mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
499d2fad98
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/47437 Test Plan: Imported from OSS Reviewed By: bhosmer Differential Revision: D24808213 Pulled By: ljk53 fbshipit-source-id: 8ec6d58952fd677ab2d97e63b060cafda052411a
318 lines
11 KiB
Python
318 lines
11 KiB
Python
from tools.codegen.model import *
|
|
from tools.codegen.api.types import *
|
|
import tools.codegen.local as local
|
|
from typing import Optional, Sequence, Union, Callable, List
|
|
|
|
# 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.
|
|
#
|
|
# Prominent characteristics of the C++ API:
|
|
#
|
|
# - dtype, layout, device and pin_memory are collected into
|
|
# a single C++ type TensorOptions (the native functions API
|
|
# also has this, but tensor options is really most relevant
|
|
# for the C++ API; it makes calling kwarg factory functions
|
|
# pleasant)
|
|
#
|
|
# - for 'use_c10_dispatcher: full' functions, optional tensors are
|
|
# represented explicitly using c10::optional
|
|
#
|
|
# - defaulting lives here (in fact, the dispatcher is completely
|
|
# oblivious of defaults!)
|
|
#
|
|
# BTW: policy on name collisions: we try not to have types with
|
|
# collisions, but functions are fair game to collide
|
|
|
|
def name(func: FunctionSchema) -> str:
|
|
name = str(func.name.name)
|
|
if func.is_out_fn():
|
|
name += '_out'
|
|
return name
|
|
|
|
# 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) -> Optional[str]:
|
|
if isinstance(t, BaseType):
|
|
if t.name == BaseTy.Tensor:
|
|
return None
|
|
elif t.name == BaseTy.int:
|
|
return 'int64_t'
|
|
elif t.name == BaseTy.float:
|
|
return 'double'
|
|
elif t.name == BaseTy.str:
|
|
return 'std::string'
|
|
elif t.name in [BaseTy.bool, BaseTy.QScheme, BaseTy.Scalar,
|
|
BaseTy.ScalarType, BaseTy.Generator, BaseTy.Storage,
|
|
BaseTy.Layout, BaseTy.Device, BaseTy.MemoryFormat,
|
|
BaseTy.Dimname, BaseTy.Stream, BaseTy.ConstQuantizerPtr]:
|
|
# These C++ names line up with their schema names
|
|
return t.name.name
|
|
else:
|
|
raise AssertionError(f"unsupported type: {t}")
|
|
elif isinstance(t, OptionalType):
|
|
elem = valuetype_type(t.elem)
|
|
if elem is None:
|
|
return None
|
|
return f"c10::optional<{elem}>"
|
|
elif isinstance(t, ListType):
|
|
if str(t.elem) == 'bool':
|
|
assert t.size is not None
|
|
return f"std::array<bool,{t.size}>"
|
|
else:
|
|
return None
|
|
else:
|
|
raise AssertionError(f"unrecognized type {repr(t)}")
|
|
|
|
# Translation of types occuring in JIT arguments to a C++ argument type.
|
|
def argumenttype_type(t: Type, *, mutable: bool) -> str:
|
|
# If it's a value type, do the value type translation
|
|
r = valuetype_type(t)
|
|
if r is not None:
|
|
return r
|
|
|
|
if isinstance(t, BaseType):
|
|
if t.name == BaseTy.Tensor:
|
|
if mutable:
|
|
return 'Tensor &'
|
|
else:
|
|
return 'const Tensor &'
|
|
else:
|
|
raise AssertionError(f"base type should have been value type {t}")
|
|
elif isinstance(t, OptionalType):
|
|
if str(t.elem) == 'Tensor':
|
|
if mutable:
|
|
return 'Tensor &' # TODO: fix this discrepancy
|
|
else:
|
|
if local.use_c10_dispatcher().dispatcher_uses_new_style():
|
|
return 'const c10::optional<Tensor>&'
|
|
else:
|
|
return 'const Tensor &'
|
|
elem = argumenttype_type(t.elem, mutable=mutable)
|
|
return f"c10::optional<{elem}>"
|
|
elif isinstance(t, ListType):
|
|
# TODO: remove these special cases, ArrayRef fallthrough works fine
|
|
if str(t.elem) == 'int':
|
|
return "IntArrayRef"
|
|
elif str(t.elem) == 'Tensor':
|
|
return "TensorList"
|
|
elif str(t.elem) == 'Dimname':
|
|
return "DimnameList"
|
|
# TODO: do something reasonable about lists of optional tensors
|
|
elif (not local.use_c10_dispatcher().dispatcher_uses_new_style()) and str(t.elem) == 'Tensor?':
|
|
return "TensorList"
|
|
elem = argumenttype_type(t.elem, mutable=mutable)
|
|
# TODO: explicitly qualify namespace here
|
|
return f"ArrayRef<{elem}>"
|
|
else:
|
|
raise AssertionError(f"unrecognized type {repr(t)}")
|
|
|
|
# Translate a JIT argument into its C++ type
|
|
def argument_type(a: Argument) -> str:
|
|
return argumenttype_type(a.type, mutable=a.is_write)
|
|
|
|
# Translation of a (non-multi) return type from JIT to C++
|
|
def returntype_type(t: Type, *, mutable: bool) -> str:
|
|
r = valuetype_type(t)
|
|
if r is not None:
|
|
return r
|
|
|
|
if isinstance(t, BaseType):
|
|
if t.name == BaseTy.Tensor:
|
|
if mutable:
|
|
return 'Tensor &'
|
|
else:
|
|
return 'Tensor'
|
|
elif isinstance(t, ListType):
|
|
elem = returntype_type(t.elem, mutable=mutable)
|
|
assert t.size is None, f"fixed size list returns not supported: {t}"
|
|
return f"std::vector<{elem}>"
|
|
|
|
raise AssertionError(f"unrecognized return type {t}")
|
|
|
|
# Translation of a single return to its C++ type
|
|
def return_type(r: Return) -> str:
|
|
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]) -> str:
|
|
if len(rs) == 0:
|
|
return 'void'
|
|
elif len(rs) == 1:
|
|
return return_type(rs[0])
|
|
else:
|
|
args = ','.join(map(return_type, rs))
|
|
return f'std::tuple<{args}>'
|
|
|
|
def return_names(f: NativeFunction) -> 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.out_arguments[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, we just name the output result,
|
|
# unless it's a multi-return, in which case it's result0,
|
|
# result1, etc (zero-indexed)
|
|
else:
|
|
name = 'result' if len(f.func.returns) == 1 else f'result{i}'
|
|
returns.append(name)
|
|
return returns
|
|
|
|
JIT_TO_CPP_DEFAULT = {
|
|
'False': 'false',
|
|
'True': 'true',
|
|
'None': 'c10::nullopt', # UGH this one is type directed
|
|
'Mean': 'at::Reduction::Mean',
|
|
'[]': '{}',
|
|
'contiguous_format': 'MemoryFormat::Contiguous',
|
|
'long': 'at::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 'c10::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_not_this(
|
|
a: Union[Argument, TensorOptionsArguments],
|
|
) -> CppArgument:
|
|
if isinstance(a, Argument):
|
|
return CppArgument(
|
|
type=argument_type(a),
|
|
name=a.name,
|
|
default=default_expr(a.default, a.type) if a.default is not None else None,
|
|
argument=a,
|
|
)
|
|
elif isinstance(a, TensorOptionsArguments):
|
|
default = None
|
|
if all(x.default == "None" for x in a.all()):
|
|
default = '{}'
|
|
elif a.dtype.default == "long":
|
|
default = 'at::kLong' # TODO: this is wrong
|
|
return CppArgument(
|
|
type='const TensorOptions &',
|
|
name='options',
|
|
default=default,
|
|
argument=a,
|
|
)
|
|
else:
|
|
assert_never(a)
|
|
|
|
def argument(
|
|
a: Union[Argument, TensorOptionsArguments, ThisArgument],
|
|
) -> Union[CppSingleArgumentPack, CppThisArgumentPack]:
|
|
if isinstance(a, ThisArgument):
|
|
return CppThisArgumentPack(argument=a, type=argument_type(a.argument))
|
|
else:
|
|
return CppSingleArgumentPack(argument_not_this(a))
|
|
|
|
def argument_faithful(
|
|
a: Union[Argument, TensorOptionsArguments, ThisArgument],
|
|
) -> CppArgumentPack:
|
|
if isinstance(a, TensorOptionsArguments):
|
|
return CppTensorOptionsArgumentPack(
|
|
argument=a,
|
|
dtype=argument_not_this(a.dtype),
|
|
layout=argument_not_this(a.layout),
|
|
device=argument_not_this(a.device),
|
|
pin_memory=argument_not_this(a.pin_memory),
|
|
)
|
|
else:
|
|
return argument(a)
|
|
|
|
# NB: this unconditionally groups arguments
|
|
def group_arguments(
|
|
func: FunctionSchema, *, method: bool
|
|
) -> Sequence[Union[Argument, TensorOptionsArguments, ThisArgument]]:
|
|
args: List[Union[Argument, ThisArgument, TensorOptionsArguments]] = []
|
|
|
|
args.extend(func.out_arguments)
|
|
|
|
if method:
|
|
args.extend(ThisArgument(a) if a.name == "self" else a for a in func.arguments)
|
|
else:
|
|
args.extend(func.arguments)
|
|
|
|
# group up arguments for tensor options
|
|
|
|
def pred(name: str, ty: Type) -> Callable[[Argument], bool]:
|
|
return lambda a: a.name == name and a.type in [ty, OptionalType(ty)]
|
|
predicates = [ # order matters
|
|
pred('dtype', Type.parse('ScalarType')),
|
|
pred('layout', Type.parse('Layout')),
|
|
pred('device', Type.parse('Device')),
|
|
pred('pin_memory', Type.parse('bool')),
|
|
]
|
|
|
|
i = 0
|
|
while i < len(func.kwarg_only_arguments):
|
|
# If there is enough space...
|
|
if i <= len(func.kwarg_only_arguments) - len(predicates):
|
|
# And the next len(predicates) arguments look like TensorOptions arguments
|
|
if all(p(a) for p, a in zip(predicates, func.kwarg_only_arguments[i : i + len(predicates)])):
|
|
# Group them together as one argument
|
|
args.append(TensorOptionsArguments(
|
|
dtype=func.kwarg_only_arguments[i],
|
|
layout=func.kwarg_only_arguments[i + 1],
|
|
device=func.kwarg_only_arguments[i + 2],
|
|
pin_memory=func.kwarg_only_arguments[i + 3],
|
|
))
|
|
i += len(predicates)
|
|
continue
|
|
args.append(func.kwarg_only_arguments[i])
|
|
i += 1
|
|
|
|
return args
|