We don’t want a compiler that can only generate code for a single platform. Our language should work on macOS, Windows, and Linux, on x86_64, ARM, and maybe some other architectures. We also don’t want to manually implement the compiler for each platform, dealing with the specifics of each architecture and operating system.
This is where LLVM comes in. LLVM (which stands for Low Level Virtual Machine), is a project which presents us with a kind of generic assembly language, an Intermediate Representation (IR). It also provides tooling to compile the IR into platform-specific instructions, as well as to apply a host of various optimizations. We can thus translate our G-machine instructions to LLVM, and then use LLVM to generate machine code, which gets us to our ultimate goal of compiling our language.
We start with adding LLVM to our CMake project.
|
|
LLVM is a huge project, and has many components. We don’t need most of them. We do need the core libraries, the x86 assembly generator, and x86 assembly parser. I’m not sure why we need the last one, but I ran into linking errors without them. We find the required link targets for these components using this CMake command:
|
|
Finally, we add the new include directories, link targets, and definitions to our compiler executable:
|
|
Great, we have the infrastructure updated to work with LLVM. It’s
now time to start using the LLVM API to compile our G-machine instructions
into assembly. We start with LLVMContext. The LLVM documentation states:
This is an important class for using LLVM in a threaded context. It (opaquely) owns and manages the core “global” data of LLVM’s core infrastructure, including the type and constant uniquing tables.
We will have exactly one instance of such a class in our program.
Additionally, we want an IRBuilder, which will help us generate IR instructions,
placing them into basic blocks (more on that in a bit). Also, we want
a Module object, which represents some collection of code and declarations
(perhaps like a C++ source file). Let’s keep these things in our own
llvm_context class. Here’s what that looks like:
|
|
We include the LLVM context, builder, and module as members of the context struct. Since the builder and the module need the context, we initialize them in the constructor, where they can safely reference it.
Besides these fields, we added
a few others, namely the functions and struct_types maps,
and the various llvm::Type subclasses such as stack_type.
We did this because we want to be able to call our runtime
functions (and use our runtime structs) from LLVM. To generate
a function call from LLVM, we need to have access to an
llvm::Function object. We thus want to have an llvm::Function
object for each runtime function we want to call. We could declare
a member variable in our llvm_context for each runtime function,
but it’s easier to leave this to be an implementation
detail, and only have a dynamically created map between runtime
function names and their corresponding llvm::Function objects.
We populate the maps and other type-related variables in the
two methods, create_functions() and create_types(). To
create an llvm::Function, we must provide an llvm::FunctionType,
an llvm::LinkageType, the name of the function, and the module
in which the function is declared. Since we only have one
module (the one we initialized in the constructor) that’s
the module we pass in. The name of the function is the same
as its name in the runtime. The linkage type is a little
more complicated - it tells LLVM the “visibility” of a function.
“Private” or “Internal” would hide this function from the linker
(like static functions in C). However, we want to do the opposite: our
generated functions should be accessible from other code.
Thus, our linkage type is “External”.
The only remaining parameter is the llvm::FunctionType, which
is created using code like:
llvm::FunctionType::get(return_type, {param_type_1, param_type_2, ...}, is_variadic)
Declaring all the functions and types in our runtime is mostly
just tedious. Here are a few lines from create_functions(), which
give a very good idea of the rest of that method:
|
|
Similarly, here are a few lines from create_types(), from
which you can extrapolate the rest:
|
|
We also tell LLVM the contents of our structs, so that
we may later reference specific fields. This is just like
forward declaration - we can forward declare a struct
in C/C++, but unless we also declare its contents,
we can’t access what’s inside. Below is the code
for specifying the body of node_base and node_app.
|
|
There’s still more functionality packed into llvm_context.
Let’s next take a look into custom_function, and
the create_custom_function method. Why do we need
these? To highlight the need for the custom class,
let’s take a look at instruction_pushglobal which
occurs at the G-machine level, and then at alloc_global,
which will be a function call generated as part of
the PushGlobal instruction. instruction_pushglobal’s
only member variable is name, which stands for
the name of the global function it’s referencing. However,
alloc_global requires an arity argument! We can
try to get this information from the llvm::Function
corresponding to the global we’re trying to reference,
but this doesn’t get us anywhere: as far as LLVM
is concerned, any global function only takes one
parameter, the stack. The rest of the parameters
are given through that stack, and their number cannot
be easily deduced from the function alone.
Instead, we decide to store global functions together
with their arity. We thus create a class to combine
these two things (custom_function), define
a map from global function names to instances
of custom_function, and add a convenience method
(create_custom_function) that takes care of
constructing an llvm::Function object, creating
a custom_function, and storing it in the map.
The implementation for custom_function is
straightforward:
|
|
We create a function type, then a function, and finally
initialize a custom_function. There’s one thing
we haven’t seen yet in this function, which is the
BasicBlock class. We’ll get to what basic blocks
are shortly, but for now it’s sufficient to
know that the basic block gives us a place to
insert code.
This isn’t the end of our llvm_context class: it also
has a variety of other create_* methods! Let’s take a look
at their signatures. Most return either void,
llvm::ConstantInt*, or llvm::Value*. Since
llvm::ConstantInt* is a subclass of llvm::Value*, let’s
just treat it as simply an llvm::Value* while trying
to understand these methods.
So, what is llvm::Value? To answer this question, let’s
first understand how the LLVM IR works.
LLVM IR
An important property of LLVM IR is that it is in Single Static Assignment
(SSA) form. This means that each variable can only be assigned to once. For instance,
if we use <- to represent assignment, the following program is valid:
x <- 1
y <- 2
z <- x + y
However, the following program is not valid:
x <- 1
x <- x + 1
But what if we do want to modify a variable x?
We can declare another “version” of x every time we modify it.
For instance, if we wanted to increment x twice, we’d do this:
x <- 1
x1 <- x + 1
x2 <- x1 + 1
In practice, LLVM’s C++ API can take care of versioning variables on its own, by auto-incrementing numbers associated with each variable we use.
Assigned to each variable is llvm::Value. The LLVM documentation states:
It is the base class of all values computed by a program that may be used as operands to other values.
It’s important to understand that llvm::Value does not store the result of the computation.
It rather represents how something may be computed. 1 is a value because it computed by
just returning 1. x + 1 is a value because it is computed by adding the value inside of
x to 1. Since we cannot modify a variable once we’ve declared it, we will
keep assigning intermediate results to new variables, constructing new values
out of values that we’ve already specified.
This somewhat elucidates what the create_* functions do: create_i8 creates an 8-bit integer
value, and create_pop creates a value that is computed by calling
our runtime stack_pop function.
Before we move on to look at the implementations of these functions, we need to understand another concept from the world of compiler design: basic blocks. A basic block is a sequence of instructions that are guaranteed to be executed one after another. This means that a basic block cannot have an if/else, jump, or any other type of control flow anywhere except at the end. If control flow could appear inside the basic block, there would be opporunity for execution of some, but not all, instructions in the block, violating the definition. Every time we add an IR instruction in LLVM, we add it to a basic block. Writing control flow involves creating several blocks, with each block serving as the destination of a potential jump. We will see this used to compile the Jump instruction.
Generating LLVM IR
Now that we understand what llvm::Value is, and have a vague
understanding of how LLVM is structured, let’s take a look at
the implementations of the create_* functions. The simplest
is create_i8:
|
|
Not much to see here. We create an instance of the llvm::ConstantInt class,
from the actual integer given to the method. As we said before,
llvm::ConstantInt is a subclass of llvm::Value. Next up, let’s look
at create_pop:
|
|
We first retrieve an llvm::Function associated with stack_pop
from our map, and then use llvm::IRBuilder::CreateCall to insert
a value that represents a function call into the currently
selected basic block (the builder’s state is what
dictates what the “selected basic block” is). CreateCall
takes as parameters the function we want to call (stack_pop,
which we store into the pop_f variable), as well as the arguments
to the function (for which we pass f->arg_begin()).
Hold on. What the heck is arg_begin()? Why do we take a function
as a paramter to this method? The answer is fairly simple: this
method is used when we are
generating a function with signature void f_(struct stack* s)
(we discussed the signature in the previous post). The
parameter that we give to create_pop is this function we’re
generating, and arg_begin() gets the value that represents
the first parameter to our function - s! Since stack_pop
takes a stack, we need to give it the stack we’re working on,
and so we use f->arg_begin() to access it.
Most of the other functions follow this exact pattern, with small deviations. However, another function uses a more complicated LLVM instruction:
|
|
unwrap_num is used to cast a given node pointer to a pointer
to a number node, and then return the integer value from
that number node. It starts fairly innocently: we ask
LLVM for the type of a pointer to a node_num struct,
and then use CreatePointerCast to create a value
that is the same node pointer we’re given, but now interpreted
as a number node pointer. We now have to access
the value field of our node. CreateGEP helps us with
this: given a pointer to a node, and two offsets
n and k, it effectively performs the following:
&(num_pointer[n]->kth_field)
The first offset, then, gives an index into the “array”
represented by the pointer, while the second offset
gives the index of the field we want to access. We
want to dereference the pointer (num_pointer[0]),
and we want the second field (1, when counting from 0).
Thus, we call CreateGEP with these offsets and our pointers.
This still leaves us with a pointer to a number, rather
than the number itself. To dereference the pointer, we use
CreateLoad. This gives us the value of the number node,
which we promptly return.
This concludes our implementation of the llvm_context -
it’s time to move on to the G-machine instructions.
G-machine Instructions to LLVM IR
Let’s now envision a gen_llvm method on the instruction struct,
which will turn the still-abstract G-machine instruction
into tangible, close-to-metal LLVM IR. As we’ve seen
in our implementation of llvm_context, to access the stack, we need access to the first
argument of the function we’re generating. Thus, we need this method
to accept the function whose instructions are
being converted to LLVM. We also pass in the
llvm_context, since it contains the LLVM builder,
context, module, and a map of globally declared functions.
With these things in mind, here’s the signature for gen_llvm:
virtual void gen_llvm(llvm_context&, llvm::Function*) const;
Let’s get right to it! instruction_pushint gives us an easy
start:
|
|
We create an LLVM integer constant with the value of our integer, and push it onto the stack.
instruction_push is equally terse:
|
|
We simply peek at the value of the stack at the given
offset (an integer of the same size as size_t, which
we create using create_size). Once we have the
result of the peek, we push it onto the stack.
instruction_pushglobal is more involved. Let’s take a look:
|
|
First, we retrive the custom_function associated with
the given global name. We then create an LLVM integer
constant representing the arity of the function,
and then push onto the stack the result of alloc_global,
giving it the function and arity just like it expects.
instruction_pop is also short, and doesn’t require much
further explanation:
|
|
Some other instructions, such as instruction_update,
instruction_pack, instruction_split, instruction_slide,
instruction_alloc and instruction_eval are equally as simple,
and we omit them for the purpose of brevity.
What remains are two “meaty” functions, instruction_jump and
instruction_binop. Let’s start with the former:
|
|
This is the one and only function in which we have to take
care of control flow. Conceptually, depending on the tag
of the node_data at the top of the stack, we want
to pick one of many branches and jump to it.
As we discussed, a basic block has to be executed in
its entirety; since the branches of a case expression
are mutually exclusive (only one of them is executed in any given case),
we have to create a separate basic block for each branch.
Given these blocks, we then want to branch to the correct one
using the tag of the node on top of the stack.
This is exactly what we do in this function. We first peek
at the node on top of the stack, and use CreateGEP through
unwrap_data_tag to get access to its tag. What we then
need is LLVM’s switch instruction, created using CreateSwitch.
We must provide the switch with a “default” case in case
the tag value is something we don’t recognize. To do this,
we create a “safety” BasicBlock. With this new safety
block in hand, we’re able to call CreateSwitch, giving it
the tag value to switch on, the safety block to default to,
and the expected number of branches (to optimize memory allocation).
Next, we create a vector of blocks, and for each branch,
we append to it a corresponding block branch_block, into
which we insert the LLVM IR corresponding to the
instructions of the branch. No matter the branch we take,
we eventually want to come back to the same basic block,
which will perform the usual function cleanup via Update and Slide.
We re-use the safety block for this, and use CreateBr at the
end of each branch_block to perform an unconditional jump.
After we create each of the blocks, we use the tag_mappings
to add cases to the switch instruction, using addCase. Finally,
we set the builder’s insertion point to the safety block,
meaning that the next instructions will insert their
LLVM IR into that block. Since we have all branches
jump to the safety block at the end, this means that
no matter which branch we take in the case expression,
we will still execute the subsequent instructions as expected.
Let’s now look at instruction_binop:
|
|
In this instruction, we pop and unwrap two integers from
the stack (assuming they are integers). Depending on
the type of operation the instruction is set to, we
then push the result of the corresponding LLVM
instruction. PLUS calls LLVM’s CreateAdd to insert
addition, MINUS calls CreateSub, and so on. No matter
what the operation was, we push the result onto the stack.
That’s all for our instructions! We’re so very close now. Let’s move on to compiling definitions.
Definitions to LLVM IR
As with typechecking, to allow for mutually recursive functions,
we need to be able each global function from any other function.
We then take the same approah as before, going in two passes.
This leads to two new methods for definition:
virtual void gen_llvm_first(llvm_context& ctx) = 0;
virtual void gen_llvm_second(llvm_context& ctx) = 0;
The first pass is intended to register all functions into
the llvm_context, making them visible to other functions.
The second pass is used to actually generate the code for
each function, now having access to all the other global
functions. Let’s see the implementation for gen_llvm_first
for definition_defn:
|
|
Since create_custom_function already creates a function
and registers it with llvm_context, this is
all we need. Note that we created a new member variable
for definition_defn which stores this newly created
function. In the second pass, we will populate this
function with LLVM IR from the definition’s instructions.
We actually create functions for each of the constructors of data types, but they’re quite special: all they do is pack their arguments! Since they don’t need access to the other global functions, we might as well create their bodies then and there:
|
|
Like in definition_defn, we use create_custom_function.
However, we then use SetInsertPoint to configure our builder to insert code into
the newly created function (which already has a BasicBlock,
thanks to that one previously unexplained line in create_custom_function!).
Since we decided to only include the Pack instruction, we generate
a call to it directly using create_pack. We follow this
up with CreateRetVoid, which tells LLVM that this is
the end of the function, and that it is now safe to return
from it.
Great! We now implement the second pass of gen_llvm. In
the case of definition_defn, we do almost exactly
what we did in the first pass of definition_data:
|
|
As for definition_data, we have nothing to do in the
second pass. We’re done!
Getting Results
We’re almost there. Two things remain. The first: our implementation
of ast_binop, implement each binary operation as simply a function call:
+ calls f_plus, and so on. But so far, we have not implemented
f_plus, or any other binary operator function. We do this
in main.cpp, creating a function gen_llvm_internal_op:
|
|
We create a simple function body. We then append G-machine
instructions that take each argument, evaluate it,
and then perform the corresponding binary operation.
With these instructions in the body, we insert
them into a new function, just like we did in our code
for definition_defn and definition_data.
Finally, we write our gen_llvm function that we will
call from main:
|
|
It first creates the functions for
+, -, *, and /. Then, it calls the first
pass of gen_llvm on all definitions, followed
by the second pass. Lastly, it uses LLVM’s built-in
functionality to print out the generated IR in
our module, and then uses a function output_llvm
to create an object file ready for linking.
To be very honest, I took the output_llvm function
almost entirely from instructional material for my university’s
compilers course. The gist of it, though, is: we determine
the target architecture and platform, specify a “generic” CPU,
create a default set of options, and then generate an object file.
Here it is:
|
|
We now add a generate_llvm call to main.
Are we there?
Let’s try to compile our first example, works1.txt. The
file:
defn main = { sum 320 6 }
defn sum x y = { x + y }
We run the following commands in our build directory:
./compiler < ../examples/work1.txt
gcc -no-pie ../runtime.c program.o
./a.out
Nothing happens. How anticlimactic! Our runtime has no way of printing out the result of the evaluation. Let’s change that:
|
|
Rerunning our commands, we get:
Result: 326
The correct result! Let’s try it with works2.txt:
defn add x y = { x + y }
defn double x = { add x x }
defn main = { double 163 }
And again, we get the right answer:
Result: 326
This is child’s play, though. Let’s try with something
more complicated, like works3.txt:
data List = { Nil, Cons Int List }
defn length l = {
case l of {
Nil -> { 0 }
Cons x xs -> { 1 + length xs }
}
}
defn main = { length (Cons 1 (Cons 2 (Cons 3 Nil))) }
Once again, our program does exactly what we intended:
Result: 3
Alright, this is neat, but we haven’t yet confirmed that
lazy evaluation works. How about we try it with
works5.txt:
data List = { Nil, Cons Int List }
defn sumZip l m = {
case l of {
Nil -> { 0 }
Cons x xs -> {
case m of {
Nil -> { 0 }
Cons y ys -> { x + y + sumZip xs ys }
}
}
}
}
defn ones = { Cons 1 ones }
defn main = { sumZip ones (Cons 1 (Cons 2 (Cons 3 Nil))) }
Yet again, the program works:
Result: 9
At last, we have a working compiler!
While this is a major victory, we are not yet
finished with the compiler altogether. While
we allocate nodes whenever we need them, we
have not once uttered the phrase free in our
runtime. Our language works, but we have no way
of comparing numbers, no lambdas, no let/in.
In the next several posts, we will improve
our compiler to properly free unused memory
usign a garbage collector, implement
lambda functions using lambda lifting,
and use our Alloc instruction to implement let/in expressions.
We get started on the first of these tasks in
Part 9 - Garbage Collection.