---
title: "Internals"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Internals}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
tcc_bind <- Rtinycc::tcc_bind
tcc_callback_close <- Rtinycc::tcc_callback_close
tcc_compile <- Rtinycc::tcc_compile
tcc_cstring <- Rtinycc::tcc_cstring
tcc_data_ptr <- Rtinycc::tcc_data_ptr
tcc_ffi <- Rtinycc::tcc_ffi
tcc_get_symbol <- Rtinycc::tcc_get_symbol
tcc_link <- Rtinycc::tcc_link
tcc_malloc <- Rtinycc::tcc_malloc
tcc_relocate <- Rtinycc::tcc_relocate
tcc_set_options <- Rtinycc::tcc_set_options
tcc_source <- Rtinycc::tcc_source
```

This article is about how `Rtinycc` works internally. It is not the stable
user-facing API contract. The pieces described here are current implementation
choices and may change as the package evolves.

At a high level, the package is built as a pipeline:

1. an R-side recipe is accumulated in a `tcc_ffi` object
2. that recipe is turned into a generated C translation unit
3. TinyCC compiles and relocates the generated code in memory
4. wrapper pointers are recovered and exposed back to R as closures

## The `tcc_ffi` Object Is a Recipe

`tcc_ffi()` does not compile anything by itself. It creates a plain R object
that accumulates:

- bound symbols
- user headers and user C code
- library and include paths
- extra compiler options
- helper declarations such as structs, unions, enums, globals, and callback use

That state lives in the `tcc_ffi` list object built by `tcc_ffi_object()`. The
important point is that `tcc_compile()` works from this declarative recipe, not
from an already-live TCC process.

```{r}
ffi <- tcc_ffi() |>
  tcc_source("int add(int a, int b) { return a + b; }") |>
  tcc_bind(add = list(args = list("i32", "i32"), returns = "i32"))

names(ffi)
```

## Code Generation Is Central

`tcc_compile()` calls the internal `generate_ffi_code()` helper to assemble one
large C source string. That generated source is the real boundary layer between
R and the target C functions.

Internally, the generated translation unit is assembled in this order:

- a TinyCC workaround for `_Complex`
- `R.h` and `Rinternals.h`
- callback trampoline declarations when needed
- user headers
- external declarations for `tcc_link()`
- user C code
- generated helpers for structs, unions, enums, globals, and raw access
- generated `SEXP` wrappers for each bound symbol

For a small binding:

```{r}
code <- Rtinycc:::generate_ffi_code(
  symbols = ffi$symbols,
  headers = ffi$headers,
  c_code = ffi$c_code,
  is_external = FALSE,
  structs = ffi$structs,
  unions = ffi$unions,
  enums = ffi$enums,
  globals = ffi$globals,
  container_of = ffi$container_of,
  field_addr = ffi$field_addr,
  struct_raw_access = ffi$struct_raw_access,
  introspect = ffi$introspect
)

grepl("SEXP R_wrap_add", code, fixed = TRUE)
```

The wrapper is where input coercion, range checks, callback trampoline setup,
actual C invocation, and return boxing happen.

## How Values Move Between R, The Wrapper, And C

The important internal boundary is not "R calls user C directly". The flow is:

1. an R closure created by `make_callable()` calls `.Call` with the compiled
   wrapper's native symbol external pointer
2. that wrapper receives `SEXP` arguments
3. wrapper code uses the R C API to decode or borrow data from those `SEXP`s
4. the wrapper calls the target C symbol using ordinary C arguments
5. the wrapper converts the C result back into a `SEXP`
6. `.Call` returns that `SEXP` to the R interpreter

So the generated wrapper is the translator between:

- R evaluation and `SEXP` objects on one side
- the target function's plain C signature on the other side

This is why `Rtinycc` includes `R.h` and `Rinternals.h` in every generated
translation unit and why the wrapper code uses constructors and accessors such
as:

- `asInteger()` / `asReal()`
- `RAW()`, `INTEGER()`, `REAL()`, `LOGICAL()`
- `STRING_ELT()` and `Rf_translateCharUTF8()`
- `ScalarInteger()`, `ScalarReal()`, `ScalarLogical()`
- `mkString()` and `R_MakeExternalPtr()`

At the R level, `make_callable()` builds a small closure around the compiled
wrapper pointer. That closure does argument-count validation, checks that the
pointer is still valid, and then hands control to `.Call`.

The wrapper itself is where the actual C API interaction happens.

## Copying Versus Borrowing Happens In The Wrapper

The copy model is mostly determined by the generated conversion code.

Scalar inputs are copied or coerced into local C values:

- integers and booleans go through `asInteger()`
- doubles go through `asReal()`
- range checks happen before the target function is called

These are not zero-copy paths.

Vector inputs are split into two groups:

- `raw`, `integer_array`, `numeric_array`, and `logical_array` borrow writable
  R vector storage directly; for ALTREP inputs, R may materialize the vector
  when the wrapper asks for a C data pointer
- `cstring_array` allocates a temporary pointer array with `R_alloc()` and
  fills it from translated R strings

String and pointer inputs need more care:

- `cstring` uses `STRING_ELT()` plus `Rf_translateCharUTF8()` for the duration
  of the call
- `ptr` reads the raw address from an external pointer with
  `R_ExternalPtrAddr()`
- `sexp` passes the original `SEXP` through unchanged

Returns have their own copy model:

- scalar returns are boxed into fresh R objects
- `cstring` returns are copied into R-managed string memory with `mkString()`
- `ptr` returns stay as external pointers to raw addresses
- array returns allocate a fresh R vector and `memcpy()` the C buffer into it

So the internal design is intentionally mixed:

- borrow when R already has contiguous vector storage that matches the C view
- copy when returning data into R-managed memory
- keep raw pointers raw when the package cannot safely invent ownership

That is the main semantic reason the generated wrapper layer exists.

## Why `lambda.r` Is Used

The large rule file `R/aaa_ffi_codegen_rules.R` uses `lambda.r` as a small
dispatch DSL. The package imports `%as%` and `UseFunction`, and defines rules
like:

- `ffi_input_rule(...)`
- `ffi_return_rule(...)`
- `array_return_alloc_line_rule(...)`
- `c_default_return_rule(...)`
- `ffi_c_type_map_rule(...)`

Those rules are not user-facing metaprogramming. They are an internal way to
register many small code-generation cases without turning
`R/ffi_codegen.R` into one enormous nest of `if` and `switch` statements.

In practice, `generate_c_input()` and `generate_c_return()` delegate into that
rule table:

```{r}
Rtinycc:::generate_c_input("x", "arg1_", "i32")
Rtinycc:::generate_c_return("res", "f64")
```

The main tradeoff is simple:

- `lambda.r` keeps the dispatch table explicit and composable
- the rule file becomes long because many integer, floating-point, and helper
  cases are still written out individually

So `lambda.r` here is being used for internal rule dispatch and code-template
selection, not because the public API depends on functional programming style.

## Wrapper Builders Work at the `SEXP` Boundary

`Rtinycc` is not using a libffi ABI layer. The generated wrappers are normal C
functions with `SEXP` signatures so that R can call them through `.Call`.

The key internal steps are:

- `generate_wrappers()` decides which wrapper variants are needed
- `generate_c_wrapper()` builds the normal synchronous wrapper body
- `generate_async_exec_wrapper()` builds the async execution path for
  `callback_async:` arguments
- `generate_callback_trampolines()` emits trampoline functions for callback
  arguments

For non-variadic bindings, the generated wrapper is named `R_wrap_<symbol>`.
Variadic bindings generate several wrapper variants and dispatch is chosen later
from R based on tail arity or inferred tail types.

This design keeps platform-specific calling conventions inside compiled C rather
than trying to reproduce them from R.

## Protection And Lifetime Rules Matter

Because wrappers use the R C API directly, protection and object lifetime are
part of the internal design.

When wrapper code allocates a fresh R object, it protects that object until the
result is fully built and returned. Typical cases include:

- array returns that allocate `out`
- `cstring` returns that construct an R string
- callback trampolines that build an argument list before calling back into R

Borrowed pointers have a different constraint: they are only sound as long as
the underlying owner stays alive and the wrapper does not invalidate the
assumption by introducing unexpected allocation patterns.

This is especially important for:

- zero-copy vector inputs
- borrowed field-address helpers for structs and unions
- callback token pointers that must remain tied to a live callback registry

The package also uses external pointer metadata and protected slots to encode
lifetime relationships. For example, borrowed field pointers can keep their
owner object alive by storing that owner in the external pointer's protected
field.

## Ownership And Lifetime Semantics In The Main Cases

The main internal cases are easier to reason about if you separate them by who
owns the underlying storage and how long the view is valid.

### Call-scoped borrows from R objects

These values are borrowed from existing R objects and are only intended to be
used during the wrapper call:

- `raw`, `integer_array`, `numeric_array`, and `logical_array` inputs borrow
  writable backing R vector storage; ALTREP inputs may be materialized by R on
  pointer access
- `cstring` input borrows the translated string pointer for the duration of the
  call
- `sexp` input borrows the original R object directly

The wrapper does not transfer ownership of these objects to C. If target C code
stores the pointer and uses it after the call returns, that is outside the safe
contract.

### Owned native allocations

These are heap allocations owned through explicit external-pointer semantics:

- `tcc_malloc()` returns `rtinycc_owned` memory with a finalizer
- `tcc_cstring()` returns a malloc-backed UTF-8 C string with the same owned tag
- generated struct and union constructors allocate native storage and attach
  type-specific finalizers

These objects have a stable native lifetime until:

- they are explicitly freed
- their owner-specific free helper is called
- or their finalizer runs during normal R lifetime

### Borrowed native views

These are external pointers that point into someone else's storage:

- `tcc_data_ptr()` returns a borrowed pointer
- field-address helpers for structs and unions return borrowed pointers
- many plain `ptr` returns are just raw addresses wrapped as external pointers

Borrowed pointers do not imply ownership and must not be freed as if they were
`rtinycc_owned`. Their validity depends entirely on the lifetime of the
underlying storage.

### Returned R objects

When the wrapper returns a scalar, string, or copied array to R, the result is
an ordinary R-managed object:

- scalar returns are fresh boxed R values
- `cstring` returns become fresh R strings
- array returns become fresh R vectors after copying

Once returned, these objects follow the normal R GC lifetime and are no longer
tied to the lifetime of the original C storage.

### Callback registry lifetime

Callbacks have a separate ownership model:

- the callback registry preserves the underlying R function
- callback tokens are external pointers referencing registry entries
- `tcc_callback_close()` releases the preserved function deterministically
- if not closed manually, finalizers and package unload eventually release it

This means the callback object is not just a function pointer. It is a managed
pairing of:

- preserved R function state
- callback metadata
- one or more external-pointer handles to the token

### Compiled object lifetime

A `tcc_compiled` object owns a live TCC state and the wrapper pointers recovered
from that state.

When that state dies, the wrapper pointers are dead as machine-code references
even though the R closures still exist. That is why the package stores a recipe
and recompiles instead of pretending those pointers survive serialization.

## Host Symbol Injection Happens Before Relocation

After the generated code is compiled, `tcc_ffi_compile_state()` calls the C
entry point `RC_libtcc_add_host_symbols()` before `tcc_relocate()`.

That host-injection step registers package-side C helpers with the live TCC
state. This matters most on macOS, where the package cannot rely on the dynamic
linker to expose every host symbol the same way TinyCC expects.

The injected symbols include:

- `RC_free_finalizer`
- callback invocation helpers
- async callback scheduling helpers
- async drain helpers
- the `RC_callback_async_exec_c()` helper used by generated async wrappers

The important semantic point is that some generated C code depends on package
runtime helpers, not just on user code and the R API.

## Callback Round-Trips Cross The Boundary Twice

Callbacks are the clearest example of value exchange between plain C and the R
interpreter.

For synchronous callbacks:

1. generated C trampoline code receives plain C arguments
2. the trampoline boxes them into a `VECSXP` argument list
3. it calls `RC_invoke_callback_id()`
4. the runtime builds and evaluates the R call with `R_tryEvalSilent()`
5. the result is converted back into the declared C return type
6. the trampoline returns that C value to the original compiled code

So a callback call is:

- C values -> boxed into R objects
- evaluated in R
- converted back from R objects -> C values

Async callbacks add one more layer: arguments are first marshaled into a
cross-thread task representation, then rebuilt as fresh R objects on the main
thread before the callback is evaluated.

## State Creation Is Separate from Compilation

The TCC state is created first, then populated and compiled.

Internally:

- `tcc_ffi_create_state()` creates the state with bundled TinyCC include/lib
  paths, user include/lib paths, and R headers/runtime library paths
- user compiler options are applied with `tcc_set_options()`
- `tcc_ffi_compile_state()` adds requested libraries, always links `R`,
  compiles the generated C string, injects host symbols, then relocates

This split is useful because both `tcc_compile()` and `tcc_link()` follow the
same broad pattern even though one starts from user C source and the other
starts from external-library declarations.

## The Compiled Object Is an Environment of Closures

After relocation, `tcc_compiled_object()` recovers wrapper symbols with
`tcc_get_symbol()` and turns them into R callables with `make_callable()`.

That compiled object is an environment, not an S4 class or external pointer
wrapper. The environment stores:

- callable closures for user symbols
- callable closures for generated helpers
- the live TCC state
- metadata such as symbol specs and helper specs

For non-variadic functions, `make_callable()` creates a closure that:

- checks arity
- checks that the wrapper pointer is still valid
- calls the wrapper through `.Call`

For variadic bindings, the closure selects the matching precompiled wrapper
first, then calls that wrapper pointer.

## Serialization Works by Recompiling the Recipe

Compiled wrapper pointers do not survive serialization as usable machine code.
`Rtinycc` handles this by storing the original recipe:

- `tcc_compile()` stores `.ffi` on the compiled object
- `tcc_link()` stores `.link_args`
- `$.tcc_compiled` checks whether the state pointer is still valid
- if not, `recompile_into()` rebuilds a fresh compiled object and copies the
  bindings back into the target environment

So serialization support is not pointer persistence. It is recipe persistence
plus transparent recompilation.

## Where To Read Next

If you want to inspect the implementation directly, the main files are:

- `R/ffi.R`: high-level FFI object, compilation flow, compiled-object assembly
- `R/ffi_codegen.R`: generated wrapper and translation-unit builders
- `R/aaa_ffi_codegen_rules.R`: rule tables for conversions and mapping
- `R/callbacks.R`: callback parsing and trampoline generation helpers
- `src/RC_libtcc.c`: TCC/R bridge, host symbol injection, callback runtime
