sadmen
/
openpilot_comma
mirror of https://github.com/commaai/openpilot.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for over 200 supported car makes and models.
6 Commits
126 Branches
82 Tags
5.2 GiB
 
 
 
 
 
 
openpilot_comma/tinygrad_repo/tinygrad/ops.py

1020 lines
54 KiB
Raw Permalink Blame History

from __future__ import annotations
from typing import Any, Optional, Union, Callable, cast, TYPE_CHECKING, Type, get_args
import sys, time, functools, itertools, math, operator, hashlib, os, types, pickle, pathlib, inspect, weakref
from enum import auto, IntEnum, Enum
from dataclasses import dataclass, field
from tinygrad.dtype import ConstType, ImageDType, dtypes, DType, truncate
from tinygrad.helpers import ContextVar, all_int, prod, getenv, all_same, Context, partition, temp, unwrap, T, argfix, Metadata, _METADATA, flatten
from tinygrad.helpers import PICKLE_BUFFERS, dedup, cdiv, cmod
if TYPE_CHECKING:
from tinygrad.shape.shapetracker import ShapeTracker
from tinygrad.device import Buffer
# wrapper around IntEnum that preserves Enum.__str__ and makes auto() unique across all FastEnum subclasses
class FastEnum(IntEnum):
def __str__(self): return Enum.__str__(self)
@staticmethod
def _generate_next_value_(_, __, ___, last_values): return 1 + max([0, *last_values, *[max(c) for c in FastEnum.__subclasses__()]])
class SimpleMathTrait:
# required to implement
def alu(self:T, arg:Ops, *src) -> T: raise NotImplementedError
def const_like(self:T, b:ConstLike) -> T: raise NotImplementedError
# great functions you get!
def ufix(self, x): return self.const_like(x) if not isinstance(x, MathTrait) else x
def _binop(self, op, x, reverse): return self.ufix(x).alu(op, self) if reverse else self.alu(op, self.ufix(x))
def logical_not(self): return self.ne(True)
def neg(self):
if (dtype:=getattr(self, 'dtype')) is None: raise TypeError(f"MathTraits __neg__ requires a dtype, {self=}")
return self.logical_not() if dtype.scalar() == dtypes.bool else self*(-1)
def add(self, x, reverse=False): return self._binop(Ops.ADD, x, reverse)
def mul(self, x, reverse=False): return self._binop(Ops.MUL, x, reverse)
def bitwise_and(self, x, reverse=False): return self._binop(Ops.AND, x, reverse)
def bitwise_or(self, x, reverse=False): return self._binop(Ops.OR, x, reverse)
def bitwise_xor(self, x, reverse=False): return self._binop(Ops.XOR, x, reverse)
def idiv(self, x, reverse=False): return self._binop(Ops.IDIV, x, reverse)
def mod(self, x, reverse=False): return self._binop(Ops.MOD, x, reverse)
def sub(self, x, reverse=False): return self.ufix(x).alu(Ops.ADD, -self) if reverse else self.alu(Ops.ADD, self.ufix(-x))
def div(self, x, reverse=False): return (self.ufix(x)*self.alu(Ops.RECIP)) if reverse else (self*self.ufix(x).alu(Ops.RECIP))
def __neg__(self): return self.neg()
def __add__(self, x): return self.add(x)
def __sub__(self, x): return self.sub(x)
def __mul__(self, x): return self.mul(x)
def __truediv__(self, x): return self.div(x)
def __floordiv__(self, x): return self.idiv(x) # TODO: idiv is trunc div, not floordiv
def __mod__(self, x): return self.mod(x)
def __and__(self, x): return self.bitwise_and(x)
def __or__(self, x): return self.bitwise_or(x)
def __xor__(self, x): return self.bitwise_xor(x)
def __radd__(self, x): return self.add(x, True)
def __rsub__(self, x): return self.sub(x, True)
def __rmul__(self, x): return self.mul(x, True)
def __rtruediv__(self, x): return self.div(x, True)
def __rfloordiv__(self, x): return self.idiv(x, True)
def __rand__(self, x): return self.bitwise_and(x, True)
def __ror__(self, x): return self.bitwise_or(x, True)
def __rxor__(self, x): return self.bitwise_xor(x, True)
def __rmod__(self, x): return self.mod(x, True)
def __lt__(self, x): return self.alu(Ops.CMPLT, self.ufix(x))
def __gt__(self, x): return self.ufix(x).alu(Ops.CMPLT, self)
def __ge__(self, x): return (self < x).logical_not()
def __le__(self, x): return (self > x).logical_not()
def ne(self, x): return self.alu(Ops.CMPNE, self.ufix(x))
def eq(self, x): return self.ne(x).logical_not()
def __ne__(self, x): return self.ne(x)
# NOTE: __eq__ isn't overridden, and means the same thing as is by default
class MathTrait(SimpleMathTrait):
# TODO: move to Tensor when new backward is done
def lshift(self, x, reverse=False): return self._binop(Ops.SHL, x, reverse)
def rshift(self, x, reverse=False): return self._binop(Ops.SHR, x, reverse)
def __lshift__(self, x): return self.lshift(x)
def __rshift__(self, x): return self.rshift(x)
def __rlshift__(self, x): return self.lshift(x, True)
def __rrshift__(self, x): return self.rshift(x, True)
def maximum(self, x): return self.alu(Ops.MAX, self.ufix(x))
def minimum(self, x): return -(-self).maximum(-x)
def where(self, x, y): return self.alu(Ops.WHERE, x, x.ufix(y))
def threefry(self, seed): return self.alu(Ops.THREEFRY, seed)
def reciprocal(self): return self.alu(Ops.RECIP)
def sqrt(self): return self.alu(Ops.SQRT)
def sin(self): return self.alu(Ops.SIN)
def log2(self): return self.alu(Ops.LOG2)
def exp2(self): return self.alu(Ops.EXP2)
def pow(self, x): return self.alu(Ops.POW, self.ufix(x))
# the order of these Ops controls the order of the toposort
class Ops(FastEnum):
# uops that aren't rendered
NAME = auto(); SINK = auto(); CONTIGUOUS = auto(); CONTIGUOUS_BACKWARD = auto(); DETACH = auto(); KERNEL = auto(); UNIQUE = auto() # noqa: E702
# TODO: empty continues to exist because of tensor
EMPTY = auto()
# MetaOps
COPY = auto(); BUFFER_VIEW = auto() # noqa: E702
# blocks in linearizer
BLOCK = auto(); BLOCKSTART = auto(); BLOCKFORK = auto(); BLOCKEND = auto() # noqa: E702
# movement ops!
RESHAPE = auto(); PERMUTE = auto(); EXPAND = auto(); PAD = auto(); SHRINK = auto(); FLIP = auto() # noqa: E702
# misc ops
UNROLL = auto(); CONTRACT = auto() # noqa: E702
VIEW = auto(); DEFINE_GLOBAL = auto(); BUFFER = auto() # noqa: E702
DEFINE_VAR = auto(); DEFINE_LOCAL = auto(); DEFINE_ACC = auto() # noqa: E702
VALID = auto(); SPECIAL = auto(); NOOP = auto() # noqa: E702
# reduce
REDUCE_AXIS = auto(); REDUCE = auto() # noqa: E702
# helper ops
GEP = auto(); VECTORIZE = auto(); CAT = auto(); PTRCAT = auto() # noqa: E702
# UnaryOps
CAST = auto(); BITCAST = auto(); EXP2 = auto(); LOG2 = auto(); SIN = auto(); SQRT = auto(); RECIP = auto(); NEG = auto() # noqa: E702
# load/store before math
LOAD = auto(); STORE = auto() # noqa: E702
# early INDEX
INDEX = auto()
# math ops
WMMA = auto()
# BinaryOps
MUL = auto(); SHL = auto(); SHR = auto(); IDIV = auto(); ADD = auto(); MAX = auto(); MOD = auto(); CMPLT = auto(); CMPNE = auto() # noqa: E702
XOR = auto(); OR = auto(); AND = auto(); THREEFRY = auto(); SUB = auto(); FDIV = auto(); POW = auto() # noqa: E702
# TernaryOps
WHERE = auto(); MULACC = auto() # noqa: E702
# assignment ops
ASSIGN = auto()
BIND = auto()
# control flow ops
BARRIER = auto(); RANGE = auto(); IF = auto(); ENDRANGE = auto(); ENDIF = auto() # noqa: E702
# consts last!
VCONST = auto(); CONST = auto() # noqa: E702
# device
DEVICE = auto()
MULTI = auto()
# CUSTOMI is inline
CUSTOM = auto(); CUSTOMI = auto() # noqa: E702
IGNORE = auto()
class GroupOp:
Unary = {Ops.EXP2, Ops.LOG2, Ops.SIN, Ops.SQRT, Ops.RECIP, Ops.NEG}
Binary = {Ops.ADD, Ops.MUL, Ops.IDIV, Ops.MAX, Ops.MOD, Ops.CMPLT, Ops.CMPNE, Ops.XOR, Ops.SHL, Ops.SHR, Ops.OR, Ops.AND, Ops.THREEFRY,
Ops.SUB, Ops.FDIV, Ops.POW}
Ternary = {Ops.WHERE, Ops.MULACC}
ALU = set.union(Unary, Binary, Ternary)
Irreducible = {Ops.CONST, Ops.DEFINE_VAR, Ops.SPECIAL, Ops.RANGE}
Movement = {Ops.RESHAPE, Ops.EXPAND, Ops.PERMUTE, Ops.PAD, Ops.SHRINK, Ops.FLIP}
Buffer = {Ops.LOAD, Ops.STORE, Ops.VALID, Ops.CONST, Ops.DEFINE_VAR}
Block = {Ops.BLOCK, Ops.BLOCKEND, Ops.BLOCKFORK, Ops.BLOCKSTART}
# BinaryOps that can be flipped
Commutative = {Ops.ADD, Ops.MUL, Ops.MAX, Ops.CMPNE, Ops.XOR, Ops.AND, Ops.OR}
# BinaryOps where f(f(a,b),c) = f(a,f(b,c))
Associative = {Ops.ADD, Ops.MUL, Ops.AND, Ops.OR, Ops.MAX}
# BinaryOps that satisfy f(x,x)=x see https://en.wikipedia.org/wiki/Idempotence
Idempotent = {Ops.OR, Ops.AND, Ops.MAX}
# do not preserve f(0) = 0
UnsafePad = {Ops.RECIP, Ops.LOG2, Ops.EXP2, Ops.IDIV, Ops.POW}
Meta = {Ops.COPY, Ops.BUFFER_VIEW}
All = set(Ops)
# some BUFFER ops can be processed with only a view
view_supported_devices = {"LLVM", "CPU", "CUDA", "NV", "AMD", "METAL", "QCOM", "DSP", "DISK"}
# https://en.wikipedia.org/wiki/Identity_element
def identity_element(op:Ops, dt:DType) -> ConstType: return dtypes.as_const({Ops.ADD:0, Ops.MUL:1, Ops.MAX:dtypes.min(dt)}[op], dt)
def can_pad(u:UOp, edges:dict[UOp, None], cache:dict[UOp, None]) -> bool:
if u.op in GroupOp.UnsafePad: return False
if u in edges or u in cache: return True
cache[u] = None
return all(can_pad(x.base, edges, cache) for x in u.src)
# With True as the default, this matches the old symbolic behavior
def resolve(x:UOp|bool, default:bool=True):
if isinstance(x, bool): return x
assert x.dtype == dtypes.bool, "UOp in resolve must be bool"
# NOTE: generating the text for the exception is expensive, so we do this
return bool(sx.vmin) if (sx:=x.simplify()).vmin == sx.vmax else default
# smax/smin are replacements for max/min that preserve symbolic
def _suop(lst, uop_fxn, python_fxn):
uops, nums = partition(lst, lambda x: isinstance(x, UOp))
return ssimplify(functools.reduce(uop_fxn, uops + ([python_fxn(nums)] if nums else [])))
def smax(*lst): return _suop(argfix(*lst), UOp.maximum, max)
def smin(*lst): return _suop(argfix(*lst), UOp.minimum, min)
def ssimplify(uop): return uop.ssimplify() if isinstance(uop, UOp) else uop
def sym_infer(uop: Union[UOp, int], var_vals: dict[UOp, int]) -> int: return uop.sym_infer(var_vals) if isinstance(uop, UOp) else uop
# used for UOp and UPat
def pretty_print(x:Any, rep:Callable, srcfn=lambda x: x.src, cache=None, d=0)->str:
def dfs(x:Any, cache:dict):
for s in srcfn(x) or []:
cache.setdefault(s, [len(cache), 0, False])[1] += 1
if cache[s][1] == 1: dfs(s, cache)
if cache is None: dfs(x, cache:={})
if (cx:=cache.setdefault(x, [0,0,False]))[2]: return f"{' '*d} x{cx[0]}"
cx[2], srcs = True, ('None' if srcfn(x) is None else ''.join(f'\n{pretty_print(s, rep, srcfn, cache, d+2)},' for s in srcfn(x)))
return f"{' '*d}{f'x{cx[0]}:=' * (cx[1]>1)}{rep(x)}" % srcs
class UOpMetaClass(type):
ucache:dict[tuple, weakref.ReferenceType[UOp]] = {}
def __call__(cls, op:Ops, dtype:DType=dtypes.void, src:tuple[UOp,...]=tuple(), arg:Any=None, _buffer:Buffer|None=None):
if (wret:=UOpMetaClass.ucache.get(key:=(op, dtype, src, arg), None)) is not None and (ret:=wret()) is not None: return ret
UOpMetaClass.ucache[key] = ref = weakref.ref(created:=super().__call__(*key))
for s in src: s.children.add(ref)
# NOTE: this will soon be set by Tensor once we remove function.py
if (metadata:=_METADATA.get()) is not None: all_metadata[created] = metadata
# NOTE: this value is set by pickle when pickling a realized tensor
if _buffer is not None:
assert op is Ops.BUFFER, f"trying to set Buffer {_buffer} for {op}"
buffers[created] = _buffer
return created
# some uops map to other stuff
buffers:weakref.WeakKeyDictionary[UOp, Buffer] = weakref.WeakKeyDictionary() # this maps BUFFER uops to their device Buffers
all_metadata:weakref.WeakKeyDictionary[UOp, Metadata] = weakref.WeakKeyDictionary()
def _toposort(u:UOp, cache:set[UOp]):
if u in cache: return {}
nodes: dict[UOp, None] = {}
# NOTE: this is a lot faster than the comprehension in parents
for parent in u.src: nodes.update(_toposort(parent, cache))
nodes[u] = None
cache.add(u)
return nodes
# NOTE: this should be frozen, but frozen is slower
@dataclass(eq=False, slots=True)
class UOp(MathTrait, metaclass=UOpMetaClass):
op:Ops
dtype:DType = dtypes.void
src:tuple[UOp, ...] = tuple()
arg:Any = None
children:set[weakref.ref[UOp]] = field(default_factory=set)
def __del__(self):
if self.op is Ops.BUFFER and (buffer:=buffers.get(self)) is not None: buffer.ref(-1)
if (ref:=UOpMetaClass.ucache.get(k:=(self.op, self.dtype, self.src, self.arg))) is not None:
for s in self.src: s.children.discard(ref)
del UOpMetaClass.ucache[k]
def __reduce__(self):
args = [self.op, self.dtype, self.src, self.arg]
if self.op is Ops.BUFFER and self.realized is not None and PICKLE_BUFFERS: args.append(self.realized)
return UOp, tuple(args)
def replace(self, **kwargs) -> UOp:
new_args = (kwargs.pop("op", self.op), kwargs.pop("dtype", self.dtype), kwargs.pop("src", self.src), kwargs.pop("arg", self.arg))
assert len(kwargs) == 0, f"unused kwargs in replace {list(kwargs)}"
if (self.op, self.dtype, self.src, self.arg) == new_args: return self
return UOp(*new_args)
@functools.cached_property
def key(self) -> bytes:
return hashlib.sha256(str((self.op, self.dtype, self.arg)).encode() + b"".join([s.key for s in self.src])).digest()
def __repr__(self): return pretty_print(self, lambda x: f"{type(self).__name__}({x.op}, {x.dtype}, arg={x.argstr()}, src=(%s))")
def argstr(self): return f'({", ".join(map(str, self.arg))})' if self.op is Ops.REDUCE_AXIS else repr(self.arg)
@property
def toposort(self) -> dict[UOp, None]:
return _toposort(self, cache=set())
# returns map of UOps to their children in the graph rooted by self
def get_children_map(self) -> dict[UOp, dict[UOp, None]]:
ret: dict[UOp, dict[UOp, None]] = {}
for u in self.toposort:
for s in u.src: ret.setdefault(s, {})[u] = None
return ret
@functools.cached_property
def tuplize(self:UOp) -> tuple[int, Any, Optional[DType], tuple]: return (self.op.value, self.arg, self.dtype, tuple(x.tuplize for x in self.src))
# *** uop shape stuff ***
@functools.cached_property
def st(self) -> ShapeTracker|None:
from tinygrad.shape.shapetracker import ShapeTracker
if self.op is Ops.MULTI:
return ShapeTracker.from_shape(
tuple(sum(y.shape[a] for y in self.real_lbs) if a == self.axis else s for a,s in enumerate(self.real_lbs[0].shape)))
if self.op in {Ops.BUFFER, Ops.BUFFER_VIEW}: return ShapeTracker.from_shape((self.size,))
if self.op is Ops.KERNEL: return ShapeTracker.from_shape((self.arg.ast.size,))
# these ops define a ShapeTracker from the arg
if self.op is Ops.VIEW: return self.arg
if self.op in GroupOp.Movement: return unwrap(self.src[0].st).mop(self.op, self.arg)
# buffer ops return the ShapeTracker from sources
if self.op in GroupOp.Buffer: return vsrc[0] if len(vsrc:=[x.st for x in self.src if x.op is Ops.VIEW]) != 0 else None
if not (src_sts := [x.st for x in self.src if x.st is not None]): return None
assert all_same([x.shape for x in src_sts]), f"UOp sources must have the same shape {self} {[x.shape for x in src_sts]}"
if self.op is Ops.BITCAST:
shape = src_sts[0].shape
if self.dtype.itemsize != (input_sz:=self.src[0].dtype.itemsize): shape = shape[:-1]+((shape[-1]*input_sz) // self.dtype.itemsize,)
# only reduce ops are allowed to change shape, everything else derives shape from sources
elif self.op in {Ops.REDUCE_AXIS, Ops.WMMA}: shape = src_sts[0].reduce(self.axis_arg)
else: shape = src_sts[0].shape
return ShapeTracker.from_shape(shape)
@functools.cached_property
def full_shape(self) -> tuple[sint, ...]:
if self.op is Ops.VIEW: return self.shape
# TODO: this should check if st is None, it cannot because local reduce has implicit movement ops
return tuple(smax(x) for x in zip(*[x.full_shape for x in self.src if x.op not in {Ops.DEFINE_GLOBAL,Ops.DEFINE_LOCAL} \
# TODO: this exists because wmma creates consts without ShapeTracker in the AST, there's probably a way to fix this
and not (x.op is Ops.CONST and x.st is None)]))
@property
def shape(self) -> tuple[sint, ...]: return unwrap(self.st).shape
@property
def size(self) -> int: return self.arg[0] if self.op is Ops.BUFFER_VIEW else self.arg if self.op is Ops.BUFFER else unwrap(self.st).size
# *** uop evaluation ***
def simplify(self):
# late import!
from tinygrad.codegen.symbolic import symbolic
with Context(TRACK_MATCH_STATS=0):
return graph_rewrite(self, symbolic)
def ssimplify(self) -> Union[UOp, ConstType]: return ret.arg if (ret:=self.simplify()).op is Ops.CONST else ret
def _eval(self, dtype, expected_type:Type[T]) -> T:
assert self.dtype in dtype, f"eval with wrong dtype {self}"
vmin, vmax = (simple_self:=self.simplify())._min_max
if vmin != vmax: raise ValueError(f"eval failed to be a single number, range is {vmin} to {vmax} in {simple_self.render()}")
assert isinstance(vmin, expected_type), f"vmin is wrong dtype {type(vmin)} != {expected_type}"
return vmin
def __bool__(self): return self._eval((dtypes.bool,), bool)
def __int__(self): return self._eval(dtypes.ints, int)
def __float__(self): return self._eval(dtypes.floats, float)
def substitute(self, dvars:dict[UOp, UOp]):
with Context(TRACK_MATCH_STATS=0):
return graph_rewrite(self, _substitute, dvars, bottom_up=True)
# *** uop syntactic sugar ***
@property
def st_arg(self) -> ShapeTracker:
assert self.op in GroupOp.Buffer, f"st_arg called on {self.op}"
return unwrap(self.st)
@property
def axis_arg(self) -> tuple[int, ...]:
assert self.op in {Ops.REDUCE_AXIS, Ops.WMMA}, f"axis_arg called on {self.op}"
ret = self.arg[1] if self.op is Ops.REDUCE_AXIS else self.arg[7]
assert isinstance(ret, tuple) and all(isinstance(x, int) for x in ret), f"axis_arg trying to return {ret}"
return ret
def sink(self, *srcs:UOp, **kwargs): return UOp(Ops.SINK, dtypes.void, (self,)+srcs, **kwargs)
def detach(self): return UOp(Ops.DETACH, self.dtype, (self,))
def index(self, idx:UOp, valid:UOp|None=None): return UOp(Ops.INDEX, self.dtype, (self,idx,valid) if valid is not None else (self,idx))
def const_like(self, b:ConstLike):
# constants can optionally have a DEVICE source
if self._device is None: return UOp.const(self.dtype, b)
if isinstance(self.device, tuple): return UOp.multi(*[UOp.metaop(Ops.CONST, self.shape, self.dtype, d, b) for d in self.device], axis=None)
return UOp.metaop(Ops.CONST, self.shape, self.dtype, self.device, b)
def broadcast(self, count:int):
assert self.dtype.count == 1
if count == 1: return self
return UOp(Ops.VECTORIZE, self.dtype.vec(count), (self,)*count)
def cast(self, dtype:DType): return UOp(Ops.CAST, dtype, (self,))
def cast_vec(self, dtype:DType): return UOp(Ops.CAST, dtype.vec(self.dtype.count), (self,))
def bitcast(self, dtype:DType): return UOp(Ops.BITCAST, dtype, (self,))
def gep(self, i:Union[tuple[int, ...], int]):
if isinstance(i, int):
# NOTE: these are just shortcuts to not have to create and fold later
if self.op is Ops.VECTORIZE: return self.src[i]
if self.op is Ops.VCONST: return UOp.const(self.dtype.scalar(), self.arg[i])
if self.op is Ops.CONST: return UOp.const(self.dtype.scalar(), self.arg)
i = (i,)
if (self.dtype.vcount == len(i) and i == tuple(range(len(i)))) or self.dtype == dtypes.void: return self
return UOp(Ops.GEP, self.dtype.scalar().vec(len(i)) if len(i) > 1 else self.dtype.scalar(), (self,), i)
def load(self, *src:UOp, **kwargs): return UOp(Ops.LOAD, src=(self,)+src, **kwargs)
def store(self, *src:UOp, **kwargs): return UOp(Ops.STORE, dtypes.void, (self,)+src, **kwargs)
def alu(self, arg, *src:UOp):
out_dtype = (self, *src)[-1].dtype
if arg in {Ops.CMPLT, Ops.CMPNE}: out_dtype = dtypes.bool.vec(out_dtype.count) if out_dtype.count > 1 else dtypes.bool
return UOp(arg, out_dtype, (self,)+src)
@staticmethod
def const(dtype:DType, b:ConstLike):
if isinstance(b, UOp): return b.unbind()[0] if b.op is Ops.BIND else b
if isinstance(b, tuple) and all_same(b): b = b[0] # doesn't have to be a VCONST if they are all the same
return UOp(Ops.VCONST if isinstance(b, tuple) else Ops.CONST, dtype, arg=dtypes.as_const(b, dtype))
def valid(self, st:ShapeTracker):
assert self.op in {Ops.CONST, Ops.DEFINE_VAR}, f"can only create VALID from a constant, got {self.op}"
from tinygrad.shape.shapetracker import ShapeTracker
# NOTE: only VALID has a masked ShapeTracker, the CONST operands are unmasked
unmasked_st = ShapeTracker.from_shape(()).reshape((1,)*len(st.shape)).expand(st.shape).to_uop()
return UOp(Ops.VALID, dtypes.bool, (st.to_uop(),)).where(self.replace(src=(unmasked_st,)), UOp.const(self.dtype, 0).replace(src=(unmasked_st,)))
@staticmethod
def range(dtype:DType, start:sint, end:sint, idx:int): return UOp(Ops.RANGE, dtype=dtype, src=(sint_to_uop(start), sint_to_uop(end)), arg=idx)
def r(self, op:Ops, axis:tuple[int, ...]):
axis = tuple(sorted([x for x in axis if resolve(self.shape[x] != 1)]))
return self if len(axis) == 0 else UOp(Ops.REDUCE_AXIS, self.dtype, (self,), (op, axis))
def assign(self, x:UOp): return UOp(Ops.ASSIGN, self.dtype, (self,x))
def contiguous(self): return self.alu(Ops.CONTIGUOUS)
def contiguous_backward(self): return self.alu(Ops.CONTIGUOUS_BACKWARD)
# *** from MultiLazyBuffer ***
def multi(self, *more:UOp, axis:int|None, real:tuple[bool,...]|None=None):
parents = (self,)+more
assert all_same([x.dtype for x in parents]), "multi parents must have the same dtype"
return UOp(Ops.MULTI, self.dtype, parents, (axis, real if real is not None else (True,)*len(parents)))
@property
def bounds(self):
if self.axis is None: raise RuntimeError("bounds is not defined when axis is None")
return tuple(itertools.pairwise(itertools.accumulate([lb.shape[self.axis] for lb in self.src], initial=0)))
@functools.cached_property
def axis(self) -> Optional[int]:
if self.op is Ops.MULTI: return self.arg[0]
# NOTE: they all have to share an axis, we always choose [-1]
if self.op in GroupOp.ALU: return axes[-1] if (axes := dedup([x.axis for x in self.src if x.axis is not None])) else None
src_axis = self.src[0].axis
if self.op is Ops.REDUCE_AXIS: return None if src_axis is not None and src_axis in self.arg[1] else src_axis
if self.op is Ops.RESHAPE:
if src_axis is None: return None
arg_acc:list[sint] = list(itertools.accumulate(self.arg, operator.mul, initial=1))
# new_axis is the last one that preserves prod(prior to new_axis) and must not move items between shards
# TODO: what to do about shrinking to self.shape[self.axis]==1 len(self.real_lbs)==1?
return len(arg_acc) - arg_acc[::-1].index(prod(self.src[0].shape[:src_axis])) - 1
if self.op is Ops.PERMUTE: return self.arg.index(src_axis) if src_axis is not None else None
return src_axis
@property
def real(self):
assert self.op is Ops.MULTI
return self.arg[1]
@property
def real_lbs(self): return [lb for lb,r in zip(self.src, self.real) if r]
def shard(self, devices:tuple[str, ...], axis:Optional[int]=None) -> UOp:
if axis is None: lbs = [self] * len(devices)
else:
if self.shape[axis] % len(devices) != 0: raise RuntimeError(f"multi axis uneven: {self.shape[axis]=} {axis=} {len(devices)=}")
# NOTE: this works for both even shards and uneven shards
sz = self.shape[axis] // len(devices)
sizes = [max(0, min(sz, self.shape[axis] - sz*i)) for i in range(len(devices))]
lbs = []
for sz,off in zip(sizes, itertools.accumulate(sizes, initial=0)):
lbs.append(self.shrink(tuple((0,s) if i != axis else (off,off+sz) for i,s in enumerate(self.shape))))
sharded_lbs = [lb.copy_to_device(d) for lb,d in zip(lbs, devices)]
return UOp.multi(*[lb.contiguous() for lb in sharded_lbs], axis=axis)
# *** from LazyBuffer ***
@staticmethod
def metaop(op:Ops, shape:tuple[sint, ...], dtype:DType, device:str, arg=None) -> UOp:
from tinygrad.shape.shapetracker import ShapeTracker
# Tensor const is CONST(VIEW(DEVICE)) -> RESHAPE -> EXPAND
if op is Ops.CONST:
assert isinstance(arg, get_args(ConstType)), f"trying to create CONST with {arg=}"
return UOp.const(dtype, unwrap(arg)).replace(src=(UOp(Ops.VIEW, dtypes.void, (UOp(Ops.DEVICE, arg=device),),
ShapeTracker.from_shape(())),)).reshape((1,)*len(shape)).expand(shape)
# Tensor variable binding is BIND(VAR(VIEW(DEVICE)), CONST(VIEW(DEVICE)))
if op is Ops.BIND:
var, val = arg.unbind()
return var.replace(src=(UOp(Ops.VIEW, dtypes.void, (UOp(Ops.DEVICE, arg=device),), ShapeTracker.from_shape(shape)),)).bind(val)
# otherwise it's just a RESHAPE(BUFFER)
if not isinstance(size:=prod([x.vmax if isinstance(x, UOp) else x for x in shape]), int): raise ValueError(f"size must be int {size}")
return UOp.new_buffer(device, size, dtype).reshape(shape)
def copy_to_device(self, device:str|tuple[str, ...], clone:bool=False): return UOp(Ops.COPY, self.dtype, (UOp(Ops.DEVICE, arg=device), self), clone)
def clone(self) -> UOp: return self.copy_to_device(self.device, clone=True)
@property
def metadata(self) -> tuple[Metadata, ...]|Metadata|None: return self.arg.metadata if self.op is Ops.KERNEL else all_metadata.get(self, None)
# *** uop movement ops ***
@property
def base(self) -> UOp:
if (self.op is Ops.VIEW and len(self.src) != 0) or self.op in GroupOp.Movement: return self.src[0].base
return self
def view(self, new_st:ShapeTracker) -> UOp: return UOp(Ops.VIEW, self.dtype, (self.base,), new_st)
def _mop(self, op:Ops, arg):
ret = UOp(op, self.dtype, (self,), arg)
if self.st == ret.st: return self # ignore NOOPs, also check ret.st
return ret
def reshape(self, arg:tuple[sint, ...]): return self._mop(Ops.RESHAPE, arg)
def pad(self, arg:tuple[tuple[sint, sint], ...]): return self._mop(Ops.PAD, arg)
def expand(self, arg:tuple[sint, ...]): return self._mop(Ops.EXPAND, arg)
def permute(self, arg:tuple[sint, ...]): return self._mop(Ops.PERMUTE, arg)
def shrink(self, arg:tuple[tuple[sint, sint], ...]): return self._mop(Ops.SHRINK, arg)
def flip(self, arg:tuple[bool, ...]): return self._mop(Ops.FLIP, arg)
# *** uop UNIQUE ***
# TODO: use this in Buffer
unique_num = itertools.count(0)
@staticmethod
def unique(): return UOp(Ops.UNIQUE, arg=next(UOp.unique_num))
# *** uop Buffer stuff ***
@staticmethod
def new_buffer(device:str, size:int, dtype:DType): return UOp(Ops.BUFFER, dtype, (UOp(Ops.DEVICE, arg=device), UOp.unique()), size)
@property
def device(self) -> str|tuple[str, ...]: return cast(str|tuple[str, ...], unwrap(self._device))
@functools.cached_property
def _device(self) -> Optional[str|tuple[str, ...]]:
if self.op is Ops.DEVICE: return self.arg
if self.op is Ops.MULTI: return tuple(cast(str, x.device) for x in self.src)
return dsrcs[0]._device if len(dsrcs:=[x for x in self.src if x._device is not None]) != 0 else None
@property
def buf_uop(self) -> UOp:
if self.op is Ops.BUFFER: return self
assert self.op is Ops.ASSIGN, f"must be ASSIGN {self.op}"
return self.src[0].base
@property
def buffer(self) -> Buffer:
if self is not self.base:
assert unwrap(self.st).contiguous, "VIEW only works here if it's contiguous"
return self.src[0].buffer
assert self.op is Ops.BUFFER, f"must be BUFFER {self.op}"
if (cret:=buffers.get(self)) is not None: return cret
from tinygrad.device import Buffer
assert isinstance(self.device, str), f"buffer not supported on multi {self.device}"
buffers[self] = ret = Buffer(self.device, self.size, self.dtype if isinstance(self.dtype, ImageDType) else self.dtype.base)
ret.ref(1)
return ret
@property
def realized(self) -> Optional[Buffer]: return self.buffer if self.op is Ops.BUFFER and self.buffer.is_allocated() else None
@property
def is_realized(self) -> bool:
return all(x.base.realized is not None for x in self.base.real_lbs) if self.base.op is Ops.MULTI else self.base.realized is not None
# *** uop Variable stuff ***
@staticmethod
def variable(name:str, min_val:ConstType, max_val:ConstType, dtype:DType=dtypes.int):
assert not isinstance(min_val, UOp) and not isinstance(max_val, UOp), f"can't create Variable {name} with {min_val}/{max_val}"
return UOp(Ops.DEFINE_VAR, dtype, arg=(name, min_val, max_val))
@property
def expr(self):
assert self.op is Ops.DEFINE_VAR, f"op is {self.op}, need DEFINE_VAR"
return self.arg[0]
def bind(self, val:int):
assert self.op is Ops.DEFINE_VAR, f"op is {self.op}, need DEFINE_VAR"
assert self.arg[1] <= val and val <= self.arg[2], f"bind {val} not in range [{self.arg[1]}, {self.arg[2]}]"
return UOp(Ops.BIND, self.dtype, (self, self.const_like(val)))
def unbind(self) -> tuple[Variable, int]:
assert self.op is Ops.BIND and self.src[0].op is Ops.DEFINE_VAR and self.src[1].op is Ops.CONST, f"can't unbind {self}"
return self.src[0], self.src[1].arg
@property
def val(self) -> int: return self.unbind()[1]
def vars(self) -> set[UOp]:
bound_vars = set([x for x in self.toposort if x.op is Ops.BIND and x.src[0].op is Ops.DEFINE_VAR])
bound_var_base = set(x.src[0] for x in bound_vars)
all_vars = set([x for x in self.toposort if x.op is Ops.DEFINE_VAR])
return bound_vars.union(set([x for x in all_vars if x not in bound_var_base]))
def variables(self) -> list[Variable]:
st_vars: list[set[Variable]] = [x.st_arg.vars() for x in self.toposort if x.op in GroupOp.Buffer]
return sorted(set.union(*st_vars, [x.unbind()[0] if x.op is not Ops.DEFINE_VAR else x for x in self.vars()]), key=lambda v: v.arg)
# *** uop symbolic stuff ***
def is_increasing(self:UOp) -> bool:
# is f a monotonically increasing function regards its input
if self.op in GroupOp.Irreducible: return True
if self.op is Ops.ADD: return self.src[0].is_increasing() and self.src[1].is_increasing()
if self.op in (Ops.MUL, Ops.IDIV) and self.src[1].op is Ops.CONST and self.src[1].arg >= 0: return self.src[0].is_increasing()
return False # False if not sure
def const_factor(self) -> int:
"""largest known int that divides self"""
if self.op is Ops.CONST: return self.arg
if self.op is Ops.VCONST: return math.gcd(*self.arg)
if self.op is Ops.ADD: return math.gcd(self.src[0].const_factor(), self.src[1].const_factor())
if self.op is Ops.MUL: return self.src[0].arg if self.src[0].op is Ops.CONST else self.src[1].arg if self.src[1].op is Ops.CONST else 1
return 1
def divides(self, v:int) -> UOp|None:
if v==1: return self
if self.op is Ops.CONST: return self.const_like(self.arg//v) if self.arg%v == 0 else None
if self.op is Ops.VCONST: return self.const_like(tuple(x//v for x in self.arg)) if all(x%v == 0 for x in self.arg) else None
if self.op is Ops.ADD: return d0+d1 if (d0:=self.src[0].divides(v)) is not None and (d1:=self.src[1].divides(v)) is not None else None
if self.op is Ops.MUL:
if (d0:=self.src[0].divides(v)) is not None: return d0 * self.src[1]
if (d1:=self.src[1].divides(v)) is not None: return self.src[0] * d1
return None # generic None if we aren't sure
@property
def vmin(self) -> ConstType: return self._min_max[0]
@property
def vmax(self) -> ConstType: return self._min_max[1]
@functools.cached_property
def _min_max(self) -> tuple[ConstType, ConstType]:
if self.op in GroupOp.Binary and not dtypes.is_float(self.dtype):
(s0_vmin, s0_vmax), (s1_vmin, s1_vmax) = self.src[0]._min_max, self.src[1]._min_max
if self.op is Ops.ADD: return s0_vmin+s1_vmin, s0_vmax+s1_vmax
if self.op is Ops.SUB: return s0_vmin-s1_vmax, s0_vmax-s1_vmin
if self.op is Ops.AND and s1_vmin == s1_vmax and s0_vmin >= 0 and s1_vmin >= 0: return min(0, s0_vmin), min(s0_vmax, s1_vmax)
if self.op is Ops.MUL: return min(vals:=(s0_vmin*s1_vmin, s0_vmin*s1_vmax, s0_vmax*s1_vmin, s0_vmax*s1_vmax)), max(vals)
# SHL/SHR on consts only
if self.op is Ops.SHL and s1_vmin == s1_vmax and all_int(t:=(s0_vmin, s0_vmax, s1_vmin)): return t[0] << t[2], t[1] << t[2]
if self.op is Ops.SHR and s1_vmin == s1_vmax and all_int(t:=(s0_vmin, s0_vmax, s1_vmin)): return t[0] >> t[2], t[1] >> t[2]
if self.op is Ops.MOD and s1_vmin > 0:
return (0, s1_vmax-1) if s0_vmin >= 0 else (-(s1_vmax-1), s1_vmax-1)
if self.op is Ops.IDIV:
if s1_vmin == s1_vmax: # min/max are equal in a CONST
if s1_vmin > 0: return s0_vmin//s1_vmin, s0_vmax//s1_vmin
if s1_vmin < 0 and s0_vmin >= 0: return -(s0_vmax//-s1_vmin), -(s0_vmin//-s1_vmin)
# don't know exact bounds, but know the sign
if (s0_vmax <= 0 and s1_vmin < 0) or (s0_vmin >= 0 and s1_vmin > 0): return 0, dtypes.max(self.dtype)
if (s0_vmax <= 0 and s1_vmin > 0) or (s0_vmin >= 0 and s1_vmin < 0): return dtypes.min(self.dtype), 0
if self.op is Ops.MAX: return max(s0_vmin, s1_vmin), max(s0_vmax, s1_vmax)
if self.op is Ops.CMPLT: return (s0_vmax<s1_vmin, s0_vmin<s1_vmax)
if self.op is Ops.CMPNE: return ((s0_vmax < s1_vmin) or (s1_vmax < s0_vmin), not (s0_vmin == s0_vmax == s1_vmin == s1_vmax))
if self.dtype == dtypes.bool:
if self.op is Ops.OR: return s0_vmin or s1_vmin, s0_vmax or s1_vmax
if self.op is Ops.AND: return s0_vmin and s1_vmin, s0_vmax and s1_vmax
# float has NAN issue and we use explicit NAN in transcendental
if self.op is Ops.WHERE and dtypes.is_int(self.dtype): return min(self.src[1].vmin, self.src[2].vmin), max(self.src[1].vmax, self.src[2].vmax)
# NOTE: returned UOp is assumed to be CONST
if self.op is Ops.DEFINE_VAR and self.arg: return self.arg[1], self.arg[2]
if self.op is Ops.RANGE: return self.src[0].vmin, (self.src[1]-1).vmax
if self.op is Ops.BIND: return self.src[0]._min_max # ignore the bound value
if self.op in {Ops.UNROLL, Ops.VECTORIZE}: return min(x.vmin for x in self.src), max(x.vmax for x in self.src)
# TODO: Ops.SPECIAL is Ops.DEFINE_VAR
if self.op is Ops.SPECIAL: return 0, self.arg[1]-1 if isinstance(self.arg[1], int) else self.arg[1].vmax
if self.op is Ops.CONST: return self.arg, self.arg
if self.op is Ops.VCONST: return (min(self.arg), max(self.arg))
if self.op is Ops.CAST: return max(dtypes.min(self.dtype), self.src[0].vmin), min(self.src[0].vmax, dtypes.max(self.dtype))
return dtypes.min(self.dtype), dtypes.max(self.dtype)
@functools.cached_property
def _sym_fxn(self):
sself = self.simplify()
varnames = tuple(x.arg[0] for x in sself.toposort if x.op is Ops.DEFINE_VAR)
# TODO: sanitize varnames, or don't use naked eval while staying fast
return eval("lambda "+','.join(varnames)+": "+sself.render()), varnames # pylint: disable=eval-used
def sym_infer(self, var_vals:dict[UOp, int]):
fxn, varnames = self._sym_fxn
return fxn(**{k.arg[0]:v for k,v in var_vals.items() if k.arg[0] in varnames})
def render(self, simplify=True) -> str:
ret = graph_rewrite(self.simplify() if simplify else self, renderer)
return ret.arg if ret.op is Ops.NOOP else str(ret)
@dataclass(frozen=True)
class KernelInfo:
name: str = "test" # name of the kernel
local_dims: int = 0 # number of local dimensions (this is remapping RANGE to SPECIAL)
upcasted: int = 0 # count that are upcasted (this is remapping RANGE to UNROLL)
dont_use_locals: bool = False # don't use local indexing
# ******** ops in python ********
def safe_exp2(x):
try: return 2 ** x
except OverflowError: return math.inf
def safe_pow(x, y):
try: return math.nan if isinstance(p:=pow(x, y), complex) else p
except ZeroDivisionError: return math.inf
except ValueError: return math.inf if x > 0 else -math.inf
python_alu: dict[Ops, Callable] = {
Ops.LOG2: lambda x: math.log2(x) if x > 0 else -math.inf if x == 0 else math.nan, Ops.EXP2: safe_exp2,
Ops.SQRT: lambda x: math.sqrt(x) if x >= 0 else math.nan, Ops.RECIP: lambda x: 1/x if x != 0 else math.copysign(math.inf, x),
Ops.SIN: lambda x: math.sin(x) if not math.isinf(x) else math.nan, Ops.POW: safe_pow,
Ops.NEG: operator.neg, Ops.ADD: operator.add, Ops.SUB: operator.sub, Ops.MUL: operator.mul, Ops.CMPNE: operator.ne, Ops.CMPLT: operator.lt,
Ops.XOR: operator.xor, Ops.OR: operator.or_, Ops.AND: operator.and_, Ops.SHR: operator.rshift, Ops.SHL: operator.lshift, Ops.MAX: max,
Ops.MOD: cmod, Ops.IDIV: cdiv, Ops.MULACC: lambda x,y,z: (x*y)+z, Ops.WHERE: lambda x,y,z: y if x else z}
def exec_alu(op:Ops, dtype:DType, operands, truncate_output=True):
if dtype.count > 1:
return tuple([exec_alu(op, dtype.scalar(), [x[i] if isinstance(x, tuple) else x for x in operands]) for i in range(dtype.count)])
alu = python_alu[op](*operands)
return truncate.get(dtype, lambda x: x)(alu) if truncate_output else alu
# ***** uop helpers *****
def print_uops(uops:list[UOp]):
for i,u in enumerate(uops):
formatted_parents = [(uops.index(x) if x.op is not Ops.CONST else f"{x.arg}") if x in uops else "--" for x in u.src]
print(f"{i:4d} {str(u.op):20s}: {str(u.dtype):30s} " f"{str(formatted_parents):32s} {u.arg}")
# ***** pattern matcher *****
def get_location() -> tuple[str, int]:
frm = sys._getframe(1)
# find the real frame in the file that has the UPat, TODO: is there a better way to do this?
while frm.f_back is not None and pathlib.Path(frm.f_back.f_code.co_filename).name in {"ops.py", "rewriter.py", "schedule.py", "multi.py",
"symbolic.py", "expander.py", "lowerer.py", "cstyle.py",
"linearize.py", "devectorizer.py"}:
frm = frm.f_back
return frm.f_code.co_filename, frm.f_lineno
@functools.lru_cache(None)
def lines(fn) -> list[str]:
with open(fn) as f: return f.readlines()
class UPat(MathTrait):
__slots__ = ("op", "dtype", "arg", "name", "src")
def __init__(self, op:Optional[Union[Ops, tuple[Ops, ...], set[Ops]]]=None, dtype:Optional[Union[DType, tuple[DType, ...]]]=None,
src:Optional[Union[tuple[UPat, ...], list[UPat], UPat]]=None, arg:Any=None,
name:Optional[str]=None, allow_any_len:bool=False, location=None, custom_early_reject:Optional[set[Ops]]=None):
assert op is None or isinstance(op, (Ops, tuple, set)), "op must be Ops or tuple of Ops"
self.op: Optional[tuple[Ops, ...]] = (op,) if isinstance(op, Ops) else (tuple(op) if isinstance(op, set) else op)
self.dtype: Optional[tuple[DType, ...]] = (dtype,) if isinstance(dtype, DType) else dtype
self.arg, self.name, self._in_src, self.custom_early_reject = arg, name, src, custom_early_reject
self.src: Any = None
assert self.name != "ctx", "UPat can't be named ctx"
# try all permutations if it's a list
if isinstance(src, list): self.src = list(itertools.permutations(src)) if not all_same(src) else [src]
# only one if it's a tuple
elif isinstance(src, tuple): self.src = [src]
# repeat if it's a UPat
elif isinstance(src, UPat): self.src = [itertools.repeat(src)]
self.allowed_len: int = -1 if allow_any_len or isinstance(src, UPat) or src is None else len(src)
self.location = location or get_location()
if custom_early_reject is not None: self.early_reject = custom_early_reject
else:
upat_match = [src] if isinstance(src, UPat) else ([] if src is None else self.src[0])
self.early_reject = {pp.op[0] for pp in upat_match if pp.op is not None and len(pp.op) == 1}
def named(self, name:str): return UPat(self.op, self.dtype, self._in_src, self.arg, name, self.allowed_len == -1, self.custom_early_reject)
@staticmethod
def any(*src): return UPatAny(src=src)
def or_casted(self, name:str|None=None): return UPat.any(self if name is None else self.named(name), UPat(Ops.CAST, name=name, src=(self,)))
@staticmethod
@functools.lru_cache(None)
def var(name:Optional[str]=None, dtype:Optional[Union[DType, tuple[DType, ...]]]=None): return UPat(dtype=dtype, name=name)
@staticmethod
@functools.lru_cache(None)
def cvar(name:Optional[str]=None, dtype:Optional[DType]=None, vec=True): return UPat((Ops.CONST,Ops.VCONST) if vec else Ops.CONST, dtype, name=name)
@staticmethod
def const(dtype:Optional[Union[DType, tuple[DType, ...]]], b:ConstType): return UPat(Ops.CONST, dtype=dtype, arg=b)
# copied from UOp
def index(self, idx:UPat, valid:Optional[UPat]=None): return UPat(Ops.INDEX, self.dtype, (self,idx,valid) if valid is not None else (self,idx))
def view(self, st=None, **kwargs): return UPat(Ops.VIEW, self.dtype, (self,), st, **kwargs)
def cast(self, dtype=None): return UPat(Ops.CAST, dtype, (self,))
def bitcast(self, dtype=None): return UPat(Ops.BITCAST, dtype, (self,))
def gep(self, i:int): return UPat(Ops.GEP, None, (self,), (i,))
def load(self, *src:UPat, **kwargs): return UPat(Ops.LOAD, src=(self,)+src, **kwargs)
def store(self, *src:UPat, **kwargs): return UPat(Ops.STORE, dtypes.void, (self,)+src, **kwargs)
def assign(self, x:UPat, **kwargs): return UPat(Ops.ASSIGN, self.dtype, (self,x), **kwargs)
def const_like(self, b:ConstLike): return UPat.const(self.dtype, cast(ConstType, b))
def alu(self, op:Ops, *src:UPat):
asrc = (self,)+src
return UPat(op, dtypes.bool if op in {Ops.CMPLT, Ops.CMPNE} else asrc[-1].dtype, list(asrc) if op in GroupOp.Commutative else asrc)
def printable(self:UPat) -> str:
try: return lines(self.location[0])[self.location[1]-1].strip()
except FileNotFoundError: return "<missing>"
def __repr__(self):
def rep(x):
form = "UPat(%s, %s, name=%s, dtype=%s, allow_any_len=%s, src=%s)"
return form % (None if x.op is None else ('(%s)'%', '.join(map(str, x.op))), x.arg, repr(x.name),
set(x.dtype) if x.dtype else None, x.allowed_len == 0, "[%s]" if x.src and len(x.src)>1 else "(%s)")
return pretty_print(self, rep, srcfn=lambda x:None if x.src is None else [next(x.src[0])] if isinstance(x.src[0], itertools.repeat) else x.src[0])
def match(self:UPat, uop:UOp, store:dict[str, UOp]) -> list[dict[str, UOp]]:
if (self.op is not None and uop.op not in self.op) or \
(self.name is not None and store.setdefault(self.name, uop) is not uop) or \
(self.dtype is not None and uop.dtype not in self.dtype and uop.dtype.scalar() not in self.dtype) or \
(self.arg is not None and self.arg != uop.arg) or \
(self.allowed_len != -1 and len(uop.src) != self.allowed_len): return []
if self.src is None: return [store]
res: list[dict[str, UOp]] = []
for vp in self.src:
stores, new_stores = [store.copy()], []
for uu, vv in zip(uop.src, vp):
for s in stores: new_stores.extend(vv.match(uu, s))
stores, new_stores = new_stores, []
res.extend(stores)
return res
class UPatAny(UPat):
def match(self:UPat, uop:UOp, store:dict[str, UOp]) -> list[dict[str, UOp]]:
matches = [x.match(uop, store.copy()) for x in self.src[0]]
return flatten([x for x in matches if x is not None])
def deconstruct_function(fxn:Callable) -> tuple:
new_globals = {k:v for k,v in fxn.__globals__.items() if k in fxn.__code__.co_names}
for co in fxn.__code__.co_consts:
if isinstance(co, types.CodeType): new_globals.update({k:v for k,v in fxn.__globals__.items() if k in co.co_names})
# NOTE: optional round trip through pickle!
assert fxn.__closure__ is None, "closures are not supported in pattern matchers"
ret = fxn.__code__, new_globals, fxn.__name__, fxn.__defaults__
return pickle.loads(pickle.dumps(ret)) if getenv("TEST_PICKLE") else ret
class PatternMatcher:
def __init__(self, patterns:list[tuple[UPat, Callable]]):
self.patterns = patterns
# NOTE: use of DefaultDict here is very dangerous! all keys will live for the lifetime of the PatternMatcher!
self.pdict: dict[Ops, list[tuple[UPat, Callable, set, bool]]] = {}
# uop is required, arg is optional
for p,fxn in self.patterns:
assert p.op is not None
tuple_fxn = fxn if isinstance(fxn, tuple) else deconstruct_function(fxn)
real_fxn = types.FunctionType(*tuple_fxn)
for uop in p.op: self.pdict.setdefault(uop, []).append((p, real_fxn, p.early_reject, 'ctx' in inspect.signature(real_fxn).parameters))
def __reduce__(self): return PatternMatcher, ([(x,deconstruct_function(fxn) if fxn.__name__ == "<lambda>" else fxn) for x,fxn in self.patterns],)
@functools.lru_cache(None) # pylint: disable=method-cache-max-size-none
def __add__(self, more:PatternMatcher): return PatternMatcher(self.patterns+more.patterns)
def rewrite(self, uop:UOp, ctx=None) -> UOp|None:
ler = {u.op for u in uop.src}
for p,fxn,early_reject,has_ctx in self.pdict.get(uop.op, []):
if not early_reject.issubset(ler): continue
for match in p.match(uop, {}):
if (ret:=(fxn(ctx=ctx, **match) if has_ctx else fxn(**match))) is not None: return ret
return None
# *** tracking pattern matcher ***
TRACK_MATCH_STATS = ContextVar("TRACK_MATCH_STATS", 2 if getenv("VIZ") else 0)
match_stats:dict[UPat, list[Union[int, float]]] = dict()
@dataclass(frozen=True)
class TrackedGraphRewrite:
loc: tuple[str, int] # location that called graph_rewrite
sink: UOp # the sink input to graph_rewrite
bottom_up: bool
matches: list[tuple[UOp, UOp, UPat]] # before+after of all the matches
name: str|None
tracked_keys:list[Any] = []
tracked_ctxs:list[list[TrackedGraphRewrite]] = []
_name_cnt:dict[str, int] = {}
def track_rewrites(named=False, name_fxn:Callable|None=None):
def _decorator(func):
def __wrapper(self, *args, **kwargs):
if TRACK_MATCH_STATS >= 2:
if (count_names:=(named or name_fxn)): _name_cnt[func.__name__] = _name_cnt.get(func.__name__, 0)+1
tracked_keys.append(f"{func.__name__}_{_name_cnt[func.__name__]}" if count_names else self)
tracked_ctxs.append([])
ret = func(self, *args, **kwargs)
if TRACK_MATCH_STATS >= 2 and name_fxn is not None: tracked_keys[-1] = f"{name_fxn(ret)} n{_name_cnt[func.__name__]}"
return ret
return __wrapper
return _decorator
active_rewrites:list[TrackedGraphRewrite] = []
def track_matches(func):
def _track_func(*args, **kwargs):
if tracking:=(TRACK_MATCH_STATS >= 2 and tracked_ctxs):
loc = ((frm:=sys._getframe(1)).f_code.co_filename, frm.f_lineno)
tracked_ctxs[-1].append(ctx:=TrackedGraphRewrite(loc, args[0], kwargs.get("bottom_up", False), [], kwargs.get("name", None)))
active_rewrites.append(ctx)
ret = func(*args, **kwargs)
if tracking: active_rewrites.pop()
return ret
return _track_func
class TrackedPatternMatcher(PatternMatcher):
def rewrite(self, uop:UOp, ctx=None) -> UOp|None:
ret = None
ler = {u.op for u in uop.src}
for p,fxn,early_reject,has_ctx in self.pdict.get(uop.op, []):
if p not in match_stats: match_stats[p] = [0,0,0.0,0.0]
st = time.perf_counter()
if not early_reject.issubset(ler):
match_stats[p][2] += time.perf_counter()-st
continue
match_stats[p][1] += 1
for match in p.match(uop, {}):
if (ret:=(fxn(ctx=ctx, **match) if has_ctx else fxn(**match))) is not None:
match_stats[p][0] += 1
match_stats[p][3] += (et:=time.perf_counter()-st)
if TRACK_MATCH_STATS >= 3: print(f"{et*1e6:7.2f} us -- ", p.printable())
if TRACK_MATCH_STATS >= 2 and isinstance(ret, UOp) and active_rewrites: active_rewrites[-1].matches.append((uop, ret, p))
return ret # NOTE: if it returns None, we keep trying to match
match_stats[p][2] += time.perf_counter()-st
return None
if TRACK_MATCH_STATS:
PatternMatcher = TrackedPatternMatcher # type: ignore
import atexit
@atexit.register
def print_match_stats():
if TRACK_MATCH_STATS >= 2:
with open(fn:=temp("rewrites.pkl", append_user=True), "wb") as f:
print(f"rewrote {len(tracked_ctxs)} graphs and matched {sum(len(r.matches) for x in tracked_ctxs for r in x)} times, saved to {fn}")
with Context(PICKLE_BUFFERS=0): pickle.dump((tracked_keys, tracked_ctxs), f)
if getenv("VIZ"): launch_viz("VIZ", temp("rewrites.pkl", append_user=True))
if getenv("PRINT_MATCH_STATS", 1):
ret = [0,0,0.0,0.0]
for k,v in sorted(list(match_stats.items()), key=lambda x: x[1][2]+x[1][3]):
loc_str = f"{k.location[0].split('/')[-1]}:{k.location[1]}"
if v[1] != 0: print(f"{v[0]:6d} / {v[1]:7d} -- {v[3]*1000.:9.2f} / {(v[2]+v[3])*1000.:9.2f} ms -- {loc_str:15s}", k.printable())
ret = [x+y for x,y in zip(ret, v)]
print(f"{ret[0]:6d} / {ret[1]:7d} -- {ret[3]*1000.:9.2f} / {(ret[2]+ret[3])*1000.:9.2f} ms -- TOTAL")
def launch_viz(env_str:str, data:str):
os.environ[env_str] = "0"
os.environ[f"{env_str}_DATA"] = data
if not int(os.getenv("VIZ", "0")) and not int(os.getenv("PROFILE", "0")):
args = ['--kernels', getenv("VIZ_DATA", "")] if getenv("VIZ_DATA", "") else []
args += ['--profile', getenv("PROFILE_DATA", "")] if getenv("PROFILE_DATA", "") else []
os.execv(sys.executable, [sys.executable] + [os.path.join(os.path.dirname(__file__), ".", "viz", "serve.py")] + args)
# *** simple graph rewrite engine ***
class RewriteContext:
def __init__(self, pm, ctx=None, children=None):
self.pm: PatternMatcher = pm
self.ctx = self if children is not None else ctx
self.replace: dict[UOp, UOp] = {}
self.children = children
# TODO: is this function always right?
def update_children(self):
# add any new children from UOps that were replaced
for u in self.replace.values():
for s in u.src: self.children.setdefault(s, {})[u] = None
# find any children that were replaced and replace them
for k,v in self.children.items():
new_child: dict[UOp, None] = {}
for tv in v:
while (nv:=self.replace.get(tv, None)) is not None and nv is not tv: tv = nv
new_child[tv] = None
self.children[k] = new_child
def top_down_rewrite(self, n:UOp) -> UOp:
if (rn := self.replace.get(n)) is not None: return rn
new_src = tuple([self.top_down_rewrite(x) for x in n.src])
new_n = self.pm.rewrite(n, self.ctx) if new_src == n.src else UOp(n.op, n.dtype, new_src, n.arg)
self.replace[n] = ret = n if new_n is None else self.top_down_rewrite(new_n)
return ret
def bottom_up_rewrite(self, n:UOp) -> UOp:
if (rn := self.replace.get(n)) is not None: return rn
new_n: UOp|None = n
while new_n is not None: last_n, new_n = new_n, self.pm.rewrite(new_n, self.ctx)
new_src = tuple([self.bottom_up_rewrite(x) for x in last_n.src])
self.replace[n] = ret = last_n if new_src == last_n.src else self.bottom_up_rewrite(UOp(last_n.op, last_n.dtype, new_src, last_n.arg))
return ret
@track_matches
def graph_rewrite(sink:UOp, pm:PatternMatcher, ctx=None, bottom_up=False, name=None, track_children=False) -> UOp:
rewrite_ctx = RewriteContext(pm, ctx, children=sink.get_children_map() if track_children else None)
return rewrite_ctx.bottom_up_rewrite(sink) if bottom_up else rewrite_ctx.top_down_rewrite(sink)
@track_matches
def graph_rewrite_map(sink:UOp, pm:PatternMatcher, ctx=None, bottom_up=False, name=None, track_children=False) -> dict[UOp, UOp]:
rewrite_ctx = RewriteContext(pm, ctx, children=sink.get_children_map() if track_children else None)
return {k:(rewrite_ctx.bottom_up_rewrite(k) if bottom_up else rewrite_ctx.top_down_rewrite(k)) for k in list(sink.toposort)[::-1]}
def sint_to_uop(x:sint, dtype:DType=dtypes.int) -> UOp: return UOp.const(dtype, x) if isinstance(x, int) else x
_substitute = PatternMatcher([(UPat(tuple(Ops), name="x"), lambda ctx,x: ctx.get(x,None))])
# for debug
syms = { Ops.ADD: "+", Ops.SUB: "-", Ops.IDIV: "//", Ops.MOD: "%", Ops.SHL: "<<", Ops.SHR: ">>",
Ops.MUL: "*", Ops.CMPLT: "<", Ops.CMPNE: "!=", Ops.AND: "&", Ops.OR: "|", Ops.XOR: "^"}
renderer = PatternMatcher([
(UPat((Ops.DEFINE_VAR, Ops.SPECIAL), name="x"), lambda x: UOp(Ops.NOOP, arg=x.arg[0])),
(UPat(Ops.RANGE, name="x"), lambda x: UOp(Ops.NOOP, arg=f"ridx{x.arg}")),
(UPat((Ops.CONST, Ops.VCONST), name="x"), lambda x: UOp(Ops.NOOP, arg=str(x.arg))),
(UPat(Ops.UNROLL, name="x"), lambda x: UOp(Ops.NOOP, arg=f"UNROLL({x.src[0].arg}, {x.arg})")),
(UPat(Ops.BIND, src=UPat(Ops.NOOP), name="x"), lambda x: x.src[0]),
(UPat(Ops.NEG, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"(-{x.src[0].arg})")),
(UPat(Ops.MAX, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"max({x.src[0].arg}, {x.src[1].arg})")),
(UPat(Ops.MULACC, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"({x.src[0].arg}*{x.src[1].arg}+{x.src[2].arg})")),
(UPat(Ops.WHERE, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"({x.src[1].arg} if {x.src[0].arg} else {x.src[2].arg})")),
(UPat(GroupOp.ALU, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"({x.src[0].arg}{syms[x.op]}{x.src[1].arg})")),
])
# *** what was symbolic.py ***
sint = Union[int, UOp]
Variable = UOp
ConstLike = Union[ConstType, Variable, tuple[ConstType, ...]]
# *** UOp merge views and swizzling ***
merge_views = PatternMatcher([
# merge adjacent views
(UPat(Ops.VIEW, src=(UPat(Ops.VIEW, name="v2"),), name="v1"), lambda v1,v2: v2.replace(arg=v2.arg+v1.arg)),
# merge unmasked const views
(UPat(Ops.VIEW, name="v", src=(UPat((Ops.CONST, Ops.DEFINE_VAR), name="const"),)),
lambda v,const: const.replace(src=(const.src[0].replace(arg=const.st+v.st),)) if all(x.mask is None for x in (const.st+v.st).views) else None),
# merge view on load/store/valid
(UPat(Ops.VIEW, name="v", src=(UPat((Ops.LOAD, Ops.STORE, Ops.VALID), name="b"),)),
lambda b,v: b.replace(src=tuple((s.st+v.st).to_uop() if s.op is Ops.VIEW else s for s in b.src))),
# remove view if it's a contiguous and the shapes match
(UPat(Ops.VIEW, name="v", src=(UPat(GroupOp.All-{Ops.DEVICE}, name="x"),)), lambda v,x: x if v.arg.contiguous and x.shape == v.shape else None),
# remove mask if there's a zero in the masked dim
(UPat(Ops.VIEW, name="v", src=(UPat(),)),
lambda v: v.const_like(0) if (mask:=v.st.views[-1].mask) is not None and any((x[1]-x[0]) == 0 for x in mask) else None),
# movement ops apply a new view on the base
(UPat(GroupOp.Movement, src=(UPat.var("x"),), name="mop"), lambda mop,x: x.view(mop.st)),
])
view_left = merge_views+PatternMatcher([
# do not push masked view before unsafe pad ops
(UPat(Ops.VIEW, src=(UPat(GroupOp.UnsafePad, name="e"),), name="view"),
lambda e,view: e.contiguous().view(view.st) if any(v.mask is not None for v in view.st.views) else None),
# view before elementwise ops
(UPat(Ops.VIEW, src=(UPat({*GroupOp.ALU, Ops.CAST, Ops.BITCAST}, name="e"),), name="view"),
lambda e,view: e.replace(src=tuple(s.view(s.st+view.st) if s.op is Ops.VIEW else s.view(view.st) for s in e.src))),
])