431 lines
19 KiB
Python
431 lines
19 KiB
Python
from typing import Dict, List, NoReturn, Sequence, Union
|
|
|
|
from torchgen.api.types import (
|
|
ArrayRefCType,
|
|
BaseCType,
|
|
Binding,
|
|
boolT,
|
|
ConstRefCType,
|
|
deviceT,
|
|
Expr,
|
|
intArrayRefT,
|
|
iOptTensorListRefT,
|
|
layoutT,
|
|
ListCType,
|
|
longT,
|
|
memoryFormatT,
|
|
MutRefCType,
|
|
NamedCType,
|
|
opmath_t,
|
|
OptionalCType,
|
|
optionalIntArrayRefT,
|
|
optionalScalarRefT,
|
|
optionalSymIntArrayRefT,
|
|
optionalTensorRefT,
|
|
scalar_t,
|
|
scalarT,
|
|
scalarTypeT,
|
|
SpecialArgName,
|
|
symIntArrayRefT,
|
|
SymIntT,
|
|
tensorOptionsT,
|
|
tensorT,
|
|
VectorCType,
|
|
)
|
|
|
|
# This file implements a small program synthesis engine that implements
|
|
# conversions between one API to another.
|
|
#
|
|
# The key data type in this file in NamedCType, short for Named C++ semantic type. A NamedCType
|
|
# represents a C++ type, plus semantic information about what it represents.
|
|
# For example, consider the argument "bool pin_memory"; its normal C++ type is
|
|
# "bool", but its C++ semantic type also keeps track that this represents a
|
|
# "pin_memory"; you can't just use a random other boolean in a context where you
|
|
# need a "pin_memory"!
|
|
#
|
|
# The translator takes a list of needed NamedCTypes, and then figures out how
|
|
# to construct expressions with these NamedCTypes from the given bindings. Many
|
|
# of these expressions are trivial (I need a Tensor other; there's a Tensor
|
|
# other scope); others are more nontrivial and may require packing/unpacking.
|
|
# Some examples of non-trivial action:
|
|
#
|
|
# - Need the "dtype" binding? Well, maybe "dtype" isn't available
|
|
# in the context, instead, "options" is, and you need to extract
|
|
# it from there. (Gather)
|
|
#
|
|
# - Need the "context" binding? Well, maybe "context" isn't available
|
|
# in the context, and you need to construct it from "dtype", "device",
|
|
# etc. (Scatter)
|
|
#
|
|
# - Need the "memory_format" binding? Well, actually, it's available
|
|
# from both "memory_format" and "options", so you had better make sure
|
|
# they are consistent. (Join)
|
|
|
|
options_ctype = NamedCType("options", ConstRefCType(BaseCType(tensorOptionsT)))
|
|
|
|
out_tensor_ctype = NamedCType("out", ConstRefCType(BaseCType(tensorT)))
|
|
|
|
longVec_ctype = VectorCType(BaseCType(longT))
|
|
longSymVec_ctype = VectorCType(BaseCType(SymIntT))
|
|
optionalLongVec_ctype = OptionalCType(VectorCType(BaseCType(longT)))
|
|
optionalScalar_ctype = OptionalCType(BaseCType(scalarT))
|
|
optionalTensor_ctype = OptionalCType(BaseCType(tensorT))
|
|
|
|
|
|
class UnsatError(RuntimeError):
|
|
pass
|
|
|
|
|
|
# Given a set of in-scope bindings and a set of target bindings, synthesize
|
|
# a list of expressions that uses only the in-scope bindings (bindings) that
|
|
# have all of the types of goals. You may want to use this function if
|
|
# you're generating code for a function like:
|
|
#
|
|
# void f({args}) {
|
|
# g({exprs}); // g is a different API
|
|
# }
|
|
#
|
|
# and you need to generate "exprs".
|
|
#
|
|
# Typically, a list of Bindings is convenient to get (you usually call something
|
|
# like arguments() to get them); but technically you only need less information:
|
|
# for 'bindings' an (un-ordered) list of Exprs is sufficient; similarly, for
|
|
# 'goals', an (ordered) list of NamedCType goals is sufficient. If you are doing
|
|
# something more complicated, e.g., tracking the set of bindings in a context,
|
|
# you may find using these smaller types more convenient.
|
|
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})"
|
|
|
|
# [Note: IOptTensorListRef]
|
|
if t.type == ConstRefCType(ListCType(OptionalCType(BaseCType(tensorT)))):
|
|
ctx[
|
|
NamedCType(t.name, BaseCType(iOptTensorListRefT))
|
|
] = f"at::IOptTensorListRef({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 torchgen.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
|
|
|
|
# TODO: These are referentially equal, shouldn't have to do this;
|
|
# ensuring we don't use type synonym IntArrayRef in codegen would
|
|
# help
|
|
if goal.type == ArrayRefCType(BaseCType(longT)):
|
|
return solve(NamedCType(goal.name, BaseCType(intArrayRefT)), direct=direct)
|
|
|
|
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))):
|
|
try:
|
|
options = direct_solve(options_ctype)
|
|
return f"c10::optTypeMetaToScalarType({options}.dtype_opt())"
|
|
except UnsatError:
|
|
out_tensor = direct_solve(out_tensor_ctype)
|
|
return f"{out_tensor}.scalar_type()"
|
|
|
|
elif goal == NamedCType("layout", OptionalCType(BaseCType(layoutT))):
|
|
try:
|
|
options = direct_solve(options_ctype)
|
|
return f"{options}.layout_opt()"
|
|
except UnsatError:
|
|
out_tensor = direct_solve(out_tensor_ctype)
|
|
return f"{out_tensor}.layout()"
|
|
|
|
elif goal == NamedCType("device", OptionalCType(BaseCType(deviceT))):
|
|
try:
|
|
options = direct_solve(options_ctype)
|
|
return f"{options}.device_opt()"
|
|
except UnsatError:
|
|
out_tensor = direct_solve(out_tensor_ctype)
|
|
return f"{out_tensor}.device()"
|
|
|
|
elif goal == NamedCType("pin_memory", OptionalCType(BaseCType(boolT))):
|
|
try:
|
|
options = direct_solve(options_ctype)
|
|
return f"{options}.pinned_memory_opt()"
|
|
except UnsatError:
|
|
# If we're calling a factory op from its out= variant,
|
|
# We don't actually care about the value of pin_memory.
|
|
out_tensor = direct_solve(out_tensor_ctype)
|
|
return "c10::nullopt"
|
|
|
|
# We can always do translations from value types to reference types, like vector<int> -> IntArrayRef
|
|
elif goal.type == BaseCType(intArrayRefT):
|
|
try:
|
|
return direct_solve(NamedCType(goal.name, longVec_ctype))
|
|
except UnsatError:
|
|
# We can also go SymIntArrayRef -> IntArrayRef
|
|
symIntArrayRef_type = direct_solve(
|
|
NamedCType(goal.name, BaseCType(symIntArrayRefT))
|
|
)
|
|
return f"C10_AS_INTARRAYREF_SLOW({symIntArrayRef_type})"
|
|
elif goal.type == BaseCType(symIntArrayRefT):
|
|
try:
|
|
r = direct_solve(NamedCType(goal.name, BaseCType(intArrayRefT)))
|
|
return f"c10::fromIntArrayRefSlow({r})"
|
|
except UnsatError:
|
|
return direct_solve(NamedCType(goal.name, longSymVec_ctype))
|
|
elif goal.type == BaseCType(SymIntT):
|
|
return direct_solve(NamedCType(goal.name, BaseCType(longT)))
|
|
elif goal.type == OptionalCType(BaseCType(SymIntT)):
|
|
argname = direct_solve(
|
|
NamedCType(goal.name, OptionalCType(BaseCType(longT)))
|
|
)
|
|
return f"{argname}.has_value() ? c10::make_optional(c10::SymInt(*{argname})) : c10::nullopt"
|
|
elif goal.type == BaseCType(longT):
|
|
symInt_type = direct_solve(NamedCType(goal.name, BaseCType(SymIntT)))
|
|
return f"{symInt_type}.guard_int(__FILE__, __LINE__)"
|
|
elif goal.type == OptionalCType(BaseCType(longT)):
|
|
argname = direct_solve(
|
|
NamedCType(goal.name, OptionalCType(BaseCType(SymIntT)))
|
|
)
|
|
return f"{argname}.has_value() ? c10::make_optional({argname}->guard_int(__FILE__, __LINE__)) : c10::nullopt"
|
|
elif goal.type == BaseCType(optionalIntArrayRefT):
|
|
try:
|
|
return direct_solve(NamedCType(goal.name, optionalLongVec_ctype))
|
|
except UnsatError:
|
|
argname = direct_solve(
|
|
NamedCType(goal.name, BaseCType(optionalSymIntArrayRefT))
|
|
)
|
|
return f"{argname}.has_value() ? c10::make_optional(C10_AS_INTARRAYREF_SLOW(*{argname})) : c10::nullopt"
|
|
elif goal.type == BaseCType(optionalSymIntArrayRefT):
|
|
# TODO: You might also want to solve this from longSymVec_ctype or
|
|
# an optional version of it
|
|
argname = direct_solve(
|
|
NamedCType(goal.name, BaseCType(optionalIntArrayRefT))
|
|
)
|
|
return f"{argname}.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*{argname})) : c10::nullopt"
|
|
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()"
|
|
if goal.type == VectorCType(BaseCType(SymIntT)):
|
|
symIntArrayRef_ctype = NamedCType(goal.name, BaseCType(symIntArrayRefT))
|
|
argname = direct_solve(symIntArrayRef_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.
|
|
|
|
# We allow const casting on tensors, since const-correctness is a bit broken for at::Tensor.
|
|
# We could probably generalize this to non-tensor types too.
|
|
if goal.type == MutRefCType(BaseCType(tensorT)):
|
|
const_ref_tensor_ctype = NamedCType(
|
|
goal.name, ConstRefCType(BaseCType(tensorT))
|
|
)
|
|
argname = direct_solve(const_ref_tensor_ctype)
|
|
return f"const_cast<Tensor&>({argname})"
|
|
|
|
unsat(goal)
|
|
|
|
return [Expr(solve(g, direct=False), g) for g in goal_ctypes]
|