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Estimated hours taken: 0.5 Various fixes for problems fjh pointed out in a review. compiler/ml_code_util.m: Fix a typo. compiler/ml_optimize.m: compiler/mlds_to_c.m: Move some comments from mlds_output_assign_args to generate_assign_args. Remove mlds_output_assign_args as it is now dead code. compiler/options.m: Fix a typo, llds-optimize should be mlds-optimize. doc/user_guide.texi: Document --no-mlds-optimize.
305 lines
9.1 KiB
Mathematica
305 lines
9.1 KiB
Mathematica
%-----------------------------------------------------------------------------%
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% Copyright (C) 2000 The University of Melbourne.
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% This file may only be copied under the terms of the GNU General
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% Public License - see the file COPYING in the Mercury distribution.
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%-----------------------------------------------------------------------------%
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% File: ml_optimize.m
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% Main author: trd, fjh
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% This module runs various optimizations on the MLDS.
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%
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% Currently the only optimization is turning tailcalls into loops.
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%
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% Note that tailcall detection is done in ml_tailcall.m.
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% It might be nice to move the detection here, and do both the
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% loop transformation (in the case of self-tailcalls) and marking
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% tailcalls at the same time.
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%
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% Ultimately this module should just consist of a skeleton to traverse
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% the MLDS, and should call various optimization modules along the way.
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%
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% It would probably be a good idea to make each transformation optional.
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% Previously the tailcall transformation depended on emit_c_loops, but
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% this is a bit misleading given the documentation of emit_c_loops.
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%-----------------------------------------------------------------------------%
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:- module ml_optimize.
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:- interface.
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:- import_module mlds, io.
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:- pred optimize(mlds, mlds, io__state, io__state).
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:- mode optimize(in, out, di, uo) is det.
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%-----------------------------------------------------------------------------%
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:- implementation.
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:- import_module bool, list, require, std_util, string.
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:- import_module builtin_ops, globals.
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:- import_module ml_util, ml_code_util.
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:- type opt_info --->
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opt_info(
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globals :: globals,
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module_name :: mlds_module_name,
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entity_name :: mlds__entity_name,
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func_params :: mlds__func_params,
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context :: mlds__context
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).
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% The label name we use for the top of the loop introduced by
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% tailcall optimization.
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:- func tailcall_loop_label_name = string.
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tailcall_loop_label_name = "loop_top".
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optimize(MLDS0, MLDS) -->
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globals__io_get_globals(Globals),
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{ MLDS0 = mlds(ModuleName, ForeignCode, Imports, Defns0) },
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{ Defns = optimize_in_defns(Defns0, Globals,
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mercury_module_name_to_mlds(ModuleName)) },
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{ MLDS = mlds(ModuleName, ForeignCode, Imports, Defns) }.
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:- func optimize_in_defns(mlds__defns, globals, mlds_module_name)
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= mlds__defns.
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optimize_in_defns(Defns, Globals, ModuleName) =
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list__map(optimize_in_defn(ModuleName, Globals), Defns).
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:- func optimize_in_defn(mlds_module_name, globals, mlds__defn) = mlds__defn.
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optimize_in_defn(ModuleName, Globals, Defn0) = Defn :-
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Defn0 = mlds__defn(Name, Context, Flags, DefnBody0),
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(
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DefnBody0 = mlds__function(PredProcId, Params, FuncBody0),
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OptInfo = opt_info(Globals, ModuleName, Name, Params, Context),
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FuncBody1 = optimize_func(OptInfo, FuncBody0),
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FuncBody = optimize_in_maybe_statement(OptInfo, FuncBody1),
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DefnBody = mlds__function(PredProcId, Params, FuncBody),
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Defn = mlds__defn(Name, Context, Flags, DefnBody)
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;
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DefnBody0 = mlds__data(_, _),
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Defn = Defn0
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;
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DefnBody0 = mlds__class(ClassDefn0),
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ClassDefn0 = class_defn(Kind, Imports, BaseClasses, Implements,
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MemberDefns0),
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MemberDefns = optimize_in_defns(MemberDefns0, Globals,
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ModuleName),
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ClassDefn = class_defn(Kind, Imports, BaseClasses, Implements,
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MemberDefns),
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DefnBody = mlds__class(ClassDefn),
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Defn = mlds__defn(Name, Context, Flags, DefnBody)
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).
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:- func optimize_in_maybe_statement(opt_info,
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maybe(mlds__statement)) = maybe(mlds__statement).
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optimize_in_maybe_statement(_, no) = no.
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optimize_in_maybe_statement(OptInfo, yes(Statement0)) = yes(Statement) :-
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Statement = optimize_in_statement(OptInfo, Statement0).
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:- func optimize_in_statements(opt_info, list(mlds__statement)) =
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list(mlds__statement).
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optimize_in_statements(OptInfo, Statements) =
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list__map(optimize_in_statement(OptInfo), Statements).
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:- func optimize_in_statement(opt_info, mlds__statement) =
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mlds__statement.
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optimize_in_statement(OptInfo, statement(Stmt, Context)) =
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statement(optimize_in_stmt(OptInfo ^ context := Context, Stmt),
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Context).
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:- func optimize_in_stmt(opt_info, mlds__stmt) = mlds__stmt.
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optimize_in_stmt(OptInfo, Stmt0) = Stmt :-
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(
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Stmt0 = call(_, _, _, _, _, _),
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Stmt = optimize_in_call_stmt(OptInfo, Stmt0)
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;
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Stmt0 = block(Defns, Statements0),
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Stmt = block(Defns, optimize_in_statements(OptInfo,
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Statements0))
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;
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Stmt0 = while(Rval, Statement0, Once),
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Stmt = while(Rval, optimize_in_statement(OptInfo,
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Statement0), Once)
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;
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Stmt0 = if_then_else(Rval, Then, MaybeElse),
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Stmt = if_then_else(Rval,
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optimize_in_statement(OptInfo, Then),
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maybe_apply(optimize_in_statement(OptInfo), MaybeElse))
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;
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Stmt0 = do_commit(_),
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Stmt = Stmt0
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;
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Stmt0 = return(_),
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Stmt = Stmt0
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;
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Stmt0 = try_commit(Ref, TryGoal, HandlerGoal),
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Stmt = try_commit(Ref,
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optimize_in_statement(OptInfo, TryGoal),
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optimize_in_statement(OptInfo, HandlerGoal))
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;
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Stmt0 = label(_Label),
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Stmt = Stmt0
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;
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Stmt0 = goto(_Label),
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Stmt = Stmt0
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;
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Stmt0 = computed_goto(_Rval, _Label),
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Stmt = Stmt0
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;
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Stmt0 = atomic(_Atomic),
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Stmt = Stmt0
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).
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:- func optimize_in_call_stmt(opt_info, mlds__stmt) = mlds__stmt.
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optimize_in_call_stmt(OptInfo, Stmt0) = Stmt :-
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% If we have a self-tailcall, assign to the arguments and
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% then goto the top of the tailcall loop.
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(
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Stmt0 = call(_Signature, _FuncRval, _MaybeObject, CallArgs,
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_Results, _IsTailCall),
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can_optimize_tailcall(qual(OptInfo ^ module_name,
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OptInfo ^ entity_name), Stmt0)
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->
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CommentStatement = statement(
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atomic(comment("direct tailcall eliminated")),
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OptInfo ^ context),
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GotoStatement = statement(goto(tailcall_loop_label_name),
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OptInfo ^ context),
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OptInfo ^ func_params = mlds__func_params(FuncArgs, _RetTypes),
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generate_assign_args(OptInfo, FuncArgs, CallArgs,
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AssignStatements, AssignDefns),
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AssignVarsStatement = statement(block(AssignDefns,
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AssignStatements), OptInfo ^ context),
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CallReplaceStatements = [
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CommentStatement,
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AssignVarsStatement,
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GotoStatement
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],
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Stmt = block([], CallReplaceStatements)
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;
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Stmt = Stmt0
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).
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%----------------------------------------------------------------------------
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% Assign the specified list of rvals to the arguments.
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% This is used as part of tail recursion optimization (see above).
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:- pred generate_assign_args(opt_info, mlds__arguments, list(mlds__rval),
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list(mlds__statement), list(mlds__defn)).
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:- mode generate_assign_args(in, in, in, out, out) is det.
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generate_assign_args(_, [_|_], [], [], []) :-
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error("generate_assign_args: length mismatch").
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generate_assign_args(_, [], [_|_], [], []) :-
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error("generate_assign_args: length mismatch").
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generate_assign_args(_, [], [], [], []).
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generate_assign_args(OptInfo,
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[Name - Type | Rest], [Arg | Args], Statements, TempDefns) :-
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(
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%
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% extract the variable name
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%
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Name = data(var(VarName))
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->
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QualVarName = qual(OptInfo ^ module_name, VarName),
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(
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%
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% don't bother assigning a variable to itself
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%
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Arg = lval(var(QualVarName))
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->
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generate_assign_args(OptInfo, Rest, Args,
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Statements, TempDefns)
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;
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% Declare a temporary variable, initialized it
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% to the arg, recursively process the remaining
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% args, and then assign the temporary to the
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% parameter:
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%
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% SomeType argN__tmp_copy = new_argN_value;
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% ...
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% new_argN_value = argN_tmp_copy;
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%
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% The temporaries are needed for the case where
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% we are e.g. assigning v1, v2 to v2, v1;
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% they ensure that we don't try to reference the old
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% value of a parameter after it has already been
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% clobbered by the new value.
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string__append(VarName, "__tmp_copy", TempName),
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QualTempName = qual(OptInfo ^ module_name,
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TempName),
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Initializer = init_obj(Arg),
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TempDefn = ml_gen_mlds_var_decl(var(TempName),
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Type, Initializer, OptInfo ^ context),
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Statement = statement(
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atomic(assign(
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var(QualVarName),
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lval(var(QualTempName)))),
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OptInfo ^ context),
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generate_assign_args(OptInfo, Rest, Args, Statements0,
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TempDefns0),
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Statements = [Statement | Statements0],
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TempDefns = [TempDefn | TempDefns0]
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)
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;
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error("generate_assign_args: function param is not a var")
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).
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%----------------------------------------------------------------------------
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:- func optimize_func(opt_info, maybe(mlds__statement))
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= maybe(mlds__statement).
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optimize_func(OptInfo, MaybeStatement) =
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maybe_apply(optimize_func_stmt(OptInfo), MaybeStatement).
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:- func optimize_func_stmt(opt_info,
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mlds__statement) = (mlds__statement).
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optimize_func_stmt(OptInfo, mlds__statement(Stmt0, Context)) =
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mlds__statement(Stmt, Context) :-
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% Tailcall optimization -- if we do a self tailcall, we
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% can turn it into a loop.
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(
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stmt_contains_statement(Stmt0, Call),
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Call = mlds__statement(CallStmt, _),
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can_optimize_tailcall(
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qual(OptInfo ^ module_name, OptInfo ^ entity_name),
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CallStmt)
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->
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Comment = atomic(comment("tailcall optimized into a loop")),
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Label = label(tailcall_loop_label_name),
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Stmt = block([], [statement(Comment, Context),
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statement(Label, Context),
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statement(Stmt0, Context)])
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;
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Stmt = Stmt0
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).
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% Maps T into V, inside a maybe .
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:- func maybe_apply(func(T) = V, maybe(T)) = maybe(V).
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maybe_apply(_, no) = no.
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maybe_apply(F, yes(T)) = yes(F(T)).
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