As part of writing my PhD I am currently thinking about the relationship between PyPy's meta-tracing approach with various previous ideas to automatically get a (JIT-)compiler from only an interpreter of a language. One of the most-researched ideas along these lines is that of partial evaluation. Partial evaluation has basically the same goals as PyPy when it comes to compilers: Write an interpreter, and get a compiler for free. The methods for reaching that goal are a bit different. In this series of blog posts, I am trying to explore the similarities and differences of partial evaluation and PyPy's meta-tracing.
A Flowgraph Language
To be able to clearly understand what "partial evaluation" is and what "meta-tracing" is I will show an "executable model" of both. To that end, I am defining a small imperative language and will then show what a partial evaluator and a tracer for that language look like. All this code will be implemented in Prolog. (Any pattern-matching functional language would do, but I happen to know Prolog best. Backtracking is not used, so you can read things simply as functional programs.) In this post I will start with the definition of the language, and a partial evaluator for it. The code written in this blog post can be found fully here: https://paste.pocoo.org/show/541004/
The language is conceptionally similar to PyPy's flow graphs, but a bit more restricted. It does not have function calls, only labelled basic blocks that consist of a series of linearly executed operations, followed by a conditional or an unconditional jump. Every operation is assigning a value to a variable, which is computed by applying some operation to some arguments.
A simple program to raise x to the yth power in that language looks like this:
power: res = 1 if y goto power_rec else goto power_done power_rec: res = res * x y = y - 1 if y goto power_rec else goto power_done power_done: print_and_stop(res)
To represent the same program as Prolog data structures, we use the following Prolog code:
block(power, op1(res, same, const(1), if(y, power_rec, power_done))). block(power_rec, op2(res, mul, var(res), var(x), op2(y, sub, var(y), const(1), if(y, power_rec, power_done)))). block(power_done, print_and_stop(var(res))).
Every rule of block declares one block by first giving the label of the block, followed by the code. Code is a series of op1 or op2 statements terminated by a jump, an if or a print_and_stop. op1 statements are operations with one argument of the form op1(res_variable, operation_name, argument, next_statement). Arguments can be either variables in the form var(name) or constants in the form const(value).
To run programs in this flowgraph language, we first need some helper functionality. The first few helper functions are concerned with the handling of environments, the data structures the interpreter uses to map variable names occuring in the program to the variables' current values. In Python dictionaries would be used for this purpose, but in Prolog we have to emulate these by lists of key/value pairs (not very efficient, but good enough):
lookup(X, , _) :- throw(key_not_found(X)). lookup(Key, [Key/Value | _], Value) :- !. lookup(Key, [_ | Rest], Value) :- lookup(Key, Rest, Value). write_env(, X, V, [X/V]). write_env([Key/_ | Rest], Key, Value, [Key/Value | Rest]) :- !. write_env([Pair | Rest], Key, Value, [Pair | NewRest]) :- write_env(Rest, Key, Value, NewRest). remove_env(, _, ). remove_env([Key/_ | Rest], Key, Rest) :- !. remove_env([Pair | Rest], Key, [Pair | NewRest]) :- remove_env(Rest, Key, NewRest). resolve(const(X), _, X). resolve(var(X), Env, Y) :- lookup(X, Env, Y).
The implementation of these functions is not too important. The lookup function finds a key in an environment list, the write_env function adds a new key/value pair to an environment, remove_env removes a key. The resolve function is used to take either a constant or a variable and return a value. If it's a constant, the value of that constant is returned, if it's a variable it is looked up in the environment. Note how the last argument of lookup and resolve is actually a return value, which is the typical approach in Prolog.
So far we have not specified what the primitive operations that can occur in the program actually mean. For that we define a do_op function which executes primitive operations:
do_op(same, X, X). do_op(mul, X, Y, Z) :- Z is X * Y. do_op(add, X, Y, Z) :- Z is X + Y. do_op(sub, X, Y, Z) :- Z is X - Y. do_op(eq, X, Y, Z) :- X == Y -> Z = 1; Z = 0. do_op(ge, X, Y, Z) :- X >= Y -> Z = 1; Z = 0. do_op(readlist, L, I, X) :- nth0(I, L, X). do_op(Op, _, _, _) :- throw(missing_op(Op)).
Again the last argument is an output variable.
Now we can start executing simple operations. For that an interp predicate is defined. It takes as its first argument the current environment and as the second argument the operation to execute. E.g. to execute primitive operations with one or two arguments:
interp(op1(ResultVar, Op, Arg, Rest), Env) :- resolve(Arg, Env, RArg), do_op(Op, RArg, Res), write_env(Env, ResultVar, Res, NEnv), interp(Rest, NEnv). interp(op2(ResultVar, Op, Arg1, Arg2, Rest), Env) :- resolve(Arg1, Env, RArg1), resolve(Arg2, Env, RArg2), do_op(Op, RArg1, RArg2, Res), write_env(Env, ResultVar, Res, NEnv), interp(Rest, NEnv).
First the arguments are resolved into values. Afterwards the operation is executed, and the result is written back into the environment. Then interp is called on the rest of the program. Similarly easy are the unconditional jump and print_and_stop:
interp(jump(L), Env) :- block(L, Block), interp(Block, Env). interp(print_and_stop(Arg), Env) :- resolve(Arg, Env, Val), print(Val), nl.
In the unconditional jump we simply get the target block and continue executing that. To execute print_and_stop we resolve the argument, print the value and then are done.
The conditional jump is only slightly more difficult:
interp(if(V, L1, L2), Env) :- lookup(V, Env, Val), (Val == 0 -> block(L2, Block) ; block(L1, Block) ), interp(Block, Env).
First the variable is looked up in the environment. If the variable is zero, execution continues at the second block, otherwise it continues at the first block.
Given this interpreter, we can execute the above example program like this, on a Prolog console:
$ swipl -s cfglang.pl ?- block(power, Block), interp(Block, [x/10, y/10]). 10000000000
Partial Evaluation of the Flowgraph Language
Let's look at what a partial evaluator for this simple flowgraph language would look like. Partial evaluation (PE), also called specialization, is a program manipuation technique. PE takes an input program and transforms it into a (hopefully) simpler and faster output program. It does this by assuming that some variables in the input program are constants. All operations that act only on such constants can be folded away. All other operations need to remain in the output program (called residual program). Thus the partial evaluator proceeds much like an interpreter, just that it cannot actually execute some operations. Also, its output is not just a value, but also list of remaining operations that could not be optimized away.
The partial evaluator cannot use normal environments, because unlike the interpreter not all variables' values are known to it. It will therefore work on partial environments, which store just the know variables. For these partial environments, some new helper functions are needed:
plookup(Key, , var(Key)). plookup(Key, [Key/Value | _], const(Value)) :- !. plookup(Key, [_ | Rest], Value) :- plookup(Key, Rest, Value). presolve(const(X), _, const(X)). presolve(var(V), PEnv, X) :- plookup(V, PEnv, X).
The function plookup takes a variable and a partial environment and returns either const(Value) if the variable is found in the partial environment or var(Key) if it is not. Equivalently, presolve is like resolve, except that it uses plookup instead of lookup.
With these helpers we can start writing a partial evaluator. The following two rules are where the main optimization in the form of constant folding happens. The idea is that when the partial evaluator sees an operation that involves only constant arguments, it can constant-fold the operation, otherwise it can't:
pe(op1(ResultVar, Op, Arg, Rest), PEnv, NewOp) :- presolve(Arg, PEnv, RArg), (RArg = const(C) -> do_op(Op, C, Res), write_env(PEnv, ResultVar, Res, NEnv), RestResidual = NewOp ; remove_env(PEnv, ResultVar, NEnv), NewOp = op1(ResultVar, Op, RArg, RestResidual) ), pe(Rest, NEnv, RestResidual). pe(op2(ResultVar, Op, Arg1, Arg2, Rest), PEnv, NewOp) :- presolve(Arg1, PEnv, RArg1), presolve(Arg2, PEnv, RArg2), (RArg1 = const(C1), RArg2 = const(C2) -> do_op(Op, C1, C2, Res), write_env(PEnv, ResultVar, Res, NEnv), RestResidual = NewOp ; remove_env(PEnv, ResultVar, NEnv), NewOp = op2(ResultVar, Op, RArg1, RArg2, RestResidual) ), pe(Rest, NEnv, RestResidual).
The pe predicate takes a partial environment, the current operations and potentially returns a new operation. To partially evaluate a simple operation, its arguments are looked up in the partial environment. If all the arguments are constants, the operation can be executed, and no new operation is produced. Otherwise, we need to produce a new residual operation which is exactly like the one currently looked at. Also, the result variable needs to be removed from the partial environment, because it was just overwritten by an unknown value.
The potentially generated residual operation is stored into the output argument NewOp. The output argument of the recursive call is the last argument of the newly created residual operation, which will then be filled by the recursive call. This is a typical approach in Prolog, but may look strange if you are not familiar with it.
Note how the first case of these two rules is just like interpretation. The second case doesn't really do anything, it just produces a residual operation. This relationship between normal evaluation and partial evaluation is very typical.
The unconditional jump and print_and_stop are not much more complex:
pe(jump(L), PEnv, jump(LR)) :- do_pe(L, PEnv, LR). pe(print_and_stop(Arg), Env, print_and_stop(RArg)) :- presolve(Arg, Env, RArg).
To partially evaluate an unconditional jump we again produce a jump. The target label of that residual jump is computed by asking the partial evaluator to produce residual code for the label L with the given partial environment. print_and_stop is simply turned into a print_and_stop. We will see the code for do_pe soon.
Conditional jumps are more interesting:
pe(if(V, L1, L2), PEnv, NewOp) :- plookup(V, PEnv, Val), (Val = const(C) -> (C = 0 -> L = L2 ; L = L1 ), do_pe(L, PEnv, LR), NewOp = jump(LR) ; do_pe(L1, PEnv, L1R), do_pe(L2, PEnv, L2R), NewOp = if(V, L1R, L2R) ).
First we look up the value of the condition variable. If it is a constant, we can produce better code, because we know statically that only one path is reachable. Thus we produce code for that path, and then emit an unconditional jump there. If the condition variable is not known at partial evaluation time, we need to partially evaluate both paths and produce a conditional jump in the residual code.
This rule is the one that causes the partial evaluator to potentially do much more work than the interpreter, because after an if sometimes both paths need to be explored. In the worst case this process never stops, so a real partial evaluator would need to ensure somehow that it terminates. There are many algorithms for doing that, but I will ignore this problem here.
Now we need to understand what the do_pe predicate is doing. Its most important task is to make sure that we don't do the same work twice by memoizing code that was already partially evaluated in the past. For that it keeps a mapping of Label, Partial Environment to Label of the residual code:
do_pe(L, PEnv, LR) :- (code_cache(L, PEnv, LR) -> true ; gensym(L, LR), assert(code_cache(L, PEnv, LR)), block(L, Code), pe(Code, PEnv, Residual), assert(block(LR, Residual)) ).
If the code cache indicates that label L was already partially evaluated with partial environment PEnv, then the previous residual code label LPrevious is returned. Otherwise, a new label is generated with gensym, the code cache is informed of that new label with assert, then the block is partially evaluated and the residual code is added to the database.
For those who know partial evaluation terminology: This partial evaluator is a polyvariant online partial evaluator. "Polyvariant" means that for every label, several specialized version of the block can be generated. "Online" means that no preprocessing is done before the partial evaluator runs.
Partial Evaluation Example
With this code we can look at the classical example of partial evaluation (it's probably the "Hello World" of partial evaluation). We can ask the partial evaluator to compute a power function, where the exponent y is a fixed number, e.g. 5, and the base x is unknown:
?- do_pe(power, [y/5], LR). LR = power1.
To find out which code was produced, we can use listing:
?- listing(code_cache) code_cache(power, [y/5], power1). code_cache(power_rec, [y/5, res/1], power_rec1). code_cache(power_rec, [y/4], power_rec2). code_cache(power_rec, [y/3], power_rec3). code_cache(power_rec, [y/2], power_rec4). code_cache(power_rec, [y/1], power_rec5). code_cache(power_done, [y/0], power_done1). ?- listing(block) .... the block definition of the user program .... block(power_done1, print_and_stop(var(res))). block(power_rec5, op2(res, mul, var(res), var(x), jump(power_done1))). block(power_rec4, op2(res, mul, var(res), var(x), jump(power_rec5))). block(power_rec3, op2(res, mul, var(res), var(x), jump(power_rec4))). block(power_rec2, op2(res, mul, var(res), var(x), jump(power_rec3))). block(power_rec1, op2(res, mul, const(1), var(x), jump(power_rec2))). block(power1, jump(power_rec1)).
The code_cache tells which residual labels correspond to which original labels under which partial environments. Thus, power1 contains the code of power under the assumption that y is 5. Looking at the block listing, the label power1 corresponds to code that simply multiplies res by x five times without using the variable x at all. The loop that was present in the original program has been fully unrolled, the loop variable y has disappeared. Hopefully this is faster than the original program.
In this blog post we saw an interpreter for a simple flow graph language in Prolog, together with a partial evaluator for it. The partial evaluator essentially duplicates every rule of the interpreter. If all the arguments of the current operation are known, it acts like the interpreter, otherwise it simply copies the operation into the residual code.
Partial evaluation can be used for a variety of applications, but the most commonly cited one is that of applying it to an interpreter. To do that, the program that the interpreter runs is assumed to be constant by the partial evaluator. Thus a specialized version of the interpreter is produced that does not use the input program at all. That residual code can be seen as a compiled version of the input program.
In the next blog post in this series we will look at writing a simple tracer for the same flowgraph language.