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Traktor/myenv/Lib/site-packages/torchgen/api/python.py

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losowanie zdjec
2024-05-26 05:12:46 +02:00
from dataclasses import dataclass
from typing import Dict, List, Optional, Sequence, Set, Tuple, Union
from torchgen.api import cpp
from torchgen.api.types import Binding, CppSignature, CppSignatureGroup
from torchgen.gen import pythonify_default
from torchgen.model import (
Argument,
BaseTy,
BaseType,
FunctionSchema,
ListType,
NativeFunction,
OptionalType,
Return,
Type,
Variant,
)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
#
# Data Models
#
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
#
# [Notes] python binding codegen
#
# The Python binding codegen produces code that takes the input list of
# PyObjects, finds the matching ATen C++ function using PythonArgParser,
# converts the PyObjects into C++ types and calls the ATen C++ function:
#
# +--------+ parsing +------------------------+ binding +-----------------------+
# | PyObjs | ---------> | PythonArgParser Output | ---------> | Cpp Function Dispatch |
# +--------+ +------------------------+ +-----------------------+
#
# The following examples demonstrate the data models the Python binding
# codegen needs to deal with and the tasks it needs to accomplish. It
# helps understand the purpose of the new data types we introduced below.
#
# - Function Schema (source of truth)
#
# aten::empty.names(int[] size, *, Dimname[]? names,
# ScalarType? dtype=None, Layout? layout=None,
# Device? device=None, bool? pin_memory=None,
# MemoryFormat? memory_format=None) -> Tensor
#
# - Python Signature
#
# It's used to generate input schema string for PythonArgParser.
# Note: TensorOptions fields are reordered and the additional
# 'requires_grad' field is added:
#
# empty(IntArrayRef size, *, DimnameList? names,
# MemoryFormat? memory_format=None, ScalarType dtype=None,
# Layout layout=torch.strided, Device device=None,
# bool pin_memory=False, bool requires_grad=False)
#
# - C++ Signature
#
# It's used to generate C++ lambda formals & dispatch call.
# Note: the scattered TensorOptions fields are packed into 'options'.
#
# auto dispatch_empty =
# [](IntArrayRef size, c10::optional<DimnameList> names,
# const TensorOptions & options,
# c10::optional<MemoryFormat> memory_format) -> Tensor {
# pybind11::gil_scoped_release no_gil;
# return torch::empty(size, names, options, memory_format);
# };
#
# - Binding between Python Arguments and C++ Arguments
#
# Given a set of Python Arguments in scope, we need produce the
# binding expressions that translate the Python API into C++ API:
#
# Python Args Cpp Args Binding Exprs
# -----------------------------------------------------------------
# 0: size size '_r.intlist(0)'
# 1: names names 'names' [special init]
# 2: memory_format -------+
# 3: dtype -----+-|--> options 'options' [special packing]
# 4: layout / |
# 5: device / +--> memory_format '_r.memoryformatOptional(2)'
# 6: pin_memory /
# 7: requires_grad -+
#
# So the full dispatch expression would look like:
#
# dispatch_empty(_r.intlist(0), names, options,
# _r.memoryformatOptional(2))
#
# Where does 'names' come from? It involves special local init:
#
# auto __names = _r.toDimnameListOptional(1);
# c10::optional<DimnameList> names =
# __names ? c10::make_optional(DimnameList(__names.value()))
# : c10::nullopt;
#
# Where does 'options' come from? It involves special local init
# for TensorOptions. Note that Python side has the additional
# 'requires_grad' field:
#
# const auto options = TensorOptions()
# .dtype(_r.scalartype(3))
# .device(_r.device(5))
# .layout(_r.layoutOptional(4))
# .requires_grad(_r.toBool(7))
# .pinned_memory(_r.toBool(6));
#
# In some other cases one Python Argument can map to multiple C++
# Arguments. For example:
#
# aten::max.names_dim(Tensor self, Dimname dim, bool keepdim=False)
# -> (Tensor values, Tensor indices)
#
# Python Args Cpp Args Binding Exprs
# ---------------------------------------------------------------------
# +----> max 'out[0]'
# /-----> max_values 'out[1]
# 0: input / self '_r.tensor(0)'
# 1: dim / dim '_r.dimname(1)'
# 2: keepdim / keepdim '_r.toBool(2)'
# 3: out -----+ [local init] out '_r.tensorlist_n<2>(3)'
#
# As demonstrated above, the binding can involve reordering,
# packing, unpacking and special local inits.
#
#
# Let's look at a concrete example:
#
# static PythonArgParser parser({
# "abs(Tensor input, *, Tensor out=None)",
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ^
# +--- Python Schema, represented by PythonSignature and PythonArgument
#
# }, /*traceable=*/true);
#
# ParsedArgs<2> parsed_args;
# auto _r = parser.parse(nullptr, args, kwargs, parsed_args);
#
# ...
#
# if (_r.isNone(1)) {
# ~~~~~~~~~~~~ <--- Scattered PythonArgParser output (arg name = 'out')
# represented by PythonArgParserOutputExpr
#
# // aten::abs(Tensor self) -> Tensor
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ^
# +--- NativeFunction schema, base version
#
# auto dispatch_abs = [](const Tensor & self) -> Tensor {
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ^
# +--- dispatch_lambda_args / dispatch_lambda_return_str
# generated from NativeFunction / CppSignature
# (deprecated PythonSignature is special)
# arguments are represented by DispatchLambdaArgument
#
# pybind11::gil_scoped_release no_gil;
# return self.abs();
# ~~~~~~~~~~~ <--- cpp_dispatch_target / cpp_dispatch_exprs
# generated from NativeFunction / CppSignature
# };
# return wrap(dispatch_abs(_r.tensor(0)));
# ~~~~~~~~~~~~~
# ^
# +--- dispatch_lambda_exprs
# binding PythonArgParserOutputExpr (python args)
# and DispatchLambdaArgument (c++ args)
#
# } else {
# // aten::abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ^
# +--- NativeFunction schema, out-variant
#
# auto dispatch_abs_out = [](Tensor out, const Tensor & self) -> Tensor {
# pybind11::gil_scoped_release no_gil;
# return at::abs_out(out, self);
# };
# return wrap(dispatch_abs_out(_r.tensor(1), _r.tensor(0)));
# }
#
#
# [Notes] python interface codegen
# The python dataclasses below are used used to generate both python binding code
# and pyi type hint signatures.
# In theory these two should look very similar, but there are number of differences
# in how pyi signatures vs. python_arg_parser signatures are generated.
# These differences have been encapsulated in signature_str() vs. signature_str_pyi()
# to display the full signatures, and argument_str() vs argument_str_pyi() to display arguments.
# For examples, only pyi signatures include return types.
@dataclass(frozen=True)
class PythonReturns:
returns: Tuple[Return, ...]
@dataclass(frozen=True)
class PythonArgument:
name: str
type: Type
default: Optional[str]
# Used to generate the default init expr for some PythonArgParser outputs, e.g.:
#
# _r.layoutWithDefault(3, layout_from_backend(self.options().backend())))
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ^
# +--- default_init str
default_init: Optional[str]
# Compute argument formal for python argument parsing.
# Needs to be consistent with torch/csrc/utils/python_arg_parser.h.
def argument_str(self, *, method: bool = False, symint: bool = True) -> str:
type_str = (
argument_type_str(self.type, symint=symint)
.replace("const ", "")
.replace(" &", "")
)
name = self.name
# s/self/input/ outside method bindings
# [old codegen] TODO: remove this? doesn't rename in codegen, it's just
# for the parse string
if name == "self" and type_str in ["Tensor", "Number"] and not method:
name = "input"
# add default
if self.default is not None:
default = {
"nullptr": "None",
"c10::nullopt": "None",
"{}": "None",
}.get(self.default, self.default)
return f"{type_str} {name}={default}"
else:
return f"{type_str} {name}"
def argument_str_pyi(
self, *, method: bool = False, deprecated: bool = False
) -> str:
type_str = argument_type_str_pyi(self.type)
name = self.name
# s/self/input/ outside method bindings
# [old codegen] TODO: remove this? doesn't rename in codegen, it's just
# for the parse string
if name == "self" and type_str == "Tensor" and not method and not deprecated:
name = "input"
if name == "from": # from is a Python keyword...
name += "_"
# pyi merges the _out and functional variants into the same signature, with an optional out arg
if name == "out" and type_str == "Tensor" and not deprecated:
type_str = "Optional[" + type_str + "]"
# pyi deprecated signatures don't get defaults for their out arg
treat_as_no_default = (
deprecated
and isinstance(self, PythonOutArgument)
and self.default == "None"
)
# add default
if self.default is not None and not treat_as_no_default:
if (
isinstance(self.type, ListType)
and self.type.elem == BaseType(BaseTy.int)
and self.default.startswith("{")
and self.default.endswith("}")
):
default = "(" + self.default[1:-1] + ")"
else:
default = {
"nullptr": "None",
"c10::nullopt": "None",
"{}": "None",
"MemoryFormat::Contiguous": "contiguous_format",
"QScheme::PER_TENSOR_AFFINE": "per_tensor_affine",
}.get(self.default, self.default)
return f"{name}: {type_str} = {default}"
else:
return f"{name}: {type_str}"
@dataclass(frozen=True)
class PythonOutArgument(PythonArgument):
# In Python signature multiple output fields are packed into one 'out' argument.
# When binding to C++, it's first binded to a local 'out' variable:
# 'auto out = _r.tensorlist_n<2>(2);',
# then binded to scattered C++ output arguments as 'out[0]', 'out[1]', and etc.
# TODO: maybe don't need keep scattered out fields for python signature?
outputs: Tuple[PythonArgument, ...]
@staticmethod
def from_outputs(
outputs: Tuple[PythonArgument, ...]
) -> Optional["PythonOutArgument"]:
if not outputs:
return None
size = len(outputs)
if size == 1:
return PythonOutArgument(
name=outputs[0].name,
type=outputs[0].type,
default="None",
default_init=None,
outputs=outputs,
)
elif size > 1:
if any(not a.type.is_tensor_like() for a in outputs):
raise RuntimeError(f"Unsupported output type: {outputs}")
return PythonOutArgument(
name="out",
# TODO: shouldn't this be OptionalType[ListType[...]], since it defaults to None?
type=ListType(BaseType(BaseTy.Tensor), size),
default="None",
default_init=None,
outputs=outputs,
)
raise AssertionError(r"Unexpected PythonOutArgument size")
@dataclass(frozen=True)
class PythonSignature:
# Base operator name, without inplace/outplace suffix.
name: str
# Positional arguments.
# TODO: create a dedicated SelfArgument type for 'self'?
input_args: Tuple[PythonArgument, ...]
# Keyword arguments excluding the 'out' argument and scattered kwargs belonging
# to TensorOptions (dtype, layout, device, pin_memory, requires_grad, etc).
input_kwargs: Tuple[PythonArgument, ...]
output_args: Optional[PythonOutArgument]
# Return types, which are only used by pyi
returns: PythonReturns
# These are scattered kwargs arguments belonging to TensorOptions.
# When binding to C++, they are packed into a TensorOptions object 'options'.
# It's possible that the C++ signature doesn't take TensorOptions object (e.g.
# for out variant), in which case they will be used as scattered fields without
# being packed into 'options'.
# TODO: maybe create a PythonTensorOptionsArgument?
tensor_options_args: Tuple[PythonArgument, ...]
# method or function signature?
method: bool
@property
def deprecated(self) -> bool:
return False
def arguments(
self, *, skip_outputs: bool = False, skip_tensor_options: bool = False
) -> Tuple[Union[PythonArgument, PythonOutArgument], ...]:
result: List[Union[PythonArgument, PythonOutArgument]] = []
result.extend(self.input_args)
result.extend(self.input_kwargs)
if self.output_args is not None and not skip_outputs:
result.append(self.output_args)
if not skip_tensor_options:
result.extend(self.tensor_options_args)
return tuple(result)
def arguments_count(self) -> int:
return len(self.arguments())
def output_idx(self) -> int:
return len(self.input_args) + len(self.input_kwargs)
# [old codegen] Compute the Python function signature for argument parsing,
# as specified in torch/csrc/utils/python_arg_parser.h. WARNING:
# this is NOT the same type signature as specified by PEP 484
# as understood by mypy; our format was independently developed
# and has some quirks to make it more suitable specifically
# for error parsing.
#
# For a translation to mypy-valid type signatures, see
# signature_str_pyi().
def signature_str(self, *, skip_outputs: bool = False, symint: bool = True) -> str:
args = self.arguments(skip_outputs=skip_outputs)
schema_formals: List[str] = [
a.argument_str(method=self.method, symint=symint) for a in args
]
positional_argc = len(self.input_args)
if len(schema_formals) > positional_argc:
schema_formals.insert(positional_argc, "*")
return f'{self.name}({", ".join(schema_formals)})'
def signature_str_pyi(self, *, skip_outputs: bool = False) -> str:
args = self.arguments(skip_outputs=skip_outputs)
schema_formals: List[str] = [
a.argument_str_pyi(method=self.method) for a in args
]
positional_argc = len(self.input_args)
if len(schema_formals) > positional_argc:
schema_formals.insert(positional_argc, "*")
# only pyi signatures include returns
returns_str = returns_str_pyi(self)
# pyi also includes self (with no typing/defaults) for methods
if self.method:
schema_formals.insert(0, "self")
return f'def {self.name}({", ".join(schema_formals)}) -> {returns_str}: ...'
def signature_str_pyi_vararg(self, *, skip_outputs: bool = False) -> Optional[str]:
# only pyi uses vararg signatures
args = self.arguments(skip_outputs=skip_outputs)
schema_formals: List[str] = [
a.argument_str_pyi(method=self.method) for a in args
]
# vararg only applies to pyi signatures. vararg variants are not generated for all signatures
num_args = self.arguments_count()
num_positionalargs = len(self.input_args)
have_vararg_version = False
if num_args > 0:
vararg_type = args[0].type
if (
isinstance(vararg_type, ListType)
and str(vararg_type.elem) in ["int", "SymInt"]
and num_positionalargs == 1
):
have_vararg_version = True
if not have_vararg_version:
return None
# Below are the major changes in vararg vs. regular pyi signatures
# vararg signatures also omit the asterix
schema_formals[0] = "*" + args[0].name + ": _int"
returns_str = returns_str_pyi(self)
# pyi also includes self (with no typing/defaults) for methods
if self.method:
schema_formals.insert(0, "self")
return f'def {self.name}({", ".join(schema_formals)}) -> {returns_str}: ...'
# The deprecated python signature involves some special logic, so create a
# dedicated data model to store these extra properties.
@dataclass(frozen=True)
class PythonSignatureDeprecated(PythonSignature):
# Schema for the deprecated function
deprecated_schema: FunctionSchema
# The deprecated signature might miss some arguments that the corresponding
# C++ signature expects. We need store the constant default values to pass in.
# For example:
# [deprecate signature]: addmm(Scalar beta, Tensor self, Tensor mat1, Tensor mat2)
# [func schema]: aten::addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
# [func call]: self.addmm(mat1, mat2, beta, 1)
# We store ['self', 'mat1', 'mat2', 'beta', '1'] in this case.
deprecated_args_exprs: Tuple[str, ...]
@property
def deprecated(self) -> bool:
return True
def signature_str(self, *, skip_outputs: bool = False, symint: bool = True) -> str:
return (
PythonSignature.signature_str(
self, skip_outputs=skip_outputs, symint=symint
)
+ "|deprecated"
)
def signature_str_pyi(self, *, skip_outputs: bool = False) -> str:
args = self.arguments(skip_outputs=skip_outputs)
schema_formals: List[str] = [
a.argument_str_pyi(method=self.method, deprecated=True) for a in args
]
positional_argc = len(self.input_args)
if len(schema_formals) > positional_argc:
schema_formals.insert(positional_argc, "*")
returns_str = returns_str_pyi(self)
return f'def {self.name}({", ".join(schema_formals)}) -> {returns_str}: ...'
def signature_str_pyi_vararg(self, *, skip_outputs: bool = False) -> Optional[str]:
# the codegen doesn't include vararg variants for deprecated signatures
return None
# This struct is used to hold the PythonSignature and its corresponding
# NativeFunction BEFORE grouping base and out-variant functions.
# Why not store NativeFunction in PythonSignature or construct PythonSignature
# from NativeFunction? Because they are not 1-1 mapped.
# One native function could have both deprecated and non-deprecated python
# signatures - NativeFunction doesn't contain information to construct the
# deprecated python signature.
# One python signature is used to handle both the base and the out-variant
# function - see 'PythonSignatureGroup'.
@dataclass(frozen=True)
class PythonSignatureNativeFunctionPair:
signature: PythonSignature
function: NativeFunction
# We merge pairs of functions with signatures that are equivalent mod
# output arguments, and use a single entry in the python_arg_parser sig
# list for both (output arguments become optional).
@dataclass(frozen=True)
class PythonSignatureGroup:
# The signature used for Python argument parsing. The outplace signature
# is preferred if exists, because it can be used to parse inputs for both
# the out-place variant and the base version (with output omitted).
signature: PythonSignature
# The regular ATen declaration (e.g. conv2d)
base: NativeFunction
# The out variant (e.g. conv2d_out)
outplace: Optional[NativeFunction]
@classmethod
def from_pairs(
cls,
functional: PythonSignatureNativeFunctionPair,
out: Optional[PythonSignatureNativeFunctionPair],
) -> "PythonSignatureGroup":
if out is None:
return PythonSignatureGroup(
signature=functional.signature,
base=functional.function,
outplace=None,
)
# prefer the signature with optional out=... arguments because it's the
# superset that can be used to parse input for both base and outplace.
signature_kwargs = out.signature.__dict__.copy()
# Out overloads in C++ don't have TensorOptions arguments,
# so take these from the functional variant
signature_kwargs[
"tensor_options_args"
] = functional.signature.tensor_options_args
return PythonSignatureGroup(
signature=type(out.signature)(**signature_kwargs),
base=functional.function,
outplace=out.function,
)
# C++ function dispatch is wrapped in a lambda function. The lambda function
# has almost the same signature as the C++ function, only with some small
# variants - see details below.
# This data model is used to represent arguments of the lambda function
# signature.
@dataclass(frozen=True)
class DispatchLambdaArgument:
name: str
type_str: str
is_out_arg: bool
# To pass PyObjects arguments to C++ function (via the lambda wrapper),
# we need first convert PyObjects into simple C++ objects. This work
# is done by PythonArgParser.
# This data model is used to represent the output of PythonArgParser.
# It has 1-1 mapping with PythonArgument in PythonSignature.
@dataclass(frozen=True)
class PythonArgParserOutputExpr:
# argument name
name: str
# RHS expression to reference PythonArgParser output.
expr: str
# In some special cases we need create different expr, e.g.:
# '_r.isNone(1)' instead of '_r.tensor(1)'.
index: int
# The python argument it maps to.
argument: PythonArgument
@property
def is_none_expr(self) -> str:
return f"_r.isNone({self.index})"
# To pass PythonArgParser output to the lambda wrapper, we need bind
# PythonArgParserOutputExpr to DispatchLambdaArgument.
# They are not always 1-1 mapped, e.g. scattered TensorOptions fields
# need be packed into a TensorOptions object, which is the argument
# that the lambda function wrapper takes.
@dataclass(frozen=True)
class DispatchLambdaArgumentExprs:
# The exprs that provide the binding for lambda arguments, e.g.:
#
# 'self' -> '_r.tensor(0)'
# 'min' -> 'out[0]' / 'min_indices' -> 'out[1]'
# 'options' -> 'options'
#
# It has 1-1 mapping with DispatchLambdaArgument.
exprs: Sequence[str]
# Special local inits, which might introduce new variables that
# the 'exprs' above reference, e.g.:
#
# 'auto out = _r.tensorlist_n<2>(2);'
#
inits: Sequence[str]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
#
# Helper Functions
#
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
def _cpp_signature(f: NativeFunction, *, method: bool = False) -> CppSignature:
return CppSignatureGroup.from_native_function(f, method=method).signature
def has_tensor_options(f: NativeFunction) -> bool:
return f.func.arguments.tensor_options is not None
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
#
# Python Signature
#
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
# 'simple_type' was introduced by the old codegen, which is slightly
# different from the python schema type, e.g.: doesn't have '?' suffix
# for optional Tensor/TensorList; doesn't have '[size]' suffix for list type.
def argument_type_str(
t: Type, *, simple_type: bool = False, symint: bool = True
) -> str:
if isinstance(t, BaseType):
if t.name == BaseTy.Tensor:
return "Tensor"
elif t.name == BaseTy.int:
return "int64_t"
elif t.name == BaseTy.float:
return "double"
elif t.name == BaseTy.str:
return "c10::string_view"
elif t.name in [
BaseTy.bool,
BaseTy.QScheme,
BaseTy.Scalar,
BaseTy.ScalarType,
BaseTy.Generator,
BaseTy.Storage,
BaseTy.Layout,
BaseTy.Device,
BaseTy.DeviceIndex,
BaseTy.MemoryFormat,
BaseTy.Dimname,
BaseTy.Stream,
BaseTy.ConstQuantizerPtr,
BaseTy.SymInt,
]:
# These python schema type names line up with their function schema names
return t.name.name
elif isinstance(t, OptionalType):
if str(t.elem) == "Tensor":
# Is it desired to keep '?' for simple_type with new style dispatcher?
return "Tensor?"
elem = argument_type_str(t.elem, simple_type=simple_type, symint=symint)
return f"{elem}?"
elif isinstance(t, ListType):
size = t.size if not simple_type else None
if str(t.elem) == "bool":
assert t.size is not None
return f"::std::array<bool,{t.size}>"
elif str(t.elem) == "int":
return f"IntArrayRef[{size}]" if size is not None else "IntArrayRef"
elif str(t.elem) == "SymInt":
if symint:
return (
f"SymIntArrayRef[{size}]" if size is not None else "SymIntArrayRef"
)
else:
return f"IntArrayRef[{size}]" if size is not None else "IntArrayRef"
elif str(t.elem) == "Tensor":
return f"TensorList[{size}]" if size is not None else "TensorList"
elif str(t.elem) == "Scalar":
return f"ScalarList[{size}]" if size is not None else "ScalarList"
elif str(t.elem) == "Tensor?":
if simple_type:
return "c10::List<c10::optional<Tensor>>"
else:
return "const c10::List<c10::optional<Tensor>> &"
elif str(t.elem) == "Dimname":
return f"DimnameList[{size}]" if size is not None else "DimnameList"
elem = argument_type_str(t.elem, simple_type=simple_type, symint=symint)
return f"ArrayRef<{elem}>"
raise RuntimeError(f"unrecognized type {repr(t)}")
def argument_type_size(t: Type) -> Optional[int]:
l = t.is_list_like()
if l is not None and str(l.elem) != "bool":
return l.size
else:
return None
def argument(a: Argument) -> PythonArgument:
return PythonArgument(
name=a.name,
type=a.type,
# TODO: directly translate a.default to python default
default=str(
pythonify_default(cpp.default_expr(a.default, a.type, symint=False))
)
if a.default is not None
else None,
default_init=None,
)
# Generates a PythonSignature that can be used for either .pyi or PythonArgParser codegen
def signature(
f: NativeFunction, *, method: bool = False, pyi: bool = False
) -> PythonSignature:
return signature_from_schema(
f.func, category_override=f.category_override, method=method, pyi=pyi
)
def signature_from_schema(
func: FunctionSchema,
*,
category_override: Optional[str],
method: bool = False,
pyi: bool = False,
) -> PythonSignature:
args: List[Argument] = []
args.extend(func.arguments.pre_self_positional)
# Skip SelfArgument if this is method.
if not method and func.arguments.self_arg is not None:
args.append(func.arguments.self_arg.argument)
args.extend(func.arguments.post_self_positional)
args.extend(func.arguments.pre_tensor_options_kwarg_only)
# Skip TensorOptionsArguments. Python side TensorOptions
# arguments are created based on different rules - see below.
args.extend(func.arguments.post_tensor_options_kwarg_only)
args.extend(func.arguments.out)
input_arg_set = {a.name for a in func.arguments.flat_positional}
kwarg_only_set = {a.name for a in func.arguments.flat_kwarg_only}
out_arg_set = {a.name for a in func.arguments.out}
input_args = tuple(map(argument, filter(lambda a: a.name in input_arg_set, args)))
input_kwargs = tuple(
map(argument, filter(lambda a: a.name in kwarg_only_set, args))
)
outputs = tuple(map(argument, filter(lambda a: a.name in out_arg_set, args)))
# Reintroduce the scattered fields of TensorOptions for Python.
# Compared to the cpp counterpart, the python arguments have new property
# (default_init) and a new argument 'requires_grad', which require some
# special handlings.
# [old codegen] TODO: because these aren't guaranteed to be 100% faithful
# to the original versions in the yaml, this recreation is a potential
# source of drift between eager and JIT. Pull this logic out to a shared place.
has_tensor_input_arg = any(
a.type.is_tensor_like() for a in func.arguments.flat_non_out
)
if any(a.name == "requires_grad" for a in func.schema_order_arguments()):
raise ValueError(
"argument named requires_grad is reserved, should not explicitly add it in the schema"
)
# [old codegen] this probably won't work if one of the returns is not a tensor,
# but it will produce a compile-time error that is obvious.
has_tensor_return = any(r.type.is_tensor_like() for r in func.returns)
name: str = cpp.name(func)
is_factory_function = category_override == "factory" or (
has_tensor_return and not has_tensor_input_arg
)
is_like_or_new_function = (
category_override in ("new", "like")
or name.startswith("new_")
or name.endswith("_like")
)
is_dummy_function = category_override == "dummy"
tensor_options_args: List[PythonArgument] = []
if (is_factory_function or is_like_or_new_function) and not is_dummy_function:
def topt_default_init(name: str) -> Optional[str]:
topt_args = func.arguments.tensor_options
if topt_args is None:
return None
a = getattr(topt_args, name)
if a.default is None or a.default == "None":
return None
return cpp.default_expr(a.default, a.type, symint=False)
tensor_options_args.append(
PythonArgument(
name="dtype",
type=OptionalType(BaseType(BaseTy.ScalarType)),
default="None",
default_init=(
None if is_like_or_new_function else topt_default_init("dtype")
),
)
)
tensor_options_args.append(
PythonArgument(
name="layout",
type=OptionalType(BaseType(BaseTy.Layout)),
default="None",
default_init=(
None if is_like_or_new_function else topt_default_init("layout")
),
)
)
tensor_options_args.append(
PythonArgument(
name="device",
type=OptionalType(BaseType(BaseTy.Device)),
default="None",
default_init=(
None
if is_like_or_new_function
else (
topt_default_init("device")
or "torch::tensors::get_default_device()"
)
),
)
)
tensor_options_args.append(
PythonArgument(
name="pin_memory",
type=OptionalType(BaseType(BaseTy.bool)),
default="False",
default_init=None,
)
)
tensor_options_args.append(
PythonArgument(
name="requires_grad",
type=OptionalType(BaseType(BaseTy.bool)),
default="False",
default_init=None,
)
)
returns = PythonReturns(returns=func.returns)
return PythonSignature(
name=str(func.name.name),
input_args=input_args,
input_kwargs=input_kwargs,
output_args=PythonOutArgument.from_outputs(outputs),
tensor_options_args=tuple(tensor_options_args),
returns=returns,
method=method,
)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
#
# Python Interface
#
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
def structseq_fieldnames(returns: Tuple[Return, ...]) -> List[str]:
if len(returns) <= 1 or all(r.name is None for r in returns):
return []
else:
if any(r.name is None for r in returns):
# When building on Windows, `PyStructSequence_UnnamedField` could not be
# resolved by the linker for some reason, which cause error in building:
#
# python_nn_functions.cpp.obj : error LNK2001: unresolved external symbol
# PyStructSequence_UnnamedField
#
# Thus, at this point in time, we do not support unnamed
# fields in structseq; you must either name all fields,
# or none of them.
raise ValueError("Unnamed field is not supported by codegen")
return [str(r.name) for r in returns]
def argument_type_str_pyi(t: Type) -> str:
add_optional = False
if isinstance(t, OptionalType):
t = t.elem
add_optional = True
if isinstance(t, BaseType):
if t.name in [BaseTy.int, BaseTy.DeviceIndex]:
ret = "_int"
if t.name == BaseTy.SymInt:
ret = "Union[_int, SymInt]"
elif t.name == BaseTy.float:
ret = "_float"
elif t.name == BaseTy.str:
ret = "str"
elif t.name == BaseTy.Scalar:
ret = "Union[Number, _complex]"
elif t.name == BaseTy.ScalarType:
ret = "_dtype"
elif t.name == BaseTy.bool:
ret = "_bool"
elif t.name == BaseTy.QScheme:
ret = "_qscheme"
elif t.name == BaseTy.Layout:
ret = "_layout"
elif t.name == BaseTy.Device:
ret = "Optional[DeviceLikeType]"
elif t.name == BaseTy.MemoryFormat:
ret = "memory_format"
elif t.name == BaseTy.Dimname:
ret = "Union[str, ellipsis, None]"
elif t.name == BaseTy.Storage:
ret = "Union[Storage, UntypedStorage]"
elif t.name in [BaseTy.Tensor, BaseTy.Generator, BaseTy.Stream]:
# These python schema type names line up with their function schema names
ret = t.name.name
elif isinstance(t, ListType):
if str(t.elem) == "int":
ret = "Union[_int, _size]" if t.size is not None else "_size"
elif t.is_tensor_like():
# TODO: this doesn't seem right...
# Tensor?[] currently translates to Optional[Union[Tuple[Tensor, ...], List[Tensor]]]
# It should probably translate to Union[Tuple[Optional[Tensor], ...], List[Optional[Tensor]]]
if isinstance(t.elem, OptionalType):
add_optional = True
ret = (
"Union[Tensor, Tuple[Tensor, ...], List[Tensor]]"
if t.size is not None
else "Union[Tuple[Tensor, ...], List[Tensor]]"
)
elif str(t.elem) == "float":
ret = "Sequence[_float]"
elif str(t.elem) == "SymInt" and t.size is not None:
elem = argument_type_str_pyi(t.elem)
ret = f"Union[{elem}, Sequence[{elem}]]"
else:
elem = argument_type_str_pyi(t.elem)
ret = f"Sequence[{elem}]"
else:
raise RuntimeError(f"unrecognized type {repr(t)}")
if add_optional:
ret = "Optional[" + ret + "]"
return ret
def return_type_str_pyi(t: Type) -> str:
# Where arguments are open to accepting Union, return types should return
# concrete types
if isinstance(t, OptionalType):
inner = return_type_str_pyi(t.elem)
return f"Optional[{inner}]"
if isinstance(t, BaseType):
if t.name == BaseTy.Device:
return "_device"
elif t.name == BaseTy.Dimname:
ret = "Optional[str]"
else:
return argument_type_str_pyi(t)
if isinstance(t, ListType):
inner = return_type_str_pyi(t.elem)
return f"Tuple[{inner}, ...]"
return argument_type_str_pyi(t)
def returns_structseq_pyi(signature: PythonSignature) -> Optional[Tuple[str, str]]:
python_returns = [return_type_str_pyi(r.type) for r in signature.returns.returns]
structseq_name = signature.name
field_names = structseq_fieldnames(signature.returns.returns)
if field_names:
# These types are structseq objects which act like named NamedTuples, but
# the constructor acts like the constructor of tuple. Using typing.NamedTuple
# does not allow us to override __init__.
field_names_str = ", ".join(repr(name) for name in field_names)
seq_type = f"Tuple[{', '.join(python_returns)}]"
structseq_def_lines = [
f"class {structseq_name}({seq_type}):",
]
for name, typ in zip(field_names, python_returns):
structseq_def_lines.extend(
[
" @property",
f" def {name}(self) -> {typ}: ...",
]
)
structseq_def_lines.extend(
[
f" def __new__(cls, sequence: {seq_type}): ...",
f" n_fields: _int = {len(field_names)}",
f" n_sequeunce_fields: _int = {len(field_names)}",
" n_unnamed_fields: _int = 0",
" def __init_subclass__(cls) -> NoReturn: ... # prohibit subclassing",
"", # add an extra newline
]
)
structseq_def = "\n".join(structseq_def_lines)
# Example:
# structseq_def = (
# "class max(Tuple[Tensor, Tensor]):\n"
# " @property\n"
# " def values(self) -> Tensor: ...\n"
# " @property\n"
# " def indices(self) -> Tensor: ...\n"
# " def __new__(cls, sequence: Tuple[Tensor, Tensor]): ...\n"
# " n_fields: _int = 2",
# " n_sequeunce_fields: _int = 2",
# " n_unnamed_fields: _int = 0",
# " def __init_subclass__(cls) -> NoReturn: ... # prohibit subclassing",
# )
return structseq_name, structseq_def
return None
def returns_str_pyi(signature: PythonSignature) -> str:
field_names = structseq_fieldnames(signature.returns.returns)
if field_names:
return f"torch.return_types.{signature.name}"
python_returns = [return_type_str_pyi(r.type) for r in signature.returns.returns]
if len(python_returns) > 1:
return "Tuple[" + ", ".join(python_returns) + "]"
if len(python_returns) == 1:
return python_returns[0]
return "None"
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
#
# C++ Function Dispatch
#
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
# This section provides APIs to generate the code that does C++ function
# dispatch. The C++ function call is wrapped by a lambda function.
# For example:
#
# // aten::selu_(Tensor(a!) self) -> Tensor(a!)
# auto dispatch_selu_ = [](Tensor self) -> Tensor {
# pybind11::gil_scoped_release no_gil;
# return at::selu_(self);
# };
#
# The lambda function's signature follows the C++ signature in common
# cases, e.g.:
#
# // aten::add.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
# [](const Tensor & self, const Tensor & other, Scalar alpha) -> Tensor
#
# For out variant the 'out' argument's type is changed from 'Tensor &'
# to 'Tensor'. It's because when calling the lambda it passes in the
# PythonArgParser output '_r.tensor(3)', which is stack allocated object
# and needs to pass by value. Also see comments in 'dispatch_lambda_return_str()'.
#
# // aten::add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
# [](Tensor out, const Tensor & self, const Tensor & other, Scalar alpha) -> Tensor
#
# For multi-output case it can keep using reference type because the
# PythonArgParser output has been unpacked to local variables, e.g.:
#
# // aten::max.names_dim_max(Tensor self, Dimname dim, bool keepdim=False, *,
# // Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices)
# [](Tensor & max, Tensor & max_values, const Tensor & self, Dimname dim, bool keepdim) -> std::tuple<Tensor,Tensor>
#
# For deprecated python signature, it should follow deprecated python arg order.
# TODO: This is to keep same byte-for-byte result as the old codegen - maybe unnecessary?
def dispatch_lambda_args(
ps: PythonSignature, f: NativeFunction, symint: bool = True
) -> Tuple[DispatchLambdaArgument, ...]:
if isinstance(ps, PythonSignatureDeprecated):
schema = ps.deprecated_schema
else:
schema = f.func
# Start with cpp arguments - dispatch lambda signature always include 'self'
cpp_args = cpp.arguments(
arguments=schema.arguments,
faithful=False,
symint=symint,
method=False,
cpp_no_default_args=f.cpp_no_default_args,
)
out_args: Set[str] = {a.name for a in schema.arguments.out}
# Convert from cpp argument to lambda argument
def dispatch_lambda_arg(cpp_arg: Binding) -> DispatchLambdaArgument:
type_str = cpp_arg.type
is_out_arg = cpp_arg.name in out_args
if ps.method and cpp_arg.name == "self":
# For method's 'self', we can use 'const Tensor &' and simply ignore mutability!
type_str = "const at::Tensor &"
else:
# For other cases we need prevent dangling refs to temps (unless it's
# unpacked scattered output)
# The reason is explained in the comments above and in 'dispatch_lambda_return_str()'.
# TODO: avoid this special handling?
ensure_temp_safe = len(out_args) <= 1 or not is_out_arg
if ensure_temp_safe:
type_str = {
"at::Tensor &": "at::Tensor",
}.get(type_str, type_str)
return DispatchLambdaArgument(
name=cpp_arg.name,
type_str=type_str,
is_out_arg=is_out_arg,
)
return tuple(map(dispatch_lambda_arg, cpp_args))
# [old codegen] XXX: if you got here because of an assertion failure, it doesn't mean
# it's enough to just extend the list here. Before you do this, make sure
# to add an appropriate wrap() overload in torch/csrc/autograd/utils/wrap_outputs.h.
SUPPORTED_RETURN_TYPES = {
"at::Tensor",
"::std::tuple<at::Tensor,at::Tensor>",
"::std::tuple<at::Tensor,at::Tensor,at::Tensor>",
"::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor>",
"::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor>",
"::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor>",
"::std::tuple<at::Tensor,at::Tensor,at::Tensor,int64_t>",
"::std::tuple<at::Tensor,at::Tensor,double,int64_t>",
"::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,int64_t>",
"::std::tuple<at::Tensor,at::Tensor,double,at::Tensor,int64_t>",
"::std::tuple<double,int64_t>",
"::std::tuple<at::Tensor,::std::vector<at::Tensor>>",
"::std::vector<at::Tensor>",
# Needed for flash attention forw/backward
"::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt,at::Tensor,at::Tensor,at::Tensor>",
"at::Scalar",
"bool",
"int64_t",
"void*",
"void",
"at::QScheme",
"double",
"at::IntArrayRef",
"at::ScalarType",
"at::Stream",
}
def dispatch_lambda_return_str(f: NativeFunction) -> str:
# [old codegen] Remove type annotation (e.g. 'Tensor' rather than 'Tensor &')
# because the dispatch lambdas take mutable arguments *by value*, not
# by reference. If you then return a reference to such an argument, you
# will now have a pointer to a dangling stack entry. Not good.
#
# You want:
#
# auto dispatch_selu_ = [](Tensor self) -> Tensor { ...; return at::selu_(self); };
# ^^^^^^
#
# *not*
#
# auto dispatch_selu_ = [](Tensor self) -> Tensor& { ...; return at::selu_(self); };
# ^^^^^^^
#
# (NB: We can't make dispatch_selu_ take Tensor&, because the enclosing
# codegen looks like dispatch_selu_(_r.tensor(0)), and you can't take a
# mutable reference to temporary. Maybe we could assign it to a
# variable itself.)
returns_without_annotation = tuple(
Return(r.name, r.type, None) for r in f.func.returns
)
return_str = cpp.returns_type(returns_without_annotation, symint=True).cpp_type()
if return_str not in SUPPORTED_RETURN_TYPES:
raise RuntimeError(f"{f.func.name} returns unsupported type {return_str}")
return return_str
def cpp_dispatch_target(f: NativeFunction) -> str:
symint = f.func.has_symint()
name = cpp.name(f.func, symint_overload=symint)
if Variant.method in f.variants:
return f"self.{name}"
if Variant.function in f.variants:
if has_tensor_options(f) or f.func.name.name.base.endswith("_like"):
namespace = "torch"
else:
namespace = "at"
return f"{namespace}::{name}"
raise RuntimeError(f"could not dispatch, neither function nor method: {f.func}")
def cpp_dispatch_exprs(
f: NativeFunction,
*,
python_signature: Optional[PythonSignature] = None,
) -> Tuple[str, ...]:
cpp_args: Sequence[Binding] = _cpp_signature(f, method=False).arguments()
exprs: Tuple[str, ...] = tuple()
if not isinstance(python_signature, PythonSignatureDeprecated):
# By default the exprs are consistent with the C++ signature.
exprs = tuple(a.name for a in cpp_args)
else:
# For deprecated python signature we may need fill in some constants.
exprs = tuple(
filter(
lambda n: n != "out" or f.func.is_out_fn(),
python_signature.deprecated_args_exprs,
)
)
if Variant.method in f.variants:
exprs = tuple(filter("self".__ne__, exprs))
return exprs
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
#
# Python / C++ Args Binding
#
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
# We explicitly enumerate the PythonArgParser unpacking methods for all
# supported types. This might be more verbose than necessary, partially
# because of the irregularity of unpacking method naming, partially
# because we want to mimic the old codegen behavior - to reject
# unexpected and/or unsupported cases which the old codegen rejects.
# For certain cases it is intentionally more restrictive than necessary,
# e.g.: it doesn't accepts doublelist with definite size.
def arg_parser_unpack_method(
t: Type, default: Optional[str], default_init: Optional[str], *, symint: bool = True
) -> str:
has_default_init = default_init is not None
if has_default_init and str(t) not in (
"ScalarType?",
"ScalarType",
"Device",
"Device?",
"Layout",
"Layout?",
"bool",
"bool?",
):
raise RuntimeError(f"type '{t}' does not supported unpacking with default")
if isinstance(t, BaseType):
if t.name in [
BaseTy.Tensor,
BaseTy.Stream,
BaseTy.Storage,
BaseTy.Scalar,
BaseTy.Dimname,
]:
# These unpack methods line up with their schema names
return t.name.name.lower()
elif t.name == BaseTy.ScalarType:
return "scalartypeWithDefault" if has_default_init else "scalartype"
elif t.name == BaseTy.Device:
return "deviceWithDefault" if has_default_init else "device"
elif t.name == BaseTy.DeviceIndex:
return "toInt64"
elif t.name == BaseTy.int:
return "toInt64"
elif t.name == BaseTy.SymInt:
return "toSymInt" if symint else "toInt64"
elif t.name == BaseTy.bool:
return "toBoolWithDefault" if has_default_init else "toBool"
elif t.name == BaseTy.float:
return "toDouble"
elif t.name == BaseTy.str:
return "stringView"
elif t.name == BaseTy.Layout:
return "layoutWithDefault" if has_default_init else "layout"
elif t.name == BaseTy.MemoryFormat:
return "memoryformat"
elif isinstance(t, OptionalType):
if str(t.elem) == "Tensor":
return "optionalTensor"
elif str(t.elem) == "Generator":
return "generator"
elif str(t.elem) == "Dimname[]":
return "toDimnameListOptional"
elif not has_default_init and default in (None, "None", "c10::nullopt"):
# If default is None: append 'Optional' to elem's unpacking method
return (
arg_parser_unpack_method(t.elem, None, None, symint=symint) + "Optional"
)
else:
# Otherwise, load as underlying type with default
return arg_parser_unpack_method(
t.elem, default, default_init, symint=symint
)
elif isinstance(t, ListType):
if str(t.elem) == "Tensor":
# accept and use definite size
return f"tensorlist_n<{t.size}>" if t.size is not None else "tensorlist"
elif str(t.elem) == "Tensor?":
return "list_of_optional_tensors"
elif str(t.elem) == "Dimname":
# accept definite size
return "dimnamelist"
elif str(t.elem) == "int":
# accept definite size
return "intlist"
elif str(t.elem) == "float":
return "doublelist"
elif str(t.elem) == "SymInt":
# accept definite size
return "symintlist" if symint else "intlist"
elif str(t.elem) == "Scalar":
return "scalarlist"
raise RuntimeError(f"type '{t}' is not supported by PythonArgParser")
# Return RHS expression for python argument using PythonArgParser output.
# e.g. for arg name 'foo', arg type 'bool', arg_index = 2, returns '_r.toBool(2)'
def arg_parser_output_expr(
arg_index: int, a: PythonArgument, *, symint: bool = True
) -> PythonArgParserOutputExpr:
has_default = a.default_init is not None
unpack_method = arg_parser_unpack_method(
t=a.type, default=a.default, default_init=a.default_init, symint=symint
)
default = f", {a.default_init}" if has_default else ""
expr = f"_r.{unpack_method}({arg_index}{default})"
return PythonArgParserOutputExpr(
name=a.name,
expr=expr,
index=arg_index,
argument=a,
)
# Returns a map with key = arg_name and value = PythonArgParserOutputExpr.
def arg_parser_output_exprs(
ps: PythonSignature, f: NativeFunction, *, symint: bool = True
) -> Dict[str, PythonArgParserOutputExpr]:
return {
e.name: e
for i, a in enumerate(ps.arguments())
for e in (arg_parser_output_expr(i, a, symint=symint),)
}
# argument name to type for scattered tensor options fields
TENSOR_OPTIONS_FIELDS = {
"dtype": "ScalarType?",
"device": "Device?",
"layout": "Layout?",
"pin_memory": "bool?",
"requires_grad": "bool?",
}
# bind arg parser outputs (python args) with dispatch lambda arguments (c++ args).
def dispatch_lambda_exprs(
ps: PythonSignature, f: NativeFunction, *, symint: bool = True
) -> DispatchLambdaArgumentExprs:
# This method is to bind 'arg_parser_outputs' and 'lambda_args' by producing
# 'inits' and 'lambda_args_exprs' for each lambda argument using arg parser
# outputs.
arg_parser_outputs = arg_parser_output_exprs(ps, f, symint=symint)
lambda_args = dispatch_lambda_args(ps, f, symint=symint)
inits: List[str] = []
lambda_args_exprs: Dict[str, str] = {}
has_toptions = has_tensor_options(f)
# 1. special inits/unpacking to provide binding exprs for lambda arguments.
for a in ps.arguments(skip_tensor_options=True):
name = a.name
arg_parser_expr = arg_parser_outputs[a.name].expr
if has_toptions and name == "self":
# TODO: why this needs to be special case?
inits.extend(
[
f"auto self = {arg_parser_expr};",
]
)
lambda_args_exprs[name] = name
elif (
isinstance(a, PythonOutArgument)
and len(a.outputs) > 1
and f.func.is_out_fn()
):
inits.extend(
[
f"auto out = {arg_parser_expr};",
]
)
for i, out_arg in enumerate(a.outputs):
lambda_args_exprs[out_arg.name] = f"out[{i}]"
elif str(a.type) == "Dimname[]?":
# [old codegen]
# TODO: make this part of something more general, or get rid of it.
# optional<ArrayRef<T>> are special. The PythonArgParser returns an
# optional<vector<T>>, which cannot be implicitly converted to
# optional<ArrayRef<T>>. One needs to unwrap the optional and rewrap.
inits.extend(
[
f"auto __{name} = {arg_parser_expr};",
f"c10::optional<DimnameList> {name} = __{name} ? c10::make_optional(DimnameList(__{name}.value())) : c10::nullopt;", # noqa: B950
]
)
lambda_args_exprs[name] = name
else:
# default case - directly using PythonArgParser output expr
lambda_args_exprs[name] = arg_parser_expr
# method's self is passed directly to python binding, rather than parsed
if ps.method:
lambda_args_exprs["self"] = "self"
# 2. special packing/checking for TensorOptions.
tensor_options_args_names = [a.name for a in ps.tensor_options_args]
if has_toptions:
if f.func.is_out_fn():
raise RuntimeError(f"{f.func}: tensor options with output arg")
for a in ps.tensor_options_args:
if a.name not in TENSOR_OPTIONS_FIELDS:
raise RuntimeError(
f"{f.func}: unrecognized tensor options field '{a.name}' in python binding arguments"
)
if str(a.type) != TENSOR_OPTIONS_FIELDS.get(a.name):
raise RuntimeError(
f"{f.func}: unrecognized type '{str(a.type)}' for tensor options field '{a.name}'"
)
if not all(
a in tensor_options_args_names for a in TENSOR_OPTIONS_FIELDS.keys()
):
raise RuntimeError(
f"{f.func}: incomplete tensor options args: {tensor_options_args_names}"
)
inits.append(
f"""\
const auto options = TensorOptions()
.dtype({arg_parser_outputs['dtype'].expr})
.device({arg_parser_outputs['device'].expr})
.layout({arg_parser_outputs['layout'].expr})
.requires_grad({arg_parser_outputs['requires_grad'].expr})
.pinned_memory({arg_parser_outputs['pin_memory'].expr});
torch::utils::maybe_initialize_device(options);
"""
)
lambda_args_exprs["options"] = "options"
# 3. special case - access scattered TensorOptions fields without packing
# TODO: maybe move to the generator side as it's not related to binding.
if not has_toptions and tensor_options_args_names:
if "dtype" in tensor_options_args_names:
# we're an output-arg variant, check these args against output tensor
if not f.func.is_out_fn():
raise RuntimeError(
f"{f.func}: dtype in tensor_options_args without output arg"
)
if not all(a in tensor_options_args_names for a in ("layout", "device")):
raise RuntimeError(
f"{f.func}: incomplete tensor options for output check"
)
inits.append(
f"""\
check_out_type_matches({arg_parser_outputs['out'].expr}, {arg_parser_outputs['dtype'].expr},
{arg_parser_outputs['dtype'].is_none_expr}, {arg_parser_outputs['layout'].expr},
{arg_parser_outputs['device'].expr}, {arg_parser_outputs['device'].is_none_expr});
"""
)
# we'll set requires_grad on outgoing tensor
if "requires_grad" not in tensor_options_args_names:
raise RuntimeError(
f'{f.func}: expected "requires_grad" in tensor_options_args absent, but found [{tensor_options_args_names}]'
)
return DispatchLambdaArgumentExprs(
exprs=tuple(lambda_args_exprs[a.name] for a in lambda_args),
inits=inits,
)
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