:

cd build
cmake -DLLVM_JIT=ON -DCMAKE_INSTALL_PREFIX=c:\ravi -DLLVM_DIR=c:\LLVM37\share\llvm\cmake -G "Visual Studio 14 Win64" ..

I then open the solution in VS2015 and do a build from there.

On Ubuntu I use:

cd build
cmake -DLLVM_JIT=ON -DCMAKE_INSTALL_PREFIX=$HOME/ravi -DLLVM_DIR=$HOME/LLVM/share/llvm/cmake -DCMAKE_BUILD_TYPE=Release -G "Unix Makefiles" ..
make

Note that on a clean install of Ubuntu 15.10 I had to install following packages:

• cmake
• git
• libreadline-dev

On MAC OS X I use:

cd build
cmake -DLLVM_JIT=ON -DCMAKE_INSTALL_PREFIX=$HOME/ravi -DLLVM_DIR=$HOME/LLVM/share/llvm/cmake -DCMAKE_BUILD_TYPE=Release -G "Xcode" ..

I open the generated project in Xcode and do a build from there. You can also use the command line build tools if you wish - generate the make files in the same way as for Linux.

You can omit -DLLVM_JIT=ON option above to build Ravi with a null JIT implementation.

By default the build generates a shared library for Ravi. You can choose to create a static library and statically linked executables by supplying the argument -DSTATIC_BUILD=ON to CMake.

The Ravi build creates a shared or static depending upon options supplied to CMake, the Ravi executable and some test programs. Additionally when JIT compilation is switched off, the ravidebug executable is generated which is the debug adapter for use by Visual Studio Code.

The ravi command recognizes following environment variables. Note that these are only for internal debugging purposes.

RAVI_DEBUG_EXPR

if set to a value this triggers debug output of expression parsing

RAVI_DEBUG_CODEGEN

if set to a value this triggers a dump of the code being generated

RAVI_DEBUG_VARS

if set this triggers a dump of local variables construction and destruction

Also see section above on available API for dumping either Lua bytecode or LLVM IR for compiled code.

I test the build by running a modified version of Lua 5.3.3 test suite. These tests are located in the lua-tests folder. Additionally I have ravi specific tests in the ravi-tests folder. There is a also a travis build that occurs upon commits - this build runs the tests as well.

• 2015 - Implemented JIT compilation using LLVM
• 2015 - Implemented libgccjit based alternative JIT
• 2016 - Implemented debugger for Ravi and Lua 5.3 for Visual Studio Code
• 2017 - Main priorities are:
  • I would like Ravi to be backward compatible with Lua 5.1 and 5.2 as far as possible
  • Lua function inlining
  • Improve performance of Ravi
  • Additional type annotations

MIT License for LLVM version.