gaussian-splatting-my/envs/gaussian_splatting/Lib/site-packages/torchgen/gen_backend_stubs.py

import pathlib
import argparse
import os
import yaml
import re
from collections import namedtuple, Counter, defaultdict
from typing import List, Dict, Union, Sequence, Optional
from torchgen.gen import (
    get_grouped_native_functions,
    parse_native_yaml,
    NamespaceHelper,
)
from torchgen.model import (
    BackendIndex,
    BackendMetadata,
    DispatchKey,
    NativeFunction,
    NativeFunctionsGroup,
    OperatorName,
)
from torchgen.selective_build.selector import SelectiveBuilder
from torchgen.utils import Target, concatMap, context, YamlLoader, FileManager
from torchgen.context import native_function_manager
from torchgen.code_template import CodeTemplate
import torchgen.dest as dest
import torchgen.api.dispatcher as dispatcher
from torchgen.api.types import DispatcherSignature


# Parses the external backend's yaml, and adds a new BackendIndex for the backend's dispatch key.
# Returns a Tuple of (backend_key, autograd_key, cpp_namespace, updated BackendIndex mapping)
ParsedExternalYaml = namedtuple(
    "ParsedExternalYaml",
    ["backend_key", "autograd_key", "class_name", "cpp_namespace", "backend_indices"],
)


def parse_backend_yaml(
    backend_yaml_path: str,
    grouped_native_functions: Sequence[Union[NativeFunction, NativeFunctionsGroup]],
    backend_indices: Dict[DispatchKey, BackendIndex],
) -> ParsedExternalYaml:

    native_functions_map: Dict[OperatorName, NativeFunction] = {
        f.func.name: f
        for f in concatMap(
            lambda f: [f] if isinstance(f, NativeFunction) else list(f.functions()),
            grouped_native_functions,
        )
    }

    with open(backend_yaml_path, "r") as f:
        yaml_values = yaml.load(f, Loader=YamlLoader)
    assert isinstance(yaml_values, dict)

    valid_keys = [
        "backend",
        "class_name",
        "cpp_namespace",
        "extra_headers",
        "supported",
        "autograd",
        "full_codegen",
    ]

    backend = yaml_values.pop("backend", None)
    assert backend is not None, 'You must provide a value for "backend"'

    class_name = yaml_values.pop("class_name", None)

    cpp_namespace = yaml_values.pop("cpp_namespace", None)
    assert cpp_namespace is not None, 'You must provide a value for "cpp_namespace"'

    # Mostly just defaulting to false to stick with LazyTensor convention.
    use_out_as_primary = yaml_values.pop("use_out_as_primary", False)
    assert isinstance(
        use_out_as_primary, bool
    ), f"You must provide either True or False for use_out_as_primary. Provided: {use_out_as_primary}"

    use_device_guard = yaml_values.pop("device_guard", False)
    assert isinstance(
        use_device_guard, bool
    ), f"You must provide either True or False for device_guard. Provided: {use_device_guard}"

    supported = yaml_values.pop("supported", [])
    if supported is None:
        supported = []  # Allow an empty list of supported ops
    assert isinstance(
        supported, list
    ), f'expected "supported" to be a list, but got: {supported} (of type {type(supported)})'

    supported_autograd = yaml_values.pop("autograd", [])
    assert isinstance(
        supported_autograd, list
    ), f'expected "autograd" to be a list, but got: {supported_autograd}'

    # full_codegen is ignored by parse_backend_yaml, and re-parsed in gen_lazy_tensor.py
    full_codegen = yaml_values.pop("full_codegen", [])
    supported.extend(full_codegen)

    assert (
        len(yaml_values.keys()) == 0
    ), f'{backend_yaml_path} contains unexpected keys: {", ".join(yaml_values.keys())}. \
Only the following keys are supported: {", ".join(valid_keys)}'

    def create_backend_index(
        backend_ops: List[str],
        dispatch_key: DispatchKey,
        *,
        use_out_as_primary: bool,
        use_device_guard: bool,
    ) -> BackendIndex:
        metadata: Dict[OperatorName, BackendMetadata] = {}
        for op in backend_ops:
            op_name = OperatorName.parse(op)
            assert (
                op_name in native_functions_map
            ), f"Found an invalid operator name: {op_name}"
            # See Note [External Backends Follow Dispatcher API]
            kernel_name = dispatcher.name(native_functions_map[op_name].func)
            # TODO: allow structured external backends later.
            m = BackendMetadata(kernel=kernel_name, structured=False)
            metadata[op_name] = m
        return BackendIndex(
            dispatch_key=dispatch_key,
            use_out_as_primary=use_out_as_primary,
            external=True,
            device_guard=use_device_guard,
            index=metadata,
        )

    backend_key: Optional[DispatchKey] = None
    if len(supported) > 0:
        with context(
            lambda: f'The provided value for "backend" must be a valid DispatchKey, but got {backend}.'
        ):
            backend_key = DispatchKey.parse(backend)

        backend_idx = create_backend_index(
            supported,
            backend_key,
            use_out_as_primary=use_out_as_primary,
            use_device_guard=use_device_guard,
        )
        assert backend_key not in backend_indices
        backend_indices[backend_key] = backend_idx

    autograd_key: Optional[DispatchKey] = None
    if len(supported_autograd) > 0:
        with context(
            lambda: f'The "autograd" key was specified, which indicates that you would like to override \
the behavior of autograd for some operators on your backend. However "Autograd{backend}" is not a valid DispatchKey.'
        ):
            autograd_key = DispatchKey.parse(f"Autograd{backend}")

        autograd_idx = create_backend_index(
            supported_autograd,
            autograd_key,
            use_out_as_primary=use_out_as_primary,
            use_device_guard=use_device_guard,
        )
        assert autograd_key not in backend_indices
        backend_indices[autograd_key] = autograd_idx

    for g in grouped_native_functions:
        if isinstance(g, NativeFunction):
            forward_kernels = (
                []
                if backend_key is None
                else [
                    m
                    for m in [backend_indices[backend_key].get_kernel(g)]
                    if m is not None
                ]
            )
            backward_kernels = (
                []
                if autograd_key is None
                else [
                    m
                    for m in [backend_indices[autograd_key].get_kernel(g)]
                    if m is not None
                ]
            )
        else:
            forward_kernels = (
                []
                if backend_key is None
                else [
                    m
                    for m in [
                        backend_indices[backend_key].get_kernel(f)
                        for f in g.functions()
                    ]
                    if m is not None
                ]
            )
            backward_kernels = (
                []
                if autograd_key is None
                else [
                    m
                    for m in [
                        backend_indices[autograd_key].get_kernel(f)
                        for f in g.functions()
                    ]
                    if m is not None
                ]
            )

        forward_kernels = [f for f in forward_kernels if f is not None]
        backward_kernels = [f for f in backward_kernels if f is not None]
        assert (
            len(forward_kernels) == 0 or len(backward_kernels) == 0
        ), f'Currently, all variants of an op must either be registered to a backend key, or to a backend\'s \
autograd key. They cannot be mix and matched. If this is something you need, feel free to create an issue! \
{forward_kernels[0].kernel} is listed under "supported", but {backward_kernels[0].kernel} is listed under "autograd".'

    return ParsedExternalYaml(
        backend_key, autograd_key, class_name, cpp_namespace, backend_indices
    )


def error_on_missing_kernels(
    native_functions: Sequence[NativeFunction],
    backend_indices: Dict[DispatchKey, BackendIndex],
    backend_key: DispatchKey,
    autograd_key: Optional[DispatchKey],
    class_name: str,
    kernel_defn_file_path: str,
    full_codegen: Optional[List[OperatorName]] = None,
) -> None:
    try:
        with open(kernel_defn_file_path, "r") as f:
            backend_defns = f.read()
    except IOError:
        raise AssertionError(
            f"Unable to read from the specified impl_path file: {kernel_defn_file_path}"
        )

    if full_codegen is None:
        full_codegen = []

    expected_backend_op_names: List[OperatorName] = (
        list(backend_indices[backend_key].index.keys()) + []
        if autograd_key is None
        else list(backend_indices[autograd_key].index.keys())
    )
    expected_backend_native_funcs: List[NativeFunction] = [
        f
        for f in native_functions
        if f.func.name in expected_backend_op_names and f.func.name not in full_codegen
    ]
    expected_backend_kernel_name_counts: Dict[str, List[NativeFunction]] = defaultdict(
        list
    )
    for native_f in expected_backend_native_funcs:
        expected_backend_kernel_name_counts[dispatcher.name(native_f.func)].append(
            native_f
        )

    kernel_defn_regex = rf"{class_name}::([\w\d]*)\([^\)]*\)\s*{{"
    actual_backend_kernel_name_counts = Counter(
        re.findall(kernel_defn_regex, backend_defns)
    )

    missing_kernels_err_msg = ""
    for expected_name, funcs in expected_backend_kernel_name_counts.items():
        expected_overload_count = len(funcs)
        actual_overload_count = actual_backend_kernel_name_counts[expected_name]
        if expected_overload_count != actual_overload_count:

            def create_decl(f: NativeFunction) -> str:
                with native_function_manager(f):
                    return DispatcherSignature.from_schema(f.func).decl()

            expected_schemas_str = "\n".join([create_decl(f) for f in funcs])
            missing_kernels_err_msg += f"""
{class_name} is missing a kernel definition for {expected_name}. We found {actual_overload_count} kernel(s) with that name,
but expected {expected_overload_count} kernel(s). The expected function schemas for the missing operator are:
{expected_schemas_str}

"""
    assert missing_kernels_err_msg == "", missing_kernels_err_msg


def main() -> None:
    parser = argparse.ArgumentParser(description="Generate backend stub files")
    parser.add_argument(
        "-s",
        "--source_yaml",
        help="path to source yaml file containing operator external definitions",
    )
    parser.add_argument("-o", "--output_dir", help="output directory")
    parser.add_argument("--dry_run", type=bool, default=False, help="output directory")
    parser.add_argument(
        "--impl_path",
        type=str,
        default=None,
        help="path to the source C++ file containing kernel definitions",
    )
    options = parser.parse_args()

    run(options.source_yaml, options.output_dir, options.dry_run, options.impl_path)


def gen_dispatchkey_nativefunc_headers(
    fm: FileManager,
    class_name: str,
    cpp_namespace: str,
    backend_indices: Dict[DispatchKey, BackendIndex],
    grouped_native_functions: Sequence[Union[NativeFunction, NativeFunctionsGroup]],
    backend_dispatch_key: DispatchKey,
    autograd_dispatch_key: Optional[DispatchKey],
    backend_name: str = "",
) -> None:
    assert class_name is not None
    generated_comment = (
        "Autogenerated file by gen_backend_stubs.py. Do not edit directly!"
    )

    # Convert to a set first to remove duplicate kernel names.
    # Backends are allowed to repeat kernel names; only generate the declaration once!
    # Sort for deterministic output.
    backend_declarations = list(
        sorted(
            set(
                concatMap(
                    lambda f: dest.compute_native_function_declaration(
                        f, backend_indices[backend_dispatch_key]
                    ),
                    grouped_native_functions,
                )
            )
        )
    )
    autograd_declarations = list(
        sorted(
            set(
                concatMap(
                    lambda f: []
                    if autograd_dispatch_key is None
                    else dest.compute_native_function_declaration(
                        f, backend_indices[autograd_dispatch_key]
                    ),
                    grouped_native_functions,
                )
            )
        )
    )

    ns_helper = NamespaceHelper(cpp_namespace)
    fm.write_with_template(
        f"{backend_dispatch_key}NativeFunctions.h",
        "DispatchKeyNativeFunctions.h",
        lambda: {
            "generated_comment": generated_comment,
            "namespace_prologue": ns_helper.prologue,
            "class_name": class_name,
            "namespace_epilogue": ns_helper.epilogue,
            "dispatch_declarations": backend_declarations + autograd_declarations,
            "BackendName": backend_name,
            "DispatchKey": backend_dispatch_key,
        },
    )


def gen_dispatcher_registrations(
    fm: FileManager,
    output_dir: str,
    class_name: str,
    cpp_namespace: str,
    backend_indices: Dict[DispatchKey, BackendIndex],
    grouped_native_functions: Sequence[Union[NativeFunction, NativeFunctionsGroup]],
    backend_dispatch_key: DispatchKey,
    dispatch_key: DispatchKey,
    selector: "SelectiveBuilder",
    # build_in_tree is true for lazy TS backend and affects include paths, not used for external backends
    build_in_tree: bool = False,
    per_operator_headers: bool = False,
    backend_name: str = "",
    eager_registration: bool = True,
) -> None:
    headers = [
        f"{output_dir}/{backend_dispatch_key}NativeFunctions.h",
    ]
    if build_in_tree:
        external_backend_headers_str = "\n".join(f"#include <{h}>" for h in headers)
    else:
        external_backend_headers_str = "\n".join(f'#include "{h}"' for h in headers)

    assert class_name is not None
    backend_index = backend_indices[dispatch_key]

    dispatch_registrations_body = list(
        concatMap(
            dest.RegisterDispatchKey(
                backend_index,
                Target.REGISTRATION,
                selector,
                rocm=False,
                cpp_namespace=cpp_namespace,
                class_method_name=f"{class_name}",
                skip_dispatcher_op_registration=False,
            ),
            grouped_native_functions,
        )
    )
    deferred_dispatch_registrations = ""
    static_init_dispatch_registrations = ""
    if eager_registration:
        static_template = CodeTemplate(
            """\
TORCH_LIBRARY_IMPL(aten, $dispatch_key, m) {
    $dispatch_registrations_body
};"""
        )
        static_init_dispatch_registrations = static_template.substitute(
            dispatch_key=dispatch_key,
            dispatch_registrations_body=dispatch_registrations_body,
        )
    else:
        deferred_template = CodeTemplate(
            """\
TORCH_API void Register${backend_name}${dispatch_key}NativeFunctions() {
    static auto m = MAKE_TORCH_LIBRARY_IMPL(aten, $dispatch_key);
    $dispatch_registrations_body
}"""
        )
        deferred_dispatch_registrations = deferred_template.substitute(
            backend_name=backend_name,
            dispatch_key=dispatch_key,
            dispatch_registrations_body=dispatch_registrations_body,
        )

    fm.write_with_template(
        f"Register{dispatch_key}.cpp",
        "RegisterDispatchKey.cpp",
        lambda: {
            "static_init_dispatch_registrations": static_init_dispatch_registrations,
            "deferred_dispatch_registrations": deferred_dispatch_registrations,
            "extra_cuda_headers": "",
            "external_backend_headers": external_backend_headers_str,
            "ops_headers": "#include <ATen/Functions.h>"
            if not per_operator_headers
            else "",
            "DispatchKey": dispatch_key,
            "dispatch_namespace": dispatch_key.lower(),
            "dispatch_headers": dest.gen_registration_headers(
                backend_index, per_operator_headers=per_operator_headers, rocm=False
            ),
            "dispatch_helpers": dest.gen_registration_helpers(backend_index),
            "dispatch_namespaced_definitions": "",
            "dispatch_anonymous_definitions": list(
                concatMap(
                    dest.RegisterDispatchKey(
                        backend_index,
                        Target.ANONYMOUS_DEFINITION,
                        selector,
                        rocm=False,
                        cpp_namespace=cpp_namespace,
                        class_method_name=f"{class_name}",
                        skip_dispatcher_op_registration=False,
                    ),
                    grouped_native_functions,
                )
            ),
        },
    )


def run(
    source_yaml: str, output_dir: str, dry_run: bool, impl_path: Optional[str] = None
) -> None:

    # Assumes that this file lives at PYTORCH_ROOT/torchgen/gen_backend_stubs.py
    pytorch_root = pathlib.Path(__file__).parent.parent.absolute()
    template_dir = os.path.join(pytorch_root, "aten/src/ATen/templates")

    def make_file_manager(install_dir: str) -> FileManager:
        return FileManager(
            install_dir=install_dir, template_dir=template_dir, dry_run=dry_run
        )

    fm = make_file_manager(output_dir)

    native_yaml_path = os.path.join(
        pytorch_root, "aten/src/ATen/native/native_functions.yaml"
    )
    tags_yaml_path = os.path.join(pytorch_root, "aten/src/ATen/native/tags.yaml")
    parsed_yaml = parse_native_yaml(native_yaml_path, tags_yaml_path)
    native_functions, backend_indices = (
        parsed_yaml.native_functions,
        parsed_yaml.backend_indices,
    )
    grouped_native_functions = get_grouped_native_functions(native_functions)
    parsed_backend_yaml = parse_backend_yaml(
        source_yaml, grouped_native_functions, backend_indices
    )
    backend_key = parsed_backend_yaml.backend_key
    autograd_key = parsed_backend_yaml.autograd_key
    cpp_namespace = parsed_backend_yaml.cpp_namespace
    class_name = parsed_backend_yaml.class_name
    backend_indices = parsed_backend_yaml.backend_indices

    selector = SelectiveBuilder.get_nop_selector()

    if backend_key is None:
        # This could be useful if a backend wants to quickly set up a noop yaml file but doesn't have any kernels ready yet.
        return

    if class_name is None:
        # class_name is an optional argument to backend yaml file.
        # if specified it allows an external backend to override
        # the name of the class that all generated kernel definitions live under.
        # if not specified, its value is given as native_function_class_name.
        class_name = backend_indices[backend_key].native_function_class_name()
    assert class_name is not None

    if impl_path is not None:
        error_on_missing_kernels(
            native_functions,
            backend_indices,
            backend_key,
            autograd_key,
            class_name,
            impl_path,
        )

    gen_dispatchkey_nativefunc_headers(
        fm,
        class_name,
        cpp_namespace,
        backend_indices,
        grouped_native_functions,
        backend_key,
        autograd_key,
    )

    for dispatch_key in (
        [backend_key] if autograd_key is None else [backend_key, autograd_key]
    ):
        gen_dispatcher_registrations(
            fm,
            output_dir,
            class_name,
            cpp_namespace,
            backend_indices,
            grouped_native_functions,
            backend_key,
            dispatch_key,
            selector,
        )


if __name__ == "__main__":
    main()