LibTorch Stable ABI

Created On: Mar 17, 2025 | Last Updated On: Oct 01, 2025

Overview

The LibTorch Stable ABI (Application Binary Interface) provides an interface for extending PyTorch functionality without being tightly coupled to specific PyTorch versions. This enables the development of custom operators and extensions that remain compatible across PyTorch releases.

The stable ABI consists of three main components:

1. Stable C headers - Low-level C API implemented by libtorch (primarily torch/csrc/inductor/aoti_torch/c/shim.h)

2. Header-only C++ library - Standalone utilities implemented in only headers such that there is no dependence on libtorch (torch/headeronly/*)

3. Stable C++ wrappers - High-level C++ convenience wrappers (torch/csrc/stable/*)

We discuss each of these in detail

torch/headeronly

This is a set of inlined C++ headers are completely decoupled from libtorch. The headers consist of certain utilities that might be familiar to custom extension writers. For example, the c10::ScalarType enum lives here as torch::headeronly::ScalarType.

torch/csrc/stable

This is a set of inlined C++ headers that provide wrappers around the C API that handle the rough edges discussed below.

It consists of

• torch/csrc/stable/library.h: Provides a stable version of TORCH_LIBRARY and similar macros.

• torch/csrc/stable/tensor_struct.h: Provides torch::stable::Tensor, a stable version of at::Tensor.

• torch/csrc/stable/ops.h: Provides a stable interface for calling ATen ops from native_functions.yaml.

• torch/csrc/stable/accelerator.h: Provides a stable interface for device-generic objects and APIs (e.g. getCurrentStream, DeviceGuard).

We are continuing to improve coverage in our torch/csrc/stable APIs. Please file an issue if you’d like to see support for particular APIs in your custom extension.

Stable C headers

The stable C headers used by AOTInductor form the foundation of the stable ABI. However, this is use at your own risk. For example, users must handle the memory lifecycle of objects returned by certain APIs. Further, the stack-based APIs discussed below which allow the user to call the PyTorch dispatcher don’t provide strong guarantees on forward and backward compatibility.

Unless absolutely necessary, we recommend the high-level C++ API in torch/csrc/stable which will handle all the rough edges of the C API for the user.

How are objects passed across the ABI boundary when interacting with the dispatcher?

When interacting with the dispatcher via the stable APIs (STABLE_TORCH_LIBRARY etc.) we use a boxed convention. Arguments and returns are represented as a stack of StableIValue which correlates with a torch::jit::stack of IValues. We discuss the following below

1. StableIValue Conversions

2. StableIValue stack Conventions

3. Stable APIs that interact with the dispatcher

StableIValue Conversions

We provide utilities for users to convert objects to and from StableIValues with the synonymous to and from APIs in torch/csrc/stable/stableivalue_conversions.h. We document the stable custom extension representation, libtorch representation and StableIValue representations below. Our confidently supported types are the ones in the table that have completed rows. You can rely on this subset for proper ABI stability, meaning that you can call to<T_custom_ext>(arg/ret) or from(T) on these types.

For a limited set of use cases, we also implicitly support any literal type that is representable within 64 bits as StableIValues, as the default reinterpret_cast will succeed. (For example: c10::Device.) These types are currently ABI-stable on best effort but might break in the future and thus should be used for short term testing only.

You can always work with StableIValue abstractions in your custom kernel for types such as c10::Device even if there is no standard defined representation of device in custom extensions by not introspecting into the StableIValue. For example, a custom operator can take as argument a StableIValue device and directly pass it through to an aten operator with aoti_torch_call_dispatcher.

1. type in custom extension: type used within the end user custom library.

2. StableIValue representation: a stable conversion of the type to liaison between the user model vs libtorch.so in an ABI-stable manner.

3. type in libtorch: type used within libtorch.so (or any code binary locked with libtorch).

4. Schema Type: type as described by the schema, which we hail as the source of truth for both ATen ops in native_functions.yaml and for user defined custom operators registered to the dispatcher via TORCH_LIBRARY or torch.library.

type in custom extension	StableIValue representation	type in libtorch	Schema Type
std::optional<S>	if there is a value, raw bitwise copy into leading bytes of uint64_t of pointer to a new StableIValue representing S. if there is no value, nullptr.	std::optional<T>	Type?
torch::stable::Tensor	raw bitwise copy of underlying AtenTensorHandle into leading bytes of uint64_t	at::Tensor	Tensor
RAIIATH (outdated)	raw bitwise copy of underlying AtenTensorHandle into leading bytes of uint64_t	at::Tensor	Tensor
torch::headeronly::ScalarType	raw bitwise copy of the translated underlying enum into leading bytes of uint64_t	torch::headeronly::ScalarType	ScalarType
int32_t	raw bitwise copy into leading bytes of uint64_t	at::Layout	Layout
int32_t	raw bitwise copy into leading bytes of uint64_t	at::MemoryFormat	MemoryFormat
bool	raw bitwise copy into leading bytes of uint64_t	bool	bool
int64_t	raw bitwise copy into leading bytes of uint64_t	int64_t	int
double	raw bitwise copy into leading bytes of uint64_t	double	float
?	?	c10::Device	Device
?	?	c10::Stream	Stream
?	?	c10::complex	complex
?	?	at::Scalar	Scalar
?	?	std::string/const char*/ivalue::ConstantString	str
?	?	at::Storage	Storage
?	?	at::Generator	Generator
?	?	c10::List<T>	Type[]
?	?	ivalue::Tuple<T>	(Type, …)
?	?	c10::SymInt	SymInt
?	?	c10::SymFloat	SymFloat
?	?	c10::SymBool	SymBool
?	?	at::QScheme	QScheme

Stack Conventions

There are two invariants for the stack:

1. The stack is populated left to right. a. For example, a stack representing arguments arg0, arg1, and arg2 will have arg0 at index 0, arg1 at index 1, and arg2 at index 2. b. Returns are also populated left to right, e.g., ret0 will be at index 0 and ret1 will be at index 1, and so on.

2. The stack always has ownership of the objects it holds. a. When calling a stack-based API, you must give owning references to the calling stack and steal references from the returned stack. b. When registering your function to be called with a stack, you must steal references from your argument stack and push onto the stack new references.

Stack-based APIs

The above is relevant in two places:

1. STABLE_TORCH_LIBRARY Unlike TORCH_LIBRARY, the dispatcher expects kernels registered via STABLE_TORCH_LIBRARY to be boxed. This means they must have the signature (StableIValue* stack, uint64_t num_args, uint64_t num_outputs) -> void.We plan to eventually abstract away the need for manual boxing, but, for the time being, please use from and to.

   Tensor my_amax_vec(Tensor t) {
       std::vector<int64_t> v = {0,1};
       return amax(t, v, false);
   }
   
   void boxed_my_amax_vec(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
       auto res = my_amax_vec(to<Tensor>(stack[0]));
       stack[0] = from(res);
   }
   

2. aoti_torch_call_dispatcher This API allows you to call the PyTorch dispatcher from C/C++ code. It has the following signature:

   aoti_torch_call_dispatcher(const char* opName, const char* overloadName, StableIValue* stack);
   

   aoti_torch_call_dispatcher will call the op overload defined by a given opName, overloadName, and a stack of StableIValues. This call will populate any return values of the op into the stack in their StableIValue form, with ret0 at index 0, ret1 at index 1, and so on.