Kernel Registration

Overview

At the last stage of ExecuTorch model exporting, we lower the operators in the dialect to the out variants of the core ATen operators. Then we serialize these operator names into the model artifact. During runtime execution, for each operator name we will need to find the actual kernels, i.e., the C++ functions that do the heavy-lifting calculations and return results.

Portable kernel library is the in-house default kernel library, it’s easy to use and portable for most of the target backends. However it’s not optimized for performance, because it’s not specialized for any certain target. Therefore we provide kernel registration APIs for ExecuTorch users to easily register their own optimized kernels.

Design Principles

What do we support? On the operator coverage side, the kernel registration APIs allow users to register kernels for all core ATen ops as well as custom ops, as long as the custom ops schemas are specified.

Notice that we also support _partial kernels, _for example the kernel only supports a subset of tensor dtypes and/or dim orders.

Kernel contract: kernels need to comply with the following requirements:

• Match the calling convention derived from operator schema. The kernel registration API will generate headers for the custom kernels as references.

• Satisfy the dtype constraints defined in edge dialect. For tensors with certain dtypes as arguments, the result of a custom kernel needs to match the expected dtypes. The constraints are available in edge dialect ops.

• Gives correct result. We will provide a testing framework to automatically test the custom kernels.

High Level Architecture

ExecuTorch users are asked to provide:

1. the custom kernel library with C++ implementations

2. a yaml file associated with the library that describes what operators are being implemented by this library. For partial kernels, the yaml file also contains information on the dtypes and dim orders supported by the kernel. More details in the API section.

Workflow

At build time, the yaml files associated with kernel libraries will be passed to the kernel resolver along with the model op info (see selective build doc) and the outcome is a mapping between a combination of operator names and tensor metadata, to kernel symbols. Then codegen tools will use this mapping to generate C++ bindings that connect the kernels to ExecuTorch runtime. ExecuTorch users need to link this generated library into their application to use these kernels.

At static object initialization time, kernels will be registered into the ExecuTorch kernel registry.

At runtime initialization stage, ExecuTorch will use the operator name and argument metadata as a key to lookup for the kernels. For example, with “aten::add.out” and inputs being float tensors with dim order (0, 1, 2, 3), ExecuTorch will go into the kernel registry and lookup for a kernel that matches the name and the input metadata.

APIs

There are two sets of APIs: yaml files that describe kernel - operator mappings and codegen tools to consume these mappings.

Yaml Entry for Core ATen Op Out Variant

Top level attributes:

• op (if the operator appears in native_functions.yaml) or func for custom operator. The value for this key needs to be the full operator name (including overload name) for op key, or a full operator schema (namespace, operator name, operator overload name and schema string), if we are describing a custom operator. For schema syntax please refer to this instruction.

• kernels: defines kernel information. It consists of arg_meta and kernel_name, which are bound together to describe “for input tensors with these metadata, use this kernel”.

• type_alias(optional): we are giving aliases to possible dtype options. T0: [Double, Float] means T0 can be one of Double or Float.

• dim_order_alias(optional): similar to type_alias, we are giving names to possible dim order options.

Attributes under kernels:

• arg_meta: a list of “tensor arg name” entries. The values for these keys are dtypes and dim orders aliases, that are implemented by the corresponding kernel_name. This being null means the kernel will be used for all types of input.

• kernel_name: the expected name of the C++ function that will implement this operator. You can put whatever you want to here, but you should follow the convention of replacing the . in the overload name with an underscore, and lowercasing all characters. In this example, add.out uses the C++ function named add_out. add.Scalar_out would become add_scalar_out, with a lowercase S. We support namespace for kernels, but note that we will be inserting a native:: to the last level of namespace. So custom::add_out in the kernel_name will point to custom::native::add_out.

Some examples of operator entry:

- op: add.out
  kernels:
    - arg_meta: null
      kernel_name: torch::executor::add_out

An out variant of a core ATen operator with a default kernel

ATen operator with a dtype/dim order specialized kernel (works for Double dtype and dim order needs to be (0, 1, 2, 3))

- op: add.out
  type_alias:
    T0: [Double]
  dim_order_alias:
    D0: [[0, 1, 2, 3]]
  kernels:
    - arg_meta:
        self: [T0, D0]
        other: [T0 , D0]
        out: [T0, D0]
      kernel_name: torch::executor::add_out

Custom Ops C++ API

For a custom kernel that implements a custom operator, we provides 2 ways to register it into ExecuTorch runtime:

1. Using EXECUTORCH_LIBRARY and WRAP_TO_ATEN C++ macros.

2. Using functions.yaml and codegen’d C++ libraries.

The first option requires C++17 and doesn’t have selective build support yet, but it’s faster than the second option where we have to go through yaml authoring and build system tweaking.

The first option is particularly suitable for fast prototyping but can also be used in production.

Similar to TORCH_LIBRARY, EXECUTORCH_LIBRARY takes the operator name and the C++ function name and register them into ExecuTorch runtime.

Prepare custom kernel implementation

Define your custom operator schema for both functional variant (used in AOT compilation) and out variant (used in ExecuTorch runtime). The schema needs to follow PyTorch ATen convention (see native_functions.yaml). For example:

custom_linear(Tensor weight, Tensor input, Tensor(?) bias) -> Tensor
custom_linear.out(Tensor weight, Tensor input, Tensor(?) bias, *, Tensor(a!) out) -> Tensor(a!)

Then write your custom kernel according to the schema using ExecuTorch types, along with APIs to register to ExecuTorch runtime:

// custom_linear.h/custom_linear.cpp
#include <executorch/runtime/kernel/kernel_includes.h>
Tensor& custom_linear_out(const Tensor& weight, const Tensor& input, optional<Tensor> bias, Tensor& out) {
   // calculation
   return out;
}

Use a C++ macro to register it into PyTorch & ExecuTorch

Append the following line in the example above:

// custom_linear.h/custom_linear.cpp
// opset namespace myop
EXECUTORCH_LIBRARY(myop, "custom_linear.out", custom_linear_out);

Now we need to write some wrapper for this op to show up in PyTorch, but don’t worry we don’t need to rewrite the kernel. Create a separate .cpp for this purpose:

// custom_linear_pytorch.cpp
#include "custom_linear.h"
#include <torch/library.h>

at::Tensor custom_linear(const at::Tensor& weight, const at::Tensor& input, std::optional<at::Tensor> bias) {
    // initialize out
    at::Tensor out = at::empty({weight.size(1), input.size(1)});
    // wrap kernel in custom_linear.cpp into ATen kernel
    WRAP_TO_ATEN(custom_linear_out, 3)(weight, input, bias, out);
    return out;
}
// standard API to register ops into PyTorch
TORCH_LIBRARY(myop, m) {
    m.def("custom_linear(Tensor weight, Tensor input, Tensor(?) bias) -> Tensor", custom_linear);
    m.def("custom_linear.out(Tensor weight, Tensor input, Tensor(?) bias, *, Tensor(a!) out) -> Tensor(a!)", WRAP_TO_ATEN(custom_linear_out, 3));
}

Compile and link the custom kernel

Link it into ExecuTorch runtime: In our `CMakeLists.txt`` that builds the binary/application, we just need to add custom_linear.h/cpp into the binary target. We can build a dynamically loaded library (.so or .dylib) and link it as well.

Link it into PyTorch runtime: We need to package custom_linear.h, custom_linear.cpp and custom_linear_pytorch.cpp into a dynamically loaded library (.so or .dylib) and load it into our python environment. One way of doing this is:

import torch
torch.ops.load_library("libcustom_linear.so/dylib")

# Now we have access to the custom op, backed by kernel implemented in custom_linear.cpp.
op = torch.ops.myop.custom_linear.default

Custom Ops Yaml Entry

As mentioned above, this option provides more support in terms of selective build and features such as merging operator libraries.

First we need to specify the operator schema as well as a kernel section. So instead of op we use func with the operator schema. As an example, here’s a yaml entry for a custom op:

- func: allclose.out(Tensor self, Tensor other, float rtol=1e-05, float atol=1e-08, bool equal_nan=False, bool dummy_param=False, *, Tensor(a!) out) -> Tensor(a!)
  kernels:
    - arg_meta: null
      kernel_name: torch::executor::allclose_out

The kernel section is the same as the one defined in core ATen ops. For operator schema, we are reusing the DSL defined in this README.md, with a few differences:

Out variants only

ExecuTorch only supports out-style operators, where:

• The caller provides the output Tensor or Tensor list in the final position with the name out.

• The C++ function modifies and returns the same out argument.

  • If the return type in the YAML file is () (which maps to void), the C++ function should still modify out but does not need to return anything.

• The out argument must be keyword-only, which means it needs to follow an argument named * like in the add.out example below.

• Conventionally, these out operators are named using the pattern <name>.out or <name>.<overload>_out.

Since all output values are returned via an out parameter, ExecuTorch ignores the actual C++ function return value. But, to be consistent, functions should always return out when the return type is non-void.

Can only return Tensor or ()

ExecuTorch only supports operators that return a single Tensor, or the unit type () (which maps to void). It does not support returning any other types, including lists, optionals, tuples, or scalars like bool.

Supported argument types

ExecuTorch does not support all of the argument types that core PyTorch supports. Here’s a list of the argument types we currently support:

• Tensor

• int

• bool

• float

• str

• Scalar

• ScalarType

• MemoryFormat

• Device

• Optional

• List

• List<Optional>

• Optional<List>

Build Tool Macros

We provide build time macros to help users to build their kernel registration library. The macro takes the yaml file describing the kernel library as well as model operator metadata, and packages the generated C++ bindings into a C++ library. The macro is available on both CMake and Buck2.

CMake

generate_bindings_for_kernels(FUNCTIONS_YAML functions_yaml CUSTOM_OPS_YAML custom_ops_yaml) takes a yaml file for core ATen op out variants and also a yaml file for custom ops, generate C++ bindings for kernel registration. It also depends on the selective build artifact generated by gen_selected_ops(), see selective build doc for more information. Then gen_operators_lib will package those bindings to be a C++ library. As an example:

# SELECT_OPS_LIST: aten::add.out,aten::mm.out
gen_selected_ops("" "${SELECT_OPS_LIST}" "")

# Look for functions.yaml associated with portable libs and generate C++ bindings
generate_bindings_for_kernels(FUNCTIONS_YAML ${EXECUTORCH_ROOT}/kernels/portable/functions.yaml)

# Prepare a C++ library called "generated_lib" with _kernel_lib being the portable library, executorch is a dependency of it.
gen_operators_lib("generated_lib" KERNEL_LIBS ${_kernel_lib} DEPS executorch)

# Link "generated_lib" into the application:
target_link_libraries(executorch_binary generated_lib)

We also provide the ability to merge two yaml files, given a precedence. merge_yaml(FUNCTIONS_YAML functions_yaml FALLBACK_YAML fallback_yaml OUTPUT_DIR out_dir) merges functions_yaml and fallback_yaml into a single yaml, if there’s duplicate entries in functions_yaml and fallback_yaml, this macro will always take the one in functions_yaml.

Example:

# functions.yaml
- op: add.out
  kernels:
    - arg_meta: null
      kernel_name: torch::executor::opt_add_out

And out fallback:

# fallback.yaml
- op: add.out
  kernels:
    - arg_meta: null
      kernel_name: torch::executor::add_out

The merged yaml will have the entry in functions.yaml.

Buck2

executorch_generated_lib is the macro that takes the yaml files and depends on the selective build macro et_operator_library. For an example:

# Yaml file for kernel library
export_file(
  name = "functions.yaml"
)

# Kernel library
cxx_library(
  name = "add_kernel",
  srcs = ["add.cpp"],
)

# Selective build artifact, it allows all operators to be registered
et_operator_library(
  name = "all_ops",
  include_all_ops = True, # Select all ops in functions.yaml
)

# Prepare a generated_lib
executorch_generated_lib(
  name = "generated_lib",
  functions_yaml_target = ":functions.yaml",
  deps = [
    ":all_ops",
    ":add_kernel",
  ],
)

# Link generated_lib to ExecuTorch binary
cxx_binary(
 name = "executorch_bin",
 deps = [
  ":generated_lib",
 ],
)