pytorch/torch/_inductor/virtualized.py

import itertools
from contextlib import contextmanager
from itertools import chain
from threading import local

import sympy

from torch._inductor.utils import IndentedBuffer

from torch.fx.graph import inplace_methods, magic_methods

from .utils import 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, default):
        self._key = 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):
    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"{name}({', '.join(fargs)})"

        return inner

    @staticmethod
    def masked(mask, body, other):
        return f"masked({mask}, {body()}, {other})"

    @staticmethod
    def indirect_indexing(index_var):
        return sympy_symbol(f"({str(index_var)})")

    @classmethod
    def _init_cls(cls):
        def make_handler(format_string):
            @staticmethod
            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()
        self.var_counter = itertools.count()

    def __getattr__(self, name):
        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 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)
_fake_mode = Virtualized("fake_mode", NullHandler)
_kernel = Virtualized("kernel", NullHandler)
_debug = Virtualized("debug", NullHandler)
_interpreter = Virtualized("interpreter", NullHandler)


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

    set_ops_handler = ops._set_handler
    get_ops_handler = ops._get_handler
    set_graph_handler = _graph._set_handler
    set_fake_mode = _fake_mode._set_handler
    set_kernel_handler = _kernel._set_handler
    set_debug_handler = _debug._set_handler
    set_interpreter_handler = _interpreter._set_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 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()


V = _V()