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, 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 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 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.BUFFER_VIEW: 1, 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, ...]: return tuple(0 if 0 in nth_dim_sizes else smax(nth_dim_sizes) for nth_dim_sizes in zip(*_align_left(*shapes))) 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/BUFFER_VIEW 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 # NOTE: this should be frozen, but frozen is slower @dataclass(eq=False, slots=True) class UOp(OpMixin, 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.CONTRACT | Ops.SINK | Ops.END | Ops.REWRITE_ERROR | Ops.PTRCAT | Ops.ENDIF | \ Ops.LINEAR | Ops.PROGRAM | Ops.SOURCE | Ops.BINARY | Ops.INS | Ops.TUPLE | Ops.CALL | Ops.FUNCTION: return None # 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: # 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:]):] # TODO: these should have the shape of the dtype.count case Ops.CONST | Ops.DEFINE_VAR: return () case Ops.GEP | Ops.STACK | Ops.VCONST | Ops.VCAT: return () # some ops init the shape case Ops.BIND | Ops.RANGE | Ops.SPECIAL | Ops.UNROLL: return () case Ops.BUFFER: return (self.arg,) case Ops.BUFFER_VIEW: # HACK: BUFFER_VIEW 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[0],) case Ops.CUSTOM_FUNCTION: return None case Ops.STAGE: return tuple([int(r.vmax+1) for r in self.src[1:]]) case Ops.DEFINE_LOCAL | Ops.DEFINE_REG: return (self.ptrdtype.size,) case Ops.PARAM: if isinstance(self.dtype, PtrDType): return (self.ptrdtype.size,) # NOTE: copied from marg if len(self.src) >= 1: return tuple(self.src[0].sgep(i) for i in range(self.src[0].dtype.count)) return 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.PATCH | Ops.LOAD | \ Ops.COPY | Ops.ALLREDUCE: return self.src[0]._shape # REDUCE with empty axis is passthrough (lowered form) case Ops.REDUCE if len(self.arg[1]) == 0: return self.src[0]._shape # 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 (axis info in PARAM sources) 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(b>=0) and resolve(e>=0) for b,e in self.marg): raise ValueError(f"invalid pad {self.marg}") return tuple(ssimplify(s+b+e) for s,(b,e) in zip(ps, self.marg)) case Ops.SHRINK: # TODO: why do i need resolve here? if len(ps) != len(self.marg) or not all(resolve(0<=b) and resolve(b<=e) and resolve(e<=s) for s,(b,e) in zip(ps, self.marg)): raise ValueError(f"invalid shrink {self.marg} for {ps}") return tuple(ssimplify(e-s) for s,e 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 self.op is Ops.REDUCE else self.arg[7] if not isinstance(axis_arg, tuple) or not all(isinstance(x, int) and x>=0 and x 0 and all(x is not None for x in input_shapes), f"None input shape not supported for {self.op}" # TODO: add broadcasting here if not all_same(input_shapes): raise RuntimeError(f"shape mismatch at {self.op}: {input_shapes} {[x.op for x in self.src]}") return input_shapes[0] # 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) @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 in {Ops.CONST, Ops.VCONST}: return self if self.op is Ops.SINK and all(s.op in {Ops.CONST, Ops.VCONST} 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]) def const_like(self, b:ConstLike, dtype:DType|None=None): # constants can optionally have a DEVICE source ret = UOp.const(dtype or self.dtype.base, b, device=self._device, shape=self.shard_shape if self.axis is not None else self._shape) return ret.multi(self.axis) if self.axis is not None else ret def ufix(self, x): if isinstance(x, UOp): return x return self.const_like(x, None if self._ufix_keep_dtype(x) else 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: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: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.VCONST: return UOp.const(self.dtype.scalar(), self.arg[i]) if self.op is Ops.CONST: return UOp.const(self.dtype.scalar(), self.arg) i = (i,) 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 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, device:str|tuple[str, ...]|None=None, shape:tuple[sint, ...]|None=None): if isinstance(b, UOp): return b.unbind()[0] if b.op is Ops.BIND else b 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 VCONST if they are all the same ret = UOp(Ops.VCONST if isinstance(b, tuple) else Ops.CONST, dtype, arg=dtype.const(b), src=(UOp(Ops.DEVICE, arg=device),) if device is not None else ()) return ret.reshape((1,)*len(shape)).expand(shape) if shape is not None and ret.shape != shape else ret @staticmethod def unique_const(fill_value:ConstType, dtype:DTypeLike|None=None, device:str|tuple[str, ...]|None=None, # type: ignore[override] shape:tuple[sint, ...]|None=None, unique=True): # NOTE: fill_value is ConstType, not ConstLike, so UOps and tuples aren't allowed assert not isinstance(fill_value, (UOp, tuple)), "unique const only works on numbers" ret = UOp.const(to_dtype(dtype) if dtype is not None else dtypes.from_py(fill_value), fill_value, canonicalize_device(device)) ret = ret.replace(src=(UOp.unique(None if unique is True else unique),) + ret.src) return ret.reshape((1,)*len(shape)).expand(shape) if shape is not None and ret.shape != shape else ret @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 self if cond.op is Ops.WHERE and cond.arg else 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.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 # *** 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 # PARAM: axis is stored as a MULTI source if self.op is Ops.PARAM: for s in self.src: if s.op is Ops.MULTI: return s.arg return None # 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) -> UOp: if len(self.shape) == 0: return self # scalars broadcast, no sharding needed dcount = len(self.device) 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) -> UOp: return self.copy_to_device(devices)._shard(axis).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.VCONST: return self.arg[i] case Ops.STACK: return self.src[i].sintify() case _: raise RuntimeError(f"no sgep on {self.op}") @functools.cached_property def marg(self): match self.op: case Ops.RESHAPE | Ops.EXPAND: return tuple(ssimplify(self.src[1].sgep(i)) for i in range(self.src[1].dtype.count)) case Ops.PAD | Ops.SHRINK: return tuple((self.src[1].sgep(i), self.src[2].sgep(i)) for i in range(self.src[1].dtype.count)) 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): buffers[uop:=UOp.new_buffer(device or opaque.device, opaque.size, opaque.dtype)] = opaque.ref(1) 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) @property def device(self) -> str|tuple[str, ...]: return unwrap(self._device) @recursive_property def _device(self) -> str|tuple[str, ...]|None: 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 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 @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.BUFFER_VIEW, 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.BUFFER_VIEW: if (cret:=buffers.get(self)) is not None: return cret buf = self.src[0].buffer if isinstance(buf, MultiBuffer): mbuf = MultiBuffer.__new__(MultiBuffer) mbuf.bufs = [b.view(self.arg[0], self.dtype, self.arg[1] * self.dtype.itemsize) for b in buf.bufs] buffers[self] = mbuf return mbuf assert isinstance(buf, Buffer), "must be a Buffer for BUFFER_VIEW" buffers[self] = bv = buf.view(self.arg[0], self.dtype, self.arg[1] * self.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 # 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: 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}, key=lambda v: v.arg) # *** uop symbolic stuff *** def is_increasing(self:UOp) -> bool: # is f a monotonically increasing function regards its input if self.op in GroupOp.Irreducible: return True if self.op is Ops.ADD: return self.src[0].is_increasing() and self.src[1].is_increasing() if self.op in (Ops.MUL, Ops.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.VCONST: return math.gcd(*self.arg) if self.op is Ops.ADD: return math.gcd(self.src[0].const_factor(), self.src[1].const_factor()) if self.op is Ops.MUL: return self.src[0].arg if self.src[0].op is Ops.CONST else self.src[1].arg if self.src[1].op is Ops.CONST else 1 return 1 def divides(self, v:int) -> UOp|None: if v==1: return self if self.op is Ops.CONST: return self.const_like(self.arg//v) if self.arg%v == 0 else None if self.op is Ops.VCONST: return self.const_like(tuple(x//v for x in self.arg)) if all(x%v == 0 for x in self.arg) else None if self.op is Ops.ADD: return d0+d1 if (d0:=self.src[0].divides(v)) is not None and (d1:=self.src[1].divides(v)) is not None else None if self.op is Ops.MUL: if (d0:=self.src[0].divides(v)) is not None: return d0 * self.src[1] if (d1:=self.src[1].divides(v)) is not None: return self.src[0] * d1 return None # generic None if we aren't sure 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 ~s0_vmax, ~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= 4: return self.src[2].arg, self.src[3].arg 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.VCONST and Invalid not in self.arg: return (min(self.arg), max(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(x.expr for x in sself.toposort() if x.op is Ops.DEFINE_VAR) # 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} ret = UOp(lookup[addrspace], dtype.ptr(prod(shape), addrspace), arg=slot) 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): src: tuple[UOp, ...] = (UOp(Ops.NOOP) if shape is None else shape_to_shape_arg(shape),) + \ (UOp(Ops.NOOP) if device is None else UOp(Ops.DEVICE, arg=device),) if vmin_vmax is not None: src += (UOp.const(dtype, vmin_vmax[0]), UOp.const(dtype.scalar(), vmin_vmax[1])) if name is not None: src += (UOp(Ops.NOOP, arg=name),) return UOp(Ops.PARAM, dtype, src, arg=slot) def param_like(self, slot:int): if self.op is Ops.BIND: return UOp.param(slot, self.dtype, self._shape, self._device, self._min_max, self.src[0].arg[0]) p = UOp.param(slot, self.dtype, self._shape, self._device) if self.axis is not None: p = p.replace(src=p.src + (UOp(Ops.MULTI, arg=self.axis),)) return p # 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.BUFFER_VIEW, 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) -> 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)) # 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)) 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: _vars.append(u) if u.op is Ops.PARAM: _globals.append(u.arg) if u.op in (Ops.STORE, Ops.LOAD): if (idx:=u.src[0]).op is Ops.INDEX or (u.src[0].op is Ops.CAST and (idx:=u.src[0].src[0]).op is Ops.INDEX): if (buf:=idx.src[0]).op is Ops.PARAM: (outs if u.op is Ops.STORE else ins).append(buf.arg) 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 is Ops.DEFINE_VAR and u.arg[0] == 'core_id': global_size[0] = u.arg[2] + 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.arg)), 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 # grad_fxn can't be pickled, but metadata can def __reduce__(self): return (CallInfo, (None, self.metadata, self.name, self.precompile, self.precompile_backward)) 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(" 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) 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) @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, vec=True, arg=None): return UPat((Ops.CONST,Ops.VCONST) if vec else 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 sink(self, *srcs:UPat|None, **kwargs): return UPat(Ops.SINK, dtypes.void, (self,)+tuple([x for x in srcs if x is not None]), **kwargs) def index(self, *srcs:UPat|None, **kwargs): return UPat(Ops.INDEX, self.match_dtype, (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, self.match_dtype, (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 contiguous(self, *args, **kwargs): return UPat(Ops.CONTIGUOUS, dtype=self.match_dtype, src=(self,)+args, **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, self.match_dtype, (self,)+src, **kwargs) def const_like(self, b:ConstLike): return UPat.const(self.match_dtype, cast(ConstType, b)) # UPat patterns are built with `upat + 1`-style operators; don't insert CAST nodes like _broadcasted does def _binop(self, op:Ops, x, reverse:bool) -> UPat: return self.ufix(x).alu(op, self) if reverse else self.alu(op, self.ufix(x)) # TODO: need these override due to dtypes.void on broadcast 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: matches = [x.match(uop, store.copy()) for x in self.src[0]] return flatten([x for x in matches if x is not None]) 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 \ (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__ == "" 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, Ops.VCONST), 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)), (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])]) 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, ...]