Thursday, January 2, 2025

Scheme Interpreter: Conclusions

This experiment with writing an MIT-Scheme S-code interpreter in C# was successful in these ways:

  • It showed that the S-code interpreter is an independent component of the Scheme system. The interpreter substrate can be replaced with a new implementation, written in a different language, using a different evaluation strategy, without replacing the Scheme runtime system written in Scheme.
  • It showed that the S-code interpreter can, on small segments of code, perform as fast as compiled code. However, growing the size of these small segment causes an exponential increase in the number of interpreter specializations. The obvious solution of automatically generating interpreter specializations on demand is the equivalent of JIT compilation.
  • It validated the idea that the lexical environment can be represented as a flattened vector of values. Mutable variables can be implemented by cell conversion. Variable values are copied from outer scopes to inner scopes when closures are created. The semantics of such an implementation is equivalent to the semantics of a nested series of frames as used in MIT-CScheme.
  • It showed that you can implement tail recursion via trampolines at each call site, and that you can implement first-class continuations by testing for a magic return value after the return of each trampoline. We don’t use the C# exception handling mechanism to unwind the stack when implementing first-class continuations, just a conditional branch and a normal return. This is far less complicated and expensive.

It was a failure in these ways:

  • Although it showed one way in which we could take incremental steps to increase the speed of the interpreter until it approached the speed of compiled code, each step resulted in an exponential increase in the number of specializations in the interpreter and had diminishing returns.
  • The ultimate outcome of this process would be an interpreter with thousands of specializations. Small Scheme programs could be completely represented by a single specialization, and they would be interpreted as fast as compiled code. But this is because the specialization is eessentially a compiled version of the Scheme program. In other words, we ultimately will have an interpreter that “optimizes” by looking up a program in a huge table that maps small programs to their precomputed compiled bodies. This is just an unusual and inefficient way to implement a compiler.
  • Because C# offers no way to dump a the heap in binary format, we must cold load the system each time we start it.
  • One of the tasks in the cold load is to initialize the unicode tables. These are big tables that take a long time to initialize.
  • It took an annoyingly long time to get to Scheme’s initial top-level prompt.
  • Debugging crashes in the Scheme system was annoying and slow because we have to cold load the Scheme system to reproduce bugs.
  • I have understated a large component of the work: providing a new C# implementation for each of the hundreds of primitives in the Scheme runtime. I only bothered to implement those primitives called as part of the cold lood boot sequence, but still there were a lot of them. For many of these primitives, the C# implementation just achieved the same effect “in spirit” as the MIT-CScheme implementation. These were easy to implement. But some were more persnickety where it was vital that the C# implementation produced exactly the same bits as the MIT-CScheme implementation. For instance, the code used to hash the types for generic method dispatch had to produce the exact same hash values in both implementations. This is because there is code that depends on the hashed multimethod ending up at a precomputed location in a method cache.
  • The C# interpreter was complete enough to boot a Scheme cold load and run it to the top-level prompt. It could run the top-level REPL. But much was missing. It could not host the SF program, which generates the S-code for the Scheme runtime. You’d have to run an original instance of MIT-CScheme to generate the S-code that you would then run in the C# interpreter.

I think the next Lisp system I will try should be based around a simple, portable JIT compiler.


Wednesday, January 1, 2025

Calling Conventions in the Interpreter

C# is not tail recursive. It could be. The IL that it compiles to supports tail recursion, but the C# compiler doesn’t generate the tail call instruction. It would be a simple thing to add: when the compiler emits a call instruction, it could check if the next instruction is a return, and if so, emit a tail call instruction. This could be controlled by a compiler flag so only us weirdos who want this feature would enable it.

But until the C# compiler adds this feature, we have to resort to other methods. I chose to use a trampoline at each call site. This is a small segment of code that awaits the result of the function call. If the callee wants to tail call, it returns the tail call target to the caller, which performs the call on the callee’s behalf. This requires a modification to the calling conventions.

EvalStep is the virtual method that all S-code objects implement to perform an evaluation. Its signature is this:

abstract class Control : SchemeObject
{
     public abstract TailRecursionFlag EvalStep (out object answer, 
                                                 ref Control expression, 
                                                 ref Environment environment);
}

The result of the evaluation is returned in the answer parameter. This is an out parameter, so the answer is allocated in the caller and a pointer to it is passed to the callee. The callee returns the answer by modifying it in the callers stack frame.

The expression and environment parameters are the expected parameters for a call to Eval. They, too, are allocated in the caller’s frame and references to them are passed to the callee. The callee is allowed to modify the caller’s values of these variables.

The returned value is a TailRecursionFlag. This is either 1, indicating that a value has been returned in the answer, or 0, indicating that the caller should perform another EvalStep. To return a value, the callee modifies the answer. To perform a tail call, the callee modifies the expression and environment references and returns 0.

Any caller must call EvalStep as follows: The caller allocates an answer variable to receive the answer of the call. It also allocates an expression, and environment variable to pass to the callee. It then calls EvalStep in a loop until the callee returns a TailRecursionFlag of 1, indicating that the answer has been set to the return value.

In the EvalStep for an S-code Conditional we see an example of the calling convention:

  object ev;
  Control unev = predicate;
  Environment env = environment;

  while (unev.EvalStep (out ev, ref unev, ref env) == TailRecursionFlag.TailCall) { };

We are making a recursive call to evaluate the predicate. We set up ev to receive the result of the evaluation. We set up unev and env to hold the expression and environment to pass to EvalStep. unev.EvalStep does the eval dispatch via virtual function dispatch.

If the predicate returns a TailRecursionFlag of ReturnValue, the loop will exit. The predicate is assumed to have put the return value in the ev variable.

If the predicate wants to tail call, it will modify the values of unev and env to the new expression and new environment, and return a TailRecursionFlag of TailCall. The loop will iterate, using the new value of unev and env to again dispatch a call to EvalStep.

When the while loop exits, the ev variable will contain the return value of the predicate. Control may be returned to the while loop several times before the loop exits. This is the trampoline in action.

Conditional expressions don’t return a value. They either tail call the consequent or the alternative. The EvalStep for a conditional ends like this:

  answer = null;
  expression = (ev is bool evb && evb == false) ? alternative :
  return TailRecursionFlag.TailCall;
}

The answer variable in the caller is set to null. out parameters must always be assigned to before the function exits, so this just keeps the compiler happy. If the return value of calling EvalStep on the predicate is the boolean false, we set the expression in the caller to the alternative, otherwise the consequent. This is the target of our tail call to EvalStep. For the scode for a conditional, we leave the environment alone — the tail call uses the same environment unchanged. We finally return TailRecursionFlag.TailCall so that the caller’s trampoline makes another iteration around its while. It will call EvalStep on the alternative or consequent that we stuffed in the caller’s expression.

This little song and dance is performed at every recursive call to EvalStep making EvalStep behave as a tail-recursive function. This calling convention is about half the speed of a normal C# method call. It is the cost of using a trampoline for tail recursion.

First Class Continuations

There is one more obscure reason that the control might return to us when evaluating the predicate. If some function further down the call chain invokes call-with-current-continuation, we need to copy the stack. The callee indicates this by returning a magic return value of Special.UnwindStack. The callee sets the caller’s environment to an UnwinderState that will accumulate the stack frames as we unwind the stack. So our calling convention says we need to check the return value of EvalStep, and if it is Special.UnwindStack, we allocate a ConditionalFrame on the heap that will contain the state of the current stack frame. We AddFrame to the UnwinderState. We propagate this up the stack by putting it in the caller’s environment, setting the caller’s value of answer to Special.UnwindStack and returning TailRecursionFlag.ReturnValue to stop the caller’s trampoline loop.

The full code of EvalStep for an S-code if expression is this:

 public override TailRecursionFlag EvalStep (out object answer, 
                                             ref Control expression,
                                             ref Environment environment)
{
    object ev;
    Control unev = predicate;
    Environment env = environment;

    // Tail recursion trampoline.
    while (unev.EvalStep (out ev, ref unev, ref env) == TailRecursionFlag.TailCall) { };
    // Support for first class continuations.
    if (ev == Special.UnwindStack)
    {
        ((UnwinderState) env).AddFrame (new ConditionalFrame (this, environment));
        environment = env;
        answer = Special.UnwindStack;

        return TailRecursionFlag.ReturnValue;
    }

    // Tail call EvalStep on the consequent or alternative.
    answer = null;
    expression = (ev is bool evb && evb == false) ? alternative : consequent;
    return TailRecursionFlag.TailCall;
}

First class continuations allow you unload and reload the pending call chain. We see that at each call site, we must check the return value and, if it is Special.UnwindStack, we create a new Frame on the heap and add it to the unwinder state befor we propagate the Special.UnwindStack up the call chain.

At the very top of the call chain, we have the outermost call to EvalStep. If the Special.UnwindStack value is returned to this call, the stack has been unwound and the UnwinderState is sitting in the environment variable. We need to rewind the stack and put the stack frames back on the stack. We create a RewindState from the UnwinderState. Each time we PopFrame from the RewindState, we get a deeper frame. We reload the stack by getting the outermost frame from the RewindState and calling EvalStep on it. The EvalStep for a Frame sets up the trampoline loop, calls PopFrame to get the next frame, and calls EvalStep on it. When we run out of stack frames to reload, the stack is reloaded and we return control the innermost frame so it can continue where it left off. This is the rewind loop.

The EvalStep for a Frame, after making the recursive call to EvalStep on the next frame, continues with code that is a duplicate of the code in the original frame before the cotinuation was captured. A specific example will make this clear. If an if expression is on the stack when it is uwound, a ConditionalFrame is created. A ConditionalFrame is a subclass of SubproblemFrame which has this EvalStep method:

public override TailRecursionFlag EvalStep (out object answer,
                                            ref Control expression,
                                            ref Environment environment)
{
    object temp;
    Control expr = ((RewindState) environment).PopFrame ();
    Environment env = environment;
    while (expr.EvalStep (out temp, ref expr, ref env) == TailRecursionFlag.TailCall) { };
    if (temp == Special.UnwindStack)
    {
        ((UnwinderState) env).AppendContinuationFrames (continuation);
        environment = env;
        answer = Special.UnwindStack;

        return TailRecursionFlag.ReturnValue;
    }
    expression = this.expression;
    environment = this.environment;
    return Continue (out answer, ref expression, ref environment, temp);
}

public abstract TailRecursionFlag Continue (out object answer,
                                            ref Control expression,
                                            ref Environment environment,
                                            object value);

That is, the EvalStep of the SubproblemFrame establishes a trampoline, pops the next frame from the RewindState, and invokes its EvalStep method. When an answer is returned, the SubproblemFrame calls its Continue method.

The Continue method is a virtual method that is implemented by each subclass of SubproblemFrame. It finishes the work of the frame. In the case of a ConditionalFrame, the Continue method is this:

public override TailRecursionFlag Continue (out object answer,
                                            ref Control expression,
                                            ref Environment environment,
                                            object value)
{
    answer = null;
    expression = value is bool bvalue && bvalue == false
      ? SCode.EnsureSCode (this.expression.Alternative)
      : SCode.EnsureSCode (this.expression.Consequent);
    return TailRecursionFlag.TailCall;
}
compare this to the code in the original Conditional:
    // Tail call EvalStep on the consequent or alternative.
    answer = null;
    expression = (ev is bool evb && evb == false) ? alternative : consequent;
    return TailRecursionFlag.TailCall;

There are only superficial differences: the Continue method gets the value returned by the predicate in an argument rather than in a local variable. It type checks the alternative and consequent components of the if expression by calling SCode.EnsureSCode. Otherwise, the code does the same thing.

It is not possible to actually rewind the stack with the original set of pending methods. What we do instead is rewind the stack with methods that do the same thing as the original pending methods. It is close enough. The same values will be computed.

There is one place you can see the difference. If you look at the stack trace in the debugger before you capture a continuation, you will see the pending recursive calls to the S-code EvalStep methods. If you look at the stack trace in the debugger after you capture a continuation, you will instead see pending calls to the EvalStep methods of a set of frames. The pending frames are in the same order and have names similar to the original pending methods. They compute the same values, too. But the debugger can notice that these are not the originals.


More Inlining

Calls to (null? x) usually appear as the predicate to a conditional. We can specialize the conditional. Instead of

[if
  [primitive-null?-argument0]
  [quote 69]
  [quote 420]]

We create a new S-code construct, if-null?-argument0, and construct the conditional as

[if-null?-argument0 
  [quote 69]
  [quote 420]]

We avoid a recursive call and generating a ’T or ’NIL value and testing it, we just test for null and jump to the appropriate branch, just like the compiled code would have done.

Multiple arguments

We can further specialize the conditional based on the types of the consequent and alternative. In this case, they are both quoted values, so we can specialize the conditional to [if-null?-argument0-q-q 69 420]. (Where the name of the S-code type is derived from the type of the consequent and alternative.)

if-null?-argument0-q-q is an esoteric S-code type that codes a test of the first argument for null, and if it is null, returns the first quoted value, otherwise the second quoted value. This S-code type runs just as fast as compiled code. Indeed the machine instructions for evaluating this S-code are the same as what the compiler would have generated for the original Lisp form.

But there is a problem

Why not continue in this vein specializing up the S-code tree? Wouldn’t our interpreter be as fast as compiled code? Well it would, but there is a problem. Every time we add a new S-code type, we add new opportunities for specialization to the containing nodes. The number of ways to specialize a node is the product of the number of ways to specialize its children, so the number of ways to specialize the S-code tree grows exponentially with the number of S-code types. The few specializations I’ve just mentioned end up producing hundreds of specialized S-code types. Many of these specialized S-code types are quite esoteric and apply at most to only one or two nodes in the S-code tree for the entire program and runtime system. Performing another round of inlining and specialization would produce thousands of specialized S-code types — too many to author by hand, and most of which would be too esoteric to ever be actually used.

The solution, of course, is to automate the specialization process. We only generate a specialized S-code type when it is actually used by a program. The number of specialized S-code types will be limited by the number of ways programs are written, which is linear in the size of the program.

But specializing the code when we first encounter it is just JIT compiling the code. We’ve just reinvented the compiler. We might as well skip the multi-level specialization of the S-code tree and write a simple JIT compiler.


Inlinig Primitive Function Calls and Argument Evaluation

Inlining some primitives

Reconsider our model of a Lisp program as a “black box” that issues a series primitive function calls. We can eliminate some of the primitive function calls by implementing them directly within our “black box”. We inline some primitives.

Take null? as an example. Instead of constructing (null? arg) as

[primitive-funcall1
  [quote [primitive null?]]
  [argument 0]]

we add a new S-code construct, primitive-null?, and construct (null? arg) as

[primitive-null?
  [argument 0]]

We don't have to evaluate the function. We don't even have to jump to the entry point of the null? primitive. After we evaluate argument 0, we just test for null in the interpreter and return T or NIL as appropriate.

There are maybe 20 or so primitives that are used frequently enough to be worth inlining in this way. Each primitive you inline like this adds bloat to the interpreter.

Inlining simple arguments

The leaves of a tree of S-code are the atomic expressions, whereas the internal nodes of the tree are compound expressions. We can eliminate the leaves by inlining them into their parent nodes. For example if a leaf node is a lexical variable reference, we inline this into the parent node. We unroll the evaluation of the leaf node thus saving a recursive call to the interpreter and an evaluation dispatch.

Consider our previous example which we consructed as

[primitive-null?
  [argument 0]]

We further specialize primitive-null? based on its argument type into primitive-null?-argument or primitive-null?-lexical. Now our S-code becomes:

[primitive-null?-argument 0]

The leaf node [argument 0] is absorbed into the parent node [primitive-null? ...] making a new leaf node [primitive-null?-argument]. The evaluator for this S-code node simply tests if argument 0 is null and returns T or NIL as appropriate.

Compare this to the original S-code:

[funcall
  [global 'null?]
  [argument 0]]

This required two recursive calls to the interpreter, a global lookup, and a primitive function call on top of the null test. We’ve eliminated all of those. There’s not much left to do. Testing null? in the interpreter is almost as fast as testing null? in compiled code.

The number of S-code types needed to perform this inlining is the number of kinds of leaf nodes raised to the power of the number of leaves in the parent node. A call to a one-argument primitive would need specializations for the cases where the argument is a quoted value, an argument, a lexical variable or a global variable — four specializations. Calls to a two-argument primitive turn into one of sixteen specializations — the product of four for each argument. A call to a three-argument primitive would turn into one of sixty-four specializations.

We can inline all the non-compound argument evaluations, both to primitive functions and to user-defined functions. In our S-code tree, we have removed all the leaf nodes and absorbed them into their parent nodes (which have now become new leaves). The interpreter is now quite a bit faster, although still not as fast as compiled code.


Tuesday, December 31, 2024

Usual Integrations, Function Calls and Primitive Calls

Near the beginning of most MIT-Scheme files you'll see (declare (usual-integrations)). This instructs the SF program, which translates Scheme to S-code, to replace the “usual” global variables with their (compile time) values. You lose the ability to change the values of these variables, but who redefines CAR or CDR? (And if you do, just remove the declare form or add CAR and CDR to the exception list.)

Now forms such as (null? x), which used to be syntaxed into this S-code:

[funcall
   [global ’null?]
   [argument 0]]

are now syntaxed into this S-code:

[funcall
   [quote [primitive null?]]
   [argument 0]]

Recall that S-code is a tree representation of Lisp code that is walked by the interpreter. The leaves of the tree are all either quote, global, argument, or lexical, and each of these only take one or two machine instructions to eval. The “overhead” of interpretation involves getting to the leaves.

The internal nodes of an S-code tree are the compound forms: function calls and special forms. IF and PROGN (in Scheme BEGIN) are the two most frequently evaluated special forms. When we construct the S-code for a compound form, we pattern match against the subforms and specialize the S-code. Specialized S-code for a compound form is called a “superoperator”. Superoperators eliminate the eval dispatch for the subforms. The machine code for interpreting a superoperator is close to the equivalent compiled code. Let us examine some of the most important superoperators.

Funcall Argument List Elimination

In general, a function call accumulates a list of arguments and jumps to APPLY to dispatch the function. We can eliminate the overhead of doing this. First, we can eliminate the call to APPLY by having applicable objects implement the applicable interface. We use virtual function dispatch to invoke the code that implements the applicable object. Applicable objects can be applied to any number of arguments passed as a list. We can eliminate the overhead of manipulating short argument lists by spreading the arguments and adding call0, call1, call2, and call3 methods to the applicable interface. The number of arguments at any particular call site is fixed, and it is usually a small number. We eliminate the overhead of accumulating the argument list for a function call by specializing the funcall S-code object on the number of arguments.

An S-code funcall is replaced by a funcall0, funcall1, funcall2, funcall3, or funcalln depending on the number of arguments. The argument accumulation loop is unrolled in the numbered cases placing the arguments in local C# variables. The funcallx superoperators directly call the appropriate calln method in the function’s applicable interface.

Primitive Function Call

Function calls to primitive procedures are the foundation of Lisp and Scheme programs. We can look at a Lisp program as if it were a “black box” that makes a series of primitive function calls. If unoptimized, a Lisp program ought to make the same series of primitive function calls with the same values whether it is interpreted or compiled. The primitive function will run at the same speed no matter how it is called, so if compiled code is faster than interpreted, it is issuing the primitive function calls faster, taking less time between them. The time between issuing primitive function calls is the interpretation overhead, and we want to reduce that.

When we construct an S-code funcall (and funcall0, funcall1, etc.), we check if the thing being called is a literal quoted primitive function, and if it is, we replace the S-code with a primitive-funcall (or primitive-funcall0, primitive-funcall1, etc.). A primitive-funcall evaluates its arguments, but it can skip evaluating the function. It can skip the apply dispatch, too, and just jump right to the primitive entry point.

We construct (null? arg) as

[primitive-funcall1
  [quote [primitive null?]]
  [argument 0]]

The MIT-Scheme interpreter and the MIT-Scheme hardware all special case function calls with small numbers of arguments and those which call primitive procedures.

In the next post, we develop this idea beyond what MIT-Scheme already does.


Eliminating Deep Search

In general, when you look up a lexical variable, you have to do a deep search. You start with the innermost scope and work your way out until you find the variable you are looking for. This takes an absurd amount of time for each variable lookup, so we want to avoid actually doing it.

As mentioned in the previous post, we special case the two most common cases: when the variable is in the innermost scope and when the variable is in the global scope. In the other cases, when the variable is in an intermediate scope, we can flatten the scopes by copying the variable values from inner scopes to outer scopes whenever the variable is closed over. This only works because we have put the mutable variables in cells and we copy pointers to the cells.

When an S-code lambda object is created, we walk the code and find the variables that are closed over. We create a closure with a copy of the variable’s value (or a copy of its cell). In essence, we replace:

(lambda (x) (+ x y))

(assuming y is a lexical variable) with

(%make-closure ’(lambda (x) (+ x y)) y)

Once we have done this, we no longer need to deep search for lexical variables. There is only one lexical frame, it contains a copy of the variable, and we know the offset of the variable in the frame. Lexical variable lookup is now just a vector-ref.

The drawback is that we have cell-converted the mutable variables, so calling a function that contains mutable variables will allocate memory. Use of SET! makes your code expensive. Furthermore, %make-closure is linear in the number of things being closed over. But typically, closures only close over one or two items.

So between these specializations (this post and the two previous ones), we have removed the overhead of variable lookup. An expression like (lambda (x) (+ y x)) (where y is a lexical variable) is converted into S-code that looks like:

[%make-closure
  [quote (lambda ’(x)
            [funcall
              [global ’+]
              [lexical 0]
              [argument 0]])]
  [argument n]  ;; wherever ’y is in the outer scope
  ]

Evaluating variables is the bulk of the work in a Lisp program. Now both the interpreter and compiler can evaluate a variable with one or two machine instructions. Interpreted code is still slower than compiled code, but this isn’t a bottleneck anymore.

Now we look for other bottlenecks. Watch this space.


Monday, December 30, 2024

Specialized Variables

In general, when you lookup a variable, you have to do a deep search through the lexical contours. This is because someone could have captured a first class environment and evaled a define in it that shadows a variable.

However, this is a very rare case. In the vast majority of cases, the variable is either a global variable or lexical variable bound in the immediately enclosing environment. We can optimize for these cases.

Symbols refer to three different kinds of variable: if the variable is free, it refers to a global or “top-level” variable, if it is bound, it may be bound in the immediately enclosing environment (i.e., it is an argument), or it may be bound in a environment further up the lexical chain. We can determine which of these cases applies statically when we construct the S-code and select the appropriate subtype of S-code variable.

A global or top-level variable can contain a pointer to the value cell of the variable in the global environment, so variable lookup simply involves a dereference. If the variable is lexically bound, and the environment is a StatcEnvironment (or subtype), then the lexical address of the variable is known statically and cannot change (the variable cannot be shadowed), so we can store the lexical address in the S-code variable. Lookup is faster because it involves constant offsets.

The variable corresponding to argument 0 of the current lambda is by far the most commonly accessed variable, followed by argument 1. We can create special S-code types for each of these two variables in j addition to the three types for global, lexical, and the other arguments. By classifying the variables into one of these five types at syntax time, we can greatly reduce the amount of work needed at runtime to access the variable.

So consider the form (lambda (n) (lambda (x) (+ x n))). n is a lexical variable bound one level deep. x is an argument and + is a global. The S-code representation of this form will have three different variable types. The S-code for n will be a LexicalVariable with a depth of 1 and offset of 0. The eval method for LexicalVariable can walk back one level in the lexical chain and then access the zeroth element of the environment.

On the other hand, the S-code for x will be an ArgumentVariable with an argument number of 0. The eval method for ArgumentVariable can call the GetArg0 method of the environment.

Finally, the S-code for + will be a GlobalVariable with a pointer to the global value cell for +. The eval method for this type of S-code variable can simply dereference the pointer.

Between specializing environments and S-code varibles, we can greatly reduce the amount of time needed to access variables.