torch/_inductor/virtualized.py - platform/external/pytorch - Git at Google

 from __future__ import annotations

 import itertools
 from contextlib import contextmanager
 from itertools import chain
 from threading import local
 from typing import Any, Callable, Union
 from unittest.mock import patch

 import sympy

 from torch._inductor.utils import IndentedBuffer

 from torch.fx.graph import inplace_methods, magic_methods

 from .utils import reduction_num_outputs, sympy_str, sympy_symbol

 threadlocal = local()


 class Virtualized:
     """
     A global variable that redirects via thread local variable

     This allows us to swap in different op implementations in codegen.
     """

     def __init__(self, vname: str, default):
         self._key: str = f"__torchinductor_{vname}"
         self._default = default

     def _set_handler(self, value):
         prior = self._get_handler()
         setattr(threadlocal, self._key, value)

         @contextmanager
         def ctx():
             try:
                 yield
             finally:
                 self._set_handler(prior)

         return ctx()

     def _get_handler(self):
         try:
             return getattr(threadlocal, self._key)
         except AttributeError:
             return self._default()

     def __getattr__(self, name):
         return getattr(self._get_handler(), name)


 class NullHandler:
     pass


 def _arg_str(a) -> str:
     if isinstance(a, sympy.Expr):
         return sympy_str(a)
     return str(a)


 class MockHandler:
     def __getattr__(self, name):
         if name == "name":
             return "MockHandler"

         def inner(*args, **kwargs):
             fargs = [_arg_str(a) for a in args]
             fargs.extend(f"{k}={v}" for k, v in kwargs.items())
             return f"ops.{name}({', '.join(fargs)})"

         return inner

     @staticmethod
     def masked(mask, body, other) -> str:
         return f"ops.masked({mask}, {body()}, {other})"

     @staticmethod
     def indirect_indexing(index_var, size, check=True) -> sympy.Symbol:
         return sympy_symbol(f"({str(index_var)})")

     @classmethod
     def _init_cls(cls):
         def make_handler(format_string):
             @staticmethod  # type: ignore[misc]
             def inner(*args):
                 return format_string.format(*args)

             return inner

         for name, format_string in chain(
             magic_methods.items(), inplace_methods.items()
         ):
             setattr(cls, name, make_handler(format_string))


 class KernelFormatterHandler:
     def __init__(self, parent_handler):
         self.parent_handler = parent_handler
         self.output = IndentedBuffer(1)
         self.var_counter = itertools.count()

     @staticmethod
     def ir_to_string(ir_fn, index, rindex=None) -> str:
         from .ir import FlexibleLayout

         args = [index, rindex] if rindex is not None else [index]
         names = ["index", "rindex"] if rindex is not None else ["index"]
         formatter = KernelFormatterHandler(MockHandler())

         with formatter.output.indent(-1):
             formatter.output.writeline(f"def inner_fn({', '.join(names)}):")
         for name, arg in zip(names, args):
             if arg:
                 lhs = ", ".join(
                     [
                         str("_" if isinstance(v, (int, sympy.Integer)) else v)
                         for v in arg
                     ]
                 )
                 formatter.output.writeline(f"{lhs} = {name}")

         with V.set_ops_handler(formatter), patch.object(  # type: ignore[call-arg]
             FlexibleLayout, "allow_indexing", True
         ):
             result = ir_fn(*args)
             return formatter.getvalue(result)

     def __getattr__(self, name) -> Callable[..., str]:
         def inner(*args, **kwargs):
             line = getattr(self.parent_handler, name)(*args, **kwargs)
             if name == "indirect_indexing":
                 return line
             # replace line with a new variable name
             varname = f"tmp{next(self.var_counter)}"
             self.output.writeline(f"{varname} = {line}")
             return varname

         return inner

     def reduction(
         self, dtype, src_dtype, reduction_type, value
     ) -> Union[tuple[str, ...], str]:
         line = self.parent_handler.reduction(dtype, src_dtype, reduction_type, value)
         num_values = reduction_num_outputs(reduction_type)
         varnames = [f"tmp{next(self.var_counter)}" for _ in range(num_values)]
         self.output.writeline(f"{','.join(varnames)} = {line}")
         return tuple(varnames) if num_values > 1 else varnames[0]

     def getvalue(self, result):
         self.output.writeline(f"return {result}")
         return self.output.getvalue()


 class WrapperHandler:
     def __init__(self, inner):
         self._inner = inner

     def __getattr__(self, item):
         return getattr(self._inner, item)


 MockHandler._init_cls()

 _ops = Virtualized("ops", MockHandler)
 _graph = Virtualized("graph", NullHandler)
 _real_inputs = Virtualized("real_inputs", NullHandler)
 _fake_mode = Virtualized("fake_mode", NullHandler)
 _kernel = Virtualized("kernel", NullHandler)
 _debug = Virtualized("debug", NullHandler)
 _interpreter = Virtualized("interpreter", NullHandler)
 _aot_compilation = Virtualized("aot_compilation", NullHandler)


 class OpsValue:
     """The return type of most ops calls.

     This exists so we can overload magic methods, and write mathematical
     expressions much more fluently. So instead of

         ops.add(ops.mul(ops.mul(ops.sub(ops.mul(_Ap2, x), _Ap3), x), x), _1)

     we can write

         (_Ap2 * x - _Ap3) * x * x + _1

     """

     value: Any

     def __init__(self, value):
         self.value = value

     def __str__(self):
         return str(self.value)

     def __repr__(self):
         return f"OpsValue({self.value!r})"

     def __add__(self, other):
         return ops.add(self, other)

     def __mul__(self, other):
         return ops.mul(self, other)

     def __sub__(self, other):
         return ops.sub(self, other)

     def __neg__(self):
         return ops.neg(self)

     def __truediv__(self, other):
         return ops.truediv(self, other)

     def __floordiv__(self, other):
         return ops.floordiv(self, other)

     def __mod__(self, other):
         return ops.mod(self, other)

     def __pow__(self, other):
         return ops.pow(self, other)


 class OpsWrapper:
     """This wraps any returned IR values into an `OpsValue` instance, so that we
     can overload the magic methods for writing mathematical expressions fluently.
     """

     def __getattr__(self, name):
         def inner(*args, **kwargs):
             new_args = [OpsWrapper._unwrap(a) for a in args]
             new_kwargs = {k: OpsWrapper._unwrap(v) for k, v in kwargs.items()}
             return OpsWrapper._wrap(getattr(_ops, name)(*new_args, **new_kwargs))

         return inner

     @staticmethod
     def _unwrap(x):
         if isinstance(x, (list, tuple)):
             return tuple(OpsWrapper._unwrap(v) for v in x)
         if isinstance(x, OpsValue):
             return x.value
         return x

     @staticmethod
     def _wrap(x):
         if isinstance(x, (list, tuple)):
             return tuple(OpsValue(v) for v in x)
         return OpsValue(x)

     @staticmethod
     def indirect_indexing(index, size, check=True):
         # Returns a sympy value, not IR value
         index = OpsWrapper._unwrap(index)
         return _ops.indirect_indexing(index, size, check)


 ops = OpsWrapper()

 _MockHandler = MockHandler


 class _V:
     MockHandler = MockHandler
     KernelFormatterHandler = KernelFormatterHandler
     WrapperHandler = WrapperHandler

     set_ops_handler: Callable[[Any], Any] = _ops._set_handler
     get_ops_handler: Callable[[], Any] = _ops._get_handler
     set_graph_handler: Callable[[Any], Any] = _graph._set_handler
     set_real_inputs: Callable[[Any], Any] = _real_inputs._set_handler
     get_real_inputs: Callable[[], Any] = _real_inputs._get_handler
     set_fake_mode: Callable[[Any], Any] = _fake_mode._set_handler
     get_fake_mode: Callable[[], Any] = _fake_mode._get_handler
     set_kernel_handler: Callable[[Any], Any] = _kernel._set_handler
     set_debug_handler: Callable[[Any], Any] = _debug._set_handler
     set_interpreter_handler: Callable[[Any], Any] = _interpreter._set_handler
     set_aot_compilation: Callable[[Any], Any] = _aot_compilation._set_handler
     get_aot_compilation: Callable[[], Any] = _aot_compilation._get_handler

     @property
     def ops(self) -> _MockHandler:
         """The operator handler specific to the current codegen task"""
         return _ops._get_handler()

     @property
     def graph(self):
         """The graph currently being generated"""
         return _graph._get_handler()

     @property
     def real_inputs(self):
         """non-fake example inputs"""
         return _real_inputs._get_handler()

     @property
     def fake_mode(self):
         """The graph currently being generated"""
         return _fake_mode._get_handler()

     @property
     def kernel(self):
         """The kernel currently being generated"""
         return _kernel._get_handler()

     @property
     def debug(self):
         return _debug._get_handler()

     @property
     def interpreter(self):
         return _interpreter._get_handler()

     @property
     def aot_compilation(self):
         return _aot_compilation._get_handler()


 V = _V()
	from __future__ import annotations

	import itertools
	from contextlib import contextmanager
	from itertools import chain
	from threading import local
	from typing import Any, Callable, Union
	from unittest.mock import patch

	import sympy

	from torch._inductor.utils import IndentedBuffer

	from torch.fx.graph import inplace_methods, magic_methods

	from .utils import reduction_num_outputs, sympy_str, sympy_symbol

	threadlocal = local()


	class Virtualized:
	"""
	A global variable that redirects via thread local variable

	This allows us to swap in different op implementations in codegen.
	"""

	def __init__(self, vname: str, default):
	self._key: str = f"__torchinductor_{vname}"
	self._default = default

	def _set_handler(self, value):
	prior = self._get_handler()
	setattr(threadlocal, self._key, value)

	@contextmanager
	def ctx():
	try:
	yield
	finally:
	self._set_handler(prior)

	return ctx()

	def _get_handler(self):
	try:
	return getattr(threadlocal, self._key)
	except AttributeError:
	return self._default()

	def __getattr__(self, name):
	return getattr(self._get_handler(), name)


	class NullHandler:
	pass


	def _arg_str(a) -> str:
	if isinstance(a, sympy.Expr):
	return sympy_str(a)
	return str(a)


	class MockHandler:
	def __getattr__(self, name):
	if name == "name":
	return "MockHandler"

	def inner(args, *kwargs):
	fargs = [_arg_str(a) for a in args]
	fargs.extend(f"{k}={v}" for k, v in kwargs.items())
	return f"ops.{name}({', '.join(fargs)})"

	return inner

	@staticmethod
	def masked(mask, body, other) -> str:
	return f"ops.masked({mask}, {body()}, {other})"

	@staticmethod
	def indirect_indexing(index_var, size, check=True) -> sympy.Symbol:
	return sympy_symbol(f"({str(index_var)})")

	@classmethod
	def _init_cls(cls):
	def make_handler(format_string):
	@staticmethod # type: ignore[misc]
	def inner(*args):
	return format_string.format(*args)

	return inner

	for name, format_string in chain(
	magic_methods.items(), inplace_methods.items()
	):
	setattr(cls, name, make_handler(format_string))


	class KernelFormatterHandler:
	def __init__(self, parent_handler):
	self.parent_handler = parent_handler
	self.output = IndentedBuffer(1)
	self.var_counter = itertools.count()

	@staticmethod
	def ir_to_string(ir_fn, index, rindex=None) -> str:
	from .ir import FlexibleLayout

	args = [index, rindex] if rindex is not None else [index]
	names = ["index", "rindex"] if rindex is not None else ["index"]
	formatter = KernelFormatterHandler(MockHandler())

	with formatter.output.indent(-1):
	formatter.output.writeline(f"def inner_fn({', '.join(names)}):")
	for name, arg in zip(names, args):
	if arg:
	lhs = ", ".join(
	[
	str("_" if isinstance(v, (int, sympy.Integer)) else v)
	for v in arg
	]
	)
	formatter.output.writeline(f"{lhs} = {name}")

	with V.set_ops_handler(formatter), patch.object( # type: ignore[call-arg]
	FlexibleLayout, "allow_indexing", True
	):
	result = ir_fn(*args)
	return formatter.getvalue(result)

	def __getattr__(self, name) -> Callable[..., str]:
	def inner(args, *kwargs):
	line = getattr(self.parent_handler, name)(args, *kwargs)
	if name == "indirect_indexing":
	return line
	# replace line with a new variable name
	varname = f"tmp{next(self.var_counter)}"
	self.output.writeline(f"{varname} = {line}")
	return varname

	return inner

	def reduction(
	self, dtype, src_dtype, reduction_type, value
	) -> Union[tuple[str, ...], str]:
	line = self.parent_handler.reduction(dtype, src_dtype, reduction_type, value)
	num_values = reduction_num_outputs(reduction_type)
	varnames = [f"tmp{next(self.var_counter)}" for _ in range(num_values)]
	self.output.writeline(f"{','.join(varnames)} = {line}")
	return tuple(varnames) if num_values > 1 else varnames[0]

	def getvalue(self, result):
	self.output.writeline(f"return {result}")
	return self.output.getvalue()


	class WrapperHandler:
	def __init__(self, inner):
	self._inner = inner

	def __getattr__(self, item):
	return getattr(self._inner, item)


	MockHandler._init_cls()

	_ops = Virtualized("ops", MockHandler)
	_graph = Virtualized("graph", NullHandler)
	_real_inputs = Virtualized("real_inputs", NullHandler)
	_fake_mode = Virtualized("fake_mode", NullHandler)
	_kernel = Virtualized("kernel", NullHandler)
	_debug = Virtualized("debug", NullHandler)
	_interpreter = Virtualized("interpreter", NullHandler)
	_aot_compilation = Virtualized("aot_compilation", NullHandler)


	class OpsValue:
	"""The return type of most ops calls.

	This exists so we can overload magic methods, and write mathematical
	expressions much more fluently. So instead of

	ops.add(ops.mul(ops.mul(ops.sub(ops.mul(_Ap2, x), _Ap3), x), x), _1)

	we can write

	(_Ap2 * x - _Ap3) * x * x + _1

	"""

	value: Any

	def __init__(self, value):
	self.value = value

	def __str__(self):
	return str(self.value)

	def __repr__(self):
	return f"OpsValue({self.value!r})"

	def __add__(self, other):
	return ops.add(self, other)

	def __mul__(self, other):
	return ops.mul(self, other)

	def __sub__(self, other):
	return ops.sub(self, other)

	def __neg__(self):
	return ops.neg(self)

	def __truediv__(self, other):
	return ops.truediv(self, other)

	def __floordiv__(self, other):
	return ops.floordiv(self, other)

	def __mod__(self, other):
	return ops.mod(self, other)

	def __pow__(self, other):
	return ops.pow(self, other)


	class OpsWrapper:
	"""This wraps any returned IR values into an `OpsValue` instance, so that we
	can overload the magic methods for writing mathematical expressions fluently.
	"""

	def __getattr__(self, name):
	def inner(args, *kwargs):
	new_args = [OpsWrapper._unwrap(a) for a in args]
	new_kwargs = {k: OpsWrapper._unwrap(v) for k, v in kwargs.items()}
	return OpsWrapper._wrap(getattr(_ops, name)(new_args, *new_kwargs))

	return inner

	@staticmethod
	def _unwrap(x):
	if isinstance(x, (list, tuple)):
	return tuple(OpsWrapper._unwrap(v) for v in x)
	if isinstance(x, OpsValue):
	return x.value
	return x

	@staticmethod
	def _wrap(x):
	if isinstance(x, (list, tuple)):
	return tuple(OpsValue(v) for v in x)
	return OpsValue(x)

	@staticmethod
	def indirect_indexing(index, size, check=True):
	# Returns a sympy value, not IR value
	index = OpsWrapper._unwrap(index)
	return _ops.indirect_indexing(index, size, check)


	ops = OpsWrapper()

	_MockHandler = MockHandler


	class _V:
	MockHandler = MockHandler
	KernelFormatterHandler = KernelFormatterHandler
	WrapperHandler = WrapperHandler

	set_ops_handler: Callable[[Any], Any] = _ops._set_handler
	get_ops_handler: Callable[[], Any] = _ops._get_handler
	set_graph_handler: Callable[[Any], Any] = _graph._set_handler
	set_real_inputs: Callable[[Any], Any] = _real_inputs._set_handler
	get_real_inputs: Callable[[], Any] = _real_inputs._get_handler
	set_fake_mode: Callable[[Any], Any] = _fake_mode._set_handler
	get_fake_mode: Callable[[], Any] = _fake_mode._get_handler
	set_kernel_handler: Callable[[Any], Any] = _kernel._set_handler
	set_debug_handler: Callable[[Any], Any] = _debug._set_handler
	set_interpreter_handler: Callable[[Any], Any] = _interpreter._set_handler
	set_aot_compilation: Callable[[Any], Any] = _aot_compilation._set_handler
	get_aot_compilation: Callable[[], Any] = _aot_compilation._get_handler

	@property
	def ops(self) -> _MockHandler:
	"""The operator handler specific to the current codegen task"""
	return _ops._get_handler()

	@property
	def graph(self):
	"""The graph currently being generated"""
	return _graph._get_handler()

	@property
	def real_inputs(self):
	"""non-fake example inputs"""
	return _real_inputs._get_handler()

	@property
	def fake_mode(self):
	"""The graph currently being generated"""
	return _fake_mode._get_handler()

	@property
	def kernel(self):
	"""The kernel currently being generated"""
	return _kernel._get_handler()

	@property
	def debug(self):
	return _debug._get_handler()

	@property
	def interpreter(self):
	return _interpreter._get_handler()

	@property
	def aot_compilation(self):
	return _aot_compilation._get_handler()


	V = _V()