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android / platform / external / pytorch / a9036e1cf8 / . / torch / _inductor / scheduler.py
blob: eba8a396b4796232f2e1f1e6edb6e0004cd8efaf [file] [log] [blame]
# mypy: disallow-untyped-defs
from __future__ import annotations
import collections
import dataclasses
import functools
import itertools
import logging
import math
import operator
import os
import pprint
import textwrap
import typing
from typing import (
Any,
Counter,
DefaultDict,
Dict,
Generic,
List,
Optional,
Sequence,
Set,
Tuple,
TypeVar,
Union,
)
import sympy
import torch
import torch._inductor.async_compile # noqa: F401 required to warm up AsyncCompile pools
from torch._dynamo.utils import counters, dynamo_timed
from torch._inductor.metrics import get_metric_table, is_metric_table_enabled
from torch.fx.experimental.symbolic_shapes import free_unbacked_symbols
from torch.utils._ordered_set import OrderedSet
from torch.utils._sympy.symbol import free_symbol_is_type, SymT
from torch.utils._triton import has_triton
from . import comms, config, dependencies, ir, metrics
from .codecache import write_text
from .codegen.common import BackendFeature, get_scheduling_for_device, Kernel
from .comm_analysis import estimate_nccl_collective_runtime
from .dependencies import Dep, MemoryDep, StarDep, WeakDep
from .ir import ComputedBuffer, MultiOutput, MultiOutputLayout
from .runtime.runtime_utils import green_text, red_text
from .sizevars import SimplifyIndexing
from .utils import (
cache_on_self,
cmp,
device_need_guard,
get_device_tflops,
get_dtype_size,
get_gpu_dram_gbps,
IndentedBuffer,
is_collective,
is_gpu,
is_wait,
sympy_product,
)
from .virtualized import V
log = logging.getLogger(__name__)
fusion_log = torch._logging.getArtifactLogger(__name__, "fusion")
@dataclasses.dataclass
class SchedulerBuffer:
scheduler: Scheduler
node: ir.Buffer
defining_op: BaseSchedulerNode
users: List[NodeUser] = dataclasses.field(default_factory=list)
def __hash__(self) -> int:
return hash(self.node.name)
def debug_str(self) -> str:
result = IndentedBuffer()
name = self.get_name()
result.writeline(f"{name}: {type(self.node).__name__}")
result.writeline(f"{name}.layout = {self.node.layout}")
if self.get_aliases():
result.writeline(f"{name}.aliases = {pformat(self.get_aliases())}")
if self.get_mutations():
result.writeline(f"{name}.mutations = {pformat(self.get_mutations())}")
if len(self.users) <= 1:
result.writeline(f"{name}.users = {self.users}")
else:
result.writeline(f"{name}.users = [")
with result.indent(1):
for user in self.users:
result.writeline(f"{user},")
result.writeline("]")
return result.getrawvalue()
def get_name(self) -> str:
return self.node.get_name()
def allocate(self) -> None:
assert self.node is not None
if not self.node.should_allocate():
return
if self.node.get_inputs_that_alias_output() or self.node.get_mutation_names():
V.graph.wrapper_code.codegen_allocation(self.node)
return
# hacky check for if V.kernel is a real kernel or NullHandler
if (
hasattr(V.kernel, "args")
and self.get_name() in V.kernel.inplace_update_buffers
):
V.graph.wrapper_code.codegen_inplace_reuse(
self.scheduler.name_to_buf[
V.kernel.inplace_update_buffers[self.get_name()]
].node,
self.node,
)
else:
V.graph.wrapper_code.codegen_allocation(self.node)
def can_free(self) -> bool:
# There's no real allocated buffer, no need to free it
assert self.node is not None
if isinstance(self.node.layout, ir.NoneLayout):
return False
for use in self.users:
if isinstance(use.node, OutputNode):
return False
return True
def set_users(self, users: List[NodeUser]) -> None:
# deduplicate
result: Dict[int, NodeUser] = {}
for use in users:
if id(use.node) in result:
result[id(use.node)] = use.merge(result[id(use.node)])
else:
result[id(use.node)] = use
self.users = list(result.values())
def get_aliases(self) -> Sequence[str]:
assert self.node is not None
return self.node.get_inputs_that_alias_output()
def get_mutations(self) -> List[str]:
assert self.node is not None
return self.node.get_mutation_names()
class BaseSchedulerNode:
group: Tuple[torch.device, Tuple[Tuple[sympy.Expr, ...], ...]]
read_writes: dependencies.ReadWrites
unmet_dependencies: OrderedSet[Dep]
def __init__(self, scheduler: Scheduler, node: ir.Operation) -> None:
self.scheduler: Scheduler = scheduler
self.node: Optional[ir.Operation] = node
self.set_read_writes(node.get_read_writes())
self.ancestors: OrderedSet[str] = OrderedSet()
# .min_order and .max_order are only relevant for "grouped" nodes such as FusedSchedulerNode.
# e.g. if the FusedSchedulerNode includes nodes (op_1, op_2, op_3), and op_X is X-th node
# in `self.scheduler.nodes`, then for this FusedSchedulerNode, .min_order is 1 and .max_order is 3.
# For non-"grouped" nodes (i.e. regular SchedulerNode),
# .min_order = .max_order = X if this node is X-th node in `self.scheduler.nodes`.
self.min_order: int
self.max_order: int
self.last_usage: OrderedSet[
str
] = OrderedSet() # buffers that won't be used after this kernel
self.written = False
self.outputs: List[SchedulerBuffer] = [
SchedulerBuffer(
scheduler=scheduler,
node=output,
defining_op=self,
)
for output in node.get_outputs()
]
self.outputs_by_name: Dict[str, SchedulerBuffer] = {
buf.get_name(): buf for buf in self.outputs
}
def __repr__(self) -> str:
return f"{type(self).__name__}(name={self.get_name()!r})"
def debug_str(self) -> str:
"""Longer form printout for trace logs"""
name = self.get_name()
buf = IndentedBuffer()
buf.splice(
f"""\
{name}: {type(self).__name__}({type(getattr(self, 'node', None)).__name__})
{name}.writes = {pformat(self.read_writes.writes)}
{name}.unmet_dependencies = {pformat(self.unmet_dependencies)}
{name}.met_dependencies = {pformat(self.read_writes.reads - self.unmet_dependencies)}
{name}.outputs = [
"""
)
with buf.indent():
for out in self.get_outputs():
buf.splice(out.debug_str())
buf.writeline("]")
try:
buf.splice(self.debug_str_extra())
except Exception:
log.warning("Ignoring error in debug_str()", exc_info=True)
return buf.getrawvalue().rstrip()
def debug_str_extra(self) -> str:
return ""
def debug_str_short(self) -> str:
maybe_data = getattr(self.node, "data", None)
data_str = ""
if isinstance(maybe_data, torch._inductor.ir.Pointwise):
data_str = ", " + maybe_data.str_helper(
[maybe_data.get_size()], shorten=False, multiline=False
)
elif isinstance(maybe_data, torch._inductor.ir.Reduction):
data_str = ", " + maybe_data.str_helper(
[maybe_data.get_reduction_size(), maybe_data.get_reduction_type()],
shorten=False,
multiline=False,
)
return f"{self}{data_str}"
def log_details(self) -> None:
log.info(
"%s: unmet_dependencies = %s, writes = %s",
self,
self.unmet_dependencies,
self.read_writes.writes,
)
def update_mutated_names(self, renames: Dict[str, str]) -> None:
self.set_read_writes(self.read_writes.rename(renames))
def add_fake_dep(self, dep: Dep) -> None:
self.set_read_writes(self.read_writes.with_read(dep))
def has_aliasing_or_mutation(self) -> bool:
return any(
buf.get_aliases() or buf.get_mutations() for buf in self.get_outputs()
)
def set_read_writes(self, rw: dependencies.ReadWrites) -> None:
self.read_writes = rw
self.unmet_dependencies = self.read_writes.reads
self.prune_deps()
def set_last_usage(
self, future_used_buffers: OrderedSet[str], mutation_real_name: Dict[str, str]
) -> None:
used_buffers = self.used_or_aliased_buffer_names()
used_buffers = OrderedSet([mutation_real_name.get(k, k) for k in used_buffers])
self.last_usage = used_buffers - future_used_buffers
def mark_run(self) -> None:
for buf in self.outputs:
buf.allocate()
def op_counts(self) -> Counter[str]:
return self.read_writes.op_counts
def used_buffer_names(self) -> OrderedSet[str]:
return OrderedSet(
dep.name
for dep in itertools.chain(self.read_writes.reads, self.read_writes.writes)
)
def used_or_aliased_buffer_names(self) -> OrderedSet[str]:
used_names: OrderedSet[str] = OrderedSet()
deps = [
dep.name
for dep in itertools.chain(self.read_writes.reads, self.read_writes.writes)
]
while len(deps) > 0:
dep = deps.pop()
used_names.add(dep)
if V.graph.name_to_buffer.get(dep):
for alias in V.graph.name_to_buffer[dep].get_inputs_that_alias_output():
if alias not in used_names:
deps.append(alias)
return used_names
def prune_deps(self) -> None:
self.unmet_dependencies = OrderedSet(
dep
for dep in self.unmet_dependencies
if dep.name not in self.scheduler.available_buffer_names
)
def prune_weak_deps(self) -> None:
# Prune weak dependencies on operations that have been removed
def should_prune(dep: Dep) -> bool:
if not isinstance(dep, WeakDep):
return False
op = self.scheduler.name_to_buf[dep.name].defining_op
return op.get_name() in V.graph.removed_operations
to_remove = OrderedSet(
dep for dep in self.read_writes.reads if should_prune(dep)
)
self.set_read_writes(self.read_writes.remove_reads(to_remove))
def prune_redundant_deps(
self, name_to_fused_node: Dict[str, BaseSchedulerNode]
) -> None:
_prune_redundant_deps(self, name_to_fused_node, self.scheduler.name_to_buf)
def get_name(self) -> str:
assert self.node is not None
return self.node.get_operation_name()
def get_first_name(self) -> str:
return self.get_name()
def get_operation_names(self) -> OrderedSet[str]:
return OrderedSet(node.get_name() for node in self.get_nodes())
def get_buffer_names(self) -> OrderedSet[str]:
return OrderedSet(out.get_name() for out in self.outputs)
def get_nodes(self) -> Sequence[BaseSchedulerNode]:
return [self]
def get_outputs(self) -> Sequence[SchedulerBuffer]:
return self.outputs
def get_output(self, buf_name: str) -> SchedulerBuffer:
return self.outputs_by_name[buf_name]
def get_device(self) -> torch.device:
assert self.node is not None
return self.node.get_device()
def is_reduction(self) -> bool:
return False
def is_split_scan(self) -> bool:
return False
def is_template(self) -> bool:
return False
def is_extern(self) -> bool:
return False
def is_foreach(self) -> bool:
return False
def can_inplace(self, read_dep: dependencies.Dep) -> bool:
return False
def has_side_effects(self) -> bool:
return False
def decide_inplace_update(self) -> None:
"""
Decide if there should be inplace updates for the node
and record the decision in the active kernel.
"""
from .codegen.wrapper import buffer_reuse_key
if not (
isinstance(self, (SchedulerNode,))
and config.inplace_buffers
and V.graph.has_feature(self.get_device(), BackendFeature.INPLACE_BUFFERS)
and (
not isinstance(V.kernel, torch._inductor.codegen.simd.SIMDKernel)
or getattr(V.kernel, "mutations", None) is not None
)
# hacky check for if V.kernel is a real kernel or NullHandler
and hasattr(V.kernel, "args")
):
return
ordered_reads = sorted(self.read_writes.reads, key=lambda x: x.name)
for buf in self.get_outputs():
buf_node = buf.node
assert buf_node is not None
if (
not buf_node.should_allocate()
or buf_node.get_inputs_that_alias_output()
or buf_node.get_mutation_names()
or buf.get_name() in V.graph.removed_buffers
):
continue
for read in ordered_reads:
input_buf: Optional[SchedulerBuffer] = self.scheduler.name_to_buf.get(
read.name
)
if (
input_buf
and V.graph.wrapper_code.can_reuse(input_buf, self)
and not isinstance(input_buf.defining_op, NopKernelSchedulerNode)
):
assert input_buf.users is not None
remaining_uses = [
x
for x in input_buf.users
if x.node.get_name() not in self.scheduler.completed_operations
]
if (
len(remaining_uses) == 1
and remaining_uses[0].can_inplace
and remaining_uses[0].node is self
and input_buf.node is not None
and not isinstance(
input_buf.node.get_layout(),
(
ir.MultiOutputLayout,
ir.MutationLayoutSHOULDREMOVE,
),
)
and not (
isinstance(
input_buf.defining_op.node,
(ir.FallbackKernel, ir.MultiOutput),
)
and len(input_buf.node.get_inputs_that_alias_output()) > 0
)
and buffer_reuse_key(input_buf.node)
== buffer_reuse_key(buf.node)
):
# if there isn't a triton kernel, then we don't need to call triton-specific things.
# but TODO this might be a convenient place to signal to the Collective kernels to inplace
# (and, can we make "kernel" less generic of a name?)
V.kernel.args.make_inplace(input_buf.get_name(), buf.get_name())
# mutations not tracked in cpp kernels
if isinstance(
V.kernel, torch._inductor.codegen.simd.SIMDKernel
):
V.kernel.mutations.add(input_buf.get_name())
V.kernel.mutations.add(buf.get_name())
# update last usage of reused node
self.last_usage.discard(input_buf.get_name())
V.kernel.inplace_update_buffers[
buf.get_name()
] = input_buf.get_name()
break
def codegen_originating_info(
self, buffer: IndentedBuffer, only_once: bool = True
) -> None:
if not config.comment_origin:
return
if only_once and self.written:
return
assert self.node is not None
origins = self.node.get_origins()
out_lines = []
for o in origins:
if o.op == "output":
# These are boring and samey
continue
out_lines.append("")
# TODO(voz): Should the pragma be constant somewhere?
out_lines.append("#pragma CMT ORIGIN:")
op_info_str = f"#pragma CMT {o.op} {o.target}"
if "seq_nr" in o.meta:
op_info_str = op_info_str + f" seq_nr:{o.meta['seq_nr']}"
out_lines.append(op_info_str)
if "stack_trace" in o.meta:
stack_trace = f"{o.meta['stack_trace']}"
stack_trace_last_line = stack_trace.split("|")[-1]
out_lines.append(
"#pragma CMT "
+ stack_trace_last_line.replace("{", "{{")
.replace("}", "}}")
.replace("\n", "\\")
)
out_lines.append("#pragma CMT END ORIGIN")
out_lines.append("")
if len(out_lines) == 0:
return
# TODO(voz): Ostensibly, we should not need this. But there are cases where C++ codegen does
# not use BracesBuffer, so we have no good indicator of a C++ buffer atm.
buffer.writelines(out_lines)
self.written = True
def get_read_write_buffers_sizes(self) -> int:
"""
Counting the number of bytes accessed for a kernel is
surprisingly tricky. In particular, there is a differentiation
between 'theoretical' memory accesses and practical memory
accesses. For example, a layernorm kernel may actually access an
input 3 times, but in theory, it only needs to access its input
once (and may be optimized to do so through say, persistent
reductions)
Another example is that even though a buffer is passed in, we may
not access the entire buffer. This may occur if we are accessing
a slice of the buffer. Another tricky case is for indirect
indexing, where the amount of bytes accessed depends on the
values of the input.
What this function aims to compute is the memory accesses for
worst-case inputs, best-case optimization. What this means is
that for each buffer we compute the amount of potential accesses in two ways and take the minimum.
1. Numel in ranges multiplied by number of deps the buffer has
2. The buffer size
"""
if isinstance(self, NopKernelSchedulerNode):
return 0
if isinstance(self, ExternKernelSchedulerNode) and isinstance(
self.node, MultiOutput
):
# todo: Calculate this - it's kinda annoying.
return 0
def try_size_hint(s: sympy.Expr) -> int:
return V.graph.sizevars.size_hint(s, fallback=0)
if isinstance(self, SchedulerNode):
node_numel = try_size_hint(
sympy_product(self.get_ranges()[0])
* sympy_product(self.get_ranges()[1]),
)
else:
node_numel = int(1e9)
buf_accesses = collections.defaultdict(list)
for dep in self.read_writes.reads | self.read_writes.writes:
buf_accesses[dep.name].append(dep)
reads = OrderedSet(dep.name for dep in self.read_writes.reads)
writes = OrderedSet(dep.name for dep in self.read_writes.writes)
def is_materialized(buf: str, snodes: Sequence[BaseSchedulerNode]) -> bool:
users = self.scheduler.name_to_buf[buf].users
buf_uses = OrderedSet(user.node for user in users)
return len(buf_uses - OrderedSet(snodes)) > 0
if isinstance(self, FusedSchedulerNode):
removed_buffers = OrderedSet(
dep for dep in writes if not is_materialized(dep, self.snodes)
)
writes = writes - removed_buffers
reads = reads - removed_buffers
node_bytes = 0
for buf_name in reads | writes:
buf_accessed_elems = sum(node_numel for dep in buf_accesses[buf_name])
buf: Union[ir.Buffer, ir.TensorBox]
if buf_name in V.graph.name_to_buffer:
buf = V.graph.name_to_buffer[buf_name]
elif buf_name in V.graph.graph_inputs:
buf = V.graph.graph_inputs[buf_name]
else:
continue
def get_buf_bytes(buf: Optional[Union[ir.Buffer, ir.TensorBox]]) -> int:
if not buf:
return 0
# Kind of a lazy way to get the MultiOutput nodes corresponding to
# a MultiOutputLayout
if isinstance(buf.layout, MultiOutputLayout):
users = self.scheduler.name_to_buf[buf.get_name()].users
tot = 0
for user in users:
assert isinstance(user.node, BaseSchedulerNode)
if isinstance(user.node.node, MultiOutput):
for sched_buf in user.node.get_outputs():
tot += get_buf_bytes(sched_buf.node)
else:
# Buf is a MultiOutputLayout but not all of its
# users are MultiOutputs...
# TODO: Figure out what's going on
return 0
return tot
elif isinstance(buf.layout, ir.NoneLayout):
return sum(
get_buf_bytes(V.graph.get_buffer(mut_name))
for mut_name in buf.get_mutation_names()
)
else:
buf_elems = try_size_hint(sympy_product(buf.get_size()))
return get_dtype_size(buf.get_dtype()) * min(
buf_accessed_elems, buf_elems
)
node_bytes += get_buf_bytes(buf)
return node_bytes
def get_estimated_runtime(self) -> float:
"""
Returns estimated op runtime in nanoseconds (ns)
"""
buf = self.get_nodes()[0].get_outputs()[0]
layout = buf.node.get_layout()
dtype = buf.node.get_dtype()
if layout.device is not None and not is_gpu(layout.device.type):
# default to no reordering based on runtime
return 0
# Collective kernels
if is_collective(self.node):
assert isinstance(self.node, ir.IRNode)
try:
return estimate_nccl_collective_runtime(self.node)
except ValueError as e:
# We don't know how to estimate runtime for this collective,
# falling back to 0
log.info(e)
return 0
elif is_wait(self.node):
# ir.Wait is only used for collective ops.
# The time needed for the collective op is already estimated and considered
# when we are processing the collective op IR node, so ir.Wait takes 0 time
# since it doesn't take extra time to get the result after the collective is completed.
return 0
try:
gpu_memory_bandwidth = get_gpu_dram_gbps()
gpu_flops = get_device_tflops(dtype) * 10**12
except Exception:
return 0
if isinstance(self, ExternKernelSchedulerNode):
assert isinstance(self.node, ir.ExternKernel), f"{type(self.node)=}"
op = kernel_name_to_op.get(
getattr(self.node, "python_kernel_name", ""), None
)
# if there is a resolved op, dry-run using fake mode and record flop count
if op is not None:
from torch._subclasses.fake_tensor import FakeTensorMode
from torch.utils.flop_counter import FlopCounterMode
if any(
len(free_unbacked_symbols(n.get_numel())) > 0
for n in self.node.inputs
):
# Tensor has unbacked symints, we don't know how to estimate
# runtime for that today
return 0
with FakeTensorMode() as fake_mode, FlopCounterMode(
display=False
) as flop_counter_mode, V.set_current_node(
self.node.fx_node
), V.set_fake_mode(
fake_mode
):
from .ir import ir_node_to_tensor
fake_inputs = [
ir_node_to_tensor(input, guard_shape=False)
for input in self.node.inputs
]
cls = self.node.__class__
cls.process_kernel(op, *fake_inputs, **self.node.kwargs)
# TODO(xmfan): find a better heuristic to model FLOPS/latency relationship
factor = 1.0
counted_flops = flop_counter_mode.get_total_flops()
counted_bytes = self.get_read_write_buffers_sizes()
compute_time = (factor * counted_flops / gpu_flops) * 1e9
transfer_time = counted_bytes / gpu_memory_bandwidth
# Return estimated runtime in nanoseconds
return max(compute_time, transfer_time)
elif isinstance(self, FusedSchedulerNode) or isinstance(
self.node, ComputedBuffer
):
# Return estimated runtime in nanoseconds (bytes / gbps)
return self.get_read_write_buffers_sizes() / gpu_memory_bandwidth
return 0
def get_template_node(self) -> Optional[ir.TemplateBuffer]:
return None
class WhyNoFuse:
# TODO when we drop support for Python < 3.10, we can use
# @dataclass(slots=True) instead of manually specifying __slots__.
__slots__ = ["node1", "node2", "reason", "args"]
reason: str
args: Tuple[Any, ...]
def __init__(self, node1: BaseSchedulerNode, node2: BaseSchedulerNode) -> None:
self.node1 = node1
self.node2 = node2
def __call__(self, reason: str, *args: Any) -> None:
self.reason = reason
self.args = args
fusion_log.debug(self)
def __str__(self) -> str:
return f"cannot fuse {self.node1.get_name()} with {self.node2.get_name()}: " + (
self.reason % self.args
)
def pformat(obj: Any) -> str:
if isinstance(obj, OrderedSet):
# pformat has trouble with sets of sympy exprs
obj = sorted(obj, key=str)
result = pprint.pformat(obj, indent=4)
if "\n" in result:
return f"\n{textwrap.indent(result, ' ' * 4)}"
return result
class OutputNode:
def __init__(self, dep: StarDep) -> None:
self.unmet_dependencies = OrderedSet([dep])
def is_reduction(self) -> bool:
return False
def get_inputs_that_alias_output(self) -> Sequence[str]:
return ()
def get_name(self) -> str:
return "OUTPUT"
__repr__ = get_name
def _prune_redundant_deps(
node: BaseSchedulerNode,
name_to_fused_node: Dict[str, BaseSchedulerNode],
name_to_buf: Dict[str, SchedulerBuffer],
) -> None:
"""
Prunes weakdeps intended for mutation ordering
on an upstream fused node if after fusion there is another dependency
on the fused upstream node, making the weakdep redundant
In essence this enforces an ordering on fusions. As fusions occur, weakdeps will
be incrementally removed, enabling other fusions, ensuring they are fused in order.
"""
name_to_dep_count: Counter[str] = collections.Counter()
for dep in node.unmet_dependencies:
if not isinstance(dep, WeakDep):
op = name_to_buf[dep.name].defining_op
name_to_dep_count[name_to_fused_node[op.get_name()].get_name()] += 1
def should_prune(dep: Dep) -> bool:
if isinstance(dep, WeakDep):
op_name = name_to_buf[dep.name].defining_op.get_name()
is_redundant = name_to_dep_count[name_to_fused_node[op_name].get_name()] > 0
# These can occur because fused nodes always gather deps from their snodes
# If B has a weakdep on A
# B gets fused with C, then any time BC is fused, the weakdep will reappear
is_self_dep = name_to_fused_node[op_name] == node
return is_redundant or is_self_dep
else:
return False
deps_to_prune = OrderedSet(
dep for dep in node.unmet_dependencies if should_prune(dep)
)
if deps_to_prune:
node.unmet_dependencies = node.unmet_dependencies - deps_to_prune
node.set_read_writes(node.read_writes.remove_reads(deps_to_prune))
# TODO(xmfan): reuse: an existing mapping for this if it exists, or formalize this into ir.py:ExternKernel
kernel_name_to_op = {
"extern_kernels.convolution": torch.ops.aten.convolution,
"extern_kernels.mm": torch.ops.aten.mm,
"extern_kernels.bmm": torch.ops.aten.bmm,
"extern_kernels.addmm": torch.ops.aten.addmm,
}
class ExternKernelSchedulerNode(BaseSchedulerNode):
def debug_str_extra(self) -> str:
return f"{self.get_name()}.node.kernel = {getattr(self.node, 'python_kernel_name', None)}"
def is_extern(self) -> bool:
return True
def has_side_effects(self) -> bool:
assert self.node is not None
return hasattr(self.node, "has_side_effects") and self.node.has_side_effects()
class NopKernelSchedulerNode(BaseSchedulerNode):
pass
class SchedulerNode(BaseSchedulerNode):
def __init__(
self,
scheduler: Scheduler,
node: Union[ir.ComputedBuffer, ir.TemplateBuffer],
) -> None:
super().__init__(scheduler, node)
self._compute_attrs()
def _compute_attrs(
self,
extra_indexing_constraints: Optional[Tuple[Dict[Any, Any], List[Any]]] = None,
) -> None:
assert isinstance(self.node, (ir.ComputedBuffer, ir.TemplateBuffer))
self._sizes, self._body = self.node.simplify_and_reorder(
extra_indexing_constraints=extra_indexing_constraints
)
group_fn = self.scheduler.get_backend(self.node.get_device()).group_fn
self.group = (self.node.get_device(), group_fn(self._sizes))
if isinstance(self.node, ir.TemplateBuffer):
self.set_read_writes(self.node.normalized_read_writes())
else:
self.set_read_writes(
dependencies.extract_read_writes(
self._body, *self._sizes, normalize=True
)
)
def recompute_size_and_body(
self, extra_indexing_constraints: Tuple[Dict[Any, Any], List[Any]]
) -> None:
self._compute_attrs(extra_indexing_constraints=extra_indexing_constraints)
def debug_str_extra(self) -> str:
name = self.get_name()
lines = [
f"{name}.group.device = {self.group[0]}",
f"{name}.group.iteration = {self.group[1]}",
f"{name}.sizes = {self._sizes}",
]
for dep in self.read_writes.reads_and_writes():
if not isinstance(dep, WeakDep):
buf_name = dep.name
buf = V.graph.get_buffer(buf_name)
lines.append(f"{buf_name}_layout = {pformat(buf.layout)}")
if isinstance(self._body, ir.LoopBody):
lines.append(f"class {name}_loop_body:")
lines.append(textwrap.indent(self._body.debug_str(), " "))
assert self.node is not None
if ir.is_triton(self.node.get_device()):
lines.extend(debug_triton_code(self))
return "\n".join(lines)
def get_ranges(self) -> Sequence[Sequence[sympy.Expr]]:
return self._sizes
def is_reduction(self) -> bool:
assert isinstance(
self.node, (ir.ComputedBuffer, ir.TemplateBuffer)
), f"{type(self.node)=}"
return bool(self.node.get_reduction_type())
def is_split_scan(self) -> bool:
assert isinstance(
self.node, (ir.ComputedBuffer, ir.TemplateBuffer)
), f"{type(self.node)=}"
return isinstance(self.node, ir.ComputedBuffer) and isinstance(
self.node.data, ir.SplitScan
)
def is_template(self) -> bool:
return isinstance(self.node, ir.TemplateBuffer)
def get_template_node(self) -> Optional[ir.TemplateBuffer]:
return self.node if isinstance(self.node, ir.TemplateBuffer) else None
def run(self, *index_vars: Sequence[sympy.Expr]) -> None:
self.decide_inplace_update()
self.mark_run()
self.codegen(index_vars)
def ranges_from_index_vars(
self, index_vars: Sequence[Sequence[sympy.Expr]]
) -> Dict[sympy.Expr, sympy.Expr]:
sizes = self._sizes
assert sum(map(len, sizes)) == sum(map(len, index_vars))
var_ranges = dict(
zip(
itertools.chain.from_iterable(index_vars),
itertools.chain.from_iterable(sizes),
)
)
return var_ranges
def codegen(self, index_vars: Sequence[Sequence[sympy.Expr]]) -> None:
var_ranges = self.ranges_from_index_vars(index_vars)
try:
with V.set_ops_handler(
SimplifyIndexing(V.get_ops_handler(), var_ranges)
), V.kernel.set_current_node(self):
self._body(*index_vars)
except Exception:
log.fatal("Error in codegen for %s", self.node)
raise
def pointwise_read_writes(self) -> dependencies.ReadWrites:
"""
Get the memory dependencies in the non-reduction axis.
"""
sizes, reduction_sizes = self._sizes
def fn(index: Sequence[sympy.Symbol]) -> str:
return self._body(index, [sympy.Integer(0) for _ in reduction_sizes])
return dependencies.extract_read_writes(fn, sizes)
def can_inplace(self, read_dep: dependencies.Dep) -> bool:
if self.is_template():
return False
if any(out.get_aliases() for out in self.get_outputs()):
return False
if len(self.read_writes.writes) == 1 and isinstance(
read_dep, dependencies.MemoryDep
):
write_dep = next(iter(self.read_writes.writes))
assert isinstance(write_dep, dependencies.MemoryDep), f"{type(write_dep)=}"
return read_dep.index == write_dep.index and read_dep.size == write_dep.size
return False
@cache_on_self
def _get_atomic_add_buffers(self) -> OrderedSet[str]:
buffers_store_as_atomic_add: OrderedSet[str] = OrderedSet()
if isinstance(self._body, ir.LoopBody):
for node in self._body.get_nodes():
if (
node.op == "call_method"
and node.target == "store"
and (
("mode" in node.kwargs and node.kwargs["mode"] == "atomic_add")
or (len(node.args) == 5 and node.args[4] == "atomic_add")
)
):
buffers_store_as_atomic_add.add(
node.kwargs["name"]
if "name" in node.kwargs
else (node.args[1] if len(node.args) >= 2 else "")
)
return buffers_store_as_atomic_add
def init_group_node(
group_snode: BaseSchedulerNode,
scheduler: Scheduler,
snodes: List[BaseSchedulerNode],
) -> None:
assert isinstance(group_snode, (FusedSchedulerNode, GroupedSchedulerNode))
group_snode.snodes = snodes
group_snode.scheduler = scheduler
group_snode.node = None
group_snode.ancestors = OrderedSet.union(
*[x.ancestors for x in snodes if x.ancestors is not None]
)
group_snode.set_read_writes(
dependencies.ReadWrites.merge_list([x.read_writes for x in snodes])
)
group_snode.unmet_dependencies = (
OrderedSet(
dep
for dep in OrderedSet.union(*[x.unmet_dependencies for x in snodes])
if dep.name not in group_snode.get_buffer_names()
)
- group_snode.read_writes.writes
)
group_snode.min_order = min(x.min_order for x in group_snode.snodes)
group_snode.max_order = max(x.max_order for x in group_snode.snodes)
group_snode.outputs_by_name = {
buf.get_name(): buf for buf in group_snode.get_outputs()
}
class FusedSchedulerNode(BaseSchedulerNode):
"""
This is a "fake" scheduler node that represents a group of scheduler nodes
that are meant to be fused together. The way it does this is by maintaining
its unmet dependencies as the union of its constituent nodes.
"""
snodes: List[BaseSchedulerNode]
@classmethod
def fuse(
cls, node1: BaseSchedulerNode, node2: BaseSchedulerNode
) -> FusedSchedulerNode:
assert node1.scheduler is node2.scheduler
assert isinstance(node1, (SchedulerNode, FusedSchedulerNode))
assert isinstance(node2, (SchedulerNode, FusedSchedulerNode))
nodes = list(itertools.chain(node1.get_nodes(), node2.get_nodes()))
return cls(node1.scheduler, nodes)
def __init__(self, scheduler: Scheduler, snodes: List[BaseSchedulerNode]) -> None:
# NB: No need to call super().__init__() because we don't need to re-use any of its logic.
init_group_node(self, scheduler, snodes)
self.users: List[NodeUser] = []
self.group = max(snodes, key=lambda x: int(x.is_reduction())).group
@cache_on_self
def get_name(self) -> str:
return "_".join([x.get_name() for x in self.snodes])
def get_first_name(self) -> str:
return self.snodes[0].get_name()
@cache_on_self
def get_buffer_names(self) -> OrderedSet[str]:
return OrderedSet.union(*[x.get_buffer_names() for x in self.snodes])
def get_outputs(self) -> List[SchedulerBuffer]:
result: List[SchedulerBuffer] = []
for node in self.snodes:
result.extend(node.get_outputs())
return result
def debug_str_extra(self) -> str:
lines = [
f"{self.get_name()}.snodes[{i}] =\n{node.debug_str()}"
for i, node in enumerate(self.snodes)
]
node = self.snodes[0].node
assert node is not None
device = node.get_device()
if ir.is_triton(device):
lines.extend(debug_triton_code(self))
return textwrap.indent("\n".join(lines).rstrip(), " ")
def debug_str_short(self) -> str:
snodes_str = [node.debug_str_short() for node in self.snodes]
return f"{self}, snodes: {snodes_str}"
def set_last_usage(
self, future_used_buffers: OrderedSet[str], mutation_real_name: Dict[str, str]
) -> None:
# Set self.last_usage using the global information
# This will be used for inter-kernel optimisations
super().set_last_usage(future_used_buffers, mutation_real_name)
# Set self.last_usage on the snodes
# This will be used for optimisations within the kernel
future_used_buffers: OrderedSet[str] = OrderedSet()
for node in reversed(self.snodes):
node.set_last_usage(future_used_buffers, mutation_real_name)
future_used_buffers.update(node.last_usage)
@cache_on_self
def used_buffer_names(self) -> OrderedSet[str]:
return OrderedSet.union(*[x.used_buffer_names() for x in self.snodes])
@cache_on_self
def used_or_aliased_buffer_names(self) -> OrderedSet[str]:
return OrderedSet.union(
*[x.used_or_aliased_buffer_names() for x in self.snodes]
)
def get_nodes(self) -> Sequence[BaseSchedulerNode]:
return self.snodes
def __repr__(self) -> str:
return f"{type(self).__name__}(nodes={self.get_name()})"
@cache_on_self
def is_reduction(self) -> bool:
return any(x.is_reduction() for x in self.snodes)
@cache_on_self
def is_split_scan(self) -> bool:
return any(x.is_split_scan() for x in self.snodes)
@cache_on_self
def is_template(self) -> bool:
return any(x.is_template() for x in self.snodes)
@cache_on_self
def get_template_node(self) -> Optional[ir.TemplateBuffer]:
for node in self.snodes:
if node.is_template():
return node.get_template_node()
return None
def get_device(self) -> torch.device:
return self.group[0]
@cache_on_self
def has_aliasing_or_mutation(self) -> bool:
return any(x.has_aliasing_or_mutation() for x in self.snodes)
@cache_on_self
def op_counts(self) -> Counter[str]:
op_counts: Counter[str] = collections.Counter()
for node in self.snodes:
op_counts.update(node.op_counts())
return op_counts
# None of these need to be implemented, as a FusedSchedulerNode is just an
# abstraction for scheduling purposes
def update_mutated_names(self, renames: Dict[str, str]) -> None:
raise NotImplementedError
def add_fake_dep(self, name: Dep) -> None:
raise NotImplementedError
def can_inplace(self, read_dep: dependencies.Dep) -> bool:
raise NotImplementedError
def debug_str(self) -> str:
"""Longer form printout for trace logs"""
name = self.get_name()
node_typestr = ",".join(type(n).__name__ for n in self.snodes)
buf = IndentedBuffer()
buf.splice(
f"""\
{name}: {type(self).__name__}({node_typestr})
{name}.writes = {pformat(self.read_writes.writes)}
{name}.unmet_dependencies = {pformat(self.unmet_dependencies)}
{name}.met_dependencies = {pformat(self.read_writes.reads - self.unmet_dependencies)}
{name}.outputs = [
"""
)
with buf.indent():
for out in self.get_outputs():
buf.splice(out.debug_str())
buf.writeline("]")
try:
buf.splice(self.debug_str_extra())
except Exception:
log.warning("Ignoring error in debug_str()", exc_info=True)
return buf.getrawvalue().rstrip()
class ForeachKernelSchedulerNode(FusedSchedulerNode):
"""
This is a schedular node that consists of a set of scheduler nodes that
has no data dependencies among them and can be executed in parallel.
"""
def get_consumer_subnode_for(
self, producer: BaseSchedulerNode
) -> Optional[BaseSchedulerNode]:
for buf in producer.get_outputs():
if buf.get_name() in self.read_to_node:
return self.read_to_node[buf.get_name()]
return None
def get_producer_subnode_for(
self, consumer: BaseSchedulerNode
) -> Optional[BaseSchedulerNode]:
producers = set()
for rd in consumer.read_writes.reads:
if rd.name not in self.scheduler.name_to_buf:
continue
node_name = self.scheduler.name_to_buf[rd.name].defining_op.get_name()
if node_name in self.name_to_node:
producers.add(self.name_to_node[node_name])
# Don't permit fusion if there are multiple subnodes
# that this consumer reads from
if len(producers) == 1:
return next(iter(producers))
else:
return None
@classmethod
def can_fuse(cls, producer: BaseSchedulerNode, consumer: BaseSchedulerNode) -> bool:
why = WhyNoFuse(producer, consumer)
if producer.is_foreach() and consumer.is_foreach():
producer = typing.cast(ForeachKernelSchedulerNode, producer)
consumer = typing.cast(ForeachKernelSchedulerNode, consumer)
foreach_match = len(producer.snodes) == len(consumer.snodes)
if not foreach_match:
why("foreach do not have same length")
return foreach_match and all(
producer.scheduler.can_fuse(l, r)
for l, r in zip(producer.snodes, consumer.snodes)
)
elif consumer.is_foreach():
if producer.is_reduction():
why(
"candidate producer is a reduction, foreach ops cannot be fused with reductions currently"
)
return False
consumer = typing.cast(ForeachKernelSchedulerNode, consumer)
consumer_subnode = consumer.get_consumer_subnode_for(producer)
if consumer_subnode is not None:
return consumer.scheduler.can_fuse(producer, consumer_subnode)
why("candidate producer is not dep of any foreach consumer")
return False
elif producer.is_foreach():
if consumer.is_reduction():
why(
"candidate consumer is a reduction, foreach ops cannot be fused with reductions currently"
)
return False
producer = typing.cast(ForeachKernelSchedulerNode, producer)
producer_subnode = producer.get_producer_subnode_for(consumer)
if producer_subnode is not None:
return producer.scheduler.can_fuse(producer_subnode, consumer)
why("candidate consumer has no dep in any foreach producer")
return False
raise AssertionError(
"At least one node passed to ForeachKernelSchedulerNode.can_fuse should be a foreach node"
)
@classmethod
def fuse(
cls, producer: BaseSchedulerNode, consumer: BaseSchedulerNode
) -> ForeachKernelSchedulerNode:
assert producer.is_foreach() or consumer.is_foreach()
if producer.is_foreach():
producer = typing.cast(ForeachKernelSchedulerNode, producer)
use_custom_partition_algo = producer.use_custom_partition_algo
enable_autotune = producer.enable_autotune
else:
consumer = typing.cast(ForeachKernelSchedulerNode, consumer)
use_custom_partition_algo = consumer.use_custom_partition_algo
enable_autotune = consumer.enable_autotune
prev_node_1 = None
prev_node_2 = None
fused_nodes: List[BaseSchedulerNode]
if producer.is_foreach() and consumer.is_foreach():
producer = typing.cast(ForeachKernelSchedulerNode, producer)
consumer = typing.cast(ForeachKernelSchedulerNode, consumer)
fused_nodes = [
FusedSchedulerNode.fuse(l, r)
for l, r in zip(producer.snodes, consumer.snodes)
]
elif producer.is_foreach():
producer = typing.cast(ForeachKernelSchedulerNode, producer)
producer_subnode = producer.get_producer_subnode_for(consumer)
fused_nodes = []
prev_node_1 = producer
prev_node_2 = None
for node in producer.snodes:
if node is producer_subnode:
new_node = FusedSchedulerNode.fuse(node, consumer)
prev_node_2 = new_node
fused_nodes.append(new_node)
else:
fused_nodes.append(node)
elif consumer.is_foreach():
consumer = typing.cast(ForeachKernelSchedulerNode, consumer)
consumer_subnode = consumer.get_consumer_subnode_for(producer)
fused_nodes = []
prev_node_1 = consumer
prev_node_2 = None
for node in consumer.snodes:
if node is consumer_subnode:
new_node = FusedSchedulerNode.fuse(producer, node)
prev_node_2 = new_node
fused_nodes.append(new_node)
else:
fused_nodes.append(node)
else:
raise AssertionError(
"At least one node passed to ForeachKernelSchedulerNode.fuse should be a foreach node"
)
return cls(
producer.scheduler,
fused_nodes,
use_custom_partition_algo=use_custom_partition_algo,
prev_node_1=prev_node_1,
prev_node_2=prev_node_2,
enable_autotune=enable_autotune,
)
def __init__(
self,
scheduler: Scheduler,
snodes: List[BaseSchedulerNode],
use_custom_partition_algo: bool,
prev_node_1: Optional[BaseSchedulerNode] = None,
prev_node_2: Optional[BaseSchedulerNode] = None,
enable_autotune: bool = False,
) -> None:
self.read_to_node = {}
self.name_to_node = {}
if prev_node_1 is None or prev_node_2 is None:
super().__init__(scheduler, snodes)
for node in snodes:
for read in node.read_writes.reads:
self.read_to_node[read.name] = node
for name in node.get_operation_names():
self.name_to_node[name] = node
else:
self.scheduler = scheduler
self.snodes = snodes
self.node = None
self.users: List[NodeUser] = []
self.set_read_writes(
dependencies.ReadWrites.merge_list(
[prev_node_1.read_writes, prev_node_2.read_writes]
)
)
self.unmet_dependencies = (
OrderedSet(
dep
for dep in OrderedSet.union(
prev_node_1.unmet_dependencies, prev_node_2.unmet_dependencies
)
if dep.name not in self.get_buffer_names()
)
- self.read_writes.writes
)
self.min_order = min([prev_node_1.min_order, prev_node_2.min_order])
self.max_order = max([prev_node_1.max_order, prev_node_2.max_order])
if prev_node_1.is_foreach():
assert isinstance(prev_node_1, ForeachKernelSchedulerNode)
foreach_node, other_node = prev_node_1, prev_node_2
else:
assert isinstance(prev_node_2, ForeachKernelSchedulerNode)
foreach_node, other_node = prev_node_2, prev_node_1
self.ancestors = foreach_node.ancestors
self.ancestors.update(other_node.ancestors)
self.name_to_node = foreach_node.name_to_node
for name in other_node.get_operation_names():
self.name_to_node[name] = other_node
self.use_custom_partition_algo = use_custom_partition_algo
self.group = (snodes[0].get_device(), ((sympy.Expr("combo_kernel"),),))
self.origins: OrderedSet[torch.fx.Node] = OrderedSet()
self.enable_autotune = enable_autotune
def mark_run(self) -> None:
raise NotImplementedError
def codegen(self) -> None:
assert isinstance(self.node, ir.ComputedBuffer), f"{type(self.node)=}"
self.node.get_store_function()(self.node.make_loader()())
def is_foreach(self) -> bool:
return True
def get_subkernel_nodes(self) -> List[BaseSchedulerNode]:
"""Returns a list of nodes which comprise the foreach kernel, operating on corresponding elements of our input lists.
These nodes may be vertically fused."""
return list(self.snodes)
def get_nodes(self) -> Sequence[BaseSchedulerNode]:
"""Returns all nodes contained in this kernel, unpacking fused nodes
into their constituent scheduler nodes."""
return list(itertools.chain.from_iterable(x.get_nodes() for x in self.snodes))
def get_first_name(self) -> str:
return self.snodes[0].get_first_name()
def prune_redundant_deps(
self, name_to_fused_node: Dict[str, BaseSchedulerNode]
) -> None:
_prune_redundant_deps(self, name_to_fused_node, self.scheduler.name_to_buf)
for node in self.snodes:
node.prune_redundant_deps(name_to_fused_node)
class GroupedSchedulerNode(BaseSchedulerNode):
"""
This is a "fake" scheduler node that represents a group of scheduler nodes
that are meant to be *grouped* together (it does not allow another node to be scheduled
in between its constituent nodes, nor does it allow another node to fuse into any of its constituent nodes).
The way it does this is by maintaining its unmet dependencies as the union of its constituent nodes.
Fusion will still happen among the nodes within each GroupedSchedulerNode.
At codegen time, this scheduler node will be unpacked and codegen is called on each constituent node.
"""
snodes: List[BaseSchedulerNode]
@classmethod
def create(cls, snodes: List[BaseSchedulerNode]) -> GroupedSchedulerNode:
scheduler = snodes[0].scheduler
assert all(node.scheduler is scheduler for node in snodes)
grouped_snode = cls(scheduler, snodes) # type: ignore[arg-type]
for snode in snodes:
scheduler.name_to_fused_node[snode.get_name()] = grouped_snode
scheduler.name_to_fused_node[grouped_snode.get_name()] = grouped_snode
return grouped_snode
def __init__(self, scheduler: Scheduler, snodes: List[BaseSchedulerNode]) -> None:
# NB: No need to call super().__init__() because we don't need to re-use any of its logic.
init_group_node(self, scheduler, snodes)
def unpack(self) -> List[BaseSchedulerNode]:
"""
Do fusion among nodes within this GroupedSchedulerNode,
and then unpack this GroupedSchedulerNode into regular nodes.
"""
for snode in self.snodes:
self.scheduler.name_to_fused_node[snode.get_name()] = snode
del self.scheduler.name_to_fused_node[self.get_name()]
return self.scheduler.fuse_nodes(self.snodes)
def add_fake_dep(self, fake_dep: Dep) -> None:
self.set_read_writes(self.read_writes.with_read(fake_dep))
self.unmet_dependencies.add(fake_dep)
@cache_on_self
def get_name(self) -> str:
return "_".join([x.get_name() for x in self.snodes])
def get_first_name(self) -> str:
return self.snodes[0].get_name()
@cache_on_self
def get_buffer_names(self) -> OrderedSet[str]:
return OrderedSet.union(*[x.get_buffer_names() for x in self.snodes])
def get_outputs(self) -> List[SchedulerBuffer]:
result: List[SchedulerBuffer] = []
for node in self.snodes:
result.extend(node.get_outputs())
return result
def get_nodes(self) -> Sequence[BaseSchedulerNode]:
return self.snodes
@classmethod
def can_fuse(cls, producer: BaseSchedulerNode, consumer: BaseSchedulerNode) -> bool:
# GroupedSchedulerNode cannot be fused with another node
return False
def pick_loop_order(
stride_lengths: List[List[int]],
sizes: List[sympy.Expr],
priority_idx: Tuple[int, ...] = (),
) -> List[int]:
"""
A heuristic to decide loop iteration orders. This has not been well
tuned and may be something we should autotune.
"""
@functools.cmp_to_key
def index_cmp(a: int, b: int) -> int:
if sizes[a] == 1 or sizes[b] == 1:
# 1-sizes don't matter, just move them to the end
return cmp(sizes[a] == 1, sizes[b] == 1)
# Take abs, otherwise flipped dimensions are treated as smaller
# strides than contiguous dims
stride_len_a = [abs(sl[a]) for sl in stride_lengths]
stride_len_b = [abs(sl[b]) for sl in stride_lengths]
# equivalent to
# np.logical_or(stride_lengths[:, b] == 0, stride_lengths[:, a] < stride_lengths[:, b]).all()
a_first = sum(
sl_b == 0 or sl_a < sl_b for sl_a, sl_b in zip(stride_len_a, stride_len_b)
)
b_first = sum(
sl_a == 0 or sl_b < sl_a for sl_a, sl_b in zip(stride_len_a, stride_len_b)
)
if a_first > b_first:
return -1
if b_first > a_first:
return 1
# otherwise contiguous
return cmp(b, a)
order = list(reversed(range(len(stride_lengths[0]))))
if len(priority_idx) > 0:
# if we have priority node, only use that node's order
stride_lengths = [stride_lengths[pi] for pi in priority_idx]
if config.pick_loop_orders:
order.sort(key=index_cmp)
return order
@dataclasses.dataclass
class NodeUser:
node: Union[BaseSchedulerNode, OutputNode]
can_inplace: bool = False
# A weak user must be scheduled after a given node, but doesn't actually
# use the result
is_weak: bool = False
def __hash__(self) -> int:
return hash((self.node.get_name(), self.can_inplace, self.is_weak))
def __eq__(self, other: object) -> bool:
return (
isinstance(other, NodeUser)
and self.get_name() == other.get_name()
and self.can_inplace == other.can_inplace
and self.is_weak == other.is_weak
)
def get_name(self) -> str:
return self.node.get_name()
def merge(self, other: NodeUser) -> NodeUser:
assert self.node is other.node
return NodeUser(
self.node,
self.can_inplace and other.can_inplace,
self.is_weak and other.is_weak,
)
_post_grad_graph_counter = itertools.count()
class Scheduler:
__dep_size_hint_cache: Dict[Dep, int]
def __init__(self, nodes: List[ir.Operation]) -> None:
with dynamo_timed("Scheduler.__init__"):
self._init(nodes)
def _init(self, nodes: List[ir.Operation]) -> None:
super().__init__()
self.__dep_size_hint_cache = {}
V.graph.scheduler = self
self.backends: Dict[torch.device, BaseScheduling] = {}
self.post_grad_graph_id = next(_post_grad_graph_counter)
self.completed_operations: OrderedSet[str] = OrderedSet()
self.available_buffer_names = OrderedSet(
[
*V.graph.graph_inputs.keys(),
*V.graph.constants.keys(),
*V.graph.torchbind_constants.keys(),
]
)
self.nodes = [self.create_scheduler_node(n) for n in nodes]
# some new constants could have been created above
self.available_buffer_names.update(V.graph.constants.keys())
for node in self.nodes:
node.prune_deps()
self.name_to_node: Dict[str, BaseSchedulerNode] = {
n.get_name(): n for n in self.nodes
}
self.name_to_buf: Dict[str, SchedulerBuffer] = {
buf.get_name(): buf for node in self.nodes for buf in node.get_outputs()
}
self.name_to_fused_node: Dict[str, BaseSchedulerNode] = self.name_to_node.copy()
# mutation_real_name: Maps back to the original name for codegen
# Example:
# If you mutate buf0 inside of buf1's kernel, then:
# mutation_real_name = {"buf0" : "buf1"}
# all subsequent uses of buf0 become buf1's usage in dependency graph
self.mutation_real_name: Dict[str, str] = {}
# We handle mutation by renaming modified versions of the same
# buffer in the dependency graph to prevent cycles.
# mutation_renames: tracks the current name for a given buffer
# (changed once per mutation)
# Example:
# If you mutate buf0 inside of buf1's kernel, then:
# mutation_renames = {"buf1" : "buf0"}
# in codegen we only use buf0, never buf1
self.mutation_renames: Dict[str, str] = {}
self.compute_dependencies()
self.nodes = self.topological_sort_schedule(self.nodes)
self.dead_node_elimination()
self.name_to_fused_node = {n.get_name(): n for n in self.nodes}
self.compute_ancestors()
if config.reorder_for_compute_comm_overlap:
self.nodes = comms.decide_global_ordering_of_comms(
self.nodes,
self.name_to_buf,
self.name_to_fused_node,
)
metrics.ir_nodes_pre_fusion += len(self.nodes)
V.debug.ir_pre_fusion(self.nodes)
self.num_orig_nodes = len(self.nodes)
self.create_foreach_nodes()
self.nodes = self.topological_sort_schedule(self.nodes)
self.logged_slow_fusion: OrderedSet[Tuple[str, str]] = OrderedSet()
if config._pre_fusion_custom_pass is not None:
self.nodes = config._pre_fusion_custom_pass(self.nodes)
self.nodes = self.fuse_nodes(self.nodes)
self.finalize_multi_template_buffers()
if config.reorder_for_compute_comm_overlap:
self.nodes = comms.reorder_compute_and_comm_for_overlap(self.nodes)
self.process_grouped_nodes()
self.compute_last_usage()
V.debug.ir_post_fusion(self.nodes)
V.debug.graph_diagram(self.nodes)
self.debug_draw_graph()
# used during codegen:
self.current_device: Optional[torch.device] = None
self.buffer_names_to_free: OrderedSet[str] = OrderedSet()
# fx graph node to the position it appears in the graph
# for debug attribution
self.origin_to_index: Dict[torch.fx.Node, int] = {}
get_metric_table("graph_stats").add_row(
lambda: {
"graph_id": self.post_grad_graph_id,
"num_nodes_before_fusion": self.num_orig_nodes,
"num_nodes_after_fusion": len(self.nodes),
}
)
def get_current_device_or_throw(self) -> torch.device:
if device := self.current_device:
return device
else:
raise RuntimeError("No current device")
def debug_draw_graph(self) -> None:
"""Generate an image of the graph for debugging"""
if os.environ.get("INDUCTOR_WRITE_SCHEDULER_GRAPH", None) == "1":
from .debug import draw_buffers
draw_buffers(self.nodes, print_graph=True)
def debug_print_nodes(self, label: str) -> None:
if log.isEnabledFor(logging.INFO):
log.info("%s:", label)
for node in self.nodes:
node.log_details()
def create_scheduler_node(self, node: ir.Operation) -> BaseSchedulerNode:
assert (
node.get_origins() is not None
), "All nodes passed to scheduling must have an origin"
if node.is_no_op():
return NopKernelSchedulerNode(self, node)
elif isinstance(node, (ir.ComputedBuffer, ir.TemplateBuffer)):
return SchedulerNode(self, node)
elif isinstance(node, ir.ExternKernel):
return ExternKernelSchedulerNode(self, node)
else:
raise NotImplementedError(node)
def create_foreach_nodes(self) -> None:
removed_node_names: OrderedSet[str] = OrderedSet()
fe_nodes = []
kept_node_names = self.name_to_fused_node.keys()
for names in V.graph.lists.values():
names = [
name
for name in names
if name in kept_node_names
and not isinstance(self.name_to_node[name], NopKernelSchedulerNode)
]
if not names:
# All nodes eliminated
continue
removed_node_names.update(names)
snodes = [self.name_to_node[name] for name in names]
enable_autotune = config.combo_kernels_autotune > 1
fe_node = ForeachKernelSchedulerNode(
self,
snodes,
use_custom_partition_algo=False,
enable_autotune=enable_autotune,
)
fe_nodes.append(fe_node)
for name in names:
self.name_to_fused_node[name] = fe_node
self.nodes = [
node for node in self.nodes if node.get_name() not in removed_node_names
] + list(fe_nodes)
def compute_dependencies(self) -> None:
"""
Create dependency edges between nodes, handling aliasing and
mutation properly.
"""
T = TypeVar("T")
class DedupList(Generic[T]):
"""
This data structure behaves like a list except it makes sure the
elements remain unique.
Normally one could use a OrderedSet/dict for this purpose however
the list in question gets elements appended as it is being
iterated over which means that we need to keep the list
semantics.
"""
def __init__(
self,
items: Optional[List[T]] = None,
membership: Optional[OrderedSet[T]] = None,
) -> None:
self.items = items or []
self.membership = membership or OrderedSet()
def append(self, node_user: T) -> None:
if node_user in self.membership:
return
self.items.append(node_user)
self.membership.add(node_user)
def __add__(self, other: DedupList[T]) -> DedupList[T]:
new_membership = OrderedSet.union(self.membership, other.membership)
new_items = self.items + [
x for x in other.items if x not in self.membership
]
return DedupList(new_items, new_membership)
name_to_users: DefaultDict[str, DedupList[NodeUser]] = collections.defaultdict(
DedupList
)
# handle aliasing by using python aliasing in name_to_users
# if foo aliases bar then we will make name_to_users["foo"] point
# to the same python list as name_to_users["bar"]
for node in self.nodes:
for buf1 in node.get_outputs():
buf1_name = buf1.get_name()
for buf2_name in buf1.get_aliases():
if buf1_name in name_to_users and buf2_name in name_to_users:
# merge the two
list1 = name_to_users[buf1_name]
list2 = name_to_users[buf2_name]
combined = list1 + list2
for key in name_to_users.keys():
if (
name_to_users[key] is list1
or name_to_users[key] is list2
):
name_to_users[key] = combined
elif buf1_name in name_to_users:
name_to_users[buf2_name] = name_to_users[buf1_name]
else:
name_to_users[buf1_name] = name_to_users[buf2_name]
def rename(n: str) -> str:
if n in self.mutation_renames:
return rename(self.mutation_renames[n])
return n
def add_user(
used_by_name: str,
user_node: Union[BaseSchedulerNode, OutputNode],
can_inplace: bool = False,
is_weak: bool = False,
) -> None:
name_to_users[rename(used_by_name)].append(
NodeUser(user_node, can_inplace, is_weak)
)
unbacked_symbol_to_origin_node: Dict[sympy.Symbol, Optional[str]] = {}
# NB: None means that the dependency is on an input. Don't actually
# generate a dependency because if we do, Inductor will start trying
# to free the unbacked int but that's pointless
for name, val in V.graph.graph_inputs.items():
if isinstance(val, sympy.Expr):
for fs in val.free_symbols:
unbacked_symbol_to_origin_node[fs] = None
for node in self.nodes:
log.debug("scheduling %s", node.node)
# unbacked symbols don't follow ordinary buffer dependencies, so
# we track their def/uses separately
assert node.node is not None
unbacked_symbol_defs = sorted(
node.node.get_unbacked_symbol_defs(), key=lambda x: x.name
)
for s in unbacked_symbol_defs:
assert isinstance(s, sympy.Symbol)
# Pick the first definer as canonical. There may be multiple
# because if a MultiOutputLayout buffer propagates an unbacked
# symint to multiple outputs, they will all claim to def it.
if s not in unbacked_symbol_to_origin_node:
unbacked_symbol_to_origin_node[s] = node.get_name()
unbacked_symbol_uses = sorted(
node.node.get_unbacked_symbol_uses(), key=lambda x: x.name
)
# if a kernel takes unbacked symints, register dependencies
for s in unbacked_symbol_uses:
assert (
s in unbacked_symbol_to_origin_node
), f"{s} not in {unbacked_symbol_to_origin_node}"
if (r := unbacked_symbol_to_origin_node[s]) is not None:
for buf in self.name_to_node[r].get_outputs():
node.add_fake_dep(StarDep(buf.get_name()))
if (
len(node.read_writes.writes) == 1
and (dep := next(iter(node.read_writes.writes)))
and isinstance(dep, MemoryDep)
):
node_mode = dep.mode
else:
node_mode = None
# Handle output mutations
for buf in node.get_outputs():
# a node will mutate either 0 or 1 buffers
assert len(buf.get_mutations()) <= 1
for alt_name in buf.get_mutations():
alt_name = rename(alt_name)
# this node must run after the prior writer
add_user(alt_name, node)
node.add_fake_dep(StarDep(alt_name, mode=node_mode))
for user in name_to_users[alt_name].items:
if user.get_name() == node.get_name():
continue
assert isinstance(user.node, BaseSchedulerNode)
for other_name in user.node.get_buffer_names():
# this node must run after all prior readers
other_name = rename(other_name)
node.add_fake_dep(
WeakDep(other_name, mutating_buf=buf.get_name())
)
add_user(other_name, node, is_weak=True)
# add normal non-mutation dependencies
for read in node.read_writes.reads:
if not isinstance(read, WeakDep):
add_user(read.name, node, node.can_inplace(read))
node.update_mutated_names(self.mutation_renames)
# update our renaming scheme for the next iteration
for buf in node.get_outputs():
for alt_name in buf.get_mutations():
self.mutation_renames[rename(alt_name)] = buf.get_name()
self.mutation_renames[alt_name] = buf.get_name()
self.mutation_real_name[
buf.get_name()
] = self.mutation_real_name.get(alt_name, alt_name)
# make sure outputs aren't dead-code-eliminated
for buf_name in V.graph.get_output_names():
log.debug("scheduling output %s", buf_name)
add_user(buf_name, OutputNode(StarDep(buf_name)))
# make sure unbacked symints aren't dead-code-eliminated
for out in V.graph.graph_outputs:
for s in out.get_unbacked_symbol_uses():
assert (
s in unbacked_symbol_to_origin_node
), f"{s} not in {unbacked_symbol_to_origin_node.keys()}"
if r := unbacked_symbol_to_origin_node[s]:
for buf_name in self.name_to_node[r].get_buffer_names():
log.debug(
"scheduling output %s for unbacked symint %s", buf_name, s
)
add_user(buf_name, OutputNode(StarDep(buf_name)))
# make sure input mutation isn't dead-code-eliminated
for name in self.mutation_renames:
if name in V.graph.graph_inputs:
add_user(name, OutputNode(StarDep(name)))
V.graph.mutated_inputs.add(name)
elif name in V.graph.constants:
# In AOTI, module parameters and buffers are not lifted as graph inputs
add_user(name, OutputNode(StarDep(name)))
inp_names = {
name: index for index, name in enumerate(V.graph.graph_inputs.keys())
}
V.graph.mutated_input_idxs = [
inp_names[name] for name in V.graph.mutated_inputs
]
# copy users information onto the nodes
for node in self.nodes:
for buf in node.get_outputs():
buf.set_users(name_to_users[buf.get_name()].items)
def dead_node_elimination(self) -> None:
"""
Remove any nodes without users
"""
# self.nodes is in topological order, so by iterating in reverse order
# we have visited (and potentially removed) all users before visiting a
# given node.
updated_nodes = []
for node in reversed(self.nodes):
def can_eliminate_user(user: NodeUser) -> bool:
return user.is_weak or user.get_name() in V.graph.removed_operations
active_buffers = False
for buf in node.get_outputs():
can_eliminate = all(can_eliminate_user(u) for u in buf.users)
if can_eliminate:
log.debug("removed dead buffer: %s", buf.get_name())
V.graph.removed_buffers.add(buf.get_name())
else:
active_buffers = True
can_eliminate = not node.has_side_effects() and not active_buffers
if not can_eliminate:
updated_nodes.append(node)
else:
# dead code
log.debug("removed dead operation: %s", node.get_name())
V.graph.removed_operations.add(node.get_name())
self.nodes = list(reversed(updated_nodes))
# Prune any WeakDeps no longer needed
for node in self.nodes:
node.prune_weak_deps()
def topological_sort_schedule(
self, nodes: List[BaseSchedulerNode]
) -> List[BaseSchedulerNode]:
"""
Ensure nodes is in topologically sorted order
"""
seen: OrderedSet[BaseSchedulerNode] = OrderedSet()
name_to_node: Dict[str, BaseSchedulerNode] = dict()
result: List[BaseSchedulerNode] = []
def visit(n: BaseSchedulerNode) -> None:
if n not in seen:
seen.add(n)
for dep in sorted(n.unmet_dependencies, key=lambda d: d.name):
# We only care about doing toposort within `nodes`
if dep.name not in name_to_node:
continue
visit(name_to_node[dep.name])
result.append(n)
for node in nodes:
for name in node.get_buffer_names():
name_to_node[name] = node
for node in nodes:
visit(node)
return result
def compute_ancestors(self) -> None:
"""
Populate each node.ancestors
"""
# note self.nodes is topologically sorted
name_to_ancestors: Dict[str, OrderedSet[str]] = {}
for node in self.nodes:
ancestors: OrderedSet[str] = OrderedSet()
for dep in node.unmet_dependencies:
dep_node_name = self.name_to_buf[dep.name].defining_op.get_name()
ancestors.add(dep_node_name)
ancestors |= name_to_ancestors[dep_node_name]
name_to_ancestors[node.get_name()] = ancestors
node.ancestors = ancestors
for order, node in enumerate(self.nodes):
node.min_order = order
node.max_order = order
def fuse_nodes(self, nodes: List[BaseSchedulerNode]) -> List[BaseSchedulerNode]:
"""
Combine eligible nodes into FusedSchedulerNodes.
"""
for i in range(10):
old_len = len(nodes)
fusion_log.debug(
"===== attempting fusion (%d/10): %d nodes =====",
i + 1,
old_len,
)
nodes = self.fuse_nodes_once(nodes)
new_len = len(nodes)
fusion_log.debug(
"completed fusion round (%d/10): fused %d nodes into %d nodes\n",
i + 1,
old_len,
new_len,
)
if new_len == old_len or new_len == 1:
fusion_log.debug("===== fusion complete (%d iterations) =====", i + 1)
break
return nodes
def process_grouped_nodes(self) -> None:
"""
Unpack GroupedSchedulerNode into regular nodes.
"""
new_nodes: List[BaseSchedulerNode] = []
for node in self.nodes:
new_nodes.extend(
node.unpack() if isinstance(node, GroupedSchedulerNode) else [node]
)
self.nodes = new_nodes
def benchmark_fused_nodes(
self, nodes: Sequence[BaseSchedulerNode]
) -> Tuple[float, str]:
"""
Benchmark fused list of nodes and return the execution time
in milliseconds on randomly generated inputs.
"""
assert len(nodes) > 0
device = nodes[0].get_device()
self.current_device = device
backend = self.get_backend(device)
return backend.benchmark_fused_nodes(nodes)
def finalize_multi_template_buffers(self) -> None:
def replace_operation_buffer(
orig_node: ir.MultiTemplateBuffer, new_node: ir.OperationBuffer
) -> None:
replaced_buf_name = new_node.get_name()
orig_buf_name = orig_node.get_name()
assert isinstance(orig_buf_name, str) and isinstance(replaced_buf_name, str)
replaced_op_name = new_node.get_operation_name()
orig_op_name = orig_node.get_operation_name()
assert isinstance(orig_op_name, str) and isinstance(replaced_op_name, str)
del V.graph.name_to_buffer[replaced_buf_name]
new_node.name = orig_buf_name
del V.graph.name_to_op[replaced_op_name]
new_node.operation_name = orig_op_name
orig = V.graph.buffers.index(orig_node)
V.graph.buffers.remove(new_node)
V.graph.buffers[orig] = new_node
V.graph.name_to_buffer[orig_buf_name] = new_node
orig = V.graph.operations.index(orig_node)
V.graph.operations.remove(new_node)
V.graph.operations[orig] = new_node
V.graph.name_to_op[orig_op_name] = new_node
for i, node in enumerate(self.nodes):
if isinstance(node, SchedulerNode) and isinstance(
node.node, ir.MultiTemplateBuffer
):
multi_node = node.node
min_node_unfused, _ = multi_node.get_min_choice()
if isinstance(
min_node_unfused,
torch._inductor.ir.TritonTemplateCallerBase,
):
node.node.finalize_as_triton_caller(min_node_unfused)
continue
out_tensorbox = min_node_unfused.output_node()
out_storage = out_tensorbox.data
assert isinstance(out_storage, ir.StorageBox)
out_buffer = out_storage.data
assert isinstance(out_buffer, ir.OperationBuffer)
out_buffer.layout = multi_node.layout
replace_operation_buffer(multi_node, out_buffer)
new_scheduler_node = self.create_scheduler_node(out_buffer)
self.nodes[i] = new_scheduler_node
self.name_to_node[node.get_name()] = new_scheduler_node
self.name_to_fused_node[node.get_name()] = new_scheduler_node
for new_out, old_out in zip(
new_scheduler_node.get_outputs(), node.get_outputs()
):
self.name_to_buf[old_out.get_name()] = new_out
new_out.users = old_out.users
new_scheduler_node.min_order = node.min_order
new_scheduler_node.max_order = node.max_order
new_scheduler_node.last_usage = node.last_usage
def speedup_by_fusion(
self, node1: BaseSchedulerNode, node2: BaseSchedulerNode
) -> bool:
"""
If config.benchmark_fusion is False, always return True.
Otherwise, return True if fusion can brings speedup.
"""
is_multi_template = node1.is_template() and isinstance(
node1.get_template_node(), ir.MultiTemplateBuffer
)
if not config.benchmark_fusion and not is_multi_template:
return True
if (
node1.is_template()
and not isinstance(node1.get_template_node(), ir.TritonTemplateBuffer)
or node1.is_foreach()
or node2.is_foreach()
):
# TODO support benchmarking epilogue fusion
return True
node_list_1 = node1.get_nodes()
device = node_list_1[0].get_device()
# don't support benchmark fusion for CPU right now.
if device.type == "cpu":
return True
node_list_2 = node2.get_nodes()
node_list_fused = list(itertools.chain(node_list_1, node_list_2))
# We can not accurately benchmark kernel using atomic_add
# due to how we generate random integer inputs.
# Skip benchmarking them by allowing fusion.
if any(
hasattr(n.node, "data")
and n.node is not None
and hasattr(n.node.data, "scatter_mode")
and n.node.data.scatter_mode == "atomic_add"
for n in node_list_fused
):
return True
from triton.compiler.errors import CompilationError
why = WhyNoFuse(node1, node2)
def log_fusion(ms_fused: float, ms1: float, ms2: float) -> None:
if fusion_log.isEnabledFor(logging.DEBUG):
if ms_fused < ms1 + ms2:
fusion_log.debug(
"can fuse (benchmark): fusing %s with %s cause %sx speedup",
node1.get_buffer_names(),
node2.get_buffer_names(),
green_text(f"{(ms1 + ms2) / ms_fused:.3f}"),
)
else:
fusion_log.debug(
"cannot fuse (benchmark): fusing %s with %s cause %sx slowdown",
node1.get_buffer_names(),
node2.get_buffer_names(),
red_text(f"{ms_fused / (ms1 + ms2):.3f}"),
)
if isinstance(node1, SchedulerNode) and isinstance(
node1.node, ir.MultiTemplateBuffer
):
multi_node = node1.node
choice_timings = multi_node.choice_timings
_, ms1 = multi_node.get_min_choice()
ms2, path2 = self.benchmark_fused_nodes(node_list_2)
min_ms_fused = float("inf")
ms_fused_choice = None
triton_choices = 0
for choice, unfused_time in sorted(
choice_timings.items(), key=lambda x: x[1]
):
if not isinstance(choice, torch._inductor.ir.TritonTemplateCallerBase):
continue
if unfused_time >= ms1 + ms2:
break
triton_choices += 1
if triton_choices > config.max_epilogue_benchmarked_choices:
break
# TODO - parallel compile triton templates
# TODO - should prune/skip choices that are not within certain % of best choice
with node1.node.swap_as_triton_caller(choice):
ms_fused, _ = self.benchmark_fused_nodes(node_list_fused)
if ms_fused < min_ms_fused:
min_ms_fused = ms_fused
ms_fused_choice = choice
log_fusion(min_ms_fused, ms1, ms2)
# after we do a fusion, we finalize a triton template.
# TODO - could preserve multi template and choices for subsequent fusions
if min_ms_fused < (ms1 + ms2) and ms_fused_choice is not None:
node1.node.finalize_as_triton_caller(ms_fused_choice)
return True
else:
return False
else:
try:
ms1, path1 = self.benchmark_fused_nodes(node_list_1)
if math.isinf(ms1):
why("register spilling of the first kernel")
return False
ms2, path2 = self.benchmark_fused_nodes(node_list_2)
if math.isinf(ms2):
why("register spilling of the second kernel")
return False
ms_fused, path_fused = self.benchmark_fused_nodes(node_list_fused)
if math.isinf(ms_fused):
why("register spilling of the fused kernel")
return False
except CompilationError as e:
# workaround triton issue: https://github.com/openai/triton/issues/2151
if "Loop-carried variable" in str(e):
return True # allow fusion
else:
raise
log_fusion(ms_fused, ms1, ms2)
if (
is_metric_table_enabled("slow_fusion")
and ms_fused >= ms1 + ms2
and (path1, path2) not in self.logged_slow_fusion
):
self.logged_slow_fusion.add((path1, path2))
get_metric_table("slow_fusion").add_row(
lambda: {
"kernel1_path": path1,
"kernel1_latency": ms1,
"kernel2_path": path2,
"kernel2_latency": ms2,
"fused_kernel_path": path_fused,
"fused_kernel_latency": ms_fused,
"slow_down_ratio": ms_fused / (ms1 + ms2),
}
)
return ms_fused < ms1 + ms2
def fuse_nodes_once(
self, nodes: List[BaseSchedulerNode]
) -> List[BaseSchedulerNode]:
"""
Combine eligible nodes into FusedSchedulerNodes.
This relies on two key functions to control the logic:
- self.can_fuse(): checks if a fusion is legal
- self.score_fusion(): assigns priority to a given fusion
"""
fused_nodes = OrderedSet(nodes)
if fusion_log.isEnabledFor(logging.DEBUG):
fusion_log.debug("fuse_nodes_once, candidates:")
for node in fused_nodes:
fusion_log.debug(" " + node.debug_str_short()) # noqa: G003
for node1, node2 in self.get_possible_fusions(nodes):
node1 = self.name_to_fused_node[node1.get_first_name()]
node2 = self.name_to_fused_node[node2.get_first_name()]
if self.can_fuse(node1, node2) and not self.will_fusion_create_cycle(
node1, node2
):
if not self.speedup_by_fusion(node1, node2):
continue
fusion_log.debug(
"fusing %s with %s", node1.get_name(), node2.get_name()
)
# above can_fuse asserts that node2 has the same device
device = node1.get_device()
node3 = self.get_backend(device).fuse(node1, node2)
fused_nodes.remove(node1)
fused_nodes.remove(node2)
fused_nodes.add(node3)
self.name_to_fused_node.update(
{n.get_name(): node3 for n in node3.get_nodes()}
)
nodes = sorted(fused_nodes, key=lambda x: x.min_order)
nodes = self.topological_sort_schedule(nodes)
self.prune_redundant_deps(nodes)
return nodes
def prune_redundant_deps(self, nodes: List[BaseSchedulerNode]) -> None:
for node in nodes:
node.prune_redundant_deps(self.name_to_fused_node)
def get_possible_fusions(
self, nodes: List[BaseSchedulerNode]
) -> List[Tuple[BaseSchedulerNode, BaseSchedulerNode]]:
"""
Helper to find all legal fusion opportunities, sorted by self.score_fusion()
"""
possible_fusions = []
seen: OrderedSet[Tuple[BaseSchedulerNode, BaseSchedulerNode]] = OrderedSet()
def check_all_pairs(nodes: List[BaseSchedulerNode]) -> None:
for node1_index, node1 in enumerate(nodes):
for node2 in nodes[node1_index + 1 :]:
key = (node1, node2)
if key in seen:
continue
seen.add(key)
if self.can_fuse(node1, node2):
possible_fusions.append(key)
elif (node2.is_template() or node2.is_foreach()) and self.can_fuse(
node2, node1
):
# foreach fusions and epilogue fusions are order dependent
possible_fusions.append((node2, node1))
buffer_names_grouping = collections.defaultdict(list)
for node in nodes:
for buf in node.used_buffer_names():
buffer_names_grouping[buf].append(node)
for node_grouping in buffer_names_grouping.values():
check_all_pairs(node_grouping)
if config.aggressive_fusion:
group_grouping = collections.defaultdict(list)
for node in nodes:
group = getattr(node, "group", None)
if group:
group_grouping[group].append(node)
for node_grouping in group_grouping.values():
check_all_pairs(node_grouping)
possible_fusions = self.get_possible_fusions_with_highest_priority(
possible_fusions
)
possible_fusions.sort(key=self.score_fusion_key, reverse=True)
fusion_log.debug("found %d possible fusions", len(possible_fusions))
return possible_fusions
def will_fusion_create_cycle(
self, node1: BaseSchedulerNode, node2: BaseSchedulerNode
) -> bool:
"""
Finds whether there's a path from node1 to node2 (or vice-versa)
caused indirectly by other fusions.
"""
# since we are just returning boolean here, use slightly faster, unordered set
visited: Set[FusedSchedulerNode] = set()
def found_path(node: BaseSchedulerNode) -> bool:
# only fused nodes can introduce new ancestors.
if isinstance(node, FusedSchedulerNode) and node not in visited:
visited.add(node)
if node.get_operation_names().issubset(combined_ancestors):
# All fusion outputs are in ancestors of node1 and node2, thus
# cannot introduce new path:
#
# 1. if output is neither descendent of node1 or node2, the
# output cannot introduce a path
# 2. due to [can_fuse]: if WLOG output is descendent of node1, it cannot be
# on path(node1->node2), hence it cannot be ancestor of node2
# 3. due to [acyclic]: if WLOG output is descendent of node1, it cannot be
# ancestor of node1
return False
else:
# continue DFS of new ancestors introduced by the fusion
return bool(combined_names & node.ancestors) or any(
found_path(self.name_to_fused_node[n])
for n in node.ancestors - combined_ancestors
)
return False
# as above - use slightly faster, unordered set
combined_names = (
node1.get_operation_names()._dict.keys()
| node2.get_operation_names()._dict.keys()
)
combined_ancestors = (
node1.ancestors._dict.keys() | node2.ancestors._dict.keys()
) - combined_names
cycle = any(found_path(self.name_to_fused_node[n]) for n in combined_ancestors)
if cycle:
WhyNoFuse(node1, node2)("will create cycle")
return cycle
def can_fusion_increase_peak_memory(
self, node1: BaseSchedulerNode, node2: BaseSchedulerNode
) -> bool:
"""
This function prevents fusion for nodes that can increase memory
footprint. This problem is more common in horizontal fusion, where nodes
that are far apart in the original order get fused, lengthening the live
intervals of tensors. This is very evident in models with activation
checkpointing, where the recomputed nodes from different checkpointed
regions get fused and significantly increase the memory footprint.
The current attempt is a quick, possibly hacky, heuristic to prevent the
fusion of nodes that are far away in the original order.
A better but difficult to implement heurisitic would be to use live
intervals of the buffers, find region of peak pressure in the original
program and prevent fusion that crosses that peak region. We might need
special care or good approximation in this implementation, as fusion of
node changes live intervals, and re-computing live intervals and peak
memory after each fusion can introduce large compilation overhead.
"""
proximity_score = max(
abs(node1.min_order - node2.max_order),
abs(node2.min_order - node1.max_order),
)
return proximity_score > 64
def decide_fusion_fail_reason(
self,
node1: BaseSchedulerNode,
node2: BaseSchedulerNode,
common_buf_names: Tuple[str, ...],
) -> str:
"""
Try to decide reasons why fusion fail due to no shared memory even though
there are common buffers.
"""
reasons = {}
node1_name2dep = {dep.name: dep for dep in node1.read_writes.reads_and_writes()}
node2_name2dep = {dep.name: dep for dep in node2.read_writes.reads_and_writes()}
for buf_name in common_buf_names:
buf = V.graph.get_buffer(buf_name)
lhs_dep = node1_name2dep[buf_name]
rhs_dep = node2_name2dep[buf_name]
if lhs_dep.get_numel() != rhs_dep.get_numel():
reasons[
buf_name
] = f"different numel: {lhs_dep.get_numel()} v.s. {rhs_dep.get_numel()}"
continue
# same numel but different MemoryDep.size. Should be broadcasting
if sympy_product(lhs_dep.size) != sympy_product(rhs_dep.size):
reasons[buf_name] = "broadcast"
continue
if not isinstance(lhs_dep, MemoryDep) or not isinstance(rhs_dep, MemoryDep):
reasons[
buf_name
] = f"not MemoryDep: {type(lhs_dep)} v.s. {type(rhs_dep)}"
continue
lhs_off = lhs_dep.get_offset()
rhs_off = rhs_dep.get_offset()
if lhs_off != rhs_off:
# One example is in transformer, we use a concatenated linear layer
# to project Q/K/V and then split the result. The 3 splits will
# point to the same buffer with different offsets.
reasons[buf_name] = f"different offset: {lhs_off} v.s. {rhs_off}"
continue
if (
lhs_dep.normalize_with_stride_order()
== rhs_dep.normalize_with_stride_order()
):
reasons[buf_name] = f"Mismatch loop orders: {lhs_dep} v.s. {rhs_dep}"
continue
# Add more rules here
reasons[
buf_name
] = f"Unknown reason: {lhs_dep} v.s. {rhs_dep}. Layout: {buf.layout}"
return str(reasons)
def can_fuse(self, node1: BaseSchedulerNode, node2: BaseSchedulerNode) -> bool:
"""
Determine if it is possible to combine node1 and node2 into a
single fused node.
"""
if node1 is node2:
return False
why = WhyNoFuse(node1, node2)
if isinstance(node1, GroupedSchedulerNode) or isinstance(
node2, GroupedSchedulerNode
):
why("grouped node must not be fused with other nodes")
return False
if (
isinstance(node1, (ExternKernelSchedulerNode, NopKernelSchedulerNode))
and not node1.is_template()
):
why("node1 is extern or nop")
return False
if (
isinstance(node2, (ExternKernelSchedulerNode, NopKernelSchedulerNode))
and not node2.is_template()
):
why("node2 is extern or nop")
return False
if node2.get_operation_names() & node1.ancestors:
why("node1 must go before node2")
return False
if node2.is_template():
why("templates can only fuse epilogues")
return False
if node1.is_template() and (
node2.has_aliasing_or_mutation()
or node2.is_reduction()
or not config.epilogue_fusion
):
why("template epilogue not satisfied")
return False
if (
node1.get_buffer_names() | node2.get_buffer_names()
) & V.graph.no_fuse_buffer_names:
why("fusion for buffer explicit disabled")
return False
device = node1.get_device()
device2 = node2.get_device()
if device != device2:
why("device mismatch (%s vs %s)", device, device2)
return False
del device2
no_shared_data = self.score_fusion_memory(node1, node2) == 0
if no_shared_data and (
not config.aggressive_fusion or node1.is_reduction() or node2.is_reduction()
):
if is_metric_table_enabled("fusion_failure_due_to_indexing_mismatch"):
common_buf_names = (
node1.read_writes.buffer_names() & node2.read_writes.buffer_names()
)
if len(common_buf_names) > 0:
get_metric_table("fusion_failure_due_to_indexing_mismatch").add_row(
lambda: {
"pre_grad_graph_id": V.graph.graph_id,
"post_grad_graph_id": V.graph.post_grad_graph_id,
"node1_name": node1.get_name(),
"node2_name": node2.get_name(),
"node1_debug_str": write_text(node1.debug_str()),
"node2_debug_str": write_text(node2.debug_str()),
"common_buffer_names": list(common_buf_names),
"failure_reason": self.decide_fusion_fail_reason(
node1, node2, common_buf_names
),
}
)
why("no shared data due to indexing mismatch")
return False
why("no shared data")
return False # heuristic not needed for correctness
if (
not node1.is_foreach()
and not node2.is_foreach()
and len(node1.get_nodes()) + len(node2.get_nodes()) > config.max_fusion_size
):
why("exceeds max fusion")
return False # heuristic not needed for correctness
if node1.get_operation_names() & node2.ancestors:
# node2 depends on node1 outputs
if not self.can_fuse_vertical(node1, node2):
return False
return self.get_backend(device).can_fuse_vertical(node1, node2)
else: # nodes don't depend on each other, but may have common reads
if self.can_fusion_increase_peak_memory(node1, node2):
why("will increase peak memory")
return False
return self.get_backend(device).can_fuse_horizontal(node1, node2)
def can_fuse_vertical(
self, node1: BaseSchedulerNode, node2: BaseSchedulerNode
) -> bool:
"""
Check if it is legal to fuse a consumer (node2) into a producer (node1).
We can fuse them if all the reads of node2 either match
corresponding writes in node1, or are written by nodes that can
be scheduled before the fusion of node1 and node2.
"""
node1_buf_names = node1.get_buffer_names()
node1_op_names = node1.get_operation_names()
computed_deps: OrderedSet[Dep] = OrderedSet()
why = WhyNoFuse(node1, node2)
for cd in node1.read_writes.writes:
if not isinstance(cd, MemoryDep):
continue
for rd in node2.unmet_dependencies:
if self.fusable_read_and_write(rd, cd):
computed_deps.add(rd)
for dep in node2.unmet_dependencies:
if isinstance(dep, WeakDep) and self.fusable_weak_dep(dep, node1, node2):
computed_deps.add(dep)
remaining_deps = OrderedSet(
dep.name for dep in node2.unmet_dependencies - computed_deps
)
if remaining_deps & node1_buf_names:
# MemoryDeps didn't match and read different locations of the same buffer.
# Examples here include:
# - MemoryDep("foo", x) != MemoryDep("foo", x + 1)
# - MemoryDep("foo", x) != StarDep("foo")
why("memory deps did not match")
return False
for name in remaining_deps:
op_name = self.name_to_buf[name].defining_op.get_name()
if node1_op_names & self.name_to_fused_node[op_name].ancestors:
why("intermediate nodes between node1 & node2")
return False
return True
def fusable_weak_dep(
self, weak_dep: WeakDep, node1: BaseSchedulerNode, node2: BaseSchedulerNode
) -> bool:
if weak_dep.name not in node1.get_buffer_names():
return False
# A weak dep can be fused if and only if the fused operation acts inplace
# on the buffer being mutated. i.e. the same index is being read then mutated
mutating_writes = [
write
for write in node2.read_writes.writes
if write.name == weak_dep.mutating_buf
]
if len(mutating_writes) != 1:
return False
write = mutating_writes[0]
assert isinstance(write, MemoryDep)
if free_symbol_is_type(write.index, SymT.TMP):
return False
real_name = self.mutation_real_name[weak_dep.mutating_buf]
relevant_reads = [
read for read in node1.read_writes.reads if read.name == real_name
]
return all(
isinstance(read, MemoryDep)
and not free_symbol_is_type(read.index, SymT.TMP)
and read.index == write.index
and read.size == write.size
for read in relevant_reads
)
# StarDep doesn't match MemoryDep, different indices don't match
# However, broadcasting sometimes strips dimensions, and if that's the case
# we still can match unmet dep
# if there's indirect indexing, don't match it
def fusable_read_and_write(self, read: Dep, write: MemoryDep) -> bool:
if isinstance(read, MemoryDep):
if read.mode == write.mode and write.mode is not None:
return True
read_name = read.name
if read_name in self.mutation_renames:
read_name = self.mutation_renames[read_name]
return (
read_name == write.name
and not free_symbol_is_type(read.index, SymT.TMP)
and not free_symbol_is_type(write.index, SymT.TMP)
and read.index == write.index
and len(read.size) >= len(write.size)
and read.size[: len(write.size)] == write.size
)
elif isinstance(read, StarDep):
read_name = self.mutation_renames.get(read.name, read.name)
write_name = self.mutation_renames.get(write.name, write.name)
if (
read.mode == write.mode
and write.mode is not None
and read_name == write_name
):
return True
return False
def score_fusion(
self, node1: BaseSchedulerNode, node2: BaseSchedulerNode
) -> Tuple[bool, bool, int, int]:
"""
Assign a score (higher comes first) to the fusion of node1
and node2. When different fusions conflict with each other,
this is the way we decide what order to run them in.
Our current score is based on:
- Estimate of the saved memory operations
- Fusions closer together in original order
"""
memory_score = self.score_fusion_memory(node1, node2)
proximity_score = -max(
abs(node1.min_order - node2.max_order),
abs(node2.min_order - node1.max_order),
)
return (
node1.is_template() == config.epilogue_fusion_first and memory_score > 0,
node1.is_reduction() == node2.is_reduction() and memory_score > 0,
memory_score,
proximity_score,
)
def dep_size_hint(self, dep: Dep) -> int:
res = 0
if dep not in self.__dep_size_hint_cache:
try:
if not dep.has_unbacked_symbols():
res = dep.numbytes_hint()
except KeyError:
# In at least one test (test/inductor/test_torchbind.py) we
# create a StarDep that doesn't exist in the graph and calling
# `has_unbacked_symbols()` throws an error.
pass
self.__dep_size_hint_cache[dep] = res
else:
res = self.__dep_size_hint_cache[dep]
return res
def score_fusion_memory(
self, node1: BaseSchedulerNode, node2: BaseSchedulerNode
) -> int:
"""
The first term in our fusion score that estimates number of saved
memory operations.
"""
node1_dep_len = len(node1.read_writes.reads) + len(node1.read_writes.writes)
node2_dep_len = len(node1.read_writes.reads) + len(node2.read_writes.writes)
# optimization: iter over smaller set
if max(node1_dep_len, node2_dep_len) * 4 > min(node1_dep_len, node2_dep_len):
if node1_dep_len > node2_dep_len:
tmp = node1
node1 = node2
node2 = tmp
deps = []
for dep in node1.read_writes.reads | node1.read_writes.writes:
if dep in node2.read_writes.reads or dep in node2.read_writes.writes:
deps.append(dep)
return sum(self.dep_size_hint(dep) for dep in deps)
common_memory_deps = (node1.read_writes.reads | node1.read_writes.writes) & (
node2.read_writes.reads | node2.read_writes.writes
)
return sum(self.dep_size_hint(dep) for dep in common_memory_deps)
def get_possible_fusions_with_highest_priority(
self, possible_fusions: List[Tuple[BaseSchedulerNode, BaseSchedulerNode]]
) -> List[Tuple[BaseSchedulerNode, BaseSchedulerNode]]:
# Group the possible fusions based on their priority from the backend.
# Only return the group of possible fusions with highest priority.
if len(possible_fusions) == 0:
return possible_fusions
possible_fusions_group_by_priority: Dict[
int, List[Tuple[BaseSchedulerNode, BaseSchedulerNode]]
] = {}
for node1, node2 in possible_fusions:
assert node1.get_device() == node2.get_device()
device = node1.get_device()
fusion_pair_priority = int(
self.get_backend(device).get_fusion_pair_priority(node1, node2)
)
if fusion_pair_priority not in possible_fusions_group_by_priority:
possible_fusions_group_by_priority[fusion_pair_priority] = [
(node1, node2),
]
else:
possible_fusions_group_by_priority[fusion_pair_priority].append(
(node1, node2)
)
# return the possible fusions with highest priority
possible_fusions_with_highest_priority = min(
possible_fusions_group_by_priority.items(), key=operator.itemgetter(0)
)[1]
assert len(possible_fusions_with_highest_priority) > 0
return possible_fusions_with_highest_priority
def score_fusion_key(
self, nodes: Tuple[BaseSchedulerNode, BaseSchedulerNode]
) -> Tuple[bool, bool, int, int]:
"""
Shim for list.sort(key=...)
"""
node1, node2 = nodes
return self.score_fusion(node1, node2)
def compute_last_usage(self) -> None:
"""
Populate node.last_usage recursively (also for the nodes within a FusedSchedulerNode)
"""
future_used_buffers: OrderedSet[str] = OrderedSet(V.graph.get_output_names())
for node in reversed(self.nodes):
node.set_last_usage(future_used_buffers, self.mutation_real_name)
future_used_buffers.update(node.last_usage)
def free_buffers(self) -> None:
"""Free any buffers that are no longer needed"""
for name in sorted(
self.buffer_names_to_free
- V.graph.removed_buffers
- V.graph.wrapper_code.freed
):
if name in self.name_to_buf:
buf = self.name_to_buf[name]
if buf.can_free():
V.graph.wrapper_code.codegen_free(buf.node)
elif name in V.graph.graph_inputs:
storage = V.graph.graph_inputs[name].data
assert isinstance(storage, ir.StorageBox) and storage.is_input_buffer()
V.graph.wrapper_code.codegen_free(storage.data)
self.buffer_names_to_free.clear()
def remove_kernel_local_buffers(self) -> None:
"""
Any buffers that are both created and have a last use in the
same kernel can be removed.
"""
fused_node_names = OrderedSet(
self.name_to_buf[buf].defining_op.get_name()
for buf in V.kernel.store_buffer_names
if buf in self.name_to_buf
)
names_to_remove = []
for out_buf in V.kernel.store_buffer_names:
if out_buf not in self.name_to_buf:
# Aux buffers created during kernel codegen
names_to_remove.append(out_buf)
continue
users = self.name_to_buf[out_buf].users
assert users is not None
users = OrderedSet(user.get_name() for user in users if not user.is_weak)
if users.issubset(fused_node_names):
names_to_remove.append(out_buf)
def remove_filter(n: str) -> bool:
return (
n not in V.kernel.must_keep_buffers
and n not in V.kernel.args.input_buffers
and n not in self.mutation_renames
and n not in self.mutation_real_name
)
names_to_remove = list(filter(remove_filter, names_to_remove))
for name in names_to_remove:
if name in V.kernel.args.inplace_buffers:
buf = V.kernel.args.inplace_buffers[name]
if isinstance(buf, str) and buf.startswith("REMOVED"):
continue
remove = all(n in names_to_remove for n in buf.other_names)
if remove:
self.remove_inplace_buffer(name)
V.kernel.inplaced_to_remove.add(name)
else:
self.remove_buffer(name)
def remove_buffer(self, name: str) -> None:
# Assign a special value instead of deleting the entry
# because we still rely on output_buffers's length to
# generate unique arg name.
log.debug("remove_buffer(%r)", name)
V.kernel.args.output_buffers[name] = "REMOVED"
V.kernel.removed_buffers.add(name)
def remove_inplace_buffer(self, name: str) -> None:
log.debug("removing_inplace_buffer(%r)", name)
inner_name = V.kernel.args.inplace_buffers[name].inner_name
V.kernel.args.inplace_buffers[name] = inner_name.replace(
"in_out_ptr", "REMOVED"
)
V.kernel.removed_buffers.add(name)
def flush(self) -> None:
for backend in self.backends.values():
backend.flush()
self.free_buffers()
def codegen_extern_call(self, scheduler_node: ExternKernelSchedulerNode) -> None:
assert isinstance(scheduler_node, ExternKernelSchedulerNode)
# 'decide_inplace_update' stores the inplace update decisions in
# the current kernel from where 'allocate' retrieve those decisions.
# We have to make sure there is a non-NULL kernel handler to store
# those inplace update decisions.
counters["inductor"]["extern_calls"] += 1
with V.set_kernel_handler(Kernel(increase_kernel_count=False)):
scheduler_node.decide_inplace_update()
scheduler_node.mark_run()
node = scheduler_node.node
assert isinstance(node, ir.ExternKernel), f"{type(node)=}"
node.codegen(V.graph.wrapper_code)
self.free_buffers()
def create_backend(self, device: torch.device) -> BaseScheduling:
assert (
not is_gpu(device.type) or device.index is not None
), f"{device} should have been normalized in lowering"
V.graph.add_device_info(device)
device_scheduling = get_scheduling_for_device(device.type)
if device_scheduling is None:
raise RuntimeError(f"Unsupported device type: {device.type}")
if not has_triton():
if (
device.type == "cuda"
and (device_props := torch.cuda.get_device_properties(device)).major < 7
):
raise RuntimeError(
f"Found {device_props.name} which is too old to be supported by the triton GPU compiler, which is used as the backend. Triton only supports devices of CUDA Capability >= 7.0, but your device is of CUDA capability {device_props.major}.{device_props.minor}" # noqa: B950
)
elif is_gpu(device.type):
raise RuntimeError(
"Cannot find a working triton installation. Either the package is not installed or it is too old. More information on installing Triton can be found at https://github.com/openai/triton" # noqa: B950
)
return device_scheduling(self)
def get_backend(self, device: torch.device) -> BaseScheduling:
if device not in self.backends:
self.backends[device] = self.create_backend(device)
return self.backends[device]
def enter_context(self, node: BaseSchedulerNode) -> None:
def get_order(n: torch.fx.Node) -> int:
if n not in self.origin_to_index:
self.origin_to_index.update({n: i for i, n in enumerate(n.graph.nodes)})
return self.origin_to_index[n]
# Use a dict to have ordering
origins = {
(get_order(e), e): None
for n in node.get_nodes()
if n.node is not None
for e in n.node.get_origins()
}
origins = list(origins.keys())
if origins:
_, last = max(origins, key=operator.itemgetter(0))
V.graph.wrapper_code.enter_context(last)
def codegen(self) -> None:
with dynamo_timed("Scheduler.codegen"):
return self._codegen()
def _codegen(self) -> None:
for node in self.nodes:
try:
log.debug(
"Generating code for node %s with estimated runtime %f",
node.get_name(),
node.get_estimated_runtime(),
)
except Exception as e:
log.debug(
"Generating code for node %s with estimated runtime 0.0",
node.get_name(),
)
self.enter_context(node)
if not isinstance(node, NopKernelSchedulerNode) and (
device := node.get_device()
):
if (
device != self.current_device
or node.is_extern()
or node.is_template()
):
self.flush()
if device != self.current_device:
if self.current_device and device_need_guard(
self.current_device.type
):
V.graph.wrapper_code.codegen_device_guard_exit()
if device_need_guard(device.type):
assert device.index is not None, "device should have an index"
V.graph.wrapper_code.codegen_device_guard_enter(device.index)
self.current_device = device
self.buffer_names_to_free.update(node.last_usage)
if node.is_template():
node, *epilogue = node.get_nodes()
self.get_backend(device).codegen_template(node, epilogue)
elif node.is_extern():
node = typing.cast(ExternKernelSchedulerNode, node)
self.codegen_extern_call(node)
elif node.is_foreach():
node = typing.cast(ForeachKernelSchedulerNode, node)
backend_ = self.get_backend(device)
from .codegen.cuda_combined_scheduling import CUDACombinedScheduling
from .codegen.simd import SIMDScheduling
if isinstance(backend_, (SIMDScheduling, CUDACombinedScheduling)):
backend = backend_
else:
raise AssertionError(f"{type(self)=}")
backend.codegen_combo_kernel(node)
elif isinstance(node, (FusedSchedulerNode, SchedulerNode)):
self.get_backend(device).codegen_node(node)
else:
assert isinstance(node, NopKernelSchedulerNode)
node.mark_run()
if config.triton.debug_sync_kernel:
self.get_backend(device).codegen_sync()
self.available_buffer_names.update(node.get_buffer_names())
self.completed_operations.update(node.get_operation_names())
if not isinstance(node, NopKernelSchedulerNode):
device = node.get_device()
if device is not None and self.get_backend(device).ready_to_flush():
self.flush()
if self.current_device and device_need_guard(self.current_device.type):
# exit the outermost CUDA device guard. this is
# important for nested indentation codegen-ing.
V.graph.wrapper_code.codegen_device_guard_exit()
self.flush()
def get_buffer_layout(self, buf_name: str) -> ir.Layout:
buf = self.name_to_buf[buf_name]
assert buf.node is not None
return buf.node.get_layout()
class BaseScheduling:
@classmethod
def get_backend_features(cls, device: torch.device) -> Sequence[BackendFeature]:
"""Return a set of .codegen.common.BackendFeature()"""
return ()
def can_fuse_vertical(
self, node1: BaseSchedulerNode, node2: BaseSchedulerNode
) -> bool:
"""
Check whether node1 and node2 can be vertically fused or not.
"""
raise NotImplementedError
def can_fuse_horizontal(
self, node1: BaseSchedulerNode, node2: BaseSchedulerNode
) -> bool:
"""
Check whether node1 and node2 can be horizontally fused or not.
"""
raise NotImplementedError
def fuse(
self, node1: BaseSchedulerNode, node2: BaseSchedulerNode
) -> FusedSchedulerNode:
"""
Fuse two nodes
"""
if node1.is_foreach() or node2.is_foreach():
return ForeachKernelSchedulerNode.fuse(node1, node2)
else:
return FusedSchedulerNode.fuse(node1, node2)
def group_fn(
self, sizes: Sequence[Sequence[sympy.Expr]]
) -> Tuple[Tuple[sympy.Expr, ...], ...]:
"""
Process the iteration sizes in case a transformation needs to be applied.
"""
raise NotImplementedError
def codegen_template(
self,
template_node: BaseSchedulerNode,
epilogue_nodes: Sequence[BaseSchedulerNode],
) -> Optional[str]:
"""
Given a template node, generate a kernel.
This function is only available for triton now. If the third-party backend behaves as a sub-class
of TritonScheduling, it can override it or reuse it.
"""
raise NotImplementedError
def codegen_node(self, node: Union[FusedSchedulerNode, SchedulerNode]) -> None:
"""
Generate a kernel given a list of pre-fused nodes.
"""
raise NotImplementedError
def codegen_sync(self) -> None:
"""
Generate synchronization code for the kernel. This method depends on the hardware characteristics.
"""
raise NotImplementedError
def ready_to_flush(self) -> bool:
"""
Check whether the backend is requesting the scheduler to flush the generated kernel.
If not supported, please return False.
"""
return False
def flush(self) -> None:
"""
Flush the generated kernel and python wrapper code to the source code file.
"""
raise NotImplementedError
def benchmark_fused_nodes(
self, nodes: Sequence[BaseSchedulerNode]
) -> Tuple[float, str]:
"""
Benchmark fused list of nodes and return the execution time
in milliseconds on randomly generated inputs.
"""
raise NotImplementedError
def get_fusion_pair_priority(
self, node1: BaseSchedulerNode, node2: BaseSchedulerNode
) -> int:
"""
Return an unsigned integer which represents the priority of this fusion pair.
The smaller is with higher priority.
"""
return 0
def debug_triton_code(node: Union[SchedulerNode, FusedSchedulerNode]) -> List[str]:
lines = []
multi_template = node.get_template_node()
assert multi_template is None or isinstance(multi_template, ir.MultiTemplateBuffer)
if multi_template and multi_template.make_kernel_render is None:
lines.append(f"{node.get_name()} Unfinalized multi template buffer")
else:
from torch._inductor.codegen.cuda_combined_scheduling import (
CUDACombinedScheduling,
)
from .codegen.simd import SIMDScheduling
snodes = (node,) if isinstance(node, SchedulerNode) else node.snodes
device = snodes[0].get_device()
backend = node.scheduler.get_backend(device)
assert isinstance(backend, (SIMDScheduling, CUDACombinedScheduling))
V.graph.scheduler.current_device = device
# Don't increment kernel count when generating debug string.
# This will confuse some unit tests that check the number of
# generated kernels.
old_generated_kernel_count = metrics.generated_kernel_count
triton_code = backend.generate_kernel_code_from_nodes(snodes).strip()
metrics.generated_kernel_count = old_generated_kernel_count
lines.append(f"{node.get_name()} Triton code:")
lines.append(textwrap.indent(triton_code, " "))
return lines