/
common.py
829 lines (703 loc) · 26.9 KB
/
common.py
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import contextlib
import dataclasses
import functools
import itertools
import logging
import re
import typing
from collections import namedtuple
from itertools import chain
import sympy
from sympy.printing.printer import Printer
import torch
from .. import metrics
from ..utils import (
DeferredLineBase,
free_symbol_startswith,
IndentedBuffer,
sympy_dot,
sympy_subs,
sympy_symbol,
unique,
)
from ..virtualized import ops, V
log = logging.getLogger(__name__)
def data_type_logger(msg):
if log.isEnabledFor(logging.DEBUG):
log.debug("Data type propagation: %s", msg)
TensorArg = namedtuple("TensorArg", ["name", "buffer", "dtype"])
SizeArg = namedtuple("SizeArg", ["name", "expr"])
def index_prevent_reordering(index: typing.List[sympy.Expr], index_vars, sizes):
from ..ir import FlexibleLayout
# added contiguous index prevents reordering
return [*index, sympy_dot(index_vars, FlexibleLayout.contiguous_strides(sizes))]
def _data_type_propagation(sub_graph: torch.fx.Graph):
def propagate_node(node: torch.fx.Node):
_node: torch.fx.Node = node
ops_to_bool = [
"is_inf",
"is_nan",
"bitwise_xor",
"logical_not",
"signbit",
"le",
"lt",
"ge",
"gt",
"eq",
"ne",
]
ops_with_dtype_arg = ["constant", "to_dtype", "rand", "randn"]
reduction_to_dtype = {
"any": torch.bool,
"argmin": torch.int64,
"argmax": torch.int64,
}
ops_without_dtype = ["ops", "get_index"]
if _node.target in ops_without_dtype:
return False
if OptimizationContext.key in _node.meta:
opt_ctx = _node.meta[OptimizationContext.key]
else:
opt_ctx = OptimizationContext()
if opt_ctx.dtype is not None:
return False
if _node.target in ops_to_bool:
opt_ctx.dtype = torch.bool
elif _node.target in ops_with_dtype_arg:
opt_ctx.dtype = _node.args[-1]
elif _node.target == "reduction":
reduction_type = _node.args[4]
if reduction_type in reduction_to_dtype:
opt_ctx.dtype = reduction_to_dtype[reduction_type]
elif _node.target == "load":
opt_ctx.dtype = V.graph.get_dtype(_node.args[1])
if opt_ctx.dtype is not None:
data_type_logger(
f"for node.target = {_node.target}, dtype is propagated to {opt_ctx.dtype}"
)
_node.meta[OptimizationContext.key] = opt_ctx
return True
# node.target not belong to any ops which can directly get the dtype
# need propogate dtype with it's input node
dtype = None
inputs = node.all_input_nodes
input_nodes = [
n
for n in inputs
if isinstance(n, torch.fx.node.Node) and n.target not in ops_without_dtype
]
if len(input_nodes) == 0:
return False
all_input_nodes_propogated = all(
OptimizationContext.key in n.meta
and n.meta[OptimizationContext.key].dtype is not None
for n in input_nodes
)
if not all_input_nodes_propogated:
return False
# all input nodes have propogated dtype, we will promot to dtype with highest precision
dtype = functools.reduce(
torch.promote_types,
[n.meta[OptimizationContext.key].dtype for n in input_nodes],
)
opt_ctx.dtype = dtype
msg = f"for node.target = {_node.target}, dtype is propagated to {opt_ctx.dtype}, "
input_msg = "inputs dtypes: "
for n in input_nodes:
input_msg += (
f"input {n.name}.dtype = {n.meta[OptimizationContext.key].dtype}"
)
data_type_logger(msg + input_msg)
_node.meta[OptimizationContext.key] = opt_ctx
return True
new_node_propogated = False
for node in sub_graph.nodes:
new_node_propogated = propagate_node(node) or new_node_propogated
if new_node_propogated:
_data_type_propagation(sub_graph)
def data_type_propagation(node):
from ..ir import LoopBody
from ..scheduler import SchedulerNode
assert isinstance(node, SchedulerNode)
_node: SchedulerNode = node
if isinstance(_node._body, LoopBody):
body: LoopBody = node._body
sub_blocks = [body.root_block] + list(body.subblocks.values())
for sub_block in sub_blocks:
_sub_graph: torch.fx.Graph = sub_block.graph
_data_type_propagation(_sub_graph)
class ExprPrinter(Printer):
@staticmethod
def paren(string):
def all_in_parens(string):
if string[0] != "(" or len(string) < 2:
return False
count = 1
for i, char in enumerate(string[1:]):
if char == "(":
count += 1
elif char == ")":
count -= 1
if count == 0 and i != len(string) - 2:
return False
assert count == 0
return True
if (
isinstance(string, CSEVariable)
or re.match(r"^[a-z0-9_.]+$", string, re.I)
or re.match(r"^\([^)]*\)$", string, re.I)
or string == ""
):
return string
# don't put extra parens for strings that are already wrapped in parens
if all_in_parens(string):
return string
return f"({string})"
def _print_Pow(self, expr):
# Pow() confuses triton
base, exp = expr.args
base = self._print(base)
# NB: Remember this is sizevar computation! You don't typically
# expect to have to do floating point computation including exponents
# in sizevar compute. Instead of adding support for floating
# point pow, you should make upstream retranslate the Sympy expression
# into Tensor expressions earlier and do that instead.
if exp == 0.5:
return f"math.sqrt({base})"
assert exp == int(exp), exp
exp = int(exp)
if exp > 0:
return "*".join([self.paren(base)] * exp)
elif exp < 0:
return "1/" + self.paren("*".join([self.paren(base)] * abs(exp)))
else: # exp == 0
return "1"
def _print_Mul(self, expr):
return "*".join(map(self.paren, map(self._print, expr.args)))
def _print_Add(self, expr):
return " + ".join(map(self.paren, map(self._print, expr.args)))
def _print_Mod(self, expr):
return " % ".join(map(self.paren, map(self._print, expr.args)))
def _print_CleanDiv(self, expr):
return self._print_FloorDiv(expr)
class PythonPrinter(ExprPrinter):
def _print_ModularIndexing(self, expr):
x, div, mod = expr.args
x = self.paren(self.doprint(x))
div = self.paren(self.doprint(div))
mod = self.paren(self.doprint(mod))
if div != "1":
x = f"({x} // {div})"
return f"{x} % {mod}"
def _print_FloorDiv(self, expr):
x, div = expr.args
x = self.paren(self.doprint(x))
div = self.paren(self.doprint(div))
return f"({x} // {div})"
def _print_floor(self, expr):
assert len(expr.args) == 1
return f"math.floor({self._print(expr.args[0])})"
def _print_ceiling(self, expr):
assert len(expr.args) == 1
return f"math.ceil({self._print(expr.args[0])})"
class OpOverrides:
def __init__(self, parent):
super().__init__()
self._parent = parent
def __getattr__(self, item):
return getattr(self._parent, item)
@staticmethod
def identity(value):
# used to trigger cse
return value
@staticmethod
def constant(value, dtype):
return repr(value)
@staticmethod
def reciprocal(x):
return ops.div("1", x)
@staticmethod
def square(x):
return ops.mul(x, x)
@staticmethod
def sign(x):
left = ops.where(ops.lt("0", x), "1", "0")
right = ops.where(ops.lt(x, "0"), "1", "0")
return ops.sub(left, right)
@staticmethod
def bitwise_not(x):
return f"~{ExprPrinter.paren(x)}"
@staticmethod
def logical_not(a):
return f"{ExprPrinter.paren(a)} == 0"
@staticmethod
def bitwise_and(x, y):
return f"{ExprPrinter.paren(x)} & {ExprPrinter.paren(y)}"
@staticmethod
def bitwise_or(x, y):
return f"{ExprPrinter.paren(x)} | {ExprPrinter.paren(y)}"
@staticmethod
def bitwise_xor(x, y):
return f"{ExprPrinter.paren(x)} ^ {ExprPrinter.paren(y)}"
@staticmethod
def bitwise_left_shift(x, y):
return f"{ExprPrinter.paren(x)} << {ExprPrinter.paren(y)}"
# TODO(fdrocha): this is currently not being used anywhere,
# pending on moving triton pin past 972b761
@staticmethod
def bitwise_right_shift(x, y):
return f"{ExprPrinter.paren(x)} >> {ExprPrinter.paren(y)}"
@staticmethod
def remainder(a, b):
r = ops.mod(a, b)
return ops.where(f"(({r} != 0) & (({r} < 0) != ({b} < 0)))", ops.add(r, b), r)
class DeferredLine(DeferredLineBase):
"""A line that can be 'unwritten' by adding name to V.graph.removed_buffers"""
def __init__(self, name, line):
super().__init__(line)
self.name = name
def __call__(self):
if (
self.name not in V.graph.removed_buffers
and self.name not in V.graph.inplaced_to_remove
):
return self.line
return None
def _new_line(self, line):
return DeferredLine(self.name, line)
class BracesBuffer(IndentedBuffer):
def indent(self, offset=1):
@contextlib.contextmanager
def ctx():
for _ in range(offset):
self.writeline("{")
self._indent += 1
for _ in range(-offset):
self._indent -= 1
self.writeline("}")
yield
for _ in range(-offset):
self.writeline("{")
self._indent += 1
for _ in range(offset):
self._indent -= 1
self.writeline("}")
return ctx()
class InplacedBuffer(typing.NamedTuple):
inner_name: str
other_names: typing.List[str]
class KernelArgs:
@staticmethod
def _lookup(prefix, odict, name):
assert isinstance(name, (str, sympy.Symbol))
if name not in odict:
odict[name] = f"{prefix}{len(odict)}"
return odict[name]
def __init__(self, sizevars=None):
self.input_buffers = dict()
self.output_buffers = dict()
self.inplace_buffers = dict()
self.sizevars = sizevars or dict()
def __repr__(self):
return "KernelArgs({})".format(
", ".join(
map(
repr,
[
self.input_buffers,
self.output_buffers,
self.inplace_buffers,
self.sizevars,
],
)
)
)
def input(self, name):
if V.graph.scheduler:
name = V.graph.scheduler.mutation_real_name.get(name, name)
assert name not in V.graph.removed_buffers, name
if name in self.output_buffers:
return self.output_buffers[name]
if name in self.inplace_buffers:
return self.inplace_buffers[name].inner_name
if name.startswith("seed"):
return self._lookup("seed", self.input_buffers, name)
return self._lookup("in_ptr", self.input_buffers, name)
def output(self, name):
if V.graph.scheduler:
name = V.graph.scheduler.mutation_real_name.get(name, name)
assert name not in V.graph.removed_buffers, name
if name in self.inplace_buffers:
return self.inplace_buffers[name].inner_name
return self._lookup("out_ptr", self.output_buffers, name)
def make_inplace(self, input_name, output_name):
assert output_name not in self.inplace_buffers
if input_name in self.inplace_buffers:
buf = self.inplace_buffers[input_name]
buf.other_names.append(output_name)
self.inplace_buffers[output_name] = buf
else:
buf = InplacedBuffer(
f"in_out_ptr{len(unique(self.inplace_buffers.values()))}",
[input_name, output_name],
)
self.inplace_buffers[input_name] = buf
self.inplace_buffers[output_name] = buf
def size(self, name):
if str(name) == "seed":
self.sizevars["seed"] = "seed"
return "seed"
return self._lookup("ks", self.sizevars, name)
def call_names(self):
return chain(
self.input_buffers.keys(), self.output_buffers.keys(), self.sizevars.keys()
)
def wrap_ptr_arg(self, buf, dtype):
return f"c_void_p({buf}.data_ptr())"
def wrap_size_arg(self, size):
return f"c_long({size})"
def cpp_argdefs(self):
from .cpp import DTYPE_TO_CPP, INDEX_TYPE
# TODO(jansel): replace this with data from scheduler
buffer_types = {x.get_name(): x.get_dtype() for x in V.graph.buffers}
for name, val in V.graph.graph_inputs.items():
if isinstance(val, sympy.Expr):
if val.is_integer:
buffer_types[name] = torch.int64
else:
buffer_types[name] = torch.float64
else:
buffer_types[name] = val.get_dtype()
buffer_types.update(
{name: val.dtype for name, val in V.graph.constants.items()}
)
call_args = []
arg_defs = []
arg_types = []
for inplaced in unique(self.inplace_buffers.values()):
outer = inplaced.other_names[-1]
inner = inplaced.inner_name
dtype = buffer_types[outer]
cpp_dtype = DTYPE_TO_CPP[dtype]
arg_defs.append(f"{cpp_dtype}* {inner}")
call_args.append(self.wrap_ptr_arg(outer, dtype))
arg_types.append(f"{cpp_dtype}*")
for outer, inner in self.input_buffers.items():
if outer in self.inplace_buffers:
continue
dtype = buffer_types[outer]
cpp_dtype = DTYPE_TO_CPP[dtype]
arg_defs.append(f"const {cpp_dtype}* {inner}")
call_args.append(self.wrap_ptr_arg(outer, dtype))
arg_types.append(f"const {cpp_dtype}*")
for outer, inner in self.output_buffers.items():
if outer in self.inplace_buffers or inner == "REMOVED":
continue
dtype = buffer_types[outer]
cpp_dtype = DTYPE_TO_CPP[dtype]
arg_defs.append(f"{cpp_dtype}* {inner}")
call_args.append(self.wrap_ptr_arg(outer, dtype))
arg_types.append(f"{cpp_dtype}*")
for outer, inner in self.sizevars.items():
arg_defs.append(f"const {INDEX_TYPE} {inner}")
call_args.append(self.wrap_size_arg(outer))
arg_types.append(f"const {INDEX_TYPE}")
return arg_defs, call_args, arg_types
def python_argdefs(self):
arg_defs = []
call_args = []
precompile_args = []
for inplaced in unique(self.inplace_buffers.values()):
arg_defs.append(inplaced.inner_name)
call_args.append(inplaced.other_names[-1])
precompile_args.append(
TensorArg(
inplaced.inner_name,
inplaced.other_names[-1],
V.graph.get_dtype(inplaced.other_names[-1]),
)
)
for outer, inner in chain(
self.input_buffers.items(), self.output_buffers.items()
):
if outer in self.inplace_buffers or inner == "REMOVED":
continue
arg_defs.append(inner)
call_args.append(outer)
precompile_args.append(TensorArg(inner, outer, V.graph.get_dtype(outer)))
for outer, inner in self.sizevars.items():
arg_defs.append(inner)
call_args.append(str(outer))
precompile_args.append(SizeArg(inner, outer))
return arg_defs, call_args, precompile_args
def aliases(self):
for inplaced in unique(self.inplace_buffers.values()):
for other in inplaced.other_names:
if other in V.graph.inplaced_to_remove:
continue
if other in self.input_buffers:
yield self.input_buffers[other], inplaced.inner_name
if other in self.output_buffers:
yield self.output_buffers[other], inplaced.inner_name
def is_removed(self, name):
def _is_removed(name, buffers):
return name not in buffers or buffers[name] == "REMOVED"
return _is_removed(name, self.output_buffers) and _is_removed(
name, self.inplace_buffers
)
# Includes inplace buffers, excludes removed buffers. Essentially,
# after you do a call into this kernel, which buffers actually contain
# updated data? Modeled off of python_argdefs.
def live_output_buffers(self):
live_outs = set()
for inplaced in unique(self.inplace_buffers.values()):
live_outs.add(inplaced.other_names[-1])
for outer, inner in self.output_buffers.items():
if outer in self.inplace_buffers or inner == "REMOVED":
continue
live_outs.add(outer)
return live_outs
class CSEVariable:
"""A CSEVariable is just a name for an expression but it is useful to be able to annotate them on a backend dependent basis.
The backends can inherit from this class and overload the "create_cse_var" Kernel to do that.
The "update_on_args" method gives you a hook for annotations, see example of TritonCSEVariable in triton.py.
"""
def __init__(self, name):
self.name = name
def __str__(self):
return self.name
def __hash__(self) -> int:
return hash(self.name)
def __eq__(self, other) -> bool:
return type(other) == type(self) and other.name == self.name
def update_on_args(self, name, args, kwargs):
pass
class CppWrapperKernelArgs(KernelArgs):
def wrap_ptr_arg(self, buf, dtype):
from .cpp import DTYPE_TO_CPP
return f"({DTYPE_TO_CPP[dtype]}*)({buf}.data_ptr())"
def wrap_size_arg(self, size):
return f"{size}"
class CSE:
"""Common subexpression elimination"""
def __init__(
self,
prefix="",
suffix="",
name_prefix="tmp",
iter_buffers=None,
store_cache=None,
reduction_cache=None,
varname_map=None,
):
self.prefix = prefix
self.suffix = suffix
self.cache = {}
self.name_prefix = name_prefix
self.store_cache = store_cache or {}
self.reduction_cache = reduction_cache or {}
self.iter_buffer_ids = iter_buffers or itertools.count()
self.invalidated_stores = set()
self.varname_map = varname_map or {}
def invalidate(self, keep_vars: typing.Set[str]):
for name, tmp in list(self.store_cache.items()):
if tmp not in keep_vars:
del self.store_cache[name]
self.invalidated_stores.add(name)
self.cache = {k: v for k, v in self.cache.items() if v in keep_vars}
def clone(self):
# Note(fdrocha): reduction_cache is not being cloned, not sure if this is intentional
return CSE(
prefix=self.prefix,
suffix=self.suffix,
name_prefix=self.name_prefix,
iter_buffers=self.iter_buffer_ids,
store_cache=self.store_cache,
varname_map=self.varname_map,
)
def generate(
self,
buffer: IndentedBuffer,
expr: typing.Union[str, CSEVariable],
write=True,
assignment=True,
) -> CSEVariable:
assert isinstance(expr, (str, CSEVariable)), type(expr)
assert write or assignment
if isinstance(expr, CSEVariable):
return expr
cache_key = expr
if cache_key not in self.cache:
var = self.newvar() if assignment else None
self.cache[cache_key] = var
if write:
if V.kernel.current_node:
V.kernel.current_node.codegen_originating_info(
buffer, only_once=True
)
if assignment:
line = f"{self.prefix}{var} = {expr}{self.suffix}"
else:
line = f"{expr}{self.suffix}"
buffer.writeline(line)
return self.cache[cache_key]
def newvar(self) -> CSEVariable:
var_name = f"{self.name_prefix}{next(self.iter_buffer_ids)}"
var = V.kernel.create_cse_var(var_name)
self.varname_map[var_name] = var
return var
class CodeGen:
def __init__(self):
super().__init__()
self.exit_stack = contextlib.ExitStack()
def __enter__(self):
self.exit_stack.__enter__()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.exit_stack.__exit__(exc_type, exc_val, exc_tb)
class Kernel(CodeGen):
newvar_prefix = ""
suffix = ""
overrides = None
load_format = None
store_format = None
def __init__(self, args=None):
super().__init__()
metrics.generated_kernel_count += 1
self.args = args or KernelArgs()
self.loads = IndentedBuffer()
self.compute = IndentedBuffer()
self.stores = IndentedBuffer()
self.cse = CSE(self.newvar_prefix, self.suffix)
self.must_keep_buffers = set()
self.current_node = None
self.store_buffer_names = set()
@contextlib.contextmanager
def set_current_node(self, node):
prior = self.current_node
self.current_node = node
try:
yield
finally:
self.current_node = prior
@contextlib.contextmanager
def swap_buffers(self, lb, cb=None, sb=None):
if cb is None:
cb = lb
loads = self.loads
compute = self.compute
stores = self.stores
cse = self.cse
self.loads = lb
self.compute = cb
self.stores = sb
self.cse = cse.clone()
try:
yield
finally:
self.loads = loads
self.compute = compute
self.stores = stores
self.cse = cse
def load(self, name: str, index: sympy.Expr):
raise NotImplementedError()
def indirect_load(self, name: str, index: sympy.Expr):
"""A load the depends on an index we have read"""
prior = self.loads
try:
# put the load in the compute section as it might have deps
self.loads = self.compute
return self.load(name, index)
finally:
self.loads = prior
def store(self, name, index, value, mode=None):
raise NotImplementedError()
def reduction(self, name, dtype, src_dtype, reduction_type, index, value):
raise NotImplementedError()
def __enter__(self):
class CSEProxy:
self.name = "CSEProxy"
@staticmethod
def __getattr__(name):
def inner(*args, **kwargs):
csevar = self.cse.generate(
self.compute, getattr(parent_handler, name)(*args, **kwargs)
)
csevar.update_on_args(name, args, kwargs)
return csevar
return inner
@staticmethod
def indirect_indexing(index_var, size):
return sympy_symbol(str(index_var))
@staticmethod
def load(name: str, index: sympy.Expr):
if name in self.cse.invalidated_stores:
# A load from an invalidated store requires us to
# keep the actual buffer around
V.kernel.must_keep_buffers.add(name)
if free_symbol_startswith(index, "tmp"):
return self.indirect_load(name, index)
store_cache = self.cse.store_cache
if name in store_cache:
return store_cache[name]
return self.load(name, index)
@staticmethod
def store(name, index, value, mode=None):
self.store_buffer_names.add(name)
if mode is None:
self.cse.store_cache[name] = value
if self.current_node:
for other_name in self.current_node.get_mutations():
self.cse.store_cache[other_name] = value
if name not in V.graph.removed_buffers:
return self.store(name, index, value, mode=mode)
@staticmethod
def reduction(name, dtype, src_dtype, reduction_type, index, value):
self.store_buffer_names.add(name)
return self.reduction(
name, dtype, src_dtype, reduction_type, index, value
)
super().__enter__()
parent_handler = self.overrides(V.get_ops_handler())
self.exit_stack.enter_context(V.set_ops_handler(CSEProxy()))
self.exit_stack.enter_context(V.set_kernel_handler(self))
return self
def __exit__(self, exc_type, exc_val, exc_tb):
if V.graph.scheduler:
V.graph.scheduler.remove_kernel_local_buffers()
super().__exit__(exc_type, exc_val, exc_tb)
def rename_indexing(self, index) -> sympy.Expr:
# adds the necessary kernel args for index expressions
# and renames variables in index expressions to kernel arg names
if isinstance(index, (list, tuple)):
return [self.rename_indexing(x) for x in index]
index = V.graph.sizevars.simplify(index)
sorted_symbols = sorted(index.free_symbols, key=lambda s: s.name)
replacements = {
x: self.args.size(x)
for x in sorted_symbols
if x.name.startswith("s") or x.name.startswith("ps")
}
return sympy_subs(index, replacements)
def create_cse_var(self, *args, **kwargs):
return CSEVariable(*args, **kwargs)
@dataclasses.dataclass
class OptimizationContext:
key: typing.ClassVar[str] = "opt_ctx"
# Load value as mask
is_load_as_mask: bool = False
# Load bfloat16 value as float32
is_load_bf16_as_fp32: bool = False
# Store float32 value as bfloat16
is_store_fp32_as_bf16: bool = False
# do not need type cast for
# for mem copy only node bf16 load -> bf16 store,
is_bf16_mem_copy: bool = False
dtype: torch.dtype = None
ops_name: str = ""
is_most_inner_loop_irrevelant: bool = False
# Load uint8 value as float32
is_load_uint8_as_float: bool = False
# Store float32 value as uint8
is_store_float_as_uint8: bool = False