def returntype_type(t: Type, *, mutable: bool) -> CType:
# placeholder is ignored
r = valuetype_type(t, binds="__placeholder__")
if r is not None:
return r.type
if isinstance(t, BaseType):
if t.name == BaseTy.Tensor:
if mutable:
if local.use_const_ref_for_mutable_tensors():
return ConstRefCType(BaseCType(tensorT))
else:
return MutRefCType(BaseCType(tensorT))
else:
# Note [Tensor Copy Returns]
# Currently, we use "Argument.is_write" to determine
# whether or not Tensor return types should be copies or references.
# If that ever changes, take a look at other locations of this note!
return BaseCType(tensorT)
elif t.name == BaseTy.Scalar:
return BaseCType(scalarT)
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 VectorCType(elem)
raise AssertionError(f"unrecognized return type {t}")
def process_ir_type(
typ: Type) -> Union[BaseCType, VectorCType, OptionalCType, ListCType]:
"""
This function takes a type from NativeFunctions and converts it for use with
lazy tensor codegen.
Type conversion for lazy currently consists of
(1) changing at::Tensors into lazy::Values
(2) wrapping everything in a BaseCType
(3) making cpp-reference types into cpp-value types (e.g. vector instead of IntArrayRef)
(1) converts at::Tensors to lazy::Values (which wrap lazy::Nodes, with which Lazy IR represents tensors.)
There is special handling for Optional[Tensor] or List[Tensor], etc- hence 'tensor-like'
This is incomplete- there are assertions in places that it's expected to need to add
more types as the codegen is used with more operators.
"""
if isinstance(typ, BaseType):
if typ.name == BaseTy.Tensor:
return BaseCType(valueT)
elif typ.name == BaseTy.Scalar:
# at::scalar has special handling,
# and is wrapped in an lazy::Value just like at::tensor
return BaseCType(valueT)
elif typ.name == BaseTy.ScalarType:
return BaseCType(scalarTypeT)
elif typ.name == BaseTy.int:
return BaseCType(longT)
elif typ.name == BaseTy.bool:
return BaseCType(boolT)
elif typ.name == BaseTy.float:
return BaseCType(doubleT)
elif typ.name == BaseTy.str:
return BaseCType(stringT)
elif typ.name == BaseTy.Device:
return BaseCType(deviceT)
elif typ.name == BaseTy.Layout:
return BaseCType(layoutT)
elif typ.name == BaseTy.MemoryFormat:
return BaseCType(memoryFormatT)
else:
raise AssertionError(f"TODO add support for type {repr(typ)}")
elif isinstance(typ, OptionalType):
return OptionalCType(process_ir_type(typ.elem))
elif isinstance(typ, ListType):
if str(typ.elem) == 'Tensor?':
# TODO(whc) is this actually correct? or should it use a Vector like above
return ListCType(OptionalCType(BaseCType(valueT)))
elif str(typ.elem) == 'Tensor':
# this is a TensorList which comes in from GetTensorList as a Value
return BaseCType(tensorListValueT)
else:
return VectorCType(process_ir_type(typ.elem))
else:
raise AssertionError(f"unrecognized type {repr(typ)}")
def emit_fw_derivatives() -> List[str]:
content: List[str] = []
fw_grad_setters: List[str] = []
for derivative in fw_derivatives:
res = derivative.var_name
if f.func.name.name.inplace:
# TODO update this when inplace namings are unified
res = "self"
assert derivative.required_inputs_fw_grad is not None
unpacked_arguments = ""
for inp in differentiable_inputs:
zeros_fn = "zeros" if inplace and inp.name == "self" else "_efficientzerotensor"
if inp.name in derivative.required_inputs_fw_grad:
unpacked_arguments += FW_DERIVATIVE_DEFINED_GRAD_TEMPLATE.substitute(inp=inp.name, zeros_fn=zeros_fn)
if inp.name in (derivative.required_inputs_primal or []):
unpacked_arguments += FW_DERIVATIVE_DEFINED_PRIMAL_TEMPLATE.substitute(inp=inp.name)
if derivative.required_original_self_value:
unpacked_arguments += FW_DERIVATIVE_DEFINED_GRAD_TEMPLATE.substitute(inp="original_self", zeros_fn=zeros_fn)
unpacked_arguments += FW_DERIVATIVE_DEFINED_PRIMAL_TEMPLATE.substitute(inp="original_self")
elif inplace and derivative.is_reusing_outplace_formula:
# The gradient wasn't already cloned, do it if grad mode is enabled
unpacked_arguments += "self_t = GradMode::is_enabled() ? self_t.clone() : self_t;"
if inplace:
is_inplace_str = "true"
else:
is_inplace_str = "false"
if isinstance(derivative.var_type, BaseType) and derivative.var_type.is_tensor_like():
# Is there a way to get from BaseType to BaseCType
opt_res_grad_type = OptionalCType(BaseCType(tensorT)).cpp_type()
fw_grad_setter = FW_DERIVATIVE_SETTER_TENSOR.substitute(out_arg=res, is_inplace=is_inplace_str)
elif isinstance(derivative.var_type, ListType) and derivative.var_type.is_tensor_like():
opt_res_grad_type = OptionalCType(VectorCType(BaseCType(tensorT))).cpp_type()
fw_grad_setter = FW_DERIVATIVE_SETTER_TENSOR_LIST.substitute(out_arg=res, is_inplace=is_inplace_str)
else:
raise RuntimeError("Unsupported output type for forward derivative")
fw_grad_opt_definition = f"{opt_res_grad_type} {res}_new_fw_grad_opt = c10::nullopt;"
# View ops create fw_grad that already is a view of the base's fw_grad so just use that
content.append(FW_DERIVATIVE_TEMPLATE.substitute(
fw_grad_opt_definition=fw_grad_opt_definition,
requires_fw_grad=get_any_has_forward_grad_name(derivative.var_name), formula=derivative.formula, out_arg=res,
unpacked_arguments=unpacked_arguments))
fw_grad_setters.append(fw_grad_setter)
# Set all the grads at the end to avoid: https://github.com/pytorch/pytorch/issues/67367
content.append('\n'.join(fw_grad_setters))
return content
def process_ir_type(typ: Type) -> Union[BaseCType, VectorCType, OptionalCType, ListCType]:
"""
This function takes a type from NativeFunctions and converts it for use with
lazy tensor codegen. Currently its output is used in several places, and so far
it has been possible for them to all use the same conversions, but that may not be
optimal or possible in the finished system.
Type conversion for lazy currently consists of
(1) changing Tensor-like things into Value-like things
(2) wrapping everything in a BaseCType
(3) making reference types into values (e.g. vector instead of IntArrayRef)
(1) converts Tensors to Values since Values are how Lazy IR represents tensors. There
is special handling for Optional[Tensor] or List[Tensor], etc- hence 'tensor-like'
This is incomplete- there are assertions in places that it's expected to need to add
more types as the codegen is used with more operators.
"""
if isinstance(typ, BaseType):
if typ.name == BaseTy.Tensor:
return BaseCType(valueT)
elif typ.name == BaseTy.Scalar:
# at::scalar has special handling,
# and is wrapped in an IR value just like at::tensor
return BaseCType(valueT)
elif typ.name == BaseTy.ScalarType:
return BaseCType(scalarTypeT)
elif typ.name == BaseTy.int:
return BaseCType(longT)
elif typ.name == BaseTy.bool:
return BaseCType(boolT)
elif typ.name == BaseTy.float:
return BaseCType(doubleT)
elif typ.name == BaseTy.str:
return BaseCType(stringT)
elif typ.name == BaseTy.Device:
return BaseCType(deviceT)
elif typ.name == BaseTy.Layout:
return BaseCType(layoutT)
else:
raise AssertionError(f"TODO add support for type {repr(typ)}")
elif isinstance(typ, OptionalType):
return OptionalCType(process_ir_type(typ.elem))
elif isinstance(typ, ListType):
if str(typ.elem) == 'Tensor?':
# TODO(whc) is this actually correct? or should it use a Vector like above
return ListCType(OptionalCType(BaseCType(valueT)))
else:
return VectorCType(process_ir_type(typ.elem))
else:
raise AssertionError(f"unrecognized type {repr(typ)}")
def argumenttype_type(t: Type, *, mutable: bool, binds: ArgName, remove_non_owning_ref_types: bool = False) -> NamedCType:
# If it's a value type, do the value type translation
r = valuetype_type(t, binds=binds, remove_non_owning_ref_types=remove_non_owning_ref_types)
if r is not None:
return r
if isinstance(t, BaseType):
if t.name == BaseTy.Tensor:
if mutable and not local.use_const_ref_for_mutable_tensors():
return NamedCType(binds, MutRefCType(BaseCType(tensorT)))
else:
return NamedCType(binds, ConstRefCType(BaseCType(tensorT)))
elif t.name == BaseTy.Scalar:
return NamedCType(binds, ConstRefCType(BaseCType(scalarT)))
else:
raise AssertionError(f"base type should have been value type {t}")
elif isinstance(t, OptionalType):
if str(t.elem) == 'Tensor':
if mutable and not local.use_const_ref_for_mutable_tensors():
return NamedCType(binds, MutRefCType(BaseCType(tensorT))) # TODO: fix this discrepancy
else:
return NamedCType(binds, ConstRefCType(OptionalCType(BaseCType(tensorT))))
elif str(t.elem) == 'Scalar':
return NamedCType(binds, ConstRefCType(OptionalCType(BaseCType(scalarT))))
elif isinstance(t.elem, ListType) and str(t.elem.elem) == 'int':
return NamedCType(binds, BaseCType(optionalIntArrayRefT))
elem = argumenttype_type(t.elem, mutable=mutable, binds=binds)
return NamedCType(binds, OptionalCType(elem.type))
elif isinstance(t, ListType):
# TODO: remove these special cases, ArrayRef fallthrough works fine
if str(t.elem) == 'int':
if remove_non_owning_ref_types:
return NamedCType(binds, VectorCType(BaseCType(longT)))
else:
return NamedCType(binds, BaseCType(intArrayRefT))
elif str(t.elem) == 'Tensor':
return NamedCType(binds, BaseCType(tensorListT))
elif str(t.elem) == 'Scalar':
return NamedCType(binds, ArrayRefCType(BaseCType(scalarT)))
elif str(t.elem) == 'Dimname':
return NamedCType(binds, BaseCType(dimnameListT))
elif str(t.elem) == 'Tensor?':
return NamedCType(binds, ConstRefCType(ListCType(OptionalCType(BaseCType(tensorT)))))
elem = argumenttype_type(t.elem, mutable=mutable, binds=binds)
return NamedCType(binds, ArrayRefCType(elem.type))
else:
raise AssertionError(f"unrecognized type {repr(t)}")
def returntype_type(t: Type, *, mutable: bool) -> CType:
# placeholder is ignored
r = valuetype_type(t, binds="__placeholder__")
if r is not None:
return r.type
if isinstance(t, BaseType):
if t.name == BaseTy.Tensor:
if mutable:
return MutRefCType(BaseCType(tensorT))
else:
return BaseCType(tensorT)
elif t.name == BaseTy.Scalar:
return BaseCType(scalarT)
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 VectorCType(elem)
raise AssertionError(f"unrecognized return type {t}")
def saved_variables(
formula: str,
nctypes: List[NamedCType],
var_names: Tuple[str, ...],
) -> Tuple[str, Tuple[SavedAttribute, ...]]:
def stride_expr(name: str) -> str:
assert var_names == (name,), (
'Replacement for ".strides()" is currently only supported for single derivatives of the same tensor '
'that ".strides()" is being called on.')
return f'strides_or_error({name}, "{name}")'
REPLACEMENTS: List[Tuple[str, Dict[str, Any]]] = [
# replace self.sizes() with self_sizes
(r'{}.sizes\(\)', {
'suffix': '_sizes',
'nctype': lambda name: NamedCType(name, BaseCType(intArrayRefT)),
}),
# replace self.options() with self_options
(r'{}.options\(\)', {
'suffix': '_options',
'nctype': lambda name: NamedCType(name, BaseCType(tensorOptionsT)),
}),
# replace zeros_like(self) with self_info
(r'zeros_like\({}\)', {
'suffix': '_info',
'nctype': lambda name: NamedCType(name, BaseCType(typeAndSizeT)),
'expr': lambda name: name, # at save-time
'res': lambda name: name + '_info.zeros()', # at eval-time
}),
# replace self.size(2) with self_size_2
(r'{}.size\((\w+)\)', {
'suffix': lambda m: '_argsize_{}'.format(*m.groups()),
'nctype': lambda name: NamedCType(name, BaseCType(intT)),
}),
# replace self.numel() with self_numel
(r'{}.numel\(\)', {
'suffix': '_numel',
'nctype': lambda name: NamedCType(name, BaseCType(intT)),
}),
# replace to_args_sizes(self) with self_args_sizes
(r'to_args_sizes\({}\)', {
'suffix': '_args_sizes',
'nctype': lambda name: NamedCType(name, VectorCType(VectorCType(BaseCType(intT)))),
}),
# replace to_args_scalartypes(self) with self_args_scalartypes
(r'to_args_scalartypes\({}\)', {
'suffix': '_args_scalartypes',
'nctype': lambda name: NamedCType(name, VectorCType(BaseCType(scalarTypeT))),
}),
# replace TensorGeometry(self) with self_geometry
(r'TensorGeometry\({}\)', {
'suffix': '_geometry',
'nctype': lambda name: NamedCType(name, BaseCType(tensorGeometryT)),
}),
(r'{}.scalar_type\(\)', {
'suffix': '_scalar_type',
'nctype': lambda name: NamedCType(name, BaseCType(scalarTypeT)),
}),
# replace self.dim() with self_dim
(r'{}.dim\(\)', {
'suffix': '_dim',
'nctype': lambda name: NamedCType(name, BaseCType(intT)),
}),
# replace self.strides() with self_strides
(r'{}.strides\(\)', {
'suffix': '_strides',
'nctype': lambda name: NamedCType(name, BaseCType(intArrayRefT)),
'expr': stride_expr,
}),
]
# find which arguments need to be saved
saved: List[SavedAttribute] = []
for nctype in nctypes:
name = nctype.name.name if isinstance(nctype.name, SpecialArgName) else nctype.name
# First search the formula for expressions which can be evaluated
# when the autograd Function is created to avoid saving variables
for regex, info in REPLACEMENTS:
def repl(m: Match[str]) -> str:
suffix: str = info['suffix'](m) if callable(info['suffix']) else info['suffix']
expr: str = info['expr'](name) if 'expr' in info else m.group(0)
saved.append(SavedAttribute(
nctype=info['nctype'](name + suffix),
expr=expr,
))
if 'res' in info:
replacement: str = info['res'](name)
return replacement
return name + suffix
formula = re.sub(regex.format(name), repl, formula)
# Find any variables which remain in the formula and save them
if re.search(IDENT_REGEX.format(name), formula):
saved.append(SavedAttribute(
nctype=nctype,
expr=name,
))
return formula, tuple(saved)
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)
#
# - 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)))
longVec_ctype = VectorCType(BaseCType(longT))
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
# }
#
# - 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)))
longVec_ctype = 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
