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env-llmeval/lib/python3.10/site-packages/torchgen/executorch/api/custom_ops.py

@@ -0,0 +1,131 @@
+from collections import defaultdict
+
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Sequence, Tuple
+
+from torchgen import dest
+
+# disable import sorting to avoid circular dependency.
+from torchgen.api.types import DispatcherSignature  # isort:skip
+from torchgen.context import method_with_native_function
+from torchgen.executorch.model import ETKernelIndex
+from torchgen.model import DispatchKey, NativeFunction, Variant
+from torchgen.selective_build.selector import SelectiveBuilder
+from torchgen.utils import concatMap, Target
+
+
+# Generates RegisterKernelStub.cpp, which provides placeholder kernels for custom operators. This will be used at
+# model authoring side.
+@dataclass(frozen=True)
+class ComputeNativeFunctionStub:
+    @method_with_native_function
+    def __call__(self, f: NativeFunction) -> Optional[str]:
+        if Variant.function not in f.variants:
+            return None
+
+        sig = DispatcherSignature.from_schema(
+            f.func, prefix=f"wrapper_CPU_{f.func.name.overload_name}_", symint=False
+        )
+        assert sig is not None
+        if len(f.func.returns) == 0:
+            ret_name = ""
+        elif len(f.func.returns) == 1:
+            if f.func.arguments.out:
+                ret_name = f.func.arguments.out[0].name
+            else:
+                ret_name = next(
+                    (
+                        a.name
+                        for a in f.func.arguments.flat_non_out
+                        if a.type == f.func.returns[0].type
+                    ),
+                    "",
+                )
+            if not ret_name:
+                raise Exception(f"Can't handle this return type {f.func}")
+        else:
+            assert len(f.func.arguments.out) == len(f.func.returns), (
+                "Out variant number of returns need to match the number of out arguments."
+                f" Got outs {str(f.func.arguments.out)} but returns {str(f.func.returns)}"
+            )
+            # returns a tuple of out arguments
+            tensor_type = "at::Tensor &"
+            comma = ", "
+            ret_name = f"""::std::tuple<{comma.join([tensor_type] * len(f.func.returns))}>(
+                {comma.join([r.name for r in f.func.arguments.out])}
+            )"""
+        ret_str = f"return {ret_name};" if len(f.func.returns) > 0 else ""
+        return f"""
+{sig.defn()} {{
+    {ret_str}
+}}
+    """
+
+
+def gen_custom_ops_registration(
+    *,
+    native_functions: Sequence[NativeFunction],
+    selector: SelectiveBuilder,
+    kernel_index: ETKernelIndex,
+    rocm: bool,
+) -> Tuple[str, str]:
+    """
+    Generate custom ops registration code for dest.RegisterDispatchKey.
+
+    :param native_functions: a sequence of `NativeFunction`
+    :param selector: for selective build.
+    :param kernel_index: kernels for all the ops.
+    :param rocm: bool for dest.RegisterDispatchKey.
+    :return: generated C++ code to register custom operators into PyTorch
+    """
+
+    # convert kernel index to BackendIndex. This is because we can't handle ETKernelIndex yet.
+    # TODO larryliu: evaluate if this code is still needed. If yes let it handle ETKernelIndex.
+
+    dispatch_key = DispatchKey.CPU
+    backend_index = kernel_index._to_backend_index()
+    static_init_dispatch_registrations = ""
+    ns_grouped_native_functions: Dict[str, List[NativeFunction]] = defaultdict(list)
+    for native_function in native_functions:
+        ns_grouped_native_functions[native_function.namespace].append(native_function)
+
+    for namespace, functions in ns_grouped_native_functions.items():
+        if len(functions) == 0:
+            continue
+        dispatch_registrations_body = "\n".join(
+            list(
+                concatMap(
+                    dest.RegisterDispatchKey(
+                        backend_index,
+                        Target.REGISTRATION,
+                        selector,
+                        rocm=rocm,
+                        symint=False,
+                        class_method_name=None,
+                        skip_dispatcher_op_registration=False,
+                    ),
+                    functions,
+                )
+            )
+        )
+        static_init_dispatch_registrations += f"""
+TORCH_LIBRARY_IMPL({namespace}, {dispatch_key}, m) {{
+{dispatch_registrations_body}
+}};"""
+    anonymous_definition = "\n".join(
+        list(
+            concatMap(
+                dest.RegisterDispatchKey(
+                    backend_index,
+                    Target.ANONYMOUS_DEFINITION,
+                    selector,
+                    rocm=rocm,
+                    symint=False,
+                    class_method_name=None,
+                    skip_dispatcher_op_registration=False,
+                ),
+                native_functions,
+            )
+        )
+    )
+    return anonymous_definition, static_init_dispatch_registrations

env-llmeval/lib/python3.10/site-packages/torchgen/executorch/api/et_cpp.py

@@ -0,0 +1,368 @@
+from typing import List, Optional, Sequence, Set, Union
+
+from torchgen import local
+from torchgen.api.types import (
+    ArgName,
+    ArrayCType,
+    BaseCType,
+    Binding,
+    ConstRefCType,
+    CType,
+    MutRefCType,
+    NamedCType,
+    SpecialArgName,
+    TupleCType,
+    VectorCType,
+    voidT,
+)
+from torchgen.model import (
+    Argument,
+    Arguments,
+    BaseTy,
+    BaseType,
+    ListType,
+    NativeFunction,
+    OptionalType,
+    Return,
+    SelfArgument,
+    TensorOptionsArguments,
+    Type,
+)
+from torchgen.utils import assert_never
+from .types import (
+    ArrayRefCType,
+    BaseTypeToCppMapping,
+    OptionalCType,
+    scalarT,
+    tensorListT,
+    tensorT,
+)
+
+"""
+This file describes the translation of JIT schema to the public C++ API, which is what people use when they call
+functions like at::add. It also serves as a native function API, which is the signature of kernels,
+since in Executorch CppSignature is the same as NativeSignature.
+
+Difference between this file and torchgen.api.cpp.py:
+
+  - Executorch doesn't support TensorOptions, however in this file we still keep the logic here to be compatible with
+    torchgen.api.cpp, so that we can do stuff like ATen mode (running ATen kernels in Executorch).
+
+  - Executorch doesn't support Dimname.
+
+  - Executorch runtime doesn't support SymInt, will treat it as int.
+"""
+
+
+# Translation of "value types" in JIT schema to C++ API type.  Value
+# types look the same no matter if they are argument types or return
+# types.  Returns None if the type in question is not a value type.
+def valuetype_type(
+    t: Type,
+    *,
+    binds: ArgName,
+    remove_non_owning_ref_types: bool = False,
+) -> Optional[NamedCType]:
+    if isinstance(t, BaseType):
+        if t.name == BaseTy.Tensor or t.name == BaseTy.Scalar:
+            return None
+        # For SymInt we simply treat it as int.
+        elif str(t) == "SymInt":
+            return NamedCType(binds, BaseCType(BaseTypeToCppMapping[BaseTy.int]))
+        if remove_non_owning_ref_types:
+            if t.name == BaseTy.str:
+                raise AssertionError(
+                    "string ref->value conversion: not implemented yet"
+                )
+        # All other BaseType currently map directly to BaseCppTypes.
+        return NamedCType(binds, BaseCType(BaseTypeToCppMapping[t.name]))
+    elif isinstance(t, OptionalType):
+        elem = valuetype_type(t.elem, binds=binds)
+        if elem is None:
+            return None
+        return NamedCType(binds, OptionalCType(elem.type))
+    elif isinstance(t, ListType):
+        if str(t.elem) == "bool":
+            assert t.size is not None
+            return NamedCType(
+                binds, ArrayCType(BaseCType(BaseTypeToCppMapping[BaseTy.bool]), t.size)
+            )
+        else:
+            return None
+    else:
+        raise AssertionError(f"unrecognized type {repr(t)}")
+
+
+# Translation of types occurring in JIT arguments to a C++ argument type.
+# If remove_non_owning_ref_types is set, we'll guarantee that the outputed CType is not a non-owning reference type.
+# For example, we'll return std::vector<int> instead of IntArrayRef.
+# See Note [translation from C++ reference to value types]
+def argumenttype_type(
+    t: Type,
+    *,
+    mutable: bool,
+    binds: ArgName,
+    remove_non_owning_ref_types: bool = False,
+) -> NamedCType:
+    # If it's a value type, do the value type translation
+    r = valuetype_type(
+        t,
+        binds=binds,
+        remove_non_owning_ref_types=remove_non_owning_ref_types,
+    )
+    if r is not None:
+        return r
+    if isinstance(t, BaseType):
+        if t.name == BaseTy.Tensor:
+            if mutable and not local.use_const_ref_for_mutable_tensors():
+                return NamedCType(binds, MutRefCType(BaseCType(tensorT)))
+            else:
+                return NamedCType(binds, ConstRefCType(BaseCType(tensorT)))
+        elif t.name == BaseTy.Scalar:
+            return NamedCType(binds, ConstRefCType(BaseCType(scalarT)))
+        else:
+            raise AssertionError(f"base type should have been value type {t}")
+    elif isinstance(t, OptionalType):
+        if str(t.elem) == "Tensor":
+            if mutable and not local.use_const_ref_for_mutable_tensors():
+                return NamedCType(
+                    binds, MutRefCType(BaseCType(tensorT))
+                )  # TODO: fix this discrepancy
+            else:
+                return NamedCType(
+                    binds, ConstRefCType(OptionalCType(BaseCType(tensorT)))
+                )
+        elif str(t.elem) == "Scalar":
+            return NamedCType(binds, ConstRefCType(OptionalCType(BaseCType(scalarT))))
+        elem = argumenttype_type(t.elem, mutable=mutable, binds=binds)
+        return NamedCType(binds, OptionalCType(elem.type))
+    elif isinstance(t, ListType):
+        # TODO: keeping these special cases for Tensor[] and Tensor?[] so that we can hookup with ATen kernels.
+        if str(t.elem) == "Tensor":
+            return NamedCType(binds, BaseCType(tensorListT))
+        elif str(t.elem) == "Dimname":
+            raise NotImplementedError("Executorch doesn't support Dimname")
+        elif str(t.elem) == "Tensor?":
+            return NamedCType(binds, ArrayRefCType(OptionalCType(BaseCType(tensorT))))
+        elem = argumenttype_type(t.elem, mutable=mutable, binds=binds)
+        return NamedCType(binds, ArrayRefCType(elem.type))
+    else:
+        raise AssertionError(f"unrecognized type {repr(t)}")
+
+
+# Translate a JIT argument into its C++ type
+def argument_type(a: Argument, *, binds: ArgName) -> NamedCType:
+    return argumenttype_type(a.type, mutable=a.is_write, binds=binds)
+
+
+# Translation of a (non-multi) return type from JIT to C++
+# N.B: returntype_type returns a CType, not a NamedCType.
+# This is mostly because of the mismatch between return types and return names.
+# e.g. a function with a return type of 'void' has 0 return names,
+# and a function with a return type of 'std::tuple' has >1 return name.
+def returntype_type(t: Type, *, mutable: bool) -> CType:
+    # placeholder is ignored
+    r = valuetype_type(t, binds="__placeholder__")
+    if r is not None:
+        return r.type
+
+    if isinstance(t, BaseType):
+        if t.name == BaseTy.Tensor:
+            if mutable:
+                if local.use_const_ref_for_mutable_tensors():
+                    return ConstRefCType(BaseCType(tensorT))
+                else:
+                    return MutRefCType(BaseCType(tensorT))
+            else:
+                # Note [Tensor Copy Returns]
+                # Currently, we use "Argument.is_write" to determine
+                # whether or not Tensor return types should be copies or references.
+                # If that ever changes, take a look at other locations of this note!
+                return BaseCType(tensorT)
+        elif t.name == BaseTy.Scalar:
+            return BaseCType(scalarT)
+    elif isinstance(t, ListType):
+        assert (
+            not mutable
+        ), "Native functions should never return a mutable tensor list. They should return void."
+        elem = returntype_type(t.elem, mutable=False)
+        assert t.size is None, f"fixed size list returns not supported: {t}"
+        return VectorCType(elem)
+
+    raise AssertionError(f"unrecognized return type {t}")
+
+
+# Translation of a single return to its C++ type
+def return_type(r: Return) -> CType:
+    return returntype_type(r.type, mutable=r.is_write)
+
+
+# Translation of a full (possibly multi) return from JIT to its C++ type
+def returns_type(rs: Sequence[Return]) -> CType:
+    if len(rs) == 0:
+        return BaseCType(voidT)
+    elif len(rs) == 1:
+        return return_type(rs[0])
+    else:
+        return TupleCType([return_type(r) for r in rs])
+
+
+def return_names(f: NativeFunction, *, fallback_name: str = "result") -> Sequence[str]:
+    returns: List[str] = []
+    for i, r in enumerate(f.func.returns):
+        # If we have an inplace function, the return argument is
+        # implicitly named self.
+        # TODO: Consider incorporating this into the data model
+        if f.func.name.name.inplace:
+            assert i == 0, "illegal inplace function with multiple returns"
+            name = "self"
+        # If we are out function, the name is the name of the
+        # corresponding output function (r.name will get recorded
+        # in field_name later.)
+        elif f.func.is_out_fn():
+            name = f.func.arguments.out[i].name
+        # If the return argument is explicitly named...
+        elif r.name:
+            name_conflict = any(
+                r.name == a.name for a in f.func.schema_order_arguments()
+            )
+            if name_conflict and not f.func.is_out_fn():
+                name = f"{r.name}_return"
+            else:
+                name = r.name
+        # If there is no explicit name and no fallback name was passed in, we just name the output result,
+        # unless it's a multi-return, in which case it's result0,
+        # result1, etc (zero-indexed)
+        else:
+            name = fallback_name if len(f.func.returns) == 1 else f"{fallback_name}{i}"
+        returns.append(name)
+    return returns
+
+
+JIT_TO_CPP_DEFAULT = {
+    "False": "false",
+    "True": "true",
+    "None": "torch::executorch::nullopt",  # UGH this one is type directed
+    "[]": "{}",
+    "contiguous_format": "torch::executorch::MemoryFormat::Contiguous",
+    "long": "torch::executorch::kLong",
+}
+
+
+# Convert a JIT default into C++ expression representing the default
+def default_expr(d: str, t: Type) -> str:
+    if d == "None" and str(t) == "Tensor?":
+        return "{}"
+    if isinstance(t, BaseType) and t.name is BaseTy.str:
+        # Schema allows single quotes but C++ needs double
+        if len(d) >= 2 and d[0] == "'" and d[-1] == "'":
+            s = ""
+            i = 1
+            while i + 1 < len(d):
+                if d[i] != "\\":
+                    if d[i] == '"':
+                        s += '\\"'
+                    else:
+                        s += d[i]
+                    i += 1
+                else:
+                    if d[i + 1] == "'":
+                        s += "'"
+                    else:
+                        s += d[i : i + 2]
+                    i += 2
+
+            return f'"{s}"'
+
+    if isinstance(t, OptionalType):
+        if d == "None":
+            return "torch::executor::nullopt"
+
+        return default_expr(d, t.elem)
+
+    if isinstance(t, ListType):
+        if d.startswith("[") and d.endswith("]"):
+            return "{" + d[1:-1] + "}"
+        elif t.size is None:
+            # NOTE: Sized lists can have scalar defaults
+            raise ValueError(f"Expected a list default '[...]' but found: '{d}'")
+
+    return JIT_TO_CPP_DEFAULT.get(d, d)
+
+
+# Convert an argument into its C++ API form
+
+
+def argument(
+    a: Union[Argument, TensorOptionsArguments, SelfArgument],
+    *,
+    cpp_no_default_args: Set[str],
+    method: bool,
+    faithful: bool,
+    has_tensor_options: bool,
+) -> List[Binding]:
+    def sub_argument(
+        a: Union[Argument, TensorOptionsArguments, SelfArgument]
+    ) -> List[Binding]:
+        return argument(
+            a,
+            cpp_no_default_args=cpp_no_default_args,
+            method=method,
+            faithful=faithful,
+            has_tensor_options=has_tensor_options,
+        )
+
+    if isinstance(a, Argument):
+        binds: ArgName
+        if a.name == "memory_format" and has_tensor_options:
+            binds = SpecialArgName.possibly_redundant_memory_format
+        else:
+            binds = a.name
+        default: Optional[str] = None
+        if a.name not in cpp_no_default_args and a.default is not None:
+            default = default_expr(a.default, a.type)
+        return [
+            Binding(
+                nctype=argument_type(a, binds=binds),
+                name=a.name,
+                default=default,
+                argument=a,
+            )
+        ]
+    elif isinstance(a, TensorOptionsArguments):
+        raise NotImplementedError("Need to implement type resolution for TensorOptions")
+    elif isinstance(a, SelfArgument):
+        if method:
+            # Caller is responsible for installing implicit this in context!
+            return []
+        else:
+            return sub_argument(a.argument)
+    else:
+        assert_never(a)
+
+
+def arguments(
+    arguments: Arguments,
+    *,
+    faithful: bool,
+    method: bool,
+    cpp_no_default_args: Set[str],
+) -> List[Binding]:
+    args: List[Union[Argument, TensorOptionsArguments, SelfArgument]] = []
+    if faithful:
+        args.extend(arguments.non_out)
+        args.extend(arguments.out)
+    else:
+        args.extend(arguments.out)
+        args.extend(arguments.non_out)
+    return [
+        r.no_default() if faithful else r
+        for a in args
+        for r in argument(
+            a,
+            faithful=faithful,
+            method=method,
+            has_tensor_options=arguments.tensor_options is not None,
+            cpp_no_default_args=cpp_no_default_args,
+        )
+    ]

env-llmeval/lib/python3.10/site-packages/torchgen/executorch/api/unboxing.py

@@ -0,0 +1,213 @@
+from dataclasses import dataclass
+from typing import Callable, List, Sequence, Tuple
+
+from torchgen.api.types import Binding, CType, NamedCType
+from torchgen.model import (
+    Argument,
+    BaseTy,
+    BaseType,
+    ListType,
+    NativeFunction,
+    OptionalType,
+    Type,
+)
+
+connector = "\n\t"
+
+
+# Return unboxing function name for a NativeFunction
+def name(f: NativeFunction) -> str:
+    return f.func.name.unambiguous_name()
+
+
+@dataclass(frozen=True)
+class Unboxing:
+    """
+    Takes a sequence of Bindings and unbox EValues to these Bindings. Return generated code that performs correct unboxing.
+    A sample generated code:
+    // aten::mul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+    void mul_out(EValue** stack) {
+        EValue& self = *stack[0];
+        EValue& other = *stack[1];
+        EValue& out = *stack[2];
+        const torch::executor::Tensor & self_base = self.to<torch::executor::Tensor>();
+        const torch::executor::Tensor & other_base = other.to<torch::executor::Tensor>();
+        torch::executor::Tensor & out_base = out.to<torch::executor::Tensor>();
+
+        EXECUTORCH_SCOPE_PROF("native_call_mul.out");
+        torch::executor::mul_outf(self_base, other_base, out_base);
+
+
+    }
+    """
+
+    # this is a callable that converts a JIT argument, into its C++ type.
+    # Translates (type, mutability, binds) to NamedCType. E.g., torchgen.api.cpp.argumenttype_type.
+    argument_type_gen: Callable[
+        ...,
+        NamedCType,
+    ]
+
+    # Convert all the arguments in a NativeFunction to C++ code
+    def convert_arguments(
+        self, args: Sequence[Binding]
+    ) -> Tuple[List[Binding], List[str]]:
+        code_list = [f"EValue& {args[i].name} = *stack[{i}];" for i in range(len(args))]
+        binding_list = []
+        for arg in args:
+            # expecting only Argument
+            if not isinstance(arg.argument, Argument):
+                raise Exception(
+                    f"Unexpected argument type, expecting `Argument` but got {arg}"
+                )
+            argument: Argument = arg.argument
+            unboxed_name, _, code, decl = self.argumenttype_evalue_convert(
+                argument.type, argument.name, mutable=argument.is_write
+            )
+            code_list.extend(decl)
+            code_list.extend(code)
+            binding_list.append(arg.with_name(unboxed_name))
+        return binding_list, code_list
+
+    def argumenttype_evalue_convert(
+        self, t: Type, arg_name: str, *, mutable: bool = False
+    ) -> Tuple[str, CType, List[str], List[str]]:
+        """
+        Takes in the type, name and mutability corresponding to an argument, and generates a tuple of:
+        (1) the C++ code necessary to unbox the argument
+        (2) A Binding corresponding to the newly created unboxed variable, including variable name and its CType
+        :param t: a `Type` of an argument
+        :param arg_name: argument name
+        :param mutable: boolean for whether this argument type is mutable
+        :return: unboxed result
+        """
+        ctype = self.argument_type_gen(t, mutable=mutable, binds=arg_name).type
+
+        if isinstance(t, BaseType):
+            out_name = f"{arg_name}_base"
+            code, decl = self._gen_code_base_type(
+                arg_name=arg_name, out_name=out_name, ctype=ctype
+            )
+        elif isinstance(t, OptionalType):
+            out_name = f"{arg_name}_opt_out"
+            code, decl = self._gen_code_optional_type(
+                arg_name=arg_name, out_name=out_name, t=t, ctype=ctype
+            )
+        elif isinstance(t, ListType):
+            out_name = f"{arg_name}_list_out"
+            code, decl = self._gen_code_list_type(
+                arg_name=arg_name, out_name=out_name, t=t, ctype=ctype
+            )
+        else:
+            raise Exception(f"Cannot handle type {t}. arg_name: {arg_name}")
+        return out_name, ctype, code, decl
+
+    def _gen_code_base_type(
+        self, arg_name: str, out_name: str, ctype: CType
+    ) -> Tuple[List[str], List[str]]:
+        return [
+            f"{ctype.cpp_type()} {out_name} = {arg_name}.to<{ctype.cpp_type(strip_ref=True)}>();"
+        ], []
+
+    def _gen_code_optional_type(
+        self, arg_name: str, out_name: str, t: OptionalType, ctype: CType
+    ) -> Tuple[List[str], List[str]]:
+        in_name = f"{arg_name}_opt_in"
+        res_name, base_type, res_code, decl = self.argumenttype_evalue_convert(
+            t.elem, in_name
+        )
+        return (
+            f"""
+    {ctype.cpp_type(strip_ref=True)} {out_name} = {arg_name}.toOptional<{base_type.cpp_type(strip_ref=True)}>();
+            """.split(
+                "\n"
+            ),
+            decl,
+        )
+
+    def _gen_code_list_type(
+        self, arg_name: str, out_name: str, t: ListType, ctype: CType
+    ) -> Tuple[List[str], List[str]]:
+        in_name = f"{arg_name}_list_in"
+        elem_name = f"{arg_name}_elem"
+        code = []
+        res_name, res_ctype, res_code, decl = self.argumenttype_evalue_convert(
+            t.elem, elem_name
+        )
+
+        if isinstance(t.elem, BaseType) and t.elem.name == BaseTy.Tensor:
+            code.extend(
+                f"""
+    {ctype.cpp_type(strip_ref=True)} {out_name} = {arg_name}.toTensorList();
+                """.split(
+                    "\n"
+                )
+            )
+        elif isinstance(t.elem, BaseType) and (
+            t.elem.name == BaseTy.int or t.elem.name == BaseTy.SymInt
+        ):
+            code.extend(
+                f"""
+    {ctype.cpp_type(strip_ref=True)} {out_name} = {arg_name}.toIntList();
+                """.split(
+                    "\n"
+                )
+            )
+        elif isinstance(t.elem, BaseType) and t.elem.name == BaseTy.float:
+            code.extend(
+                f"""
+    {ctype.cpp_type(strip_ref=True)} {out_name} = {arg_name}.toDoubleList();
+                """.split(
+                    "\n"
+                )
+            )
+        elif isinstance(t.elem, BaseType) and t.elem.name == BaseTy.bool:
+            # handle list type with size, e.g., bool[4]
+            code.extend(
+                f"""
+    {ctype.cpp_type(strip_ref=True)} {out_name} = {arg_name}.toBoolList();
+                """.split(
+                    "\n"
+                )
+            )
+        # pytorch codegen:
+        # we have to use c10::List for optional element. e.g., Tensor?[] -> c10::List<c10::optional<at::Tensor>>
+        elif (
+            isinstance(t.elem, OptionalType)
+            and isinstance(t.elem.elem, BaseType)
+            and t.elem.elem.name == BaseTy.Tensor
+        ):
+            code.extend(
+                f"""
+#ifdef USE_ATEN_LIB
+at::ArrayRef<c10::optional<at::Tensor>> {in_name} = {arg_name}.toListOptionalTensor();
+c10::List<c10::optional<at::Tensor>> {out_name};
+for (auto {elem_name}: {in_name}) {{
+    {out_name}.push_back({elem_name});
+}}
+#else
+torch::executor::ArrayRef<torch::executor::optional<torch::executor::Tensor>> {out_name} = {arg_name}.toListOptionalTensor();
+#endif
+                """.split(
+                    "\n"
+                )
+            )
+        else:
+            # use ArrayRef as default.
+            vec_name = arg_name + "_vec"
+            # need to bring vector instantiation out of scope so that ArrayRef has valid data
+            decl.append(
+                f"std::vector<{res_ctype.cpp_type(strip_ref=True)}> {vec_name};"
+            )
+            code.extend(
+                f"""
+    for (EValue {elem_name}: {in_name}) {{
+        {connector.join(res_code)}
+        {vec_name}.push_back({res_name});
+    }}
+    {ctype.cpp_type(strip_ref=True)} {out_name}({vec_name});
+                """.split(
+                    "\n"
+                )
+            )
+        return code, decl

env-llmeval/lib/python3.10/site-packages/torchgen/executorch/model.py

@@ -0,0 +1,220 @@
+# Represents all kernels used by an Executorch model.
+# It maintains a Dict[OperatorName, Dict[ETKernelKey, BackendMetadata]] structure.
+
+import itertools
+from collections import defaultdict, namedtuple
+from dataclasses import dataclass
+from enum import IntEnum
+from typing import Dict, List, Tuple, Union
+
+from torchgen.model import (
+    BackendIndex,
+    BackendMetadata,
+    DispatchKey,
+    NativeFunction,
+    NativeFunctionsGroup,
+    OperatorName,
+)
+from torchgen.utils import assert_never
+
+KERNEL_KEY_VERSION = 1
+
+
+# TODO: Duplicated Subset from codegen.tool.gen_oplist, remove declaration in codegen
+class ScalarType(IntEnum):
+    Byte = 0
+    Char = 1
+    Short = 2
+    Int = 3
+    Long = 4
+    Float = 6
+    Double = 7
+    Bool = 11
+
+
+ETParsedYaml = namedtuple("ETParsedYaml", ["native_functions", "kernel_index"])
+
+
+@dataclass(frozen=True)
+class ETKernelKeyOpArgMeta:
+    arg_name: str
+    dtype: str
+    # The order of the dimensions if entry is a Tensor
+    dim_order: Tuple[int, ...]
+
+    def to_native_string(self) -> str:
+        dtype_str = ScalarType[self.dtype].value
+        dim_str = str(self.dim_order)[1:-1].replace(" ", "")
+        return f"{dtype_str};{dim_str}"
+
+
+@dataclass(frozen=True)
+class ETKernelKey:
+    # Field undefined is default = True
+    arg_meta: Tuple[ETKernelKeyOpArgMeta, ...] = ()
+
+    # Indicator for this kernel being used as a catch all
+    default: bool = False
+
+    version: int = KERNEL_KEY_VERSION
+
+    @staticmethod
+    def gen_from_yaml(
+        args: Dict[str, Tuple[str, str]],
+        type_alias_map: Dict[str, List[str]],  # TODO: Support unwrapped str val
+        dim_order_alias_map: Dict[str, List[int]],
+    ) -> List["ETKernelKey"]:
+        """Generate ETKernelKeys from arg kernel specs
+        Multiple ETKernelKeys are returned due to dtype permutations from utilizing
+        type_alias_map (actualizing each potential type permutation as a KernelKey)
+
+        Args:
+            args: Mapping from argument name to kernel specs
+                Kernel specs are a tuple of (dtype, dim_order).
+                Currently tuple entries must be aliased via the alias map arguments
+            type_alias_map: Mapping from type alias to potential type enums
+                i.e { T0 : [Double, Int] } means T0 can be either Double or Int
+                Used for lookup by args
+            dim_order_alias_map: Mapping from alias to a list of dimension orders
+                Used for lookup by args
+        """
+        # Cast to dim order to int
+        dim_order_alias_map = {
+            k: [int(alias) for alias in v] for k, v in dim_order_alias_map.items()
+        }
+        kernel_keys = []
+
+        # Get all used Dtype Alias
+        dtype_alias_used = set()
+        for type_alias, dim_order in args.values():
+            # Enforce usage of alias initially
+            # TODO: Support inlined arguments
+            assert type_alias in type_alias_map, "Undefined type alias: " + str(
+                type_alias
+            )
+            assert (
+                dim_order in dim_order_alias_map
+            ), "Undefined dim_order alias: " + str(dim_order)
+            dtype_alias_used.add(type_alias)
+
+        # Generate all permutations of dtype alias values
+        alias_dtypes = [
+            [(alias, dtype) for dtype in type_alias_map[alias]]
+            for alias in dtype_alias_used
+        ]
+        alias_permutations = [
+            dict(permutation) for permutation in list(itertools.product(*alias_dtypes))
+        ]
+
+        # Using each alias value permutation, generate kernel keys
+        op_arg_cache = {}
+        for permutation in alias_permutations:
+            arg_list = []
+            for arg_name, arg_spec in args.items():
+                dtype = permutation[arg_spec[0]]
+                dim_order = dim_order_alias_map[arg_spec[1]]  # type: ignore[assignment]
+                if (
+                    cache_key := (arg_name, dtype, tuple(dim_order))
+                ) not in op_arg_cache:
+                    op_arg_cache[cache_key] = ETKernelKeyOpArgMeta(*cache_key)  # type: ignore[arg-type]
+
+                arg_list.append(op_arg_cache[cache_key])
+            kernel_keys.append(ETKernelKey(tuple(arg_list)))
+
+        return kernel_keys
+
+    def to_native_string(self) -> str:
+        if self.default:
+            return "default"
+        return (
+            "v"
+            + str(KERNEL_KEY_VERSION)
+            + "/"
+            + "|".join([arg.to_native_string() for arg in self.arg_meta])
+        )
+
+
+@dataclass(frozen=True)
+class ETKernelIndex:
+    index: Dict[OperatorName, Dict[ETKernelKey, BackendMetadata]]
+
+    def has_kernels(self, g: Union[NativeFunction, NativeFunctionsGroup]) -> bool:
+        m = self.get_kernels(g)
+        return m is not None
+
+    def get_kernels(
+        self, g: Union[NativeFunction, NativeFunctionsGroup]
+    ) -> Dict[ETKernelKey, BackendMetadata]:
+        if isinstance(g, NativeFunction):
+            f = g
+        elif isinstance(g, NativeFunctionsGroup):
+            f = g.functional
+        else:
+            assert_never(g)
+        if f.func.name not in self.index:
+            return {}
+        return self.index[f.func.name]
+
+    @staticmethod
+    def grow_from_backend_indices(
+        kernel_index: Dict[OperatorName, Dict[ETKernelKey, BackendMetadata]],
+        backend_indices: Dict[DispatchKey, Dict[OperatorName, BackendMetadata]],
+    ) -> None:
+        for dk in backend_indices:
+            index = backend_indices[dk]
+            for op, backend_metadata in index.items():
+                if op in kernel_index:
+                    kernel_index[op][ETKernelKey(default=True)] = backend_metadata
+                else:
+                    kernel_index[op] = {ETKernelKey(default=True): backend_metadata}
+
+    @staticmethod
+    def from_backend_indices(
+        backend_indices: Dict[DispatchKey, Dict[OperatorName, BackendMetadata]]
+    ) -> "ETKernelIndex":
+        kernel_index: Dict[
+            OperatorName, Dict[ETKernelKey, BackendMetadata]
+        ] = defaultdict(dict)
+        ETKernelIndex.grow_from_backend_indices(kernel_index, backend_indices)
+        return ETKernelIndex(kernel_index)
+
+    def grow(
+        self, backend_indices: Dict[DispatchKey, Dict[OperatorName, BackendMetadata]]
+    ) -> "ETKernelIndex":
+        ETKernelIndex.grow_from_backend_indices(self.index, backend_indices)
+        return self
+
+    def _to_backend_index(self) -> BackendIndex:
+        """
+        WARNING: this will be deprecated once all the codegen places know how to handle ETKernelIndex.
+        """
+        index: Dict[OperatorName, BackendMetadata] = {}
+        for op in self.index:
+            kernel_dict = self.index[op]
+            assert (
+                len(kernel_dict.values()) == 1
+            ), f"Can't convert ETKernelIndex to BackendIndex because {op} has more than one kernels. Got {kernel_dict}"
+            index[op] = kernel_dict.get(
+                ETKernelKey(default=True),
+                BackendMetadata(kernel="", structured=False, cpp_namespace=""),
+            )
+        return BackendIndex(
+            dispatch_key=DispatchKey.CPU,
+            use_out_as_primary=False,
+            device_guard=False,
+            external=False,
+            index=index,
+        )
+
+    # Note duplicate ETKernelKey from index_b will clobber the metadata from index_a
+    @staticmethod
+    def merge_indices(
+        index_a: "ETKernelIndex", index_b: "ETKernelIndex"
+    ) -> "ETKernelIndex":
+        combined = defaultdict(dict, index_a.index.copy())
+
+        for op, entry in index_b.index.items():
+            for key, metadata in entry.items():
+                combined[op][key] = metadata
+
+        return ETKernelIndex(combined)

env-llmeval/lib/python3.10/site-packages/torchgen/executorch/parse.py

@@ -0,0 +1,151 @@
+from collections import defaultdict, namedtuple
+from typing import Any, Dict, List, Optional, Set, Tuple
+
+import yaml
+
+from torchgen.executorch.model import ETKernelIndex, ETKernelKey
+
+from torchgen.gen import LineLoader, parse_native_yaml
+from torchgen.model import (
+    BackendMetadata,
+    DispatchKey,
+    FunctionSchema,
+    NativeFunction,
+    OperatorName,
+)
+from torchgen.utils import NamespaceHelper
+
+# Parse native_functions.yaml into a sequence of NativeFunctions and ET Backend Indices.
+ETParsedYaml = namedtuple("ETParsedYaml", ["native_functions", "et_kernel_indices"])
+
+# Fields in native_functions.yaml used to determine which kernels should be used
+ET_FIELDS = ["kernels", "type_alias", "dim_order_alias"]
+
+
+def parse_from_yaml(ei: Dict[str, object]) -> Dict[ETKernelKey, BackendMetadata]:
+    """Given a loaded yaml representing kernel assignment information, extract the
+    mapping from `kernel keys` to `BackendMetadata` (the latter representing the kernel instance)
+
+    Args:
+        ei: Dict keys {kernels, type_alias, dim_order_alias}
+            See ETKernelKey for description of arguments
+    """
+    e = ei.copy()
+    if (kernels := e.pop("kernels", None)) is None:
+        return {}
+
+    type_alias: Dict[str, List[str]] = e.pop("type_alias", {})  # type: ignore[assignment]
+    dim_order_alias: Dict[str, List[str]] = e.pop("dim_order_alias", {})  # type: ignore[assignment]
+    dim_order_alias.pop("__line__", None)
+
+    kernel_mapping: Dict[ETKernelKey, BackendMetadata] = {}
+
+    for entry in kernels:  # type: ignore[attr-defined]
+        arg_meta = entry.get("arg_meta")
+        if arg_meta is not None:
+            arg_meta.pop("__line__")
+
+        kernel_name = entry.get("kernel_name")
+        namespace_helper = NamespaceHelper.from_namespaced_entity(
+            kernel_name, max_level=3
+        )
+        kernel_namespace = namespace_helper.get_cpp_namespace(default="at")
+        backend_metadata = BackendMetadata(
+            kernel=namespace_helper.entity_name,
+            structured=False,
+            cpp_namespace=(kernel_namespace + "::native"),
+        )
+
+        kernel_keys = (
+            [ETKernelKey((), default=True)]
+            if arg_meta is None
+            else ETKernelKey.gen_from_yaml(arg_meta, type_alias, dim_order_alias)  # type: ignore[arg-type]
+        )
+
+        for kernel_key in kernel_keys:
+            assert kernel_key not in kernel_mapping, (
+                "Duplicate kernel key: " + str(kernel_key) + " " + str(e)
+            )
+            kernel_mapping[kernel_key] = backend_metadata
+
+    return kernel_mapping
+
+
+def parse_et_yaml_struct(es: object) -> ETKernelIndex:
+    """Given a loaded yaml representing a list of operators, for each op extract the mapping
+    of `kernel keys` to `BackendMetadata` (the latter representing the kernel instance
+    that should be used by the kernel key).
+    """
+    indices: Dict[OperatorName, Dict[ETKernelKey, BackendMetadata]] = {}
+    for ei in es:  # type: ignore[attr-defined]
+        e = ei.copy()
+
+        funcs = e.pop("func")
+        assert isinstance(funcs, str), f"not a str: {funcs}"
+        namespace_helper = NamespaceHelper.from_namespaced_entity(
+            namespaced_entity=funcs, max_level=1
+        )
+        opname = FunctionSchema.parse(namespace_helper.entity_name).name
+
+        assert opname not in indices, f"Duplicate func found in yaml: {opname} already"
+
+        if len(index := parse_from_yaml(e)) != 0:
+            indices[opname] = index
+
+    return ETKernelIndex(indices)
+
+
+def extract_kernel_fields(es: object) -> Dict[OperatorName, Dict[str, Any]]:
+    """Given a loaded yaml representing a list of operators, extract the
+    kernel key related fields indexed by the operator name.
+    """
+    fields: Dict[OperatorName, Dict[str, Any]] = defaultdict(dict)
+    for ei in es:  # type: ignore[attr-defined]
+        funcs = ei.get("func")
+        assert isinstance(funcs, str), f"not a str: {funcs}"
+        namespace_helper = NamespaceHelper.from_namespaced_entity(
+            namespaced_entity=funcs, max_level=1
+        )
+        opname = FunctionSchema.parse(namespace_helper.entity_name).name
+
+        for field in ET_FIELDS:
+            if (value := ei.get(field)) is not None:
+                fields[opname][field] = value
+
+    return fields
+
+
+def parse_et_yaml(
+    path: str,
+    tags_yaml_path: str,
+    ignore_keys: Optional[Set[DispatchKey]] = None,
+    skip_native_fns_gen: bool = False,
+) -> Tuple[List[NativeFunction], Dict[OperatorName, Dict[str, Any]]]:
+    """Parse native_functions.yaml into NativeFunctions and an Operator Indexed Dict
+    of fields to persist from native_functions.yaml to functions.yaml
+    """
+    with open(path) as f:
+        es = yaml.load(f, Loader=LineLoader)
+
+    et_kernel = extract_kernel_fields(es)
+
+    # Remove ET specific fields from entries for BC compatibility
+    strip_et_fields(es)
+
+    native_yaml = parse_native_yaml(
+        path,
+        tags_yaml_path,
+        ignore_keys,
+        skip_native_fns_gen=skip_native_fns_gen,
+        loaded_yaml=es,
+    )
+    return native_yaml.native_functions, et_kernel
+
+
+def strip_et_fields(es: object) -> None:
+    """Given a loaded yaml representing a list of operators,
+    remove ET specific fields from every entries for BC compatibility
+    """
+    for entry in es:  # type: ignore[attr-defined]
+        for field in ET_FIELDS:
+            entry.pop(field, None)