from __future__ import annotations
import collections
import contextlib
import dataclasses
import functools
import itertools
import logging
import math
import operator
import os
import textwrap
from typing import Any, Counter, Dict, Iterable, List, Optional, Set, Tuple, Union
import sympy
import torch
import torch._logging
from torch._prims_common import is_integer_dtype
from torch.utils._sympy.functions import FloorDiv, ModularIndexing
from torch.utils._sympy.value_ranges import ValueRanges
from ..._dynamo.utils import counters
from .. import config, ir, scheduler
from ..codecache import code_hash, get_path, PyCodeCache
from ..dependencies import MemoryDep, StarDep
from ..ir import IRNode, ReductionHint, TritonTemplateBuffer
from ..optimize_indexing import indexing_dtype_strength_reduction
from ..scheduler import BaseScheduling, WhyNoFuse
from ..triton_heuristics import AutotuneHint
from ..utils import (
do_bench,
get_fused_kernel_name,
get_kernel_metadata,
green_text,
is_welford_reduction,
next_power_of_2,
Placeholder,
sympy_product,
sympy_subs,
sympy_symbol,
unique,
yellow_text,
)
from ..virtualized import ops, V
from ..wrapper_benchmark import get_kernel_category_by_source_code
from .common import (
CSEVariable,
DeferredLine,
free_symbol_startswith,
IndentedBuffer,
index_prevent_reordering,
Kernel,
OpOverrides,
PythonPrinter,
SizeArg,
TensorArg,
)
from .triton_utils import config_of, signature_of, signature_to_meta
log = logging.getLogger(__name__)
perf_hint_log = torch._logging.getArtifactLogger(__name__, "perf_hints")
schedule_log = torch._logging.getArtifactLogger(__name__, "schedule")
fusion_log = torch._logging.getArtifactLogger(__name__, "fusion")
class TritonPrinter(PythonPrinter):
def _print_floor(self, expr):
assert len(expr.args) == 1
return f"tl.math.floor({self.paren(self._print(expr.args[0]))})"
def _helper_sqrt(self, expr):
return f"tl.math.sqrt({self.paren(self._print(expr))}.to(tl.float32))"
def _print_Where(self, expr):
c = self.doprint(expr.args[0])
p = self.doprint(expr.args[1])
q = self.doprint(expr.args[2])
return f"tl.where({c}, {p}, {q})"
def _print_Min(self, expr):
nargs = len(expr.args)
if len(expr.args) == 1:
return self._print(expr.args[0])
mid = len(expr.args) // 2
a = self._print(sympy.Min(*expr.args[:mid]))
b = self._print(sympy.Min(*expr.args[mid:]))
return f"tl.math.min({a}, {b})"
def _print_Max(self, expr):
nargs = len(expr.args)
if len(expr.args) == 1:
return self._print(expr.args[0])
mid = len(expr.args) // 2
a = self._print(sympy.Max(*expr.args[:mid]))
b = self._print(sympy.Max(*expr.args[mid:]))
return f"tl.math.max({a}, {b})"
def _print_Abs(self, expr):
assert len(expr.args) == 1
return f"tl.abs({self._print(expr.args[0])})"
texpr = TritonPrinter().doprint
pexpr = PythonPrinter().doprint
def triton_compute_type(dtype):
triton_type_name = str(dtype).split(".")[-1]
if triton_type_name == "bool":
triton_type_name = "int1"
elif triton_type_name in ("float16", "bfloat16"):
# float16 math is done in float32 inside the kernel
triton_type_name = "float32"
elif triton_type_name == "float8_e4m3fn":
triton_type_name = "float8e4nv"
elif triton_type_name == "float8_e5m2":
triton_type_name = "float8e5"
return f"tl.{triton_type_name}"
def triton_acc_type(dtype):
if is_integer_dtype(dtype) and dtype.is_signed:
nbits = 64 if dtype == torch.int64 else 32
return f"tl.int{nbits}"
return triton_compute_type(dtype)
def triton_constant(value):
if value == float("inf"):
return 'float("inf")'
elif value == float("-inf"):
return 'float("-inf")'
elif math.isnan(value):
return 'float("nan")'
return repr(value)
class TritonCSEVariable(CSEVariable):
def __init__(self, name, bounds: ValueRanges):
super().__init__(name, bounds)
# We'll use this to track which masks the variable needs when used for indirect indexing
self.mask_vars: Set[str] = set()
def update_on_args(self, name, args, kwargs):
# When making a variable that is going to be used in indirect indexing
# if a where clause is used it should mean that the result is always a
# valid index, so you shouldn't include any of the dependent variables
# in the resulting load mask
if name == "where":
return
for arg in args:
if isinstance(arg, TritonCSEVariable):
self.mask_vars.update(arg.mask_vars)
elif isinstance(arg, sympy.Symbol) and arg.name[0] in "xyr":
# most of the time index vars don't need masks associated with them
# however, when index vars are used to compute indices for indirect reads
# those reads should subsequently be masked,
self.mask_vars.update({f"{arg.name[0]}mask"})
class TritonOverrides(OpOverrides):
"""Map element-wise ops to Triton"""
@staticmethod
def to_dtype(x, dtype: torch.dtype, src_dtype: Optional[torch.dtype] = None):
def _get_min_elements_per_thread(
src_dtype: torch.dtype, dst_dtype: torch.dtype
) -> int:
if src_dtype == dst_dtype:
# No data type conversion is needed. No requirements on min_elem_per_thread.
return 0
# fp8 data type conversions has min_elem_per_thread requirements.
# Refer to Triton implementations here:
# https://github.com/openai/triton/blob/10f59d8ce04052521c1bc0cb3a3f8b98918fc7e3/lib/Conversion/TritonGPUToLLVM/ElementwiseOpToLLVM.cpp#L10.
fp8_dtypes = {
torch.float8_e4m3fn,
torch.float8_e5m2,
}
# Triton doesn't support type conversions between fp8_e4m3 and fp8_e5m2.
assert not (
src_dtype in fp8_dtypes
and dst_dtype in fp8_dtypes
and src_dtype != dst_dtype
), "Conversions between float8_e5m2 and float8_e4m3fn is not supported!"
if src_dtype == torch.float8_e5m2 or dst_dtype == torch.float8_e5m2:
return 4
if src_dtype == torch.float8_e4m3fn or dst_dtype == torch.float8_e4m3fn:
return 2
# No requirements on min_elem_per_thread.
return 0
if src_dtype is not None:
# Both dtype and src_dtype are set. This is used by torch to(dtype=dtype).
# It takes the maximum min_elem_per_thread if there are multiple fp8 conversions
# in the same kernel.
V.kernel.min_elem_per_thread = max(
_get_min_elements_per_thread(src_dtype, dtype),
V.kernel.min_elem_per_thread,
)
if dtype == torch.bool:
return f"({x} != 0)"
elif dtype == torch.uint8:
# to work around llvm uint conversion semantics
# that produces 0's for negative values
return f"{x}.to(tl.int8).to(tl.uint8)"
return f"{x}.to({triton_compute_type(dtype)})"
@staticmethod
def to_dtype_bitcast(x, dtype: torch.dtype):
return f"{x}.to({triton_compute_type(dtype)}, bitcast=True)"
@classmethod
def constant(cls, value, dtype):
if dtype == torch.uint8:
# tl.full is broken for uint8, remove once triton is fixed.
# See openai/triton#1919
tmp = cls.constant(value, torch.int16)
return cls.to_dtype(tmp, dtype)
type_ = torch._prims_common.dtype_to_type(dtype)
triton_val = triton_constant(type_(value))
triton_type = triton_compute_type(dtype)
if triton_type == "tl.float32":
# Float constants are always f32 in triton
return triton_val
# NOTE: We use a tensor here in order to get the expected type.
# Otherwise, e.g. float64 constants would be trunctated to float32.
# Also, we could just use shape=[1] here but starting with the correct
# ndim avoids extra `tt.expand_dim` ops appearing in the triton IR.
ndim = V.kernel.triton_tensor_ndim()
shape = [1] * ndim
return f"tl.full({shape}, {triton_val}, {triton_type})"
@staticmethod
def abs(x):
return f"tl.abs({x})"
@staticmethod
def libdevice_abs(x):
return f"tl.math.abs({x})"
@staticmethod
def exp(x):
return f"tl.exp({x})"
@staticmethod
def libdevice_exp(x):
return f"tl.math.exp({x})"
@staticmethod
def exp2(x):
return f"tl.math.exp2({x})"
@staticmethod
def expm1(x):
return f"tl.math.expm1({x})"
@staticmethod
def sqrt(x):
return f"tl.sqrt({x})"
@staticmethod
def libdevice_sqrt(x):
return f"tl.math.sqrt({x})"
@staticmethod
def relu(x):
bug = config.triton.inject_relu_bug_TESTING_ONLY
if bug == "compile_error":
return "compile error!"
elif bug == "runtime_error":
# NB: this only triggers runtime error as long as input
# is not all zero
return f'triton_helpers.device_assert_then({x} == 0, "injected assert fail", {x})'
elif bug == "accuracy":
return f"{x} + 1"
elif bug is None:
return ops.maximum("0", x)
else:
raise AssertionError(
f"unrecognized config triton.inject_relu_bug_TESTING_ONLY = {bug!r}"
)
@staticmethod
def minimum(a, b):
return f"triton_helpers.minimum({a}, {b})"
@staticmethod
def maximum(a, b):
return f"triton_helpers.maximum({a}, {b})"
@staticmethod
def where(a, b, c):
return f"tl.where({a}, {b}, {c})"
@staticmethod
def cos(x):
return f"tl.cos({x})"
@staticmethod
def libdevice_cos(x):
return f"tl.math.cos({x})"
@staticmethod
def sin(x):
return f"tl.sin({x})"
@staticmethod
def libdevice_sin(x):
return f"tl.math.sin({x})"
@classmethod
def index_expr(cls, expr, dtype):
index_str, mask_vars, mask, expand_str = V.kernel.indexing(expr)
# This is called from CSEProxy.__getattr__, so we'll set the bounds there
var = V.kernel.cse.generate(V.kernel.compute, index_str)
if dtype not in {torch.int32, torch.int64}:
var = V.kernel.cse.generate(V.kernel.compute, cls.to_dtype(var, dtype))
var.mask_vars = mask_vars
return var
@staticmethod
def masked(mask, body, other):
with V.kernel.mask_loads(mask) as new_mask:
result = body()
# Take dtype from result to prevent accidental promotion
other = V.kernel.cse.generate(
V.kernel.compute,
f"tl.full({result}.shape, {triton_constant(other)}, {result}.dtype)",
)
return ops.where(new_mask, result, other)
@staticmethod
def lgamma(x):
return f"tl.math.lgamma({x})"
@staticmethod
def erf(x):
return f"tl.math.erf({x})"
@staticmethod
def cosh(x):
return f"tl.math.cosh({x})"
@staticmethod
def sinh(x):
return f"tl.math.sinh({x})"
@staticmethod
def acos(x):
return f"tl.math.acos({x})"
@staticmethod
def acosh(x):
return f"tl.math.acosh({x})"
@staticmethod
def asin(x):
return f"tl.math.asin({x})"
@staticmethod
def asinh(x):
return f"tl.math.asinh({x})"
@staticmethod
def atan2(x, y):
return f"tl.math.atan2({x}, {y})"
@staticmethod
def atan(x):
return f"tl.math.atan({x})"
@staticmethod
def atanh(x):
return f"tl.math.atanh({x})"
@staticmethod
def copysign(x, y):
return f"tl.math.copysign({x}, {y})"
@staticmethod
def erfc(x):
return f"tl.math.erfc({x})"
@staticmethod
def erfinv(x):
return f"tl.math.erfinv({x})"
@staticmethod
def hypot(x, y):
return f"tl.math.hypot({x}, {y})"
@staticmethod
def log10(x):
return f"tl.math.log10({x})"
@staticmethod
def nextafter(x, y):
return f"tl.math.nextafter({x}, {y})"
@staticmethod
def logical_and(a, b):
return f"{a} & {b}"
@staticmethod
def logical_not(a):
return f"{a} == 0"
@staticmethod
def logical_or(a, b):
return f"{a} | {b}"
@staticmethod
def logical_xor(a, b):
return f"({a} ^ {b})"
@staticmethod
def bitwise_and(a, b):
return f"{a} & {b}"
@staticmethod
def bitwise_not(a):
return f"~{a}"
@staticmethod
def bitwise_or(a, b):
return f"{a} | {b}"
@staticmethod
def bitwise_xor(a, b):
return f"{a} ^ {b}"
@staticmethod
def bitwise_left_shift(a, b):
return f"{a} << {b}"
@staticmethod
def bitwise_right_shift(a, b):
return f"{a} >> {b}"
@staticmethod
def rand(seed, offset):
offset = f"({offset}).to(tl.uint32)"
return f"tl.rand({seed}, {offset})"
@staticmethod
def randn(seed, offset):
offset = f"({offset}).to(tl.uint32)"
return f"tl.randn({seed}, {offset})"
@staticmethod
def randint64(seed, offset, low, high):
offset = f"({offset}).to(tl.uint32)"
return f"triton_helpers.randint64({seed}, {offset}, {low}, {high})"
@staticmethod
def load_seed(name, offset):
var = V.kernel.args.input(name)
return (
f"tl.load({var} + {V.kernel.args.seed_offset('load_seed_offset', offset)})"
)
@staticmethod
def rsqrt(x):
return f"tl.math.rsqrt({x})"
@staticmethod
def log1p(x):
return f"tl.math.log1p({x})"
@staticmethod
def tan(x):
return f"tl.math.tan({x})"
@staticmethod
def tanh(x):
return f"tl.math.tanh({x})"
@staticmethod
def sigmoid(x):
return f"tl.sigmoid({x})"
@staticmethod
def libdevice_sigmoid(x):
return f"1/(1 + tl.math.exp(-({x})))"
@staticmethod
def signbit(x):
# XX: This is wrong for the value -0.0 in floating point
return f"tl.math.signbit({x}) if ({x}).dtype is tl.float32 else {x} < 0"
@staticmethod
def fmod(a, b):
return f"tl.math.fmod({a}, {b})"
@staticmethod
def pow(a, b):
return f"tl.math.pow({a}, {b})"
@staticmethod
def log(x):
return f"tl.log({x})"
@staticmethod
def libdevice_log(x):
return f"tl.math.log({x})"
@staticmethod
def isinf(x):
return f"tl.math.isinf({x}).to(tl.int1)"
@staticmethod
def isnan(x):
return f"tl.math.isnan({x}).to(tl.int1)"
@staticmethod
def round(x):
return f"tl.math.nearbyint({x})"
@staticmethod
def floor(x):
return f"tl.math.floor({x})"
@staticmethod
def floordiv(a, b):
# See the comment in lowering.div_mode. a and b are integer type.
# Similar to div_floor_kernel_cuda in pytorch core.
# Notice that // in triton behaves as truncdiv instead of floordiv
quot = f"{a} // {b}"
rem = f"{a} % {b}"
return f"tl.where(({a} < 0) != ({b} < 0), tl.where({rem} != 0, {quot} - 1, {quot}), {quot})"
@staticmethod
def sign(x):
def to_int(s):
return f"{s}.to(tl.int8)"
left = to_int(ops.lt("0", x))
right = to_int(ops.lt(x, "0"))
sub = ops.sub(left, right)
return f"{sub}.to({x}.dtype)"
@staticmethod
def trunc(x):
return f"tl.math.trunc({x})"
@staticmethod
def truncdiv(a, b):
# See the comment in lowering.div_mode. a and b are integer type.
# Notice that // in triton behaves as truncdiv instead of floordiv
return f"{a} // {b}"
@staticmethod
def ceil(x):
return f"tl.math.ceil({x})"
@dataclasses.dataclass
class IterationRanges:
"""
Each range tree represents multiple sets of iteration indexing
in a single tiled dimension in the output kernel.
If you have two loops ranges one (4, 3, 2) and another (4, 6),
then the range tree will be:
4 (i0)
3 (i1) 6 (i3)
2 (i2)
Where i0 is shared between both loops, but then the split into
different indexing vars. All loop ranges must iterate over
the same number of elements.
"""
def __init__(
self,
name: str,
var_list: List[sympy.Symbol],
var_ranges: Dict[sympy.Symbol, sympy.Expr],
numel: sympy.Expr,
prefix: str,
*,
kernel: TritonKernel,
divisor=sympy.Integer(1),
length=sympy.Integer(1),
):
super().__init__()
self.name = name
self.var_list = var_list
self.var_ranges = var_ranges
self.numel = numel
self.prefix = prefix
self.divisor = divisor
self.length = length
self.kernel = kernel
def is_loop(self):
return self.prefix == "r" and not self.kernel.persistent_reduction
class IterationRangesRoot(IterationRanges):
def __init__(
self,
name: str,
numel: sympy.Expr,
prefix: str,
index: int,
kernel: TritonKernel,
pid_cache=None,
):
if pid_cache is None:
pid_cache = {}
super().__init__(
name=name,
var_list=[],
var_ranges={},
numel=numel,
prefix=prefix,
kernel=kernel,
)
self.index = index
# Store all the nodes in one flat list
self.nodes: Dict[sympy.Expr, IterationRangesEntry] = {}
# This is for re-ordering program ID in triton mm template
# pid_cache["tl.program_id(0)"] = pid_m
self.pid_cache: Dict[str, str] = pid_cache
def cache_clear(self):
for node in self.nodes.values():
node.cache_clear()
def lookup(self, divisor, length):
"""
Lookup a given RangeTreeEntry, creating it if needed
"""
if V.graph.sizevars.statically_known_equals(divisor * length, self.numel):
expr = FloorDiv(sympy_symbol(f"{self.prefix}index"), divisor)
else:
expr = ModularIndexing(sympy_symbol(f"{self.prefix}index"), divisor, length)
if expr not in self.nodes:
node = IterationRangesEntry(
f"{self.prefix}{next(V.kernel.iter_vars_count)}",
divisor,
length,
expr,
self,
)
V.kernel.range_tree_nodes[node.symbol()] = node
self.var_list.append(node.symbol())
self.var_ranges[node.symbol()] = length
self.nodes[expr] = node
return self.nodes[expr]
def construct_entries(self, lengths: List[sympy.Expr]):
divisor = sympy.Integer(1)
itervars = []
for length in reversed(lengths):
itervars.append(self.lookup(divisor, length))
divisor = divisor * length
return list(reversed(itervars))
def construct(self, lengths: List[sympy.Expr]):
return [e.symbol() for e in self.construct_entries(lengths)]
def vars_and_sizes(self, index: sympy.Expr):
"""Figure out vars from this tree used in index"""
nodes = [V.kernel.range_tree_nodes.get(s) for s in index.free_symbols]
nodes = [n for n in nodes if n and n.prefix == self.prefix]
nodes.sort(key=lambda x: V.graph.sizevars.size_hint(x.divisor))
divisor = sympy.Integer(1)
index_vars = []
sizes = []
def add(node):
nonlocal divisor
index_vars.append(node.symbol())
sizes.append(node.length)
divisor = divisor * node.length
for node in nodes:
if not V.graph.sizevars.statically_known_equals(node.divisor, divisor):
# fill in unused index var
add(self.lookup(divisor, FloorDiv(node.divisor, divisor)))
divisor = node.divisor
add(node)
if not V.graph.sizevars.statically_known_equals(self.numel, divisor):
# fill in unused index var
add(self.lookup(divisor, FloorDiv(self.numel, divisor)))
return list(reversed(index_vars)), list(reversed(sizes))
def ranges_code(self):
size = self.kernel.indexing_size_str(self.index, self.prefix)
index_dtype = self.kernel.index_dtype
convert = f".to({index_dtype})" if index_dtype != "tl.int32" else ""
return f"tl.arange(0, {self.prefix.upper()}BLOCK){size}{convert}"
def scalar_code(self, value):
index_dtype = self.kernel.index_dtype
ndim = self.kernel.triton_tensor_ndim()
size = [1] * ndim
return f"tl.full({size}, {value}, {index_dtype})"
def get_pid(self):
key = f"tl.program_id({self.index})"
pid = self.pid_cache.get(key, key)
if self.kernel.index_dtype != "tl.int32":
return f"{pid}.to({self.kernel.index_dtype})"
return pid
def codegen_header(self, code, no_x_dim=False):
x = self.prefix
if self.is_loop():
code.writeline(f"{self.name} = {x}offset + {x}base")
elif x == "r" and self.kernel.persistent_reduction:
# no need to "roffset = "
code.writeline(
f"{self.name} = {self.ranges_code()}",
)
else:
if not no_x_dim:
line = f"{x}offset + {self.ranges_code()}"
else:
line = self.scalar_code(f"{x}offset")
code.writelines(
[
f"{x}offset = {self.get_pid()} * {x.upper()}BLOCK",
f"{self.name} = {line}",
]
)
code.writeline(f"{x}mask = {self.name} < {x}numel")
class IterationRangesEntry(IterationRanges):
def __init__(
self,
name: str,
divisor: sympy.Expr,
length: sympy.Expr,
expr: sympy.Expr,
parent: IterationRanges,
):
super().__init__(
name=name,
numel=parent.numel / length,
var_list=parent.var_list,
var_ranges=parent.var_ranges,
prefix=parent.prefix,
divisor=divisor,
length=length,
kernel=parent.kernel,
)
self.parent = parent
self.codegen = functools.lru_cache(None)(self._codegen)
self.expr = expr
def set_name(self, name):
self.codegen = lambda: name # type: ignore[assignment]
self.codegen.cache_clear = lambda: None # type: ignore[method-assign]
self.name = name
def cache_clear(self):
self.codegen.cache_clear()
def writeline(self, line):
if self.is_loop():
V.kernel.indexing_code.writeline(line)
else:
# lift non-reduction stores outside loop
V.kernel.body.writeline(line)
def _codegen(self):
self.writeline(f"{self.name} = " + texpr(V.kernel.rename_indexing(self.expr)))
return self.name
def precomputed_args(self):
# for dynamic shapes, find parts of indexing expressions that have to be precomputed
precomputed_args: List[sympy.Expr] = []
if isinstance(self.expr, sympy.Symbol):
return precomputed_args
assert isinstance(self.expr, (FloorDiv, ModularIndexing)), type(self.expr)
for arg in self.expr.args[1:]:
if not isinstance(arg, (sympy.Integer, sympy.Symbol)):
symbols = arg.free_symbols
if len(symbols) > 0 and all(s.name.startswith("s") for s in symbols):
precomputed_args.append(arg)
return precomputed_args
def symbol(self):
return sympy_symbol(self.name)
def __hash__(self):
return hash(self.name)
def __eq__(self, other):
return self.name == other.name
class TritonKernel(Kernel):
overrides = TritonOverrides # type: ignore[assignment]
sexpr = pexpr
def __init__(
self,
*groups,
index_dtype: str,
mutations: Optional[Set[str]] = None,
pid_cache=None,
reduction_hint=ReductionHint.DEFAULT,
min_elem_per_thread=0,
):
if pid_cache is None:
pid_cache = {}
super().__init__()
self.numels = [V.graph.sizevars.simplify(s) for s in groups]
self.mutations: Set[str] = mutations if mutations is not None else set()
self.range_trees: List[IterationRangesRoot] = []
self.range_tree_nodes: Dict[sympy.Symbol, IterationRangesEntry] = {}
self.iter_vars_count = itertools.count()
self.inside_reduction = self.numels[-1] != 1
self.body = IndentedBuffer()
self.indexing_code = IndentedBuffer()
self.suffix: IndentedBuffer = IndentedBuffer() # type: ignore[assignment]
self.outside_loop_vars: Set[Any] = set()
self.reduction_hint = reduction_hint
self.index_dtype: str = index_dtype
self.min_elem_per_thread = min_elem_per_thread
self.last_usage: Set[str] = set()
self.persistent_reduction: bool = self.should_use_persistent_reduction()
self.no_x_dim = (
self.reduction_hint == ReductionHint.INNER
and self.persistent_reduction
and len(self.numels) == 2
and self.numels[-1] >= 256
)
self.initialize_range_tree(pid_cache)
# A set of autotuning hints to pass as part of triton_meta
self.autotune_hints: Set[AutotuneHint] = set()
# define this in a closure to make cache local to object
@functools.lru_cache(None)
def simplify_indexing(index: sympy.Expr):
index = V.graph.sizevars.simplify_with_ranges(index, self.var_ranges())
for tree in self.range_trees:
index = self.combine_contiguous_dims(index, tree)
return index
self.simplify_indexing = simplify_indexing
def need_numel_args(self):
r"""
Indicate whether we need provide numel as arguments for the generated
kernel calls in the benchmark.
Should be true for pointwise/reduction kernels but false for triton
matmul kernels.
"""
return True
def should_use_persistent_reduction(self) -> bool:
"""
Heuristic to set self.persistent_reduction and add guards
if needed.
"""
if not (self.inside_reduction and config.triton.persistent_reductions):
return False
threshold = {
ReductionHint.INNER: 1024,
}.get(self.reduction_hint, 64)
last_numel = self.numels[-1]
if not isinstance(last_numel, (int, sympy.Integer)):
# Not static
return False
hint = V.graph.sizevars.size_hint(last_numel)
if hint > threshold:
return False
# will need to recompile if we cross a larger power of 2 boundary
V.graph.sizevars.guard_leq(self.numels[-1], next_power_of_2(hint))
return True
def set_last_usage(self, nodes):
if not self.inside_reduction or self.persistent_reduction:
return
self.last_usage = set(
itertools.chain.from_iterable(
n.last_usage for n in nodes if n is not EnableReduction
)
)
def initialize_range_tree(self, pid_cache):
names = list(
reversed(["xindex", "yindex", "zindex"][: len(self.numels) - 1])
) + ["rindex"]
for i in range(len(self.numels)):
pid_idx = i if names[i][0] == "r" else "xyz".find(names[i][0])
self.range_trees.append(
IterationRangesRoot(
names[i], self.numels[i], names[i][0], pid_idx, self, pid_cache
)
)
for tree in self.range_trees:
# reduction indexing goes inside a loop
if not tree.is_loop():
tree.codegen_header(self.body, self.no_x_dim)
if self.inside_reduction and self.range_trees[-1].is_loop():
# workaround for this issue:
# https://gist.github.com/jansel/6527126f781559095c5531f98a4235a7
self.body.writeline(f"rbase = {self.range_trees[-1].ranges_code()}")
def disable_reduction(self):
@contextlib.contextmanager
def ctx():
if self.numels[-1] == 1:
assert not self.inside_reduction
yield
return
if not self.persistent_reduction:
# calling codegen_body() will flush all the pending buffers
# and write out a reduction loop
self.codegen_body()
self.inside_reduction = False
try:
yield
if not self.persistent_reduction:
# flush out any code before opening the next loop
self.codegen_body()
finally:
self.inside_reduction = True
return ctx()
def set_ranges(self, *lengths):
assert len(lengths) == len(self.range_trees)
return [
ranges.construct(length)
for length, ranges in zip(lengths, self.range_trees)
]
@staticmethod
def _split_iteration_ranges(
groups: Iterable[sympy.Expr], lengths: List[List[sympy.Expr]]
):
sv = V.graph.sizevars
new_ranges: List[List[sympy.Expr]] = [[] for _ in groups]
remaining = [sv.simplify(g) for g in groups]
var_count = itertools.count()
def add_range(i, expr):
expr = sv.simplify(expr)
if not sv.statically_known_multiple_of(remaining[i], expr):
raise CantSplit()
# guard on the last item out
remaining[i] = FloorDiv(remaining[i], expr)
new_ranges[i].append(expr)
return next(var_count)
def make_combined(size, idx1, idx2):
def getter(flat_vars):
return size * flat_vars[idx1] + flat_vars[idx2]
return getter
return_getters_groups = []
current_group = 0
for length_group in lengths:
return_getters = []
for size in length_group:
if sv.statically_known_equals(size, 1):
return_getters.append(lambda _: sympy.Integer(0))
continue
while (
current_group < len(remaining)
and sv.size_hint(remaining[current_group]) == 1
):
# scroll to next group with remaining elements
current_group += 1
if sv.size_hint(size) > sv.size_hint(remaining[current_group]):
# need to break size in two
if not sv.statically_known_multiple_of(
size, remaining[current_group]
):
raise CantSplit()
size1 = remaining[current_group]
size2 = FloorDiv(size, remaining[current_group])
return_getters.append(
make_combined(
size2,
add_range(current_group, size1),
add_range(current_group + 1, size2),
)
)
else:
return_getters.append(
operator.itemgetter(add_range(current_group, size))
)
return_getters_groups.append(return_getters)
assert all(
V.graph.sizevars.size_hint(s) == 1 for s in remaining
), f"failed to set ranges {remaining} {lengths}"
return new_ranges, return_getters_groups
@classmethod
def is_compatible(
cls, groups: Iterable[sympy.Expr], lengths: List[List[sympy.Expr]]
):
try:
cls._split_iteration_ranges(groups, lengths)
return True
except CantSplit:
return False
def split_and_set_ranges(self, lengths: List[List[sympy.Expr]]):
"""
We may want to fuse `for i0 in s0*s1` into a tiled kernel with groups (s0, s1).
To do this we need to split up the iteration space of i0 into something like:
for i1 in s0:
for i2 in s1:
i0 = i1*s1 + i2
....
This function matches and resplits lengths to the groups of
this kernel to enable tiled + non-tiled fusions.
"""
groups = [rt.numel for rt in self.range_trees]
if not self.inside_reduction:
groups[-1] = sympy.Integer(1)
if len(lengths) == len(self.range_trees) and all(
V.graph.sizevars.simplify(sympy_product(x) - g) == 0
for x, g in zip(lengths, groups)
):
return self.set_ranges(*lengths)
new_ranges, return_getters_groups = self._split_iteration_ranges(
groups, lengths
)
itervars = list(itertools.chain(*self.set_ranges(*new_ranges)))
return [[fn(itervars) for fn in fns] for fns in return_getters_groups]
def is_indirect_indexing(self, index: sympy.Expr):
# tmpX means indirect indexing
return free_symbol_startswith(index, "tmp")
def is_broadcasted(self, index: sympy.Expr):
# Note. This may not be correct when there is indirect indexing
if self.is_indirect_indexing(index):
return False
index_numels = [1] * len(self.numels)
for symbol in index.free_symbols:
if symbol not in self.range_tree_nodes:
# Non-iterated variables, e.g. strides
continue
entry = self.range_tree_nodes[symbol]
assert isinstance(entry.parent, IterationRangesRoot)
index_numels[entry.parent.index] *= entry.length
# If the index variables only iterate over a subset of the kernel
# numels, then it must be broadcasted.
simplify = V.graph.sizevars.simplify
return any(
simplify(idx_range) != simplify(iter_range)
for idx_range, iter_range in zip(index_numels, self.numels)
)
def combine_contiguous_dims(self, index: sympy.Expr, tree: IterationRangesRoot):
"""
More aggressive simplification to merge contiguous dims
"""
if isinstance(index, (sympy.Integer, sympy.Symbol)):
return index
index_vars, sizes = tree.vars_and_sizes(index)
if len(sizes) <= 1:
return index
new_sizes, reindex, prune = V.graph.sizevars._simplify_loops(
index_vars, sizes, index_prevent_reordering([index], index_vars, sizes)
)
if new_sizes == sizes:
return index
new_index_vars = tree.construct(new_sizes)
new_index = sympy_subs(index, dict(zip(index_vars, reindex(new_index_vars))))
return new_index
def index_to_str(self, index: sympy.Expr) -> str:
"""
Convert an index expr to a string that can be used in triton code.
e.g. a sympy expression "s2" may actually appear as "ks1" in the triton kernel.
Index expressions often need to be passed in as arguments to the triton kernel.
Rename_indexing and codegen_indexing keep track of the needed indices and add
new parameters to the function signature.
"""
return texpr(self.rename_indexing(self.codegen_indexing(index)))
def indexing(
self,
index: sympy.Expr,
*,
copy_shape=None,
dense_indexing=False,
override_mask=None,
):
"""
Compute the index and mask to pass to tl.load() or tl.store()
"""
index = self.simplify_indexing(index)
index = sympy_subs(index, V.graph.sizevars.precomputed_replacements)
# if simple replacements didn't get rid of floor/ceil, try full subs
if len(index.atoms(sympy.floor)) or len(index.atoms(sympy.ceiling)):
index = index.subs(V.graph.sizevars.precomputed_replacements)
# last resort, if no range vars are in the expr, hoist it
# TODO instead of trying to blindly find complicated exprs, we should hoist the
# inputs/outputs sizes and strides, but at the time indexing is generated
# kernel inputs and outputs are not set yet, we'd need a deeper refactor
# to do it this way
if len(index.atoms(sympy.ceiling)):
for a in index.atoms(sympy.ceiling):
# for nested exprs, atoms yields top level first (?)
# so if everything goes fine, lower level replacements will come up empty
symbols = a.free_symbols
if len(symbols) > 0 and all(
s.name.startswith("s") or s.name.startswith("ps") for s in symbols
):
replacements = {a: V.graph.sizevars.lookup_precomputed_size(a)}
index = sympy_subs(index, replacements)
index_vars = index.free_symbols
index = self.simplify_indexing(index)
index_str = self.index_to_str(index)
mask_vars: Set[str] = set()
for var in index_vars:
assert isinstance(var, sympy.Symbol)
if override_mask:
pass
elif var.name.startswith("tmp"):
# indirect indexing
cse_var = self.cse.varname_map[var.name]
mask_vars.update(cse_var.mask_vars)
elif var.name.startswith(("s", "ps", "i")):
pass
else:
# var is one of xN, yN or rN
assert var.name[0] in "xyr", var.name
mask_vars.add(f"{var.name[0]}mask")
need_dense = (
config.triton.dense_indexing
or dense_indexing
or self._load_mask is not None
) and index != 0
have_dense = True
have_loop_vars = False
dense_mask_vars = set()
for tree in self.range_trees:
if tree.prefix == "r" and not self.inside_reduction:
continue
if index_vars.intersection(tree.var_list):
have_loop_vars = True
else:
have_dense = False
dense_mask_vars.add(f"{tree.prefix}mask")
expand_str = None
if isinstance(index, sympy.Integer):
expand_str = f"{copy_shape}.shape" if copy_shape else self.dense_size_str()
index_str = f"tl.full({expand_str}, {index_str}, tl.int32)"
return index_str, set(), "None", expand_str
if need_dense and not have_dense:
expand_str = f"{copy_shape}.shape" if copy_shape else self.dense_size_str()
index_str = f"tl.broadcast_to({index_str}, {expand_str})"
mask_vars = dense_mask_vars
elif not have_loop_vars and copy_shape:
index_str = f"tl.broadcast_to({index_str}, {copy_shape}.shape)"
mask_vars = dense_mask_vars
if override_mask:
mask_vars = {override_mask}
if self._load_mask:
mask_vars.add(self._load_mask)
self.filter_masks(mask_vars)
mask_str = " & ".join(sorted(map(str, mask_vars))) if mask_vars else "None"
return index_str, mask_vars, mask_str, expand_str
def filter_masks(self, mask_vars):
for tree in self.range_trees:
# Masks are superfluous if we only have one element
if V.graph.sizevars.statically_known_equals(tree.numel, 1):
mask_vars.discard(f"{tree.prefix}mask")
continue
# Masks are superfluous if numel is a multiple of BLOCK
# (We use the fact that BLOCK is required by triton to be a power of 2)
if tree.prefix.upper() not in config.triton.max_block:
continue
max_block = config.triton.max_block[tree.prefix.upper()]
# Optional optimization: if block divides numel exactly, we will
# never need to do a masked load to handle stragglers at the end.
# It's faster to avoid masking at all. But it is sound to always
# mask.
if V.graph.sizevars.statically_known_multiple_of(tree.numel, max_block):
mask_vars.discard(f"{tree.prefix}mask")
def var_ranges(self):
return dict(
itertools.chain.from_iterable(
tree.var_ranges.items() for tree in self.range_trees
)
)
def codegen_indexing(self, expr: sympy.Expr):
expr = V.graph.sizevars.simplify_with_ranges(expr, self.var_ranges())
for sym in sorted(expr.free_symbols, key=str):
if sym in self.range_tree_nodes:
# if indexing expression is complicated, we precompute it on the host side
# and send the result as a kernel argument
replacements = {}
for ps in self.range_tree_nodes[sym].precomputed_args():
replacements[ps] = V.graph.sizevars.lookup_precomputed_size(ps)
if len(replacements) > 0:
self.range_tree_nodes[sym].expr = sympy_subs(
self.range_tree_nodes[sym].expr, replacements
)
self.range_tree_nodes[sym].codegen()
return expr
@contextlib.contextmanager
def mask_loads(self, mask):
"""Context manager to add an additional mask to tl.load/store"""
prior = self._load_mask
if prior:
mask = self.cse.generate(self.compute, f"{mask} & {prior}")
self._load_mask = mask
try:
# TODO(jansel): do we need a reshape here?
yield mask
finally:
self._load_mask = prior
def generate_assert(self, check):
return torch.version.hip is None and super().generate_assert(check)
def load_mask(self, var):
mask = ""
mask_vars = set(var.mask_vars)
if self._load_mask:
mask_vars.add(self._load_mask)
if mask_vars:
mask = (
f"{next(iter(mask_vars))}"
if len(mask_vars) == 1
else f"({' & '.join(str(v) for v in mask_vars)})"
)
return mask
@property
def assert_function(self) -> str:
return "tl.device_assert"
def get_strides_of_load(self, index: sympy.Expr):
"""
This gets the stride of the index for each of the tiling variables
(technically, it does it at index 0)
For example, if
xindex = x0 + 512*x1 + 1024*r0
x0 = (xindex//512)
x1 = (xindex % 512)
r0 = rindex // 1024
this function would return
{xindex: 512, rindex: 1024}
"""
index_to_tile_indexes = {k: v.expr for k, v in self.range_tree_nodes.items()}
index_in_tile_vars = sympy_subs(index, index_to_tile_indexes)
strides = {}
for range_tree in self.range_trees:
s = sympy_symbol(range_tree.name)
strides[s] = sympy_subs(index_in_tile_vars, {s: 1}) - sympy_subs(
index_in_tile_vars, {s: 0}
)
return strides
def load(self, name: str, index: sympy.Expr):
var = self.args.input(name)
indirect_indexing = self.is_indirect_indexing(index)
original_index = index
index, mask_vars, mask, expand_str = self.indexing(index)
# Keep the variable in cache if were going to reuse it. Equiv., if any of the following hold
# 1) We are doing broadcasting
# 2) It is a non-coalesced load. The intuition is that if it's
# non-coalesced, we will likely load each element multiple times in
# practice.
# 3) It will be used later and it won't be CSE'd. Equiv., if all the following hold
# 3.1) We are in a reduction loop
# 3.2) Its not its last use
# 3.3) This load will not be lifted to the body
#
is_coalesced = any(
i == 1 for i in self.get_strides_of_load(original_index).values()
)
if self.is_broadcasted(original_index):
ep = ", eviction_policy='evict_last'"
elif not is_coalesced:
ep = ", eviction_policy='evict_last'"
elif self.inside_reduction and not self.persistent_reduction:
if name in self.args.inplace_buffers:
names = set(self.args.inplace_buffers[name].other_names)
else:
names = {name}
last_use = len(names & self.last_usage) > 0
evict_last = not last_use and ("rmask" in mask or indirect_indexing)
if evict_last:
ep = ", eviction_policy='evict_last'"
else:
ep = ", eviction_policy='evict_first'"
else:
ep = ""
# "other" below is a workaround for https://github.com/openai/triton/issues/737
# for bool, even though it's likely subject to the same bug, setting `other` leads
# to LLVM errors so we are skipping it for now
if ("tmp" in mask or "rmask" in mask) and V.graph.get_dtype(name) != torch.bool:
other = ", other=0.0"
else:
other = ""
append_broadcast = None
if V.graph.is_unspec_arg(name):
line = var
else:
if isinstance(original_index, sympy.Integer):
line = f"tl.load({var} + ({original_index}))"
append_broadcast = expand_str
else:
line = f"tl.load({var} + ({index}), {mask}{ep}{other})"
dtype = V.graph.get_dtype(name)
if dtype in (torch.float16, torch.bfloat16):
line += ".to(tl.float32)"
if dtype == torch.bool and torch.version.hip is None:
# Workaround for https://github.com/openai/triton/issues/2151
# tl.load returns int8 when loading from pointer to int1
# NOTE: Currently causes hangs on bool UTs for ROCm
line += ".to(tl.int1)"
if "tmp" in mask:
# Masked loads must come after the mask is computed
load_buffer = self.compute
elif (
self.inside_reduction
and not self.persistent_reduction
and "rmask" not in mask
and not indirect_indexing
):
# can lift a common load outside of reduction loop
# One exception is when this is an indirect_load.
load_buffer = self.body
else:
load_buffer = self.loads
result_var = self.cse.generate(load_buffer, line)
assert isinstance(result_var, TritonCSEVariable)
result_var.mask_vars = mask_vars
if append_broadcast:
line = f"tl.broadcast_to({result_var}, {append_broadcast})"
result_var = self.cse.generate(load_buffer, line)
if not self.inside_reduction or "rmask" not in mask:
self.outside_loop_vars.add(result_var)
return result_var
def store(self, name, index, value, mode=None):
var = self.args.output(name)
indirect_indexing = self.is_indirect_indexing(index)
original_index = index
index, mask_vars, mask, expand_str = self.indexing(index, dense_indexing=True)
# Guard against write-after-read corruption in triton.
# See # https://github.com/openai/triton/issues/1615
# This triton bug means that a load which is broadcasted over multiple
# warps may see the result of a store that happens later in the triton
# program. The workaround is to add a barrier before storing, which
# enforces that all warps have already read the data.
is_inplace = name in self.args.inplace_buffers
is_broadcasted = self.is_broadcasted(original_index)
if is_inplace and is_broadcasted:
self.stores.writeline(DeferredLine(name, "tl.debug_barrier()"))
if mode is None:
line = f"tl.store({var} + ({index}), {value}, {mask})"
elif mode == "atomic_add":
line = f"tl.atomic_add({var} + ({index}), {value}, {mask})"
else:
raise NotImplementedError(f"store mode={mode}")
self.stores.writeline(DeferredLine(name, line))
if not self.inside_reduction:
self.outside_loop_vars.add(value)
def bucketize(
self,
values: CSEVariable,
offsets_name: str,
offsets_size: sympy.Expr,
indexing_dtype: torch.dtype,
right: bool,
):
"""
See [Note: Inductor bucketize op]
"""
# Triton performance for bucketize_binary_search is much better when the number
# of threads equals the number of elements.
# If we're trying to use a bucketize kernel, we should make sure that an
# autotuning config with num_elements_per_warp=32 exists.
self.autotune_hints.add(AutotuneHint.ELEMENTS_PER_WARP_32)
offsets_ptr = self.args.input(offsets_name)
block_size = self.dense_size_str()
offsets_size_str = self.index_to_str(offsets_size)
if indexing_dtype == torch.int32:
triton_dtype = "tl.int32"
elif indexing_dtype == torch.int64:
triton_dtype = "tl.int64"
else:
raise NotImplementedError(
"Bucketize only supports indexing with int32 and int64"
)
result = self.cse.generate(
self.compute,
f"triton_helpers.bucketize_binary_search({values}, {offsets_ptr}, {triton_dtype}, {right}, {offsets_size_str}, {block_size})", # noqa: B950 line too long
)
return result
def reduction_resize(self, value):
ndims = self.triton_tensor_ndim()
if ndims == 1:
return f"triton_helpers.promote_to_tensor({value})"
sizes = [":"] * ndims
sizes[-1] = "None"
return f"{value}[{', '.join(sizes)}]"
@staticmethod
def _map_tuple_or_scalar(fn, value):
if isinstance(value, tuple):
return tuple(map(fn, value))
return fn(value)
def reduction(self, dtype, src_dtype, reduction_type, value):
assert self.inside_reduction
masks = {f"{tree.prefix}mask" for tree in self.range_trees}
self.filter_masks(masks)
masks = sorted(masks)
if self._load_mask:
masks.append(self._load_mask)
reduction_range_prefix = self.range_trees[-1].prefix
reduction_sizes = ["None" for _ in self.range_trees]
reduction_sizes[-1] = ":"
# Say we have
# tmp0 = ops.constant(1, torch.int64)
# tmp1 = ops.reduction(torch.int64, torch.int64, "sum", tmp0)
# tmp0 in the triton code is either a scalar, or single-element tensor
# so if we emit tl.sum directly, it will only give 1 instead of RBLOCK * 1
# To avoid this, we broadcast to the expected shape first.
dense_size_str = self.dense_size_str()
value = self._map_tuple_or_scalar(
lambda v: self.cse.generate(
self.compute, f"tl.broadcast_to({v}, {dense_size_str})"
),
value,
)
dim: int
root_op: str
def final_reduction(value):
use_helper = reduction_type in {"any", "max", "min", "prod"}
module = "triton_helpers" if use_helper else "tl"
if reduction_type in {"max", "min"}:
return self.reduction_resize(
f"{module}.{reduction_type}2({value}, {dim})"
)
return self.reduction_resize(f"{module}.{reduction_type}({value}, {dim})")
def final_argreduce(buffer, result_var, value, index):
buffer.splice(
f"""\
_, {result_var}_tmp = triton_helpers.{root_op}_with_index({value}, {index}, {dim})
{result_var} = {self.reduction_resize(f'{result_var}_tmp')}
"""
)
cache_key = (src_dtype, reduction_type, value)
if cache_key in self.cse.reduction_cache:
return self.cse.reduction_cache[cache_key]
dim = len(self.range_trees) - 1 - int(bool(self.no_x_dim))
acc_type = triton_acc_type(src_dtype)
result_var: Any = self.cse.newvar()
result_var.mask_vars = {var for var in masks if var[0] != "r"}
cond = " & ".join(masks)
if self.persistent_reduction:
default = ir.Reduction.default_value(reduction_type, src_dtype)
default = self._map_tuple_or_scalar(triton_constant, default)
def _mask_value(value, default):
return self.cse.generate(
self.compute, f"tl.where({cond}, {value}, {default})"
)
if isinstance(value, tuple):
masked_value = [_mask_value(v, d) for v, d in zip(value, default)]
else:
masked_value = _mask_value(value, default)
if reduction_type in {"argmax", "argmin"}:
accumulator_index = str(
self.cse.generate(
self.compute,
f"tl.broadcast_to({reduction_range_prefix}index, {masked_value}.shape)",
)
)
root_op = {"argmax": "max", "argmin": "min"}[reduction_type]
final_argreduce(
self.compute, result_var, masked_value, accumulator_index
)
elif reduction_type == "welford_reduce":
# For persistent reductions, don't bother with
# welford's algorithm since it uses more registers, and
# taking two reductions doesn't increase memory usage.
sum_ = ops.reduction(dtype, dtype, "sum", value)
self.inside_reduction = False
rnumel = ops.index_expr(self.numels[-1], dtype)
mean = ops.truediv(sum_, rnumel)
self.inside_reduction = True
dx = ops.sub(value, mean)
dx2 = ops.mul(dx, dx)
m2 = ops.reduction(dtype, dtype, "sum", dx2)
result_var = (mean, m2, rnumel)
elif reduction_type == "welford_combine":
mean, m2, weight = masked_value
welford = f"triton_helpers.welford({mean}, {m2}, {weight}, {dim})"
mean, m2, weight = (self.cse.newvar() for _ in range(3))
self.compute.writeline(f"{mean}, {m2}, {weight} = {welford}")
result_var = tuple(
self.cse.generate(self.compute, self.reduction_resize(var_name))
for var_name in (mean, m2, weight)
)
else:
result_var = self.cse.generate(
self.compute, final_reduction(masked_value)
)
else:
accumulator = f"_{result_var}"
default = ir.Reduction.default_accumulator(reduction_type, src_dtype)
default = self._map_tuple_or_scalar(triton_constant, default)
if not isinstance(default, tuple):
self.body.writeline(
f"{accumulator} = tl.full({self.dense_size_str()}, {default}, {acc_type})"
)
if reduction_type in {"argmax", "argmin"}:
accumulator_index = f"_{result_var}_index"
long_max = torch.iinfo(torch.int64).max
self.body.writeline(
f"{accumulator_index} = tl.full({self.dense_size_str()}, {long_max}, tl.int64)"
)
root_op = {"argmax": "max", "argmin": "min"}[reduction_type]
self.compute.splice(
f"""\
{accumulator}_next, {accumulator_index}_next = triton_helpers.{root_op}imum_with_index(
{accumulator}, {accumulator_index}, {value}, {reduction_range_prefix}index
)
{accumulator} = tl.where({cond}, {accumulator}_next, {accumulator})
{accumulator_index} = tl.where({cond}, {accumulator_index}_next, {accumulator_index})
"""
)
final_argreduce(self.suffix, result_var, accumulator, accumulator_index)
elif is_welford_reduction(reduction_type):
accumulator = f"{result_var}_mean"
accumulator_m2 = f"{result_var}_m2"
accumulator_weight = f"{result_var}_weight"
self.body.writeline(
f"{accumulator} = tl.zeros({self.dense_size_str()}, {acc_type})"
)
self.body.writeline(
f"{accumulator_m2} = tl.zeros({self.dense_size_str()}, {acc_type})"
)
self.body.writeline(
f"{accumulator_weight} = tl.zeros({self.dense_size_str()}, {acc_type})"
)
if reduction_type == "welford_combine":
mean, m2, weight = value
self.compute.splice(
f"""\
{accumulator}_next, {accumulator_m2}_next, {accumulator_weight}_next = triton_helpers.welford_combine(
{accumulator}, {accumulator_m2}, {accumulator_weight},
{mean}, {m2}, {weight}
)
"""
)
else:
assert reduction_type == "welford_reduce"
self.compute.splice(
f"""\
{accumulator}_next, {accumulator_m2}_next, {accumulator_weight}_next = triton_helpers.welford_reduce(
{value}, {accumulator}, {accumulator_m2}, {accumulator_weight},
)
"""
)
self.compute.splice(
f"""\
{accumulator} = tl.where({cond}, {accumulator}_next, {accumulator})
{accumulator_m2} = tl.where({cond}, {accumulator_m2}_next, {accumulator_m2})
{accumulator_weight} = tl.where({cond}, {accumulator_weight}_next, {accumulator_weight})
"""
)
result_mean = result_var
result_m2 = self.cse.newvar()
result_weight = self.cse.newvar()
self.suffix.splice(
f"""\
{result_mean}_tmp, {result_m2}_tmp, {result_weight}_tmp = triton_helpers.welford(
{accumulator}, {accumulator_m2}, {accumulator_weight}, {dim}
)
{result_mean} = {self.reduction_resize(f'{result_mean}_tmp')}
{result_m2} = {self.reduction_resize(f'{result_m2}_tmp')}
{result_weight} = {self.reduction_resize(f'{result_weight}_tmp')}
"""
)
result_var = result_mean, result_m2, result_weight
else:
combine_fn = ir.get_reduction_combine_fn(reduction_type, src_dtype)
updated = combine_fn(accumulator, value)
self.compute.writeline(
f"{accumulator} = tl.where({cond}, {updated}, {accumulator})"
)
if src_dtype == torch.bool:
# This is only really used for aten.any. It changes the
# final reduction of a non-persistent reduction from
# tmp5 = triton_helpers.max(_tmp5, 1)[:, None]
# to
# tmp5 = triton_helpers.max(_tmp5.to(tl.int8), 1)[:, None].to(tl.int1)
# which is needed because tl.reduce doesn't support tl.int1
accumulator = f"{accumulator}.to(tl.int8)"
result_type = triton_compute_type(dtype)
self.suffix.writeline(
f"{result_var} = {final_reduction(accumulator)}.to({result_type})"
)
else:
self.suffix.writeline(
f"{result_var} = {final_reduction(accumulator)}"
)
self.cse.reduction_cache[cache_key] = result_var
if isinstance(result_var, tuple):
self.outside_loop_vars |= set(result_var)
else:
self.outside_loop_vars.add(result_var)
return result_var
def store_reduction(self, name, index, value):
assert self.inside_reduction
self.inside_reduction = False
index, mask_vars, mask, _ = self.indexing(index)
assert "rmask" not in index
self.inside_reduction = True
var = self.args.output(name)
self.suffix.writeline(
DeferredLine(name, f"tl.store({var} + ({index}), {value}, {mask})")
)
def codegen_body(self):
"""
Concat output code from index_code, loads, compute, stores,
suffix into self.body.
For pointwise kernels, this is called just once at the end.
For reduction kernels, this generates a loop over the reduction
axis.
"""
if not (
self.indexing_code
or self.loads
or self.stores
or self.compute
or self.suffix
):
return
if self.inside_reduction and not self.persistent_reduction:
self.body.writeline("for roffset in range(0, rnumel, RBLOCK):")
with self.body.indent():
# last range tree is always reduction
self.range_trees[-1].codegen_header(self.body)
self.body.splice(self.indexing_code)
self.body.splice(self.loads)
self.body.splice(self.compute)
self.body.splice(self.stores)
# invalidate any caches that came from inside the reduction loop
self.cse.invalidate(self.outside_loop_vars)
self.range_trees[-1].cache_clear()
else:
self.body.splice(self.indexing_code)
self.body.splice(self.loads)
self.body.splice(self.compute)
self.body.splice(self.stores)
self.body.splice(self.suffix)
self.indexing_code.clear()
self.loads.clear()
self.compute.clear()
self.stores.clear()
self.suffix.clear()
def codegen_kernel_benchmark(self):
result = IndentedBuffer()
argdefs, call_args, signature = self.args.python_argdefs()
result.writelines(["", "", "def get_args():"])
with result.indent():
name_cnt = itertools.count()
var_names = []
for arg_name, arg_sig in zip(call_args, signature):
var_name = f"arg_{next(name_cnt)}"
buf = V.graph.get_buffer(arg_name)
if buf:
result.writeline(
f"{var_name} = rand_strided({V.graph.sizevars.size_hints(buf.get_size())}, {V.graph.sizevars.size_hints(buf.get_stride())}, device='{buf.get_device()}', dtype={buf.get_dtype()})" # noqa: B950 line too long
)
elif arg_name in V.graph.constants:
# note that random seed is put in V.graph.constants
const_tensor = V.graph.constants[arg_name]
result.writeline(
f"{var_name} = rand_strided({V.graph.sizevars.size_hints(const_tensor.size())}, {V.graph.sizevars.size_hints(const_tensor.stride())}, device='{const_tensor.device}', dtype={const_tensor.dtype})" # noqa: B950 line too long
)
elif isinstance(arg_sig, SizeArg):
symval_hint = V.graph.sizevars.size_hint(arg_sig.expr)
# Force the seed_offset to be 0 so calls to the same kernel
# using different seed offset will have the same benchmark harness.
# We can dedup kernel definitions in this case.
if "seed_offset" in arg_sig.name:
symval_hint = 0
result.writeline(f"{var_name} = {symval_hint}")
else:
raise KeyError(
f"Don't find the buffer or const tensor for {arg_name}"
)
var_names.append(var_name)
result.writeline(f"return {', '.join(var_names)},")
result.writelines(["\n", "\n", "def call(args):"])
grid = []
extra_args = []
extra_args_str = None
index = V.graph.scheduler.current_device.index
with result.indent():
result.writeline(f"with torch.cuda._DeviceGuard({index}):")
with result.indent():
result.writeline(
f"torch.cuda.set_device({index})"
) # no-op to ensure context
for tree in self.range_trees:
expr = pexpr(V.graph.sizevars.size_hint(tree.numel))
if tree.prefix != "r" or self.inside_reduction:
extra_args.append(expr)
if tree.prefix != "r":
grid.append(expr)
stream_name = f"stream{index}"
result.writeline(f"{stream_name} = get_cuda_stream({index})")
if self.need_numel_args():
extra_args_str = ", ".join(map(str, extra_args)) + ", "
else:
extra_args_str = ""
result.writeline(
f"{str(Placeholder.KERNEL_NAME)}.run(*args, {extra_args_str}grid=grid({', '.join(grid)}), stream={stream_name})"
)
# benchmark all configs
result.writelines(["\n", "\n", "def benchmark_all_configs(args):"])
with result.indent():
result.writeline(f"with torch.cuda._DeviceGuard({index}):")
with result.indent():
result.writeline(
f"torch.cuda.set_device({index})"
) # no-op to ensure context
result.writeline(
f"return {str(Placeholder.KERNEL_NAME)}.benchmark_all_configs(*args, {extra_args_str}grid=grid({', '.join(grid)}))" # noqa: B950 line too long
)
ninplace_args = len(unique(self.args.inplace_buffers.values()))
result.writelines(["\n", "\n", "if __name__ == '__main__':"])
with result.indent():
result.writeline("from torch._inductor.utils import get_num_bytes")
result.writeline("from triton.testing import do_bench")
result.writeline("")
result.writeline("args = get_args()")
result.writeline(
"ms = do_bench(lambda: call(args), rep=40, fast_flush=True)"
)
result.writeline(
f"num_gb = get_num_bytes(*args, num_in_out_args={ninplace_args}) / 1e9"
)
result.writeline("gb_per_s = num_gb / (ms / 1e3)")
result.writeline(
'print(f"{ms:.3f}ms {num_gb:.3f}GB {gb_per_s:.2f}GB/s")'
)
return result
def imports_for_benchmark_kernel(self):
return textwrap.dedent(
"""
from torch._dynamo.testing import rand_strided
from torch._C import _cuda_getCurrentRawStream as get_cuda_stream
import torch
from torch._inductor.triton_heuristics import grid
"""
)
def codegen_kernel(self, name=None):
from triton import next_power_of_2
code = IndentedBuffer()
size_hints = []
for numel in self.numels:
numel_hint = V.graph.sizevars.symbolic_hint(numel)
if not isinstance(numel_hint, (int, sympy.Integer)):
# This default heuristic hint was picked carefully: it is
# large, to ensure that we don't shrink the block size (since
# if you don't have many elements, it'd be wasteful to pick a
# large block size). Since we don't know how many elements we
# might have, we should be OK with some inefficiency to make
# sure we handle the large case well. 8192 is the largest
# block size we support, so we pick that.
#
# If we have a better hint for unbacked SymInts (e.g., because
# a user told us, or we are tracking upper bounds) we could
# use that here.
size_hint = 8192
else:
size_hint = next_power_of_2(int(numel_hint))
size_hints.append(size_hint)
if self.persistent_reduction:
assert self.inside_reduction
heuristics = "persistent_reduction"
elif self.inside_reduction:
heuristics = "reduction"
else:
size_hints.pop()
heuristics = "pointwise"
if name is None:
code.splice(
f"""
import triton
import triton.language as tl
from torch._inductor.ir import ReductionHint
from torch._inductor.ir import TileHint
from torch._inductor.triton_heuristics import AutotuneHint, {heuristics}
from torch._inductor.utils import instance_descriptor
from torch._inductor import triton_helpers
"""
)
if config.benchmark_kernel:
code.splice(self.imports_for_benchmark_kernel())
argdefs, _, signature = self.args.python_argdefs()
# maps actual expression to SizeArg if its in sizevars replacements
for i, arg in enumerate(signature):
if (
isinstance(arg, SizeArg)
and arg.expr in V.graph.sizevars.inv_precomputed_replacements
):
signature[i] = SizeArg(
arg.name, V.graph.sizevars.inv_precomputed_replacements[arg.expr]
)
mutated_args = set()
for mutation in self.mutations:
if mutation in self.args.input_buffers:
mutated_args.add(self.args.input_buffers[mutation])
if (
mutation in self.args.inplace_buffers
and mutation not in V.graph.removed_buffers
and mutation not in self.removed_buffers
):
mutated_args.add(self.args.inplace_buffers[mutation].inner_name)
if mutation in self.args.output_buffers:
mutated_args.add(self.args.output_buffers[mutation])
mutated_args = sorted(mutated_args)
triton_meta_signature = signature_to_meta(
signature, size_dtype=self.index_dtype
)
triton_meta = {
"signature": triton_meta_signature,
"device": V.graph.scheduler.current_device.index,
"device_type": V.graph.scheduler.current_device.type,
"constants": {},
}
inductor_meta = {
"autotune_hints": set(self.autotune_hints),
"kernel_name": str(Placeholder.DESCRIPTIVE_NAME),
"mutated_arg_names": mutated_args,
}
for tree in self.range_trees:
if tree.prefix != "r" or self.inside_reduction:
sizearg = SizeArg(f"{tree.prefix}numel", tree.numel)
signature.append(sizearg)
triton_meta_signature[len(argdefs)] = signature_of(
sizearg, size_dtype=self.index_dtype
)
argdefs.append(f"{tree.prefix}numel")
# constexpr version causes issues, see
# https://github.com/pytorch/torchdynamo/pull/1362
# triton_meta["constants"][len(argdefs)] = V.graph.sizevars.size_hint(
# tree.numel
# )
# argdefs.append(f"{tree.prefix}numel: tl.constexpr")
triton_meta["configs"] = [config_of(signature)]
for tree in self.range_trees:
if tree.prefix == "r" and (
not self.inside_reduction or self.persistent_reduction
):
continue
if tree.prefix == "x" and self.no_x_dim:
continue
argdefs.append(f"{tree.prefix.upper()}BLOCK : tl.constexpr")
if self.inside_reduction:
reduction_hint = self.reduction_hint
heuristics_line = f"""
@{heuristics}(
size_hints={size_hints!r},
reduction_hint={reduction_hint},
filename=__file__,
triton_meta={triton_meta!r},
inductor_meta={inductor_meta!r}
)
@triton.jit
"""
else:
tile_hint = ""
if len(size_hints) == 2:
if len(signature) == 4: # input, output and 2 args
tile_hint = "tile_hint=TileHint.SQUARE,"
else:
tile_hint = "tile_hint=TileHint.DEFAULT,"
heuristics_line = f"""
@{heuristics}(
size_hints={size_hints!r}, {tile_hint}
filename=__file__,
triton_meta={triton_meta!r},
inductor_meta={inductor_meta!r},
min_elem_per_thread={self.min_elem_per_thread}
)
@triton.jit
"""
code.splice(heuristics_line)
code.writeline(
f"def {name or str(Placeholder.KERNEL_NAME)}({', '.join(argdefs)}):"
)
self.codegen_body()
with code.indent():
self.codegen_static_numels(code)
for old, new in self.args.aliases():
code.writeline(f"{old} = {new}")
code.splice(self.body)
if config.benchmark_kernel:
code.splice(self.codegen_kernel_benchmark())
return code.getvalue()
def codegen_static_numels(self, code):
"""
We get a small speedup from hard coding numels if they are static.
This code stomps on the passed-in values by writing an constant to the top of the kernel.
In a kernel like:
def KERNEL_NAME(in_ptr0, in_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
We would add
xnumel = 4096
rnumel = 768
After the signature, before the kernel code, if we decided to make these static. As its hardcoded, it becomes
a better signal to triton on how to unroll and do some static indexing. So, it's not so much that downstream
knows that its a static numel, as that you just plop a constant into the kernel.
"""
for tree in self.range_trees:
if tree.prefix != "r" or self.inside_reduction:
simplified_tree_numel = V.graph.sizevars.simplify(tree.numel)
if isinstance(simplified_tree_numel, (sympy.Integer, int)):
code.writeline(f"{tree.prefix}numel = {int(simplified_tree_numel)}")
if tree.prefix == "r" and self.persistent_reduction:
simplified_tree_numel = V.graph.sizevars.simplify(tree.numel)
if isinstance(simplified_tree_numel, (sympy.Integer, int)):
val = int(simplified_tree_numel)
else:
continue
val = next_power_of_2(val)
code.writeline(f"RBLOCK: tl.constexpr = {val}")
if tree.prefix == "x" and self.no_x_dim:
code.writeline("XBLOCK: tl.constexpr = 1")
def triton_tensor_ndim(self):
no_x_dim = int(bool(self.no_x_dim))
no_r_dim = self.numels[-1] == 1
return len(self.range_trees) - no_x_dim - no_r_dim
def indexing_size_str(self, i=None, x=None):
# no_x_dim is sympy.logic.boolalg.BooleanTrue
no_x_dim = int(bool(self.no_x_dim))
sizes = ["None"] * self.triton_tensor_ndim()
if i is not None:
idx = i - no_x_dim
sizes[idx] = ":"
return f"[{', '.join(sizes)}]"
def dense_size_str(self):
sizes = []
for tree in self.range_trees:
if self.no_x_dim and tree.prefix == "x":
continue
if tree.prefix != "r" or self.inside_reduction:
sizes.append(f"{tree.prefix.upper()}BLOCK")
elif tree.prefix == "r" and tree.numel != 1:
sizes.append("1")
if sizes[0:3] == ["ZBLOCK", "YBLOCK", "XBLOCK"]:
sizes[0:3] = reversed(sizes[0:3])
if sizes[0:2] == ["YBLOCK", "XBLOCK"]:
sizes[0:2] = reversed(sizes[0:2])
return f"[{', '.join(sizes)}]"
def call_kernel(self, name: str, node: Optional[IRNode] = None):
wrapper = V.graph.wrapper_code
_, call_args, _ = self.args.python_argdefs()
# dynamo wraps unspec variable as 0d CPU tensor, need convert to scalar
for i in range(len(call_args)):
if V.graph.is_unspec_arg(call_args[i]):
call_args[i] = call_args[i] + ".item()"
grid = []
# TODO(jansel): if there are constants, we shouldn't bother passing them as args
for tree in self.range_trees:
if isinstance(tree.numel, (sympy.Integer, sympy.Symbol)):
expr = tree.numel
else:
expr = wrapper.generate_numel_expr(name, tree)
if tree.prefix != "r" or self.inside_reduction:
call_args.append(expr)
if tree.prefix != "r":
grid.append(expr)
grid = wrapper.generate_default_grid(name, grid)
wrapper.generate_kernel_call(
name,
call_args,
grid,
V.graph.scheduler.current_device.index,
cuda=True,
triton=True,
)
def codegen_nan_check(self):
if not config.nan_asserts:
return
wrapper = V.graph.wrapper_code
_, call_args, arg_types = self.args.python_argdefs()
for arg, arg_type in zip(call_args, arg_types):
if isinstance(arg_type, TensorArg):
line = f"assert not {arg}.isnan().any().item()"
wrapper.writeline(line)
line = f"assert not {arg}.isinf().any().item()"
wrapper.writeline(line)
def warn_mix_layout(self, kernel_name):
"""
Print message if the kernel have mixed layout inputs.
Only care about 4D tensor for now.
"""
if (
len(self.args.input_buffers) == 1
and len(self.args.output_buffers) == 1
and len(self.args.inplace_buffers) == 0
):
# even if input buffer and output buffer have different layout,
# this can be a layout conversion kernel. No need to warn for
# the mix layouts.
return
argdefs, call_args, signature = self.args.python_argdefs()
uniform_stride_order = None
for arg_name in call_args:
buf = V.graph.get_buffer(arg_name)
if buf and len(buf.layout.size) == 4:
# ignore the tensor if only 1 dimension is non-zero
if len([x for x in buf.layout.size if x == 1]) == 3:
continue
stride_order = ir.get_stride_order(buf.layout.stride)
if uniform_stride_order is None:
uniform_stride_order = stride_order
elif uniform_stride_order != stride_order:
msg = yellow_text(
f"Expected stride order {uniform_stride_order}, but found stride order"
+ f" {stride_order} for kernel {kernel_name}"
)
log.warning(msg)
stride_order_list = [
ir.get_stride_order(V.graph.get_buffer(name).layout.stride)
if V.graph.get_buffer(name)
else None
for name in call_args
]
size_list = [
V.graph.get_buffer(name).layout.size
if V.graph.get_buffer(name)
else None
for name in call_args
]
source_list = [
"GraphInput"
if name in V.graph.graph_inputs
else "IntermediateBuffer"
if name in V.graph.name_to_buffer
else None
for name in call_args
]
msg = yellow_text(
f" param names {argdefs}\n buf names {call_args}\n strides {stride_order_list}"
+ f"\n sizes {size_list}\n sources {source_list}\n"
)
log.warning(msg)
return
msg = green_text(
f"All the inputs for the triton kernel {kernel_name} have uniform layout"
)
log.warning(msg)
def create_cse_var(self, *args, **kwargs):
return TritonCSEVariable(*args, **kwargs)
class TritonScheduling(BaseScheduling):
def __init__(self, scheduler):
self.scheduler = scheduler
def group_fn(self, sizes):
return tuple(V.graph.sizevars.simplify(sympy_product(s)) for s in sizes)
def can_fuse(self, node1, node2):
"""
Hook called by Scheduler to determine if the Triton backend
can fuse node1 and node2. These nodes might already be
FusedSchedulerNodes.
"""
if isinstance(node1, scheduler.ForeachKernelSchedulerNode) or isinstance(
node2, scheduler.ForeachKernelSchedulerNode
):
return scheduler.ForeachKernelSchedulerNode.can_fuse(node1, node2)
_, (numel1, rnumel1) = node1.group
_, (numel2, rnumel2) = node2.group
why = WhyNoFuse(node1, node2)
if node1.is_reduction() and node2.is_reduction():
reduction_can_fuse = numel1 == numel2 and rnumel1 == rnumel2
if not reduction_can_fuse:
why(
"numel/rnumel mismatch (reduce) (%s, %s), (%s, %s)",
numel1,
numel2,
rnumel1,
rnumel2,
)
return reduction_can_fuse
if not node1.is_reduction() and not node2.is_reduction():
if not (numel1 == numel2 and rnumel1 == rnumel2):
why(
"numel/rnumel mismatch (non-reduce) (%s, %s), (%s, %s)",
numel1,
numel2,
rnumel1,
rnumel2,
)
return False
if node1.is_template():
# Only allow fusion for TritonTemplates for now.
# Fusion for CUDATemplates are not supported.
is_triton_template = isinstance(node1.node, TritonTemplateBuffer)
if not is_triton_template:
why("node1 is not TritonTemplateBuffer")
return is_triton_template
# check for a bad combined tiling
tiling1 = self.select_tiling(node1.get_nodes(), numel1, rnumel1)
tiling2 = self.select_tiling(node2.get_nodes(), numel1, rnumel1)
tiling3 = self.select_tiling(
node1.get_nodes() + node2.get_nodes(), numel1, rnumel1
)
if config.triton.tiling_prevents_pointwise_fusion:
cond = True
if len(tiling1) > 2:
if len(tiling2) > 2:
cond = tiling1 == tiling2 == tiling3
else:
cond = tiling1 == tiling3
elif len(tiling2) > 2:
cond = tiling2 == tiling3
if not cond:
why(
"tiling mismatch (%s, %s, %s)",
tiling1,
tiling2,
tiling3,
)
return False
return True
if not node1.is_reduction() and node2.is_reduction():
assert rnumel1 == 1 and rnumel2 != 1
if numel1 == numel2 * rnumel2:
if not all(
TritonKernel.is_compatible((numel2, rnumel2), n.get_ranges())
for n in node1.get_nodes()
):
why("nodes numel/rnumel incompatibility")
return False
if (
config.triton.tiling_prevents_reduction_fusion
and not node1.is_template()
):
is_reduction_tiling_valid = self.select_tiling(
node1.get_nodes(), numel1
) in (
(numel1, 1),
(numel2, rnumel2, 1),
)
if not is_reduction_tiling_valid:
why("invalid tiling for reduction")
return is_reduction_tiling_valid
return True
if numel1 != numel2:
why("nodes numel incompatibility")
return numel1 == numel2
assert node1.is_reduction() and not node2.is_reduction()
# swap args to hit the case above
return self.can_fuse_horizontal(node2, node1)
can_fuse_vertical = can_fuse
can_fuse_horizontal = can_fuse
def generate_node_schedule(self, nodes, numel, rnumel):
node_schedule: List[Any] = []
current_loop_writes: Set[str] = set()
is_current_reductions = set()
done = set()
def fits_in_main_body(n):
_, (node_numel, node_rnumel) = n.group
return (node_numel == numel and node_rnumel == rnumel) or (
node_numel == numel * rnumel and node_rnumel == 1
)
def fits_outside_reduction(n):
_, (node_numel, node_rnumel) = n.group
return node_numel == numel and node_rnumel == 1 and rnumel != 1
@contextlib.contextmanager
def end_current_reduction_loop():
if current_loop_writes:
# flush out any other runnable nodes to reduce number of loops
for other_node in nodes[index + 1 :]:
if (
node not in done
and fits_in_main_body(other_node)
and not (current_loop_writes & other_node.ancestors)
):
done.add(node)
current_loop_writes.add(node.get_name())
is_current_reductions.add(node.is_reduction())
node_schedule.append(node)
if node_schedule and node_schedule[-1] is EnableReduction:
node_schedule.pop()
else:
node_schedule.append(DisableReduction)
yield
node_schedule.append(EnableReduction)
current_loop_writes.clear()
is_current_reductions.clear()
for index, node in enumerate(nodes):
if node in done:
continue
done.add(node)
def requires_closing_previous_reduction(node, node_schedule):
if rnumel == 1:
return False
if not current_loop_writes & node.ancestors:
return False
assert node_schedule and not isinstance(
node_schedule[-1], (EnableReduction, DisableReduction)
)
return True in is_current_reductions
if fits_in_main_body(node):
if requires_closing_previous_reduction(node, node_schedule):
with end_current_reduction_loop():
pass # need to start a new reduction loop
current_loop_writes.add(node.get_name())
is_current_reductions.add(node.is_reduction())
node_schedule.append(node)
elif fits_outside_reduction(node):
with end_current_reduction_loop():
node_schedule.append(node)
else:
raise NotImplementedError(
f"unexpected group: ({numel}, {rnumel}) != {node.group[1]}"
)
return node_schedule
def codegen_nodes(self, nodes):
"""
Given a set of pre-fused nodes, generate a Triton kernel.
"""
_, (numel, rnumel) = max(nodes, key=lambda x: int(x.is_reduction())).group
node_schedule = self.generate_node_schedule(nodes, numel, rnumel)
schedule_log.debug("Schedule:\n %s", node_schedule)
return self.codegen_node_schedule(node_schedule, numel, rnumel)
@staticmethod
def reduction_hint(node):
assert node.is_reduction()
if all(
dep.is_contiguous()
for dep in itertools.chain(node.read_writes.reads, node.read_writes.writes)
):
return ReductionHint.INNER
else:
return node.node.data.reduction_hint
@staticmethod
def can_use_32bit_indexing(
numel: sympy.Expr, buffers: Iterable[Union[ir.Buffer, ir.TensorBox]]
) -> bool:
int_max = torch.iinfo(torch.int32).max
size_hint = V.graph.sizevars.size_hint
has_hint = V.graph.sizevars.shape_env.has_hint
def within_32bit(e):
# Allow for unhinted e as long as we can still statically prove
# (e.g., via ValueRanges) that it is still in bounds
if V.graph.sizevars.is_expr_static_and_true(e <= int_max):
return True
# Otherwise, the hint MUST exist and be in range
return has_hint(e) and size_hint(e) <= int_max
if not within_32bit(numel):
return False
# Any use of a MultiOutputLayout will create a buffer with a
# Layout whose sizes are accounted for
buf_sizes = [
buf.get_layout().storage_size()
for buf in buffers
if not isinstance(buf.get_layout(), ir.MultiOutputLayout)
]
if not all(within_32bit(size) for size in buf_sizes):
return False
# Only install guards for 32-bit indexing as there is no correctness
# issue with using 64-bit for everything
V.graph.sizevars.guard_leq(numel, int_max)
for size in buf_sizes:
V.graph.sizevars.guard_leq(size, int_max)
return True
@staticmethod
def select_index_dtype(node_schedule, numel, reduction_numel):
# Gather all used buffer names
buffer_names = set()
for node in node_schedule:
if not isinstance(node, scheduler.BaseSchedulerNode):
continue
buffer_names.update(node.get_names())
buffer_names.update(node.used_buffer_names())
# Get buffers objects
def _get_buffer(name: str) -> Union[ir.Buffer, ir.TensorBox]:
if name in V.graph.name_to_buffer:
return V.graph.name_to_buffer[name]
elif name in V.graph.graph_inputs:
return V.graph.graph_inputs[name]
elif name in V.graph.constants:
data = V.graph.constants[name]
return ir.ConstantBuffer(
name,
ir.FixedLayout(
data.device, data.dtype, *V.graph.static_sizes_strides(data)
),
)
raise RuntimeError(f"Failed to find buffer matching name {name}")
buffers = [_get_buffer(name) for name in buffer_names]
# In theory we can separately check xnumel and rnumel are <= int_max
# but some indexers do use the full linear index so we need to be
# conservative here.
total_numel = numel * reduction_numel
if TritonScheduling.can_use_32bit_indexing(total_numel, buffers):
return "tl.int32"
return "tl.int64"
def get_kernel_args(self, node_schedule, numel, reduction_numel):
reductions = list(
filter(
lambda n: n not in (EnableReduction, DisableReduction)
and n.is_reduction(),
node_schedule,
)
)
if len(reductions) > 0:
hints = [self.reduction_hint(n) for n in reductions]
if hints.count(hints[0]) == len(hints):
reduction_hint_val = hints[0]
else:
reduction_hint_val = ReductionHint.DEFAULT
else:
reduction_hint_val = ReductionHint.DEFAULT
mutations = set()
for node in node_schedule:
if hasattr(node, "get_mutations"):
mutations.update(node.get_mutations())
index_dtype = self.select_index_dtype(node_schedule, numel, reduction_numel)
return reduction_hint_val, mutations, index_dtype
def codegen_comment(self, node_schedule):
wrapper = V.graph.wrapper_code
origins, detailed_origins = get_kernel_metadata(node_schedule, wrapper)
if origins:
wrapper.writeline(origins)
if config.debug_fusion:
from torch._inductor.scheduler import (
BaseSchedulerNode,
ForeachKernelSchedulerNode,
)
if not any(
isinstance(n, ForeachKernelSchedulerNode) for n in node_schedule
):
# We probably should look what are the nodes inside a foreach
# schedule node
node_names = [
n.get_name()
for n in node_schedule
if isinstance(n, BaseSchedulerNode)
]
wrapper.writeline(
f"{wrapper.comment} Fused node name list: {', '.join(node_names)}"
)
def codegen_node_schedule(self, node_schedule, numel, reduction_numel):
tiled_groups = self.select_tiling(node_schedule, numel, reduction_numel)
reduction_hint_val, mutations, index_dtype = self.get_kernel_args(
node_schedule, numel, reduction_numel
)
kernel = TritonKernel(
*tiled_groups,
reduction_hint=reduction_hint_val,
mutations=mutations,
index_dtype=index_dtype,
)
self.codegen_node_schedule_with_kernel(node_schedule, kernel)
with V.set_kernel_handler(kernel):
src_code = kernel.codegen_kernel()
for node in node_schedule:
if node not in (EnableReduction, DisableReduction):
node.mark_run()
kernel_name = self.define_kernel(src_code, node_schedule)
log.debug("Generating kernel code with kernel_name: %s", kernel_name)
self.codegen_comment(node_schedule)
kernel.call_kernel(kernel_name)
kernel.codegen_nan_check()
V.graph.removed_buffers |= kernel.removed_buffers
V.graph.inplaced_to_remove |= kernel.inplaced_to_remove
if config.warn_mix_layout:
kernel.warn_mix_layout(kernel_name)
if (
V.graph.wrapper_code.supports_intermediate_hooks
and config.generate_intermediate_hooks
):
# Not every node in the schedule will actually be live on output;
# we can't check dead buffers.
live_outs = kernel.args.live_output_buffers()
for node in node_schedule:
if not isinstance(node, scheduler.BaseSchedulerNode):
continue
name = node.get_name()
if name not in live_outs:
continue
origin_node = node.node.get_origin_node()
if origin_node is not None:
counters["inductor"]["intermediate_hooks"] += 1
V.graph.wrapper_code.writeline(
f"run_intermediate_hooks({origin_node.name!r}, {name})"
)
self.scheduler.free_buffers()
def codegen_node_schedule_with_kernel(self, node_schedule, kernel):
def current_reduction_nodes(nodes):
return itertools.takewhile(lambda n: n is not DisableReduction, nodes)
with kernel:
stack = contextlib.ExitStack()
kernel.set_last_usage(current_reduction_nodes(node_schedule))
for node in node_schedule:
if node not in (EnableReduction, DisableReduction):
node.decide_inplace_update()
for i, node in enumerate(node_schedule):
if node is DisableReduction:
stack.enter_context(kernel.disable_reduction())
elif node is EnableReduction:
stack.close()
kernel.set_last_usage(current_reduction_nodes(node_schedule[i:]))
else:
# TODO - use split ranges ?
indexing_dtype_strength_reduction(node._body)
index_vars = kernel.split_and_set_ranges(node.get_ranges())
node.codegen(index_vars)
def define_kernel(self, src_code, node_schedule):
wrapper = V.graph.wrapper_code
if src_code in wrapper.src_to_kernel:
kernel_name = wrapper.src_to_kernel[src_code]
else:
fused_name = (
get_fused_kernel_name(node_schedule, config.triton.descriptive_names)
if config.triton.descriptive_names
else ""
)
kernel_category = get_kernel_category_by_source_code(src_code)[:3]
kernel_name = "_".join(
["triton", kernel_category, fused_name, wrapper.next_kernel_suffix()]
)
# use the original src_code as the key
wrapper.src_to_kernel[src_code] = kernel_name
subs_name = kernel_name if config.triton.unique_kernel_names else "triton_"
# DESCRIPTIVE_NAME is used for profiling purposes; it shows the full kernel name
# even when unique_kernel_names is turned off. Meanwhile, KERNEL_NAME is sometimes set
# to "triton_" to maximize caching opportunities (when unique_kernel_names = False).
src_code = src_code.replace(str(Placeholder.DESCRIPTIVE_NAME), kernel_name)
src_code = src_code.replace(str(Placeholder.KERNEL_NAME), subs_name)
# 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.
src_code = src_code.replace("#pragma CMT", "#")
basename, _, kernel_path = get_path(code_hash(src_code.strip()), "py")
compile_wrapper = IndentedBuffer()
compile_wrapper.writeline(f"async_compile.triton({subs_name!r}, '''")
compile_wrapper.splice(src_code, strip=True)
compile_wrapper.writeline("''')")
metadata_comment = f"# kernel path: {kernel_path}"
origins, detailed_origins = get_kernel_metadata(node_schedule, wrapper)
metadata_comment += "\n" + origins + "\n" + detailed_origins
wrapper.define_kernel(
kernel_name, compile_wrapper.getvalue(), metadata_comment
)
return kernel_name
def codegen_template(self, template_node, epilogue_nodes):
"""
Codegen a triton template
"""
_, (numel, rnumel) = template_node.group
assert rnumel == 1
kernel, render = template_node.node.make_kernel_render(template_node.node)
with kernel:
for node in [template_node, *epilogue_nodes]:
node.mark_run()
partial_code = render()
for node in epilogue_nodes:
node.codegen(kernel.split_and_set_ranges(node.get_ranges()))
# finalize must be called after adding epilogue above
with V.set_kernel_handler(kernel):
# TODO: Maybe unify CUDATemplateKernel to also use PartialRender for flexible epilogue fusion.
src_code = (
partial_code
if isinstance(partial_code, str)
else partial_code.finalize()
)
node_schedule = [template_node, *epilogue_nodes]
if config.benchmark_kernel:
src_code = f"{kernel.imports_for_benchmark_kernel()}\n{src_code}\n{kernel.codegen_kernel_benchmark().getvalue()}"
kernel_name = self.define_kernel(src_code, node_schedule)
self.codegen_comment(node_schedule)
kernel.call_kernel(kernel_name, template_node.node)
V.graph.removed_buffers |= kernel.removed_buffers
V.graph.inplaced_to_remove |= kernel.inplaced_to_remove
self.scheduler.free_buffers()
def codegen_sync(self):
V.graph.wrapper_code.writeline("torch.cuda.synchronize()")
def codegen_foreach(self, foreach_node):
from .triton_foreach import ForeachKernel
for partitions_with_metadata in ForeachKernel.horizontal_partition(
foreach_node.get_subkernel_nodes(), self
):
kernel = ForeachKernel()
for nodes, tiled_groups, numel, rnumel in partitions_with_metadata:
node_schedule = self.generate_node_schedule(nodes, numel, rnumel)
(
reduction_hint_val,
mutations,
index_dtype,
) = self.get_kernel_args(node_schedule, numel, rnumel)
subkernel = kernel.create_sub_kernel(
*tiled_groups,
reduction_hint=reduction_hint_val,
mutations=mutations,
index_dtype=index_dtype,
)
self.codegen_node_schedule_with_kernel(
node_schedule,
subkernel,
)
with V.set_kernel_handler(subkernel):
for node in node_schedule:
if node not in (EnableReduction, DisableReduction):
node.mark_run()
V.graph.removed_buffers |= subkernel.removed_buffers
V.graph.inplaced_to_remove |= subkernel.inplaced_to_remove
src_code = kernel.codegen_kernel()
kernel_name = self.define_kernel(src_code, [foreach_node])
self.codegen_comment([foreach_node])
kernel.call_kernel(V.graph.wrapper_code, kernel_name)
self.scheduler.free_buffers()
@staticmethod
@functools.lru_cache(32)
def candidate_tilings(node):
ranges, reduction_ranges = node.get_ranges()
if len(ranges) <= 1:
return ()
rw = node.pointwise_read_writes()
assert len(rw.range_vars) == len(ranges)
# isinstance(dep, MemoryDep): this filters out StarDeps. StarDeps refer to reads
# that need to access the entire tensor; they don't contribute read indexing
# information (and practically, they don't have dep.index so they can't be used
# for stride_hints below
dep_sources = [rw.reads, rw.writes]
assert all(
isinstance(dep, (MemoryDep, StarDep))
for dep in itertools.chain(*dep_sources)
)
deps = [
dep
for dep in itertools.chain(*dep_sources)
if dep.name not in V.graph.removed_buffers and isinstance(dep, MemoryDep)
]
write_names = {dep.name for dep in rw.writes}
tilings: List[CandidateTiling] = []
for dep in deps:
strides = V.graph.sizevars.stride_hints(dep.index, rw.range_vars)
assert len(strides) == len(ranges)
try:
split = strides.index(1) + 1
if split == len(ranges):
continue
if all(s == 0 for s in strides[split:]):
# if this is a broadcasted tensor and all dimensions after split are broadcast,
# this is not a real split
continue
except ValueError:
continue
tiled_groups = (
V.graph.sizevars.simplify(sympy_product(ranges[:split])),
V.graph.sizevars.simplify(sympy_product(ranges[split:])),
)
# score by number of elements
score = V.graph.sizevars.size_hint(
sympy_product(
size for size, stride in zip(ranges, strides) if stride != 0
)
)
if dep.name in write_names:
# ngimel said contiguous writes is more important than reads
score *= 2
if CandidateTiling.is_good_size(tiled_groups[0]):
score *= 2
if CandidateTiling.is_good_size(tiled_groups[1]):
score *= 2
if (
V.graph.sizevars.size_hint(
score - sympy_product(itertools.chain(ranges, reduction_ranges))
)
>= 0
):
tilings.append(CandidateTiling(tiled_groups, score, dep.name))
return tilings
@classmethod
def select_tiling(cls, node_schedule, numel, reduction_numel=sympy.Integer(1)):
"""
Heuristics to decide how to tile kernels.
Currently, we tile based on stride-1 dimensions.
Returns:
`(tile1, tile2, reduction_numel)` s.t. `tile1 * tile2 == numel`
"""
if reduction_numel != 1 or config.triton.max_tiles <= 1:
# TODO(jansel): should we tile reductions?
# do perf hint here if stride-1 dim is not being reduced
if perf_hint_log.level <= logging.WARNING:
for node in EnableReduction.filter(node_schedule):
if len(cls.candidate_tilings(node)) > 0:
perf_hint_log.info("reduction over non-contiguous dims")
break
return (numel, reduction_numel)
seen_names = set()
candidate_tiles: Counter[Any] = collections.Counter()
for node in EnableReduction.filter(node_schedule):
for tiling in cls.candidate_tilings(node):
if tiling.name in seen_names:
continue
seen_names.add(tiling.name)
candidate_tiles[tiling.tiling] += tiling.score
ranked_tilings = [tiling for tiling, score in candidate_tiles.most_common()]
if config.triton.max_tiles >= 3:
# Consider adding a third dimension of tiling, but only
# when a1 is a multiple of b1; otherwise, you have a lot
# of stragglers which is annoying to generate code for.
#
# NB: More than three max tiles is not enabled by default.
# Add one 3D tiling choice
for i in range(1, len(ranked_tilings)):
a0, a1 = ranked_tilings[0]
b0, b1 = ranked_tilings[i]
if V.graph.sizevars.size_hint(a1 - b1) == 0:
continue
if V.graph.sizevars.size_hint(a1 - b1) < 0:
# swap so a0 is bigger
a0, a1 = ranked_tilings[i]
b0, b1 = ranked_tilings[0]
assert V.graph.sizevars.size_hint(a1 - b1) > 0
if V.graph.sizevars.statically_known_multiple_of(a1, b1):
tiling = (a0, FloorDiv(a1, b1), b1)
ranked_tilings = [tiling] + ranked_tilings
break # only 1 choice for now
if len(ranked_tilings) > 1:
perf_hint_log.info("possibly bad tiling: %s", ranked_tilings)
for tiled_groups in ranked_tilings:
new_groups = (*tiled_groups, reduction_numel)
if all(
TritonKernel.is_compatible(new_groups, node.get_ranges())
for node in node_schedule
if isinstance(node, scheduler.SchedulerNode)
):
return new_groups
return (numel, reduction_numel)
def flush(self):
pass
def ready_to_flush(self) -> bool:
return False
def benchmark_fused_nodes(self, nodes):
_, (numel, rnumel) = max(nodes, key=lambda x: int(x.is_reduction())).group
node_schedule = self.generate_node_schedule(nodes, numel, rnumel)
tiled_groups = self.select_tiling(node_schedule, numel, rnumel)
reduction_hint_val, mutations, index_dtype = self.get_kernel_args(
node_schedule, numel, rnumel
)
kernel = TritonKernel(
*tiled_groups,
reduction_hint=reduction_hint_val,
mutations=mutations,
index_dtype=index_dtype,
)
# empty last_usage. May cause more aggressive 'evict_last'. Should be fine.
for n in nodes:
n.last_usage = set()
self.codegen_node_schedule_with_kernel(node_schedule, kernel)
with config.patch("benchmark_kernel", True), V.set_kernel_handler(kernel):
src_code = kernel.codegen_kernel()
src_code = src_code.replace(str(Placeholder.KERNEL_NAME), "triton_")
mod = PyCodeCache.load(src_code)
def cache_file_path():
assert mod.__file__ is not None
return os.path.splitext(mod.__file__)[0] + ".kernel_perf"
def load_cache():
path = cache_file_path()
if os.path.exists(path):
with open(path) as fd:
return float(fd.read())
return None
def store_cache():
path = cache_file_path()
with open(path, "w") as fd:
fd.write(str(ms))
log.debug(
"kernel src code for %s written to: %s",
{n.get_name() for n in nodes},
mod.__file__,
)
ms = load_cache()
if ms is not None:
return ms, mod.__file__
args = mod.get_args()
call = mod.call
wrapped_jit_function = mod.triton_
# call once to trigger the compilation
call(wrapped_jit_function.clone_args(*args)[0])
launchers = wrapped_jit_function.launchers
assert len(launchers) == 1
if launchers[0].n_spills > 0:
# skip benchmarking the kernel if there are register spills
ms = float("inf")
else:
# We have to clone the inplace updated arguments to avoid earlier calls
# generating out of range indices for later calls.
ms = do_bench(lambda: call(wrapped_jit_function.clone_args(*args)[0]))
log.debug(
"The fused kernel for %s took %.3f ms to run",
{n.get_name() for n in nodes},
ms,
)
store_cache()
return ms, mod.__file__
@dataclasses.dataclass
class CandidateTiling:
tiling: Tuple[sympy.Expr, sympy.Expr]
score: int # higher is better
name: Optional[str] = None
@staticmethod
def is_good_size(s):
"""Somewhat arbitrary heuristic used to boost scores for some sizes"""
s = V.graph.sizevars.size_hint(s)
return s >= 32 and (s % 32 == 0)
class DisableReduction:
"""
Marker to invoke `kernel.disable_reduction()`. This closes a
reduction loop and allows for pointwise ops to occur on the output
of a reduction.
"""
class EnableReduction:
"""
Marker to end a DisableReduction block.
"""
@staticmethod
def filter(node_schedule):
"""
Get the nodes from node_schedule skipping those in a
DisableReduction block.
"""
disabled = False
for node in node_schedule:
if node in (EnableReduction, DisableReduction):
# Don't tile stuff outside the main reduction loop
disabled = node is DisableReduction
elif disabled:
pass
else:
yield node
class CantSplit(Exception):
pass