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 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 "" 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__ == "" 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))), ])