def compute_ufunc_cuda_dtype_body(g: NativeFunctionsGroup, dtype: ScalarType,
inner_loops: Dict[UfuncKey,
UfunctorSignature],
parent_ctx: Sequence[Binding]) -> str:
body = "using opmath_t = at::opmath_type<scalar_t>;"
body += "if (false) {}\n" # for ease of codegen
for config in BinaryScalarSpecializationConfigs:
if config.ufunc_key not in inner_loops:
continue
ufunctor_sig = inner_loops[config.ufunc_key]
scalar_idx = config.scalar_idx + 1
# Make a copy and at the same time widen the type (not permissible
# without copy; we don't want to mutate the input argument anyway)
ctx: List[Union[Expr, Binding]] = list(parent_ctx)
ctx.append(
Expr(
expr=f"iter.scalar_value<opmath_t>({scalar_idx})",
type=NamedCType(config.ctor_tensor, BaseCType(opmath_t)),
))
ufunctor_ctor_exprs_str = ', '.join(
a.expr for a in translate(ctx,
ufunctor_sig.arguments().ctor))
# NB: ufunctor must be allocated before iter.remove_operand is called,
# as it relies on iter
body += f"""\
else if (iter.is_cpu_scalar({scalar_idx})) {{
{ufunctor_sig.name}<scalar_t> ufunctor({ufunctor_ctor_exprs_str});
iter.remove_operand({scalar_idx});
gpu_kernel(iter, ufunctor);
}}"""
ufunctor_sig = inner_loops[UfuncKey.CUDAFunctor]
ufunctor_ctor_exprs_str = ', '.join(
a.expr for a in translate(parent_ctx,
ufunctor_sig.arguments().ctor))
body += f"""
else {{
gpu_kernel(iter, {ufunctor_sig.name}<scalar_t>({ufunctor_ctor_exprs_str}));
}}
"""
return body
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"""
def translate(
bindings: Sequence[Union[Expr, Binding]],
goals: Sequence[Union[NamedCType, Binding]],
*, method: bool = False,
allow_expensive_conversions: bool = False
) -> List[Expr]:
binding_exprs: List[Expr] = []
for b in bindings:
if isinstance(b, Binding):
binding_exprs.append(Expr(
expr=b.name,
type=b.nctype,
))
else:
binding_exprs.append(b)
goal_ctypes: List[NamedCType] = []
for g in goals:
if isinstance(g, Binding):
goal_ctypes.append(g.nctype)
else:
goal_ctypes.append(g)
# Add all the bindings to the context
ctx: Dict[NamedCType, str] = {}
for b in binding_exprs:
ctx[b.type] = b.expr
# While we're at it, do some simple forward inference, looking through
# constructors.
#
# NB: When should you do forward inference versus backward inference?
# The general idea:
#
# - Backward inference WHEN the goal gets smaller
# - Forward inference WHEN the hypothesis gets smaller
#
# This helps ensure termination: backward inference starts with a goal
# and tries to make it simpler and simpler until it's trivial; if the
# goal can grow in size, we blow up to a really huge goal size.
# Similarly, with forward inference we take hypotheses and decompose
# them into simpler hypotheses; if hypotheses could expand in size,
# we also have potential nontermination. (In the code below, forward
# inference is only ever carried out at a single step, but you could
# imagine repeated application of forward inference being profitable.)
#
# A good starting point in the literature for exploring more about proof
# search are these lecture notes
# https://www.cs.cmu.edu/~fp/courses/oregon-m10/04-focusing.pdf
#
# TODO: My kingdom for a pattern matcher
# https://www.python.org/dev/peps/pep-0634/
#
# TODO: This could get us in recomputation trouble if b.expr is nontrivial.
# Fix this by implementing some sort of sharing so that if multiple
# goals share the same expression, we only compute it once. This seems
# to matter in practice as compiler is often unwilling to CSE nontrivial
# expressions like scalar.to<scalar_t>()
t = b.type
if isinstance(t, ConstRefCType) and isinstance(t.elem, OptionalCType) and \
isinstance(t.elem.elem, BaseCType) and str(t.elem.elem.type) == 'at::Tensor':
ctx[NamedCType(t.elem.elem.name, ConstRefCType(BaseCType(tensorT)))] = \
f'({b.expr}.has_value() ? *{b.expr} : at::Tensor())'
if t.type == ConstRefCType(OptionalCType(BaseCType(tensorT))):
ctx[NamedCType(t.name, BaseCType(optionalTensorRefT))] = \
f'(({b.expr}.has_value() && (*{b.expr}).defined()) ? at::OptionalTensorRef(*{b.expr}) : at::OptionalTensorRef())'
if t.type == ConstRefCType(BaseCType(scalarT)):
ctx[NamedCType(t.name, BaseCType(opmath_t))] = f'({b.expr}).to<opmath_t>()'
if t.type == ConstRefCType(OptionalCType(BaseCType(scalarT))):
ctx[NamedCType(t.name, BaseCType(optionalScalarRefT))] = \
f'({b.expr}.has_value() ? at::OptionalScalarRef(&({b.expr}.value())) : at::OptionalScalarRef())'
if t.type == BaseCType(scalar_t):
ctx[NamedCType(t.name, BaseCType(opmath_t))] = f'static_cast<opmath_t>({b.expr})'
# Add implicit bindings if the generated code is inside a Tensor method
if method:
ctx[NamedCType("self", MutRefCType(BaseCType(tensorT)))] = "const_cast<Tensor&>(*this)"
ctx[NamedCType("self", ConstRefCType(BaseCType(tensorT)))] = "const_cast<Tensor&>(*this)"
# This is better! Byte-for-byte compat
# ctx[NamedCType("self", ConstRefCType(BaseCType(tensorT)))] = "*this"
def unsat(goal: NamedCType) -> NoReturn:
ctx_desc = '\n'.join(f" {t.cpp_type()} {t.name}; // {e}" for t, e in ctx.items())
raise UnsatError(f'''
Failed to synthesize the expression "{goal.cpp_type()} {goal.name}".
When I failed, the following bindings were available in the context:
{ctx_desc}
This probably means there is a missing rule in the rules of tools.codegen.api.translate.
Check this module for more information.
''')
# A shitty backtracking search implementation. It's shitty because it
# does backtracking via stack (bad idea!) and for the most part tries to
# avoid backtracking. In particular, if
# direct=True, we won't try to do any fancy synthesis, just trivial
# conversions (e.g., "T a" is OK for "const T& a"). So all of the
# existing rules in this function simply try to solve immediately,
# and bail if things don't work out.
def solve(goal: NamedCType, *, direct: bool) -> str:
def direct_solve(goal: NamedCType) -> str:
return solve(goal, direct=True)
if goal in ctx:
# Trivial
return ctx[goal]
# const & is satisfied with mutable &
if isinstance(goal.type, ConstRefCType):
try:
# WARNING: not strictly decreasing; be careful not
# to add a direct conversion that goes satisfies
# mutable& with const&
return solve(NamedCType(goal.name, MutRefCType(goal.type.elem)), direct=direct)
except UnsatError:
pass
# mutable & is satisfied with value
if isinstance(goal.type, MutRefCType):
try:
return solve(NamedCType(goal.name, goal.type.elem), direct=direct)
except UnsatError:
pass
if direct:
unsat(goal)
# For now, all of these rules are mutually exclusive.
if goal == NamedCType("memory_format", OptionalCType(BaseCType(memoryFormatT))):
memory_format = direct_solve(
NamedCType(SpecialArgName.possibly_redundant_memory_format, OptionalCType(BaseCType(memoryFormatT)))
)
# No need to join "memory_format" and "options" if the target API takes "options" directly.
# Otherwise it will cause the redundant memory_format error.
if options_ctype in goal_ctypes:
return memory_format
try:
options = direct_solve(options_ctype)
return f"c10::impl::check_tensor_options_and_extract_memory_format({options}, {memory_format})"
except UnsatError:
return memory_format
elif goal == NamedCType("options", BaseCType(tensorOptionsT)):
dtype = direct_solve(NamedCType("dtype", OptionalCType(BaseCType(scalarTypeT))))
pin_memory = direct_solve(NamedCType("pin_memory", OptionalCType(BaseCType(boolT))))
device = direct_solve(NamedCType("device", OptionalCType(BaseCType(deviceT))))
layout = direct_solve(NamedCType("layout", OptionalCType(BaseCType(layoutT))))
return f'TensorOptions().dtype({dtype}).layout({layout}).device({device}).pinned_memory({pin_memory})'
elif goal == NamedCType("dtype", OptionalCType(BaseCType(scalarTypeT))):
options = direct_solve(options_ctype)
return f'optTypeMetaToScalarType({options}.dtype_opt())'
elif goal == NamedCType("layout", OptionalCType(BaseCType(layoutT))):
options = direct_solve(options_ctype)
return f'{options}.layout_opt()'
elif goal == NamedCType("device", OptionalCType(BaseCType(deviceT))):
options = direct_solve(options_ctype)
return f'{options}.device_opt()'
elif goal == NamedCType("pin_memory", OptionalCType(BaseCType(boolT))):
options = direct_solve(options_ctype)
return f'{options}.pinned_memory_opt()'
# We can always do translations from value types to reference types, like vector<int> -> IntArrayRef
elif goal.type == BaseCType(intArrayRefT):
return direct_solve(NamedCType(goal.name, longVec_ctype))
elif goal.type == BaseCType(optionalIntArrayRefT):
return direct_solve(NamedCType(goal.name, optionalLongVec_ctype))
elif goal.type == BaseCType(optionalScalarRefT):
return direct_solve(NamedCType(goal.name, optionalScalar_ctype))
elif goal.type == BaseCType(optionalTensorRefT):
return direct_solve(NamedCType(goal.name, optionalTensor_ctype))
# Note [translation from C++ reference to value types]
# The below cases are all for when we have an argument with a reference type,
# and a corresponding goal with a value type.
# These are needed when we populate the inputs to a lambda capture and we need
# to guarantee the lifetime of each captured argument.
# We guard it with an explicit kwarg because converting to a value type is expensive
# (O(n)) to convert from IntArrayRef to vector<int>),
# so the caller of translate() should be explicit that they need it.
if allow_expensive_conversions:
if goal.type == VectorCType(BaseCType(longT)):
intArrayRef_ctype = NamedCType(goal.name, BaseCType(intArrayRefT))
argname = direct_solve(intArrayRef_ctype)
return f'{argname}.vec()'
elif goal.type == OptionalCType(VectorCType(BaseCType(longT))):
optionalIntArrayRef_ctype = NamedCType(goal.name, BaseCType(optionalIntArrayRefT))
argname = direct_solve(optionalIntArrayRef_ctype)
return f'{argname}.has_value() ? c10::make_optional({argname}->vec()) : c10::nullopt'
elif goal.type == OptionalCType(BaseCType(scalarT)):
optionalScalarRef_ctype = NamedCType(goal.name, BaseCType(optionalScalarRefT))
argname = direct_solve(optionalScalarRef_ctype)
return f'{argname}.has_value() ? c10::make_optional({argname}) : c10::nullopt'
elif goal.type == OptionalCType(BaseCType(scalarT)):
optionalTensorRef_ctype = NamedCType(goal.name, BaseCType(optionalTensorRefT))
argname = direct_solve(optionalTensorRef_ctype)
return f'{argname}.has_value() ? c10::make_optional({argname}) : c10::nullopt'
# Technically, we also need to handle cases of C++ containers holding reference types.
# But there currently aren't any ops that require lambda capture codegen
# With arguments like std::vector<IntArrayRef>.
# If that changes, we'll have to add the translation here.
unsat(goal)
return [Expr(solve(g, direct=False), g) for g in goal_ctypes]
def gen_one(self, f: NativeFunction) -> Optional[str]:
assert not f.manual_kernel_registration
if self.target is Target.REGISTRATION and not self.selector.is_native_function_selected(
f):
return None
# TODO: Now, there is something interesting going on here. In the code below,
# we generate CompositeExplicitAutograd implementations of functional and inplace
# based on the out implementation. But in fact, out is definable by
# functional too (just not very efficiently), and this is honestly the
# MORE likely situation for a backend implementor. How do we pick?
# Well, taking a page from Haskell type classes and default methods,
# we could conceivably register a circular definition (out in terms
# of functional, and functional in terms of out) and just require
# someone to implement one or the other. We'd have to do a little bit
# of work to not register one of these "weak" definitions unless there
# is a strong definition somewhere in the DAG! So it's not implemented yet.
if self.backend_index.dispatch_key == DispatchKey.CompositeExplicitAutograd and f.func.kind(
) is SchemaKind.out:
# Never generate a default implementation for out, that's what you
# have to define as a backend implementor
return None
# Note [Direct dispatch bindings]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Signature of the non-dispatched function we'll expose in a header
# (e.g., at::cpu::add). We don't generate methods (TODO: do this
# when CPUTensor class is a thing); nor do we generate fallback
# bindings for manual_cpp_binding functions.
cpp_sig_group = CppSignatureGroup.from_native_function(
f, method=False, fallback_binding=False)
# Signature of the wrapper function we'll register to the dispatcher
sig = NativeSignature(f.func, prefix="wrapper_")
if self.target is Target.NAMESPACED_DECLARATION:
result = f"TORCH_API {cpp_sig_group.signature.decl()};\n"
if cpp_sig_group.faithful_signature is not None:
result += f"TORCH_API {cpp_sig_group.faithful_signature.decl()};\n"
return result
elif self.target is Target.NAMESPACED_DEFINITION:
def generate_defn(cpp_sig: CppSignature) -> str:
return f"""
{cpp_sig.defn()} {{
return {sig.name()}({', '.join(e.expr for e in translate(cpp_sig.arguments(), sig.arguments()))});
}}
"""
result = generate_defn(cpp_sig_group.signature)
if cpp_sig_group.faithful_signature is not None:
result += generate_defn(cpp_sig_group.faithful_signature)
return result
elif self.target is Target.ANONYMOUS_DEFINITION:
k = f.func.kind()
# Construct the body of the wrapper function with signature sig
sig_body = []
# We'll use context to keep track of any variables we've brought
# into scope while generating code
context: List[Union[Binding, Expr]] = list(sig.arguments())
# Initialize the class corresponding to this structured
# operator; feeding it the output argument(s) if it is known
if self.backend_index.dispatch_key is DispatchKey.Meta:
class_name = f"structured_{meta.name(self.g)}_meta_{k.name}"
parent_class = f"at::meta::structured_{meta.name(self.g)}"
elif self.backend_index.dispatch_key is DispatchKey.CompositeExplicitAutograd:
# TODO: dedup this branch
class_name = f"structured_{meta.name(self.g)}_default_backend_{k.name}"
parent_class = f"at::meta::structured_{meta.name(self.g)}"
else:
metadata = self.backend_index.get_kernel(self.g)
assert metadata is not None
class_name = f"structured_{metadata.kernel}_{k.name}"
parent_class = f"{self.cpp_namespace}::structured_{metadata.kernel}"
if is_cuda_dispatch_key(self.backend_index.dispatch_key):
device_check_args = itertools.chain(
f.func.arguments.out, f.func.arguments.flat_positional)
sig_body.append(
RegisterDispatchKey.gen_device_check(
f.device_check, list(device_check_args), sig.name()))
if k is SchemaKind.functional:
sig_body.append(f"{class_name} op;")
elif k is SchemaKind.inplace:
sig_body.append(f"{class_name} op(self);")
elif k is SchemaKind.out:
out_args_str = ', '.join(a.name for a in f.func.arguments.out)
sig_body.append(f"{class_name} op({out_args_str});")
# Translate the input native arguments into structured
# arguments for the meta call
meta_exprs = ', '.join(e.expr for e in translate(
context, structured.meta_arguments(self.g), method=False))
if self.g.out.precomputed:
# If this function group has precomputed elements, the meta function
# returns a struct containing them which must be saved so that it
# can be unpacked when generating code to call the impl.
sig_body.append(f"auto precompute = op.meta({meta_exprs});")
# Put all of the contents of the precompute struct into the context
# so that translate will be able to return the correct args for the
# call to the impl.
for precomputed_elems in self.g.out.precomputed.replace.values(
):
for arg in precomputed_elems:
context.append(
Expr(
expr=f"precompute.{arg.name}",
type=structured.argument_type(arg,
binds=arg.name),
))
# Add a use of the precompute struct so FB internal compilers don't
# complain that there is an unused variable.
sig_body.append("(void)precompute;")
else:
sig_body.append(f"op.meta({meta_exprs});")
# After running meta, op.outputs_ is guaranteed to be valid;
# add it to the context
out_args = structured.out_arguments(self.g)
maybe_star = '*' if k is SchemaKind.functional else ''
for i, out_arg in enumerate(out_args):
assert ConstRefCType(BaseCType(tensorT)) == out_arg.nctype.type
context.append(
Expr(
expr=f"{maybe_star}op.outputs_[{i}]",
# TODO: Stop hardcoding that the output type is a Tensor. Note
# that for the codegen here this is fine because outputs_ is
# hardcoded to be tensor already
type=NamedCType(out_arg.nctype.name,
MutRefCType(BaseCType(tensorT)))))
# With the expanded context, do the impl call (if not a meta
# function)
if self.backend_index.dispatch_key == DispatchKey.CompositeExplicitAutograd:
# TODO: https://github.com/pytorch/pytorch/issues/53023
out_sig_group = CppSignatureGroup.from_native_function(
self.g.out,
method=False,
fallback_binding=f.manual_cpp_binding)
out_sig = out_sig_group.most_faithful_signature()
api_name = out_sig.name()
out_exprs = ', '.join(e.expr for e in translate(
context, out_sig.arguments(), method=False))
# TODO: I think this means structured won't work with method
# only functions (but maybe you're saved by faithful? iunno.)
# NB: Originally I wrote this as an at::redispatch call, but
# I got in trouble because that meant I needed a DispatchKeySet
# in the wrapper function, which meant I needed a DispatchKeySet
# in the DispatchKeyFunctions declarations, but the defined API
# there does NOT permit a dispatch key set. I think you can
# probably unwind this by calling some function to do the TLS
# fetch and get the DispatchKeySet when you don't have it, but
# I didn't do it for this version
sig_body.append(f"at::{api_name}({out_exprs});")
elif self.backend_index.dispatch_key != DispatchKey.Meta:
impl_exprs = ', '.join(e.expr for e in translate(
context, structured.impl_arguments(self.g), method=False))
sig_body.append(f"op.impl({impl_exprs});")
# Destructively return the final tensors
# TODO: Do this in translate instead
if k is SchemaKind.functional:
if len(f.func.returns) == 1:
ret_expr = "std::move(op.outputs_[0]).take()" # small optimization
else:
moved = ', '.join(f"std::move(op.outputs_[{i}]).take()"
for i in range(len(f.func.returns)))
ret_expr = f"std::make_tuple({moved})"
elif k is SchemaKind.inplace:
ret_expr = "self"
elif k is SchemaKind.out:
if len(f.func.returns) == 1:
ret_expr = f.func.arguments.out[0].name
else:
refs = ', '.join(a.name for a in f.func.arguments.out)
ret_expr = f"std::forward_as_tuple({refs})"
sig_body.append(f"return {ret_expr};")
sig_body_str = "\n".join(sig_body)
# For an overview of what this template code looks like, see
# https://github.com/pytorch/rfcs/pull/9
return f"""\
{self.gen_class(
f, k,
class_name=class_name,
parent_class=parent_class,
generate_super=self.g.out.structured_inherits is not None
)}
{sig.defn()} {{
{sig_body_str}
}}
"""
elif self.target is Target.REGISTRATION:
return f'm.impl("{f.func.name}", TORCH_FN({sig.name()}));'
else:
assert_never(self.target)
# Silence mypy's "Missing return statement" error
return None
def translate(
bindings: Sequence[Union[Expr, Binding]],
goals: Sequence[Union[NamedCType, Binding]],
*, method: bool = False
) -> List[Expr]:
binding_exprs: List[Expr] = []
for b in bindings:
if isinstance(b, Binding):
binding_exprs.append(Expr(
expr=b.name,
type=b.nctype,
))
else:
binding_exprs.append(b)
goal_ctypes: List[NamedCType] = []
for g in goals:
if isinstance(g, Binding):
goal_ctypes.append(g.nctype)
else:
goal_ctypes.append(g)
# Add all the bindings to the context
ctx: Dict[NamedCType, str] = {}
for b in binding_exprs:
ctx[b.type] = b.expr
# While we're at it, do some simple forward inference, looking through
# constructors.
# TODO: My kingdom for a pattern matcher
# https://www.python.org/dev/peps/pep-0634/
# TODO: This could get us in recomputation trouble if b.expr is nontrivial
t = b.type
if isinstance(t, ConstRefCType) and isinstance(t.elem, OptionalCType) and \
isinstance(t.elem.elem, BaseCType) and str(t.elem.elem.type) == 'at::Tensor':
ctx[NamedCType(t.elem.elem.name, ConstRefCType(BaseCType(tensorT)))] = \
f'({b.expr}.has_value() ? *{b.expr} : at::Tensor())'
# Add implicit bindings if the generated code is inside a Tensor method
if method:
ctx[NamedCType("self", MutRefCType(BaseCType(tensorT)))] = "const_cast<Tensor&>(*this)"
ctx[NamedCType("self", ConstRefCType(BaseCType(tensorT)))] = "const_cast<Tensor&>(*this)"
# This is better! Byte-for-byte compat
# ctx[NamedCType("self", ConstRefCType(BaseCType(tensorT)))] = "*this"
def unsat(goal: NamedCType) -> NoReturn:
ctx_desc = '\n'.join(f" {t.cpp_type()} {t.name}; // {e}" for t, e in ctx.items())
raise UnsatError(f'''
Failed to synthesize the expression "{goal.cpp_type()} {goal.name}".
When I failed, the following bindings were available in the context:
{ctx_desc}
This probably means there is a missing rule in the rules of tools.codegen.api.translate.
Check this module for more information.
''')
# A shitty backtracking search implementation. It's shitty because it
# doesn't actually do backtracing or search. In particular, if
# direct=True, we won't try to do any fancy synthesis, just trivial
# conversions (e.g., "T a" is OK for "const T& a"). So all of the
# existing rules in this function simply try to solve immediately,
# and bail if things don't work out.
def solve(goal: NamedCType, *, direct: bool) -> str:
def direct_solve(goal: NamedCType) -> str:
return solve(goal, direct=True)
if goal in ctx:
# Trivial
return ctx[goal]
# const & is satisfied with mutable &
if isinstance(goal.type, ConstRefCType):
try:
# WARNING: not strictly decreasing; be careful not
# to add a direct conversion that goes satisfies
# mutable& with const&
return solve(NamedCType(goal.name, MutRefCType(goal.type.elem)), direct=direct)
except UnsatError:
pass
# mutable & is satisfied with value
if isinstance(goal.type, MutRefCType):
try:
return solve(NamedCType(goal.name, goal.type.elem), direct=direct)
except UnsatError:
pass
if direct:
unsat(goal)
# For now, all of these rules are mutually exclusive.
if goal == NamedCType("memory_format", OptionalCType(BaseCType(memoryFormatT))):
memory_format = direct_solve(
NamedCType(SpecialArgName.possibly_redundant_memory_format, OptionalCType(BaseCType(memoryFormatT)))
)
# No need to join "memory_format" and "options" if the target API takes "options" directly.
# Otherwise it will cause the redundant memory_format error.
if options_ctype in goal_ctypes:
return memory_format
try:
options = direct_solve(options_ctype)
return f"c10::impl::check_tensor_options_and_extract_memory_format({options}, {memory_format})"
except UnsatError:
return memory_format
elif goal == NamedCType("options", BaseCType(tensorOptionsT)):
dtype = direct_solve(NamedCType("dtype", OptionalCType(BaseCType(scalarTypeT))))
pin_memory = direct_solve(NamedCType("pin_memory", OptionalCType(BaseCType(boolT))))
device = direct_solve(NamedCType("device", OptionalCType(BaseCType(deviceT))))
layout = direct_solve(NamedCType("layout", OptionalCType(BaseCType(layoutT))))
return f'TensorOptions().dtype({dtype}).layout({layout}).device({device}).pinned_memory({pin_memory})'
elif goal == NamedCType("dtype", OptionalCType(BaseCType(scalarTypeT))):
options = direct_solve(options_ctype)
return f'optTypeMetaToScalarType({options}.dtype_opt())'
elif goal == NamedCType("layout", OptionalCType(BaseCType(layoutT))):
options = direct_solve(options_ctype)
return f'{options}.layout_opt()'
elif goal == NamedCType("device", OptionalCType(BaseCType(deviceT))):
options = direct_solve(options_ctype)
return f'{options}.device_opt()'
elif goal == NamedCType("pin_memory", OptionalCType(BaseCType(boolT))):
options = direct_solve(options_ctype)
return f'{options}.pinned_memory_opt()'
unsat(goal)
return [Expr(solve(g, direct=False), g) for g in goal_ctypes]
