MLPY/Lib/site-packages/torch/_inductor/dependencies.py

import collections
import dataclasses
import itertools
import logging
import re
import typing
from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
from unittest.mock import patch

import sympy

import torch
from torch.fx.experimental.symbolic_shapes import free_unbacked_symbols

from .codegen.common import index_prevent_reordering
from .utils import (
    get_dtype_size,
    reduction_num_outputs,
    sympy_index_symbol,
    sympy_str,
    sympy_subs,
    VarRanges,
)
from .virtualized import OpsHandler, ReductionType, V

log = logging.getLogger(__name__)
is_indirect = re.compile(r"indirect|tmp").search
Dep = Union["MemoryDep", "StarDep", "WeakDep"]


class MemoryDep(typing.NamedTuple):
    name: str
    index: sympy.Expr  # type: ignore[assignment]
    var_names: Tuple[sympy.Symbol, ...]
    size: Tuple[sympy.Expr, ...]

    def __repr__(self):
        return f"MemoryDep({self.name!r}, {self.index}, {self.ranges})"

    @property
    def ranges(self) -> Dict[sympy.Symbol, sympy.Expr]:
        """{c0: 128, c1: 512, ...}"""
        return dict(zip(self.var_names, self.size))

    def get_numel(self) -> sympy.Expr:
        if self.is_indirect():
            numel = V.graph.get_numel(self.name)
        else:
            vars = set(self.index.free_symbols)
            numel = sympy.Integer(1)
            for var, size in zip(self.var_names, self.size):
                if var in vars:
                    numel = numel * size
        return numel

    def rename(self, renames: Dict[str, str]) -> "MemoryDep":
        if self.name in renames:
            return MemoryDep(
                renames[self.name], self.index, var_names=self.var_names, size=self.size
            )
        return self

    def numbytes_hint(self):
        return V.graph.sizevars.size_hint(self.get_numel()) * get_dtype_size(
            V.graph.get_dtype(self.name)
        )

    def has_unbacked_symbols(self):
        return len(free_unbacked_symbols(self.get_numel())) > 0

    def is_contiguous(self) -> bool:
        return isinstance(self.index, sympy.Symbol) and self.index in self.var_names

    def is_scalar(self) -> bool:
        if isinstance(self.index, sympy.Symbol):
            return self.index not in self.var_names and not self.is_indirect()
        return isinstance(self.index, (int, sympy.Integer))

    def is_indirect(self) -> bool:
        return any(is_indirect(v.name) for v in self.index.free_symbols)  # type: ignore[attr-defined]


class StarDep(typing.NamedTuple):
    # depends on the entire buffer
    name: str

    @property
    def index(self):
        raise NotImplementedError("StarDep does not have an index")

    def get_numel(self) -> sympy.Expr:
        return V.graph.get_numel(self.name)

    def rename(self, renames: Dict[str, str]) -> "StarDep":
        if self.name in renames:
            return StarDep(renames[self.name])
        return self

    def numbytes_hint(self):
        return V.graph.sizevars.size_hint(self.get_numel()) * get_dtype_size(
            V.graph.get_dtype(self.name)
        )

    def has_unbacked_symbols(self):
        return len(free_unbacked_symbols(self.get_numel())) > 0

    def is_contiguous(self) -> bool:
        return False

    def is_scalar(self) -> bool:
        return False

    def is_indirect(self) -> bool:
        return False


# Used for tracking mutation ordering
# if A reads a buffer and B mutates it
# B must be ordered after A
#
# It is weak because if it turns out A's read is never used, we can still
# eliminate it
class WeakDep(typing.NamedTuple):
    name: str

    @property
    def index(self):
        raise NotImplementedError("WeakDep does not have an index")

    def get_numel(self) -> sympy.Expr:
        return sympy.Integer(1)

    def rename(self, renames: Dict[str, str]) -> "WeakDep":
        if self.name in renames:
            return WeakDep(renames[self.name])
        return self

    def numbytes_hint(self):
        return 1  # Purely inserted for ordering, not an actual dep

    def has_unbacked_symbols(self):
        return False

    def is_contiguous(self) -> bool:
        return False


class IndexExprDep(typing.NamedTuple):
    index: sympy.Expr  # type: ignore[assignment]
    var_names: Tuple[sympy.Symbol, ...]
    size: Tuple[sympy.Expr, ...]


@dataclasses.dataclass
class ReadWrites:
    reads: Set[Dep]
    writes: Set[Dep]
    index_exprs: Set[IndexExprDep]
    range_vars: Optional[List[sympy.Expr]] = None
    var_ranges: Optional[VarRanges] = None
    op_counts: typing.Counter[str] = dataclasses.field(
        default_factory=collections.Counter
    )

    def rename(self, renames: typing.Dict[str, str]) -> "ReadWrites":
        return ReadWrites(
            {dep.rename(renames) for dep in self.reads},
            {dep.rename(renames) for dep in self.writes},
            self.index_exprs,
            self.range_vars,
            self.var_ranges,
            op_counts=self.op_counts,
        )

    def with_read(self, dep: Dep) -> "ReadWrites":
        assert isinstance(dep, (WeakDep, StarDep))
        return ReadWrites(
            set.union(self.reads, {dep}),
            self.writes,
            self.index_exprs,
            self.range_vars,
            self.var_ranges,
            op_counts=self.op_counts,
        )

    def merge(self, other: "ReadWrites"):
        reads = set.union(self.reads, other.reads)
        writes = set.union(self.writes, other.writes)
        index_exprs = set.union(self.index_exprs, other.index_exprs)
        op_counts = collections.Counter(self.op_counts)
        op_counts.update(other.op_counts)
        return ReadWrites(reads - writes, writes, index_exprs, op_counts=op_counts)

    @staticmethod
    def merge_list(read_writes: List["ReadWrites"]):
        all_writes = set.union(*[rw.writes for rw in read_writes])
        all_reads = set.union(*[rw.reads for rw in read_writes]) - all_writes
        all_index_exprs = set.union(*[rw.index_exprs for rw in read_writes])

        op_counts: typing.Counter[Any] = collections.Counter()
        for rw in read_writes:
            op_counts.update(rw.op_counts)

        return ReadWrites(all_reads, all_writes, all_index_exprs, op_counts=op_counts)

    def remove_reads(self, rem_reads):
        return ReadWrites(
            self.reads - rem_reads,
            self.writes,
            self.index_exprs,
            self.range_vars,
            self.var_ranges,
            op_counts=self.op_counts,
        )

    def reads_and_writes(self):
        return itertools.chain(self.reads, self.writes)


class _RecordLoadStoreInner(V.MockHandler):  # type: ignore[name-defined]
    def __init__(self, var_ranges: VarRanges, normalize: bool):
        super().__init__()
        self._reads: Set[Dep] = set()
        self._writes: Set[MemoryDep] = set()
        self._index_exprs: Set[IndexExprDep] = set()
        self._var_ranges: VarRanges = var_ranges
        self._normalize: bool = normalize

    def canonicalize(
        self, index: sympy.Expr
    ) -> Tuple[sympy.Expr, Tuple[sympy.Symbol, ...], Tuple[sympy.Expr, ...]]:
        if not self._normalize:
            sizes = [V.graph.sizevars.simplify(x) for x in self._var_ranges.values()]
            var_names = tuple(
                k for k, v in zip(self._var_ranges.keys(), sizes) if v != 1
            )
            sizes = tuple(v for v in sizes if v != 1)
            return index, var_names, sizes  # type: ignore[return-value]

        # Try to further simplify the indexes even if simplify_loops didn't
        # convert it to the simplest form because of the interference from
        # different indexing formulas.
        free_symbols = index.free_symbols
        var_ranges = {
            k: V.graph.sizevars.simplify(v)
            for k, v in self._var_ranges.items()
            # TODO(jansel): explore this further normalization
            # if k in free_symbols
        }
        index_vars = [*var_ranges.keys()]
        sizes = tuple(var_ranges.values())
        new_sizes, reindex, prune = V.graph.sizevars._simplify_loops(
            index_vars,
            sizes,
            index_prevent_reordering([index], index_vars, sizes),
        )

        # assign new variables each dimension to deal with numbering mismatches
        # d0, d1, d2 could become d0, d2 -- which won't match d0, d1
        new_vars, add_var = var_builder(canonicalization_prefix())
        replacement = dict(zip(index_vars, reindex([add_var(x) for x in new_sizes])))
        index = sympy_subs(sympy.expand(index), replacement)

        new_vars = [*new_vars.keys()]
        new_sizes = [*new_sizes]
        free_symbols = index.free_symbols
        while new_vars and new_vars[-1] not in free_symbols:
            # Reduction has last (reduced) dim in its sizes, but
            # downstream users won't.  Normalize this away.
            new_vars.pop()
            new_sizes.pop()
        return index, tuple(new_vars), tuple(new_sizes)  # type: ignore[arg-type]

    def load(self, name: str, index: sympy.Expr) -> str:
        self._reads.add(MemoryDep(name, *self.canonicalize(index)))
        return f"load({name}, {sympy_str(index)})"

    def load_seed(self, name: str, index: int):
        assert isinstance(index, int)
        return self.load(name, sympy.Integer(index))

    def store(self, name: str, index: sympy.Expr, value: str, mode=None) -> str:
        self._writes.add(MemoryDep(name, *self.canonicalize(index)))
        return f"store({name}, {sympy_str(index)}, {value}, {mode})"

    def store_reduction(self, name: str, index, value) -> str:
        return self.store(name, index, f"store_reduction({value})")

    def index_expr(self, index: sympy.Expr, dtype) -> str:
        self._index_exprs.add(IndexExprDep(*self.canonicalize(index)))
        return f"index_expr({sympy_str(index)}, {dtype})"

    def bucketize(
        self,
        values,
        offsets_name: str,
        offsets_size: sympy.Expr,
        indexing_dtype: torch.dtype,
        right: bool,
    ):
        self._reads.add(StarDep(offsets_name))
        return f"bucketize({values}, {offsets_name}, {sympy_str(offsets_size)}, {indexing_dtype}, {right})"


class _OpCounter:
    """Shim to count how many times each op is used"""

    def __init__(self, inner):
        super().__init__()
        self.parent_handler = inner
        self._op_counts: typing.Counter[Any] = collections.Counter()

    def __getattr__(self, name):
        self._op_counts[name] += 1
        return getattr(self.parent_handler, name)


class RecordLoadStore(V.KernelFormatterHandler):  # type: ignore[name-defined]
    def __init__(self, var_ranges: VarRanges, normalize: bool):
        parent_handler = _RecordLoadStoreInner(
            var_ranges=var_ranges, normalize=normalize
        )
        parent_handler = _OpCounter(parent_handler)
        super().__init__(parent_handler=parent_handler)


def var_builder(prefix: str) -> Tuple[VarRanges, Callable[[sympy.Expr], sympy.Symbol]]:
    cnt = itertools.count()
    var_ranges: VarRanges = dict()

    def add_var(length: sympy.Expr) -> sympy.Symbol:
        v = sympy_index_symbol(f"{prefix}{next(cnt)}")
        var_ranges[v] = length
        return v

    return var_ranges, add_var


def index_vars_no_squeeze(*argsizes: Tuple[sympy.Expr, ...], prefix: str):
    var_ranges, add_var = var_builder(prefix)
    args: List[List[sympy.Symbol]] = []
    for size in argsizes:
        args.append(list(map(add_var, size)))
    return args, var_ranges


def index_vars_squeeze(*argsizes: Tuple[sympy.Expr, ...], prefix: str = "d"):
    from .ir import SqueezeView

    var_ranges, add_var = var_builder(prefix)
    args: List[List[sympy.Expr]] = []
    new_sizes: List[List[sympy.Expr]] = []
    for size in argsizes:
        new_size, reindex = SqueezeView.squeezer(size)
        new_sizes.append(new_size)
        args.append(reindex(list(map(add_var, new_size))))
    return args, var_ranges


def extract_read_writes(
    fn: Callable[..., Any],
    *argsizes: Tuple[sympy.Expr, ...],
    normalize: bool = False,
    prefix: str = "d",
):
    args, var_ranges = index_vars_squeeze(*argsizes, prefix=prefix)
    rw = RecordLoadStore(var_ranges, normalize=normalize)
    with V.set_ops_handler(rw):
        fn(*args)

    if normalize:
        range_vars = []  # Number of vars could differ due to normalization
    else:
        range_vars = list(itertools.chain.from_iterable(args))

    inner = rw.parent_handler.parent_handler
    return ReadWrites(
        set(inner._reads),
        set(inner._writes),
        inner._index_exprs,
        range_vars,
        var_ranges,
        rw.parent_handler._op_counts,
    )


def extract_input_node_reduction_ranges(
    input_node: "torch._inductor.ir.TensorBox",
) -> Tuple[Optional[List[sympy.Expr]], Optional[List[sympy.Expr]]]:
    """
    Returns the size and reduction size of all inputs, if the sizes and reduction_sizes (if exist) are all the same.
    It's possible that a node has multiple inputs, some are Reduction nodes and others are Pointwise nodes.
    In this case, reduction_sizes of the Reduction nodes need to be the same.
    Otherwise returns (None, None).
    """

    from .ir import ComputedBuffer, Loops

    if isinstance(input_node.data, ComputedBuffer):
        # Input node has already been realized. Return its size and reduction_size.
        size = input_node.get_size()
        reduction_size = input_node.get_reduction_size()
        if len(reduction_size) > 0:
            return (size, reduction_size)
        else:
            return (None, None)

    if not isinstance(input_node.data.data, Loops):  # type: ignore[attr-defined]
        # Other IRNodes do not have reduction_ranges.
        return (None, None)

    # There is one issue: what if there are views / permutations between the input node and its dependent realized nodes?
    # The current method still uses reduction ranges from the dependent realized node, which is not ideal.
    # Is there a way to check whether there are permutations inbetween?
    reads = input_node.get_reads()
    reduction_size = None
    size = None
    while reduction_size is None and len(reads) > 0:
        seen = set()
        new_reads = []
        for read in reads:
            if not isinstance(read, MemoryDep):
                continue
            if read.name in seen:
                continue
            seen.add(read.name)
            buffer = V.graph.get_buffer(read.name)
            if buffer is None:
                continue
            if (
                isinstance(buffer, ComputedBuffer)
                and len(buffer.get_reduction_size()) > 0
            ):
                if reduction_size is None:
                    reduction_size = buffer.get_reduction_size()
                    size = buffer.get_size()
                elif (
                    reduction_size != buffer.get_reduction_size()
                    or size != buffer.get_size()
                ):
                    return (None, None)
            else:
                new_reads.extend(buffer.get_reads())
        if reads == new_reads:
            return (size, reduction_size)
        else:
            reads = new_reads
    return (size, reduction_size)


def canonicalization_prefix():
    return "c"


# ops handler which computes all the free unbacked symbols for an IR
class FreeUnbackedSymbolsOpsHandler:
    symbols: Set[sympy.Symbol]

    def __init__(self):
        self.symbols = set()

    def __getattr__(self, name: str) -> Callable[..., Any]:
        def inner(*args, **kwargs):
            for a in itertools.chain(args, kwargs.values()):
                if isinstance(a, (sympy.Expr, sympy.logic.boolalg.Boolean)):
                    self.symbols |= free_unbacked_symbols(a)

        return inner

    def indirect_indexing(self, index_var, size, check=True) -> sympy.Symbol:
        assert not isinstance(index_var, (sympy.Expr, sympy.logic.boolalg.Boolean))
        self.symbols |= free_unbacked_symbols(size)
        return sympy_index_symbol(f"({str(index_var)})")

    def frexp(self, x):
        return (None,) * 2

    def reduction(
        self,
        dtype: torch.dtype,
        src_dtype: torch.dtype,
        reduction_type: ReductionType,
        value: Union[None, Tuple[None, ...]],
    ) -> Union[None, Tuple[None, ...]]:
        num_values = reduction_num_outputs(reduction_type)
        return (None,) * num_values if num_values > 1 else None


def _typecheck_FreeUnbackedSymbolsOpsHandler(
    h: FreeUnbackedSymbolsOpsHandler,
) -> OpsHandler[None]:
    return h


def extract_free_unbacked_symbols(fn: Callable[..., Any], index, rindex=None):
    from .ir import FlexibleLayout

    args = [index, rindex] if rindex is not None else [index]
    handler = FreeUnbackedSymbolsOpsHandler()
    # NB: I cargo culted the allow_indexing patch here, I don't understand why
    # people do this all over
    with V.set_ops_handler(handler), patch.object(
        FlexibleLayout, "allow_indexing", True
    ):
        fn(*args)
    return handler.symbols