def argument(a: Union[Argument, SelfArgument, TensorOptionsArguments], *,
is_out: bool) -> List[Binding]:
# Ideally, we NEVER default native functions. However, there are a number
# of functions that call native:: directly and rely on the defaulting
# existing. So for BC, we generate defaults for non-out variants (but not
# for out variants, where it is impossible to generate an appropriate
# default)
should_default = not is_out
if isinstance(a, Argument):
default: Optional[str] = None
if should_default and a.default is not None:
default = cpp.default_expr(a.default, a.type)
return [
Binding(
nctype=argument_type(a, binds=a.name),
name=a.name,
default=default,
argument=a,
)
]
elif isinstance(a, SelfArgument):
# Erase SelfArgument from the distinction
return argument(a.argument, is_out=is_out)
elif isinstance(a, TensorOptionsArguments):
default = None
if should_default:
default = '{}'
# TODO: Not sure why the arguments assigned here are for
# TensorOptionsArguments and not the constituent pieces. It seems
# to matter
return [
Binding(
nctype=NamedCType('dtype',
OptionalCType(BaseCType(scalarTypeT))),
name='dtype',
default=default,
argument=a,
),
Binding(
nctype=NamedCType('layout', OptionalCType(BaseCType(layoutT))),
name='layout',
default=default,
argument=a,
),
Binding(
nctype=NamedCType('device', OptionalCType(BaseCType(deviceT))),
name='device',
default=default,
argument=a,
),
Binding(
nctype=NamedCType('pin_memory',
OptionalCType(BaseCType(boolT))),
name='pin_memory',
default=default,
argument=a,
)
]
else:
assert_never(a)
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):
if faithful:
return sub_argument(a.dtype) + sub_argument(a.layout) + \
sub_argument(a.device) + sub_argument(a.pin_memory)
else:
default = None
# Enforced by NativeFunction.__post_init__
assert 'options' not in cpp_no_default_args
if all(x.default == "None" for x in a.all()):
default = '{}'
elif a.dtype.default == "long":
default = 'at::kLong' # TODO: this is wrong
return [
Binding(
nctype=NamedCType('options', BaseCType(tensorOptionsT)),
name='options',
default=default,
argument=a,
)
]
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 ufunctor_ctor_argument(a: Argument, scalar_t: BaseCppType) -> Binding:
return Binding(
nctype=ufunctor_ctor_type(a.type, binds=a.name, scalar_t=scalar_t),
name=a.name,
default=None,
argument=a,
)
def ufunc_argument(a: Argument, compute_t: CType) -> Binding:
return Binding(
nctype=ufunc_type(a.type, binds=a.name, compute_t=compute_t),
name=a.name,
default=None,
argument=a,
)
def arguments(func: FunctionSchema) -> List[Binding]:
return [
Binding(
nctype=argument_type(a, binds=a.name),
name=a.name,
argument=a,
) for a in jit_arguments(func)
]
def argument(a: Union[Argument, SelfArgument, TensorOptionsArguments]) -> List[Binding]:
if isinstance(a, Argument):
return [Binding(
nctype=argument_type(a, binds=a.name),
name=a.name,
default=None,
argument=a,
)]
elif isinstance(a, SelfArgument):
return argument(a.argument)
elif isinstance(a, TensorOptionsArguments):
raise AssertionError("structured kernels don't support TensorOptions yet")
else:
assert_never(a)
def argument(
a: Union[Argument, TensorOptionsArguments,
SelfArgument]) -> List[Binding]:
if isinstance(a, Argument):
return [
Binding(
ctype=argument_type(a, binds=a.name),
name=a.name,
argument=a,
)
]
elif isinstance(a, SelfArgument):
return argument(a.argument)
elif isinstance(a, TensorOptionsArguments):
return argument(a.dtype) + argument(a.layout) + argument(
a.device) + argument(a.pin_memory)
else:
assert_never(a)
def unpack_args(f: NativeFunction) -> Tuple[List[str], List[Binding]]:
body: List[str] = []
unpacked_bindings: List[Binding] = []
bindings = [
r for a in f.func.schema_order_arguments()
for r in cpp.argument(a,
method=False,
cpp_no_default_args=set(),
faithful=False,
has_tensor_options=False)
]
for i, binding in enumerate(bindings):
assert not isinstance(binding.argument, SelfArgument)
if isinstance(binding.argument, TensorOptionsArguments):
raise RuntimeError("VariableKernel shouldn't take TensorOptions")
is_nullable = binding.argument.type.is_nullable()
if not binding.argument.type.is_tensor_like() or is_nullable:
unpacked_bindings.append(binding)
continue
is_tensor_list = is_tensor_list_type(binding.argument.type)
ref = (not is_nullable) and not is_tensor_list
suffix = '_opt' if is_nullable and not is_tensor_list else ''
body.append(
UNPACK_TENSOR.substitute(
arg_name=binding.name,
arg_pos=i,
suffix=suffix,
ref='&' if ref else '',
))
unpacked_bindings.append(
Binding(
name=unpacked_name(binding.name),
nctype=binding.nctype,
argument=binding.argument,
default=binding.default,
))
return body, unpacked_bindings
def compute_ufunc_cpu_dtype_body(g: NativeFunctionsGroup, dtype: ScalarType,
inner_loops: Dict[UfuncKey, UfuncSignature],
parent_ctx: Sequence[Binding]) -> str:
assert UfuncKey.CPUScalar in inner_loops, f"{dtype}, {inner_loops.keys()}"
assert inner_loops.keys() <= {UfuncKey.CPUScalar, UfuncKey.CPUVector}
scalar_loop = inner_loops[UfuncKey.CPUScalar]
vec_loop = None
if UfuncKey.CPUVector in inner_loops:
vec_loop = inner_loops[UfuncKey.CPUVector]
# NB: We DON'T use translate here, because translate is
# incapable of CSE'ing the scalar accesses in case it is also
# used by Vectorized; also, the unpacking here is very simple
# and only affects Scalar; everything else is implicitly captured
# by the lambda
# Setup scalar in scope
body = []
ctx = []
for b in parent_ctx:
if isinstance(b.argument,
Argument) and b.argument.type != BaseType(BaseTy.Scalar):
continue
body.append(f"auto _s_{b.name} = {b.name}.to<scalar_t>();")
ctx.append(
Expr(f"_s_{b.name}", NamedCType(b.nctype.name,
BaseCType(scalar_t))))
if vec_loop is not None:
for b in parent_ctx:
if isinstance(
b.argument,
Argument) and b.argument.type != BaseType(BaseTy.Scalar):
continue
body.append(
f"auto _v_{b.name} = at::vec::Vectorized<scalar_t>(_s_{b.name});"
)
ctx.append(
Expr(
f"_v_{b.name}",
NamedCType(b.nctype.name,
VectorizedCType(BaseCType(scalar_t)))))
# Setup lambda signature
# NB: simplified version of ufunctor_arguments
scalar_bindings = []
vec_bindings = []
for a in g.functional.func.arguments.flat_non_out:
if not a.type.is_tensor_like():
continue
assert a.type == BaseType(BaseTy.Tensor)
scalar_bindings.append(
Binding(
name=a.name,
nctype=NamedCType(a.name, BaseCType(scalar_t)),
argument=a,
))
if vec_loop is not None:
vec_bindings.append(
Binding(
name=a.name,
nctype=NamedCType(a.name,
VectorizedCType(BaseCType(scalar_t))),
argument=a,
))
def with_ctx(b: Sequence[Binding]) -> List[Union[Expr, Binding]]:
r: List[Union[Expr, Binding]] = []
r.extend(ctx)
r.extend(b)
return r
body_str = '\n'.join(body)
if vec_loop is not None:
return f"""
{body_str}
cpu_kernel_vec(iter,
[=]({', '.join(b.decl() for b in scalar_bindings)}) {{ return {scalar_loop.call(with_ctx(scalar_bindings))}; }},
[=]({', '.join(b.decl() for b in vec_bindings)}) {{ return {vec_loop.call(with_ctx(vec_bindings))}; }}
);
"""
else:
return f"""
# - While the forward lambda just directly calls into the at::_ops API
# (following the dispatcher convention), the logic here for the reverse lambda
# is responsible for generating both the call-site, and the declarations
# (which are implemented manually in the at::functionalization::impl namespace).
# The lambdas generated for each view op in the functionalization pass are of the form
# [capture_arguments](outer_arguments) -> returns_type {
# return name(inner_arguments);
# }
# Define some specific lambda input arguments.
base_binding = Binding(name='base',
nctype=NamedCType(name='base',
type=ConstRefCType(
BaseCType(tensorT))),
argument=Argument(name='base',
type=BaseType(BaseTy.Tensor),
default=None,
annotation=None),
default=None)
mutated_view_binding = Binding(name='mutated_view',
nctype=NamedCType(name='mutated_view',
type=ConstRefCType(
BaseCType(tensorT))),
argument=Argument(name='base',
type=BaseType(BaseTy.Tensor),
default=None,
annotation=None),
default=None)
mutated_view_idx_binding = Binding(name='mutated_view_idx',
nctype=NamedCType(name='mutated_view_idx',
def argument(a: Argument, *, remove_non_owning_ref_types: bool = False) -> Binding:
return Binding(
nctype=argument_type(a, binds=a.name, remove_non_owning_ref_types=remove_non_owning_ref_types),
name=a.name,
argument=a
)
