/*
This file is part of TON Blockchain Library.
TON Blockchain Library is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as published by
the Free Software Foundation, either version 2 of the License, or
(at your option) any later version.
TON Blockchain Library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License
along with TON Blockchain Library. If not, see .
*/
#include "tolk.h"
#include "src-file.h"
#include "ast.h"
#include "ast-visitor.h"
#include "type-system.h"
#include "common/refint.h"
#include "constant-evaluator.h"
#include
/*
* This pipe is the last one operating AST: it transforms AST to IR.
* IR is described as "Op" struct. So, here AST is transformed to Ops, and then all the rest "legacy"
* kernel (initially forked from FunC) comes into play.
* Up to this point, all types have been inferred, all validity checks have been passed, etc.
* All properties in AST nodes are assigned and can be safely used (fun_ref, etc.).
* So, if execution reaches this pass, the input is (almost) correct, and code generation should succeed.
* (previously, there was a check for one variable modified twice like `(t.0, t.0) = rhs`, but after changing
* execution order of assignment to "first lhs, then lhs", it was removed for several reasons)
*
* A noticeable property for IR generation is "target_type" used to extend/shrink stack.
* Example: `var a: (int,int)? = null`. This `null` has inferred_type "null literal", but target_type "nullable tensor",
* and when it's assigned, it's "expanded" from 1 stack slot to 3 (int + int + null flag).
* Example: `fun analyze(t: (int,int)?)` and a call `analyze((1,2))`. `(1,2)` is `(int,int)` (2 stack slots),
* and when passed to target (3 slots, one for null flag), this null flag is implicitly added (zero value).
* Example: `nullableInt!`; for `nullableInt` inferred_type is `int?`, and target_type is `int`
* (this doesn't lead to stack reorganization, but in case `nullableTensor!` does)
* (inferred_type of `nullableInt!` is `int`, and its target_type depends on its usage).
* The same mechanism will work for union types in the future.
*/
namespace tolk {
class LValContext;
std::vector pre_compile_expr(AnyExprV v, CodeBlob& code, TypePtr target_type = nullptr, LValContext* lval_ctx = nullptr);
std::vector pre_compile_symbol(SrcLocation loc, const Symbol* sym, CodeBlob& code, LValContext* lval_ctx);
void process_any_statement(AnyV v, CodeBlob& code);
// The goal of VarsModificationWatcher is to detect such cases: `return (x, x += y, x)`.
// Without any changes, ops will be { _Call $2 = +($0_x, $1_y); _Return $0_x, $2, $0_x } - incorrect
// Correct will be to introduce tmp var: { _Let $3 = $0_x; _Call $2 = ...; _Return $3, $2, $0_x }
// This "introducing" is done when compiling tensors, whereas this class allows to watch vars for modification.
class VarsModificationWatcher {
struct WatchedVar {
var_idx_t ir_idx;
std::function on_modification_callback;
WatchedVar(var_idx_t ir_idx, std::function on_modification_callback)
: ir_idx(ir_idx), on_modification_callback(std::move(on_modification_callback)) {}
};
std::vector all_callbacks;
public:
bool empty() const { return all_callbacks.empty(); }
void push_callback(var_idx_t ir_idx, std::function callback) {
all_callbacks.emplace_back(ir_idx, std::move(callback));
}
void pop_callback(var_idx_t ir_idx) {
for (auto it = all_callbacks.rbegin(); it != all_callbacks.rend(); ++it) {
if (it->ir_idx == ir_idx) {
all_callbacks.erase((it + 1).base());
return;
}
}
tolk_assert(false);
}
void trigger_callbacks(const std::vector& left_lval_indices, SrcLocation loc) const {
for (const WatchedVar& w : all_callbacks) {
for (var_idx_t changed_var : left_lval_indices) {
if (w.ir_idx == changed_var) {
w.on_modification_callback(loc, w.ir_idx);
}
}
}
}
};
static VarsModificationWatcher vars_modification_watcher;
// Main goal of LValContext is to handle non-primitive lvalues. At IR level, a usual local variable
// exists, but on its change, something non-trivial should happen.
// Example: `globalVar = 9` actually does `Const $5 = 9` + `Let $6 = $5` + `SetGlob "globVar" = $6`
// Example: `tupleVar.0 = 9` actually does `Const $5 = 9` + `Let $6 = $5` + `Const $7 = 0` + `Call tupleSetAt($4, $6, $7)`
// Of course, mixing globals with tuples should also be supported.
// To achieve this, treat tupleObj inside "tupleObj.i" like "rvalue inside lvalue".
// For instance, `globalTuple.0 = 9` reads global (like rvalue), assigns 9 to tmp var, modifies tuple, writes global.
// Note, that tensors (not tuples) `tensorVar.0 = 9` do not emit anything special (unless global).
class LValContext {
// every global variable used as lvalue is registered here
// example: `globalInt = 9`, implicit var is created `$tmp = 9`, and `SetGlob "globalInt" $tmp` is done after
struct ModifiedGlobal {
GlobalVarPtr glob_ref;
std::vector lval_ir_idx; // typically 1, generally get_width_on_stack() of global var (tensors)
// for 1-slot globals int/cell/slice, assigning to them is just SETGLOB
// same for tensors, if they are fully rewritten in an expression: `gTensor = (5,6)`
void apply_fully_rewrite(CodeBlob& code, SrcLocation loc) const {
Op& op = code.emplace_back(loc, Op::_SetGlob, std::vector{}, lval_ir_idx, glob_ref);
op.set_impure_flag();
}
// for N-slot globals tensor/struct/union, assigning to their parts, like `gTensor.1 = 6`
// we need to read gTensor as a whole (0-th and 1-th component), rewrite 1-th component, and SETGLOB a whole back
void apply_partially_rewrite(CodeBlob& code, SrcLocation loc, std::vector&& was_modified_by_let) const {
LValContext local_lval;
local_lval.enter_rval_inside_lval();
std::vector local_ir_idx = pre_compile_symbol(loc, glob_ref, code, &local_lval);
for (size_t i = 0; i < local_ir_idx.size(); ++i) {
if (was_modified_by_let[i]) {
code.emplace_back(loc, Op::_Let, std::vector{local_ir_idx[i]}, std::vector{lval_ir_idx[i]});
}
}
Op& op = code.emplace_back(loc, Op::_SetGlob, std::vector{}, local_ir_idx, glob_ref);
op.set_impure_flag();
}
};
// every tensor index, when a tensor is a global, is registered here (same for structs and fields)
// example: `global v: (int, int); v.1 = 5`, implicit var is created `$tmp = 5`, and when it's modified,
// we need to partially update w; essentially, apply_partially_rewrite() above will be called
struct ModifiedFieldOfGlobal {
AnyExprV tensor_obj;
int index_at;
std::vector lval_ir_idx;
void apply(CodeBlob& code, SrcLocation loc) const {
LValContext local_lval;
local_lval.enter_rval_inside_lval();
std::vector obj_ir_idx = pre_compile_expr(tensor_obj, code, nullptr, &local_lval);
const TypeDataTensor* t_tensor = tensor_obj->inferred_type->try_as();
tolk_assert(t_tensor);
int stack_width = t_tensor->items[index_at]->get_width_on_stack();
int stack_offset = 0;
for (int i = 0; i < index_at; ++i) {
stack_offset += t_tensor->items[i]->get_width_on_stack();
}
std::vector field_ir_idx = {obj_ir_idx.begin() + stack_offset, obj_ir_idx.begin() + stack_offset + stack_width};
tolk_assert(field_ir_idx.size() == lval_ir_idx.size());
vars_modification_watcher.trigger_callbacks(field_ir_idx, loc);
code.emplace_back(loc, Op::_Let, field_ir_idx, lval_ir_idx);
local_lval.after_let(std::move(field_ir_idx), code, loc);
}
};
// every tuple index used as lvalue is registered here
// example: `t.0 = 9`, implicit var is created `$tmp = 9`, as well as `$tmp_idx = 0` and `tupleSetAt()` is done after
// for `t.0.0` if t is `[[int, ...]]`, `tupleAt()` for it is done since it's rvalue, and `tupleSetAt()` is done 2 times
struct ModifiedTupleIndex {
AnyExprV tuple_obj;
int index_at;
std::vector lval_ir_idx;
void apply(CodeBlob& code, SrcLocation loc) const {
LValContext local_lval;
local_lval.enter_rval_inside_lval();
std::vector tuple_ir_idx = pre_compile_expr(tuple_obj, code, nullptr, &local_lval);
std::vector index_ir_idx = code.create_tmp_var(TypeDataInt::create(), loc, "(tuple-idx)");
code.emplace_back(loc, Op::_IntConst, index_ir_idx, td::make_refint(index_at));
vars_modification_watcher.trigger_callbacks(tuple_ir_idx, loc);
FunctionPtr builtin_sym = lookup_global_symbol("tupleSetAt")->try_as();
code.emplace_back(loc, Op::_Call, std::vector{tuple_ir_idx}, std::vector{tuple_ir_idx[0], lval_ir_idx[0], index_ir_idx[0]}, builtin_sym);
local_lval.after_let(std::move(tuple_ir_idx), code, loc);
}
};
int level_rval_inside_lval = 0;
std::vector> modifications;
static bool vector_contains(const std::vector& ir_vars, var_idx_t ir_idx) {
for (var_idx_t var_in_vector : ir_vars) {
if (var_in_vector == ir_idx) {
return true;
}
}
return false;
}
public:
void enter_rval_inside_lval() { level_rval_inside_lval++; }
void exit_rval_inside_lval() { level_rval_inside_lval--; }
bool is_rval_inside_lval() const { return level_rval_inside_lval > 0; }
void capture_global_modification(GlobalVarPtr glob_ref, std::vector lval_ir_idx) {
modifications.emplace_back(ModifiedGlobal{glob_ref, std::move(lval_ir_idx)});
}
void capture_field_of_global_modification(AnyExprV tensor_obj, int index_at, std::vector lval_ir_idx) {
modifications.emplace_back(ModifiedFieldOfGlobal{tensor_obj, index_at, std::move(lval_ir_idx)});
}
void capture_tuple_index_modification(AnyExprV tuple_obj, int index_at, std::vector lval_ir_idx) {
modifications.emplace_back(ModifiedTupleIndex{tuple_obj, index_at, std::move(lval_ir_idx)});
}
void after_let(std::vector&& let_left_vars, CodeBlob& code, SrcLocation loc) const {
for (const auto& modification : modifications) {
if (const auto* m_glob = std::get_if(&modification)) {
int n_modified_by_let = 0;
std::vector was_modified_by_let;
was_modified_by_let.resize(m_glob->lval_ir_idx.size());
for (size_t i = 0; i < m_glob->lval_ir_idx.size(); ++i) {
if (vector_contains(let_left_vars, m_glob->lval_ir_idx[i])) {
was_modified_by_let[i] = true;
n_modified_by_let++;
}
}
if (n_modified_by_let == static_cast(m_glob->lval_ir_idx.size())) {
m_glob->apply_fully_rewrite(code, loc);
} else if (n_modified_by_let > 0) {
m_glob->apply_partially_rewrite(code, loc, std::move(was_modified_by_let));
}
} else if (const auto* m_tup = std::get_if(&modification)) {
bool was_tuple_index_modified = false;
for (var_idx_t field_ir_idx : m_tup->lval_ir_idx) {
was_tuple_index_modified |= vector_contains(let_left_vars, field_ir_idx);
}
if (was_tuple_index_modified) {
m_tup->apply(code, loc);
}
} else if (const auto* m_tens = std::get_if(&modification)) {
bool was_tensor_index_modified = false;
for (var_idx_t field_ir_idx : m_tens->lval_ir_idx) {
was_tensor_index_modified |= vector_contains(let_left_vars, field_ir_idx);
}
if (was_tensor_index_modified) {
m_tens->apply(code, loc);
}
}
}
}
};
// given `{some_expr}!`, return some_expr
static AnyExprV unwrap_not_null_operator(AnyExprV v) {
while (auto v_notnull = v->try_as()) {
v = v_notnull->get_expr();
}
return v;
}
// given `{some_expr}.{i}`, check it for pattern `some_var.0` / `some_var.0.1` / etc.
// return some_var if satisfies (it may be a local or a global var, a tensor or a tuple)
// return nullptr otherwise: `f().0` / `(v = rhs).0` / `some_var.method().0` / etc.
static V calc_sink_leftmost_obj(V v) {
AnyExprV leftmost_obj = unwrap_not_null_operator(v->get_obj());
while (auto v_dot = leftmost_obj->try_as()) {
if (!v_dot->is_target_indexed_access()) {
break;
}
leftmost_obj = unwrap_not_null_operator(v_dot->get_obj());
}
return leftmost_obj->type == ast_reference ? leftmost_obj->as() : nullptr;
}
static std::vector> pre_compile_tensor_inner(CodeBlob& code, const std::vector& args,
const TypeDataTensor* tensor_target_type, LValContext* lval_ctx) {
const int n = static_cast(args.size());
if (n == 0) { // just `()`
return {};
}
tolk_assert(!tensor_target_type || tensor_target_type->size() == n);
if (n == 1) { // just `(x)`: even if x is modified (e.g. `f(x=x+2)`), there are no next arguments
TypePtr child_target_type = tensor_target_type ? tensor_target_type->items[0] : nullptr;
return {pre_compile_expr(args[0], code, child_target_type, lval_ctx)};
}
// the purpose is to handle such cases: `return (x, x += y, x)`
// without this, ops will be { _Call $2 = +($0_x, $1_y); _Return $0_x, $2, $0_x } - invalid
// with this, ops will be { _Let $3 = $0_x; _Call $2 = ...; _Return $3, $2, $0_x } - valid, tmp var for x
// how it works: for every arg, after transforming to ops, start tracking ir_idx inside it
// on modification attempt, create Op::_Let to a tmp var and replace old ir_idx with tmp_idx in result
struct WatchingVarList {
std::vector watched_vars;
std::vector> res_lists;
explicit WatchingVarList(int n_args) {
res_lists.reserve(n_args);
}
bool is_watched(var_idx_t ir_idx) const {
return std::find(watched_vars.begin(), watched_vars.end(), ir_idx) != watched_vars.end();
}
void add_and_watch_modifications(std::vector&& vars_of_ith_arg, CodeBlob& code) {
for (var_idx_t ir_idx : vars_of_ith_arg) {
if (!code.vars[ir_idx].name.empty() && !is_watched(ir_idx)) {
watched_vars.emplace_back(ir_idx);
vars_modification_watcher.push_callback(ir_idx, [this, &code](SrcLocation loc, var_idx_t ir_idx) {
on_var_modified(ir_idx, loc, code);
});
}
}
res_lists.emplace_back(std::move(vars_of_ith_arg));
}
void on_var_modified(var_idx_t ir_idx, SrcLocation loc, CodeBlob& code) {
tolk_assert(is_watched(ir_idx));
std::vector tmp_idx_arr = code.create_tmp_var(code.vars[ir_idx].v_type, loc, "(pre-modified)");
tolk_assert(tmp_idx_arr.size() == 1);
var_idx_t tmp_idx = tmp_idx_arr[0];
code.emplace_back(loc, Op::_Let, std::vector{tmp_idx}, std::vector{ir_idx});
for (std::vector& prev_vars : res_lists) {
std::replace(prev_vars.begin(), prev_vars.end(), ir_idx, tmp_idx);
}
}
std::vector> clear_and_stop_watching() {
for (var_idx_t ir_idx : watched_vars) {
vars_modification_watcher.pop_callback(ir_idx);
}
watched_vars.clear();
return std::move(res_lists);
}
};
WatchingVarList watched_vars(n);
for (int arg_idx = 0; arg_idx < n; ++arg_idx) {
TypePtr child_target_type = tensor_target_type ? tensor_target_type->items[arg_idx] : nullptr;
std::vector vars_of_ith_arg = pre_compile_expr(args[arg_idx], code, child_target_type, lval_ctx);
watched_vars.add_and_watch_modifications(std::move(vars_of_ith_arg), code);
}
return watched_vars.clear_and_stop_watching();
}
static std::vector pre_compile_tensor(CodeBlob& code, const std::vector& args,
LValContext* lval_ctx = nullptr) {
std::vector types_list;
types_list.reserve(args.size());
for (AnyExprV item : args) {
types_list.push_back(item->inferred_type);
}
const TypeDataTensor* tensor_target_type = TypeDataTensor::create(std::move(types_list))->try_as();
std::vector> res_lists = pre_compile_tensor_inner(code, args, tensor_target_type, lval_ctx);
std::vector res;
for (const std::vector& list : res_lists) {
res.insert(res.end(), list.cbegin(), list.cend());
}
return res;
}
static std::vector pre_compile_let(CodeBlob& code, AnyExprV lhs, AnyExprV rhs, SrcLocation loc) {
// [lhs] = [rhs]; since type checking is ok, it's the same as "lhs = rhs"
if (lhs->type == ast_typed_tuple && rhs->type == ast_typed_tuple) {
// note: there are no type transitions (adding nullability flag, etc.), since only 1-slot elements allowed in tuples
LValContext local_lval;
std::vector left = pre_compile_tensor(code, lhs->as()->get_items(), &local_lval);
vars_modification_watcher.trigger_callbacks(left, loc);
std::vector rvect = pre_compile_tensor(code, rhs->as()->get_items());
code.emplace_back(loc, Op::_Let, left, rvect);
local_lval.after_let(std::move(left), code, loc);
std::vector right = code.create_tmp_var(TypeDataTuple::create(), loc, "(tuple)");
code.emplace_back(lhs->loc, Op::_Tuple, right, std::move(rvect));
return right;
}
// [lhs] = rhs; it's un-tuple to N left vars
if (lhs->type == ast_typed_tuple) {
LValContext local_lval;
std::vector left = pre_compile_tensor(code, lhs->as()->get_items(), &local_lval);
vars_modification_watcher.trigger_callbacks(left, loc);
std::vector right = pre_compile_expr(rhs, code, nullptr);
const TypeDataTypedTuple* inferred_tuple = rhs->inferred_type->try_as();
std::vector types_list = inferred_tuple->items;
std::vector rvect = code.create_tmp_var(TypeDataTensor::create(std::move(types_list)), rhs->loc, "(unpack-tuple)");
code.emplace_back(lhs->loc, Op::_UnTuple, rvect, std::move(right));
code.emplace_back(loc, Op::_Let, left, rvect);
local_lval.after_let(std::move(left), code, loc);
return right;
}
// small optimization: `var x = rhs` or `local_var = rhs` (90% cases), LValContext not needed actually
if (lhs->type == ast_local_var_lhs || (lhs->type == ast_reference && lhs->as()->sym->try_as())) {
std::vector left = pre_compile_expr(lhs, code, nullptr); // effectively, local_var->ir_idx
vars_modification_watcher.trigger_callbacks(left, loc);
std::vector right = pre_compile_expr(rhs, code, lhs->inferred_type);
code.emplace_back(loc, Op::_Let, std::move(left), right);
return right;
}
// lhs = rhs
LValContext local_lval;
std::vector left = pre_compile_expr(lhs, code, nullptr, &local_lval);
vars_modification_watcher.trigger_callbacks(left, loc);
std::vector right = pre_compile_expr(rhs, code, lhs->inferred_type);
code.emplace_back(loc, Op::_Let, left, right);
local_lval.after_let(std::move(left), code, loc);
return right;
}
static std::vector gen_op_call(CodeBlob& code, TypePtr ret_type, SrcLocation loc,
std::vector&& args_vars, FunctionPtr fun_ref, const char* debug_desc) {
std::vector rvect = code.create_tmp_var(ret_type, loc, debug_desc);
Op& op = code.emplace_back(loc, Op::_Call, rvect, std::move(args_vars), fun_ref);
if (!fun_ref->is_marked_as_pure()) {
op.set_impure_flag();
}
return rvect;
}
// "Transition to target (runtime) type" is the following process.
// Imagine `fun analyze(t: (int,int)?)` and a call `analyze((1,2))`.
// `(1,2)` (inferred_type) is 2 stack slots, but `t` (target_type) is 3 (one for null-flag).
// So, this null flag should be implicitly added (non-zero, since a variable is not null).
// Another example: `var t: (int, int)? = null`.
// `null` (inferred_type) is 1 stack slots, but target_type is 3, we should add 2 nulls.
// Another example: `var t1 = (1, null); var t2: (int, (int,int)?) = t1;`.
// Then t1's rvect is 2 vars (1 and null), but t1's `null` should be converted to 3 stack slots (resulting in 4 total).
// The same mechanism will work for union types in the future.
// Here rvect is a list of IR vars for inferred_type, probably patched due to target_type.
GNU_ATTRIBUTE_NOINLINE
static std::vector transition_expr_to_runtime_type_impl(std::vector&& rvect, CodeBlob& code, TypePtr original_type, TypePtr target_type, SrcLocation loc) {
// pass `T` to `T`
// could occur for passing tensor `(..., T, ...)` to `(..., T, ...)` while traversing tensor's components
if (target_type == original_type) {
return rvect;
}
int target_w = target_type->get_width_on_stack();
const TypeDataNullable* t_nullable = target_type->try_as();
const TypeDataNullable* o_nullable = original_type->try_as();
// handle `never`
// it may occur due to smart cast and in unreachable branches
// we can't do anything reasonable here, but (hopefully) execution will never reach this point, and stack won't be polluted
if (original_type == TypeDataNever::create()) {
std::vector dummy_rvect;
dummy_rvect.reserve(target_w);
for (int i = 0; i < target_w; ++i) {
dummy_rvect.push_back(code.create_tmp_var(TypeDataUnknown::create(), loc, "(never)")[0]);
}
return dummy_rvect;
}
if (target_type == TypeDataNever::create()) {
return {};
}
// pass `null` to `T?`
// for primitives like `int?`, no changes in rvect, null occupies the same TVM slot
// for tensors like `(int,int)?`, `null` is represented as N nulls + 1 null flag, insert N nulls
if (t_nullable && original_type == TypeDataNullLiteral::create()) {
tolk_assert(rvect.size() == 1);
if (target_w == 1 && !t_nullable->is_primitive_nullable()) { // `null` to `()?`
rvect = code.create_tmp_var(TypeDataInt::create(), loc, "(NNFlag)");
code.emplace_back(loc, Op::_IntConst, rvect, td::make_refint(0));
}
if (target_w > 1) {
FunctionPtr builtin_sym = lookup_global_symbol("__null")->try_as();
rvect.reserve(target_w + 1);
for (int i = 1; i < target_w - 1; ++i) {
std::vector ith_null = code.create_tmp_var(TypeDataNullLiteral::create(), loc, "(null-literal)");
code.emplace_back(loc, Op::_Call, ith_null, std::vector{}, builtin_sym);
rvect.push_back(ith_null[0]);
}
std::vector null_flag_ir = code.create_tmp_var(TypeDataInt::create(), loc, "(NNFlag)");
var_idx_t null_flag_ir_idx = null_flag_ir[0];
code.emplace_back(loc, Op::_IntConst, std::move(null_flag_ir), td::make_refint(0));
rvect.push_back(null_flag_ir_idx);
}
return rvect;
}
// pass `T` to `T?`
// for primitives like `int?`, no changes in rvect: `int` and `int?` occupy the same TVM slot (null is represented as NULL TVM value)
// for passing `(int, int)` to `(int, int)?` / `(int, null)` to `(int, (int,int)?)?`, add a null flag equals to 0
if (t_nullable && !o_nullable) {
if (!t_nullable->is_primitive_nullable()) {
rvect = transition_expr_to_runtime_type_impl(std::move(rvect), code, original_type, t_nullable->inner, loc);
tolk_assert(target_w == static_cast(rvect.size() + 1));
std::vector null_flag_ir = code.create_tmp_var(TypeDataInt::create(), loc, "(NNFlag)");
var_idx_t null_flag_ir_idx = null_flag_ir[0];
code.emplace_back(loc, Op::_IntConst, std::move(null_flag_ir), td::make_refint(-1));
rvect.push_back(null_flag_ir_idx);
}
return rvect;
}
// pass `T1?` to `T2?`
// for example, `int8?` to `int16?`
// transition inner types, leaving nullable flag unchanged for tensors
if (t_nullable && o_nullable) {
if (target_w > 1) {
var_idx_t null_flag_ir_idx = rvect.back();
rvect.pop_back();
rvect = transition_expr_to_runtime_type_impl(std::move(rvect), code, o_nullable->inner, t_nullable->inner, loc);
rvect.push_back(null_flag_ir_idx);
}
return rvect;
}
// pass `T?` to `null`
// it may occur due to smart cast, when a `T?` variable is guaranteed to be always null
// (for instance, always-null `(int,int)?` will be represented as 1 TVM NULL value, not 3)
if (target_type == TypeDataNullLiteral::create() && original_type->can_rhs_be_assigned(target_type)) {
tolk_assert(o_nullable || original_type == TypeDataUnknown::create());
if (o_nullable && !o_nullable->is_primitive_nullable()) {
FunctionPtr builtin_sym = lookup_global_symbol("__null")->try_as();
rvect = code.create_tmp_var(TypeDataNullLiteral::create(), loc, "(null-literal)");
code.emplace_back(loc, Op::_Call, rvect, std::vector{}, builtin_sym);
}
return rvect;
}
// pass `T?` to `T` (or, more generally, `T1?` to `T2`)
// it may occur due to operator `!` or smart cast
// for primitives like `int?`, no changes in rvect
// for passing `(int, int)?` to `(int, int)`, drop the null flag from the tail
// for complex scenarios like passing `(int, (int,int)?)?` to `(int, null)`, recurse the call
// (it may occur on `someF(t = (3,null))` when `(3,null)` at first targeted to lhs, but actually its result is rhs)
if (!t_nullable && o_nullable) {
if (!o_nullable->is_primitive_nullable()) {
rvect.pop_back();
rvect = transition_expr_to_runtime_type_impl(std::move(rvect), code, original_type->try_as()->inner, target_type, loc);
}
return rvect;
}
// pass `bool` to `int`
// in code, it's done via `as` operator, like `boolVar as int`
// no changes in rvect, boolVar is guaranteed to be -1 or 0 at TVM level
if (target_type == TypeDataInt::create() && original_type == TypeDataBool::create()) {
return rvect;
}
// pass something to `unknown`
// probably, it comes from `_ = rhs`, type of `_` is unknown, it's target_type of rhs
// no changes in rvect
if (target_type == TypeDataUnknown::create()) {
return rvect;
}
// pass `unknown` to something
// probably, it comes from `arg` in exception, it's inferred as `unknown` and could be cast to any value
if (original_type == TypeDataUnknown::create()) {
tolk_assert(rvect.size() == 1);
return rvect;
}
// pass tensor to tensor, e.g. `(1, null)` to `(int, slice?)` / `(1, null)` to `(int, (int,int)?)`
// every element of rhs tensor should be transitioned
if (target_type->try_as() && original_type->try_as()) {
const TypeDataTensor* target_tensor = target_type->try_as();
const TypeDataTensor* inferred_tensor = original_type->try_as();
tolk_assert(target_tensor->size() == inferred_tensor->size());
tolk_assert(inferred_tensor->get_width_on_stack() == static_cast(rvect.size()));
std::vector result_rvect;
result_rvect.reserve(target_w);
int stack_offset = 0;
for (int i = 0; i < inferred_tensor->size(); ++i) {
int ith_w = inferred_tensor->items[i]->get_width_on_stack();
std::vector rvect_i{rvect.begin() + stack_offset, rvect.begin() + stack_offset + ith_w};
std::vector result_i = transition_expr_to_runtime_type_impl(std::move(rvect_i), code, inferred_tensor->items[i], target_tensor->items[i], loc);
result_rvect.insert(result_rvect.end(), result_i.begin(), result_i.end());
stack_offset += ith_w;
}
return result_rvect;
}
// pass tuple to tuple, e.g. `[1, null]` to `[int, int?]` / `[1, null]` to `[int, [int?,int?]?]`
// to changes to rvect, since tuples contain only 1-slot elements
if (target_type->try_as() && original_type->try_as()) {
tolk_assert(target_type->get_width_on_stack() == original_type->get_width_on_stack());
return rvect;
}
throw Fatal("unhandled transition_expr_to_runtime_type_impl() combination");
}
// invoke the function above only if potentially needed to
// (if an expression is targeted to another type)
#ifndef TOLK_DEBUG
GNU_ATTRIBUTE_ALWAYS_INLINE
#endif
static std::vector transition_to_target_type(std::vector&& rvect, CodeBlob& code, TypePtr target_type, AnyExprV v) {
if (target_type != nullptr && target_type != v->inferred_type) {
rvect = transition_expr_to_runtime_type_impl(std::move(rvect), code, v->inferred_type, target_type, v->loc);
}
return rvect;
}
// the second overload of the same function, invoke impl only when original and target differ
#ifndef TOLK_DEBUG
GNU_ATTRIBUTE_ALWAYS_INLINE
#endif
static std::vector transition_to_target_type(std::vector&& rvect, CodeBlob& code, TypePtr original_type, TypePtr target_type, SrcLocation loc) {
if (target_type != original_type) {
rvect = transition_expr_to_runtime_type_impl(std::move(rvect), code, original_type, target_type, loc);
}
return rvect;
}
std::vector pre_compile_symbol(SrcLocation loc, const Symbol* sym, CodeBlob& code, LValContext* lval_ctx) {
if (GlobalVarPtr glob_ref = sym->try_as()) {
// handle `globalVar = rhs` / `mutate globalVar`
if (lval_ctx && !lval_ctx->is_rval_inside_lval()) {
std::vector lval_ir_idx = code.create_tmp_var(glob_ref->declared_type, loc, "(lval-glob)");
lval_ctx->capture_global_modification(glob_ref, lval_ir_idx);
return lval_ir_idx;
}
// `globalVar` is used for reading, just create local IR var to represent its value, Op GlobVar will fill it
// note, that global tensors are stored as a tuple an unpacked to N vars on read, N determined by declared_type
std::vector local_ir_idx = code.create_var(glob_ref->declared_type, loc, "g_" + glob_ref->name);
code.emplace_back(loc, Op::_GlobVar, local_ir_idx, std::vector{}, glob_ref);
if (lval_ctx) { // `globalVar.0 = rhs`, globalVar is rval inside lval
lval_ctx->capture_global_modification(glob_ref, local_ir_idx);
}
return local_ir_idx;
}
if (GlobalConstPtr const_ref = sym->try_as()) {
if (const_ref->is_int_const()) {
std::vector rvect = code.create_tmp_var(TypeDataInt::create(), loc, "(glob-const)");
code.emplace_back(loc, Op::_IntConst, rvect, const_ref->as_int_const());
return rvect;
} else {
std::vector rvect = code.create_tmp_var(TypeDataSlice::create(), loc, "(glob-const)");
code.emplace_back(loc, Op::_SliceConst, rvect, const_ref->as_slice_const());
return rvect;
}
}
if (FunctionPtr fun_ref = sym->try_as()) {
std::vector rvect = code.create_tmp_var(fun_ref->inferred_full_type, loc, "(glob-var-fun)");
code.emplace_back(loc, Op::_GlobVar, rvect, std::vector{}, fun_ref);
return rvect;
}
if (LocalVarPtr var_ref = sym->try_as()) {
#ifdef TOLK_DEBUG
tolk_assert(static_cast(var_ref->ir_idx.size()) == var_ref->declared_type->get_width_on_stack());
#endif
return var_ref->ir_idx;
}
throw Fatal("pre_compile_symbol");
}
static std::vector process_reference(V v, CodeBlob& code, TypePtr target_type, LValContext* lval_ctx) {
std::vector rvect = pre_compile_symbol(v->loc, v->sym, code, lval_ctx);
// a local variable might be smart cast at this point, for example we're in `if (v != null)`
// it means that we must drop the null flag (if it's a tensor), or maybe perform other stack transformations
// (from original var_ref->ir_idx to fit smart cast)
if (LocalVarPtr var_ref = v->sym->try_as()) {
// note, inside `if (v != null)` when `v` is used for writing, v->inferred_type is an original (declared_type)
// (smart casts apply only for rvalue, not for lvalue, we don't check it here, it's a property of inferring)
rvect = transition_to_target_type(std::move(rvect), code, var_ref->declared_type, v->inferred_type, v->loc);
}
return transition_to_target_type(std::move(rvect), code, target_type, v);
}
static std::vector process_assignment(V v, CodeBlob& code, TypePtr target_type) {
AnyExprV lhs = v->get_lhs();
AnyExprV rhs = v->get_rhs();
if (auto lhs_decl = lhs->try_as()) {
std::vector rvect = pre_compile_let(code, lhs_decl->get_expr(), rhs, v->loc);
return transition_to_target_type(std::move(rvect), code, target_type, v);
} else {
std::vector rvect = pre_compile_let(code, lhs, rhs, v->loc);
// now rvect contains rhs IR vars constructed to fit lhs (for correct assignment, lhs type was target_type for rhs)
// but the type of `lhs = rhs` is RHS (see type inferring), so rvect now should fit rhs->inferred_type (= v->inferred_type)
// example: `t1 = t2 = null`, we're at `t2 = null`, earlier declared t1: `int?`, t2: `(int,int)?`
// currently "null" matches t2 (3 null slots), but type of this assignment is "plain null" (1 slot) assigned later to t1
rvect = transition_to_target_type(std::move(rvect), code, lhs->inferred_type, v->inferred_type, v->loc);
return transition_to_target_type(std::move(rvect), code, target_type, v);
}
}
static std::vector process_set_assign(V v, CodeBlob& code, TypePtr target_type) {
// for "a += b", emulate "a = a + b"
// seems not beautiful, but it works; probably, this transformation should be done at AST level in advance
std::string_view calc_operator = v->operator_name; // "+" for operator +=
auto v_apply = createV(v->loc, calc_operator, static_cast(v->tok - 1), v->get_lhs(), v->get_rhs());
v_apply->assign_inferred_type(v->inferred_type);
v_apply->assign_fun_ref(v->fun_ref);
std::vector rvect = pre_compile_let(code, v->get_lhs(), v_apply, v->loc);
return transition_to_target_type(std::move(rvect), code, target_type, v);
}
static std::vector process_binary_operator(V v, CodeBlob& code, TypePtr target_type) {
TokenType t = v->tok;
if (v->fun_ref) { // almost all operators, fun_ref was assigned at type inferring
std::vector args_vars = pre_compile_tensor(code, {v->get_lhs(), v->get_rhs()});
std::vector rvect = gen_op_call(code, v->inferred_type, v->loc, std::move(args_vars), v->fun_ref, "(binary-op)");
return transition_to_target_type(std::move(rvect), code, target_type, v);
}
if (t == tok_logical_and || t == tok_logical_or) {
// do the following transformations:
// a && b -> a ? (b != 0) : 0
// a || b -> a ? 1 : (b != 0)
AnyExprV v_0 = createV(v->loc, td::make_refint(0), "0");
v_0->mutate()->assign_inferred_type(TypeDataInt::create());
AnyExprV v_1 = createV(v->loc, td::make_refint(-1), "-1");
v_1->mutate()->assign_inferred_type(TypeDataInt::create());
auto v_b_ne_0 = createV(v->loc, "!=", tok_neq, v->get_rhs(), v_0);
v_b_ne_0->mutate()->assign_inferred_type(TypeDataInt::create());
v_b_ne_0->mutate()->assign_fun_ref(lookup_global_symbol("_!=_")->try_as());
std::vector cond = pre_compile_expr(v->get_lhs(), code, nullptr);
tolk_assert(cond.size() == 1);
std::vector rvect = code.create_tmp_var(v->inferred_type, v->loc, "(ternary)");
Op& if_op = code.emplace_back(v->loc, Op::_If, cond);
code.push_set_cur(if_op.block0);
code.emplace_back(v->loc, Op::_Let, rvect, pre_compile_expr(t == tok_logical_and ? v_b_ne_0 : v_1, code, nullptr));
code.close_pop_cur(v->loc);
code.push_set_cur(if_op.block1);
code.emplace_back(v->loc, Op::_Let, rvect, pre_compile_expr(t == tok_logical_and ? v_0 : v_b_ne_0, code, nullptr));
code.close_pop_cur(v->loc);
return transition_to_target_type(std::move(rvect), code, target_type, v);
}
throw UnexpectedASTNodeType(v, "process_binary_operator");
}
static std::vector process_unary_operator(V v, CodeBlob& code, TypePtr target_type) {
std::vector rhs_vars = pre_compile_expr(v->get_rhs(), code, nullptr);
std::vector rvect = gen_op_call(code, v->inferred_type, v->loc, std::move(rhs_vars), v->fun_ref, "(unary-op)");
return transition_to_target_type(std::move(rvect), code, target_type, v);
}
static std::vector process_ternary_operator(V v, CodeBlob& code, TypePtr target_type) {
std::vector cond = pre_compile_expr(v->get_cond(), code, nullptr);
tolk_assert(cond.size() == 1);
std::vector rvect = code.create_tmp_var(v->inferred_type, v->loc, "(cond)");
if (v->get_cond()->is_always_true) {
code.emplace_back(v->get_when_true()->loc, Op::_Let, rvect, pre_compile_expr(v->get_when_true(), code, v->inferred_type));
} else if (v->get_cond()->is_always_false) {
code.emplace_back(v->get_when_false()->loc, Op::_Let, rvect, pre_compile_expr(v->get_when_false(), code, v->inferred_type));
} else {
Op& if_op = code.emplace_back(v->loc, Op::_If, cond);
code.push_set_cur(if_op.block0);
code.emplace_back(v->get_when_true()->loc, Op::_Let, rvect, pre_compile_expr(v->get_when_true(), code, v->inferred_type));
code.close_pop_cur(v->get_when_true()->loc);
code.push_set_cur(if_op.block1);
code.emplace_back(v->get_when_false()->loc, Op::_Let, rvect, pre_compile_expr(v->get_when_false(), code, v->inferred_type));
code.close_pop_cur(v->get_when_false()->loc);
}
return transition_to_target_type(std::move(rvect), code, target_type, v);
}
static std::vector process_cast_as_operator(V v, CodeBlob& code, TypePtr target_type, LValContext* lval_ctx) {
TypePtr child_target_type = v->cast_to_type;
std::vector rvect = pre_compile_expr(v->get_expr(), code, child_target_type, lval_ctx);
return transition_to_target_type(std::move(rvect), code, target_type, v);
}
static std::vector process_not_null_operator(V v, CodeBlob& code, TypePtr target_type, LValContext* lval_ctx) {
TypePtr child_target_type = v->get_expr()->inferred_type;
if (const auto* as_nullable = child_target_type->try_as()) {
child_target_type = as_nullable->inner;
}
std::vector rvect = pre_compile_expr(v->get_expr(), code, child_target_type, lval_ctx);
return transition_to_target_type(std::move(rvect), code, target_type, v);
}
static std::vector process_is_null_check(V v, CodeBlob& code, TypePtr target_type) {
std::vector expr_ir_idx = pre_compile_expr(v->get_expr(), code, nullptr);
std::vector isnull_ir_idx = code.create_tmp_var(TypeDataBool::create(), v->loc, "(is-null)");
TypePtr expr_type = v->get_expr()->inferred_type;
if (const TypeDataNullable* t_nullable = expr_type->try_as()) {
if (!t_nullable->is_primitive_nullable()) {
std::vector zero_ir_idx = code.create_tmp_var(TypeDataInt::create(), v->loc, "(zero)");
code.emplace_back(v->loc, Op::_IntConst, zero_ir_idx, td::make_refint(0));
FunctionPtr eq_sym = lookup_global_symbol("_==_")->try_as();
code.emplace_back(v->loc, Op::_Call, isnull_ir_idx, std::vector{expr_ir_idx.back(), zero_ir_idx[0]}, eq_sym);
} else {
FunctionPtr builtin_sym = lookup_global_symbol("__isNull")->try_as();
code.emplace_back(v->loc, Op::_Call, isnull_ir_idx, expr_ir_idx, builtin_sym);
}
} else {
bool always_null = expr_type == TypeDataNullLiteral::create();
code.emplace_back(v->loc, Op::_IntConst, isnull_ir_idx, td::make_refint(always_null ? -1 : 0));
}
if (v->is_negated) {
FunctionPtr not_sym = lookup_global_symbol("!b_")->try_as();
code.emplace_back(v->loc, Op::_Call, isnull_ir_idx, std::vector{isnull_ir_idx}, not_sym);
}
return transition_to_target_type(std::move(isnull_ir_idx), code, target_type, v);
}
static std::vector process_dot_access(V v, CodeBlob& code, TypePtr target_type, LValContext* lval_ctx) {
// it's NOT a method call `t.tupleSize()` (since such cases are handled by process_function_call)
// it's `t.0`, `getUser().id`, and `t.tupleSize` (as a reference, not as a call)
if (!v->is_target_fun_ref()) {
TypePtr obj_type = v->get_obj()->inferred_type;
int index_at = std::get(v->target);
// `tensorVar.0`
if (const auto* t_tensor = obj_type->try_as()) {
// handle `tensorVar.0 = rhs` if tensors is a global, special case, then the global will be read on demand
if (lval_ctx && !lval_ctx->is_rval_inside_lval()) {
if (auto sink = calc_sink_leftmost_obj(v); sink && sink->sym->try_as()) {
std::vector lval_ir_idx = code.create_tmp_var(v->inferred_type, v->loc, "(lval-global-tensor)");
lval_ctx->capture_field_of_global_modification(v->get_obj(), index_at, lval_ir_idx);
return lval_ir_idx;
}
}
// since a tensor of N elems are N vars on a stack actually, calculate offset
std::vector lhs_vars = pre_compile_expr(v->get_obj(), code, nullptr, lval_ctx);
int stack_width = t_tensor->items[index_at]->get_width_on_stack();
int stack_offset = 0;
for (int i = 0; i < index_at; ++i) {
stack_offset += t_tensor->items[i]->get_width_on_stack();
}
std::vector rvect{lhs_vars.begin() + stack_offset, lhs_vars.begin() + stack_offset + stack_width};
// a tensor index might be smart cast at this point, for example we're in `if (t.1 != null)`
// it means that we must drop the null flag (if `t.1` is a tensor), or maybe perform other stack transformations
// (from original rvect = (vars of t.1) to fit smart cast)
rvect = transition_to_target_type(std::move(rvect), code, t_tensor->items[index_at], v->inferred_type, v->loc);
return transition_to_target_type(std::move(rvect), code, target_type, v);
}
// `tupleVar.0`
if (obj_type->try_as() || obj_type->try_as()) {
// handle `tupleVar.0 = rhs`, "0 SETINDEX" will be called when this was is modified
if (lval_ctx && !lval_ctx->is_rval_inside_lval() && calc_sink_leftmost_obj(v)) {
std::vector lval_ir_idx = code.create_tmp_var(v->inferred_type, v->loc, "(lval-tuple-field)");
lval_ctx->capture_tuple_index_modification(v->get_obj(), index_at, lval_ir_idx);
return lval_ir_idx;
}
// `tupleVar.0` as rvalue: the same as "tupleAt(tupleVar, 0)" written in terms of IR vars
std::vector tuple_ir_idx = pre_compile_expr(v->get_obj(), code);
std::vector index_ir_idx = code.create_tmp_var(TypeDataInt::create(), v->get_identifier()->loc, "(tuple-idx)");
code.emplace_back(v->loc, Op::_IntConst, index_ir_idx, td::make_refint(index_at));
std::vector field_ir_idx = code.create_tmp_var(v->inferred_type, v->loc, "(tuple-field)");
tolk_assert(tuple_ir_idx.size() == 1 && field_ir_idx.size() == 1); // tuples contain only 1-slot values
FunctionPtr builtin_sym = lookup_global_symbol("tupleAt")->try_as();
code.emplace_back(v->loc, Op::_Call, field_ir_idx, std::vector{tuple_ir_idx[0], index_ir_idx[0]}, builtin_sym);
if (lval_ctx && calc_sink_leftmost_obj(v)) { // `tupleVar.0.1 = rhs`, then `tupleVar.0` is rval inside lval
lval_ctx->capture_tuple_index_modification(v->get_obj(), index_at, field_ir_idx);
}
// like tensor index, `tupleVar.1` also might be smart cast, for example we're in `if (tupleVar.1 != null)`
// but since tuple's elements are only 1-slot width (no tensors and unions), no stack transformations required
return transition_to_target_type(std::move(field_ir_idx), code, target_type, v);
}
tolk_assert(false);
}
// okay, v->target refs a function, like `obj.method`, filled at type inferring
// (currently, nothing except a global function can be referenced, no object-scope methods exist)
FunctionPtr fun_ref = std::get(v->target);
tolk_assert(fun_ref);
std::vector rvect = pre_compile_symbol(v->loc, fun_ref, code, lval_ctx);
return transition_to_target_type(std::move(rvect), code, target_type, v);
}
static std::vector process_function_call(V v, CodeBlob& code, TypePtr target_type) {
// v is `globalF(args)` / `globalF(args)` / `obj.method(args)` / `local_var(args)` / `getF()(args)`
FunctionPtr fun_ref = v->fun_maybe;
if (!fun_ref) {
// it's `local_var(args)`, treat args like a tensor:
// 1) when variables are modified like `local_var(x, x += 2, x)`, regular mechanism of watching automatically works
// 2) when `null` is passed to `(int, int)?`, or any other type transitions, it automatically works
std::vector args;
args.reserve(v->get_num_args());
for (int i = 0; i < v->get_num_args(); ++i) {
args.push_back(v->get_arg(i)->get_expr());
}
std::vector params_types = v->get_callee()->inferred_type->try_as()->params_types;
const TypeDataTensor* tensor_tt = TypeDataTensor::create(std::move(params_types))->try_as();
std::vector> vars_per_arg = pre_compile_tensor_inner(code, args, tensor_tt, nullptr);
std::vector args_vars;
for (const std::vector& list : vars_per_arg) {
args_vars.insert(args_vars.end(), list.cbegin(), list.cend());
}
std::vector tfunc = pre_compile_expr(v->get_callee(), code, nullptr);
tolk_assert(tfunc.size() == 1);
args_vars.push_back(tfunc[0]);
std::vector rvect = code.create_tmp_var(v->inferred_type, v->loc, "(call-ind)");
Op& op = code.emplace_back(v->loc, Op::_CallInd, rvect, std::move(args_vars));
op.set_impure_flag();
return transition_to_target_type(std::move(rvect), code, target_type, v);
}
int delta_self = v->is_dot_call();
AnyExprV obj_leftmost = nullptr;
std::vector args;
args.reserve(delta_self + v->get_num_args());
if (delta_self) {
args.push_back(v->get_dot_obj());
obj_leftmost = v->get_dot_obj();
while (obj_leftmost->type == ast_function_call && obj_leftmost->as()->is_dot_call() && obj_leftmost->as()->fun_maybe && obj_leftmost->as()->fun_maybe->does_return_self()) {
obj_leftmost = obj_leftmost->as()->get_dot_obj();
}
}
for (int i = 0; i < v->get_num_args(); ++i) {
args.push_back(v->get_arg(i)->get_expr());
}
// the purpose of tensor_tt ("tensor target type") is to transition `null` to `(int, int)?` and so on
// the purpose of calling `pre_compile_tensor_inner` is to have 0-th IR vars to handle return self
std::vector params_types = fun_ref->inferred_full_type->try_as()->params_types;
const TypeDataTensor* tensor_tt = TypeDataTensor::create(std::move(params_types))->try_as();
std::vector> vars_per_arg = pre_compile_tensor_inner(code, args, tensor_tt, nullptr);
TypePtr op_call_type = v->inferred_type;
TypePtr real_ret_type = v->inferred_type;
if (delta_self && fun_ref->does_return_self()) {
real_ret_type = TypeDataVoid::create();
if (!fun_ref->parameters[0].is_mutate_parameter()) {
op_call_type = TypeDataVoid::create();
}
}
if (fun_ref->has_mutate_params()) {
std::vector types_list;
for (int i = 0; i < delta_self + v->get_num_args(); ++i) {
if (fun_ref->parameters[i].is_mutate_parameter()) {
types_list.push_back(fun_ref->parameters[i].declared_type);
}
}
types_list.push_back(real_ret_type);
op_call_type = TypeDataTensor::create(std::move(types_list));
}
std::vector args_vars;
for (const std::vector& list : vars_per_arg) {
args_vars.insert(args_vars.end(), list.cbegin(), list.cend());
}
std::vector rvect_apply = gen_op_call(code, op_call_type, v->loc, std::move(args_vars), fun_ref, "(fun-call)");
if (fun_ref->has_mutate_params()) {
LValContext local_lval;
std::vector left;
for (int i = 0; i < delta_self + v->get_num_args(); ++i) {
if (fun_ref->parameters[i].is_mutate_parameter()) {
AnyExprV arg_i = obj_leftmost && i == 0 ? obj_leftmost : args[i];
tolk_assert(arg_i->is_lvalue || i == 0);
if (arg_i->is_lvalue) {
std::vector ith_var_idx = pre_compile_expr(arg_i, code, nullptr, &local_lval);
left.insert(left.end(), ith_var_idx.begin(), ith_var_idx.end());
} else {
left.insert(left.end(), vars_per_arg[0].begin(), vars_per_arg[0].end());
}
}
}
std::vector rvect = code.create_tmp_var(real_ret_type, v->loc, "(fun-call)");
left.insert(left.end(), rvect.begin(), rvect.end());
vars_modification_watcher.trigger_callbacks(left, v->loc);
code.emplace_back(v->loc, Op::_Let, left, rvect_apply);
local_lval.after_let(std::move(left), code, v->loc);
rvect_apply = rvect;
}
if (obj_leftmost && fun_ref->does_return_self()) {
if (obj_leftmost->is_lvalue) { // to handle if obj is global var, potentially re-assigned inside a chain
rvect_apply = pre_compile_expr(obj_leftmost, code, nullptr);
} else { // temporary object, not lvalue, pre_compile_expr
rvect_apply = vars_per_arg[0];
}
}
return transition_to_target_type(std::move(rvect_apply), code, target_type, v);
}
static std::vector process_tensor(V v, CodeBlob& code, TypePtr target_type, LValContext* lval_ctx) {
// tensor is compiled "as is", for example `(1, null)` occupies 2 slots
// and if assigned/passed to something other, like `(int, (int,int)?)`, a whole tensor is transitioned, it works
std::vector rvect = pre_compile_tensor(code, v->get_items(), lval_ctx);
return transition_to_target_type(std::move(rvect), code, target_type, v);
}
static std::vector process_typed_tuple(V v, CodeBlob& code, TypePtr target_type, LValContext* lval_ctx) {
if (lval_ctx) { // todo some time, make "var (a, [b,c]) = (1, [2,3])" work
v->error("[...] can not be used as lvalue here");
}
std::vector left = code.create_tmp_var(v->inferred_type, v->loc, "(pack-tuple)");
std::vector right = pre_compile_tensor(code, v->get_items(), lval_ctx);
code.emplace_back(v->loc, Op::_Tuple, left, std::move(right));
return transition_to_target_type(std::move(left), code, target_type, v);
}
static std::vector process_int_const(V v, CodeBlob& code, TypePtr target_type) {
std::vector rvect = code.create_tmp_var(v->inferred_type, v->loc, "(int-const)");
code.emplace_back(v->loc, Op::_IntConst, rvect, v->intval);
return transition_to_target_type(std::move(rvect), code, target_type, v);
}
static std::vector process_string_const(V v, CodeBlob& code, TypePtr target_type) {
ConstantValue value = eval_const_init_value(v);
std::vector rvect = code.create_tmp_var(v->inferred_type, v->loc, "(str-const)");
if (value.is_int()) {
code.emplace_back(v->loc, Op::_IntConst, rvect, value.as_int());
} else {
code.emplace_back(v->loc, Op::_SliceConst, rvect, value.as_slice());
}
return transition_to_target_type(std::move(rvect), code, target_type, v);
}
static std::vector process_bool_const(V v, CodeBlob& code, TypePtr target_type) {
FunctionPtr builtin_sym = lookup_global_symbol(v->bool_val ? "__true" : "__false")->try_as();
std::vector rvect = gen_op_call(code, v->inferred_type, v->loc, {}, builtin_sym, "(bool-const)");
return transition_to_target_type(std::move(rvect), code, target_type, v);
}
static std::vector process_null_keyword(V v, CodeBlob& code, TypePtr target_type) {
FunctionPtr builtin_sym = lookup_global_symbol("__null")->try_as();
std::vector rvect = gen_op_call(code, v->inferred_type, v->loc, {}, builtin_sym, "(null-literal)");
return transition_to_target_type(std::move(rvect), code, target_type, v);
}
static std::vector process_local_var(V v, CodeBlob& code, TypePtr target_type) {
if (v->marked_as_redef) {
std::vector rvect = pre_compile_symbol(v->loc, v->var_ref, code, nullptr);
return transition_to_target_type(std::move(rvect), code, target_type, v);
}
tolk_assert(v->var_ref->ir_idx.empty());
v->var_ref->mutate()->assign_ir_idx(code.create_var(v->inferred_type, v->loc, v->var_ref->name));
std::vector rvect = v->var_ref->ir_idx;
return transition_to_target_type(std::move(rvect), code, target_type, v);
}
static std::vector process_local_vars_declaration(V, CodeBlob&) {
// it can not appear as a standalone expression
// `var ... = rhs` is handled by ast_assign
tolk_assert(false);
}
static std::vector process_underscore(V v, CodeBlob& code) {
// when _ is used as left side of assignment, like `(cs, _) = cs.loadAndReturn()`
return code.create_tmp_var(v->inferred_type, v->loc, "(underscore)");
}
std::vector pre_compile_expr(AnyExprV v, CodeBlob& code, TypePtr target_type, LValContext* lval_ctx) {
switch (v->type) {
case ast_reference:
return process_reference(v->as(), code, target_type, lval_ctx);
case ast_assign:
return process_assignment(v->as(), code, target_type);
case ast_set_assign:
return process_set_assign(v->as(), code, target_type);
case ast_binary_operator:
return process_binary_operator(v->as(), code, target_type);
case ast_unary_operator:
return process_unary_operator(v->as(), code, target_type);
case ast_ternary_operator:
return process_ternary_operator(v->as(), code, target_type);
case ast_cast_as_operator:
return process_cast_as_operator(v->as(), code, target_type, lval_ctx);
case ast_not_null_operator:
return process_not_null_operator(v->as(), code, target_type, lval_ctx);
case ast_is_null_check:
return process_is_null_check(v->as(), code, target_type);
case ast_dot_access:
return process_dot_access(v->as(), code, target_type, lval_ctx);
case ast_function_call:
return process_function_call(v->as(), code, target_type);
case ast_parenthesized_expression:
return pre_compile_expr(v->as()->get_expr(), code, target_type, lval_ctx);
case ast_tensor:
return process_tensor(v->as(), code, target_type, lval_ctx);
case ast_typed_tuple:
return process_typed_tuple(v->as(), code, target_type, lval_ctx);
case ast_int_const:
return process_int_const(v->as