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from __future__ import annotations
import contextlib
import functools
import itertools
import logging
import operator
import os
import re
import sys
import time
from collections import defaultdict
from contextlib import contextmanager
from typing import Any, Callable, NoReturn, Optional, TYPE_CHECKING, Union
import sympy
from sympy import Expr
import torch
import torch._logging
import torch.fx
from torch import device, Tensor
from torch._decomp import get_decompositions
from torch._dynamo.utils import defake, dynamo_timed
from torch._library.fake_class_registry import FakeScriptObject
from torch._library.utils import get_layout_constraint_tag
from torch._logging import LazyString, trace_structured
from torch._prims_common import (
compute_required_storage_length,
make_channels_last_strides_for,
)
from torch._subclasses.fake_tensor import FakeTensor
from torch._utils_internal import full_aoti_runtime_assert
from torch.fx.experimental._backward_state import BackwardState
from torch.fx.experimental.sym_node import magic_methods, method_to_operator
from torch.fx.experimental.symbolic_shapes import (
_get_placeholder_expr,
free_unbacked_symbols,
has_free_symbols,
resolve_unbacked_bindings,
RuntimeAssert,
ShapeEnv,
SympyBoolean,
SymTypes,
)
from torch.fx.node import Node
from torch.utils._mode_utils import no_dispatch
from torch.utils._ordered_set import OrderedSet
from torch.utils._sympy.numbers import int_oo
from . import config, ir, metrics
from .codegen.common import (
BackendFeature,
DeviceOpOverrides,
FileBackedGraphModule,
get_backend_features,
get_device_op_overrides,
get_wrapper_codegen_for_device,
init_backend_registration,
WorkspaceArg,
)
from .exc import (
CppWrapperCodegenError,
LoweringException,
MissingOperatorWithDecomp,
MissingOperatorWithoutDecomp,
)
from .ir import (
Constant,
DonatedBuffer,
FixedLayout,
get_device_type,
GraphPartitionSignature,
InputBuffer,
Pointwise,
Reduction,
StorageBox,
TensorBox,
TorchBindObject,
)
from .lowering import (
constrain_to_fake_tensors,
constrain_to_fx_strides,
FALLBACK_ALLOW_LIST,
fallback_handler,
fallback_node_due_to_unsupported_type,
lowerings,
make_fallback,
maybe_layout_constraints,
needs_realized_inputs,
require_contiguous,
tag_to_layout_constraint,
unsupported_output_tensor,
)
from .runtime import autotune_cache
from .runtime.autotune_cache import AutotuneCacheBundler
from .sizevars import SizeVarAllocator
from .utils import (
convert_shape_to_inductor,
gather_origins,
get_cloned_parameter_buffer_name,
get_donated_idxs,
get_sympy_Expr_dtype,
GraphPartitionMap,
is_same_tensor,
maybe_get_suppress_shape_guards_ctx,
normalize_name,
should_assume_input_aligned,
SUPPORTED_MKLDNN_DEVICES,
ValueWithLineMap,
)
from .virtualized import NullHandler, V
if TYPE_CHECKING:
from collections.abc import Iterable, Iterator, Sequence
from types import ModuleType
from torch._higher_order_ops.effects import _EffectType
from torch.fx import GraphModule
from torch.fx.graph import Graph
from .codegen.wrapper import PythonWrapperCodegen
from .scheduler import BaseSchedulerNode
CompiledModule = Union[ModuleType, FileBackedGraphModule]
from torch._inductor.codecache import output_code_log
log = logging.getLogger(__name__)
perf_hint_log = torch._logging.getArtifactLogger(__name__, "perf_hints")
aten = torch.ops.aten
_post_grad_graph_counter = itertools.count()
if config.is_fbcode():
from torch._inductor.fb.utils import log_module_code
else:
def log_module_code(*args: Any, **kwargs: Any) -> None:
pass
def may_get_constant_buffer_dtype(constant_buffer: sympy.Expr) -> Optional[torch.dtype]:
assert isinstance(
constant_buffer, (sympy.Symbol, sympy.Expr, sympy.core.numbers.Integer)
), (
"get_constant_buffer_dtype only supports input of sympy.Symbol, sympy.Expr or sympy.core.numbers.Integer"
)
if isinstance(constant_buffer, sympy.core.numbers.Integer):
return torch.int64
if isinstance(constant_buffer, sympy.Expr):
return get_sympy_Expr_dtype(constant_buffer)
if constant_buffer.is_integer:
return torch.int64
elif constant_buffer.is_float:
return torch.float32
else:
return None
def is_magic_method(op: Any) -> bool:
magic_ops = OrderedSet(method_to_operator(m) for m in magic_methods)
return op in magic_ops
def getattr_recursive(
obj: GraphModule, target: str
) -> Union[Tensor, torch._C.ScriptObject, GraphModule]:
target_atoms = target.split(".")
attr_itr = obj
for i, atom in enumerate(target_atoms):
if not hasattr(attr_itr, atom):
raise RuntimeError(
f"Node referenced nonexistent target {'.'.join(target_atoms[:i])}"
)
attr_itr = getattr(attr_itr, atom)
return attr_itr
def get_user_visible_output_strides(g: Graph) -> dict[Node, tuple[int, ...]]:
ret: dict[Node, tuple[int, ...]] = {}
output_node = g.find_nodes(op="output")[0]
if "user_visible_output_idxs" not in output_node.meta:
return ret
if not isinstance(output_node.args[0], torch.fx.Node):
output_node_args = output_node.args[0]
else:
output_node_args = output_node.args
for idx, node in enumerate(output_node_args):
if idx in output_node.meta["user_visible_output_idxs"]:
ret[node] = output_node.meta["original_output_strides"][idx]
return ret
def mark_nodes_dislike_padding(
g: Graph, user_visible_output_strides: dict[Node, tuple[int, ...]]
) -> None:
"""
Nodes like convolution/convolution_backward want its input to be dense.
If we pad their inputs, we result in extra calls to copy kernels! On the other hand, padding usually helps reduction.
The pass finds nodes that dislike padding. These are nodes that can be reached
from a convolution/convolution_backward in the backward direction without
going thru a reduction.
"""
if not config.comprehensive_padding:
return
ops_dislike_padding = OrderedSet(
[
aten.convolution,
aten.convolution_backward,
aten._scaled_mm,
]
)
# what's a better way to collect the reduction ops?
ops_like_padding = OrderedSet(
[
aten.var_mean,
aten.sum,
aten.mean,
aten.prod,
aten.any,
aten.amin,
aten.amax,
aten.min,
aten.max,
aten.argmin,
aten.argmax,
aten.scatter_reduce,
]
)
def _get_overload_packet(
node: torch.fx.Node,
) -> Optional[torch._ops.OpOverloadPacket]:
return (
node.target._overloadpacket
if node.op == "call_function"
# hasattr on OpOverloadPacket is slow, do isinstance first
and isinstance(node.target, torch._ops.OpOverload)
and hasattr(node.target, "_overloadpacket")
else None
)
for cur in reversed(g.nodes):
if isinstance(
cur.target,
torch._higher_order_ops.triton_kernel_wrap.TritonKernelWrapperMutation,
):
cur.meta["dislike_padding"] = True
continue
if (
isinstance(cur.target, torch._ops.OpOverload)
and get_layout_constraint_tag(cur.target)
== torch._C.Tag.needs_exact_strides
):
cur.meta["dislike_padding"] = True
continue
op = _get_overload_packet(cur)
if not op:
continue
if op in ops_dislike_padding:
cur.meta["dislike_padding"] = True
if cur.meta.get("dislike_padding", False):
# propagate
for prior in cur.all_input_nodes:
prior_op = _get_overload_packet(prior)
if not prior_op:
continue
if prior_op not in ops_like_padding:
prior.meta["dislike_padding"] = True
# We only want to mark output nodes. So, move it after the above prior nodes process.
if not config.pad_outputs and cur in user_visible_output_strides:
cur.meta["dislike_padding"] = True
class GraphLowering(torch.fx.Interpreter):
graph_outputs: list[ir.IRNode]
def __init__(
self,
gm: torch.fx.GraphModule,
example_inputs: Optional[Sequence[object]] = None,
shape_env: Optional[ShapeEnv] = None,
graph_id: Optional[int] = None,
cpp_wrapper: bool = False,
aot_mode: bool = False,
layout_opt: Optional[bool] = None,
extern_node_serializer: Optional[
Callable[[list[ir.ExternKernelNode]], Any]
] = None,
is_inference: bool = False,
is_backward: bool = False,
is_const_graph: bool = False,
const_output_index: Optional[dict[str, int]] = None,
const_wrapper_code: Optional[str] = None,
const_kernel_code: Optional[str] = None,
const_module: Optional[GraphLowering] = None,
name: Optional[str] = None,
inputs_to_check: Optional[Sequence[int]] = None,
) -> None:
super().__init__(gm)
self.example_inputs = example_inputs
self.layout_opt = (
layout_opt
if layout_opt is not None
else self.decide_layout_opt(gm, is_inference=is_inference)
)
self.num_channels_last_conv = 0
self.is_inference = is_inference
self.is_backward = is_backward
self.is_const_graph = is_const_graph
self.const_wrapper_code = const_wrapper_code
self.const_kernel_code = const_kernel_code
self.const_module = const_module
self.inputs_to_check = inputs_to_check
self.extra_traceback = False # we do our own error wrapping
if shape_env is None:
shape_env = ShapeEnv()
self.reuse_shape_env = False
else:
self.reuse_shape_env = True
self._shape_env = shape_env
# We're going to mutate ras_by_symbol as we finish generating them
self.ras_by_symbol: dict[Optional[sympy.Symbol], list[RuntimeAssert]] = (
shape_env.deferred_runtime_asserts.copy()
)
self.bound_unbacked_symbols = OrderedSet[sympy.Symbol]()
self.sizevars = SizeVarAllocator(shape_env)
self.graph_input_names: list[str] = []
self.graph_inputs: dict[str, Union[TensorBox, TorchBindObject, sympy.Expr]] = {}
self.graph_inputs_original: dict[str, InputBuffer] = {}
self.partition_maps: Optional[list[GraphPartitionMap]] = None
self.zero_dim_cpu_tensor_list: OrderedSet[str] = OrderedSet()
self.device_types: OrderedSet[str] = (
const_module.device_types if const_module else OrderedSet()
)
self.device_idxs: OrderedSet[int] = (
const_module.device_idxs if const_module else OrderedSet()
)
self.device_type = "cpu"
# Inplace padding may require Inductor to allocate slightly larger
# tensor for padding.
self.buffer_to_padded_size: dict[str, list[int]] = {}
self.buffers: list[ir.Buffer] = []
self.operations: list[ir.Operation] = []
self.const_output_index: dict[str, int] = (
const_output_index if const_output_index else {}
)
self.folded_constants: OrderedSet[str] = (
OrderedSet(const_output_index.keys())
if const_output_index
else OrderedSet()
)
self.constants: dict[str, torch.Tensor] = (
const_module.constants if const_module else {}
)
self.named_buffers: dict[str, torch.Tensor] = (
const_module.named_buffers if const_module else {}
)
self.named_parameters: dict[str, torch.Tensor] = (
const_module.named_parameters if const_module else {}
)
self.torchbind_constants: dict[
str, Union[torch._C.ScriptObject, FakeScriptObject]
] = {}
self.seen_subgraphs: dict[str, ir.Subgraph] = {}
self.constant_reprs: dict[str, str] = {}
self.removed_operations: OrderedSet[str] = OrderedSet()
self.removed_buffers: OrderedSet[str] = OrderedSet()
self.removed_inplace_buffers: OrderedSet[str] = OrderedSet()
self.mutated_buffers: OrderedSet[str] = OrderedSet()
self.never_reuse_buffers: OrderedSet[str] = OrderedSet()
self.inplaced_to_remove: OrderedSet[str] = OrderedSet()
self.device_ops: DeviceOpOverrides = None # type: ignore[assignment]
self.wrapper_code: PythonWrapperCodegen = None # type: ignore[assignment]
# See `ProxyExecutor Design Note` in ir.py for more details
self.extern_kernel_nodes: list[ir.ExternKernelNode] = []
from torch._inductor.extern_node_serializer import extern_node_json_serializer
self.extern_node_serializer: Callable[[list[ir.ExternKernelNode]], Any] = (
extern_node_serializer
if config.is_fbcode() and extern_node_serializer
else extern_node_json_serializer
)
self.current_node: torch.fx.Node = None # type: ignore[assignment]
self.lists: dict[str, list[str]] = {}
self.mutated_inputs: OrderedSet[str] = OrderedSet()
self.mutated_input_idxs: list[int] = []
self.name_to_buffer: dict[str, ir.Buffer] = {}
self.name_to_users: defaultdict[str, list[ir.IRNode]] = defaultdict(list)
self.name_to_op: dict[str, ir.Operation] = {}
self.creation_time = time.time()
self.name = name # type: ignore[assignment]
self.cpp_wrapper = cpp_wrapper
# record multi_kernel choice for cpp_wrapper so the second pass knows
# which sub-kernel is picked. Copy cpp_wrapper to another variable
# since cpp_wrapper flag is OrderedSet to false for the first pass of codegen.
self.record_multi_kernel_choice = cpp_wrapper
self.multi_kernel_to_choice: dict[str, str] = {}
self.aot_mode = aot_mode
self.graph_id = graph_id
self.post_grad_graph_id = next(_post_grad_graph_counter)
self.scheduler: torch._inductor.scheduler.Scheduler = None # type: ignore[assignment]
# record intermediate results for input of UsedDefinedTritonKernels
# This will be used if autotuning is done in one pass.
self.autotuning_inputs: Optional[list[torch.Tensor]] = None
self.autotuning_mapping: Optional[dict[str, dict[str, int]]] = None
self.autotuning_grids: Optional[dict[str, Any]] = None
# current_device is set only during codegen of a device-specific kernel
# a graph can have many devices
self.current_device: Optional[torch.device] = None
self.nodes_prefer_channels_last = (
self.find_nodes_prefer_channels_last() if self.layout_opt else OrderedSet()
)
self._warned_fallback = OrderedSet(["aten.convolution_backward"])
self.user_visible_output_strides = get_user_visible_output_strides(gm.graph)
mark_nodes_dislike_padding(gm.graph, self.user_visible_output_strides)
self.cache_key: str = "" # This is the cache key for the compiled artifact
self.cache_path: str = "" # This is the path in the filesystem where the compiled artifact is stored
self.cache_linemap: list[
tuple[int, str]
] = [] # This is the linemap used by the profiler to mark custom compiled kernels getting run
# Used if lowering encounters cases where cudagraphs are not supported
self.disable_cudagraphs_reason: Optional[str] = None
# only keeping one node per device for stack trace purposes
self.device_node_mapping: dict[torch.device, torch.fx.Node] = {}
self.orig_gm: torch.fx.GraphModule = gm.__copy__()
for k, v in self.orig_gm.named_buffers():
self.named_buffers[k] = v
for k, v in self.orig_gm.named_parameters():
self.named_parameters[k] = v
self.dynamo_flat_name_to_original_fqn = self.module.meta.get( # type: ignore[operator, union-attr]
"dynamo_flat_name_to_original_fqn", {}
)
self.allocated_constant_name: dict[str, str] = (
const_module.allocated_constant_name if const_module is not None else {}
)
init_backend_registration()
self.get_backend_features = functools.lru_cache(None)(get_backend_features)
self.effectful_ops: dict[_EffectType, ir.Buffer] = {}
# Track the buffers that we know is unaligned
# This can either be a graph input or the output of fallback
# kernels.
self.unaligned_buffers: OrderedSet[str] = OrderedSet()
self.no_fuse_buffer_names: OrderedSet[str] = OrderedSet()
self.low_precision_codegen_ops: OrderedSet[str] = OrderedSet()
# more aggressive prologue fusion
self.invoke_quant_ops: OrderedSet[str] = OrderedSet()
# Below field is related to printing debug intermediate tensor values info for debugging
self.all_codegen_kernel_names: OrderedSet[str] = OrderedSet()
# state used by for Kernel.workspace
self.workspace_id = itertools.count()
# track the current placeholder index that we are processing
self.placeholder_idx = -1
self.bw_donated_idxs = get_donated_idxs()
def freeze_runtime_asserts(self) -> None:
self._shape_env.freeze_runtime_asserts()
def symbolic_sizes_strides(
self, ex: torch.Tensor
) -> tuple[Sequence[Union[int, Expr]], Sequence[Union[int, Expr]]]:
"""
Support dynamic shapes and dynamic strides by assigning variables
to each dimension. We duck-shape tensors, so if two tensors
have the same size they get assigned the same symbolic variable.
"""
if self.reuse_shape_env:
return convert_shape_to_inductor(ex.size()), convert_shape_to_inductor(
ex.stride()
)
else:
from torch._dynamo.source import ConstantSource
# TODO: this should not be needed once #93059 lands
# https://github.com/pytorch/pytorch/pull/94031#discussion_r1096044816
# TODO: make a dedicated UnknownSource for this?
# NB: This is using the legacy default behavior from
# create_symbolic_sizes_strides_storage_offset but we hope we can
# just delete this entirely
source = ConstantSource(
f"__inductor_unknown_tensor_{len(self._shape_env.var_to_val)}"
)
(
size,
stride,
_,
) = self._shape_env.create_symbolic_sizes_strides_storage_offset(
ex,
source,
)
r_size = [i.node.expr if isinstance(i, torch.SymInt) else i for i in size]
r_stride = [i.node.expr if isinstance(i, torch.SymInt) else i for i in stride]
return r_size, r_stride
def static_sizes_strides(
self, ex: torch.Tensor
) -> tuple[list[sympy.Expr], list[sympy.Expr]]:
"""
Primarily used to weights
"""
size = [sympy.Integer(i) for i in ex.size()]
stride = [sympy.Integer(i) for i in ex.stride()]
return size, stride
def get_allocation_size(
self,
node: Union[
ir.TensorBox, ir.StorageBox, ir.Buffer, WorkspaceArg, ir.TorchBindObject
],
) -> Sequence[Expr]:
if isinstance(node, ir.TensorBox):
node = node.data # type: ignore[assignment]
if isinstance(node, ir.StorageBox):
node = node.data # type: ignore[assignment]
if (
isinstance(node, ir.ComputedBuffer)
and node.name in self.buffer_to_padded_size
):
return self.buffer_to_padded_size[node.name]
else:
return node.get_size()
def get_allocation_storage_size(
self, node: Union[ir.Buffer, WorkspaceArg, ir.TorchBindObject]
) -> Expr:
layout = node.get_layout()
size = self.get_allocation_size(node) # consider inplace padding
stride = layout.stride
offset = layout.offset
return compute_required_storage_length(size, stride, offset) # type: ignore[arg-type]
def has_feature(
self,
device: Union[torch._inductor.ir.IRNode, device, None],
feature: BackendFeature,
) -> bool:
assert isinstance(feature, BackendFeature), feature
return feature in self.get_backend_features(get_device_type(device))
def get_current_device_or_throw(self) -> torch.device:
if device := self.current_device:
return device
else:
raise RuntimeError("No current device")
@contextlib.contextmanager
def set_current_device(self, device: torch.device) -> Iterator[None]:
prior = self.current_device
self.current_device = device
try:
yield
finally:
self.current_device = prior
def get_training_phase(self) -> str:
if self.is_inference:
return "inference"
if self.is_backward:
return "backward"
return "forward"
@staticmethod
def decide_layout_opt(gm: GraphModule, *, is_inference: bool) -> bool:
"""
Decide if we should enable layout optimization for this graph based on
heuristics.
"""
if not config.layout_optimization:
return False
if config.force_layout_optimization:
return True
conv_nodes = [
n for n in gm.graph.nodes if n.target == torch.ops.aten.convolution.default
]
nconv = len(conv_nodes)
if nconv == 0:
return False
# For cpu backend and mkldnn enabled, we always use channels_last for better performance.
if (
torch.backends.mkldnn.enabled
and torch.backends.mkldnn.is_available()
and all(
n.args[idx].meta["val"].device.type in SUPPORTED_MKLDNN_DEVICES
for n in conv_nodes
for idx in [0, 1]
)
):
return True
# Following models are skipped due to this:
# jx_nest_base
# volo_d1_224
if len(list(gm.graph.nodes)) >= 300 * nconv:
log.debug("Skipped layout opt because only a few conv")
return False
if any(
has_free_symbols(n.args[idx].meta["val"])
for n in conv_nodes
for idx in [0, 1]
):
log.debug(
"See perf regression with dynamic shape. Follow up in https://github.com/pytorch/pytorch/issues/102670"
)
return False
def is_grouped(n: Any) -> bool:
meta_val = n.args[1].meta["val"] # type: ignore[union-attr, operator]
assert isinstance(meta_val, torch.Tensor)
return n.args[-1] > 1 and meta_val.size(1) > 1 # type: ignore[union-attr, operator]
def is_in_out_channel(n: torch.fx.Node) -> bool:
return (
n.args[1].meta["val"].size(0) * 2 <= n.args[1].meta["val"].size(1) # type: ignore[union-attr, operator]
and n.args[1].meta["val"].size(2) > 1 # type: ignore[union-attr, operator]
)
def is_small_channel(n: torch.fx.Node) -> bool:
return (
n.args[1].meta["val"].size(0) <= 64 # type: ignore[union-attr, operator]
and n.args[1].meta["val"].size(1) <= 64 # type: ignore[union-attr, operator]
)
# only grouped convolutions benchmarked as slower in conv samples for inference only
if is_inference:
from torch.utils.flop_counter import FlopCounterMode
flop_counts: dict[str, float] = defaultdict(float)
for node in conv_nodes:
success, args, kwargs = torch._inductor.fx_utils.get_fake_args_kwargs(
node
)
if success:
with FlopCounterMode(display=False) as flop_counter_mode:
with V.fake_mode:
node.target(*args, **kwargs)
counted_flops = flop_counter_mode.get_total_flops()
if is_grouped(node):
node_type = "grouped"
elif is_small_channel(node):
node_type = "small"
elif is_in_out_channel(node):
node_type = "in_out"
else:
node_type = "default"
flop_counts[node_type] += counted_flops
else:
log.debug("Conv inputs meta not found")
# average benchmarked channels last speedup / slowdown, < 1 is speedup.
# taken from the set of convolution inputs in benchmarks/dynamo/microbenchmarks/operator_inp_logs/torchbench_train/
# To regenerate these numbers follow https://gist.github.com/eellison/55d7a6ed6f39829d68ac56f95f4df5bb
GROUPED_MULTIPLIER = 1.358
DEFAULT_MULTIPLIER = 0.823
IN_OUT_MULTIPLIER = 0.725
SMALL_MULTIPLIER = 0.783
total_flops = sum(flop_counts.values())
# TODO - get different values per hardware
weighted_flops = (
flop_counts["grouped"] * GROUPED_MULTIPLIER
+ flop_counts["small"] * SMALL_MULTIPLIER
+ flop_counts["in_out"] * IN_OUT_MULTIPLIER
+ flop_counts["default"] * DEFAULT_MULTIPLIER
)
do_layout_opt = weighted_flops <= total_flops
if not do_layout_opt:
log.debug(
"Skipped layout opt in inference because weighted flops indicate slowdown, default: %d, channels last: %d",
total_flops,
weighted_flops,
)
return do_layout_opt
# Channels last layout can dramatically hurt grouped conv perf. E.g.
# Conv with arguments like
# {"input_shape": [32, 224, 112, 112], "weight_shape": [224, 112, 3, 3],
# "stride": [2, 2], "padding": [1, 1], "groups": 2}
# slows down 31x using channels last..
# But a lot of timm models use depthwise separable convolution which will
# result in grouped convolution with in-channel size == 1.
# For those grouped convolution, channels last still helps a lot.
# E.g.
# Conv with arguments
# {"input_shape": [128, 58, 56, 56], "weight_shape": [58, 1, 3, 3],
# "stride": [2, 2], "padding": [1, 1], "groups": 58}
# get 1.86x speedup with channels last layout.
#
# The following heuristics skip using channels-last if the model contains
# grouped convolution with in-channels > 1.
if any(map(is_grouped, conv_nodes)):
log.debug(
"Skip layout opt because found grouped convolution with >1 in_channels!"
)
return False
# For some models that contain convolution with larger in-channel than out-channel, applying
# channels last hurts performance.
# Following models are skipped due to this:
# - pytorch_unet
# - phlippe_densenet (slightly worse)
# - Background_Matting (1.22x -> 0.821x)
# - pytorch_CycleGAN_and_pix2pix (1.597x -> 1.294x)
if any(map(is_in_out_channel, conv_nodes)):
log.debug(
"Skip layout opt because some convolutions have smaller out_channel"
)
return False
# Following models are skipped due to this:
# - functorch_maml_omniglot
if all(map(is_small_channel, conv_nodes)):
log.debug("Skip layout opt because all convolution channels are too small")
return False
return True
def qualify_name(self, name: str) -> str:
"""Prepend the given name with the graph name if any."""
if self.name is not None:
return f"{self.name}_{name}"
return name
def make_subgraph(
self,
gm: torch.fx.GraphModule,
example_inputs: list[torch.Tensor],
subgraph_name: str,
) -> SubgraphLowering:
"""
Make a subgraph of the current graph with all inherited parts, except
the graph module (`gm`) and `example_inputs`. The subgraphs are lowered
separately and lifted into a separate function in the parent output
wrapper code. The subgraph name is qualified by the parent graph's
name. Note that the lifting of subgraph is supported for python wrapper
only. For cpp wrapper, we inline the subgraphs in the parent wrapper.
"""
return SubgraphLowering(
parent=self,
gm=gm,
example_inputs=example_inputs,
shape_env=self._shape_env,
cpp_wrapper=self.cpp_wrapper,
aot_mode=self.aot_mode,
extern_node_serializer=self.extern_node_serializer,
is_inference=self.is_inference,
is_backward=self.is_backward,
name=self.qualify_name(subgraph_name),
)
def find_nodes_prefer_channels_last(self) -> OrderedSet[Node]:
"""
The rule to decide if an node prefer channels last is simple.
1. if it's input/output of a convolution
2. if one of its user prefers channels last
We have rule 1 because cudnn runs a faster convolution kernel for channels last inputs;
Rule 2 is also important. It makes sure that indirect inputs to convolution also prefers
channels last.
Consider the scenario: conv -> batch-norm -> relu -> conv
Without rule 2, batch-norm output may use a contiguous layout. That will cause 2 extra copies:
1. the output of batch-norm should be channels last initially since its input is a conv's output.
Forcing the batch-norm's output to be contiguous results in the first copy
2. The second conv's input is initially contiguous. This layout is propagated from the batch-norm's output.
We need convert it to channels last layout which results in the second copy.
With rule 2, we makes sure all the tensors in the chain uses channels last layout. So both copies
can be saved.
"""
output_set = OrderedSet[Node]()
for n in reversed(self.module.graph.nodes): # type: ignore[arg-type, union-attr]
if n.target == torch.ops.aten.convolution.default:
output_set.add(n)
continue
for user in n.users:
if user in output_set:
output_set.add(n)
break
# need a second pass to add downstream nodes of those channel last nodes to the sets.
# This pass is especially needed to avoid mix-layout kernel inputs in backward pass.
#
# Let's say a conv-batchnorm 's output is passed to relu whose output is in turn returned
# from the fwd graph. Without this second pass, we will force relu's output to be contiguous.
# Then in the kernel in backward pass, the contiguous output of relu may be mix with other channels last
# tensors and passed to a kernel.
#
# This pass improve yolov3 training speedup from 1.116x (worse than disabling layout optimization speedup 1.196x) to 1.457x.
# It also improves dla102 training speedup from 1.240x (worse than disabling layout optimization speedup 1.523x) to 1.835x .
# This also helps the following models:
# - res2net101_26w_4s
# - res2net50_14w_8s
# - sebotnet33ts_256
for n in self.module.graph.nodes: # type: ignore[union-attr]
if n in output_set:
output_set.update(n.users)
return output_set
def warn_fallback(self, name: str) -> None:
if name not in self._warned_fallback:
self._warned_fallback.add(name)
perf_hint_log.info("Using FallbackKernel: %s", name)
def add_device_info(self, device: torch.device) -> None:
self.device_types.add(device.type)
if device.index is not None:
self.device_idxs.add(device.index)
if V.graph.current_node and device not in self.device_node_mapping:
self.device_node_mapping[device] = V.graph.current_node
@property
def fake_mode(self) -> torch._subclasses.fake_tensor.FakeTensorMode:
return V.fake_mode
def try_get_buffer(
self, buffer_name: str
) -> Optional[Union[ir.TensorBox, ir.Buffer, ir.TorchBindObject]]:
if buffer_name in self.name_to_buffer:
return self.name_to_buffer[buffer_name]
if buffer_name in self.graph_inputs:
return self.graph_inputs[buffer_name]
if buffer_name in self.constants:
data = V.graph.constants[buffer_name]
return ir.ConstantBuffer(
name=buffer_name,
layout=ir.FixedLayout(
data.device, data.dtype, *V.graph.static_sizes_strides(data)
),
)
return None
def add_symbol_graph_input(self, symbol: sympy.Expr) -> None:
raise RuntimeError("Should not be called for the main graph")
def get_buffer(
self, buffer_name: str
) -> Union[ir.TensorBox, ir.Buffer, ir.TorchBindObject]:
buf = self.try_get_buffer(buffer_name)
if buf is not None:
return buf
raise RuntimeError(f"Failed to find buffer matching name {buffer_name}")
def get_dtype(self, buffer_name: str) -> torch.dtype:
if buffer_name in self.constants:
return self.constants[buffer_name].dtype
# For a mutation op we should return the dtype of the buffer being mutated
if (
hasattr(self.scheduler, "mutation_real_name")
and buffer_name in self.scheduler.mutation_real_name
):
mutated_buf = self.scheduler.mutation_real_name[buffer_name]
if mutated_buf in self.name_to_buffer:
return self.name_to_buffer[mutated_buf].get_dtype()
if mutated_buf in self.graph_inputs:
return self.graph_inputs[mutated_buf].get_dtype()
if buffer_name in self.name_to_buffer:
return self.name_to_buffer[buffer_name].get_dtype()
if buffer_name in self.graph_inputs:
return self.graph_inputs[buffer_name].get_dtype()
m = re.match(r"(as_strided|reinterpret_tensor)\(([a-zA-Z0-9_]+),", buffer_name)
if m:
return self.get_dtype(m.group(1))
raise KeyError(f"could not find {buffer_name}")
def get_numel(self, buffer_name: str) -> Union[int, Expr]:
if buffer_name in self.constants:
return self.constants[buffer_name].numel()
if buffer_name in self.name_to_buffer:
buf = self.name_to_buffer[buffer_name]
if not buf.has_tensor_output():
return 1
return buf.get_numel()
if buffer_name in self.graph_inputs:
return self.graph_inputs[buffer_name].get_numel()
raise KeyError(f"could not find {buffer_name}")
def run(self, *args: Any) -> Any: # type: ignore[override]
with dynamo_timed("GraphLowering.run"):
return super().run(*args)
def register_operation(self, op: ir.Operation) -> str:
assert op.operation_name is None, f"Operation registered twice: {op}"
assert isinstance(op, ir.Operation)
name = self.qualify_name(f"op{len(self.operations)}")
self.operations.append(op)
self.name_to_op[name] = op
op.operation_name = name
return name
def register_buffer(self, buffer: ir.Buffer, *, set_name: bool = False) -> str:
name = self.qualify_name(f"buf{len(self.buffers)}")
self.buffers.append(buffer)
self.name_to_buffer[name] = buffer
device = buffer.get_device()
if (
# Skip empty CPU tensor so that CUDA graphs can succeed, see https://github.com/pytorch/pytorch/pull/114144
device is not None
and not (
isinstance(buffer, ir.ComputedBuffer)
and buffer.is_zero_elements()
and device == torch.device("cpu")
)
):
self.add_device_info(device)
if set_name:
buffer.name = name
return name
def register_operation_list(self, operation_names: list[str]) -> str:
name = self.qualify_name("list_" + "_".join(operation_names))
self.lists[name] = operation_names
return name
def register_users_of(
self, node_output: Union[Iterable[ir.IRNode], ir.IRNode]
) -> None:
def register(value: Union[Iterable[ir.IRNode], ir.IRNode]) -> None:
if isinstance(value, (list, tuple)):
for x in value:
register(x)
if isinstance(value, ir.TensorBox):
for read_name in value.get_read_names():
self.name_to_users[read_name].append(value)
register(node_output)
def mark_buffer_mutated(self, name: str) -> None:
"""
When a buffer is mutated we need to make sure all the reads to
the old version are realized before the mutation happens.
"""
assert isinstance(name, str)
self.mutated_buffers.add(name)
if name not in self.name_to_users:
return
for user in self.name_to_users[name]:
user.realize()
def get_original_value_of_constant(self, name: str) -> torch.Tensor:
"""
In AOTI, module buffers may have been mutated during the tracing and compilation.
Thus we need to read from previously stored original buffers, to make sure the
generated model.so uses correct initial values.
"""
assert name in self.allocated_constant_name and name in self.constants, (
"Can not find the original value for " + name
)
orig_name = get_cloned_parameter_buffer_name(self.allocated_constant_name[name])
return (
self.module.meta[orig_name] # type: ignore[index]
if orig_name in self.module.meta # type: ignore[operator]
else self.constants[name]
)
def allocate_non_dup_const_name(
self, name: Optional[str], data: Union[Tensor]
) -> str:
if not config.aot_inductor.use_runtime_constant_folding:
for constant_name, value in self.constants.items():
if is_same_tensor(data, value):
return constant_name
if name is None:
name = f"constant{len(self.constants)}"
orig_name = name
if name[0].isdigit():
name = f"constant_{name}"
name = self.qualify_name(name)
# We may generate a var name for each constant in the codegen.
# Let's only keep sane characters.
prefix = normalize_name(name)
name = prefix
cnt = 0
while name in self.constants:
name = f"{prefix}_{cnt}"
cnt += 1
self.constants[name] = data
self.constant_reprs[name] = (
f"{data.device!r} {data.dtype!r} "
f"{tuple(data.size())!r} {tuple(data.stride())!r} "
f"{hash(data):x}"
)
self.allocated_constant_name[name] = orig_name # type: ignore[assignment]
return name
def add_tensor_constant(
self, data: Tensor, name: Optional[str] = None
) -> TensorBox:
new_name = self.allocate_non_dup_const_name(name, data)
return TensorBox.create(
ir.ConstantBuffer(
name=new_name,
layout=FixedLayout(
data.device, data.dtype, *self.static_sizes_strides(data)
),
)
)
def constant_name(self, name: str, device_override: Optional[torch.device]) -> str:
"""
We AOT copy constants to the devices they are needed on.
If device_override doesn't match the constant's device, then
copy it and return a different name.
"""
if self.constants[name].device == device_override or device_override is None:
return name
with torch.utils._python_dispatch._disable_current_modes():
# caller might have OrderedSet fake tensor mode which will create a fake tensor
# when calling .to, so unset modes here
return self.allocate_non_dup_const_name(
f"{name}_{device_override.type}{device_override.index or 0}",
self.constants[name].to(device_override),
)
def placeholder(
self,
target: str, # type: ignore[override]
args: tuple[object], # type: ignore[override]
kwargs: dict[str, object],
) -> Union[Expr, TensorBox, None]:
self.placeholder_idx += 1
example = super().placeholder(target, args, kwargs) # type: ignore[arg-type]
target = self.qualify_name(target)
if isinstance(example, SymTypes):
# TODO fix partitioning issue and re-enable for backward
# https://github.com/pytorch/pytorch/issues/155468.
if not V.graph.is_backward:
expr = _get_placeholder_expr(example.node)
else:
expr = example.node.expr
self.graph_inputs[target] = expr
self.graph_input_names.append(target)
return expr
elif isinstance(example, (int, bool, float)):
expr = sympy.sympify(example)
self.graph_inputs[target] = expr
self.graph_input_names.append(target)
return expr
elif isinstance(example, FakeScriptObject):
obj = TorchBindObject(name=target, value=example)
self.graph_inputs[target] = obj
self.graph_input_names.append(target)
return obj
elif example is None:
self.graph_input_names.append(target)
return None
if isinstance(example, BackwardState):
# Ignored arg, must be unused
# Alternately we could filter this out in AotAutograd
self.graph_input_names.append(target)
return None
# See note: Note: [Generator arguments in AOTDispatcher]
elif isinstance(example, torch.Generator):
assert (
len(V.graph.current_node.users) == 1
and next(iter(V.graph.current_node.users)).target
is torch._prims.rng_prims.graphsafe_run_with_rng_state
)
gen = ir.GeneratorState(name=target, device=example.device)
self.graph_inputs[target] = gen # type: ignore[assignment]
self.graph_input_names.append(target)
return gen
assert isinstance(example, torch.Tensor), example
# todo(chilli): We can remove the last check once we turn buffers into
# static shape tensors. That's a hack to workaround Inductor believing
# the buffer should be static but us passing in a fake tensor with
# symbolic shapes.
if not example._has_symbolic_sizes_strides:
# the first N inputs are weights
sizes, strides = self.static_sizes_strides(example)
else:
sizes, strides = self.symbolic_sizes_strides(example) # type: ignore[assignment]
if (
self.is_backward
and self.bw_donated_idxs
and self.placeholder_idx in self.bw_donated_idxs
):
tensor = TensorBox.create(
DonatedBuffer(
name=target,
layout=FixedLayout(example.device, example.dtype, sizes, strides),
)
)
else:
# TODO(jansel): handle input aliasing
tensor = TensorBox.create(
InputBuffer(
name=target,
layout=FixedLayout(example.device, example.dtype, sizes, strides),
)
)
self.graph_inputs[target] = tensor
self.graph_input_names.append(target)
self.graph_inputs_original[target] = tensor.data.data
if self.current_node.users: # cudagraphs should work with an unused CPU input
self.add_device_info(example.device)
# Note: [Input Alignment handling in Inductor]
# Alignment matters for generating efficient code. Some operations,
# e.g. vectorized loads, can only be performed on aligned inputs.
#
# But if we codegen assuming aligned inputs and then get unaligned
# inputs at runtime, then we are forced to clone - which is bad for
# both perf and memory usage.
#
# One option would be to guard on storage_offset%ALIGNMENT, and then
# codegen based on this. But storage_offset guards turned out to be
# expensive and cause recompiles; Instead, we're generating code
# based on the alignment of the example input without guarding.
with maybe_get_suppress_shape_guards_ctx():
if not should_assume_input_aligned(example):
self.unaligned_buffers.add(target)
return tensor
def call_function(self, target: Callable, args: Any, kwargs: dict[str, Any]) -> Any: # type: ignore[type-arg, override]
if target is operator.getitem and isinstance(args[0], (list, tuple, dict)):
return super().call_function(target, args, kwargs)
# hasattr on OpOverloadPacket is slow, check isinstance first
if not isinstance(target, torch._ops.OpOverloadPacket) and hasattr(
target, "_inductor_lowering_function"
):
# passthrough lowerings from .pattern_matcher
return target(*args, **kwargs)
if target not in lowerings:
assert isinstance(target, torch._ops.OpOverload), (
f"{target} is not an OpOverload"
)
base_name = target.name().split(".")[0]
if base_name in FALLBACK_ALLOW_LIST:
make_fallback(target, warn=False, override_decomp=True)
elif config.implicit_fallbacks:
error = (
MissingOperatorWithDecomp
if get_decompositions([target])
else MissingOperatorWithoutDecomp
)
log.info(
"Creating implicit fallback for:\n%s",
error.operator_str(target, args, kwargs),
)
tag = get_layout_constraint_tag(target, with_default=False)
if (
tag is None
and torch._library.utils.is_builtin(target)
and self.is_backward
):
# for implicit fallback ATen ops during backward, if there
# is no layout constraint tag, we conservatively require contiguous
# input since some eager kernels do not
# support non-contiguous inputs. Otherwise they may silently cause
# accuracy problems. Check https://github.com/pytorch/pytorch/issues/140452
# We only do this For ATen ops and for backward.
#
# TODO: should really switch to "needs_fixed_stride" constraint on these
# and identify them one by one.
decided_constraint = require_contiguous # type: ignore[assignment]
else:
tag = get_layout_constraint_tag(target, with_default=True)
decided_constraint = tag_to_layout_constraint(tag)
make_fallback(target, layout_constraint=decided_constraint)
elif get_decompositions([target]):
# There isn't a good way to dynamically patch this in
# since AOT Autograd already ran. The error message tells
# the user how to fix it.
raise MissingOperatorWithDecomp(target, args, kwargs)
else:
raise MissingOperatorWithoutDecomp(target, args, kwargs)
try:
log.debug(" via %s", lowerings[target]) # type: ignore[index]
n = self.current_node
layout_constraints = maybe_layout_constraints(target)
if layout_constraints:
old_args, old_kwargs = args, kwargs
if layout_constraints is constrain_to_fake_tensors:
# only constrain_to_fake_tensor if this exists.
# otherwise, no constraints at all: the implication is
# that this operator was inserted by a custom pass
# so we'll give them the freedom.
if "eager_input_vals" in n.meta:
fake_args, fake_kwargs = n.meta["eager_input_vals"]
# (fake_args, fake_kwargs) might not align with (args, kwargs).
# we need to normalize them based on the schema
assert isinstance(target, torch._ops.OpOverload)
def normalize(args: Any, kwargs: Any) -> tuple[Any, Any]:
result = torch.fx.operator_schemas.normalize_function(
target, args, kwargs
)
assert result is not None
return result[0], result[1]
fake_args, fake_kwargs = normalize(fake_args, fake_kwargs)
args, kwargs = normalize(args, kwargs)
old_args, old_kwargs = normalize(old_args, old_kwargs)
args, kwargs = constrain_to_fake_tensors(
args, kwargs, fake_args, fake_kwargs
)
else:
args, kwargs = layout_constraints(n, *args, **kwargs)
out = lowerings[target](*args, **kwargs) # type: ignore[index]
if layout_constraints:
# layout_constraints are allowed to make new copies of the inputs.
# if they do, and if the target is mutable, then we need to
# write the new values back into the original inputs.
self.propagate_mutation(n, old_args, old_kwargs, args, kwargs) # type: ignore[possibly-undefined]
return out
except Exception as e:
raise LoweringException(e, target, args, kwargs).with_traceback(
e.__traceback__
) from None
@staticmethod
def can_inline_constant(t: torch.Tensor) -> bool:
"""
True if this is a small constant attr that will be inlined.
"""
return len(t.shape) == 1 and t.shape[0] <= 8
def get_attr(
self,
target: str, # type: ignore[override]
args: tuple[()], # type: ignore[override]
kwargs: dict[str, object],
) -> Union[Constant, TensorBox, ir.Subgraph, TorchBindObject]:
# this is a constant
value = getattr_recursive(self.module, target) # type: ignore[arg-type]
if isinstance(value, torch.fx.GraphModule):
# Reuse the existing subgraph if we have seen it before already.
if target in self.seen_subgraphs:
return self.seen_subgraphs[target]
out = ir.Subgraph(name=target, graph_module=value)
self.seen_subgraphs[target] = out
return out
if isinstance(value, torch._C.ScriptObject):
self.torchbind_constants[target] = value
self.constant_reprs[target] = ""
return TorchBindObject(name=target, value=value)
elif isinstance(value, FakeScriptObject):
self.torchbind_constants[target] = value
self.constant_reprs[target] = ""
return TorchBindObject(name=target, value=value)
assert isinstance(value, torch.Tensor)
if (
config.aot_inductor.use_runtime_constant_folding
or config.always_keep_tensor_constants
or unsupported_output_tensor(value)
):
return self.add_tensor_constant(value, target)
with no_dispatch():
if value.shape == ():
return Constant(
value=value.item(), dtype=value.dtype, device=value.device
)
if self.can_inline_constant(value):
log.debug("Inlining constant: %s ", str(target))
# tensor lowering has constant inlining logic
from .lowering import tensor
return tensor(value.tolist(), dtype=value.dtype, device=value.device)
return self.add_tensor_constant(value, target)
def call_module(self, target: Any, args: Any, kwargs: Any) -> NoReturn:
raise AssertionError
def call_method(self, target: Any, args: Any, kwargs: Any) -> NoReturn:
raise AssertionError
def output(
self,
target: str, # type: ignore[override]
args: tuple[object], # type: ignore[override]
kwargs: dict[str, object],
) -> None:
result = super().output(target, args, kwargs) # type: ignore[arg-type]
if not isinstance(result, (tuple, list)):
# nested subgraphs can have singleton outputs
result = (result,)
assert isinstance(result, (tuple, list)), type(result)
assert all(
isinstance(
x,
(
TensorBox,
ir.Constant,
type(None),
ir.ConstantBuffer,
sympy.Expr,
sympy.logic.boolalg.Boolean,
int,
ir.EffectfulKernel,
ir.ShapeAsConstantBuffer,
),
)
for x in result
), result
fx_node_args = V.graph.current_node.args[0] # type: ignore[arg-type]
if not isinstance(fx_node_args, (tuple, list)):
# nested subgraphs can have singleton outputs
fx_node_args = (fx_node_args,)
result = [ir.ExternKernel.realize_input(x) for x in result]
result_correct_strides = []
assert len(fx_node_args) == len(result)
for r, fx_node in zip(result, fx_node_args):
if not isinstance(r, (ir.TensorBox, ir.BaseView)):
result_correct_strides.append(r)
elif isinstance(r.get_output_spec(), ir.CommBufferLayout):
# Active references to persistent comm buffers are not allowed
# outside of graphs
result_correct_strides.append(ir.ExternKernel.copy_input(r))
else:
# AOT Autograd tries to detect stride divergence of inductor from output metadata.
# Here, we try to avoid spurious divergence by matching insignificant strides such as
# should have already been realized
assert torch._inductor.ir.is_storage_and_layout(r)
meta_strides = [
s.node.expr if isinstance(s, torch.SymInt) else s
for s in fx_node.meta["val"].stride()
]
result_correct_strides.append(
ir.try_match_insignificant_strides(r, meta_strides)
)
self.graph_outputs = result_correct_strides
value: ir.IRNode
for name, value in self.graph_inputs.items():
if isinstance(value, TorchBindObject):
continue
assert isinstance(
value, (TensorBox, sympy.Expr, torch._inductor.ir.GeneratorState)
), f"Unsupported inductor graph input type: {type(value)}"
if not isinstance(value, TensorBox):
continue
value.realize()
assert isinstance(value, TensorBox)
value = value.data
assert isinstance(value, ir.StorageBox)
value_storage_box = value
value = value.data
if not isinstance(value, InputBuffer) or value.get_name() != name:
# one of our inputs was mutated, need to turn that into a copy
ir.MutationLayoutSHOULDREMOVE.realize_into(
value, self.graph_inputs_original[name]
)
# replace output with mutated input
try:
ind = self.graph_outputs.index(value_storage_box)
self.graph_outputs[ind] = self.graph_inputs_original[name]
except ValueError:
pass
self.finalize()
log.debug(
"Force channels last inputs for %d conv for the current graph with id %d",
self.num_channels_last_conv,
self.graph_id if self.graph_id is not None else -1,
)
def finalize(self) -> None:
for buf in self.buffers:
buf.decide_layout()
@contextmanager
def set_current_node(self, node: torch.fx.Node): # type: ignore[no-untyped-def]
old = self.current_node
try:
self.current_node = node
yield
finally:
self.current_node = old
@contextmanager
def set_current_wrapper_code(self) -> Iterator[None]:
old = self.wrapper_code
try:
yield
finally:
self.wrapper_code = old
def propagate_mutation(
self,
fx_node: torch.fx.Node,
old_args: tuple[Any],
old_kwargs: dict[str, Any],
new_args: tuple[Any],
new_kwargs: dict[str, Any],
) -> None:
"""Propagate mutations on new_args/new_kwargs back to old_args/old_kwargs.
Assumes we may have cloned old_args/old_kwargs into new_args/new_kwargs
and then called fx_node(*new_args, **new_kwargs).
If fx_node mutates any of new_args/new_kwargs, and they are different from
old_args/old_kwargs, then we need to update the original tensor.
"""
assert len(old_args) == len(new_args)
assert len(old_kwargs) == len(new_kwargs)
if fx_node.target is torch.ops.higher_order.triton_kernel_wrapper_mutation:
kwargs = fx_node.kwargs["kwargs"]
assert isinstance(kwargs, dict)
mutated = torch._higher_order_ops.triton_kernel_wrap.get_mutated_tensors(
old_kwargs["kernel_idx"],
old_kwargs["constant_args_idx"],
{
k: v.meta["val"] if isinstance(v, torch.fx.Node) else v
for k, v in kwargs.items()
},
old_kwargs["tma_descriptor_metadata"],
)
for name in mutated:
old_arg = old_kwargs["kwargs"][name]
new_arg = new_kwargs["kwargs"][name]
if old_arg is new_arg:
continue
self.call_function(torch.ops.aten.copy_.default, (old_arg, new_arg), {})
return
assert isinstance(fx_node.target, torch._ops.OpOverload)
def maybe_propagate(
schema_arg: torch._C.Argument, old_arg: ir.IRNode, new_arg: ir.IRNode
) -> None:
if old_arg is new_arg:
return
if schema_arg.alias_info is not None and schema_arg.alias_info.is_write:
# The lowering for copy_ is smart enough to "replace" old_arg with
# new_arg in all future uses so a copy_ kernel never gets emitted.
# old_arg, new_arg may be immutable_list
if isinstance(old_arg, ir.IRNode):
old_arg = (old_arg,) # type: ignore[assignment]
new_arg = (new_arg,) # type: ignore[assignment]
for old_arg_item, new_arg_item in zip(old_arg, new_arg): # type: ignore[call-overload]
if old_arg_item is new_arg_item:
continue
self.call_function(
torch.ops.aten.copy_.default, (old_arg_item, new_arg_item), {}
)
schema = fx_node.target._schema
for idx, (old_arg, new_arg) in enumerate(zip(old_args, new_args)):
schema_arg = schema.arguments[idx]
maybe_propagate(schema_arg, old_arg, new_arg)
schema_kwargs = {arg.name: arg for arg in schema.arguments}
for key in old_kwargs.keys():
old_arg = old_kwargs[key]
new_arg = new_kwargs[key]
schema_arg = schema_kwargs[key]
maybe_propagate(schema_arg, old_arg, new_arg)
def run_node(self, n: torch.fx.Node) -> object:
def debug(msg: str) -> None:
log.debug("lowering %s %s", LazyString(n.format_node), msg) # type: ignore[arg-type]
from torch._inductor.compiler_bisector import CompilerBisector
buffer_watermark = len(self.buffers)
operation_watermark = len(self.operations)
# origins: OrderedSet[Union[Node, ir.IRNode]] = OrderedSet([n])
origins: OrderedSet[Any] = OrderedSet([n])
is_call_function = n.op == "call_function"
if is_call_function:
args, kwargs = self.fetch_args_kwargs_from_env(n)
origins |= gather_origins(args, kwargs)
with (
ir.IRNode.current_origins(origins),
self.set_current_node(n),
V.set_current_node(n),
):
if (
n.op == "call_function"
and n.target
not in (operator.getitem, torch._higher_order_ops.invoke_subgraph)
and (
fallback_node_due_to_unsupported_type(n)
or CompilerBisector.disable_subsystem(
"inductor", "lowerings", lambda: repr(n)
)
)
):
debug("fallback_handler")
result = fallback_handler(n.target, add_to_fallback_set=False)(
*args, # type: ignore[possibly-undefined]
**kwargs, # type: ignore[possibly-undefined]
)
elif (
n.op == "call_function"
and n.target is torch.ops.higher_order.triton_kernel_wrapper_mutation
and config.triton_kernel_default_layout_constraint != "flexible_layout"
):
debug("user_defined_triton_kernel_layout_constraints")
if (
config.triton_kernel_default_layout_constraint
== "needs_fixed_stride_order"
):
old_args = args # type: ignore[possibly-undefined]
old_kwargs = kwargs # type: ignore[possibly-undefined]
if eager_input_vals := n.meta.get("eager_input_vals"):
inp_args = eager_input_vals[0]
inp_kwargs = eager_input_vals[1]
args, kwargs = constrain_to_fake_tensors(
args, kwargs, inp_args, inp_kwargs
)
else:
args, kwargs = constrain_to_fx_strides(n, *args, **kwargs) # type: ignore[index]
result = self.call_function(n.target, args, kwargs) # type: ignore[arg-type]
self.propagate_mutation(n, old_args, old_kwargs, args, kwargs) # type: ignore[possibly-undefined]
else:
raise RuntimeError(
f"Unknown triton_kernel_default_layout_constraint: {config.triton_kernel_default_layout_constraint}"
)
elif is_magic_method(n.target):
# TODO: this is sus, it probably should be handled in the
# lowerings themselves similarly to sym_size/sym-stride
# https://github.com/pytorch/pytorch/issues/127789
debug("is_magic_method")
if isinstance(
n.meta["val"], (torch.SymInt, torch.SymFloat, torch.SymBool)
):
result = n.meta["val"].node.expr
else:
result = super().run_node(n)
else:
debug("")
result = super().run_node(n)
# require the same stride order for dense outputs,
# 1. user-land view() will not throw because inductor
# output different strides than eager
# long term the solution is to make view() always succeed
# with infallible strides.
# 2: as_strided ops, we need make sure its input has same size/stride with
# eager model to align with eager behavior.
as_strided_ops = [
torch.ops.aten.as_strided.default,
torch.ops.aten.as_strided_.default,
torch.ops.aten.as_strided_scatter.default,
torch.ops.aten.resize.default,
torch.ops.aten.resize_as.default,
]
is_output = any(user.op == "output" for user in n.users)
is_user_visible = n in self.user_visible_output_strides
is_input_for_as_strided = any(
user.target in as_strided_ops for user in n.users
)
if n.meta.get("inductor_realize_to_strides", False) and isinstance(
result, TensorBox
):
result.realize()
strides = n.meta["val"].stride()
sym_strides = torch._inductor.utils.any_is_symbolic(*strides)
if result.maybe_get_stride() != strides and not sym_strides:
stride_order = ir.get_stride_order(strides)
result = ir.ExternKernel.require_stride_order(result, stride_order)
if (
is_output
and isinstance(result, TensorBox)
and isinstance(result.data, ir.BaseView)
):
# Realize so that outputs are correctly aliased
result.realize()
if (is_output or is_input_for_as_strided) and isinstance(
n.meta["val"], torch.Tensor
):
if is_user_visible:
strides = self.user_visible_output_strides.get(n)
else:
strides = n.meta["val"].stride()
if strides is not None and len(strides) > 0:
allow_padding = (
config.pad_outputs or not is_user_visible
) and not is_input_for_as_strided
dense = torch._prims_common.is_non_overlapping_and_dense(
n.meta["val"]
)
unbacked_symbols_in_strides = (
len(free_unbacked_symbols(strides)) > 0
)
if (
not unbacked_symbols_in_strides
and dense
and len(result.get_size()) == 4
and n in self.nodes_prefer_channels_last
and not is_user_visible
and not is_input_for_as_strided
):
strides = ir.FlexibleLayout.stride_ordered_for_memory_format(
result.get_size(), torch.channels_last
)
if not unbacked_symbols_in_strides and len(strides):
# To avoid converting possible view ops to a copy kernel, we use the previous
# require_exact_strides to handle views. But ultimately it's better to require
# the right strides at the tensor definition.
if n.meta["val"]._is_view() or isinstance(
result.data, ir.BaseView
):
result = ir.ExternKernel.require_stride_order(
result,
ir.get_stride_order(strides),
allow_padding=allow_padding,
)
else:
strides = [
s.node.expr if isinstance(s, torch.SymInt) else s
for s in strides
]
result = ir.ExternKernel.require_exact_strides(
result, strides, allow_padding=allow_padding
)
# Realize if (1) any user need inputs realized, or (2) there is
# already too many reads and rematerializing can be bad.
num_users = len(OrderedSet(n.users))
if num_users > 1 and isinstance(result, TensorBox):
for user in n.users:
if user.target in needs_realized_inputs:
result.realize_hint()
# This inclusion is somewhat controversial (from
# discussion between Horace, Natalia, and Elias).
# Currently, it's not very clear why this is helpful.
# The general idea here is that even though a node may
# have FlexibleLayout, we still often *treat* it as if
# it was contiguous. This appears to sometimes result in
# suboptimal behavior.
#
# When we do a better job selecting layout, we should
# revisit this.
need_fixed_layout = [
torch.ops.aten.convolution_backward.default,
torch.ops.aten.mm.default,
torch.ops.aten._int_mm.default,
]
need_fixed_channels_last_layout = []
if not self.layout_opt:
need_fixed_layout.append(torch.ops.aten.convolution.default)
if torch._C._has_mkldnn:
need_fixed_layout += [
torch.ops.mkldnn._linear_pointwise.default,
torch.ops.mkldnn._linear_pointwise.binary,
torch.ops.aten.mkldnn_rnn_layer.default,
torch.ops.onednn.qlinear_pointwise.default,
torch.ops.onednn.qlinear_pointwise.tensor,
torch.ops.onednn.qlinear_pointwise.binary,
torch.ops.onednn.qlinear_pointwise.binary_tensor,
]
need_fixed_channels_last_layout += [
torch.ops.mkldnn._convolution_pointwise.default,
torch.ops.mkldnn._convolution_pointwise.binary,
torch.ops.mkldnn._convolution_pointwise_.binary,
torch.ops.mkldnn._convolution_transpose_pointwise.default,
torch.ops.onednn.qconv_pointwise.default,
torch.ops.onednn.qconv2d_pointwise.binary,
]
if torch._C.has_mkl:
need_fixed_layout += [torch.ops.mkl._mkl_linear.default]
if user.target in need_fixed_layout:
result = ir.ExternKernel.require_stride_order(
result,
ir.get_stride_order(n.meta["val"].stride()),
allow_padding=True,
)
if (
user.target in need_fixed_channels_last_layout
and n is user.args[0]
):
result = ir.ExternKernel.require_stride_order(
result,
ir.get_stride_order(
make_channels_last_strides_for(n.meta["val"].shape)
),
)
if user.op == "output":
if isinstance(result.data.data, (Pointwise, Reduction)):
result.realize()
# TODO(jansel): introduce a store vs inline choice
result.mark_reuse(len(n.users))
# Realize if the IRNode already has accumulated lots of reads
if isinstance(result, TensorBox) and result.has_exceeded_max_reads():
# Prevent excessive accumulation in a computed buffer, when
# there are multiple branches each with small number of memory
# reads, but they converge to a user.
result.realize_hint()
# Realize if a Pointwise has too much stuff to be inlined.
# As this may cause RecursionError during Inductor's evaluation.
if isinstance(result, TensorBox) and isinstance(result.data, StorageBox):
curr = result.data.data
if isinstance(curr, Pointwise):
# Use inner fn as a rough proxy. Good enough.
if curr.has_large_inner_fn(threshold=100):
result.realize()
# This is not complete, but it doesn't have to be: origin_node
# tracking is best effort. The logic here critically relies on direct
# TensorBox -> StorageBox denoting a non-view; we don't bother trying
# to get views to work. Feel free to add any extra cases as needed.
#
# Note: we can't YOLO tree_map over this result, because if there are
# buffers or a view involved, we might not be able to validly assign
# the origin_node here.
if isinstance(result, TensorBox) and isinstance(result.data, ir.StorageBox):
if isinstance(result.data.data, ir.Loops):
result.data.data._post_init_setattr("origin_node", n)
elif isinstance(result.data.data, ir.Buffer):
result.data.data._post_init_setattr("origin_node", n)
if isinstance(result.data.data, ir.ComputedBuffer) and isinstance(
result.data.data.data, ir.Loops
):
result.data.data.data._post_init_setattr("origin_node", n)
# Not really multi-output, can straightforwardly recurse in
elif (
isinstance(result.data.data, ir.MultiOutput)
and not result.data.data.indices
):
if isinstance(result.data.data.inputs[0], ir.Buffer):
result.data.data.inputs[0]._post_init_setattr("origin_node", n)
self.register_users_of(result)
new_unbacked_defs = OrderedSet[sympy.Symbol]()
for buf in self.buffers[buffer_watermark:]:
new_unbacked_defs |= buf.get_unbacked_symbol_defs()
for op in self.operations[operation_watermark:]:
new_unbacked_defs |= op.get_unbacked_symbol_defs()
shape_env = V.graph.sizevars.shape_env
# An input can an unbacked symint i.e.: when mark_unabcked is used.
# in that case add it to new_unbacked_defs.
if (
n.op == "placeholder"
and isinstance(result, sympy.Symbol)
and shape_env.is_unbacked_symint(result)
):
new_unbacked_defs.add(result)
def format_new_defs() -> str:
r = [
f"unbacked_symbol_defs={buf.get_unbacked_symbol_defs()} in:\n{buf}\n"
for buf in self.buffers[buffer_watermark:]
]
r.extend(
f"unbacked_symbol_defs={op.get_unbacked_symbol_defs()} in:\n{op}\n"
for op in self.operations[operation_watermark:]
)
return "***\n".join(r)
# We do not skip unbacked symints that are input for backward see the note below.
if V.graph.is_backward and n.op == "placeholder":
return result
# Note [Backwards runtime asserts]
# Backwards poses an interesting problem for deferred runtime
# asserts. In the easy case, we may solely close over data
# dependent sized tensors, and there are no binding sites for
# unbacked SymInts. In this case, we can just drop all the
# runtime asserts on the floor: no non-placeholder bindings, no
# problem.
#
# However, it is *possible* for a fresh runtime assert to show up
# between forwards and backwards. Right now, the freezing process
# that happens when we lower forwards means that we will freeze
# runtime asserts, and then the moment the backwards lowering
# process attempts to add a new deferred runtime assert, we will
# fail. Let's say you remove that assert. Now when we get here,
# we need to make sure we actually emit these asserts (because we
# can't emit them in forwards, we already compiled it). So we
# have to do something here. But we don't want to reemit ALL
# deferred runtime asserts, we only want to emit the NEW ones.
# Therefore needing some sort of stratification in the ShapeEnv.
# This is all doable, it just hasn't been done yet.
unbacked_bindings = resolve_unbacked_bindings(
V.graph.sizevars.shape_env, n.meta.get("unbacked_bindings", {})
)
assert unbacked_bindings is not None
# When we do lowering, it is possible we reallocate unbacked SymInts.
# So we need to line up the unbacked SymInts when performing the test
# here
#
# In principle, we could permit lowering to introduce MORE unbacked
# SymInts: as long as all the old unbacked ones are accounted for,
# it's fine for inductor to introduce extra calls to item()/unbacked()
# whatever. This actually happens in practice when an unbacked SymInt
# gets memoized away; naively, when Inductor reprocesses a kernel, it
# doesn't know that the memo still applies, and ends up allocating a
# new symbol. However, this is generally a bad thing: we may still
# end up needing to test equalities on the symbols, and a fresh
# symbol is likely to hit lots of GuardOnDataDependent errors that
# we already know facts for.
renamed_unbacked_bindings = OrderedSet(
V.fake_mode.shape_env.unbacked_renamings.get(s, s)
for s in unbacked_bindings.keys()
)
assert new_unbacked_defs >= renamed_unbacked_bindings, (
f"failed {new_unbacked_defs} >= {renamed_unbacked_bindings} (inductor >= fx)\n"
f"fx node is: {n.format_node()}\n"
f"new operations are:\n\n{format_new_defs()}"
)
self.create_deferred_runtime_asserts(n, new_unbacked_defs)
return result
def create_deferred_runtime_asserts(
self, n: torch.fx.Node, new_unbacked_defs: OrderedSet[sympy.Symbol]
) -> None:
# [NOTE] Codegen runtime asserts in Inductor
#
# We need to generate runtime asserts directly in Inductor instead
# of just reusing the asserts from input graphs because we reuse the
# same ShapeEnv as before. In particular, on subsequent graph passes,
# we would immediately turn all of these assertions into noops,
# because when we evaluated their expressions, we would see that
# because we had a deferred runtime assert in the ShapeEnv, we
# know "oh, of course this expression is True" already.
# One example is below:
#
# class Model(torch.nn.Module):
# def forward(self, a, b, c):
# nz = torch.nonzero(a)
# ones = a.new_ones([nz.size(0), b.size(0)])
# torch._check(ones.size(0) >= 1)
# equals = torch.add(ones, c)
# return equals
# torch._dynamo.mark_dynamic(c, 0)
# When we reuse the ShapeEnv in Inductor lowering, the check that checks
# a and nonzero have the same shape would be evaluated to True after we resolve
# unbacked bindings using the ShapeEnv.
# See test_unbacked_equals_input_size_runtime_assertion in test_aot_inductor.
#
#
# In addition to the Inductor generated runtime asserts, we also
# need the runtime asserts from the input graph, because some derived
# runtime asserts on backed symints are not generated in Inductor. One example is
# this: `y = x.reshape(100, -1).clone()`. x.shape[0] needs to be a multiple of 100.
# See test_aoti_runtime_asserts_backed_symint in test_aot_inductor.
def make_assert(expr: SympyBoolean, msg: str) -> None:
assert_op = ir.AssertScalar(expr, msg)
self.register_buffer(assert_op, set_name=True)
self.register_operation(assert_op)
if (
full_aoti_runtime_assert()
and n.target == torch.ops.aten._assert_scalar.default
and self.aot_mode
):
node_args, _ = self.fetch_args_kwargs_from_env(n)
if node_args[0] != True: # noqa: E712
make_assert(node_args[0], f"{node_args[0]} to be True")
else:
# bound_unbacked_symbols tracks the symbols that are created so far,
# we use it to make sure that runtime assertions are added after all
# symbols used in them are defined.
self.bound_unbacked_symbols |= new_unbacked_defs
shape_env = V.graph.sizevars.shape_env
# Emit code for runtime asserts that can be inserted at this point.
for i0 in new_unbacked_defs:
ras = self.ras_by_symbol.pop(i0, [])
# NB: size-like not needed, we won't retrace
vr = shape_env.var_to_range[i0]
if not shape_env._default_unspecified_value_range().issubset(vr):
def is_convertible(s: Expr) -> bool:
if s in (int_oo, -int_oo):
return False
try:
int(s)
return True
except TypeError:
return False
if is_convertible(vr.lower):
make_assert(i0 >= vr.lower, f"{i0} >= {vr.lower}")
if is_convertible(vr.upper):
make_assert(i0 <= vr.upper, f"{i0} <= {vr.upper}")
for ra in ras:
fvs = free_unbacked_symbols(ra.expr)
missing = fvs - self.bound_unbacked_symbols
if missing:
i1 = min(missing, key=str)
self.ras_by_symbol.setdefault(i1, []).append(ra)
else:
make_assert(ra.expr, f"{ra.expr}")
def validate_can_generate_cpp_wrapper(self) -> None:
if config.disable_cpp_codegen:
raise CppWrapperCodegenError("C++ codegen is disabled")
if sys.platform not in ("linux", "darwin", "win32"):
raise CppWrapperCodegenError(f"Unsupported platform {sys.platform}")
def init_wrapper_code(
self,
is_subgraph: bool = False,
subgraph_name: Optional[str] = None,
parent_wrapper_code: Optional[PythonWrapperCodegen] = None,
partition_signatures: Optional[GraphPartitionSignature] = None,
) -> None:
device_types = self.device_types.copy()
device_types.discard("cpu")
device_types.discard("meta")
# TODO(Eikan): Only support mixing cpu and other device now.
assert len(device_types) <= 1, "Does not support mixing {}".format(
"+".join(device_types)
)
only_cpu = len(device_types) == 0
self.device_type = "cpu" if only_cpu else device_types.pop()
if self.cpp_wrapper:
self.validate_can_generate_cpp_wrapper()
self.device_ops = get_device_op_overrides(self.device_type)
wrapper_code_gen_cls = get_wrapper_codegen_for_device(
self.device_type, self.cpp_wrapper
)
assert wrapper_code_gen_cls is not None, (
f"Device {self.device_type} not supported"
)
self.wrapper_code = wrapper_code_gen_cls.create(
is_subgraph,
subgraph_name,
parent_wrapper_code,
partition_signatures,
)
if self.const_module:
self.wrapper_code._names_iter = self.const_module.wrapper_code._names_iter
def extract_autotune_inputs(
self, example_inputs: list[Union[int, float, torch.Tensor]]
) -> None:
import copy
cloned_gm = copy.deepcopy(self.orig_gm)
example_inputs = copy.deepcopy(example_inputs)
triton_nodes = []
for node in cloned_gm.graph.nodes:
if (
node.op == "call_function"
and node.target is torch.ops.higher_order.triton_kernel_wrapper_mutation
):
triton_nodes.append(node)
# Store grid related nodes
grid_inputs: list[torch.fx.Node] = []
visited_grids: dict[torch.fx.Node, int] = {}
# Store kwargs related nodes
triton_inputs: dict[str, Any] = {}
kwargs_inputs: list[torch.fx.Node] = []
visited_kwargs: dict[Any, int] = {}
for node in triton_nodes:
# first check whether we have fx node in grid settings.
for grid in node.kwargs["grid"]:
for val in grid:
if val in visited_grids:
continue
if isinstance(val, torch.fx.Node):
visited_grids[val] = len(grid_inputs)
grid_inputs.append(val)
kwargs = node.kwargs["kwargs"]
# identify which args might be mutated, those should be cloned.
mutated = torch._higher_order_ops.triton_kernel_wrap.get_mutated_tensors(
node.kwargs["kernel_idx"],
node.kwargs["constant_args_idx"],
{
k: v.meta["val"] if isinstance(v, torch.fx.Node) else v
for k, v in kwargs.items()
},
node.kwargs["tma_descriptor_metadata"],
)
new_kwargs: dict[str, int] = {}
with cloned_gm.graph.inserting_before(node):
for k, v in kwargs.items():
if k in mutated:
new_node = cloned_gm.graph.call_function(torch.clone, args=(v,))
new_kwargs[k] = len(kwargs_inputs)
kwargs_inputs.append(new_node)
continue
if v in visited_kwargs:
new_kwargs[k] = visited_kwargs[v]
continue
visited_kwargs[v] = len(kwargs_inputs)
kwargs_inputs.append(v)
new_kwargs[k] = visited_kwargs[v]
triton_inputs[node.name] = new_kwargs
new_outputs = kwargs_inputs + grid_inputs
for node in cloned_gm.graph.nodes:
if node.op == "output":
node.args = (tuple(new_outputs),)
break
cloned_gm.recompile()
runner = torch.fx.Interpreter(cloned_gm)
returned_outputs = runner.run(example_inputs)
# Extract and store the grid for autotuning
if len(grid_inputs) > 0:
grid_outputs = returned_outputs[len(kwargs_inputs) :]
self.autotuning_grids = {}
for node in triton_nodes:
dynamic_grid = False
new_grids: list[tuple[Any]] = []
for grid in node.kwargs["grid"]:
new_grid = []
for val in grid:
if not isinstance(val, torch.fx.Node):
new_grid.append(val)
continue
dynamic_grid = True
new_grid.append(grid_outputs[visited_grids[val]])
new_grids.append(tuple(new_grid))
if dynamic_grid:
self.autotuning_grids[node.name] = new_grids
# Store the kwargs input for autotuning
self.autotuning_inputs = returned_outputs[: len(kwargs_inputs)]
self.autotuning_mapping = triton_inputs
def codegen_with_cpp_wrapper(
self,
) -> tuple[ValueWithLineMap, ValueWithLineMap]:
"""
For GPU, Triton kernels are autotuned and stored as cubin files
"""
if any(device in self.device_types for device in ["cuda", "xpu"]):
def extract_real_inputs() -> list[Union[int, float, torch.Tensor]]:
def materialize(
x: Union[torch.SymInt, torch.SymFloat, torch.Tensor],
) -> Union[int, float, torch.Tensor]:
if x is None:
return None
elif isinstance(x, (torch.SymInt, torch.SymFloat)):
# Need concrete value to run dynamic shapes and tune the result
return x.node.hint
elif isinstance(x, FakeTensor):
return defake(x)
else:
assert isinstance(x, torch.Tensor), (
"Unknown type when creating real inputs" + str(type(x))
)
return x
tracing_context = torch._guards.TracingContext.try_get()
if tracing_context is not None and not isinstance(
V.real_inputs, NullHandler
):
if tracing_context.output_strides:
tracing_context.output_strides.clear()
params_flat = [
param
for param in tracing_context.params_flat # type: ignore[union-attr]
if param is not None
]
real_inputs = [
materialize(x)
for x in itertools.chain(params_flat, V.real_inputs)
]
else:
# In the backward pass, V.real_inputs is not OrderedSet.
# Generating random inputs based on self.example_inputs sometimes can be problematic,
# e.g. illegal memory access. A comprehensive fix is to autotune in a separate process.
real_inputs = [
materialize(x) # type:ignore[arg-type]
for x in (
self.example_inputs # type:ignore[union-attr]
if isinstance(V.real_inputs, NullHandler)
else V.real_inputs
)
]
if self.mutated_inputs:
from .compile_fx import clone_preserve_strides
mutated_input_idxs = [
idx
for idx, name in enumerate(self.graph_inputs)
if name in self.mutated_inputs
and isinstance(real_inputs[idx], torch.Tensor)
]
for idx in mutated_input_idxs:
# clone mutated Tensor inputs to avoid mutating them in
# the first pass of the CPP wrapper-based compilation, as
# this will lead to a side effect on the example inputs:
# e.g. if torch.compile(f)(x) if called on input-mutating
# f, the inputs x will be mutated twice in the process:
# once here, and again when running the compiled model;
# this will also lead to a numerically incorrect output
mutated_inp = real_inputs[idx]
assert isinstance(mutated_inp, torch.Tensor)
real_inputs[idx] = clone_preserve_strides(mutated_inp)
del mutated_inp
return real_inputs
if config.triton.autotune_at_compile_time:
# If autotune_at_compile_time is True, we can do the codegen in one-pass
# We will construct the autotuning values if user defined kernel exists.
if config.triton.autotune_with_sample_inputs:
user_defined_kernels = False
for op in self.operations:
if isinstance(op, ir.UserDefinedTritonKernel):
user_defined_kernels = True
break
if user_defined_kernels:
real_inputs = extract_real_inputs()
self.extract_autotune_inputs(real_inputs)
return self.codegen()
else:
# first pass
self.cpp_wrapper = False
compiled = self.compile_to_module().call
real_inputs = extract_real_inputs()
with torch.utils._python_dispatch._disable_current_modes():
compiled(real_inputs)
del real_inputs
# second pass
self.cpp_wrapper = True
self.removed_buffers.clear()
self.removed_operations.clear()
self.inplaced_to_remove.clear()
V.graph.sizevars.precomputed_replacements.clear()
V.graph.sizevars.inv_precomputed_replacements.clear()
metrics.reset()
with config.patch({"triton.autotune_at_compile_time": False}):
return self.codegen()
else:
# cpu
return self.codegen()
def _update_scheduler(self) -> None:
"""
(Re)initializes the scheduler member. When initializing the scheduler, no CUBIN
files should be generated (to avoid biasing any benchmarks and pessimizing
fusion decisions).
"""
from .scheduler import Scheduler
with config.patch("triton.store_cubin", False):
self.scheduler = Scheduler(self.operations)
def codegen(self) -> tuple[ValueWithLineMap, ValueWithLineMap]:
with dynamo_timed("GraphLowering.codegen", log_pt2_compile_event=True):
self.init_wrapper_code()
self._update_scheduler()
V.debug.draw_orig_fx_graph(self.orig_gm, self.scheduler.nodes)
self.wrapper_code.push_codegened_graph(self)
self.scheduler.codegen()
log.debug(
"Finished codegen for all nodes. The list of kernel names available: %s",
V.graph.all_codegen_kernel_names,
)
result = self.wrapper_code.generate(self.is_inference)
self.wrapper_code.pop_codegened_graph()
return result
def codegen_subgraph(self, parent_graph: GraphLowering) -> None:
"""
This is a more compact version of the `codegen()` above
where we codegen this graph as a subgraph of some parent
graph. The parent graph is passed as an argument: the
intention is to inline codegening of the subgraph in
the parent graph's wrapper code (including the generated
kernels). The wrapper code is not finalized (via `.generate()`
call), as this will be done in the parent graph's `codegen()`.
"""
with dynamo_timed("GraphLowering.codegen_subgraph", log_pt2_compile_event=True):
self.wrapper_code = parent_graph.wrapper_code
self.device_ops = parent_graph.device_ops
self.cpp_wrapper = parent_graph.cpp_wrapper
self._update_scheduler()
self.scheduler.codegen()
def count_bytes(
self,
) -> tuple[
int, list[tuple[BaseSchedulerNode, int]], list[tuple[BaseSchedulerNode, float]]
]:
total_bytes = 0
node_counts = []
node_runtimes = []
for node in self.scheduler.nodes:
num_bytes = node.get_read_write_buffers_sizes()
total_bytes += num_bytes
node_counts.append((node, num_bytes // 4))
node_runtimes.append((node, node.get_estimated_runtime()))
return total_bytes, node_counts, node_runtimes
# No-op to be patched for unit tests
save_output_code: Optional[Callable[[str], None]] = None
def compile_to_module(self) -> CompiledModule:
with dynamo_timed(
"GraphLowering.compile_to_module",
phase_name="code_gen",
log_pt2_compile_event=True,
dynamo_compile_column_us="inductor_code_gen_cumulative_compile_time_us",
):
return self._compile_to_module()
def _compile_to_module(self) -> CompiledModule:
# If we're here, we don't have to worry about the kernel code, which is only
# returned separately in AOTInductor mode.
wrapper_code, _ = (
self.codegen_with_cpp_wrapper() if self.cpp_wrapper else self.codegen()
)
if isinstance(wrapper_code, ValueWithLineMap):
mod = self._compile_to_module_lines(wrapper_code)
elif isinstance(wrapper_code, FileBackedGraphModule):
mod = wrapper_code
else:
raise NotImplementedError(
f"Unrecognized wrapper code type: {type(wrapper_code)}"
)
# Logged twice as per https://github.com/pytorch/pytorch/pull/99038#discussion_r1167826029
# TODO. Revisit this once the logging API is more mature
assert mod.__file__ is not None
log_module_code(mod.__file__)
log.debug("Output code written to: %s", mod.__file__)
output_code_log.info("Output code written to: %s", mod.__file__)
if config.benchmark_kernel:
print(f"Compiled module path: {mod.__file__}", file=sys.stderr)
V.debug.output_code(mod.__file__)
V.debug.copy(os.path.splitext(mod.__file__)[0] + ".debug")
return mod
def _compile_to_module_lines(
self, wrapper_code: ValueWithLineMap
) -> CompiledModule:
from .codecache import PyCodeCache
if config.triton.autotune_at_compile_time:
# sanitize docstrings in kernel defs (#155006)
kernel_autotune_defs = self.wrapper_code.kernel_autotune_defs.getvalue()
kernel_autotune_defs = kernel_autotune_defs.replace('"""', '\\"\\"\\"')
tuning_code = (
'"""\n'
+ "Compile-time auto-tuning block: \n"
+ kernel_autotune_defs
+ self.wrapper_code.kernel_autotune_calls.getvalue()
+ '"""\n'
)
wrapper_code.value = tuning_code + wrapper_code.value
if GraphLowering.save_output_code is not None:
GraphLowering.save_output_code(wrapper_code.value)
output_code_log.debug("Output code: \n%s", wrapper_code.value)
inductor_meta = autotune_cache.inductor_meta_from_config()
AutotuneCacheBundler.begin_compile(inductor_meta, code=wrapper_code.value)
try:
linemap = [
(line_no, node.stack_trace) # type: ignore[attr-defined]
for line_no, node in wrapper_code.line_map
]
key, path = PyCodeCache.write(wrapper_code.value)
output_code_log.debug("Output code written to: %s", path)
except Exception:
trace_structured(
"inductor_output_code",
# Just omit the filename, I still want the code though!
payload_fn=lambda: wrapper_code.value,
)
raise
else:
trace_structured(
"inductor_output_code",
lambda: {"filename": path},
payload_fn=lambda: wrapper_code.value,
)
with dynamo_timed("PyCodeCache.load_by_key_path", log_pt2_compile_event=True):
mod = PyCodeCache.load_by_key_path(
key,
path,
linemap=linemap, # type: ignore[arg-type]
attrs={**self.constants, **self.torchbind_constants},
)
self.cache_key = key
self.cache_path = path
self.cache_linemap = linemap # type: ignore[assignment]
if config.benchmark_harness and config.profile_bandwidth_output:
# run the inputs code gen to get the bandwidth info
mod.benchmark_compiled_module(times=1, repeat=1)
return mod
def get_output_names(self) -> list[str]:
names = []
shape_counter = itertools.count(0)
none_counter = itertools.count(0)
for node in self.graph_outputs:
if isinstance(node, ir.NoneAsConstantBuffer):
names.append(f"{self.name}_none{next(none_counter)}")
elif isinstance(node, ir.ShapeAsConstantBuffer):
names.append(f"{self.name}_shape{next(shape_counter)}")
else:
names.append(node.get_name())
return names
def is_unspec_arg(self, name: str) -> bool:
# dynamo wraps unspec variable as 0d CPU tensor,
# need to convert to scalar during codegen (triton only)
return (
name in self.graph_inputs.keys()
and self.graph_inputs[name].get_numel() == 1
and len(self.graph_inputs[name].get_size()) == 0
and get_device_type(self.graph_inputs[name]) == "cpu"
) or name in self.zero_dim_cpu_tensor_list
class SubgraphLowering(GraphLowering):
"""
Mostly a helper class for the subgraph lowering. The main goal is to call
init_wrapper_code with the subgraph related arguments.
"""
def __init__(self, parent: GraphLowering, *args: Any, **kwargs: Any) -> None:
self.parent = parent
super().__init__(*args, **kwargs)
def init_wrapper_code(
self,
is_subgraph: bool = False,
subgraph_name: Optional[str] = None,
parent_wrapper_code: Optional[PythonWrapperCodegen] = None,
partition_signatures: Optional[GraphPartitionSignature] = None,
) -> None:
super().init_wrapper_code(
is_subgraph=True,
subgraph_name=self.name,
parent_wrapper_code=self.parent.wrapper_code,
)
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