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from __future__ import annotations
import collections
import itertools
from typing import Any, Dict, Iterable, List, Type, Union
import sympy
import torch
from ...utils._ordered_set import OrderedSet
from ..dependencies import Dep, MemoryDep
from ..runtime.hints import ReductionHint
from ..scheduler import SchedulerNode
from ..utils import cache_on_self
from ..virtualized import V
class NodeScheduleMarker:
@staticmethod
def only_nodes(it: Iterable[NodeScheduleEntry]) -> Iterable[SchedulerNode]:
for item in it:
if not (item is DisableReduction or item is EnableReduction):
yield item # type: ignore[misc]
@staticmethod
def is_reduction() -> bool:
return False
NodeScheduleEntry = Union[SchedulerNode, Type[NodeScheduleMarker]]
class DisableReduction(NodeScheduleMarker):
"""
Marker to invoke `kernel.disable_reduction()`. This closes a
reduction loop and allows for pointwise ops to occur on the output
of a reduction.
"""
class EnableReduction(NodeScheduleMarker):
"""
Marker to end a DisableReduction block.
"""
@staticmethod
def filter(node_schedule: List[NodeScheduleEntry]) -> Iterable[SchedulerNode]:
"""
Get the nodes from node_schedule skipping those in a
DisableReduction block.
"""
disabled = False
for node in node_schedule:
if node in (EnableReduction, DisableReduction):
# Don't tile stuff outside the main reduction loop
disabled = node is DisableReduction
elif disabled:
pass
else:
yield node # type: ignore[misc]
class SIMDKernelFeatures:
"""
An ordered schedule of nodes that will become a single kernel.
"""
def __init__(
self,
node_schedule: List[NodeScheduleEntry],
numel: sympy.Expr,
reduction_numel: sympy.Expr = sympy.S.One,
):
self.node_schedule = node_schedule
# numel excludes reduction_numel
self.numel: sympy.Expr = V.graph.sizevars.simplify(numel)
self.reduction_numel: sympy.Expr = V.graph.sizevars.simplify(reduction_numel)
@cache_on_self
def is_reduction(self) -> bool:
return self.reduction_numel != 1
@cache_on_self
def scheduler_nodes(self) -> Iterable[SchedulerNode]:
return tuple(NodeScheduleMarker.only_nodes(self.node_schedule))
def reduction_nodes(self) -> List[SchedulerNode]:
return [n for n in self.scheduler_nodes() if n.is_reduction()]
@cache_on_self
def buf_accesses(self) -> Dict[str, List[Dep]]:
"""only needed for config.benchmark_kernel"""
buf_accesses = collections.defaultdict(list)
for node in self.scheduler_nodes():
for access in node.read_writes.reads | node.read_writes.writes:
buf_accesses[access.name].append(access)
return buf_accesses
@cache_on_self
def op_counts(self) -> collections.Counter[str]:
counts: collections.Counter[str] = collections.Counter()
for node in self.scheduler_nodes():
counts.update(node._body.op_counts)
return counts
def contains_op(self, op_name: str) -> bool:
"""True if V.ops.{op_name} is used in node_schedule"""
return bool(self.op_counts().get(op_name))
def get_mutations(self) -> OrderedSet[str]:
mutations: OrderedSet[str] = OrderedSet()
for node in self.scheduler_nodes():
for buf in node.get_outputs():
mutations.update(buf.get_mutations())
return mutations
@cache_on_self
def select_index_dtype(self) -> torch.dtype:
# Gather all used buffer names
buffer_names: OrderedSet[str] = OrderedSet()
for node in self.scheduler_nodes():
buffer_names.update(node.get_buffer_names())
buffer_names.update(node.used_buffer_names())
buffers = [V.graph.get_buffer(name) for name in buffer_names]
# In theory we can separately check xnumel and rnumel are <= int_max
# but some indexers do use the full linear index so we need to be
# conservative here.
total_numel = self.numel * self.reduction_numel
from .simd import SIMDScheduling
if SIMDScheduling.can_use_32bit_indexing(total_numel, buffers):
return torch.int32
return torch.int64
@cache_on_self
def get_reduction_hint(self) -> ReductionHint:
reductions = self.reduction_nodes()
if len(reductions) > 0:
hints = [self.reduction_hint(n) for n in reductions]
if hints.count(hints[0]) == len(hints):
reduction_hint_val = hints[0]
else:
reduction_hint_val = ReductionHint.DEFAULT
if (
reduction_hint_val == ReductionHint.INNER
and self.has_non_contiguous_pw_in_reduction_kernel()
):
reduction_hint_val = ReductionHint.DEFAULT
else:
reduction_hint_val = ReductionHint.DEFAULT
return reduction_hint_val
def has_non_contiguous_pw_in_reduction_kernel(self) -> bool:
pointwise_nodes = [
n
for n in self.scheduler_nodes()
if not n.is_reduction()
and n.group[1][0] == self.numel * self.reduction_numel
]
for node in pointwise_nodes:
# An index can be an integer when loading a random seed.
if not all(
not isinstance(dep, MemoryDep)
or dep.is_contiguous()
or isinstance(dep.index, (sympy.Integer, int))
or dep.stride1_for_last_dim()
for dep in itertools.chain(
node.read_writes.reads, node.read_writes.writes
)
):
return True
return False
@staticmethod
def reduction_hint(node: Any) -> ReductionHint:
assert node.is_reduction()
if node.node.data.reduction_hint != ReductionHint.INNER and all(
dep.is_contiguous()
for dep in itertools.chain(node.read_writes.reads, node.read_writes.writes)
):
return ReductionHint.INNER
else:
return node.node.data.reduction_hint
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