mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-01-10 07:28:15 -05:00
d726eb6f48
* uop bool and int and stuff [run_process_replay] * add ne support * can't even be None anymore * BinaryOps.AND support * less compare
687 lines
37 KiB
Python
687 lines
37 KiB
Python
from __future__ import annotations
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from typing import Any, List, Optional, Set, Union, Tuple, Dict, Callable, cast, TYPE_CHECKING, TypeVar
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from types import FrameType
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import sys, time, functools, itertools, math, operator, hashlib, os, types, pickle
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from enum import auto, IntEnum, Enum
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from dataclasses import dataclass, field
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from tinygrad.dtype import ConstType, ImageDType, PtrDType, dtypes, DType, truncate
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from tinygrad.helpers import ContextVar, pretty_print, prod, getenv, all_same
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from tinygrad.shape.symbolic import Variable, sint
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if TYPE_CHECKING:
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from tinygrad.shape.shapetracker import ShapeTracker
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from tinygrad.codegen.kernel import Kernel
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# wrapper around IntEnum that preserves Enum.__str__ and makes auto() unique across all FastEnum subclasses
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class FastEnum(IntEnum):
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def __str__(self): return Enum.__str__(self)
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@staticmethod
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def _generate_next_value_(_, __, ___, last_values): return 1 + max([0, *last_values, *[max(c) for c in FastEnum.__subclasses__()]])
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# the Enum class doesn't work with mypy, this is static. sorry it's ugly
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# NOTE: MOD, CMPLT don't have to be implemented on vectors, just scalars
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# NOTE: many GPUs don't have DIV, but UnaryOps.RECIP doesn't work for integer division
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class UnaryOps(FastEnum):
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"""A -> A (elementwise)"""
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EXP2 = auto(); LOG2 = auto(); CAST = auto(); BITCAST = auto(); SIN = auto(); SQRT = auto(); RECIP = auto(); NEG = auto() # noqa: E702
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class BinaryOps(FastEnum):
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"""A + A -> A (elementwise)"""
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ADD = auto(); MUL = auto(); IDIV = auto(); MAX = auto(); MOD = auto(); CMPLT = auto(); CMPNE = auto(); XOR = auto() # noqa: E702
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SHL = auto(); SHR = auto(); OR = auto(); AND = auto(); THREEFRY = auto(); SUB = auto() # noqa: E702
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class TernaryOps(FastEnum):
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"""A + A + A -> A (elementwise)"""
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WHERE = auto(); MULACC = auto() # noqa: E702
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class ReduceOps(FastEnum):
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"""A -> B (reduce)"""
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SUM = auto(); PROD = auto(); MAX = auto() # noqa: E702
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class MetaOps(FastEnum):
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EMPTY = auto(); CONST = auto(); COPY = auto(); CONTIGUOUS = auto(); CUSTOM = auto(); ASSIGN = auto(); VIEW = auto() # noqa: E702
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Op = Union[UnaryOps, BinaryOps, ReduceOps, MetaOps, TernaryOps]
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T = TypeVar("T")
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class MathTrait:
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# required to implement
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def alu(self:T, arg:Union[UnaryOps, BinaryOps, TernaryOps], *src) -> T: raise NotImplementedError
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def const_like(self, b:ConstType|Variable|Tuple[ConstType]): raise NotImplementedError
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# great functions you get!
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def ufix(self, x): return self.const_like(x) if not isinstance(x, MathTrait) else x
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def __neg__(self):
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dtype = getattr(self, 'dtype', None)
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assert dtype is not None, "MathTraits __neg__ requires a dtype"
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return self.ne(True) if dtype.scalar() == dtypes.bool else self*(-1)
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def __add__(self, x): return self.alu(BinaryOps.ADD, self.ufix(x))
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def __radd__(self, x): return self.ufix(x).alu(BinaryOps.ADD, self)
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def __sub__(self, x): return self.alu(BinaryOps.ADD, self.ufix(-x))
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def __rsub__(self, x): return self.ufix(x).alu(BinaryOps.ADD, -self)
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def __mul__(self, x): return self.alu(BinaryOps.MUL, self.ufix(x))
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def __rmul__(self, x): return self.ufix(x).alu(BinaryOps.MUL, self)
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def __floordiv__(self, x): return self.alu(BinaryOps.IDIV, self.ufix(x))
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def __truediv__(self, x): return self.alu(BinaryOps.MUL, self.ufix(x).alu(UnaryOps.RECIP))
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def __mod__(self, x): return self.alu(BinaryOps.MOD, self.ufix(x))
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def __xor__(self, x): return self.alu(BinaryOps.XOR, self.ufix(x))
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def __and__(self, x): return self.alu(BinaryOps.AND, self.ufix(x))
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def __or__(self, x): return self.alu(BinaryOps.OR, self.ufix(x))
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def ne(self, x): return self.alu(BinaryOps.CMPNE, self.ufix(x))
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def eq(self, x): return self.ne(x).ne(True)
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def lt(self, x): return self.alu(BinaryOps.CMPLT, self.ufix(x))
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def gt(self, x): return self.ufix(x).alu(BinaryOps.CMPLT, self)
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def ge(self, x): return self.lt(x).ne(True)
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def le(self, x): return self.gt(x).ne(True)
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# NOTE: __eq__/__ne__ can't be overridden, and means the same thing as is and is not
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def __lt__(self, x): return self.lt(x)
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def __gt__(self, x): return self.gt(x)
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def __ge__(self, x): return self.ge(x)
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def __le__(self, x): return self.le(x)
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def max(self, x): return self.alu(BinaryOps.MAX, self.ufix(x))
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def min(self, x): return -(-self).max(-x)
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def where(self, x, y): return self.alu(TernaryOps.WHERE, x, y)
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def threefry(self, seed): return self.alu(BinaryOps.THREEFRY, seed)
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def recip(self): return self.alu(UnaryOps.RECIP)
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def sqrt(self): return self.alu(UnaryOps.SQRT)
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def sin(self): return self.alu(UnaryOps.SIN)
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def log2(self): return self.alu(UnaryOps.LOG2)
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def exp2(self): return self.alu(UnaryOps.EXP2)
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# do not preserve f(0) = 0
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UNSAFE_PAD_OPS = {UnaryOps.RECIP, UnaryOps.LOG2, UnaryOps.EXP2, BinaryOps.IDIV}
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REDUCE_ALU: Dict[ReduceOps, BinaryOps] = {ReduceOps.SUM:BinaryOps.ADD, ReduceOps.PROD:BinaryOps.MUL, ReduceOps.MAX:BinaryOps.MAX}
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# https://en.wikipedia.org/wiki/Identity_element
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def identity_element(op:BinaryOps, dt:DType): return dtypes.as_const({BinaryOps.ADD:0, BinaryOps.MUL:1, BinaryOps.MAX:dtypes.min(dt)}[op], dt)
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# the order of these UOps controls the order of the toposort
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class UOps(FastEnum):
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# uops that aren't rendered
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SINK = auto()
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EXT = auto()
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EXPAND = auto()
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CONTRACT = auto()
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SHAPETRACKER = auto()
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SWIZZLE = auto()
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DEFINE_GLOBAL = auto()
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BUFFER = auto()
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DEFINE_VAR = auto()
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DEFINE_LOCAL = auto()
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DEFINE_ACC = auto()
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VCONST = auto()
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CONST = auto()
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VALID = auto()
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SPECIAL = auto()
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NOOP = auto()
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REDUCE = auto()
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REDUCE_AXIS = auto()
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# helper ops
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GEP = auto()
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VECTORIZE = auto()
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CAST = auto()
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BITCAST = auto()
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# loads before math
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LOAD = auto()
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# math ops
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ALU = auto()
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WMMA = auto()
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# assignment ops
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STORE = auto()
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ASSIGN = auto()
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# control flow ops
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BARRIER = auto()
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IF = auto()
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RANGE = auto()
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# ops that are not graph nodes
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ENDRANGE = auto()
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ENDIF = auto()
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BUFFER_UOPS = {UOps.LOAD, UOps.STORE, UOps.VALID}
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COMMUTATIVE = {BinaryOps.ADD, BinaryOps.MUL, BinaryOps.MAX, BinaryOps.CMPNE, BinaryOps.XOR, BinaryOps.AND, BinaryOps.OR}
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END_FOR_UOP = {UOps.IF:(UOps.STORE, UOps.ENDIF), UOps.RANGE:(UOps.ASSIGN, UOps.ENDRANGE)}
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class UOp(MathTrait):
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__slots__ = ["op", "dtype", "src", "arg"]
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def __init__(self, op: UOps, dtype:DType=dtypes.void, src: Tuple[UOp,...]=tuple(), arg:Any=None):
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# TODO: instant check rules here make debugging easier
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#if op is UOps.ALU and arg is BinaryOps.CMPNE: assert dtype.scalar() == dtypes.bool
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#if op is UOps.VECTORIZE and dtype != dtypes.void: assert len(src) == dtype.count, f"{len(src)} invalid for {dtype}"
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#if op is UOps.ALU and arg not in (BinaryOps.CMPNE, BinaryOps.CMPLT, TernaryOps.WHERE): assert all_same([dtype] + [x.dtype for x in src])
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#if op is UOps.CAST: assert dtype.count == src[0].dtype.count, f"cast can't change vectorization {src[0].dtype} --> {dtype}"
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self.op, self.dtype, self.src, self.arg = op, dtype, src, arg
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def replace(self, **kwargs) -> UOp:
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for k in kwargs: assert k in self.__slots__, f"unkown replace arg, expected one of {self.__slots__}, got {k}"
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return UOp(kwargs.get("op", self.op), kwargs.get("dtype", self.dtype), kwargs.get("src", self.src), kwargs.get("arg", self.arg))
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@property
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def has_st(self) -> bool: return self.op not in {UOps.DEFINE_LOCAL, UOps.DEFINE_GLOBAL, UOps.BUFFER, UOps.CONST, UOps.DEFINE_VAR}
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@functools.cached_property
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def st(self) -> Optional[ShapeTracker]:
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if not self.has_st: return None
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if self.op in BUFFER_UOPS: return self.st_arg
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if self.op in {UOps.SHAPETRACKER, UOps.SWIZZLE}: return self.arg
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src_sts = [x.st for x in self.src if x.st is not None]
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assert all_same([x.shape for x in src_sts]), f"UOp parents must have the same shape {self} {[x.shape for x in src_sts]}"
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from tinygrad.shape.shapetracker import ShapeTracker
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return ShapeTracker.from_shape(src_sts[0].reduce(self.axis_arg)) if self.op is UOps.REDUCE_AXIS else src_sts[0]
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@functools.cached_property
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def key(self) -> bytes:
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return hashlib.sha256(str((self.op, self.dtype, self.arg)).encode() + b"".join([s.key for s in self.src])).digest()
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def __repr__(self): return pretty_print(self, lambda x: f"{type(self).__name__}({x.op}, {x.dtype}, arg={x.argstr()}, src=(%s))")
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def argstr(self): return f'({", ".join(map(str, self.arg))})' if self.op is UOps.REDUCE_AXIS else self.arg
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# *** uop evaluation ***
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def _eval(self, dtype, expected_type) -> ConstType:
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assert self.dtype in dtype, f"eval with wrong dtype {self}"
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vmin, vmax = self._min_max
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if vmin != vmax: raise ValueError(f"eval failed to be a single number, range is {vmin} to {vmax}")
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assert type(vmin) is expected_type, f"vmin is wrong dtype {vmin} != {expected_type}"
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return vmin
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def __bool__(self): return self._eval((dtypes.bool,), bool)
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def __int__(self): return self._eval(dtypes.ints, int)
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def __float__(self): return self._eval(dtypes.floats, float)
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# *** uop syntactic sugar
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@property
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def st_arg(self) -> ShapeTracker:
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assert self.op in BUFFER_UOPS, f"st_arg called on {self.op}"
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ret = self.src[0 if self.op is UOps.VALID else 1]
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assert ret.op is UOps.SHAPETRACKER, f"st_arg trying to return {ret}"
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return ret.arg
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@property
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def axis_arg(self) -> Tuple[int, ...]:
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assert self.op in {UOps.REDUCE_AXIS, UOps.WMMA}, f"axis_arg called on {self.op}"
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ret = self.arg[1] if self.op is UOps.REDUCE_AXIS else self.arg[7]
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assert isinstance(ret, tuple) and all(isinstance(x, int) for x in ret), f"axis_arg trying to return {ret}"
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return ret
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def sink(self, *srcs:UOp): return UOp(UOps.SINK, dtypes.void, (self,)+srcs)
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def swizzle(self, st:ShapeTracker): return UOp(UOps.SWIZZLE, self.dtype, (self,), st)
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def const_like(self, b:ConstType|Variable|Tuple[ConstType, ...]): return UOp.const(self.dtype, b)
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def broadcast(self, count:int):
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assert self.dtype.count == 1
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if count == 1: return self
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return UOp(UOps.VECTORIZE, self.dtype.vec(count), (self,)*count)
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def cast(self, dtype:DType): return UOp(UOps.CAST, dtype, (self,))
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def bitcast(self, dtype:DType): return UOp(UOps.BITCAST, dtype, (self,))
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def gep(self, i:Union[Tuple[int, ...], int]):
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if isinstance(i, int):
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# NOTE: these are just shortcuts to not have to create and fold later
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if self.op is UOps.VECTORIZE: return self.src[i]
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if self.op is UOps.VCONST: return UOp.const(self.dtype.scalar(), self.arg[i])
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if self.op is UOps.CONST: return UOp.const(self.dtype.scalar(), self.arg)
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i = (i,)
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if self.dtype == dtypes.void or (i == tuple(range(len(i))) and self.dtype.count == len(i)): return self
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assert len(i) >= 1 and all(x < self.dtype.count for x in i), f"bad GEP on {self.dtype}, {i}"
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return UOp(UOps.GEP, self.dtype.scalar().vec(len(i)) if len(i) > 1 else self.dtype.scalar(), (self,), i)
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@staticmethod
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def load(*src:UOp, dtype:DType): return UOp(UOps.LOAD, dtype, src)
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@staticmethod
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def store(*src:UOp): return UOp(UOps.STORE, dtypes.void, src)
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def alu(self, arg, *src:UOp):
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out_dtype = (self, *src)[-1].dtype
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if arg in {BinaryOps.CMPLT, BinaryOps.CMPNE} and out_dtype is not None:
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out_dtype = dtypes.bool.vec(out_dtype.count) if out_dtype.count > 1 else dtypes.bool
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return UOp(UOps.ALU, out_dtype, (self,)+src, arg)
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@staticmethod
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@functools.lru_cache(None)
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def const(dtype:DType, b:Tuple[ConstType, ...]|ConstType|Variable): return UOp._const(dtype, b)
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@staticmethod
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def _const(dtype:DType, b:Tuple[ConstType, ...]|ConstType|Variable):
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# TODO: fix dtype of b.max after Variable is just an UOp
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if isinstance(b, Variable): return UOp.define_var(b.expr, dtype, b.min, cast(int, b.max))
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if isinstance(b, tuple) and all_same(b): b = b[0] # doesn't have to be a VCONST if they are all the same
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return UOp(UOps.VCONST if isinstance(b, tuple) else UOps.CONST, dtype, arg=dtypes.as_const(b, dtype) if dtype is not None else b) # type: ignore
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@staticmethod
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def define_var(name:str, dtype:DType, min_val:ConstType, max_val:ConstType): return UOp(UOps.DEFINE_VAR, dtype, arg=(name, min_val, max_val))
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@staticmethod
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def define_global(dtype:DType, arg): return UOp(UOps.DEFINE_GLOBAL, dtype if isinstance(dtype, ImageDType) else PtrDType(dtype), (), arg)
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@staticmethod
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def range(dtype:DType, start:ConstType, end:ConstType, idx:int):
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return UOp(UOps.RANGE, dtype=dtype, src=(UOp.const(dtype, start), UOp.const(dtype, end)), arg=(idx,))
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def reduce(self, op:BinaryOps, *rng:UOp): return UOp(UOps.REDUCE, self.dtype, (self,) + rng, op)
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@functools.cached_property
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def parents(self) -> Dict[UOp, None]: return {**{x:None for x in self.src}, **{k:None for x in self.src for k in x.parents}}
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@property # parents with self
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def sparents(self) -> Dict[UOp, None]: return {**self.parents, self:None}
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@functools.cached_property
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def full_shape(self) -> Tuple[sint, ...]:
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return self.arg.shape if self.op is UOps.SHAPETRACKER else tuple(max(x) for x in zip(*[x.full_shape for x in self.src if x.has_st]))
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def vars(self) -> Set[UOp]: return set([x for x in self.sparents if x.op is UOps.DEFINE_VAR])
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def variables(self) -> List[Variable]:
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st_vars: List[Set[Variable]] = [x.st_arg.vars() for x in self.sparents if x.op in BUFFER_UOPS]
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return sorted(set.union(*st_vars, [Variable(x.arg[0], x.arg[1], x.arg[2]) for x in self.vars()]), key=lambda v: v.expr)
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def const_factor(self) -> int:
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"""largest known int that divides self"""
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if self.op is UOps.CONST: return self.arg
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if self.op is UOps.VCONST: return functools.reduce(math.gcd, self.arg)
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if self.op is UOps.ALU:
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if self.arg is BinaryOps.ADD: return math.gcd(self.src[0].const_factor(), self.src[1].const_factor())
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if self.arg is BinaryOps.MUL: return self.src[0].arg if self.src[0].op is UOps.CONST else self.src[1].arg if self.src[1].op is UOps.CONST else 1
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return 1
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def divides(self, v) -> Optional[UOp]:
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if v==1: return self
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if self.op is UOps.CONST: return self.const_like(self.arg//v) if self.arg%v == 0 else None
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if self.op is UOps.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
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if self.op is UOps.ALU:
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if self.arg is BinaryOps.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
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if self.arg is BinaryOps.MUL:
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if (d0:=self.src[0].divides(v)) is not None: return d0 * self.src[1]
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if (d1:=self.src[1].divides(v)) is not None: return self.src[0] * d1
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return None # generic None if we aren't sure
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@property
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def vmin(self) -> ConstType: return self._min_max[0]
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@property
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def vmax(self) -> ConstType: return self._min_max[1]
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@functools.cached_property
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def _min_max(self) -> Tuple[ConstType, ConstType]:
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# NOTE: returned UOp is assumed to be CONST
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if self.op is UOps.DEFINE_VAR and self.arg: return self.arg[1], self.arg[2]
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if self.op is UOps.RANGE: return self.src[0].vmin, (self.src[1]-1).vmax
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if self.op is UOps.EXPAND: return min(x.vmin for x in self.src), max(x.vmax for x in self.src)
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# TODO: UOps.SPECIAL is UOps.DEFINE_VAR
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if self.op is UOps.SPECIAL: return 0, self.arg[1]-1 if isinstance(self.arg[1], int) else dtypes.max(self.dtype)
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if self.op is UOps.CONST: return self.arg, self.arg
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if self.op is UOps.VCONST: return (min(self.arg), max(self.arg))
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if self.op is UOps.ALU and self.dtype.count == 1:
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s0,s1,s2 = [cast(UOp, self.src[i] if i < len(self.src) else None) for i in range(3)]
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if self.arg is BinaryOps.ADD: return s0.vmin+s1.vmin, s0.vmax+s1.vmax
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if self.arg is BinaryOps.MUL:
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# both are non-positive
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if (s0.vmax <= 0 and s1.vmax <= 0): return s0.vmax*s1.vmax, s0.vmin*s1.vmin
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# at lease one is non-negative
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if (s0.vmin >= 0 or s1.vmin >= 0):
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Lmin, Lmax = (s0.vmin, s0.vmax) if s1.vmin >= 0 else (s0.vmax, s0.vmin)
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Rmin, Rmax = (s1.vmin, s1.vmax) if s0.vmin >= 0 else (s1.vmax, s1.vmin)
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return Lmin*Rmin, Lmax*Rmax
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if self.arg is BinaryOps.MOD and s1.vmin > 0: return 0, s1.vmax-1
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if self.arg is BinaryOps.IDIV and s1.op is UOps.CONST:
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if s1.arg > 0: return s0.vmin//s1.arg, s0.vmax//s1.arg
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if s1.arg < 0: return -(s0.vmax//-s1.arg), -(s0.vmin//-s1.arg)
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if self.arg is BinaryOps.MAX: return max(s0.vmin, s1.vmin), max(s0.vmax, s1.vmax)
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if self.arg is BinaryOps.CMPLT: return (s0.vmax<s1.vmin, s0.vmin<s1.vmax)
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if self.arg is BinaryOps.CMPNE:
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always_ne = (s0.vmax < s1.vmin) or (s1.vmin > s0.vmax)
|
|
sometimes_ne = not (s0.vmin == s0.vmax == s1.vmin == s1.vmax)
|
|
return (always_ne, sometimes_ne)
|
|
# float has NAN issue and we use explicit NAN in transcendental
|
|
if self.arg is TernaryOps.WHERE and dtypes.is_int(s1.dtype): return min(s1.vmin, s2.vmin), max(s1.vmax, s2.vmax)
|
|
if self.dtype is dtypes.bool:
|
|
if self.arg is BinaryOps.OR: return s0.vmin or s1.vmin, s0.vmax or s1.vmax
|
|
if self.arg is BinaryOps.AND: return s0.vmin and s1.vmin, s0.vmax and s1.vmax
|
|
return dtypes.min(self.dtype), dtypes.max(self.dtype)
|
|
|
|
@dataclass(frozen=True)
|
|
class KernelInfo:
|
|
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 EXPAND)
|
|
dont_use_locals: bool = False # don't use local indexing
|
|
|
|
# ***** ops in python *****
|
|
|
|
def hook_overflow(dv, fxn):
|
|
def wfxn(*args):
|
|
try: return fxn(*args)
|
|
except OverflowError: return dv
|
|
return wfxn
|
|
|
|
python_alu: Dict[Op, Callable] = {
|
|
UnaryOps.LOG2: lambda x: math.log2(x) if x > 0 else -math.inf if x == 0 else math.nan, UnaryOps.EXP2: hook_overflow(math.inf, lambda x: 2**x),
|
|
UnaryOps.SQRT: lambda x: math.sqrt(x) if x >= 0 else math.nan, UnaryOps.RECIP: lambda x: 1/x if x != 0 else math.copysign(math.inf, x),
|
|
UnaryOps.SIN: lambda x: math.sin(x) if not math.isinf(x) else math.nan,
|
|
UnaryOps.NEG: operator.neg, BinaryOps.ADD: operator.add, BinaryOps.SUB: operator.sub,
|
|
BinaryOps.SHR: operator.rshift, BinaryOps.SHL: operator.lshift, BinaryOps.MUL: operator.mul,
|
|
BinaryOps.XOR: operator.xor, BinaryOps.MAX: max, BinaryOps.CMPNE: operator.ne, BinaryOps.CMPLT: operator.lt,
|
|
BinaryOps.OR: operator.or_, BinaryOps.AND: operator.and_,
|
|
BinaryOps.MOD: lambda x,y: abs(int(x))%abs(int(y))*(1,-1)[x<0], BinaryOps.IDIV: lambda x,y: abs(x)//abs(y)*(1,-1)[x*y<0] if y != 0 else x*math.inf,
|
|
TernaryOps.MULACC: lambda x,y,z: (x*y)+z, TernaryOps.WHERE: lambda x,y,z: y if x else z}
|
|
|
|
def exec_alu(op:Op, dtype:DType, operands):
|
|
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)])
|
|
return truncate.get(dtype, lambda x: x)(python_alu[op](*operands))
|
|
|
|
def uop_alu_resolve(u:UOp) -> sint:
|
|
if u.op is UOps.CONST: return u.arg
|
|
if u.op is UOps.DEFINE_VAR: return Variable(u.arg[0], u.arg[1], u.arg[2])
|
|
if u.op is UOps.ALU: return exec_alu(u.arg, u.dtype, tuple(map(uop_alu_resolve, u.src)))
|
|
raise RuntimeError(f"ALU resolve fail @ {u.op}")
|
|
|
|
# ***** uop helpers *****
|
|
|
|
def print_uops(uops:List[UOp]):
|
|
for i,u in enumerate(uops):
|
|
formatted_parents = [uops.index(x) if x.op is not UOps.CONST else f"{x.arg}" for x in u.src]
|
|
print(f"{i:4d} {str(u.op):20s}: {str(u.dtype):25s} " f"{str(formatted_parents):32s} {u.arg}")
|
|
|
|
def flops_mem(uops:List[UOp], ignore_indexing=False) -> Tuple[sint, sint]:
|
|
flops: sint = 0
|
|
mem: sint = 0
|
|
mults: sint = 1
|
|
mult_stack: List[sint] = []
|
|
dont_count: Set[UOp] = set()
|
|
if ignore_indexing:
|
|
for u in uops:
|
|
if u.op is UOps.LOAD:
|
|
dont_count = dont_count.union(u.src[1].sparents)
|
|
if len(u.src) > 3: dont_count = dont_count.union(u.src[2].sparents)
|
|
elif u.op is UOps.STORE:
|
|
dont_count = dont_count.union(u.src[1].sparents)
|
|
if len(u.src) > 3: dont_count = dont_count.union(u.src[3].sparents)
|
|
elif u.op is UOps.IF:
|
|
dont_count = dont_count.union(u.src[0].sparents)
|
|
for u in uops:
|
|
if u.op is UOps.RANGE:
|
|
mult_stack.append(mults)
|
|
mults *= uop_alu_resolve(u.src[1] - u.src[0])
|
|
elif u.op is UOps.ENDRANGE:
|
|
mults = mult_stack.pop(-1)
|
|
elif u.op is UOps.SPECIAL:
|
|
mults *= u.arg[1] # NOTE: we don't push to the mult_stack here, you can't end these
|
|
elif u.op is UOps.LOAD:
|
|
mem += u.dtype.itemsize * mults
|
|
elif u.op is UOps.STORE:
|
|
mem += u.src[2].dtype.itemsize * mults
|
|
elif u.op is UOps.ALU and u not in dont_count:
|
|
flops += (mults * (2 if u.arg == TernaryOps.MULACC else 1)) * u.dtype.count
|
|
elif u.op is UOps.WMMA and u not in dont_count:
|
|
flops += 2 * prod(u.arg[1]) // u.arg[5] * mults
|
|
return flops, mem
|
|
|
|
# ***** 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 frm.f_back.f_code.co_filename.split("/")[-1] in {"ops.py", "uopgraph.py", "schedule.py", "lowerer.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", "_any"]
|
|
def __init__(self, op:Optional[Union[UOps, Tuple[UOps, ...]]]=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[Tuple[UOps, Any]]]=None):
|
|
self.op: Optional[Tuple[UOps, ...]] = (op,) if isinstance(op, UOps) else op
|
|
self.dtype: Optional[Tuple[DType, ...]] = (dtype,) if isinstance(dtype, DType) else dtype
|
|
self.arg, self.name = arg, name
|
|
self.src: Any = None
|
|
|
|
# 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 = set((pp.op[0], pp.arg) for pp in upat_match if pp.op is not None and len(pp.op) == 1)
|
|
|
|
@staticmethod
|
|
def any(*src): return UPatAny(src=src)
|
|
|
|
@staticmethod
|
|
@functools.lru_cache(None)
|
|
def var(name:Optional[str]=None, dtype:Optional[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((UOps.CONST, UOps.VCONST) if vec else UOps.CONST, dtype=dtype, name=name)
|
|
@staticmethod
|
|
@functools.lru_cache(None)
|
|
def const(dtype:Optional[DType], b:ConstType): return UPat(UOps.CONST, dtype=dtype, arg=b)
|
|
|
|
# copied from UOp
|
|
def cast(self, dtype=None): return UPat(UOps.CAST, dtype, (self,))
|
|
def bitcast(self, dtype=None): return UPat(UOps.BITCAST, dtype, (self,))
|
|
def gep(self, i:int): return UPat(UOps.GEP, None, (self,), (i,))
|
|
@staticmethod
|
|
def load(*src:UPat, dtype:Optional[DType]=None): return UPat(UOps.LOAD, dtype, src)
|
|
@staticmethod
|
|
def store(*src:UPat): return UPat(UOps.STORE, dtypes.void, src)
|
|
|
|
def const_like(self, b:ConstType|Variable|Tuple[ConstType]): return UPat.const(self.dtype, b)
|
|
def alu(self, arg, *src:UPat):
|
|
asrc = (self,)+src
|
|
return UPat(UOps.ALU, None if arg in {BinaryOps.CMPLT, BinaryOps.CMPNE} else asrc[-1].dtype, list(asrc) if arg in COMMUTATIVE else asrc, arg)
|
|
|
|
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.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.op is not None and uop.op not in self.op) 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]]:
|
|
for x in self.src[0]:
|
|
if (match:=x.match(uop, store.copy())): return match
|
|
return []
|
|
|
|
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})
|
|
new_code_obj = pickle.loads(pickle.dumps(fxn.__code__)) if getenv("TEST_PICKLE") else fxn.__code__ # NOTE: optional round trip through pickle!
|
|
assert fxn.__closure__ is None, "closures are not supported in pattern matchers"
|
|
return new_code_obj, new_globals, fxn.__name__, fxn.__defaults__
|
|
|
|
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[Tuple[UOps, Any], List[Tuple[UPat, Callable, Set]]] = {}
|
|
# 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)
|
|
tuple_fxn[1]['__builtins__'] = __builtins__ # NOTE: Python 3.8 requires this for "all" and "len" and friends
|
|
real_fxn = types.FunctionType(*tuple_fxn)
|
|
for uop in p.op: self.pdict.setdefault((uop, p.arg), []).append((p, real_fxn, p.early_reject))
|
|
|
|
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) -> Optional[UOp]:
|
|
ler = set([v for u in uop.src for v in ((u.op, u.arg), (u.op, None))])
|
|
for p,fxn,early_reject in self.pdict.get((uop.op, uop.arg), []) + ([] if uop.arg is None else self.pdict.get((uop.op, None), [])):
|
|
if not early_reject.issubset(ler): continue
|
|
if (matches := p.match(uop, {})) and (ret:=(fxn(ctx, **matches[0]) if ctx is not None else fxn(**matches[0]))) 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 TrackedRewriteContext:
|
|
loc: Tuple[str, int] # location that called graph_rewrite
|
|
sink: UOp # the sink passed into the rewrite
|
|
kernel: Optional[Kernel] = None # the kernel being rewritten
|
|
rewrites: List[Tuple[UOp, UOp, UPat]] = field(default_factory=list) # all rewrites of sparents. (before, after, UPat)
|
|
contexts: List[TrackedRewriteContext] = []
|
|
class TrackedPatternMatcher(PatternMatcher):
|
|
def __init__(self, patterns:List[Tuple[UPat, Callable]]):
|
|
super().__init__(patterns)
|
|
for p,_ in self.patterns:
|
|
if p not in match_stats: match_stats[p] = [0,0,0.0,0.0]
|
|
|
|
def rewrite(self, uop:UOp, ctx=None) -> Optional[UOp]:
|
|
ret = None
|
|
ler = set([v for u in uop.src for v in ((u.op, u.arg), (u.op, None))])
|
|
for p,fxn,early_reject in self.pdict.get((uop.op, uop.arg), []) + ([] if uop.arg is None else self.pdict.get((uop.op, None), [])):
|
|
st = time.perf_counter()
|
|
if not early_reject.issubset(ler):
|
|
match_stats[p][2] += time.perf_counter()-st
|
|
continue
|
|
match_stats[p][1] += 1
|
|
if (matches := p.match(uop, {})) and (ret:=(fxn(ctx, **matches[0]) if ctx is not None else fxn(**matches[0]))) is not None:
|
|
match_stats[p][0] += 1
|
|
match_stats[p][2] += (et:=time.perf_counter()-st)
|
|
match_stats[p][3] += et
|
|
if TRACK_MATCH_STATS >= 3: print(f"{et*1e6:7.2f} us -- ", p.printable())
|
|
if TRACK_MATCH_STATS >= 2 and contexts: contexts[-1].rewrites.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("/tmp/rewrites.pkl", "wb") as f:
|
|
print(f"rewrote {len(contexts)} graphs and applied {sum(len(x.rewrites) for x in contexts)} rules, saved to /tmp/rewrites.pkl")
|
|
pickle.dump(contexts, f)
|
|
if getenv("VIZ"):
|
|
os.environ["VIZ"] = "0"
|
|
os.execv(sys.executable, [sys.executable] + [os.path.join(os.path.dirname(__file__), "..", "viz", "serve.py")])
|
|
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]):
|
|
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]*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]*1000.:9.2f} ms -- TOTAL")
|
|
|
|
# *** simple graph rewrite engine ***
|
|
|
|
class RewriteContext:
|
|
def __init__(self, pm, ctx):
|
|
self.pm: PatternMatcher = pm
|
|
self.ctx = ctx
|
|
self.nodes: Dict[Tuple, UOp] = {}
|
|
self.replace: Dict[UOp, UOp] = {}
|
|
def rewrite(self, n:UOp) -> UOp:
|
|
if (rn := self.replace.get(n)) is not None: return rn
|
|
replace_source = (n.op, n.dtype, new_src:=tuple(map(self.rewrite, n.src)), n.arg)
|
|
if (found := self.nodes.get(replace_source)) is not None: self.replace[n] = found
|
|
else:
|
|
x = UOp(*replace_source) if new_src != n.src else n
|
|
self.nodes[replace_source] = self.replace[n] = found = self.rewrite(new_x) if (new_x := self.pm.rewrite(x, self.ctx)) is not None else x
|
|
return found
|
|
def graph_rewrite(sink:UOp, pm:PatternMatcher, ctx=None) -> UOp:
|
|
if TRACK_MATCH_STATS >= 2:
|
|
from tinygrad.codegen.kernel import Kernel
|
|
frm = sys._getframe(1)
|
|
# get Kernel we are rewriting in the context of
|
|
frm_walk: Optional[FrameType] = frm
|
|
while frm_walk is not None and not isinstance(kernel:=frm_walk.f_locals.get("self", None), Kernel): kernel, frm_walk = None, frm_walk.f_back
|
|
contexts.append(TrackedRewriteContext((frm.f_code.co_filename, frm.f_lineno), sink, kernel))
|
|
return RewriteContext(pm, ctx).rewrite(sink)
|
|
|
|
# ***** uop type spec *****
|
|
|
|
# this is the matcher for the final rendered UOps
|
|
# matcher functions returns True or False (or None to not match)
|
|
spec = PatternMatcher([
|
|
(UPat(UOps.DEFINE_GLOBAL, name="x"), lambda x: isinstance(x.dtype, (PtrDType, ImageDType)) and not x.dtype.local),
|
|
(UPat(UOps.DEFINE_LOCAL, name="x"), lambda x: isinstance(x.dtype, PtrDType) and x.dtype.local),
|
|
(UPat(UOps.DEFINE_ACC, src=(UPat.var("c"),), name="x", allow_any_len=True),
|
|
lambda x,c: all(y.op is UOps.RANGE for y in x.src[1:]) and c.dtype == x.dtype),
|
|
(UPat(UOps.DEFINE_VAR, src=(), name="x"), lambda x: isinstance(x.arg[1], int) and isinstance(x.arg[2], int)),
|
|
|
|
(UPat(UOps.RANGE, src=(UPat(name="x"), UPat(name="y")), name="rng"), lambda rng,x,y: rng.dtype == x.dtype == y.dtype),
|
|
(UPat(UOps.SPECIAL, src=()), lambda: True),
|
|
|
|
# no pyint allowed here!
|
|
(UPat(UOps.ALU, dtype=dtypes.pyint), lambda: False),
|
|
|
|
# TODO: confirm the args of both of these are shapetrackers
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(UPat(UOps.SHAPETRACKER, src=()), lambda: True),
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(UPat(UOps.SWIZZLE, src=(UPat(),)), lambda: True),
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(UPat(UOps.VALID, dtypes.bool, (UPat(UOps.SHAPETRACKER),)), lambda: True),
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(UPat(UOps.CONST, name="x"), lambda x: x.dtype == x.dtype.scalar() and (type(x.arg) is type(dtypes.as_const(x.arg, x.dtype)))),
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# early LOAD has a <buf, shapetracker, store?>
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(UPat(UOps.LOAD, src=(UPat((UOps.DEFINE_GLOBAL, UOps.DEFINE_LOCAL)), UPat(UOps.SHAPETRACKER))), lambda: True),
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(UPat(UOps.LOAD, src=(UPat((UOps.DEFINE_GLOBAL, UOps.DEFINE_LOCAL)), UPat(UOps.SHAPETRACKER), UPat(UOps.STORE))), lambda: True),
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# LOAD takes a <buf, idx, alt?, gate?, barrier?>
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(UPat(UOps.LOAD, src=(UPat((UOps.DEFINE_GLOBAL, UOps.DEFINE_LOCAL)), UPat())), lambda: True),
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(UPat(UOps.LOAD, src=(UPat((UOps.DEFINE_GLOBAL, UOps.DEFINE_LOCAL)), UPat(), UPat((UOps.IF, UOps.BARRIER)))), lambda: True),
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(UPat(UOps.LOAD, src=(UPat((UOps.DEFINE_GLOBAL, UOps.DEFINE_LOCAL)), UPat(), UPat(name="alt"), UPat(dtype=dtypes.bool)), name="ld"),
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lambda ld,alt: ld.dtype == alt.dtype),
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# STORE takes a <buf, idx, val, gate?>
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(UPat(UOps.STORE, dtype=dtypes.void, src=(UPat((UOps.DEFINE_GLOBAL, UOps.DEFINE_LOCAL)), UPat(), UPat())), lambda: True),
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(UPat(UOps.STORE, dtype=dtypes.void, src=(UPat((UOps.DEFINE_GLOBAL, UOps.DEFINE_LOCAL)), UPat(), UPat(), UPat(dtype=dtypes.bool))), lambda: True),
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(UPat(UOps.STORE, dtype=dtypes.void, src=(UPat((UOps.DEFINE_GLOBAL, UOps.DEFINE_LOCAL)), UPat(), UPat(), UPat(UOps.IF))), lambda: True),
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# most ALUs have all matching dtypes, except CMPLT, CMPNE, and WHERE
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(UPat(UOps.ALU, name="w", src=(UPat(dtype=dtypes.bool), UPat(name="x"), UPat(name="y")), arg=TernaryOps.WHERE),
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lambda w,x,y: w.dtype == x.dtype == y.dtype),
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(UPat(UOps.ALU, dtype=dtypes.bool, src=(UPat(name="x"), UPat(name="y")), arg=BinaryOps.CMPLT), lambda x,y: x.dtype == y.dtype),
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(UPat(UOps.ALU, dtype=dtypes.bool, src=(UPat(name="x"), UPat(name="y")), arg=BinaryOps.CMPNE), lambda x,y: x.dtype == y.dtype),
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# and SHL/SHR, the shift distance is an int
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(UPat(UOps.ALU, src=(UPat(name="x"), UPat()), name="alu", arg=BinaryOps.SHL), lambda alu,x: alu.dtype == x.dtype),
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(UPat(UOps.ALU, src=(UPat(name="x"), UPat()), name="alu", arg=BinaryOps.SHR), lambda alu,x: alu.dtype == x.dtype),
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(UPat(UOps.ALU, arg=BinaryOps.IDIV, name="x"), lambda x: None if dtypes.is_int(x.dtype) else False),
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(UPat(UOps.ALU, name="x"), lambda x: all(x.dtype == y.dtype for y in x.src)),
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(UPat(UOps.ASSIGN, src=(UPat((UOps.DEFINE_ACC, UOps.DEFINE_GLOBAL)), UPat())), lambda: True),
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(UPat(UOps.ENDRANGE, dtype=dtypes.void, src=(UPat(UOps.RANGE),)), lambda: True),
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# all WMMA has 3 args, <x, w, acc>
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(UPat(UOps.WMMA, src=(UPat(), UPat(), UPat())), lambda: True),
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(UPat(UOps.CONTRACT, name="x"), lambda x: x.dtype.count == prod(y[1] for y in x.arg)),
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(UPat(UOps.EXPAND, name="x"), lambda x: x.src[0].dtype.count == prod(y[1] for y in x.arg)),
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# if has a <gate, barrier?>
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(UPat(UOps.IF, dtype=dtypes.void, src=(UPat(),)), lambda: True),
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(UPat(UOps.IF, dtype=dtypes.void, src=(UPat(), UPat(UOps.BARRIER))), lambda: True),
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(UPat(UOps.ENDIF, dtype=dtypes.void, src=(UPat(UOps.IF),)), lambda: True),
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(UPat(UOps.REDUCE_AXIS, name="x"), lambda x: isinstance(x.arg, tuple) and len(x.arg) == 2 and x.arg[0] in BinaryOps),
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(UPat(UOps.GEP, src=(UPat(name="src"),), name="gep"), lambda gep,src: gep.dtype == src.dtype.scalar()),
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(UPat(UOps.VECTORIZE, name="x"), lambda x: len(x.src)>1 and len(x.src) == x.dtype.count and all(x.dtype == y.dtype.vec(len(x.src)) for y in x.src)),
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(UPat((UOps.BITCAST, UOps.CAST), src=(UPat(),), name="x"), lambda x: x.arg is None and x.dtype.count == 1),
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(UPat(UOps.BARRIER, dtypes.void, src=UPat(UOps.STORE, src=(UPat(UOps.DEFINE_LOCAL),), allow_any_len=True)), lambda: True),
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# NOTE: for testing, we let sinks be anything
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#(UPat(UOps.SINK, src=UPat(UOps.STORE)), lambda: True),
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(UPat(UOps.SINK, dtypes.void), lambda: True),
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(UPat(UOps.NOOP), lambda: True),
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# PTX LOAD/STORE
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(UPat((UOps.LOAD, UOps.STORE), src=(UPat(dtype=dtypes.int64),), allow_any_len=True), lambda: True),
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(UPat(UOps.BARRIER, dtypes.void, src=UPat(UOps.STORE, src=(UPat(dtype=dtypes.int64),), allow_any_len=True)), lambda: True),
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])
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def type_verify(uops:List[UOp]):
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for u in uops:
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chk = cast(bool, spec.rewrite(u))
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assert chk is True, f"UOp verification failed on {u.op} {u.dtype} {len(u.src)} {[x.op for x in u.src]} {u.arg}"
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