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StarPilot/tinygrad_repo/tinygrad/uop/ops.py
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from __future__ import annotations
from typing import Any, Callable, cast, TYPE_CHECKING, Type, Sequence, Iterable, Final, Iterator
import sys, time, functools, itertools, math, operator, hashlib, os, types, pickle, pathlib, inspect, weakref, collections, struct
from dataclasses import dataclass
from enum import Enum, auto
from tinygrad.uop import Ops, GroupOp
from tinygrad.dtype import ConstType, ImageDType, dtypes, DType, DTypeLike, to_dtype, truncate, PtrDType, least_upper_dtype, Invalid, AddrSpace
from tinygrad.dtype import ConstFloat, PyConst, InvalidType, storage_fmt_for_dtype, to_storage_scalar, from_storage_scalar
from tinygrad.device import Buffer, MultiBuffer, canonicalize_device
from tinygrad.helpers import ContextVar, all_int, prod, getenv, all_same, Context, partition, temp, unwrap, T, argfix, Metadata, flatten, TRACEMETA
from tinygrad.helpers import PROFILE, dedup, cdiv, cmod, floordiv, floormod, diskcache_put, to_function_name, cpu_profile, TracingKey
from tinygrad.helpers import VIZ, SPEC, CAPTURE_PROCESS_REPLAY, DISALLOW_BROADCAST, get_shape, fully_flatten
from tinygrad.helpers import colored, ansilen, printable
if TYPE_CHECKING:
from tinygrad.renderer import Estimates
class AxisType(Enum):
def __repr__(self): return str(self)
GLOBAL = auto(); WARP = auto(); LOCAL = auto(); LOOP = auto(); GROUP_REDUCE = auto(); REDUCE = auto(); UPCAST = auto(); UNROLL = auto() # noqa: E702
THREAD = auto(); PLACEHOLDER = auto() # noqa: E702
@dataclass(frozen=True, order=True)
class ParamArg:
slot: int
vmin_vmax: tuple[PyConst, PyConst]|None = None
name: str|None = None
addrspace: AddrSpace|None = AddrSpace.GLOBAL
axis: int|None = None
device: str|tuple[str, ...]|None = None
def __repr__(self):
fields = (("vmin_vmax", None), ("name", None), ("addrspace", AddrSpace.GLOBAL), ("axis", None), ("device", None))
args = [repr(self.slot)] + [f"{k}={v!r}" for k,default in fields if (v:=getattr(self, k)) != default]
return f"ParamArg({', '.join(args)})"
axis_letters = {AxisType.GLOBAL: "g", AxisType.THREAD: "t", AxisType.LOCAL: "l", AxisType.WARP: "w", AxisType.LOOP: "L", AxisType.UPCAST: "u",
AxisType.GROUP_REDUCE: "G", AxisType.REDUCE: "R", AxisType.UNROLL: "r"}
axis_colors = {AxisType.GLOBAL: "blue", AxisType.THREAD: "BLUE", AxisType.LOCAL: "cyan", AxisType.WARP: "CYAN", AxisType.LOOP: "WHITE",
AxisType.UPCAST: "yellow", AxisType.GROUP_REDUCE: "RED", AxisType.REDUCE: "red", AxisType.UNROLL: "magenta"}
# NOTE: LOCAL and GROUP_REDUCE have the same priority. the order here matters
axis_to_pos = {AxisType.LOOP: -1, AxisType.THREAD: 0, AxisType.GLOBAL: 0, AxisType.WARP: 1, AxisType.LOCAL: 2, AxisType.UPCAST: 3,
AxisType.GROUP_REDUCE: 2, AxisType.REDUCE: 4, AxisType.UNROLL: 5}
range_start = {Ops.STAGE: 1, Ops.REDUCE: 1, Ops.WMMA: 3, Ops.END: 1, Ops.CALL: 1, Ops.FUNCTION: 1,
Ops.COPY: 2, Ops.SLICE: 2, Ops.LINEAR: 0}
# https://en.wikipedia.org/wiki/Identity_element
def identity_element(op:Ops, dt:DType) -> PyConst: return dt.const({Ops.ADD:0, Ops.MUL:1, Ops.MAX:dt.min}[op])
# 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) -> sint: return _suop(argfix(*lst), UOp.maximum, max)
def smin(*lst) -> sint: return _suop(argfix(*lst), UOp.minimum, min)
def srender(x:sint) -> str: return x.render() if isinstance(x, UOp) else str(x)
def _align_left(*shapes:tuple[sint, ...]) -> tuple[tuple[sint, ...], ...]:
max_dim = max(len(s) for s in shapes)
return tuple((1,)*(max_dim-len(s))+s for s in shapes)
def _broadcast_shape(*shapes:tuple[sint, ...]) -> tuple[sint, ...]:
shaped_aligned_left = _align_left(*shapes)
ret = tuple(0 if 0 in nth_dim_sizes else smax(nth_dim_sizes) for nth_dim_sizes in zip(*shaped_aligned_left))
if not all(resolve(s == ns) or resolve(s == 1) for shape in shaped_aligned_left for s,ns in zip(shape, ret)):
raise IndexError(f"shape mismatch: objects cannot be broadcast to a single shape {shapes}")
return ret
def ssimplify(uop:sint): return uop.ssimplify() if isinstance(uop, UOp) else uop
def sym_infer(uop: UOp|int, var_vals: dict[str, int]) -> int: return uop.sym_infer(var_vals) if isinstance(uop, UOp) else uop
def range_str(u:UOp, color=False) -> str:
ret = '_'.join([str(x) if x >= 0 else "m"+str(-x) for x in u.arg[0:-1]])
return colored(ret, axis_colors[u.arg[-1]]) if color else ret
def multirange_str(rngs:Iterable[UOp], color=False, pad=None) -> str:
ret = ','.join([range_str(x, color=color) for x in sorted(rngs, key=lambda x: x.arg)])
if pad is not None: ret += " " * (pad-ansilen(ret))
return ret
def shape_to_shape_arg(arg:tuple[sint, ...]) -> UOp:
if len(arg) == 0: return UOp(Ops.STACK, dtypes.weakint.vec(0))
elif all_int(arg): return UOp.const(dtypes.weakint.vec(len(arg)), arg)
else: return UOp(Ops.STACK, dtypes.weakint.vec(len(arg)), tuple(UOp.const(dtypes.weakint, x) if isinstance(x, int) else x for x in arg))
def consumer_map_from_toposort(lst:Iterable[UOp]):
ret: dict[UOp, dict[UOp, None]] = {}
for u in lst:
ret[u] = {}
for s in u.src:
if s in ret: ret[s][u] = None
return ret
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, tag:Any=None,
metadata:tuple[Metadata,...]|None=None, _buffer:Buffer|None=None):
if (wret:=UOpMetaClass.ucache.get(key:=(op, dtype, src, arg, tag), None)) is not None and (ret:=wret()) is not None: return ret
UOpMetaClass.ucache[key] = weakref.ref(created:=super().__call__(*key))
if metadata 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
if SPEC > 1:
from tinygrad.uop.spec import spec_full, test_pyrender
if SPEC > 2:
# SPEC=3 checks the shape
_ = created._shape
if SPEC > 3:
test_pyrender(created)
with Context(CHECK_OOB=0): fret = cast(bool|None, spec_full.rewrite(created))
if fret is not True: raise RuntimeError(f"SPEC ISSUE {fret}: {created}")
return created
# some uops map to other stuff
buffers:weakref.WeakKeyDictionary[UOp, Buffer|MultiBuffer] = weakref.WeakKeyDictionary() # this maps BUFFER/SLICE uops to their device Buffers
all_metadata:weakref.WeakKeyDictionary[UOp, tuple[Metadata, ...]] = weakref.WeakKeyDictionary() # TODO: should this be here?
# recursive_property replaces functools.cached_property in recursive UOp functions to prevent RecursionError
class recursive_property(property):
def __init__(self, fxn):
self.fxn = fxn
self.nm = "_RECURSIVE_PROPERTY_"+fxn.__name__
self.__doc__ = fxn.__doc__
def __get__(self, x:UOp|None, owner=None):
if x is None: return self
if self.nm in x.__dict__: return x.__dict__[self.nm]
for node in x.toposort(gate=lambda node: self.nm not in node.__dict__): node.__dict__[self.nm] = self.fxn(node)
return x.__dict__[self.nm]
# we import this late so we can use resolve/smax in mixins
from tinygrad.mixin import OpMixin
from tinygrad.mixin.rand import RandMixin
# NOTE: this should be frozen, but frozen is slower
@dataclass(eq=False, slots=True)
class UOp(RandMixin, metaclass=UOpMetaClass):
op:Ops
dtype:DType = dtypes.void
src:tuple[UOp, ...] = tuple()
arg:Any = None
tag:Any = None
def __del__(self):
if Ops is not None and self.op is Ops.BUFFER and (buffer:=buffers.get(self)) is not None: buffer.ref(-1)
try: del UOpMetaClass.ucache[(self.op, self.dtype, self.src, self.arg, self.tag)]
except AttributeError: pass
def __reduce__(self):
args = [self.op, self.dtype, self.src, self.arg, self.tag, self.metadata]
if self.op is Ops.BUFFER and self.realized is not None: 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), kwargs.pop("tag", self.tag))
assert len(kwargs) == 0, f"unused kwargs in replace {list(kwargs)}"
if (self.op, self.dtype, self.src, self.arg, self.tag) == new_args: return self
return UOp(*new_args)
def rtag(self, tag=True): return self.replace(tag=tag)
@recursive_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):
from tinygrad.uop.render import pretty_print
return pretty_print(self)
def argstr(self):
if self.op is Ops.REDUCE: return f'({", ".join(map(str, self.arg))})'
return f"ConstFloat({float.__repr__(self.arg)})" if isinstance(self.arg, ConstFloat) else repr(self.arg)
def tagstr(self): return f", tag={self.tag}" if self.tag is not None else ""
def f(self, op, **kwargs): return UOp(op, dtype=kwargs.pop("dtype", self.dtype), src=(self,), **kwargs)
@functools.cached_property
def backward_slice(self:UOp) -> dict[UOp, None]:
res: dict[UOp, None] = self.toposort()
res.pop(self)
return res
@property
def backward_slice_with_self(self:UOp) -> dict[UOp, None]: return {self:None, **self.backward_slice}
def op_in_backward_slice_with_self(self, *ops:Ops) -> bool:
# Check self first, then iterate backward_slice (avoids creating intermediate dict)
return self.op in ops or any(x.op in ops for x in self.backward_slice)
def toposort(self, gate:Callable|None=None, enter_calls=True) -> dict[UOp, None]:
cache: dict[UOp, None] = {}
stack: list[tuple[UOp, bool]] = [(self, False)] # each stack entry is (node, visited_flag)
while stack:
node, visited = stack.pop()
if node in cache: continue
if not visited:
if gate is None or gate(node):
stack.append((node, True)) # push node back on stack to process after its srcs
for s in reversed(node.src if enter_calls or node.op not in {Ops.CALL, Ops.FUNCTION} else node.src[1:]):
stack.append((s, False)) # push srcs on the stack
else: cache[node] = None # second time i'm seeing this node, add it to returned toposort
return cache
def topovisit(self, visitor:Callable[[UOp], T], cache:dict[UOp, T]) -> T:
# NOTE: this shares a lot of code with toposort
stack: list[tuple[UOp, bool]] = [(self, False)]
while stack:
node, visited = stack.pop()
if node in cache: continue
if not visited:
stack.append((node, True))
for s in reversed(node.src): stack.append((s, False))
else: cache[node] = visitor(node)
return cache[self]
@functools.cached_property
def tuplize(self:UOp) -> tuple:
return (self.op.value, self.arg, self.dtype,)+tuple([x.tuplize for x in self.src])
@property
def ptrdtype(self) -> PtrDType:
if not isinstance(self.dtype, PtrDType): raise RuntimeError(f"ptrdtype called on UOp with type {self.dtype}")
return self.dtype
# *** uop shape stuff ***
@recursive_property
def _shape(self) -> tuple[sint, ...]|None:
match self.op:
# late ops don't have shape
case Ops.UNIQUE | Ops.LUNIQUE | Ops.DEVICE | Ops.IF | Ops.BARRIER | Ops.CUSTOM | Ops.CUSTOMI | \
Ops.SINK | Ops.END | Ops.REWRITE_ERROR | Ops.PTRCAT | Ops.ENDIF | \
Ops.LINEAR | Ops.PROGRAM | Ops.SOURCE | Ops.INS | Ops.TUPLE | Ops.CALL | Ops.FUNCTION:
return None
# special (terrible) case for RESHAPE on NOOP
case Ops.RESHAPE:
if self.src[0].op is Ops.NOOP: return self.marg
# hacks for NOOP
case Ops.NOOP:
return self.src[0]._shape if len(self.src) >= 1 else None
case Ops.GETTUPLE:
# GETTUPLE extracts from a TUPLE (possibly through a FUNCTION)
in_tuple = self.src[0].src[0] if self.src[0].op is Ops.FUNCTION else self.src[0]
assert in_tuple.op is Ops.TUPLE
inner_shape = in_tuple.src[self.arg]._shape
if inner_shape is None: return None
# if through a FUNCTION, substitute internal PARAMs in the shape with corresponding args
if self.src[0].op is Ops.FUNCTION:
return tuple(graph_rewrite(s, _pm_resolve_params, self.src[0].src[1:], walk=True) if isinstance(s, UOp) else s for s in inner_shape)
return inner_shape
case Ops.CAST:
# if it has a vec dtype, set the shape
if self.dtype.count > 1: return (self.dtype.count,)
# when PTX casts from ptr to non ptr, remove the shape of the buffer
if isinstance(self.src[0].dtype, PtrDType) and not isinstance(self.src[0].dtype, ImageDType) and not isinstance(self.dtype, PtrDType):
return ()
case Ops.INDEX:
shp:list[sint] = []
for s in self.src[1:]: shp.extend(list(s.shape))
return tuple(shp) + self.src[0].shape[len(self.src[1:]):]
case Ops.GEP:
return (len(self.arg),) if len(self.arg) > 1 else ()
case Ops.STACK:
if len(self.src) == 0: return ()
if isinstance(self.dtype, PtrDType):
# TODO: this is broken
return self.src[0].shape
else:
return (len(self.src),) + self.src[0].shape
# TODO: contract and unroll should be deleted
case Ops.CONST | Ops.DEFINE_VAR | Ops.CONTRACT | Ops.UNROLL | Ops.VCAT:
return (self.dtype.count,) if self.dtype.count > 1 else ()
# some ops init the shape
case Ops.GETADDR: return ()
case Ops.BIND | Ops.RANGE | Ops.SPECIAL: return ()
case Ops.BINARY: return (len(self.arg),)
case Ops.BUFFER: return self.src[0].as_shape if isinstance(self.arg, ParamArg) else (self.arg,)
case Ops.SLICE:
# HACK: SLICE is used inside kernels, so we set the shape to () if it's on an INDEX
if self.src[0].op is Ops.INDEX: return ()
return (self.arg,)
case Ops.CUSTOM_FUNCTION: return None
case Ops.STAGE:
# STAGE adds the existing shape to the front, opposite of INDEX
return tuple([int(r.vmax+1) for r in self.src[1:]])+self.src[0].shape
case Ops.DEFINE_LOCAL | Ops.DEFINE_REG:
if len(self.src) >= 1:
# NOTE: this is the same as PARAM
return tuple(self.src[0].sgep(i) for i in range(self.src[0].dtype.count))
if isinstance(self.dtype, PtrDType):
return (self.ptrdtype.size, self.dtype.count) if self.dtype.count > 1 else (self.ptrdtype.size,)
return (self.dtype.count,) if self.dtype.count > 1 else ()
case Ops.PARAM:
if isinstance(self.dtype, ImageDType): return self.dtype.shape
if isinstance(self.dtype, PtrDType): return (self.ptrdtype.size,)
return self.src[0].as_shape if len(self.src) >= 1 else None
# wmma output shape = accumulator shape (src[2])
case Ops.WMMA | Ops.SHAPED_WMMA: return self.src[2]._shape
# passthrough ops
case Ops.MSTACK | Ops.MSELECT | Ops.DETACH | Ops.CONTIGUOUS | Ops.CONTIGUOUS_BACKWARD | Ops.AFTER | Ops.LOAD | \
Ops.COPY | Ops.ALLREDUCE | Ops.STORE:
return self.src[0]._shape
# REDUCE with empty axis is passthrough (lowered form)
case Ops.REDUCE if len(self.arg[1]) == 0:
# these can mismatch if there's a horizonal reduce
return (self.dtype.count,) if self.dtype.count > 1 else ()
# TODO: disallow shape changing bitcast
case Ops.BITCAST:
ps = self.src[0]._shape
if ps is None: return None
if (output_sz:=self.dtype.itemsize) != (input_sz:=self.src[0].dtype.itemsize):
return ps[:-1]+(ssimplify((ps[-1]*input_sz) // output_sz),) if len(ps) > 0 else ps
return ps
# MULTI marker has no shape
case Ops.MULTI if len(self.src) == 0: return None
# movement ops change the shape
# NOTE: ssimplify is required because the shape needs to be canonical for broadcasting and same shape checking
if self.op in GroupOp.Movement.union({Ops.MULTI, Ops.REDUCE}):
ps = self.src[0]._shape
if ps is None: raise RuntimeError(f"movement op {self.op} requires shape, {self.src[0].op} doesn't have one")
match self.op:
case Ops.RESHAPE:
if not all(x >= 0 for x in self.marg): raise ValueError(f"shape can't contain negative numbers {self.marg}")
if prod(ps) != prod(self.marg): raise ValueError(f"bad reshape: {ps} -> {self.marg}")
return self.marg
case Ops.EXPAND:
if len(ps) != len(self.marg) or not all(s==ns or (s==1 and ns>=0) for s,ns in zip(ps, self.marg)):
raise ValueError(f"bad expand: {ps} -> {self.marg}")
return self.marg
case Ops.PERMUTE:
if sorted(self.marg) != list(range(len(ps))): raise ValueError(f"invalid permutation {self.marg} of len {len(ps)}")
return tuple(ps[i] for i in self.marg)
case Ops.PAD:
# TODO: why do i need resolve here?
if len(ps) != len(self.marg) or not all(resolve(sz>=0) and resolve(0<=o) and resolve(o+s<=sz) for s,(o,sz) in zip(ps, self.marg)):
raise ValueError(f"invalid pad {self.marg} for {ps}")
return tuple(ssimplify(sz) for _,sz in self.marg)
case Ops.SHRINK:
# TODO: why do i need resolve here?
if len(ps) != len(self.marg) or not all(resolve(0<=o) and resolve(sz>=0) and resolve(o+sz<=s) for s,(o,sz) in zip(ps, self.marg)):
raise ValueError(f"invalid shrink {self.marg} for {ps}")
return tuple(ssimplify(sz) for _,sz in self.marg)
case Ops.FLIP:
if len(ps) != len(self.marg) or not all(isinstance(x, bool) for x in self.marg): raise ValueError(f"bad flip on {ps}, {self.marg}")
return ps
case Ops.MULTI: return tuple(s*len(self.device) if a == self.axis else s for a,s in enumerate(ps))
case Ops.REDUCE:
axis_arg = self.arg[1]
if not isinstance(axis_arg, tuple) or not all(isinstance(x, int) and x>=0 and x<len(ps) for x in axis_arg):
raise ValueError(f"invalid type for axis: {axis_arg}")
return tuple(1 if i in axis_arg else s for i,s in enumerate(ps))
if self.op in GroupOp.Unary.union({Ops.CAST}):
assert len(self.src) == 1, "unary ops must have 1 src"
return self.src[0]._shape
# elementwise ops keep the shape the same. all inputs with shape must match
if self.op in GroupOp.Broadcastable:
input_shapes = [x._shape for x in self.src]
assert len(self.src) > 0 and all(x is not None for x in input_shapes), f"None input shape not supported for {self.op}"
if DISALLOW_BROADCAST and not all_same(input_shapes):
raise RuntimeError(f"shape mismatch at {self.op}: {input_shapes} {[x.op for x in self.src]}")
# broadcasting lives in _shape property now
return _broadcast_shape(*input_shapes)
# all Ops must be explicitly handled
raise NotImplementedError(f"no shape handling for {self.op} with {self.dtype}")
@property
def shape(self) -> tuple[sint, ...]:
if (ret:=self._shape) is None: raise RuntimeError(f"shape requested, but {self.op} doesn't have a shape")
return ret
@property
def max_shape(self) -> tuple[int, ...]: return to_max_shape(self.shape)
def max_numel(self) -> int: return prod(self.max_shape)
@property
def shard_shape(self) -> tuple[sint, ...]:
if not isinstance(self.device, tuple) or self.axis is None: return self.shape
return tuple(x//len(self.device) if i == self.axis else x for i,x in enumerate(self.shape))
@property
def max_shard_shape(self) -> tuple[int, ...]:
if not isinstance(self.device, tuple) or self.axis is None: return self.max_shape
return tuple(x//len(self.device) if i == self.axis else x for i,x in enumerate(self.max_shape))
@functools.cached_property
def ended_ranges(self) -> tuple[UOp, ...]:
if self.op in range_start: return self.src[range_start[self.op]:]
if self.op is Ops.AFTER: return tuple(flatten([x.ended_ranges for x in self.src[1:]]))
if self.op is Ops.CONTRACT:
contract_rng_ids = {rng_id for rng_id, _ in self.arg}
return tuple(r for r in self.src[0].ranges if r.op is Ops.RANGE and r.arg[0] in contract_rng_ids)
return ()
# determine what ranges this is in
@recursive_property
def _ranges(self) -> dict[UOp, None]:
ret: dict[UOp, None] = {}
for s in self.src: ret.update(s.ranges)
for er in self.ended_ranges:
if er.op is Ops.RANGE:
# if it's a single RANGE, we don't flow through it.
ret.pop(er, None)
else:
# if it's not a RANGE, we include all ranges in srcs.
# technically we shouldn't flow through these ranges either, but this is pre pm_add_control_flow so it's the same.
for s in er.ranges: ret.pop(s, None)
return ret
@property
def ranges(self) -> dict[UOp, None]:
if self.op is Ops.RANGE: return {self:None} | self._ranges
return self._ranges
# *** uop evaluation ***
def simplify(self, tracked=False):
if self.op is Ops.CONST: return self
if self.op is Ops.SINK and all(s.op is Ops.CONST or (s.op is Ops.STACK and len(s.src) == 0) for s in self.src): return self
# late import!
from tinygrad.uop.symbolic import symbolic
with Context(TRACK_MATCH_STATS=0 if not tracked else TRACK_MATCH_STATS.value):
return graph_rewrite(self, symbolic, name="simplify")
def ssimplify(self) -> UOp|ConstType: return ret.arg if (ret:=self.simplify()).op is Ops.CONST else ret
def sintify(self) -> sint: return self.arg if self.op is Ops.CONST else self
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 float(self._eval(dtypes.floats, float))
def substitute(self, dvars:dict[UOp, UOp], name:str|None=None, extra_pm:PatternMatcher|None=None, walk:bool=False):
dvars = {k:v for k,v in dvars.items() if k is not v}
if len(dvars) == 0: return self
with Context(TRACK_MATCH_STATS=(0 if name is None else TRACK_MATCH_STATS.value)):
return graph_rewrite(self, (extra_pm+_substitute) if extra_pm is not None else _substitute, dvars,
bottom_up=True, walk=walk, name=name)
# NOTE: this is not called by Tensor slice (Tensor handles UOps directly), but satisfies SupportsIndex for type checking
def __index__(self): return self.__int__()
# *** uop tracing stuff ***
@recursive_property
def trace_num(self):
num = next(ucount)
uop_fields[num] = (self.op, self.dtype, tuple(s.trace_num for s in self.src), self.arg, self.tag)+((self.metadata,) if TRACEMETA>=2 else ())
return num
# *** uop syntactic sugar ***
def sink(*srcs:UOp|None, **kwargs): # pylint: disable=no-self-argument
return UOp(Ops.SINK, dtypes.void, tuple([x for x in srcs if x is not None]), **kwargs)
def maketuple(*srcs:UOp): # pylint: disable=no-self-argument
return UOp(Ops.TUPLE, dtypes.void, srcs)
def gettuple(self, idx:int) -> UOp:
in_tuple = self.src[0] if self.op is Ops.FUNCTION else self
assert in_tuple.op is Ops.TUPLE, f"gettuple requires FUNCTION or TUPLE source, got {self.op}"
return UOp(Ops.GETTUPLE, in_tuple.src[idx].dtype, (self,), idx)
def group(*srcs:UOp|None): # pylint: disable=no-self-argument
if len(srcs) == 1 and isinstance(srcs[0], UOp): return srcs[0]
return UOp(Ops.GROUP, dtypes.void, tuple([x for x in srcs if x is not None]))
def vectorize(self, *srcs):
return UOp(Ops.STACK, self.dtype.vec(len(srcs)+1), (self,)+srcs)
def index(self, *srcs:UOp|None, ptr=False, **kwargs):
return UOp(Ops.INDEX, kwargs.pop("dtype", self.dtype if ptr else self.dtype.base), (self,)+tuple([x for x in srcs if x is not None]), **kwargs)
def __getitem__(self, idx):
# pointers index into INDEX UOps (scalar lookup); everything else uses the shared mixin view path
if not isinstance(self.dtype, PtrDType): return super(UOp, self).__getitem__(idx)
idx = self._normalize_indices(list(argfix(idx)))
if len(slice_idx:=[i for i,x in enumerate(idx) if isinstance(x, slice)]):
# apply SHRINK for slices that aren't the full range
bounds = tuple((s.start or 0, s.stop if s.stop is not None else self.shape[i]) if isinstance(s, slice) else (0, self.shape[i])
for i, s in enumerate(idx))
src = self if all(b == (0, self.shape[i]) for i, b in enumerate(bounds)) else self.shrink(bounds)
non_slice_args = [UOp.const(dtypes.weakint, x) if isinstance(x, int) else x for x in idx if not isinstance(x, slice)]
if not non_slice_args: return src # all dims are slices, no indexing needed
perm = src.permute(tuple([i for i in range(src.ndim) if i not in slice_idx] + slice_idx))
return perm.index(*non_slice_args, ptr=True)
return self.index(*[UOp.const(dtypes.weakint, x) if isinstance(x, int) else x for x in idx])
@property
def _uop(self) -> UOp: return self
def _wrap_uop(self, u:UOp) -> UOp: return u
def const_like(self, b:ConstLike, dtype:DType|None=None):
return UOp.const(dtype or self.dtype.base, b, shape=self._shape)
def vconst_like(self, b:ConstLike, dtype:DType|None=None):
# for use after movement ops have been removed
ret = UOp.const(dtype or self.dtype.base, b)
if self.shape == (): return ret
if len(self.shape) == 1: return UOp(Ops.STACK, ret.dtype, (ret,)*self.max_numel())
raise RuntimeError(f"vconst_like only works on 0 or 1D shapes, not {self.shape}")
def ufix(self, x):
if isinstance(x, UOp): return x
# float self keeps its dtype for any scalar, int self only for int/Invalid scalars
if dtypes.is_float(self.dtype) or (dtypes.is_int(self.dtype) and isinstance(x, (int, InvalidType))): return self.const_like(x)
return self.const_like(x, dtypes.from_py(x).vec(self.dtype.vcount))
def broadcast(self, count:int):
assert self.dtype.vcount == 1
if count == 1: return self
return UOp(Ops.STACK, self.dtype.vec(count), (self,)*count)
def cast(self, dtype:DTypeLike):
dtype = to_dtype(dtype)
# TODO: we shouldn't have to check for dtype.count == 1 here, but CAST is misused in AMD LLVM
if dtype.count == 1 and dtype.count != self.dtype.count: dtype = dtype.vec(self.dtype.count)
if self.dtype == dtype: return self
return UOp(Ops.CAST, dtype, (self,))
def bitcast(self, dtype:DTypeLike):
dtype = to_dtype(dtype)
return self if self.dtype == dtype else UOp(Ops.BITCAST, dtype, (self,))
def gep(self, i:tuple[int, ...]|int):
if isinstance(i, tuple) and len(i) == 1: return self.gep(i[0])
if isinstance(i, int):
# NOTE: these are just shortcuts to not have to create and fold later
if self.op is Ops.STACK: return self.src[i]
if self.op is Ops.CONST: return UOp.const(self.dtype.scalar(), self.arg)
i = (i,)
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, dtype=kwargs.pop("dtype", self.dtype.base), src=(self,)+src, **kwargs)
def store(self, src:UOp|ConstType, gate:UOp|None=None, **kwargs):
srcs = (self, self.const_like(src) if not isinstance(src, UOp) else src) + ((gate,) if gate is not None else ())
return UOp(Ops.STORE, dtypes.void, srcs, **kwargs)
def wait(self, src:UOp|ConstType, **kwargs):
return UOp(Ops.WAIT, dtypes.void, (self, self.const_like(src) if not isinstance(src, UOp) else src), **kwargs)
def end(self, *src:UOp): return UOp(Ops.END, src=(self,)+src) if len(src) else self
def after(self, *src:UOp, **kwargs): return UOp(Ops.AFTER, self.dtype, (self,)+src, **kwargs) if len(src) else self
def barrier(self, *src:UOp): return UOp(Ops.BARRIER, src=(self,)+src)
def ins(self, arg, **kwargs): return UOp(Ops.INS, kwargs.pop("dtype", self.dtype), kwargs.pop("src", self.src), arg, kwargs.pop("tag", self.tag))
def contract(self, *rngs:UOp):
assert all(x.arg[-1] == AxisType.UPCAST for x in rngs), "all contract ranges must be upcast"
return UOp(Ops.CONTRACT, dtype=self.dtype.vec(prod([x.vmax+1 for x in rngs])), src=(self,), arg=tuple((x.arg[0], x.vmax+1) for x in rngs))
def alu(self, op, *src:UOp, **kwargs):
all_srcs = (self, *src)
# broadcast shaped operands to a common shape (None and () are falsy, so only real shapes participate)
if (shapes := [s for x in all_srcs if (s:=x._shape)]) and not all_same(shapes):
out_shape = _broadcast_shape(*shapes)
all_srcs = tuple(x._broadcast_to(out_shape) if x._shape else x for x in all_srcs)
out_dtype = all_srcs[-1].dtype
if op in {Ops.CMPLT, Ops.CMPNE, Ops.CMPEQ}: out_dtype = dtypes.bool.vec(out_dtype.count) if out_dtype.count > 1 else dtypes.bool
return UOp(op, out_dtype, all_srcs, **kwargs)
@staticmethod
def const(dtype:DType, b:ConstLike, shape:tuple[sint, ...]|None=None):
if isinstance(b, UOp): return b.cast(dtype)
if isinstance(b, tuple) and all_same(b):
assert len(b) > 0, "can't create const from empty tuple"
b = b[0] # doesn't have to be a STACK if they are all the same
if isinstance(b, tuple):
stk = [UOp(Ops.CONST, dtype.scalar(), arg=dtype.const(c), src=()) for c in b]
ret = UOp.vectorize(*stk)
else:
ret = UOp(Ops.CONST, dtype, arg=dtype.const(b), src=())
return ret.reshape((1,)*len(shape)).expand(shape) if shape is not None and shape != () and ret.shape != shape else ret
@staticmethod
def invalids(shape:tuple[sint, ...]|None=None, dtype:DTypeLike|None=None, device:str|tuple[str, ...]|None=None) -> UOp:
dt = to_dtype(dtype) if dtype is not None else dtypes.from_py(Invalid)
return UOp.const(dt, Invalid, shape=shape).clone(device=device)
@staticmethod
def range(end:sint, axis_id, axis_type=AxisType.LOOP, *arg, dtype=dtypes.weakint, src=(), **kwargs):
return UOp(Ops.RANGE, dtype=dtype, src=(sint_to_uop(end, dtype),)+src, arg=(axis_id, axis_type)+arg, **kwargs)
@staticmethod
def special(end:sint, name:str, dtype=dtypes.weakint): return UOp(Ops.SPECIAL, dtype=dtype, src=(sint_to_uop(end, dtype),), arg=name)
def _rop(self, op:Ops, axis:tuple[int, ...]):
axis = tuple(sorted([x for x in axis if resolve(self.shape[x] != 1)]))
return UOp(Ops.REDUCE, self.dtype, (self,), (op, axis)) if len(axis) else self
@staticmethod
def invalid(count=1): return UOp(Ops.CONST, dtypes.weakint.vec(count), src=(), arg=Invalid)
def valid(self, cond):
return cond.where(self.cast(dtypes.weakint), UOp.invalid(self.dtype.count))
def get_idx(self) -> UOp:
assert self.dtype.scalar() is dtypes.weakint, "Can only call get_idx on index dtype"
return self.src[1] if self.op is Ops.WHERE and self.src[2].arg is Invalid else self
def get_valid(self) -> UOp:
assert self.dtype.scalar() is dtypes.weakint, "Can only call get_valid on index dtype"
return self.src[0] if self.op is Ops.WHERE and self.src[2].arg is Invalid else UOp.const(dtypes.bool, self.arg is not Invalid)
def reduce(self, *src:UOp, **kwargs):
arg = kwargs.pop('arg', None)
if isinstance(arg, Ops): arg = (arg, ())
return UOp(Ops.REDUCE, kwargs.pop('dtype', self.dtype), src=(self,)+src, arg=arg, **kwargs)
def contiguous(self, *args, **kwargs):
if self.op is Ops.CONTIGUOUS: return self
if self.device is None: return self
if self.has_buffer_identity(): return self
return UOp(Ops.CONTIGUOUS, dtype=self.dtype, src=(self,)+args, **kwargs)
def bufferize(self, *args, **kwargs): return UOp(Ops.STAGE, dtype=self.dtype, src=(self,)+args, **kwargs)
def allreduce(self, op, device:str|tuple[str, ...]|UOp):
assert isinstance(self.device, tuple), f"allreduce must be on tuple {self.device} isn't"
return UOp(Ops.ALLREDUCE, self.dtype, (self, UOp(Ops.DEVICE, arg=device) if not isinstance(device, UOp) else device), op)
def overflows(self, dtype:DType) -> bool: return self.vmin < dtype.min or dtype.max < self.vmax
def split_uop(self:UOp, sep:Ops) -> Iterator[UOp]:
if self.op is sep:
for s in self.src: yield from s.split_uop(sep)
else: yield self
@property
def reg(self:UOp):
# TODO: add a way to access the nth element in src
if self.op in (Ops.NOOP, Ops.AFTER) and self.src: return self.src[0].reg
if isinstance(self.tag, tuple): return self.tag[0]
return self.tag
# *** multi-device helpers ***
def multi(self, axis:int|None):
assert isinstance(self.device, tuple), f"multi device must be tuple, {self.device} isn't"
assert axis is not None, "multi None is no longer supported"
return UOp(Ops.MULTI, self.dtype, (self,), axis)
@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([self.src[0].shape[self.axis] for _ in self.device], initial=0)))
@functools.cached_property
def axis(self) -> int|None:
# COPY removes axis. TODO: add more tests for this, and consider MSELECT/MSTACK
if self.op is Ops.COPY: return None
if self.op is Ops.MULTI: return self.arg
# GETTUPLE: axis comes from the specific TUPLE element, not src[0]
if self.op is Ops.GETTUPLE:
in_tuple = self.src[0].src[0] if self.src[0].op is Ops.FUNCTION else self.src[0]
return in_tuple.src[self.arg].axis if in_tuple.op is Ops.TUPLE else None
if self.op is Ops.PARAM: return self.arg.axis
# 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
if len(self.src) == 0: return None
src_axis = self.src[0].axis
if self.op is Ops.SHRINK and src_axis is not None and self.marg[src_axis] != (0, self.src[0].shape[src_axis]):
return None # SHRINK will remove the sharding if it's on axis
if self.op is Ops.REDUCE: 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.marg, operator.mul, initial=1))
# new_axis is the last one that preserves prod(prior to new_axis) and must not move items between shards
new_axis = len(arg_acc) - arg_acc[::-1].index(prod(self.src[0].shape[:src_axis])) - 1
if self.shape[new_axis] % len(self.device) != 0: raise RuntimeError(f"reshape {self.src[0].shape} -> {self.shape} moved items between shards")
return new_axis
if self.op is Ops.PERMUTE: return self.marg.index(src_axis) if src_axis is not None else None
return src_axis
def _unshard(self, axis:int) -> UOp:
bsz, dcount = self.shape[axis], len(self.device)
dnum = UOp.variable("_device_num", 0, dcount-1)
return self.pad(tuple((0,0) if a != axis else (bsz*dnum, bsz*(dcount-1) - bsz*dnum) for a in range(len(self.shape))))
def _shard(self, axis:int, dcount:int) -> UOp:
if len(self.shape) == 0: return self # scalars broadcast, no sharding needed
dnum = UOp.variable("_device_num", 0, dcount-1)
if self.shape[axis] % dcount != 0: raise RuntimeError(f"multi axis uneven: {self.shape[axis]=} {axis=} {dcount=}")
sz = self.shape[axis] // dcount
return self.shrink(tuple((0,s) if i != axis else (dnum*sz,dnum*sz+sz) for i,s in enumerate(self.shape)))
def shard(self, devices:tuple[str, ...], axis:int|None=None) -> UOp:
copied = self.copy_to_device(devices)
return copied if axis is None else copied._shard(axis, len(devices)).multi(axis)
def copy_to_device(self, device:str|tuple[str, ...]|UOp, arg=None):
assert arg is None or isinstance(self.device, tuple)
inp = self if arg is None else UOp(Ops.MSELECT, self.dtype, src=(self,), arg=arg)
return UOp(Ops.COPY, self.dtype, (inp, UOp(Ops.DEVICE, arg=device) if not isinstance(device, UOp) else device))
def mselect(self, arg:int) -> UOp: return UOp(Ops.MSELECT, self.dtype, (self,), arg)
def mstack(self, *srcs: UOp) -> UOp: return UOp(Ops.MSTACK, self.dtype, (self,)+srcs)
@property
def metadata(self) -> tuple[Metadata, ...]|None: return all_metadata.get(self, None)
# *** uop movement ops ***
@property
def base(self) -> UOp:
if self.op in GroupOp.Movement: return self.src[0].base
if self.op is Ops.MULTI: return self.src[0].base # MULTI is really a VIEW
if self.op is Ops.DETACH: return self.src[0].base # DETACH can't change base
return self
@property
def multibase(self) -> UOp:
if self.op in GroupOp.Movement: return self.src[0].base
if self.op is Ops.DETACH: return self.src[0].base # DETACH can't change base
return self
# like gep, but might return an integer
def sgep(self, i:int) -> sint:
match self.op:
case Ops.CONST: return self.arg
case Ops.STACK: return self.src[i].sintify()
case _: raise RuntimeError(f"no sgep on {self.op}")
# cached property here makes external_uop_gc fail, why?
@property
def as_shape(self) -> tuple[sint, ...]:
if self.op is Ops.CONST: return (self.arg,)*self.dtype.count # NOTE: this will break
if self.op is not Ops.STACK: return (ssimplify(self),)
return tuple(ssimplify(self.sgep(i)) for i in range(len(self.src)))
@functools.cached_property
def marg(self):
match self.op:
case Ops.RESHAPE | Ops.EXPAND: return self.src[1].as_shape
case Ops.PAD | Ops.SHRINK: return tuple(zip(self.src[1].as_shape, self.src[2].as_shape))
case Ops.PERMUTE | Ops.FLIP: return self.arg
case _: raise RuntimeError(f"{self.op} is not a MovementOp")
def _mop(self, op:Ops, arg) -> UOp:
# early NOOP
if op in {Ops.SHRINK, Ops.PAD, Ops.EXPAND} and len(arg) == 0:
assert len(self.shape) == 0, "0 len arg only valid on zero length shape"
return self
match op:
case Ops.RESHAPE | Ops.EXPAND: src_args = [arg]
case Ops.PAD | Ops.SHRINK: src_args = list(zip(*arg))
case Ops.PERMUTE | Ops.FLIP: src_args = []
case _: raise RuntimeError(f"{op} is not a MovementOp")
usrcs = [shape_to_shape_arg(arg) for arg in src_args]
if len(usrcs) == 0: return UOp(op, self.dtype, (self,), arg)
return UOp(op, self.dtype, (self,)+UOp.sink(*usrcs).simplify().src)
# *** uop UNIQUE ***
# TODO: use this in Buffer
unique_num = itertools.count(0)
@staticmethod
def unique(arg:int|None=None): return UOp(Ops.UNIQUE, arg=next(UOp.unique_num) if arg is None else arg)
# *** uop Buffer stuff ***
@staticmethod
def new_buffer(device:str|tuple[str, ...], size:int, dtype:DType, num=None):
return UOp(Ops.BUFFER, dtype, (UOp.unique(num), UOp(Ops.DEVICE, arg=device)), size)
@staticmethod
def from_buffer(opaque:Buffer, device:str|tuple[str, ...]|None=None):
if (uop:=UOp.new_buffer(device or opaque.device, opaque.size, opaque.dtype, num=-id(opaque))) not in buffers: buffers[uop] = opaque.ref(1)
else: assert buffers[uop] is opaque
return uop
@staticmethod
def empty(shape:tuple[sint, ...], dtype:DTypeLike|None=None, device:str|tuple[str, ...]|None=None, axis:int|None=None, num=None) -> UOp:
dtype, device = to_dtype(dtype) if dtype is not None else dtypes.default_float, canonicalize_device(device)
max_shape = to_max_shape(shape)
ret = UOp.new_buffer(device, prod(max_shape), dtype, num).reshape(max_shape).shrink_to(shape)
return ret.multi(axis) if isinstance(device, tuple) and axis is not None else ret
def empty_like(self, dtype:DTypeLike|None=None, device:str|tuple[str, ...]|None=None) -> UOp:
device = canonicalize_device(self.device if device is None else device)
axis = self.axis if isinstance(device, tuple) else None
return UOp.empty(self.shard_shape if axis is not None else self.shape, self.dtype if dtype is None else dtype, device, axis)
@staticmethod
def _frompy(x:list|tuple|bytes, dtype:DType, device:str|tuple[str, ...]|None=None) -> UOp:
device = canonicalize_device(device)
if isinstance(x, bytes): ret, data = UOp.new_buffer("PYTHON", len(x)//dtype.itemsize, dtype), x
else:
# bfloat16 and fp8 have no struct format, so pack a float32 buffer and cast
bdtype = dtypes.float32 if dtype in [dtypes.bfloat16, *dtypes.fp8s] else dtype
assert bdtype.fmt is not None, f"{bdtype=} has None fmt"
ret = UOp.empty(shape:=get_shape(x), bdtype, "PYTHON")
data = struct.pack(f"{prod(shape)}{bdtype.fmt}", *[truncate[bdtype](bdtype.const(xi)) for xi in fully_flatten(x)])
# fake realize. if target device is PYTHON it needs bytearray to be writable
ret.buffer.allocate(memoryview(data if device != "PYTHON" else bytearray(data)))
if ret.dtype != dtype: ret = ret.cast(dtype)
return ret if ret.device == device else ret.copy_to_device(device)
def clone(self, device=None) -> UOp:
device = device or self.device
ret = self.empty_like(device=device)
src = self if self.device is None or self.device == device else self.copy_to_device(device)
return ret.after(ret.store(src))
@recursive_property
def device(self) -> str|tuple[str, ...]|None:
if self.op is Ops.PARAM: return self.arg.device
if self.op is Ops.DEVICE: return self.arg
if self.op is Ops.STAGE: return self.arg.device
if self.op is Ops.AFTER: return self.src[0].device
if self.op is Ops.MSELECT:
assert isinstance(self.src[0].device, tuple), f"mselect must be on tuple device, getting {self.src[0].device}"
return self.src[0].device[self.arg]
if self.op is Ops.MSTACK: return tuple(cast(str, x.device) for x in self.src)
if self.op is Ops.BUFFER and isinstance(self.arg, ParamArg): return self.arg.device
if self.op in {Ops.COPY, Ops.BUFFER, Ops.ALLREDUCE}: return self.src[1].device
for x in self.src:
if x.device is not None: return x.device
return None
@recursive_property
def addrspace(self) -> AddrSpace|None:
if self.op is Ops.PARAM: return self.arg.addrspace
if self.op is Ops.BUFFER: return self.arg.addrspace if isinstance(self.arg, ParamArg) else AddrSpace.GLOBAL
if self.op is Ops.DEFINE_LOCAL: return AddrSpace.LOCAL
if self.op is Ops.DEFINE_REG: return AddrSpace.REG
if self.op is Ops.LOAD: return AddrSpace.ALU # LOAD brings things into the ALU
if self.op in {Ops.INDEX, Ops.CAST, Ops.AFTER, Ops.REDUCE, Ops.GEP, Ops.STORE, Ops.MSTACK, Ops.MSELECT}:
return self.src[0].addrspace
if self.op in GroupOp.Movement: return self.src[0].addrspace
if self.op in {Ops.STACK, Ops.WMMA} or self.op in GroupOp.Elementwise:
ad = [x.addrspace for x in self.src if x.addrspace is not None]
if not len(ad) or not all_same(ad): return None
return ad[0]
return None
@property
def buf_uop(self) -> UOp:
if self.op in {Ops.BUFFER, Ops.PARAM}: return self
if self.op is Ops.MSELECT: return self.src[0].buf_uop.mselect(self.arg)
if self.op is Ops.MSTACK: return UOp(Ops.MSTACK, self.dtype, src=tuple(x.buf_uop for x in self.src))
if self.base.op is Ops.AFTER: return self.base.src[0].buf_uop.base
s = self
while len(s.src) and s.op not in {Ops.BUFFER, Ops.PARAM, Ops.STAGE, Ops.MSTACK}: s = s.src[0]
return s
def contiguous_view_offset(self) -> int|None:
"""If movement ops on a BUFFER collapse to a contiguous range, return `offset` in elements. Otherwise None."""
from tinygrad.schedule.rangeify import pm_mops
from tinygrad.uop.symbolic import symbolic
numel = self.numel()
out = graph_rewrite(self.flatten().index(UOp.range(numel, 0)), pm_mops+symbolic, name="contiguous_view_offset")
if out.op is not Ops.INDEX: return None
if out.src[1].op is Ops.CONST and resolve(numel == 1, False):
if not isinstance(out.src[1].arg, int): return None # masked/padded regions produce InvalidType
return out.src[1].arg
if out.src[1].op is Ops.RANGE: return 0
if out.src[1].op is Ops.ADD and out.src[1].src[0].op is Ops.RANGE and out.src[1].src[1].op is Ops.CONST:
if not isinstance(out.src[1].src[1].arg, int): return None # masked/padded regions produce InvalidType
return out.src[1].src[1].arg
return None
def has_buffer_identity(self):
"""Check if this UOp has a concrete buffer identity in the graph (RESHAPE/MULTI -> BUFFER chain)."""
if self.op in {Ops.RESHAPE, Ops.MULTI}: return self.src[0].has_buffer_identity()
if self.op is Ops.GETTUPLE and self.src[0].op is Ops.TUPLE: return self.src[0].src[self.arg].has_buffer_identity()
return self.op in {Ops.BUFFER, Ops.SLICE, Ops.PARAM}
def _base_buffer_is_realized(self) -> bool:
"""Walk through AFTER chain to find if the underlying buffer is realized (has allocated memory)."""
u = self.base
while u.op is Ops.AFTER: u = u.src[0]
return u.is_realized
@property
def buffer(self) -> Buffer|MultiBuffer:
if self.op in {Ops.CONTIGUOUS, Ops.RESHAPE, Ops.DETACH, Ops.AFTER}: return self.src[0].buffer
# this buffer can process disk tensors and simple movement ops
if self is not self.base:
buf = self.base.buffer
assert isinstance(buf, Buffer), "must be a Buffer for movement ops"
offset = self.contiguous_view_offset()
if offset is None: raise RuntimeError(f"non-contiguous view is not supported for {buf.device} buffer")
return buf.view(prod(self.max_shape), self.dtype, offset*self.dtype.itemsize)
if self.op is Ops.BITCAST:
buf = self.src[0].buffer
assert isinstance(buf, Buffer), "must be a Buffer for BITCAST"
return buf.view(prod(self.max_shape), self.dtype, 0)
if self.op is Ops.SLICE:
if (cret:=buffers.get(self)) is not None: return cret
buf = self.src[0].buffer
offset = self.src[1].arg
if isinstance(buf, MultiBuffer):
mbuf = MultiBuffer.__new__(MultiBuffer)
mbuf.bufs = [b.view(self.arg, self.dtype, offset * self.src[0].dtype.itemsize) for b in buf.bufs]
buffers[self] = mbuf
return mbuf
assert isinstance(buf, Buffer), "must be a Buffer for SLICE"
buffers[self] = bv = buf.view(self.arg, self.dtype, offset * self.src[0].dtype.itemsize)
return bv
if self.op is Ops.MSELECT:
ret = self.src[0].buffer
assert isinstance(ret, MultiBuffer)
return ret.bufs[self.arg]
if self.op is Ops.MSTACK:
ret = MultiBuffer.__new__(MultiBuffer)
ret.bufs = [cast(Buffer, x.buffer) for x in self.src]
assert all_same([(x.size, x.dtype) for x in ret.bufs]), "multibuffers mismatch buffers"
return ret
assert self.op is Ops.BUFFER, f"must be BUFFER {self.op}"
assert self.src[0].op is Ops.UNIQUE, f"buffer src[0] must be UNIQUE, not {self.src[0].op}"
if (cret:=buffers.get(self)) is not None: return cret
rdtype = self.dtype if isinstance(self.dtype, ImageDType) else self.dtype.base
if isinstance(self.device, tuple): ret = MultiBuffer(self.device, self.arg, rdtype).ref(1)
else: ret = Buffer(self.device, self.arg, rdtype).ref(1)
buffers[self] = ret
return ret
@property
def realized(self) -> Buffer|MultiBuffer|None:
# only these can be realized
if self.op not in (Ops.BUFFER, Ops.MSTACK): return None
# ParamArg is LOCAL/REG and never realized
if self.op is Ops.BUFFER and isinstance(self.arg, ParamArg): return None
# LUNIQUEs are never realized
if self.op_in_backward_slice_with_self(Ops.LUNIQUE): return None
# NOTE: this is used by the JIT to determine which inputs we capture
return self.buffer if self.buffer.is_allocated() else None
@property
def is_realized(self) -> bool: return self.base.realized is not None
# *** uop Variable stuff ***
@staticmethod
def variable(name:str, min_val:ConstType, max_val:ConstType, dtype:DType=dtypes.weakint) -> UOp:
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) -> str:
if self.op is Ops.PARAM and self.arg.addrspace is None: return unwrap(self.arg.name)
assert self.op is Ops.DEFINE_VAR, f"op is {self.op}, need DEFINE_VAR"
return self.arg[0]
def bind(self, val:int|UOp):
assert self.op is Ops.DEFINE_VAR, f"op is {self.op}, need DEFINE_VAR"
uval = self.const_like(val) if isinstance(val, int) else val
assert self.arg[1] <= uval.vmin and uval.vmax <= self.arg[2], f"bind {val} not in range [{self.arg[1]}, {self.arg[2]}]"
return UOp(Ops.BIND, self.dtype, (self, uval))
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
def unbind_all(self) -> tuple[UOp, dict[Variable, int]]:
ret:dict[Variable, int] = {}
return graph_rewrite(self, pm_unbind, ctx=ret), ret
@property
def val(self) -> int: return self.unbind()[1]
def variables(self) -> list[Variable]:
return sorted({x for x in self.backward_slice_with_self if x.op is Ops.DEFINE_VAR or (x.op is Ops.PARAM and x.arg.addrspace is None)},
key=lambda v: v.expr)
# *** 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.CDIV, Ops.FLOORDIV) 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"""
# TODO: for negatives it's not the largest
if self.op is Ops.CONST: return self.arg
if self.op is Ops.STACK: return math.gcd(*[x.const_factor() for x in self.src])
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.STACK:
srcs = tuple(s.divides(v) for s in self.src)
return None if any(s is None for s in srcs) else UOp(Ops.STACK, self.dtype, cast(tuple[UOp, ...], srcs))
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
def pop_const(self, op=Ops.ADD) -> tuple[UOp, PyConst]: # NOTE: assume Invalid ALU is resolved
return (self.src[0], self.src[1].arg) if self.op is op and self.src[1].op is Ops.CONST else (self, identity_element(op, self.dtype))
@staticmethod
def gcd(*uops: UOp) -> UOp:
terms, factors = zip(*[(u.divides(f:=u.const_factor()),f) for u in uops])
count = functools.reduce(operator.and_, [collections.Counter(term.split_uop(Ops.MUL)) for term in terms])
return math.prod([*count.elements(), terms[0].const_like(math.gcd(*factors))]) # put the const at the top
def divide_exact(self, v:UOp) -> UOp|None:
if self is v: return self.const_like(1)
if v.op is Ops.CONST: return self.divides(v.arg)
if self.op is Ops.ADD: return None if (s0:=self.src[0].divide_exact(v)) is None or (s1:=self.src[1].divide_exact(v)) is None else s0+s1
if self.op is Ops.MUL:
(fac, const), (div_fac, div_const) = self.pop_const(Ops.MUL), v.pop_const(Ops.MUL)
new_count = collections.Counter(fac.split_uop(Ops.MUL))
new_count.subtract(div_fac.split_uop(Ops.MUL))
if const%div_const==0 and all(v>=0 for v in new_count.values()): return math.prod(new_count.elements(), start=self.const_like(const//div_const))
return None # generic None if we aren't sure
@property
def vmin(self) -> PyConst: return self._min_max[0]
@property
def vmax(self) -> PyConst: return self._min_max[1]
@functools.cached_property
def _min_max(self) -> tuple[PyConst, PyConst]:
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 dtypes.is_int(self.dtype) and s1_vmin == s1_vmax >= 0:
return 0, s1_vmax if s0_vmin < 0 else 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.CMOD:
if (c:=s1_vmin) == s1_vmax > 0:
return (0 if s0_vmin > 0 else s0_vmin if 0 >= s0_vmin > -c else -(s1_vmax-1), 0 if s0_vmax < 0 else s0_vmax if 0 <= s0_vmax < c else c-1)
if s1_vmin > 0: return (0, s1_vmax-1) if s0_vmin >= 0 else (-(s1_vmax-1), 0) if s0_vmax <= 0 else (-(s1_vmax-1), s1_vmax-1)
if s1_vmax < 0: return (0, -s1_vmin-1) if s0_vmin >= 0 else (-(-s1_vmin-1), 0) if s0_vmax <= 0 else (-(-s1_vmin-1), -s1_vmin-1)
if self.op is Ops.CDIV:
assert isinstance(s0_vmin, int) and isinstance(s0_vmax, int) and isinstance(s1_vmin, int) and isinstance(s1_vmax, int)
if s1_vmin*s1_vmax>0:
return min(vals:=(cdiv(s0_vmin, s1_vmin), cdiv(s0_vmin, s1_vmax), cdiv(s0_vmax, s1_vmin), cdiv(s0_vmax, s1_vmax))), max(vals)
if self.op is Ops.FLOORDIV:
assert isinstance(s0_vmin, int) and isinstance(s0_vmax, int) and isinstance(s1_vmin, int) and isinstance(s1_vmax, int)
if s0_vmin > s0_vmax: return 0, 0 # numerator range is empty (e.g. RANGE with end=0)
if s1_vmin*s1_vmax>0: return min(vals:=(s0_vmin//s1_vmin, s0_vmin//s1_vmax, s0_vmax//s1_vmin, s0_vmax//s1_vmax)), max(vals)
if self.op is Ops.FLOORMOD:
assert isinstance(s0_vmin, int) and isinstance(s0_vmax, int) and isinstance(s1_vmin, int) and isinstance(s1_vmax, int)
if s0_vmin > s0_vmax: return 0, 0 # numerator range is empty (e.g. RANGE with end=0)
if (c:=s1_vmin) == s1_vmax > 0: return (s0_vmin%c, s0_vmax%c) if s0_vmin//c == s0_vmax//c else (0, c-1)
if (c:=s1_vmin) == s1_vmax < 0: return (s0_vmin%c, s0_vmax%c) if s0_vmin//c == s0_vmax//c else (c+1, 0)
if s1_vmin > 0: return (0, s1_vmax-1)
if s1_vmax < 0: return (s1_vmin+1, 0)
if self.op is Ops.XOR and s1_vmin == s1_vmax == -1 and isinstance(s0_vmin, int) and isinstance(s0_vmax, int):
return ~int(s0_vmax), ~int(s0_vmin)
if self.op is Ops.MAX: return max(s0_vmin, s1_vmin), max(s0_vmax, s1_vmax)
if self.op is Ops.CMPLT: return (s0_vmax<s1_vmin, s0_vmin<s1_vmax)
if self.op is Ops.CMPNE: return ((s0_vmax < s1_vmin) or (s1_vmax < s0_vmin), not (s0_vmin == s0_vmax == s1_vmin == s1_vmax))
if self.op is Ops.OR and self.dtype == dtypes.bool: return s0_vmin or s1_vmin, s0_vmax or s1_vmax
if self.op is Ops.AND and self.dtype == dtypes.bool: return s0_vmin and s1_vmin, s0_vmax and s1_vmax
# float has NAN issue and we use explicit NAN in transcendental
if self.op is Ops.WHERE and dtypes.is_int(self.dtype): return min(self.src[1].vmin, self.src[2].vmin), max(self.src[1].vmax, self.src[2].vmax)
# NOTE: returned UOp is assumed to be CONST
if self.op is Ops.PARAM and self.arg.vmin_vmax is not None: return self.arg.vmin_vmax
if self.op is Ops.DEFINE_VAR and self.arg: return self.arg[1], self.arg[2]
if self.op in (Ops.RANGE, Ops.SPECIAL): return 0, (self.src[0]-1).vmax
if self.op is Ops.BIND: return self.src[0]._min_max # ignore the bound value
if self.op in {Ops.UNROLL, Ops.STACK}: return min(x.vmin for x in self.src), max(x.vmax for x in self.src)
if self.op is Ops.CONST and self.arg is not Invalid: return self.arg, self.arg
if self.op is Ops.GEP: return self.src[0]._min_max
# TODO: CAST to bool/unsigned is not monotone, still some case can be simplified
if self.op is Ops.CAST and self.dtype in dtypes.floats+dtypes.sints+(dtypes.weakint,):
return max(self.dtype.min, self.src[0].vmin), min(self.src[0].vmax, self.dtype.max)
return self.dtype.min, self.dtype.max
@functools.cached_property
def _sym_fxn(self):
from tinygrad.uop.render import _render_with_splits, renderer_infer
sself = self.simplify()
varnames = tuple(dedup(x.expr for x in sself.toposort() if x.op is Ops.DEFINE_VAR or (x.op is Ops.PARAM and x.arg.addrspace is None)))
# TODO: sanitize varnames, or don't use naked eval while staying fast
ret = _render_with_splits(list(sself.toposort()), renderer_infer, {sself})
lines = [f" {k}={v}" for k,v in ret.items() if k != "ast"] + [f" return {ret['ast']}"]
ns: dict[str, Any] = {"max": max, "cdiv": cdiv, "cmod": cmod, "floordiv": floordiv, "floormod": floormod, "bitcast": bitcast, "dtypes": dtypes}
exec(f"def _f({','.join(varnames)}):\n"+'\n'.join(lines), ns) # pylint: disable=exec-used
return ns["_f"], varnames
def sym_infer(self, var_vals:dict[str, int]):
fxn, varnames = self._sym_fxn
return fxn(**{k:v for k,v in var_vals.items() if k in varnames})
def render(self, simplify=True, pm:PatternMatcher|None=None) -> str:
ctx: dict[UOp, str] = {}
from tinygrad.uop.render import renderer
pm = renderer if pm is None else pm
for u in (s:=self.simplify() if simplify else self).toposort():
ctx[u] = cast(str, pm.rewrite(u, ctx=ctx))
return ctx[s]
def pyrender(self):
from tinygrad.uop.render import pyrender
return pyrender(self)
# *** uop high level syntactic sugar ***
@staticmethod
def placeholder(shape:tuple[int, ...], dtype:DType, slot:int, addrspace=AddrSpace.GLOBAL):
lookup = {AddrSpace.GLOBAL: Ops.PARAM, AddrSpace.LOCAL: Ops.DEFINE_LOCAL, AddrSpace.REG: Ops.DEFINE_REG}
arg = ParamArg(slot, addrspace=addrspace) if addrspace is AddrSpace.GLOBAL else slot
ret = UOp(lookup[addrspace], dtype.ptr(prod(shape), addrspace), arg=arg)
if len(shape) > 1: ret = ret.reshape(shape)
return ret
def placeholder_like(self, slot:int):
assert all_int(self.shape), "no placeholder-like on symbolic shape"
return UOp.placeholder(self.max_shard_shape, self.dtype, slot)
# set is store+end+after
def set(self:UOp, val:UOp|ConstType, end:UOp|tuple[UOp, ...]|list[UOp]=()) -> UOp:
return self.src[0].after(self.store(val).end(*argfix(end)))
# TODO: this should replace placeholder
@staticmethod
def param(slot:int, dtype:DType, shape:tuple[sint, ...]|None=None, device=None, vmin_vmax:tuple[PyConst, PyConst]|None=None, name=None,
addrspace=AddrSpace.GLOBAL, axis:int|None=None):
if shape is not None and axis is not None and isinstance(device, tuple):
shape = tuple(s*len(device) if i == axis else s for i,s in enumerate(shape))
src: tuple[UOp, ...] = (UOp(Ops.NOOP) if shape is None else shape_to_shape_arg(shape),)
return UOp(Ops.PARAM, dtype, src, arg=ParamArg(slot, vmin_vmax, name, addrspace, axis, device))
def param_like(self, slot:int):
addrspace = self.addrspace if isinstance(self.dtype, (PtrDType, ImageDType)) else AddrSpace.GLOBAL
if self.op is Ops.BIND:
return UOp.param(slot, self.dtype, self._shape, self.device, cast(tuple[int, int], self._min_max), self.src[0].arg[0], addrspace)
return UOp.param(slot, self.dtype, self.shard_shape if self.axis is not None else self._shape, self.device, addrspace=addrspace, axis=self.axis)
# opaque bodies stay as Ops.CALL; value-producing bodies become Ops.FUNCTION (wrapped in TUPLE)
_OPAQUE_CALL_BODIES = {Ops.SINK, Ops.PROGRAM, Ops.LINEAR, Ops.COPY, Ops.SLICE, Ops.CUSTOM_FUNCTION}
def call(self, *srcs:UOp, grad_fxn:Callable|None=None, metadata:tuple[Metadata, ...]=(),
name:str|None=None, precompile:bool=False, precompile_backward:bool=False, aux:Any=None) -> UOp:
assert len(self.ranges) == 0, f"ranges {self.ranges} are leaking out of the call in {self.pyrender()}"
if self.op in UOp._OPAQUE_CALL_BODIES:
return UOp(Ops.CALL, dtypes.void, (self,)+srcs, CallInfo(grad_fxn, metadata, name, precompile, precompile_backward, aux))
# value-producing bodies are always wrapped in TUPLE so FUNCTION dtype is always void
body = self if self.op is Ops.TUPLE else UOp.maketuple(self)
return UOp(Ops.FUNCTION, dtypes.void, (body,)+srcs, CallInfo(grad_fxn, metadata, name, precompile, precompile_backward, aux))
def custom_kernel(*srcs:UOp, fxn:Callable, grad_fxn:Callable|None=None) -> list[UOp]:
contig_srcs = tuple(x.contiguous() if x.op is not Ops.AFTER else x for x in srcs)
placeholders = [UOp.placeholder_like(s, slot=i) for i,s in enumerate(contig_srcs)]
kernel = fxn(*placeholders).call(*contig_srcs, grad_fxn=grad_fxn)
return [s.after(kernel) for s in contig_srcs]
@dataclass(frozen=True)
class KernelInfo:
name: str = "test" # name of the kernel
axis_types: tuple[AxisType, ...] = tuple()
dont_use_locals: bool = False # don't use local indexing
applied_opts: tuple = tuple()
opts_to_apply: tuple|None = None
estimates: Estimates|None = None
beam: int = 0
@property
def function_name(self): return to_function_name(self.name)
@dataclass(frozen=True)
class ProgramInfo:
name: str = "test"
global_size: tuple[int|float, ...] = (1, 1, 1)
local_size: tuple[int, ...]|None = None
vars: tuple[UOp, ...] = ()
globals: tuple[int, ...] = ()
outs: tuple[int, ...] = ()
ins: tuple[int, ...] = ()
aux: tuple = ()
@property
def function_name(self): return to_function_name(self.name)
@property
def runtimevars(self) -> dict[str, int]: return {v.expr: i for i, v in enumerate(self.vars) if v.expr == 'core_id'}
def launch_dims(self, var_vals:dict[str, int]) -> tuple[tuple[int, ...], tuple[int, ...]|None]:
global_size = tuple([sym_infer(sz, var_vals) for sz in self.global_size]) # type: ignore[arg-type]
local_size = tuple([sym_infer(sz, var_vals) for sz in self.local_size]) if self.local_size is not None else None
return global_size, local_size
def vals(self, var_vals:dict[str, int]): return tuple(var_vals[k.expr] if k.expr not in self.runtimevars else None for k in self.vars)
@staticmethod
def from_sink(sink:UOp, aux:tuple=()) -> ProgramInfo:
_vars: list[UOp] = []
_globals: list[int] = []
outs: list[int] = []
ins: list[int] = []
global_size: list[int] = [1, 1, 1]
local_size: list[int]|None = [1, 1, 1]
for u in sink.toposort():
if u.op is Ops.DEFINE_VAR or (u.op is Ops.PARAM and u.addrspace is None): _vars.append(u)
if u.op is Ops.PARAM and u.addrspace is not None: _globals.append(u.arg.slot)
if u.op in (Ops.STORE, Ops.LOAD):
if (idx:=u.src[0]).op in (Ops.INDEX, Ops.SHRINK) or (u.src[0].op is Ops.CAST and (idx:=u.src[0].src[0]).op is Ops.INDEX):
if (buf:=idx.src[0].buf_uop).op is Ops.PARAM: (outs if u.op is Ops.STORE else ins).append(buf.arg.slot)
if u.op is Ops.SPECIAL:
if u.arg[0] == 'i': local_size = None
special_size = local_size if u.arg[0] == 'l' else global_size
if special_size is not None: special_size[int(u.arg[-1])] = cast(int, u.src[0].ssimplify())
if u.op in (Ops.DEFINE_VAR, Ops.PARAM) and u in _vars and u.expr == 'core_id': global_size[0] = int(u.vmax) + 1
return ProgramInfo(sink.arg.name if isinstance(sink.arg, KernelInfo) else "test", tuple(global_size),
tuple(local_size) if local_size is not None else None, tuple(sorted(_vars, key=lambda v: v.expr)),
tuple(sorted(dedup(_globals))), tuple(sorted(dedup(outs))), tuple(sorted(dedup(ins))), aux)
@dataclass(frozen=True)
class CallInfo:
grad_fxn: Callable|None = None
metadata: tuple[Metadata, ...] = ()
name: str|None = None
precompile: bool = False
precompile_backward: bool = False
aux: Any = None
# grad_fxn can't be pickled, but metadata can
def __reduce__(self): return (CallInfo, (None, self.metadata, self.name, self.precompile, self.precompile_backward, self.aux))
def __repr__(self):
gf = id(self.grad_fxn) if self.grad_fxn else None
return f"CallInfo({gf}, {self.metadata}, {repr(self.name)}, {self.precompile}, {self.precompile_backward})"
# ******** 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.RECIPROCAL: 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.TRUNC: math.trunc,
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.CMOD: cmod, Ops.CDIV: cdiv, Ops.FLOORDIV: floordiv, Ops.FLOORMOD: floormod,
Ops.MULACC: lambda x,y,z: (x*y)+z, Ops.WHERE: lambda x,y,z: y if x else z, Ops.CMPEQ: operator.eq}
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)])
if dtype==dtypes.weakint and op in GroupOp.Binary and Invalid in operands: return Invalid
alu = python_alu[op](*operands)
return truncate.get(dtype, lambda x: x)(alu) if truncate_output else alu
def bitcast(x, in_dtype:DType, out_dtype:DType):
assert in_dtype.itemsize == out_dtype.itemsize, "bitcast itemsize mismatch"
in_count, out_count = in_dtype.count, out_dtype.count
in_vals = (x,) if in_count == 1 else tuple(x)
assert len(in_vals) == in_count, f"bitcast expected {in_count} values, got {len(in_vals)}"
packed = struct.pack(f"{in_count}{storage_fmt_for_dtype(in_dtype.scalar())}", *[to_storage_scalar(v, in_dtype.scalar()) for v in in_vals])
out_vals = struct.unpack(f"{out_count}{storage_fmt_for_dtype(out_dtype.scalar())}", packed)
ret = tuple(from_storage_scalar(v, out_dtype.scalar()) for v in out_vals)
return ret[0] if out_count == 1 else ret
# ***** pattern matcher *****
def get_location() -> tuple[str, int]:
frm = sys._getframe(1)
# skip over ops.py and anything in mixin
while frm.f_back is not None and not frm.f_back.f_code.co_filename.startswith("<frozen"):
fn = frm.f_code.co_filename.replace("\\", "/")
if not (fn.endswith("/ops.py") or "/mixin/" in fn): break
frm = frm.f_back
return frm.f_code.co_filename, frm.f_lineno
class UPat(OpMixin):
__slots__ = ("op", "match_dtype", "match_tag", "arg", "name", "src", "is_any")
def __init__(self, op:Ops|tuple[Ops, ...]|set[Ops]|None=None, dtype:DType|tuple[DType, ...]|set[DType]|None=None,
src:tuple[UPat, ...]|list[UPat]|UPat|None=None, arg:Any=None,
name:str|None=None, allow_any_len:bool=False, custom_early_reject:set[Ops]|None=None, location=None, is_any:bool=False,
tag:Any=None):
assert op is None or isinstance(op, (Ops, tuple, set)), f"op must be Ops or tuple of Ops, not {op!r}"
self.op: tuple[Ops, ...]|None = (op,) if isinstance(op, Ops) else (tuple(op) if isinstance(op, set) else op)
self.match_dtype: tuple[DType, ...]|None = (dtype,) if isinstance(dtype, DType) else (tuple(dtype) if isinstance(dtype, set) else dtype)
self.match_tag: tuple[Any, ...]|None = (tag,) if isinstance(tag, str) else (tuple(tag) if isinstance(tag, set) else tag)
self.arg, self.name, self._in_src, self.custom_early_reject = arg, name, src, custom_early_reject
self.src: Any = None
self.is_any = is_any
assert self.name != "ctx", "UPat can't be named ctx"
assert dtype is None or isinstance(dtype, DType) or all(isinstance(x, DType) for x in dtype), f"invalid dtype {dtype}"
# try all permutations if it's a list
if isinstance(src, list): self.src = list(itertools.permutations(src)) if not all_same(src) else [tuple(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.strict_length = not (allow_any_len or isinstance(src, UPat) or src is None)
self.required_len: int = 0 if 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}
@property
def dtype(self) -> DType: return self.match_dtype[0] if self.match_dtype is not None else dtypes.void
def _check_dtype(self) -> None: pass
def _ensure_float(self) -> UPat: return self
def __reduce__(self):
return UPat, (self.op, self.match_dtype, self._in_src, self.arg, self.name, not self.strict_length, self.custom_early_reject, self.location,
self.is_any, self.match_tag)
def named(self, name:str):
return UPat(self.op, self.match_dtype, self._in_src, self.arg, name, not self.strict_length, self.custom_early_reject, tag=self.match_tag)
@staticmethod
def any(*src): return UPat(src=src, is_any=True)
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,)))
def or_after(self, name:str|None=None):
return UPat.any(self if name is None else self.named(name), UPat(Ops.AFTER, name=name, src=(self,), allow_any_len=True))
@staticmethod
@functools.cache
def var(name:str|None=None, dtype:DType|tuple[DType, ...]|None=None): return UPat(dtype=dtype, name=name)
@staticmethod
@functools.cache
def cvar(name:str|None=None, dtype:DType|tuple[DType, ...]|None=None, arg=None): return UPat(Ops.CONST, dtype, name=name, arg=arg)
@staticmethod
def const(dtype:DType|tuple[DType, ...]|None, b:ConstType): return UPat(Ops.CONST, dtype=dtype, arg=b)
# lil helper
def f(self, op, **kwargs): return UPat(op, src=(self,), **kwargs)
# copied from UOp
def index(self, *srcs:UPat|None, **kwargs):
return UPat(Ops.INDEX, src=(self,)+tuple(x for x in srcs if x is not None), **kwargs)
def cast(self, dtype=None, **kwargs):
if dtype is not None and self.match_dtype == (dtype,): return self
return UPat(Ops.CAST, dtype, (self,), **kwargs)
def bitcast(self, dtype=None): return UPat(Ops.BITCAST, dtype, (self,))
def gep(self, i:int|None=None, **kwargs): return UPat(Ops.GEP, None, (self,), (i,) if i is not None else None, **kwargs)
def load(self, *src:UPat, **kwargs): return UPat(Ops.LOAD, src=(self,)+src, **kwargs)
def store(self, *src:UPat, **kwargs): return UPat(Ops.STORE, src=(self,)+src, **kwargs)
def reduce(self, *src:UPat, **kwargs):
arg = kwargs.pop('arg', None)
if isinstance(arg, Ops): arg = (arg, ())
return UPat(Ops.REDUCE, self.match_dtype, src=(self,)+src, arg=arg, **kwargs)
def broadcast(self, **kwargs): return UPat(Ops.STACK, self.match_dtype, src=self, **kwargs)
def after(self, *src:UPat, **kwargs): return UPat(Ops.AFTER, self.match_dtype, (self,)+src, **kwargs)
def end(self, *src:UPat, **kwargs): return UPat(Ops.END, src=(self,)+src, **kwargs)
def const_like(self, b:ConstLike): return UPat.const(self.match_dtype, cast(ConstType, b))
def _broadcasted(self, y, reverse=False) -> tuple[UPat, UPat]:
y = self.ufix(y)
return (y, self) if reverse else (self, y)
def ufix(self, x): return self.const_like(x) if not isinstance(x, UPat) else x
def __floordiv__(self, x): return self._binop(Ops.FLOORDIV, x, False)
def __rfloordiv__(self, x): return self._binop(Ops.FLOORDIV, x, True)
def mod(self, x, reverse=False): return self._binop(Ops.FLOORMOD, x, reverse)
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].match_dtype, list(asrc) if op in GroupOp.Commutative else asrc)
def match(self:UPat, uop:UOp, store:dict[str, UOp]) -> list[dict[str, UOp]]:
if self.is_any: return flatten([x.match(uop, store.copy()) for x in self.src[0]])
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.match_dtype is not None and uop.dtype not in self.match_dtype and uop.dtype.scalar() not in self.match_dtype) or \
(self.arg is not None and self.arg != uop.arg) or \
(self.match_tag is not None and uop.tag not in self.match_tag) or \
(len(uop.src) < self.required_len) or \
(self.strict_length and len(uop.src) != self.required_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
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
@functools.cache
def upat_interpret(p:UPat, fxn:Callable) -> Callable:
real_fxn = types.FunctionType(*deconstruct_function(fxn))
if 'ctx' in inspect.signature(real_fxn).parameters:
def universal_match(uop, ctx):
for match in p.match(uop, {}):
if (ret:=real_fxn(ctx=ctx, **match)) is not None: return ret # pylint: disable=not-callable
return None
else:
def universal_match(uop, _):
for match in p.match(uop, {}):
if (ret:=real_fxn(**match)) is not None: return ret # pylint: disable=not-callable
return None
return universal_match
def upat_deferred_compile(p:UPat, fxn:Callable, entry:list) -> Callable:
def lazy_compile(uop, ctx):
from tinygrad.uop.upat import upat_compile
entry[1] = upat_compile(p, fxn) or upat_interpret(p, fxn)
return entry[1](uop, ctx)
return lazy_compile
class PatternMatcher:
def __init__(self, patterns:Sequence[tuple[UPat, Callable|tuple]], compiled=bool(getenv("UPAT_COMPILE", 1))):
# if this comes from a pickle, we reconstruct the lambda functions here
self.patterns:list[tuple[UPat, Callable]] = [(p,types.FunctionType(*fxn) if isinstance(fxn, tuple) else fxn) for p,fxn in patterns]
# NOTE: use of DefaultDict here is very dangerous! all keys will live for the lifetime of the PatternMatcher!
self.pdict: dict[Ops, list[list]] = {}
# uop is required, arg is optional
for p,fxn in self.patterns:
assert p.op is not None
entry: list = [p, None, p.early_reject]
entry[1] = upat_deferred_compile(p, fxn, entry) if compiled else upat_interpret(p, fxn)
for uop in p.op: self.pdict.setdefault(uop, []).append(entry)
def __reduce__(self): return PatternMatcher, ([(x,deconstruct_function(fxn) if fxn.__name__ == "<lambda>" else fxn) for x,fxn in self.patterns],)
@functools.cache # pylint: disable=method-cache-max-size-none
def __add__(self, more:PatternMatcher) -> PatternMatcher: return PatternMatcher(self.patterns+more.patterns)
def rewrite(self, uop:UOp, ctx=None):
if len(pats:=self.pdict.get(uop.op, [])):
if (ler:=uop.__dict__.get('_src_ops')) is None: uop.__dict__['_src_ops'] = ler = {u.op for u in uop.src}
for _,match,early_reject in pats:
if not early_reject.issubset(ler): continue
if (ret:=match(uop, ctx)) is not None and ret is not uop: return ret
return None
# *** tracking pattern matcher ***
TRACK_MATCH_STATS = ContextVar("TRACK_MATCH_STATS", 2 if VIZ else 0)
REWRITE_STACK_LIMIT = ContextVar("REWRITE_STACK_LIMIT", 250000)
match_stats:dict[UPat, list[int|float]] = dict()
# TRACK_MATCH_STATS>=2 or VIZ=1 saves all matches
ucount = itertools.count()
uop_fields:dict[int, tuple] = {}
@dataclass(frozen=True)
class TrackedGraphRewrite:
loc:tuple[str, int] # location that called graph_rewrite
sink:int # the sink input to graph_rewrite
matches:list[tuple[int, int, tuple, float]] # before/after UOp, UPat location and time
name:str # name of the rewrite
depth:int # depth if it's a subrewrite
bottom_up:bool
tracked_keys:list[TracingKey] = []
tracked_ctxs:list[list[TrackedGraphRewrite]] = []
_name_cnt:dict[str, itertools.count] = {}
if CAPTURE_PROCESS_REPLAY:
replay_capture: list[bytes] = []
import atexit, uuid
@atexit.register
def save_to_diskcache():
uid = uuid.uuid4() # one id per process
for i,v in enumerate(replay_capture): diskcache_put("process_replay", f"{uid}_{i}", v, prepickled=True)
def add_trace_group(kt:TracingKey) -> None:
tracked_keys.append(kt)
tracked_ctxs.append([])
active_group:list[int] = []
def track_rewrites(name:Callable[..., str|TracingKey]|bool=True, replay:bool=False):
def _decorator(func):
def __wrapper(*args, **kwargs):
fn = key = func.__name__
idx = -1
if TRACK_MATCH_STATS >= 2:
add_trace_group(key:=TracingKey(n:=f"{fn} n{next(_name_cnt.setdefault(fn, itertools.count(1)))}", (n,)))
active_group.append(idx:=len(tracked_keys)-1)
with cpu_profile(key, "TINY") as e:
ret = func(*args, **kwargs)
if TRACK_MATCH_STATS >= 2: active_group.pop()
if TRACK_MATCH_STATS >= 2 and callable(name):
name_ret = name(*args, **kwargs, ret=ret)
assert isinstance(name_ret, (TracingKey, str)), f"name function returned {type(name_ret)}"
tracked_keys[idx] = k = TracingKey(n:=tracked_keys[idx].display_name.replace(fn, name_ret), (n,)) if isinstance(name_ret, str) else name_ret
e.name = TracingKey(k.display_name if isinstance(name_ret, str) else f"{fn} for {k.display_name}", k.keys)
if CAPTURE_PROCESS_REPLAY and replay:
# find the unittest frame we're capturing in
frm = sys._getframe(1)
while (f_back:=frm.f_back) is not None and "unittest" not in f_back.f_code.co_filename: frm = f_back
loc = f"{frm.f_code.co_filename.split('/')[-1]}:{frm.f_lineno} {frm.f_code.co_name}"
# capture global context vars and all the args passed in
inputs = (fn, args, kwargs, ContextVar._cache)
replay_capture.append(pickle.dumps(inputs+(loc, ret)))
return ret
return __wrapper
return _decorator
active_rewrites:list[TrackedGraphRewrite] = []
def profile_matches(fxn:Callable):
def wrap_profile_matches(*args, **kwargs):
if TRACK_MATCH_STATS >= 2:
name = str(kwargs.get("name", None) or fxn.__name__)
assert args and isinstance(args[0], UOp), f"invalid match tracing inputs for {name} with {args}"
loc = ((frm:=sys._getframe(1)).f_code.co_filename, frm.f_lineno)
depth = len(active_rewrites)
if not tracked_ctxs: add_trace_group(TracingKey(f"default {fxn.__name__}"))
dest_group = active_group[-1] if active_group else len(tracked_ctxs)-1
tracked_ctxs[dest_group].append(ctx:=TrackedGraphRewrite(loc, args[0].trace_num, [], name, depth, kwargs.get("bottom_up", False)))
active_rewrites.append(ctx)
with cpu_profile(name, "TINY"):
ret = fxn(*args, **kwargs)
active_rewrites.pop()
return ret
# without tracking, we just call the function
return fxn(*args, **kwargs)
return wrap_profile_matches
class TrackedPatternMatcher(PatternMatcher):
def rewrite(self, uop:UOp, ctx=None):
if len(pats:=self.pdict.get(uop.op, [])):
ret = None
ler = {u.op for u in uop.src}
for p,match,early_reject in pats:
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
try: ret = match(uop, ctx)
except Exception:
if TRACK_MATCH_STATS >= 2 and active_rewrites:
active_rewrites[-1].matches.append((uop.trace_num, UOp(Ops.REWRITE_ERROR,src=uop.src,arg=str(sys.exc_info()[1])).trace_num,p.location,0))
raise
if ret is not None and ret is not uop:
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 -- ", printable(p.location))
if TRACK_MATCH_STATS >= 2 and isinstance(ret, UOp) and active_rewrites:
active_rewrites[-1].matches.append((uop.trace_num, ret.trace_num, p.location, et))
return ret
match_stats[p][2] += time.perf_counter()-st
return None
@dataclass(frozen=True)
class RewriteTrace: keys:list[TracingKey]; rewrites:list[list[TrackedGraphRewrite]]; uop_fields:dict[int, tuple] # noqa: E702
if TRACK_MATCH_STATS or PROFILE:
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}")
pickle.dump(RewriteTrace(tracked_keys, tracked_ctxs, uop_fields), f)
TRACK_MATCH_STATS.value = 0
launch_viz("REWRITE_DATA", temp("rewrites.pkl", append_user=True))
if getenv("PRINT_MATCH_STATS", TRACK_MATCH_STATS.value and not VIZ):
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:20s}", printable(k.location))
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")
print(f"{len(match_stats)} rules, {sum(v[0] > 0 for v in match_stats.values())} matched once")
def launch_viz(env_str:str, data:str):
os.environ[f"{env_str}_DATA"] = data
if not TRACK_MATCH_STATS and not PROFILE:
os.environ["VIZ"], os.environ["PROFILE"], os.environ["TRACK_MATCH_STATS"] = "0", "0", "0"
args = ['--rewrites-path', os.getenv("REWRITE_DATA", "")] if os.getenv("REWRITE_DATA", "") else []
args += ['--profile-path', os.getenv("PROFILE_DATA", "")] if os.getenv("PROFILE_DATA", "") else []
viz_path = pathlib.Path(__file__).resolve().parent.parent / "viz" / "serve.py"
if VIZ > 0 and sys.stdout.isatty(): os.execv(sys.executable, [sys.executable, viz_path.as_posix()] + args)
if VIZ: print("saved viz files, view using: python -m tinygrad.viz.cli")
VIZ.value = 0
# *** simple graph rewrite engine ***
# A pure Python sentinel, but *typed* as UOp so it fits all the dict annotations
SENTINEL: Final[UOp] = cast(UOp, object())
class BottomUpGate(Exception): pass
class RewriteContext:
def __init__(self, pm, bpm, ctx=None, enter_calls=False):
self.pm: PatternMatcher|None = pm
self.bpm: PatternMatcher|None = bpm
self.bpm_cache: dict[UOp, UOp|None] = {}
self.ctx = ctx
self.replace: dict[UOp, UOp] = {}
self.enter_calls = enter_calls
# no cache needed: pm_rewrite is called at most once per UOp due to the replace dict check in unified_rewrite
def pm_rewrite(self, x:UOp) -> UOp|None: return unwrap(self.pm).rewrite(x, self.ctx)
def cached_bpm_rewrite(self, x:UOp) -> UOp|None:
if (ret:=self.bpm_cache.get(x,SENTINEL)) is not SENTINEL: return ret
ret = self.bpm_cache[x] = unwrap(self.bpm).rewrite(x, self.ctx)
return ret
def walk_rewrite(self, root:UOp) -> UOp:
"""MLIR-style Walk Pattern Rewrite Driver: single-pass, no re-traversal into rewritten subtrees."""
stack: list[tuple[UOp, bool]] = [(root, False)]
while stack:
n, processed = stack.pop()
if n in self.replace: continue
if not processed:
# bottom-up: try bpm on original node first, if it rewrites, use result as-is (no traversal into replacement)
if self.bpm is not None and (rewritten:=self.cached_bpm_rewrite(n)) is not None:
self.replace[n] = rewritten
continue
# no rewrite, process children then come back to rebuild
stack.append((n, True))
if not self.enter_calls and n.op in {Ops.CALL, Ops.FUNCTION}: self.replace[n.src[0]] = n.src[0]
for x in reversed(n.src):
if x not in self.replace: stack.append((x, False))
else:
# rebuild node with rewritten srcs
new_src = tuple(self.replace.get(x, x) for x in n.src)
new_n = UOp(n.op, n.dtype, new_src, n.arg, n.tag) if new_src != n.src else n
# top-down: try pm on rebuilt node, use result as-is (no re-traversal)
if self.pm is not None and (rewritten:=self.pm_rewrite(new_n)) is not None: new_n = rewritten
self.replace[n] = new_n
return self.replace.get(root, root)
def unified_rewrite(self, root:UOp) -> UOp:
stack: collections.deque[tuple[UOp, int, UOp]] = collections.deque([(root, 0, root)])
on_stack = {root} # all UOps either on the stack or in self.replace, i.e. dont have to be placed again
waitlist: dict[UOp, list[tuple[UOp, int, UOp]]] = {} # UOps waiting on a dependency to be in self.replace
while stack:
if len(stack) > REWRITE_STACK_LIMIT: raise RuntimeError("infinite loop in graph_rewrite (stack too big)")
n, stage, new_n = stack.pop()
if n in self.replace: continue # skip any nodes we have seen
if stage == 0:
# if bottom up, we rewrite this node early. in both cases, we add its srcs to the stack
if self.bpm is not None:
# apply rewrite rules until a fixed point is reached. may return `uop` itself if PatternMatcher doesn't match
test_n: UOp|None = n
seen = set()
try:
while test_n is not None:
if test_n in seen: raise RuntimeError("infinite loop in fixed_point_rewrite")
seen.add(test_n)
new_n, test_n = test_n, self.cached_bpm_rewrite(test_n)
except BottomUpGate:
# if the bpm matching raised a gate, we are done with this node and dont continue down the srcs
self.replace[n] = unwrap(test_n)
if n in waitlist: stack.extend(waitlist.pop(n))
continue
stack.append((n, 1, new_n))
# NOTE: CALL/FUNCTION are handled as a special case.
# The function that is called is not included in the graph_rewrite.
# If you want to graph_rewrite a call, you can
if not self.enter_calls and new_n.op in {Ops.CALL, Ops.FUNCTION}: self.replace[new_n.src[0]] = new_n.src[0]
for x in reversed(new_n.src):
if x in on_stack: continue
stack.append((x, 0, x))
on_stack.add(x)
elif stage == 1:
tmp = []
for x in new_n.src:
if (rx:=self.replace.get(x, SENTINEL)) is SENTINEL:
# source not ready: register in waitlist instead of spinning
waitlist.setdefault(x, []).append((n, 1, new_n))
break
tmp.append(rx)
else:
# in stage 1, once all srcs are rewritten, rebuild (if changed) or run top-down rewrite
if (new_src:=tuple(tmp)) == new_n.src:
# if top down, do the rewrite. if no rewrite or bottom up, we are done rewriting this node so we add it to the dict
if self.pm is None or (new_src_n:=self.pm_rewrite(new_n)) is None:
self.replace[n] = new_n
if n in waitlist: stack.extend(waitlist.pop(n))
continue
else:
# if srcs changed from rewrites, construct a new UOp with the new srcs
new_src_n = UOp(new_n.op, new_n.dtype, new_src, new_n.arg, new_n.tag)
# trigger a rewrite of new_src_n, then after that rewrite is done, link it back to n
stack.append((n, 2, new_src_n))
stack.append((new_src_n, 0, new_src_n))
else:
# in stage 2, we link the result of new_n to the result of n
if (replaced_new_n:=self.replace.get(new_n, SENTINEL)) is SENTINEL:
# not ready: register in waitlist instead of spinning
waitlist.setdefault(new_n, []).append((n, 2, new_n))
else:
# otherwise we are done
self.replace[n] = replaced_new_n
if n in waitlist: stack.extend(waitlist.pop(n))
return self.replace[root]
@profile_matches
def graph_rewrite(sink:UOp, pm:PatternMatcher, ctx=None, bottom_up=False, name=None, bpm=None, walk=False, enter_calls=False) -> UOp:
rewrite_ctx = RewriteContext(pm if not bottom_up else None, pm if bottom_up else bpm, ctx, enter_calls)
return rewrite_ctx.walk_rewrite(sink) if walk else rewrite_ctx.unified_rewrite(sink)
def sint_to_uop(x:sint, dtype=dtypes.weakint) -> UOp: return UOp.const(dtype, x) if isinstance(x, int) else x.cast(dtype)
def to_max_shape(shape:tuple[sint, ...]) -> tuple[int, ...]: return tuple(int(x.vmax) if isinstance(x, UOp) else x for x in shape)
def select_dtype(u): return (dtypes.long if u.overflows(dtypes.int32) else dtypes.int).vec(u.dtype.count)
pm_lower_index_dtype = PatternMatcher([
# There are no Unary ops at this point in symbolic, those are introduced later
(UPat(GroupOp.Binary, name="u", src=(UPat.var("x").cast(dtypes.weakint), UPat.var("y").cast(dtypes.weakint))), lambda u,x,y:
x.cast(dt:=least_upper_dtype(select_dtype(u), x.dtype, y.dtype)).alu(u.op, y.cast(dt)).cast(u.dtype)),
(UPat(Ops.CONST, dtype=dtypes.weakint, name="u"), lambda u: u.replace(dtype=select_dtype(u)).cast(u.dtype) if u.arg!=Invalid else None),
(UPat(Ops.WHERE, dtypes.weakint, src=(UPat.var("cond"), UPat.var("x").cast(dtypes.weakint), UPat.var("y").cast(dtypes.weakint))), lambda cond,x,y:
cond.where(x.cast(dt:=least_upper_dtype(x.dtype, y.dtype)), y.cast(dt)).cast(dtypes.weakint)),
(UPat(Ops.RANGE, src=(UPat.var("end").cast(dtypes.weakint)), name="r"), lambda r,end: r.replace(dtype=end.dtype, src=(end,)).cast(dtypes.weakint)),
(UPat(Ops.STACK, src=UPat().cast(dtypes.weakint), name="v"),
lambda v: v.replace(dtype=(dt:=select_dtype(v)), src=tuple(s.src[0].cast(dt.scalar()) for s in v.src)).cast(dtypes.weakint)),
# special can only be int32
(UPat(Ops.SPECIAL, src=(UPat.var("var").cast(dtypes.weakint),), name="u"),
lambda u,var: u.replace(dtype=dtypes.int, src=(var,)).cast(dtypes.weakint)),
(UPat(Ops.DEFINE_VAR, dtype=dtypes.weakint, name="u"), lambda u: u.replace(dtype=dtypes.int).cast(dtypes.weakint)),
(UPat(Ops.BIND, src=(UPat.var("var").cast(dtypes.weakint), UPat.cvar("val").cast(dtypes.weakint))),
lambda var,val: var.bind(val).cast(dtypes.weakint)),
# remove hanging casts
(UPat(Ops.INDEX, src=(UPat.var("buf"), UPat.var("idx", dtypes.ints).cast()),), lambda buf,idx: buf.index(idx, ptr=True)),
(UPat(Ops.INDEX, src=(UPat.var("buf"), UPat.var("gate").where(UPat.var("idx", dtypes.ints).cast(), UPat(Ops.CONST, arg=Invalid)))),
lambda buf,idx,gate: buf.index(gate.where(idx, idx.const_like(Invalid)), ptr=True)),
# remove hanging casts for images
(UPat(Ops.INDEX, src=(UPat.var("buf"), UPat.var("idx_y", dtypes.ints).cast(), UPat.var("idx_x", dtypes.ints).cast()),),
lambda buf,idx_x,idx_y: buf.index(idx_y, idx_x, ptr=True)),
(UPat(Ops.INDEX, src=(UPat.var("buf"),
UPat.var("gate").where(UPat.var("idx_y", dtypes.ints).cast(), UPat(Ops.CONST, arg=Invalid)),
UPat.var("gate").where(UPat.var("idx_x", dtypes.ints).cast(), UPat(Ops.CONST, arg=Invalid)))),
lambda buf,idx_x,idx_y,gate: buf.index(gate.where(idx_y, idx_y.const_like(Invalid)),
gate.where(idx_x, idx_x.const_like(Invalid)), ptr=True)),
(UPat((Ops.SINK, Ops.NOOP, Ops.END), name="n"),
lambda n: n.replace(src=tuple(s.src[0] if s.op is Ops.CAST and s.dtype == dtypes.weakint else s for s in n.src))),
])
def _index_to_concrete_int(u:UOp) -> UOp: return graph_rewrite(u.sink(), pm_lower_index_dtype).src[0]
_substitute = PatternMatcher([(UPat(tuple(Ops), name="x"), lambda ctx,x: ctx.get(x,None))])
_pm_resolve_params = PatternMatcher([(UPat(Ops.PARAM, name="p"), lambda ctx,p: ctx[p.arg.slot])])
remove_all_tags = PatternMatcher([(UPat(GroupOp.All, name="x"), lambda x: x.replace(tag=None) if x.tag is not None else None)])
def gate_kernel_sink(x:UOp) -> bool:
if x.op is Ops.LINEAR: return False
if x.op is Ops.SINK and isinstance(x.arg, KernelInfo): return False
return True
def do_unbind(ctx:dict[Variable, int], x:UOp):
v,i = x.unbind()
ctx[v] = i
return v
pm_unbind = PatternMatcher([(UPat(Ops.BIND, name="x"), do_unbind)])
# *** what was symbolic.py ***
sint = int|UOp
Variable = UOp
ConstLike = ConstType|Variable|tuple[ConstType, ...]
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