mirror of
https://github.com/ton-blockchain/ton
synced 2025-03-09 15:40:10 +00:00
7bcb8b895f
With the introduction of nullable types, we want the compiler to be smart in cases like > if (x == null) return; > // x is int now or > if (x == null) x = 0; > // x is int now These are called smart casts: when the type of variable at particular usage might differ from its declaration. Implementing smart casts is very challenging. They are based on building control-flow graph and handling every AST vertex with care. Actually, I represent cfg not a as a "graph with edges". Instead, it's a "structured DFS" for the AST: 1) at every point of inferring, we have "current flow facts" 2) when we see an `if (...)`, we create two derived contexts 3) after `if`, finalize them at the end and unify 4) if we detect unreachable code, we mark that context In other words, we get the effect of a CFG but in a more direct approach. That's enough for AST-level data-flow. Smart casts work for local variables and tensor/tuple indices. Compilation errors have been reworked and now are more friendly. There are also compilation warnings for always true/false conditions inside if, assert, etc.
1437 lines
71 KiB
C++
1437 lines
71 KiB
C++
/*
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This file is part of TON Blockchain Library.
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TON Blockchain Library is free software: you can redistribute it and/or modify
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it under the terms of the GNU Lesser General Public License as published by
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the Free Software Foundation, either version 2 of the License, or
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(at your option) any later version.
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TON Blockchain Library is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU Lesser General Public License for more details.
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You should have received a copy of the GNU Lesser General Public License
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along with TON Blockchain Library. If not, see <http://www.gnu.org/licenses/>.
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*/
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#include "tolk.h"
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#include "src-file.h"
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#include "ast.h"
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#include "ast-visitor.h"
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#include "type-system.h"
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#include "common/refint.h"
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#include "constant-evaluator.h"
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#include <unordered_set>
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/*
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* This pipe is the last one operating AST: it transforms AST to IR.
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* IR is described as "Op" struct. So, here AST is transformed to Ops, and then all the rest "legacy"
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* kernel (initially forked from FunC) comes into play.
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* Up to this point, all types have been inferred, all validity checks have been passed, etc.
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* All properties in AST nodes are assigned and can be safely used (fun_ref, etc.).
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* So, if execution reaches this pass, the input is (almost) correct, and code generation should succeed.
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* (previously, there was a check for one variable modified twice like `(t.0, t.0) = rhs`, but after changing
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* execution order of assignment to "first lhs, then lhs", it was removed for several reasons)
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*
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* A noticeable property for IR generation is "target_type" used to extend/shrink stack.
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* Example: `var a: (int,int)? = null`. This `null` has inferred_type "null literal", but target_type "nullable tensor",
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* and when it's assigned, it's "expanded" from 1 stack slot to 3 (int + int + null flag).
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* Example: `fun analyze(t: (int,int)?)` and a call `analyze((1,2))`. `(1,2)` is `(int,int)` (2 stack slots),
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* and when passed to target (3 slots, one for null flag), this null flag is implicitly added (zero value).
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* Example: `nullableInt!`; for `nullableInt` inferred_type is `int?`, and target_type is `int`
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* (this doesn't lead to stack reorganization, but in case `nullableTensor!` does)
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* (inferred_type of `nullableInt!` is `int`, and its target_type depends on its usage).
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* The same mechanism will work for union types in the future.
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*/
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namespace tolk {
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class LValContext;
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std::vector<var_idx_t> pre_compile_expr(AnyExprV v, CodeBlob& code, TypePtr target_type = nullptr, LValContext* lval_ctx = nullptr);
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std::vector<var_idx_t> pre_compile_symbol(SrcLocation loc, const Symbol* sym, CodeBlob& code, LValContext* lval_ctx);
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void process_any_statement(AnyV v, CodeBlob& code);
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// The goal of VarsModificationWatcher is to detect such cases: `return (x, x += y, x)`.
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// Without any changes, ops will be { _Call $2 = +($0_x, $1_y); _Return $0_x, $2, $0_x } - incorrect
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// Correct will be to introduce tmp var: { _Let $3 = $0_x; _Call $2 = ...; _Return $3, $2, $0_x }
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// This "introducing" is done when compiling tensors, whereas this class allows to watch vars for modification.
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class VarsModificationWatcher {
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struct WatchedVar {
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var_idx_t ir_idx;
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std::function<void(SrcLocation, var_idx_t)> on_modification_callback;
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WatchedVar(var_idx_t ir_idx, std::function<void(SrcLocation, var_idx_t)> on_modification_callback)
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: ir_idx(ir_idx), on_modification_callback(std::move(on_modification_callback)) {}
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};
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std::vector<WatchedVar> all_callbacks;
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public:
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bool empty() const { return all_callbacks.empty(); }
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void push_callback(var_idx_t ir_idx, std::function<void(SrcLocation, var_idx_t)> callback) {
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all_callbacks.emplace_back(ir_idx, std::move(callback));
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}
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void pop_callback(var_idx_t ir_idx) {
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for (auto it = all_callbacks.rbegin(); it != all_callbacks.rend(); ++it) {
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if (it->ir_idx == ir_idx) {
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all_callbacks.erase((it + 1).base());
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return;
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}
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}
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tolk_assert(false);
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}
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void trigger_callbacks(const std::vector<var_idx_t>& left_lval_indices, SrcLocation loc) const {
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for (const WatchedVar& w : all_callbacks) {
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for (var_idx_t changed_var : left_lval_indices) {
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if (w.ir_idx == changed_var) {
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w.on_modification_callback(loc, w.ir_idx);
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}
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}
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}
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}
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};
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static VarsModificationWatcher vars_modification_watcher;
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// Main goal of LValContext is to handle non-primitive lvalues. At IR level, a usual local variable
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// exists, but on its change, something non-trivial should happen.
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// Example: `globalVar = 9` actually does `Const $5 = 9` + `Let $6 = $5` + `SetGlob "globVar" = $6`
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// Example: `tupleVar.0 = 9` actually does `Const $5 = 9` + `Let $6 = $5` + `Const $7 = 0` + `Call tupleSetAt($4, $6, $7)`
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// Of course, mixing globals with tuples should also be supported.
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// To achieve this, treat tupleObj inside "tupleObj.i" like "rvalue inside lvalue".
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// For instance, `globalTuple.0 = 9` reads global (like rvalue), assigns 9 to tmp var, modifies tuple, writes global.
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// Note, that tensors (not tuples) `tensorVar.0 = 9` do not emit anything special (unless global).
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class LValContext {
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// every global variable used as lvalue is registered here
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// example: `globalInt = 9`, implicit var is created `$tmp = 9`, and `SetGlob "globalInt" $tmp` is done after
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struct ModifiedGlobal {
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GlobalVarPtr glob_ref;
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std::vector<var_idx_t> lval_ir_idx; // typically 1, generally get_width_on_stack() of global var (tensors)
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// for 1-slot globals int/cell/slice, assigning to them is just SETGLOB
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// same for tensors, if they are fully rewritten in an expression: `gTensor = (5,6)`
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void apply_fully_rewrite(CodeBlob& code, SrcLocation loc) const {
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Op& op = code.emplace_back(loc, Op::_SetGlob, std::vector<var_idx_t>{}, lval_ir_idx, glob_ref);
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op.set_impure_flag();
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}
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// for N-slot globals tensor/struct/union, assigning to their parts, like `gTensor.1 = 6`
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// we need to read gTensor as a whole (0-th and 1-th component), rewrite 1-th component, and SETGLOB a whole back
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void apply_partially_rewrite(CodeBlob& code, SrcLocation loc, std::vector<bool>&& was_modified_by_let) const {
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LValContext local_lval;
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local_lval.enter_rval_inside_lval();
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std::vector<var_idx_t> local_ir_idx = pre_compile_symbol(loc, glob_ref, code, &local_lval);
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for (size_t i = 0; i < local_ir_idx.size(); ++i) {
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if (was_modified_by_let[i]) {
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code.emplace_back(loc, Op::_Let, std::vector{local_ir_idx[i]}, std::vector{lval_ir_idx[i]});
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}
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}
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Op& op = code.emplace_back(loc, Op::_SetGlob, std::vector<var_idx_t>{}, local_ir_idx, glob_ref);
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op.set_impure_flag();
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}
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};
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// every tensor index, when a tensor is a global, is registered here (same for structs and fields)
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// example: `global v: (int, int); v.1 = 5`, implicit var is created `$tmp = 5`, and when it's modified,
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// we need to partially update w; essentially, apply_partially_rewrite() above will be called
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struct ModifiedFieldOfGlobal {
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AnyExprV tensor_obj;
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int index_at;
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std::vector<var_idx_t> lval_ir_idx;
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void apply(CodeBlob& code, SrcLocation loc) const {
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LValContext local_lval;
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local_lval.enter_rval_inside_lval();
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std::vector<var_idx_t> obj_ir_idx = pre_compile_expr(tensor_obj, code, nullptr, &local_lval);
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const TypeDataTensor* t_tensor = tensor_obj->inferred_type->try_as<TypeDataTensor>();
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tolk_assert(t_tensor);
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int stack_width = t_tensor->items[index_at]->get_width_on_stack();
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int stack_offset = 0;
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for (int i = 0; i < index_at; ++i) {
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stack_offset += t_tensor->items[i]->get_width_on_stack();
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}
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std::vector<var_idx_t> field_ir_idx = {obj_ir_idx.begin() + stack_offset, obj_ir_idx.begin() + stack_offset + stack_width};
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tolk_assert(field_ir_idx.size() == lval_ir_idx.size());
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vars_modification_watcher.trigger_callbacks(field_ir_idx, loc);
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code.emplace_back(loc, Op::_Let, field_ir_idx, lval_ir_idx);
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local_lval.after_let(std::move(field_ir_idx), code, loc);
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}
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};
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// every tuple index used as lvalue is registered here
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// example: `t.0 = 9`, implicit var is created `$tmp = 9`, as well as `$tmp_idx = 0` and `tupleSetAt()` is done after
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// for `t.0.0` if t is `[[int, ...]]`, `tupleAt()` for it is done since it's rvalue, and `tupleSetAt()` is done 2 times
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struct ModifiedTupleIndex {
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AnyExprV tuple_obj;
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int index_at;
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std::vector<var_idx_t> lval_ir_idx;
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void apply(CodeBlob& code, SrcLocation loc) const {
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LValContext local_lval;
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local_lval.enter_rval_inside_lval();
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std::vector<var_idx_t> tuple_ir_idx = pre_compile_expr(tuple_obj, code, nullptr, &local_lval);
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std::vector<var_idx_t> index_ir_idx = code.create_tmp_var(TypeDataInt::create(), loc, "(tuple-idx)");
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code.emplace_back(loc, Op::_IntConst, index_ir_idx, td::make_refint(index_at));
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vars_modification_watcher.trigger_callbacks(tuple_ir_idx, loc);
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FunctionPtr builtin_sym = lookup_global_symbol("tupleSetAt")->try_as<FunctionPtr>();
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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);
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local_lval.after_let(std::move(tuple_ir_idx), code, loc);
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}
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};
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int level_rval_inside_lval = 0;
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std::vector<std::variant<ModifiedGlobal, ModifiedTupleIndex, ModifiedFieldOfGlobal>> modifications;
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static bool vector_contains(const std::vector<var_idx_t>& ir_vars, var_idx_t ir_idx) {
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for (var_idx_t var_in_vector : ir_vars) {
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if (var_in_vector == ir_idx) {
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return true;
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}
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}
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return false;
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}
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public:
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void enter_rval_inside_lval() { level_rval_inside_lval++; }
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void exit_rval_inside_lval() { level_rval_inside_lval--; }
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bool is_rval_inside_lval() const { return level_rval_inside_lval > 0; }
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void capture_global_modification(GlobalVarPtr glob_ref, std::vector<var_idx_t> lval_ir_idx) {
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modifications.emplace_back(ModifiedGlobal{glob_ref, std::move(lval_ir_idx)});
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}
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void capture_field_of_global_modification(AnyExprV tensor_obj, int index_at, std::vector<var_idx_t> lval_ir_idx) {
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modifications.emplace_back(ModifiedFieldOfGlobal{tensor_obj, index_at, std::move(lval_ir_idx)});
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}
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void capture_tuple_index_modification(AnyExprV tuple_obj, int index_at, std::vector<var_idx_t> lval_ir_idx) {
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modifications.emplace_back(ModifiedTupleIndex{tuple_obj, index_at, std::move(lval_ir_idx)});
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}
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void after_let(std::vector<var_idx_t>&& let_left_vars, CodeBlob& code, SrcLocation loc) const {
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for (const auto& modification : modifications) {
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if (const auto* m_glob = std::get_if<ModifiedGlobal>(&modification)) {
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int n_modified_by_let = 0;
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std::vector<bool> was_modified_by_let;
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was_modified_by_let.resize(m_glob->lval_ir_idx.size());
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for (size_t i = 0; i < m_glob->lval_ir_idx.size(); ++i) {
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if (vector_contains(let_left_vars, m_glob->lval_ir_idx[i])) {
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was_modified_by_let[i] = true;
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n_modified_by_let++;
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}
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}
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if (n_modified_by_let == static_cast<int>(m_glob->lval_ir_idx.size())) {
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m_glob->apply_fully_rewrite(code, loc);
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} else if (n_modified_by_let > 0) {
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m_glob->apply_partially_rewrite(code, loc, std::move(was_modified_by_let));
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}
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} else if (const auto* m_tup = std::get_if<ModifiedTupleIndex>(&modification)) {
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bool was_tuple_index_modified = false;
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for (var_idx_t field_ir_idx : m_tup->lval_ir_idx) {
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was_tuple_index_modified |= vector_contains(let_left_vars, field_ir_idx);
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}
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if (was_tuple_index_modified) {
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m_tup->apply(code, loc);
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}
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} else if (const auto* m_tens = std::get_if<ModifiedFieldOfGlobal>(&modification)) {
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bool was_tensor_index_modified = false;
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for (var_idx_t field_ir_idx : m_tens->lval_ir_idx) {
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was_tensor_index_modified |= vector_contains(let_left_vars, field_ir_idx);
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}
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if (was_tensor_index_modified) {
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m_tens->apply(code, loc);
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}
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}
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}
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}
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};
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// given `{some_expr}!`, return some_expr
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static AnyExprV unwrap_not_null_operator(AnyExprV v) {
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while (auto v_notnull = v->try_as<ast_not_null_operator>()) {
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v = v_notnull->get_expr();
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}
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return v;
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}
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// given `{some_expr}.{i}`, check it for pattern `some_var.0` / `some_var.0.1` / etc.
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// return some_var if satisfies (it may be a local or a global var, a tensor or a tuple)
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// return nullptr otherwise: `f().0` / `(v = rhs).0` / `some_var.method().0` / etc.
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static V<ast_reference> calc_sink_leftmost_obj(V<ast_dot_access> v) {
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AnyExprV leftmost_obj = unwrap_not_null_operator(v->get_obj());
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while (auto v_dot = leftmost_obj->try_as<ast_dot_access>()) {
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if (!v_dot->is_target_indexed_access()) {
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break;
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}
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leftmost_obj = unwrap_not_null_operator(v_dot->get_obj());
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}
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return leftmost_obj->type == ast_reference ? leftmost_obj->as<ast_reference>() : nullptr;
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}
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static std::vector<std::vector<var_idx_t>> pre_compile_tensor_inner(CodeBlob& code, const std::vector<AnyExprV>& args,
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const TypeDataTensor* tensor_target_type, LValContext* lval_ctx) {
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const int n = static_cast<int>(args.size());
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if (n == 0) { // just `()`
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return {};
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}
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tolk_assert(!tensor_target_type || tensor_target_type->size() == n);
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if (n == 1) { // just `(x)`: even if x is modified (e.g. `f(x=x+2)`), there are no next arguments
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TypePtr child_target_type = tensor_target_type ? tensor_target_type->items[0] : nullptr;
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return {pre_compile_expr(args[0], code, child_target_type, lval_ctx)};
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}
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// the purpose is to handle such cases: `return (x, x += y, x)`
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// without this, ops will be { _Call $2 = +($0_x, $1_y); _Return $0_x, $2, $0_x } - invalid
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// with this, ops will be { _Let $3 = $0_x; _Call $2 = ...; _Return $3, $2, $0_x } - valid, tmp var for x
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// how it works: for every arg, after transforming to ops, start tracking ir_idx inside it
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// on modification attempt, create Op::_Let to a tmp var and replace old ir_idx with tmp_idx in result
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struct WatchingVarList {
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std::vector<var_idx_t> watched_vars;
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std::vector<std::vector<var_idx_t>> res_lists;
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explicit WatchingVarList(int n_args) {
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res_lists.reserve(n_args);
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}
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bool is_watched(var_idx_t ir_idx) const {
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return std::find(watched_vars.begin(), watched_vars.end(), ir_idx) != watched_vars.end();
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}
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void add_and_watch_modifications(std::vector<var_idx_t>&& vars_of_ith_arg, CodeBlob& code) {
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for (var_idx_t ir_idx : vars_of_ith_arg) {
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if (!code.vars[ir_idx].name.empty() && !is_watched(ir_idx)) {
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watched_vars.emplace_back(ir_idx);
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vars_modification_watcher.push_callback(ir_idx, [this, &code](SrcLocation loc, var_idx_t ir_idx) {
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on_var_modified(ir_idx, loc, code);
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});
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}
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}
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res_lists.emplace_back(std::move(vars_of_ith_arg));
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}
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void on_var_modified(var_idx_t ir_idx, SrcLocation loc, CodeBlob& code) {
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tolk_assert(is_watched(ir_idx));
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std::vector<var_idx_t> tmp_idx_arr = code.create_tmp_var(code.vars[ir_idx].v_type, loc, "(pre-modified)");
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tolk_assert(tmp_idx_arr.size() == 1);
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var_idx_t tmp_idx = tmp_idx_arr[0];
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code.emplace_back(loc, Op::_Let, std::vector{tmp_idx}, std::vector{ir_idx});
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for (std::vector<var_idx_t>& prev_vars : res_lists) {
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std::replace(prev_vars.begin(), prev_vars.end(), ir_idx, tmp_idx);
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}
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}
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std::vector<std::vector<var_idx_t>> clear_and_stop_watching() {
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for (var_idx_t ir_idx : watched_vars) {
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vars_modification_watcher.pop_callback(ir_idx);
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}
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watched_vars.clear();
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return std::move(res_lists);
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}
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};
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WatchingVarList watched_vars(n);
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for (int arg_idx = 0; arg_idx < n; ++arg_idx) {
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TypePtr child_target_type = tensor_target_type ? tensor_target_type->items[arg_idx] : nullptr;
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std::vector<var_idx_t> vars_of_ith_arg = pre_compile_expr(args[arg_idx], code, child_target_type, lval_ctx);
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watched_vars.add_and_watch_modifications(std::move(vars_of_ith_arg), code);
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}
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return watched_vars.clear_and_stop_watching();
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}
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static std::vector<var_idx_t> pre_compile_tensor(CodeBlob& code, const std::vector<AnyExprV>& args,
|
|
LValContext* lval_ctx = nullptr) {
|
|
std::vector<TypePtr> 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<TypeDataTensor>();
|
|
std::vector<std::vector<var_idx_t>> res_lists = pre_compile_tensor_inner(code, args, tensor_target_type, lval_ctx);
|
|
std::vector<var_idx_t> res;
|
|
for (const std::vector<var_idx_t>& list : res_lists) {
|
|
res.insert(res.end(), list.cbegin(), list.cend());
|
|
}
|
|
return res;
|
|
}
|
|
|
|
static std::vector<var_idx_t> 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<var_idx_t> left = pre_compile_tensor(code, lhs->as<ast_typed_tuple>()->get_items(), &local_lval);
|
|
vars_modification_watcher.trigger_callbacks(left, loc);
|
|
std::vector<var_idx_t> rvect = pre_compile_tensor(code, rhs->as<ast_typed_tuple>()->get_items());
|
|
code.emplace_back(loc, Op::_Let, left, rvect);
|
|
local_lval.after_let(std::move(left), code, loc);
|
|
std::vector<var_idx_t> 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<var_idx_t> left = pre_compile_tensor(code, lhs->as<ast_typed_tuple>()->get_items(), &local_lval);
|
|
vars_modification_watcher.trigger_callbacks(left, loc);
|
|
std::vector<var_idx_t> right = pre_compile_expr(rhs, code, nullptr);
|
|
const TypeDataTypedTuple* inferred_tuple = rhs->inferred_type->try_as<TypeDataTypedTuple>();
|
|
std::vector<TypePtr> types_list = inferred_tuple->items;
|
|
std::vector<var_idx_t> 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<ast_reference>()->sym->try_as<LocalVarPtr>())) {
|
|
std::vector<var_idx_t> left = pre_compile_expr(lhs, code, nullptr); // effectively, local_var->ir_idx
|
|
vars_modification_watcher.trigger_callbacks(left, loc);
|
|
std::vector<var_idx_t> 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<var_idx_t> left = pre_compile_expr(lhs, code, nullptr, &local_lval);
|
|
vars_modification_watcher.trigger_callbacks(left, loc);
|
|
std::vector<var_idx_t> 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<var_idx_t> gen_op_call(CodeBlob& code, TypePtr ret_type, SrcLocation loc,
|
|
std::vector<var_idx_t>&& args_vars, FunctionPtr fun_ref, const char* debug_desc) {
|
|
std::vector<var_idx_t> 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<var_idx_t> transition_expr_to_runtime_type_impl(std::vector<var_idx_t>&& 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<TypeDataNullable>();
|
|
const TypeDataNullable* o_nullable = original_type->try_as<TypeDataNullable>();
|
|
|
|
// 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<var_idx_t> 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<FunctionPtr>();
|
|
rvect.reserve(target_w + 1);
|
|
for (int i = 1; i < target_w - 1; ++i) {
|
|
std::vector<var_idx_t> ith_null = code.create_tmp_var(TypeDataNullLiteral::create(), loc, "(null-literal)");
|
|
code.emplace_back(loc, Op::_Call, ith_null, std::vector<var_idx_t>{}, builtin_sym);
|
|
rvect.push_back(ith_null[0]);
|
|
}
|
|
std::vector<var_idx_t> 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<int>(rvect.size() + 1));
|
|
std::vector<var_idx_t> 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<FunctionPtr>();
|
|
rvect = code.create_tmp_var(TypeDataNullLiteral::create(), loc, "(null-literal)");
|
|
code.emplace_back(loc, Op::_Call, rvect, std::vector<var_idx_t>{}, 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<TypeDataNullable>()->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<TypeDataTensor>() && original_type->try_as<TypeDataTensor>()) {
|
|
const TypeDataTensor* target_tensor = target_type->try_as<TypeDataTensor>();
|
|
const TypeDataTensor* inferred_tensor = original_type->try_as<TypeDataTensor>();
|
|
tolk_assert(target_tensor->size() == inferred_tensor->size());
|
|
tolk_assert(inferred_tensor->get_width_on_stack() == static_cast<int>(rvect.size()));
|
|
std::vector<var_idx_t> 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<var_idx_t> rvect_i{rvect.begin() + stack_offset, rvect.begin() + stack_offset + ith_w};
|
|
std::vector<var_idx_t> 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<TypeDataTypedTuple>() && original_type->try_as<TypeDataTypedTuple>()) {
|
|
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<var_idx_t> transition_to_target_type(std::vector<var_idx_t>&& 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<var_idx_t> transition_to_target_type(std::vector<var_idx_t>&& 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<var_idx_t> pre_compile_symbol(SrcLocation loc, const Symbol* sym, CodeBlob& code, LValContext* lval_ctx) {
|
|
if (GlobalVarPtr glob_ref = sym->try_as<GlobalVarPtr>()) {
|
|
// handle `globalVar = rhs` / `mutate globalVar`
|
|
if (lval_ctx && !lval_ctx->is_rval_inside_lval()) {
|
|
std::vector<var_idx_t> 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<var_idx_t> 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<var_idx_t>{}, 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<GlobalConstPtr>()) {
|
|
if (const_ref->is_int_const()) {
|
|
std::vector<var_idx_t> 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<var_idx_t> 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<FunctionPtr>()) {
|
|
std::vector<var_idx_t> rvect = code.create_tmp_var(fun_ref->inferred_full_type, loc, "(glob-var-fun)");
|
|
code.emplace_back(loc, Op::_GlobVar, rvect, std::vector<var_idx_t>{}, fun_ref);
|
|
return rvect;
|
|
}
|
|
if (LocalVarPtr var_ref = sym->try_as<LocalVarPtr>()) {
|
|
#ifdef TOLK_DEBUG
|
|
tolk_assert(static_cast<int>(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<var_idx_t> process_reference(V<ast_reference> v, CodeBlob& code, TypePtr target_type, LValContext* lval_ctx) {
|
|
std::vector<var_idx_t> 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<LocalVarPtr>()) {
|
|
// 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<var_idx_t> process_assignment(V<ast_assign> v, CodeBlob& code, TypePtr target_type) {
|
|
AnyExprV lhs = v->get_lhs();
|
|
AnyExprV rhs = v->get_rhs();
|
|
|
|
if (auto lhs_decl = lhs->try_as<ast_local_vars_declaration>()) {
|
|
std::vector<var_idx_t> 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<var_idx_t> 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<var_idx_t> process_set_assign(V<ast_set_assign> 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<ast_binary_operator>(v->loc, calc_operator, static_cast<TokenType>(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<var_idx_t> 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<var_idx_t> process_binary_operator(V<ast_binary_operator> 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<var_idx_t> args_vars = pre_compile_tensor(code, {v->get_lhs(), v->get_rhs()});
|
|
std::vector<var_idx_t> 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<ast_int_const>(v->loc, td::make_refint(0), "0");
|
|
v_0->mutate()->assign_inferred_type(TypeDataInt::create());
|
|
AnyExprV v_1 = createV<ast_int_const>(v->loc, td::make_refint(-1), "-1");
|
|
v_1->mutate()->assign_inferred_type(TypeDataInt::create());
|
|
auto v_b_ne_0 = createV<ast_binary_operator>(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<FunctionPtr>());
|
|
std::vector<var_idx_t> cond = pre_compile_expr(v->get_lhs(), code, nullptr);
|
|
tolk_assert(cond.size() == 1);
|
|
std::vector<var_idx_t> 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<var_idx_t> process_unary_operator(V<ast_unary_operator> v, CodeBlob& code, TypePtr target_type) {
|
|
std::vector<var_idx_t> rhs_vars = pre_compile_expr(v->get_rhs(), code, nullptr);
|
|
std::vector<var_idx_t> 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<var_idx_t> process_ternary_operator(V<ast_ternary_operator> v, CodeBlob& code, TypePtr target_type) {
|
|
std::vector<var_idx_t> cond = pre_compile_expr(v->get_cond(), code, nullptr);
|
|
tolk_assert(cond.size() == 1);
|
|
std::vector<var_idx_t> 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<var_idx_t> process_cast_as_operator(V<ast_cast_as_operator> v, CodeBlob& code, TypePtr target_type, LValContext* lval_ctx) {
|
|
TypePtr child_target_type = v->cast_to_type;
|
|
std::vector<var_idx_t> 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<var_idx_t> process_not_null_operator(V<ast_not_null_operator> 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<TypeDataNullable>()) {
|
|
child_target_type = as_nullable->inner;
|
|
}
|
|
std::vector<var_idx_t> 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<var_idx_t> process_is_null_check(V<ast_is_null_check> v, CodeBlob& code, TypePtr target_type) {
|
|
std::vector<var_idx_t> expr_ir_idx = pre_compile_expr(v->get_expr(), code, nullptr);
|
|
std::vector<var_idx_t> 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<TypeDataNullable>()) {
|
|
if (!t_nullable->is_primitive_nullable()) {
|
|
std::vector<var_idx_t> 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<FunctionPtr>();
|
|
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<FunctionPtr>();
|
|
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<FunctionPtr>();
|
|
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<var_idx_t> process_dot_access(V<ast_dot_access> 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<int>(v->target);
|
|
// `tensorVar.0`
|
|
if (const auto* t_tensor = obj_type->try_as<TypeDataTensor>()) {
|
|
// 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<GlobalVarPtr>()) {
|
|
std::vector<var_idx_t> 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<var_idx_t> 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<var_idx_t> 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<TypeDataTypedTuple>() || obj_type->try_as<TypeDataTuple>()) {
|
|
// 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<var_idx_t> 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<var_idx_t> tuple_ir_idx = pre_compile_expr(v->get_obj(), code);
|
|
std::vector<var_idx_t> 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<var_idx_t> 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<FunctionPtr>();
|
|
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<FunctionPtr>(v->target);
|
|
tolk_assert(fun_ref);
|
|
std::vector<var_idx_t> 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<var_idx_t> process_function_call(V<ast_function_call> v, CodeBlob& code, TypePtr target_type) {
|
|
// v is `globalF(args)` / `globalF<int>(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<AnyExprV> 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<TypePtr> params_types = v->get_callee()->inferred_type->try_as<TypeDataFunCallable>()->params_types;
|
|
const TypeDataTensor* tensor_tt = TypeDataTensor::create(std::move(params_types))->try_as<TypeDataTensor>();
|
|
std::vector<std::vector<var_idx_t>> vars_per_arg = pre_compile_tensor_inner(code, args, tensor_tt, nullptr);
|
|
std::vector<var_idx_t> args_vars;
|
|
for (const std::vector<var_idx_t>& list : vars_per_arg) {
|
|
args_vars.insert(args_vars.end(), list.cbegin(), list.cend());
|
|
}
|
|
std::vector<var_idx_t> tfunc = pre_compile_expr(v->get_callee(), code, nullptr);
|
|
tolk_assert(tfunc.size() == 1);
|
|
args_vars.push_back(tfunc[0]);
|
|
std::vector<var_idx_t> 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<AnyExprV> 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<ast_function_call>()->is_dot_call() && obj_leftmost->as<ast_function_call>()->fun_maybe && obj_leftmost->as<ast_function_call>()->fun_maybe->does_return_self()) {
|
|
obj_leftmost = obj_leftmost->as<ast_function_call>()->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<TypePtr> params_types = fun_ref->inferred_full_type->try_as<TypeDataFunCallable>()->params_types;
|
|
const TypeDataTensor* tensor_tt = TypeDataTensor::create(std::move(params_types))->try_as<TypeDataTensor>();
|
|
std::vector<std::vector<var_idx_t>> 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<TypePtr> 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<var_idx_t> args_vars;
|
|
for (const std::vector<var_idx_t>& list : vars_per_arg) {
|
|
args_vars.insert(args_vars.end(), list.cbegin(), list.cend());
|
|
}
|
|
std::vector<var_idx_t> 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<var_idx_t> 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<var_idx_t> 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<var_idx_t> 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<var_idx_t> process_tensor(V<ast_tensor> 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<var_idx_t> 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<var_idx_t> process_typed_tuple(V<ast_typed_tuple> 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<var_idx_t> left = code.create_tmp_var(v->inferred_type, v->loc, "(pack-tuple)");
|
|
std::vector<var_idx_t> 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<var_idx_t> process_int_const(V<ast_int_const> v, CodeBlob& code, TypePtr target_type) {
|
|
std::vector<var_idx_t> 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<var_idx_t> process_string_const(V<ast_string_const> v, CodeBlob& code, TypePtr target_type) {
|
|
ConstantValue value = eval_const_init_value(v);
|
|
std::vector<var_idx_t> 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<var_idx_t> process_bool_const(V<ast_bool_const> v, CodeBlob& code, TypePtr target_type) {
|
|
FunctionPtr builtin_sym = lookup_global_symbol(v->bool_val ? "__true" : "__false")->try_as<FunctionPtr>();
|
|
std::vector<var_idx_t> 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<var_idx_t> process_null_keyword(V<ast_null_keyword> v, CodeBlob& code, TypePtr target_type) {
|
|
FunctionPtr builtin_sym = lookup_global_symbol("__null")->try_as<FunctionPtr>();
|
|
std::vector<var_idx_t> 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<var_idx_t> process_local_var(V<ast_local_var_lhs> v, CodeBlob& code, TypePtr target_type) {
|
|
if (v->marked_as_redef) {
|
|
std::vector<var_idx_t> 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<var_idx_t> rvect = v->var_ref->ir_idx;
|
|
return transition_to_target_type(std::move(rvect), code, target_type, v);
|
|
}
|
|
|
|
static std::vector<var_idx_t> process_local_vars_declaration(V<ast_local_vars_declaration>, CodeBlob&) {
|
|
// it can not appear as a standalone expression
|
|
// `var ... = rhs` is handled by ast_assign
|
|
tolk_assert(false);
|
|
}
|
|
|
|
static std::vector<var_idx_t> process_underscore(V<ast_underscore> 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<var_idx_t> pre_compile_expr(AnyExprV v, CodeBlob& code, TypePtr target_type, LValContext* lval_ctx) {
|
|
switch (v->type) {
|
|
case ast_reference:
|
|
return process_reference(v->as<ast_reference>(), code, target_type, lval_ctx);
|
|
case ast_assign:
|
|
return process_assignment(v->as<ast_assign>(), code, target_type);
|
|
case ast_set_assign:
|
|
return process_set_assign(v->as<ast_set_assign>(), code, target_type);
|
|
case ast_binary_operator:
|
|
return process_binary_operator(v->as<ast_binary_operator>(), code, target_type);
|
|
case ast_unary_operator:
|
|
return process_unary_operator(v->as<ast_unary_operator>(), code, target_type);
|
|
case ast_ternary_operator:
|
|
return process_ternary_operator(v->as<ast_ternary_operator>(), code, target_type);
|
|
case ast_cast_as_operator:
|
|
return process_cast_as_operator(v->as<ast_cast_as_operator>(), code, target_type, lval_ctx);
|
|
case ast_not_null_operator:
|
|
return process_not_null_operator(v->as<ast_not_null_operator>(), code, target_type, lval_ctx);
|
|
case ast_is_null_check:
|
|
return process_is_null_check(v->as<ast_is_null_check>(), code, target_type);
|
|
case ast_dot_access:
|
|
return process_dot_access(v->as<ast_dot_access>(), code, target_type, lval_ctx);
|
|
case ast_function_call:
|
|
return process_function_call(v->as<ast_function_call>(), code, target_type);
|
|
case ast_parenthesized_expression:
|
|
return pre_compile_expr(v->as<ast_parenthesized_expression>()->get_expr(), code, target_type, lval_ctx);
|
|
case ast_tensor:
|
|
return process_tensor(v->as<ast_tensor>(), code, target_type, lval_ctx);
|
|
case ast_typed_tuple:
|
|
return process_typed_tuple(v->as<ast_typed_tuple>(), code, target_type, lval_ctx);
|
|
case ast_int_const:
|
|
return process_int_const(v->as<ast_int_const>(), code, target_type);
|
|
case ast_string_const:
|
|
return process_string_const(v->as<ast_string_const>(), code, target_type);
|
|
case ast_bool_const:
|
|
return process_bool_const(v->as<ast_bool_const>(), code, target_type);
|
|
case ast_null_keyword:
|
|
return process_null_keyword(v->as<ast_null_keyword>(), code, target_type);
|
|
case ast_local_var_lhs:
|
|
return process_local_var(v->as<ast_local_var_lhs>(), code, target_type);
|
|
case ast_local_vars_declaration:
|
|
return process_local_vars_declaration(v->as<ast_local_vars_declaration>(), code);
|
|
case ast_underscore:
|
|
return process_underscore(v->as<ast_underscore>(), code);
|
|
default:
|
|
throw UnexpectedASTNodeType(v, "pre_compile_expr");
|
|
}
|
|
}
|
|
|
|
|
|
static void process_sequence(V<ast_sequence> v, CodeBlob& code) {
|
|
for (AnyV item : v->get_items()) {
|
|
process_any_statement(item, code);
|
|
}
|
|
}
|
|
|
|
static void process_assert_statement(V<ast_assert_statement> v, CodeBlob& code) {
|
|
std::vector<AnyExprV> args(3);
|
|
if (auto v_not = v->get_cond()->try_as<ast_unary_operator>(); v_not && v_not->tok == tok_logical_not) {
|
|
args[0] = v->get_thrown_code();
|
|
args[1] = v->get_cond()->as<ast_unary_operator>()->get_rhs();
|
|
args[2] = createV<ast_bool_const>(v->loc, true);
|
|
args[2]->mutate()->assign_inferred_type(TypeDataInt::create());
|
|
} else {
|
|
args[0] = v->get_thrown_code();
|
|
args[1] = v->get_cond();
|
|
args[2] = createV<ast_bool_const>(v->loc, false);
|
|
args[2]->mutate()->assign_inferred_type(TypeDataInt::create());
|
|
}
|
|
|
|
FunctionPtr builtin_sym = lookup_global_symbol("__throw_if_unless")->try_as<FunctionPtr>();
|
|
std::vector<var_idx_t> args_vars = pre_compile_tensor(code, args);
|
|
gen_op_call(code, TypeDataVoid::create(), v->loc, std::move(args_vars), builtin_sym, "(throw-call)");
|
|
}
|
|
|
|
static void process_catch_variable(AnyExprV v_catch_var, CodeBlob& code) {
|
|
if (auto v_ref = v_catch_var->try_as<ast_reference>(); v_ref && v_ref->sym) { // not underscore
|
|
LocalVarPtr var_ref = v_ref->sym->try_as<LocalVarPtr>();
|
|
tolk_assert(var_ref->ir_idx.empty());
|
|
var_ref->mutate()->assign_ir_idx(code.create_var(v_catch_var->inferred_type, v_catch_var->loc, var_ref->name));
|
|
}
|
|
}
|
|
|
|
static void process_try_catch_statement(V<ast_try_catch_statement> v, CodeBlob& code) {
|
|
code.require_callxargs = true;
|
|
Op& try_catch_op = code.emplace_back(v->loc, Op::_TryCatch);
|
|
code.push_set_cur(try_catch_op.block0);
|
|
process_any_statement(v->get_try_body(), code);
|
|
code.close_pop_cur(v->get_try_body()->loc_end);
|
|
code.push_set_cur(try_catch_op.block1);
|
|
|
|
// transform catch (excNo, arg) into TVM-catch (arg, excNo), where arg is untyped and thus almost useless now
|
|
const std::vector<AnyExprV>& catch_vars = v->get_catch_expr()->get_items();
|
|
tolk_assert(catch_vars.size() == 2);
|
|
process_catch_variable(catch_vars[0], code);
|
|
process_catch_variable(catch_vars[1], code);
|
|
try_catch_op.left = pre_compile_tensor(code, {catch_vars[1], catch_vars[0]});
|
|
process_any_statement(v->get_catch_body(), code);
|
|
code.close_pop_cur(v->get_catch_body()->loc_end);
|
|
}
|
|
|
|
static void process_repeat_statement(V<ast_repeat_statement> v, CodeBlob& code) {
|
|
std::vector<var_idx_t> tmp_vars = pre_compile_expr(v->get_cond(), code, nullptr);
|
|
Op& repeat_op = code.emplace_back(v->loc, Op::_Repeat, tmp_vars);
|
|
code.push_set_cur(repeat_op.block0);
|
|
process_any_statement(v->get_body(), code);
|
|
code.close_pop_cur(v->get_body()->loc_end);
|
|
}
|
|
|
|
static void process_if_statement(V<ast_if_statement> v, CodeBlob& code) {
|
|
std::vector<var_idx_t> cond = pre_compile_expr(v->get_cond(), code, nullptr);
|
|
tolk_assert(cond.size() == 1);
|
|
|
|
if (v->get_cond()->is_always_true) {
|
|
process_any_statement(v->get_if_body(), code); // v->is_ifnot does not matter here
|
|
return;
|
|
}
|
|
if (v->get_cond()->is_always_false) {
|
|
process_any_statement(v->get_else_body(), code);
|
|
return;
|
|
}
|
|
|
|
Op& if_op = code.emplace_back(v->loc, Op::_If, std::move(cond));
|
|
code.push_set_cur(if_op.block0);
|
|
process_any_statement(v->get_if_body(), code);
|
|
code.close_pop_cur(v->get_if_body()->loc_end);
|
|
code.push_set_cur(if_op.block1);
|
|
process_any_statement(v->get_else_body(), code);
|
|
code.close_pop_cur(v->get_else_body()->loc_end);
|
|
if (v->is_ifnot) {
|
|
std::swap(if_op.block0, if_op.block1);
|
|
}
|
|
}
|
|
|
|
static void process_do_while_statement(V<ast_do_while_statement> v, CodeBlob& code) {
|
|
Op& until_op = code.emplace_back(v->loc, Op::_Until);
|
|
code.push_set_cur(until_op.block0);
|
|
process_any_statement(v->get_body(), code);
|
|
|
|
// in TVM, there is only "do until", but in Tolk, we want "do while"
|
|
// here we negate condition to pass it forward to legacy to Op::_Until
|
|
// also, handle common situations as a hardcoded "optimization": replace (a<0) with (a>=0) and so on
|
|
// todo these hardcoded conditions should be removed from this place in the future
|
|
AnyExprV cond = v->get_cond();
|
|
AnyExprV until_cond;
|
|
if (auto v_not = cond->try_as<ast_unary_operator>(); v_not && v_not->tok == tok_logical_not) {
|
|
until_cond = v_not->get_rhs();
|
|
} else if (auto v_eq = cond->try_as<ast_binary_operator>(); v_eq && v_eq->tok == tok_eq) {
|
|
until_cond = createV<ast_binary_operator>(cond->loc, "!=", tok_neq, v_eq->get_lhs(), v_eq->get_rhs());
|
|
} else if (auto v_neq = cond->try_as<ast_binary_operator>(); v_neq && v_neq->tok == tok_neq) {
|
|
until_cond = createV<ast_binary_operator>(cond->loc, "==", tok_eq, v_neq->get_lhs(), v_neq->get_rhs());
|
|
} else if (auto v_leq = cond->try_as<ast_binary_operator>(); v_leq && v_leq->tok == tok_leq) {
|
|
until_cond = createV<ast_binary_operator>(cond->loc, ">", tok_gt, v_leq->get_lhs(), v_leq->get_rhs());
|
|
} else if (auto v_lt = cond->try_as<ast_binary_operator>(); v_lt && v_lt->tok == tok_lt) {
|
|
until_cond = createV<ast_binary_operator>(cond->loc, ">=", tok_geq, v_lt->get_lhs(), v_lt->get_rhs());
|
|
} else if (auto v_geq = cond->try_as<ast_binary_operator>(); v_geq && v_geq->tok == tok_geq) {
|
|
until_cond = createV<ast_binary_operator>(cond->loc, "<", tok_lt, v_geq->get_lhs(), v_geq->get_rhs());
|
|
} else if (auto v_gt = cond->try_as<ast_binary_operator>(); v_gt && v_gt->tok == tok_gt) {
|
|
until_cond = createV<ast_binary_operator>(cond->loc, "<=", tok_geq, v_gt->get_lhs(), v_gt->get_rhs());
|
|
} else if (cond->inferred_type == TypeDataBool::create()) {
|
|
until_cond = createV<ast_unary_operator>(cond->loc, "!b", tok_logical_not, cond);
|
|
} else {
|
|
until_cond = createV<ast_unary_operator>(cond->loc, "!", tok_logical_not, cond);
|
|
}
|
|
until_cond->mutate()->assign_inferred_type(TypeDataInt::create());
|
|
if (auto v_bin = until_cond->try_as<ast_binary_operator>(); v_bin && !v_bin->fun_ref) {
|
|
v_bin->mutate()->assign_fun_ref(lookup_global_symbol("_" + static_cast<std::string>(v_bin->operator_name) + "_")->try_as<FunctionPtr>());
|
|
} else if (auto v_un = until_cond->try_as<ast_unary_operator>(); v_un && !v_un->fun_ref) {
|
|
v_un->mutate()->assign_fun_ref(lookup_global_symbol(static_cast<std::string>(v_un->operator_name) + "_")->try_as<FunctionPtr>());
|
|
}
|
|
|
|
until_op.left = pre_compile_expr(until_cond, code, nullptr);
|
|
tolk_assert(until_op.left.size() == 1);
|
|
code.close_pop_cur(v->get_body()->loc_end);
|
|
}
|
|
|
|
static void process_while_statement(V<ast_while_statement> v, CodeBlob& code) {
|
|
Op& while_op = code.emplace_back(v->loc, Op::_While);
|
|
code.push_set_cur(while_op.block0);
|
|
while_op.left = pre_compile_expr(v->get_cond(), code, nullptr);
|
|
tolk_assert(while_op.left.size() == 1);
|
|
code.close_pop_cur(v->get_body()->loc);
|
|
code.push_set_cur(while_op.block1);
|
|
process_any_statement(v->get_body(), code);
|
|
code.close_pop_cur(v->get_body()->loc_end);
|
|
}
|
|
|
|
static void process_throw_statement(V<ast_throw_statement> v, CodeBlob& code) {
|
|
if (v->has_thrown_arg()) {
|
|
FunctionPtr builtin_sym = lookup_global_symbol("__throw_arg")->try_as<FunctionPtr>();
|
|
std::vector<var_idx_t> args_vars = pre_compile_tensor(code, {v->get_thrown_arg(), v->get_thrown_code()});
|
|
gen_op_call(code, TypeDataVoid::create(), v->loc, std::move(args_vars), builtin_sym, "(throw-call)");
|
|
} else {
|
|
FunctionPtr builtin_sym = lookup_global_symbol("__throw")->try_as<FunctionPtr>();
|
|
std::vector<var_idx_t> args_vars = pre_compile_tensor(code, {v->get_thrown_code()});
|
|
gen_op_call(code, TypeDataVoid::create(), v->loc, std::move(args_vars), builtin_sym, "(throw-call)");
|
|
}
|
|
}
|
|
|
|
static void process_return_statement(V<ast_return_statement> v, CodeBlob& code) {
|
|
std::vector<var_idx_t> return_vars;
|
|
if (v->has_return_value()) {
|
|
TypePtr child_target_type = code.fun_ref->inferred_return_type;
|
|
if (code.fun_ref->does_return_self()) {
|
|
child_target_type = code.fun_ref->parameters[0].declared_type;
|
|
}
|
|
return_vars = pre_compile_expr(v->get_return_value(), code, child_target_type);
|
|
}
|
|
if (code.fun_ref->does_return_self()) {
|
|
return_vars = {};
|
|
}
|
|
if (code.fun_ref->has_mutate_params()) {
|
|
std::vector<var_idx_t> mutated_vars;
|
|
for (const LocalVarData& p_sym: code.fun_ref->parameters) {
|
|
if (p_sym.is_mutate_parameter()) {
|
|
mutated_vars.insert(mutated_vars.end(), p_sym.ir_idx.begin(), p_sym.ir_idx.end());
|
|
}
|
|
}
|
|
return_vars.insert(return_vars.begin(), mutated_vars.begin(), mutated_vars.end());
|
|
}
|
|
code.emplace_back(v->loc, Op::_Return, std::move(return_vars));
|
|
}
|
|
|
|
// append "return" (void) to the end of the function
|
|
// if it's not reachable, it will be dropped
|
|
// (IR cfg reachability may differ from FlowContext in case of "never" types, so there may be situations,
|
|
// when IR will consider this "return" reachable and leave it, but actually execution will never reach it)
|
|
static void append_implicit_return_statement(SrcLocation loc_end, CodeBlob& code) {
|
|
std::vector<var_idx_t> mutated_vars;
|
|
if (code.fun_ref->has_mutate_params()) {
|
|
for (const LocalVarData& p_sym: code.fun_ref->parameters) {
|
|
if (p_sym.is_mutate_parameter()) {
|
|
mutated_vars.insert(mutated_vars.end(), p_sym.ir_idx.begin(), p_sym.ir_idx.end());
|
|
}
|
|
}
|
|
}
|
|
code.emplace_back(loc_end, Op::_Return, std::move(mutated_vars));
|
|
}
|
|
|
|
|
|
void process_any_statement(AnyV v, CodeBlob& code) {
|
|
switch (v->type) {
|
|
case ast_sequence:
|
|
return process_sequence(v->as<ast_sequence>(), code);
|
|
case ast_return_statement:
|
|
return process_return_statement(v->as<ast_return_statement>(), code);
|
|
case ast_repeat_statement:
|
|
return process_repeat_statement(v->as<ast_repeat_statement>(), code);
|
|
case ast_if_statement:
|
|
return process_if_statement(v->as<ast_if_statement>(), code);
|
|
case ast_do_while_statement:
|
|
return process_do_while_statement(v->as<ast_do_while_statement>(), code);
|
|
case ast_while_statement:
|
|
return process_while_statement(v->as<ast_while_statement>(), code);
|
|
case ast_throw_statement:
|
|
return process_throw_statement(v->as<ast_throw_statement>(), code);
|
|
case ast_assert_statement:
|
|
return process_assert_statement(v->as<ast_assert_statement>(), code);
|
|
case ast_try_catch_statement:
|
|
return process_try_catch_statement(v->as<ast_try_catch_statement>(), code);
|
|
case ast_empty_statement:
|
|
return;
|
|
default:
|
|
pre_compile_expr(reinterpret_cast<AnyExprV>(v), code, nullptr);
|
|
}
|
|
}
|
|
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static void convert_function_body_to_CodeBlob(FunctionPtr fun_ref, FunctionBodyCode* code_body) {
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auto v_body = fun_ref->ast_root->as<ast_function_declaration>()->get_body()->as<ast_sequence>();
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CodeBlob* blob = new CodeBlob{fun_ref->name, fun_ref->loc, fun_ref};
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std::vector<var_idx_t> rvect_import;
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int total_arg_width = 0;
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for (int i = 0; i < fun_ref->get_num_params(); ++i) {
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total_arg_width += fun_ref->parameters[i].declared_type->get_width_on_stack();
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}
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rvect_import.reserve(total_arg_width);
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for (int i = 0; i < fun_ref->get_num_params(); ++i) {
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const LocalVarData& param_i = fun_ref->parameters[i];
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std::vector<var_idx_t> ir_idx = blob->create_var(param_i.declared_type, param_i.loc, param_i.name);
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rvect_import.insert(rvect_import.end(), ir_idx.begin(), ir_idx.end());
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param_i.mutate()->assign_ir_idx(std::move(ir_idx));
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}
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blob->emplace_back(fun_ref->loc, Op::_Import, rvect_import);
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blob->in_var_cnt = blob->var_cnt;
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tolk_assert(blob->var_cnt == total_arg_width);
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for (AnyV item : v_body->get_items()) {
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process_any_statement(item, *blob);
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}
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append_implicit_return_statement(v_body->loc_end, *blob);
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blob->close_blk(v_body->loc_end);
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code_body->set_code(blob);
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tolk_assert(vars_modification_watcher.empty());
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}
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static void convert_asm_body_to_AsmOp(FunctionPtr fun_ref, FunctionBodyAsm* asm_body) {
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int cnt = fun_ref->get_num_params();
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int width = fun_ref->inferred_return_type->get_width_on_stack();
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std::vector<AsmOp> asm_ops;
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for (AnyV v_child : fun_ref->ast_root->as<ast_function_declaration>()->get_body()->as<ast_asm_body>()->get_asm_commands()) {
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std::string_view ops = v_child->as<ast_string_const>()->str_val; // <op>\n<op>\n...
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std::string op;
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for (char c : ops) {
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if (c == '\n' || c == '\r') {
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if (!op.empty()) {
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asm_ops.push_back(AsmOp::Parse(op, cnt, width));
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if (asm_ops.back().is_custom()) {
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cnt = width;
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}
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op.clear();
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}
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} else {
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op.push_back(c);
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}
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}
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if (!op.empty()) {
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asm_ops.push_back(AsmOp::Parse(op, cnt, width));
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if (asm_ops.back().is_custom()) {
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cnt = width;
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}
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}
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}
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asm_body->set_code(std::move(asm_ops));
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}
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class UpdateArgRetOrderConsideringStackWidth final {
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public:
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static bool should_visit_function(FunctionPtr fun_ref) {
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return !fun_ref->is_generic_function() && (!fun_ref->ret_order.empty() || !fun_ref->arg_order.empty());
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}
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static void start_visiting_function(FunctionPtr fun_ref, V<ast_function_declaration> v_function) {
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int total_arg_mutate_width = 0;
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bool has_arg_width_not_1 = false;
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for (const LocalVarData& param : fun_ref->parameters) {
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int arg_width = param.declared_type->get_width_on_stack();
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has_arg_width_not_1 |= arg_width != 1;
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total_arg_mutate_width += param.is_mutate_parameter() * arg_width;
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}
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// example: `fun f(a: int, b: (int, (int, int)), c: int)` with `asm (b a c)`
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// current arg_order is [1 0 2]
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// needs to be converted to [1 2 3 0 4] because b width is 3
|
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if (has_arg_width_not_1) {
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int total_arg_width = 0;
|
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std::vector<int> cum_arg_width;
|
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cum_arg_width.reserve(1 + fun_ref->get_num_params());
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cum_arg_width.push_back(0);
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for (const LocalVarData& param : fun_ref->parameters) {
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cum_arg_width.push_back(total_arg_width += param.declared_type->get_width_on_stack());
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}
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std::vector<int> arg_order;
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for (int i = 0; i < fun_ref->get_num_params(); ++i) {
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int j = fun_ref->arg_order[i];
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int c1 = cum_arg_width[j], c2 = cum_arg_width[j + 1];
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while (c1 < c2) {
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arg_order.push_back(c1++);
|
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}
|
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}
|
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fun_ref->mutate()->assign_arg_order(std::move(arg_order));
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}
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// example: `fun f(mutate self: slice): slice` with `asm(-> 1 0)`
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// ret_order is a shuffled range 0...N
|
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// validate N: a function should return value and mutated arguments onto a stack
|
|
if (!fun_ref->ret_order.empty()) {
|
|
size_t expected_width = fun_ref->inferred_return_type->get_width_on_stack() + total_arg_mutate_width;
|
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if (expected_width != fun_ref->ret_order.size()) {
|
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v_function->get_body()->error("ret_order (after ->) expected to contain " + std::to_string(expected_width) + " numbers");
|
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}
|
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}
|
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}
|
|
};
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|
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class ConvertASTToLegacyOpVisitor final {
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public:
|
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static bool should_visit_function(FunctionPtr fun_ref) {
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return !fun_ref->is_generic_function();
|
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}
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static void start_visiting_function(FunctionPtr fun_ref, V<ast_function_declaration>) {
|
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tolk_assert(fun_ref->is_type_inferring_done());
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if (fun_ref->is_code_function()) {
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convert_function_body_to_CodeBlob(fun_ref, std::get<FunctionBodyCode*>(fun_ref->body));
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} else if (fun_ref->is_asm_function()) {
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convert_asm_body_to_AsmOp(fun_ref, std::get<FunctionBodyAsm*>(fun_ref->body));
|
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}
|
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}
|
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};
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void pipeline_convert_ast_to_legacy_Expr_Op() {
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visit_ast_of_all_functions<UpdateArgRetOrderConsideringStackWidth>();
|
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visit_ast_of_all_functions<ConvertASTToLegacyOpVisitor>();
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}
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} // namespace tolk
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