from __future__ import annotations import itertools, functools, math from dataclasses import dataclass from collections import defaultdict from typing import Optional, cast, Final, Callable, Sequence from enum import Enum, auto from tinygrad.uop.ops import GroupOp, KernelInfo, UOp, Ops, can_pad, resolve, Variable, sint, graph_rewrite, smax from tinygrad.uop.spec import type_verify, ast_spec from tinygrad.device import Device from tinygrad.opt.tc import TensorCore from tinygrad.renderer import Renderer, ProgramSpec from tinygrad.dtype import ImageDType from tinygrad.helpers import all_same, colored, ansilen, dedup, prod, round_up, to_function_name, unwrap, DEBUG, TC_SELECT, TC_OPT, AMX from tinygrad.shape.shapetracker import ShapeTracker from tinygrad.shape.view import strides_for_shape, get_contraction from tinygrad.kernelize.kernelize import view_left class OptOps(Enum): TC = auto(); UPCAST = auto(); UNROLL = auto(); LOCAL = auto() # noqa: E702 GROUP = auto(); GROUPTOP = auto(); NOLOCALS = auto(); PADTO = auto(); SWAP = auto() # noqa: E702 def __lt__(self, x:OptOps): return self.value < x.value @dataclass(frozen=True, order=True) class Opt: op: OptOps axis: Optional[int] = None arg: Optional[int | tuple] = None def __repr__(self): return f"Opt(op={self.op}, axis={self.axis}, arg={self.arg})" class AxisType(Enum): GLOBAL = auto(); LOCAL = auto(); GROUP_REDUCE = auto(); REDUCE = auto(); UPCAST = auto(); UNROLL = auto() # noqa: E702 axis_letters = {AxisType.GLOBAL: "g", AxisType.LOCAL: "l", AxisType.UPCAST: "u", AxisType.GROUP_REDUCE: "G", AxisType.REDUCE: "R", AxisType.UNROLL: "r"} axis_colors = {AxisType.GLOBAL: "blue", AxisType.LOCAL: "cyan", AxisType.UPCAST: "yellow", AxisType.GROUP_REDUCE: "green", AxisType.REDUCE: "red", AxisType.UNROLL: "magenta"} class KernelOptError(Exception): pass def check(cond:bool, msg:str=""): if not cond: raise KernelOptError(msg) @dataclass class TensorCoreOptions: axes: tuple[int, ...] # the location of the original N and M axes if still in the shape axes_exist: tuple[bool, ...] # true if the original N and M axes are still in the shape axis_pads: tuple[tuple[int, int], ...] def fix_axes(self, removed_axis:int): # adjust the TC axes if necessary when a dimension is removed axes, axes_exist = list(self.axes), list(self.axes_exist) for tc_dim in [i for i in range(2) if axes_exist[i]]: if removed_axis < axes[tc_dim]: axes[tc_dim] -= 1 elif removed_axis == axes[tc_dim]: axes_exist[tc_dim] = False self.axes, self.axes_exist = tuple(axes), tuple(axes_exist) class Kernel: def __init__(self, ast:UOp, opts:Optional[Renderer]=None): assert ast.op is Ops.SINK, ast.op self.ast = ast self.opts = opts if opts is not None else Device[Device.DEFAULT].renderer # verify AST matches the spec if __debug__: type_verify(list(self.ast.toposort()), ast_spec) self.vars: list[Variable] = self.ast.variables() # NOTE: this requires a specific order with the [::-1], this is likely a bug self.bufs: list[UOp] = [x for x in self.ast.toposort() if x.op in GroupOp.Buffer][::-1] # create new shapetrackers inside this kernel, we will permute them self.sts: list[ShapeTracker] = [x.st_arg for x in self.bufs] # add the shapetrackers for each reduce # we use this to track which axes are reduced in each reduce self.reduceops = [x for x in self.ast.toposort() if x.op is Ops.REDUCE_AXIS] for x in self.reduceops: self.sts.append(unwrap(x.st)) self.sts.append(unwrap(x.src[0].st)) # add a shapetracker to the end to track the full shape, with 0 strides so it can merge self.sts.append(ShapeTracker.from_shape(tuple([smax(*s) for s in zip(*[x.shape for x in self.sts])]), (0,)*len(self.sts[0].shape))) # parameters for optimization self.tensor_core: Optional[TensorCore] = None self.tensor_core_opts: Optional[TensorCoreOptions] = None self.use_tensor_cores: int = 0 self.applied_opts: list[Opt] = [] self.dont_use_locals = False self.finalized: bool = False # group simplifies self.simplify_ones() self.simplify_merge_adjacent() # axis types self.axis_types: list[AxisType] = [AxisType.REDUCE if resolve(x!=y) else AxisType.GLOBAL for x,y in zip(self.sts[0].shape, self.sts[-1].shape)] # confirm all reduce axes are at the end final_reduces = [i for i,(s,n) in enumerate(zip(self.full_shape, self.output_shape)) if resolve(s != n)] if final_reduces != list(range(len(self.full_shape)-len(final_reduces), len(self.full_shape))): raise RuntimeError(f"reduces are not at the end of the shape {self.full_shape} -> {self.output_shape}") def copy(self): ret = type(self).__new__(type(self)) # base linearizer params ret.opts, ret.ast = self.opts, self.ast # things downstream of the AST ret.reduceops, ret.vars, ret.bufs = self.reduceops, self.vars, self.bufs ret.sts = self.sts[:] ret.axis_types = self.axis_types[:] # parameters for optimizations ret.applied_opts, ret.dont_use_locals = self.applied_opts[:], self.dont_use_locals ret.tensor_core, ret.tensor_core_opts, ret.use_tensor_cores = self.tensor_core, self.tensor_core_opts, self.use_tensor_cores ret.finalized = self.finalized return ret @property def first_reduce(self) -> int: for i in range(self.first_upcast): if self.axis_types[i] in (AxisType.GROUP_REDUCE, AxisType.REDUCE): return i return self.first_upcast @property def first_upcast(self) -> int: return self.shape_len-self.upcasted @property def reduceop(self) -> UOp|None: return self.reduceops[0] if len(self.reduceops) > 0 else None @property def full_shape(self) -> tuple[sint, ...]: return self.sts[-1].shape @property def full_unupcasted_shape(self) -> tuple[sint, ...]: return self.full_shape[:self.first_upcast] @property def output_shape(self) -> tuple[sint, ...]: return self.sts[0].shape @property def shape_len(self) -> int: return len(self.sts[0].shape) @property def global_dims(self) -> int: return sum([1 for x in self.axis_types if x == AxisType.GLOBAL]) if hasattr(self, 'axis_types') else 0 @property def local_dims(self) -> int: return sum([1 for x in self.axis_types if x == AxisType.LOCAL]) if hasattr(self, 'axis_types') else 0 @property def upcasted(self) -> int: return sum([1 for x in self.axis_types if x in {AxisType.UPCAST, AxisType.UNROLL}]) if hasattr(self, 'axis_types') else 0 @property def group_for_reduces(self) -> int: return sum([1 for x in self.axis_types if x == AxisType.GROUP_REDUCE]) if hasattr(self, 'axis_types') else 0 # ******************** colors and names ******************** def colors(self) -> list[str]: assert len(self.axis_types) == self.shape_len, "colors size mismatch" return [axis_colors[x] if not self.dont_use_locals or not x == AxisType.GLOBAL else "BLUE" for x in self.axis_types] def colored_shape(self, pad:Optional[int]=None, dense=False) -> str: shape_strs = [(s if dense else f"{s:4d}") if isinstance(s, int) else s.render() for s in self.full_shape] ret = ' '.join(colored(s, color) for s,color in zip(shape_strs, self.colors())) if pad: ret += ' '*(pad-ansilen(ret)) return ret kernel_cnt: Final[defaultdict[str, int]] = defaultdict(int) @functools.cached_property def name(self) -> str: # kernel name (before late upcast) kernel_type = "r" if self.reduceop is not None else ("C" if all(x.op is Ops.SINK or x.op in GroupOp.Buffer for x in self.ast.toposort()) else "E") suffix = colored('_', 'BLACK').join([colored(x.render() if isinstance(x, UOp) else str(x), c) for x,c in zip(self.full_shape, self.colors())]) name = kernel_type + (f"{len(self.ast.src)}" if len(self.ast.src) > 1 else "") + "_" + suffix # name the function something unique Kernel.kernel_cnt[(function_name := to_function_name(name))] += 1 num = f"n{Kernel.kernel_cnt[function_name]-1}" if Kernel.kernel_cnt[function_name] > 1 else "" return name + colored(num, 'BLACK') # ******************** base simplifiers ******************** # apply reshape and permute to all shapetrackers def reshape(self, new_shape_fxn:Callable[[tuple[sint, ...]], Sequence[sint]]): self.sts = [st.reshape(tuple(new_shape_fxn(st.shape))) for st in self.sts] def permute(self, new_axes:Sequence[int]): self.sts = [st.permute(tuple(new_axes)) for st in self.sts] # axis : the axis to pull from # amount : the amount to take # top : if you want to pull that amount from the top # insert_before : place to insert the new stuff def shift_to(self, axis:int, amount:int, new_type:AxisType, top:bool=False, insert_before:int|None=None): if insert_before is None: insert_before = self.shape_len self.axis_types.insert(insert_before, new_type) move_axis = axis if top else axis+1 if move_axis < insert_before: insert_before += 1 def new_shape_fxn(x): return x[0:axis] + (((amount,x[axis]//amount) if top else (x[axis]//amount,amount)) if x[axis] > 1 else (1,1)) + x[axis+1:] new_axes = [i for i in range(insert_before) if i != move_axis]+[move_axis]+[i for i in range(insert_before, self.shape_len+1) if i != move_axis] self.reshape(new_shape_fxn) self.permute(new_axes) # ******************** complex simplifiers ******************** def simplify_ones(self) -> bool: # remove places where the shape is all ones if any(all_ones:=[s==1 for s in self.full_shape]): if hasattr(self, 'axis_types'): self.axis_types = [x for i,x in enumerate(self.axis_types) if not all_ones[i]] self.reshape(lambda shape: [x for i,x in enumerate(shape) if not all_ones[i]]) return True return False def simplify_merge_adjacent(self): if self.shape_len == 0: return shapes, strides = [x.shape for x in self.sts], [x.real_strides() for x in self.sts] # NOTE: we can't use self.first_reduce yet first_reduce = [resolve(x!=y) for x,y in zip(self.sts[0].shape[:self.first_upcast]+(0,), self.full_shape[:self.first_upcast]+(1,))].index(True) # if it's an image, insert fake strides such that this fusion doesn't happen across image axes if isinstance(self.membufs[0].dtype, ImageDType): base_shape = self.membufs[0].dtype.shape if shape_idx_groups := get_contraction(self.output_shape, base_shape): special_strides: tuple[sint, ...] = tuple() for i,g in enumerate(shape_idx_groups): shape_piece = tuple(self.output_shape[x] for x in g) assert prod(shape_piece) == base_shape[i], f"get_contraction was wrong? {shape_piece} != {base_shape[i]}" special_strides += strides_for_shape(shape_piece) # adding the fake image shape shapes.append(self.output_shape) strides.append(special_strides) # merge dimensions if we can, multi _merge_dims # NOTE: this does not always preserve the reduce dimension # TODO: move this into shapetracker, with tests! # TODO: how does this work with multi-reduce? rets = [[(s[0], st[0])] for s,st in zip(shapes, strides)] for i in range(1, len(shapes[0])): can_merge = [] for s,st,ret in zip(shapes, strides, rets): # TODO: added the always mergeability of 1s, is this right? if so, add to shapetracker in the 1 case si, sti, last_st = s[i], st[i], ret[-1][1] can_merge.append((sti is not None) and ((sti != 0 and last_st == si*sti) or (sti == 0 and last_st == 0))) # more can merge than this mergeable = all(can_merge) and i != first_reduce for j,(s,st) in enumerate(zip(shapes, strides)): if mergeable: rets[j][-1] = (rets[j][-1][0] * s[i], st[i]) else: rets[j].append((s[i], st[i])) # do the reshapes for i,x in enumerate(rets[:len(self.sts)]): self.sts[i] = self.sts[i].reshape(tuple([y[0] for y in x])) # ******************** apply optimizations ******************** def real_axis(self, opt:Opt): if opt.axis is None: return -1 if opt.op is OptOps.UNROLL: return self.first_reduce+opt.axis if opt.op in {OptOps.GROUP, OptOps.GROUPTOP}: return self.first_reduce+self.group_for_reduces+opt.axis return opt.axis def apply_opt(self, opt:Opt, append_opt:bool=True): if self.finalized: raise RuntimeError("can't optimize Kernel after it's finalized") if self.dont_use_locals: check(opt.op not in {OptOps.LOCAL, OptOps.GROUP, OptOps.GROUPTOP}, "not using locals") if opt.op is OptOps.TC: check(len(self.applied_opts) == 0, "tensor core opts must be first") # TODO: things like PADTO might be fine check(len(self.opts.tensor_cores) > 0, "must have tensor cores") check(opt.axis is not None, "tensor core opts must have an axis") check(opt.arg is not None and isinstance(opt.arg, tuple) and len(opt.arg) == 3, "tensor core opts must have valid arg") check(-1 <= (tc_select:=cast(tuple, opt.arg)[0]) < len(self.opts.tensor_cores), "tensor core opts must have valid tc_select") check(0 <= (tc_opt:=cast(tuple, opt.arg)[1]) <= 2, "tensor core opts must have valid tc_opt") check(0 < (use_tensor_cores:=cast(tuple, opt.arg)[2]) <= 2, "use_tensor_cores value is not valid") check(self._apply_tc_opt(use_tensor_cores, cast(int, opt.axis), tc_select, tc_opt), "no tensor core available") self.applied_opts.append(opt) return axis = self.real_axis(opt) check(axis < len(self.full_shape), "invalid axis") if opt.op is OptOps.SWAP: amt = cast(int, opt.arg) # arg is an axis in the SWAPs elif opt.arg is not None: check(isinstance(opt.arg, int), "arg should be int") amt = arg if (arg:=cast(int, opt.arg)) != 0 else self.full_shape[axis] check(isinstance(amt, int) and amt != 1, f"shift/padto of {amt=}, 1 or symbolic amount is meaningless") if opt.op is not OptOps.PADTO: check(self.full_shape[axis] % amt == 0, f"no longer valid shift {self.full_shape[axis]=}, {amt=}") else: amt = -1 if self.reduceop is not None and (opt.op in {OptOps.GROUP, OptOps.GROUPTOP} or \ (self.group_for_reduces and opt.op not in {OptOps.NOLOCALS, OptOps.PADTO})): acc_sz = self.reduceop.dtype.itemsize upcast_sz = prod([a for a,b in zip(self.full_shape[self.first_upcast:], self.sts[0].shape[self.first_upcast:]) if a == b]) local_sz = prod(self.full_shape[self.first_reduce-self.local_dims:self.first_reduce+self.group_for_reduces]) smem_sz = amt*acc_sz*upcast_sz*local_sz check(smem_sz <= self.opts.shared_max, f"exceeds maximum shared memory size: needs {smem_sz}, max {self.opts.shared_max}") if opt.op is OptOps.LOCAL: # cyan # NOTE: LLVM/CPU can use locals too, but they are treated the same as globals (still helpful for L1 cache) # it's disabled for now since it makes BEAM slow for little gain check(self.opts.has_local, "target does not support local") check(axis < self.global_dims, "local is for globals") self.shift_to(axis, amt, AxisType.LOCAL, insert_before=self.first_reduce) elif opt.op in {OptOps.GROUP, OptOps.GROUPTOP}: # green check(self.opts.has_local and self.opts.has_shared, "target does not support local or shared mem") check(self.first_reduce + self.group_for_reduces <= axis < self.first_upcast, "must be reduce axis to group") check(not self.tensor_core, "can't group with tensor cores") check(len(reduce_axes:=[i for r in self.reduceops for i in r.axis_arg]) == len(set(reduce_axes)), "can't group with parallel reduces") self.shift_to(axis, amt, AxisType.GROUP_REDUCE, top=(opt.op is OptOps.GROUPTOP), insert_before=self.first_reduce + self.group_for_reduces) elif opt.op is OptOps.UNROLL: # purple check(axis < self.first_upcast, "can't upcasted already upcasted") check(amt <= 32, "don't unroll more than 32") self.shift_to(axis, amt, AxisType.UNROLL, insert_before=None) elif opt.op is OptOps.UPCAST: # yellow check(axis < self.first_reduce, "upcast is for non-reduce") check(not (self.tensor_core and self.global_dims <= axis < self.global_dims+len(self.tensor_core.get_local_axes())), "can't upcast TC locals") check((self.opts is not None and self.opts.device == "DSP") or amt <= 16, "don't upcast more than 16") self.shift_to(axis, amt, AxisType.UPCAST, insert_before=None) elif opt.op is OptOps.NOLOCALS: check(self.opts.has_local and not self.dont_use_locals, "NOLOCALS is meaningless if target does not support local or already not using locals") check(self.local_dims == 0 and self.group_for_reduces == 0, "can't have no locals with locals") self.dont_use_locals = True elif opt.op is OptOps.SWAP: check(axis < amt < self.global_dims, f"swap is only for globals with axis < amt, getting {amt=}, {axis=}, {self.global_dims=}") permute = list(range(self.shape_len)) permute[axis], permute[amt] = permute[amt], permute[axis] self.permute(tuple(permute)) elif opt.op is OptOps.PADTO: check(not self.vars, "does not work with symbolic shape") check(axis < self.first_upcast, "cannot pad upcasted") # ok to pad SUM if all parent ALU ops have f(0) = 0 if (r:=self.reduceop) is not None and self.first_reduce <= axis: check(r.arg[0] is Ops.ADD and can_pad(r, {}), f"cannot pad {r}") padded = False for i,st in enumerate(self.sts): if (s:=st.shape[axis]) == 1: continue # reduced check(s > amt//4, f"pad adds more than quadruple the work {st.shape[axis]=} > {amt//4=}") if (ru := round_up(cast(int, s), amt) - s): # pad right seems to be faster self.sts[i] = st.pad(((0,0),) * axis + ((0,ru),) + ((0,0),) * (len(st.shape)-axis-1)) padded = True check(padded, "nothing was padded") if append_opt: self.applied_opts.append(opt) if self.simplify_ones() and self.tensor_core_opts: self.tensor_core_opts.fix_axes(axis) # fix up axes in TC opts if required after simplify_ones() def apply_opts(self, opts:Sequence[Opt]) -> Kernel: for opt in opts: self.apply_opt(opt) return self # **** kernel outputs, mostly tensor cores **** def _create_tc_opts(self, reduceop:UOp, tc:TensorCore, axis:int, opt_level:int) -> Optional[TensorCoreOptions]: has_cast = tc.dtype_in != tc.dtype_out if has_cast and not (reduceop.src[0].op is Ops.CAST and reduceop.src[0].dtype == tc.dtype_out): return None mul_op = reduceop.src[0].src[0] if has_cast else reduceop.src[0] if mul_op.op is not Ops.MUL: return None def buf_index(src:UOp) -> Optional[int]: # TODO: apply tc even if the sources are not from LOAD if src.op is Ops.LOAD and src.dtype == tc.dtype_in: return self.bufs.index(src) try: if opt_level >= 1 and src.op is Ops.CAST and src.dtype == tc.dtype_in: return self.bufs.index(src.src[0]) except ValueError: return None return None if (buf0:=buf_index(mul_op.src[0])) is None or (buf1:=buf_index(mul_op.src[1])) is None: return None buf0_strides, buf1_strides = self.sts[buf0].real_strides(), self.sts[buf1].real_strides() axis_buf0 = [(i,self.full_shape[i],buf1_strides[i]) for i,s in enumerate(buf0_strides[:self.first_reduce]) if s == 0] axis_buf1 = [(i,self.full_shape[i],buf0_strides[i]) for i,s in enumerate(buf1_strides[:self.first_reduce]) if s == 0] if not (axis_buf0 and axis_buf1 and ((self.shape_len-self.first_reduce) == 1 or (opt_level >= 1))): return None axis_choices = list(itertools.product(axis_buf0, axis_buf1, range(self.first_reduce, self.shape_len))) if not (axis < len(axis_choices)): return None s0, s1, s2 = axis_choices[-(axis+1)][0][0], axis_choices[-(axis+1)][1][0], axis_choices[-(axis+1)][2] # s0 is n, s1 is m, s2 is k axis_pads = tuple((x, tc.dims[i]) for i, x in enumerate([s0, s1, s2]) if resolve(self.full_shape[x]%tc.dims[i] != 0)) if axis_pads and (opt_level < 2): return None if DEBUG >= 3: print("TENSOR CORES", axis_buf0, axis_buf1, tc) return TensorCoreOptions(axes=(s0, s1, s2), axes_exist=(True, True), axis_pads=axis_pads) def _apply_tc_opt(self, use_tensor_cores:int, axis:int, tc_select:int, opt_level:int) -> bool: if use_tensor_cores and self.reduceop is not None and self.reduceop.arg[0] is Ops.ADD: tensor_cores = self.opts.tensor_cores if tc_select == -1 else [self.opts.tensor_cores[tc_select]] for tc in tensor_cores: tensor_core_opts = [self._create_tc_opts(reduceop, tc, axis, opt_level) for reduceop in self.reduceops] # can only fuse reduces with the same tc options assert all_same(tensor_core_opts) if tensor_core_opts[0] is None: continue self.tensor_core_opts = tc_opts = tensor_core_opts[0] # attempt to pad the tensor axes that require it try: for axis, dim in tc_opts.axis_pads: self.apply_opt(Opt(OptOps.PADTO, axis, dim), append_opt=False) # PADTO might fail except KernelOptError: continue # tensor core -- unroll the reduce dim (K), upcast and local the inner and outer dims (N, M) for opt in tc.opts: self.apply_opt(Opt({"u":OptOps.UPCAST, "l":OptOps.LOCAL}[opt[0]], tc_opts.axes[int(opt[1])], 2), append_opt=False) for dim, amt in tc.get_reduce_axes(): self.apply_opt(Opt(OptOps.UNROLL, 0, amt), append_opt=False) # TODO: this should be the reduce, not 0 self.tensor_core = tc self.use_tensor_cores = use_tensor_cores # TC=2 will do the shape ops without the WMMA return True return False def apply_tensor_cores(self, use_tensor_cores=1, extra_opts:Optional[list[Opt]]=None, axis:int=0, tc_select:Optional[int]=None, tc_opt:Optional[int]=None) -> bool: """ Attempts to apply a tensor core optimization to the kernel. If one exists and applies properly, return true, otherwise return false. Tensor cores are optimized instructions that matrix multiply-accumulate across a wave of threads: D(M, N) = A(M, K) * B(K, N) + C(M, N). Keyword arguments: use_tensor_cores -- controls how tensor cores are applied (default 1) 0: will disable any tensor core matching 1: enable tensor cores 2: apply tensor core shape but don't use UOp.WMMA extra_opts -- additional Opt's to apply after the tensor core instead of the hand-coded additional Opt's (default None) tc_select -- specifies which tensor core(s) to use for optimization (default -1) -1: iterates through all available tensor cores in order and uses the first one that matches the requirements (dims and dtypes) [0-N]: uses only the n'th tensor core available; useful for search tc_opt -- controls which kinds of kernels may be eligible for tensor cores application (default 2 during BEAM, 0 otherwise) 0: applies to only kernels with a single reduce axis and direct Ops.LOAD into Ops.MUL 1: allows kernels with multiple reduce axes and also multiplication of Ops.CAST'd buffers 2: allows kernels with M, N, K axes that are not multiples of the tensor core dimensions by applying padding those axes as needed """ if tc_select is None: tc_select = TC_SELECT.value if tc_opt is None: tc_opt = TC_OPT.value if not self.opts.tensor_cores: return False try: # check TC first and apply hand-coded opts if successful self.apply_opt(Opt(OptOps.TC, axis, (tc_select, tc_opt, use_tensor_cores))) if (tc_opts:=self.tensor_core_opts) is not None: if extra_opts is not None: self.apply_opts(extra_opts) else: if AMX: return True # skip hand-coded TC opts if AMX, upcasting will make kernel slower # hand-coded TC opts for tc_dim in [tc_dim for tc_dim in [1,0] if tc_opts.axes_exist[tc_dim]]: # attempt to upcast M and N szs = [sz for sz in [5,4,3,2] if self.full_shape[tc_opts.axes[tc_dim]] % sz == 0] if szs: self.apply_opt(Opt(OptOps.UPCAST, tc_opts.axes[tc_dim], szs[0])) if tc_opts.axes_exist[0] and (szs := [sz for sz in [4,2] if self.full_shape[tc_opts.axes[0]] % sz == 0]): # attempt to local N self.apply_opt(Opt(OptOps.LOCAL, tc_opts.axes[0], szs[0])) return True except KernelOptError: return False # strings like ['g0', 'g1', 'l0', 'l1', 'l2', 'l3', 'l4', 'l5', 'R0', 'r0', 'r1', 'r2', 'u0', 'u1', 'u2'] def shape_str(self) -> list[str]: ret: list[str] = [] cnt: dict[AxisType, int] = {} for x in self.axis_types: cnt[x] = (cnt[x] + 1) if x in cnt else 0 ret.append(f"{axis_letters[x]}{cnt[x]}") return ret def shape_str_to_axis(self, nms:list[str]) -> tuple[int, ...]: return tuple([self.shape_str().index(x) for x in nms]) def get_optimized_ast(self, name_override:Optional[str]=None) -> UOp: @functools.cache def fixup_ast(op:UOp) -> UOp: ret = op.replace(src=tuple(fixup_ast(x) for x in op.src)) # noqa: F821 if op.op in GroupOp.Buffer and op in self.bufs: st = self.sts[self.bufs.index(op)] # NOTE: if CONST got masked after applying opts, we create a new VALID if op.op is Ops.CONST and any(v.mask is not None for v in st.views): return op.view(st).valid() # otherwise we just replace the VIEW source return ret.replace(src=(ret.src[0].replace(arg=st),)+ret.src[1:]) if op.op is Ops.SINK: # NOTE: should group_for_reduces be added to the local_dims? return ret.replace(arg=KernelInfo(ret.arg.name if ret.arg is not None else self.name if name_override is None else name_override, self.global_dims if self.opts.has_local else 0, self.local_dims + self.group_for_reduces, self.upcasted, self.dont_use_locals, tuple(self.applied_opts))) if op.op is Ops.REDUCE_AXIS: reduce_idx = len(self.bufs) + self.reduceops.index(op) * 2 axes = tuple(i for i in range(0, self.shape_len) if self.axis_types[i] in {AxisType.REDUCE, AxisType.UNROLL} and resolve(self.sts[reduce_idx].shape[i] != self.sts[reduce_idx + 1].shape[i])) grouped_axes = tuple(i for i in range(0, self.shape_len) if self.axis_types[i] is AxisType.GROUP_REDUCE and resolve(self.sts[reduce_idx].shape[i] != self.sts[reduce_idx + 1].shape[i])) if (tc := self.tensor_core) and self.use_tensor_cores == 1: # get reduce/upcast axes for the tensor cores tc_reduce_axes = self.shape_str_to_axis([f"r{i}" for i in range(len(tc.get_reduce_axes()))]) base_upcast_axes = tuple([(s,2) for s in self.shape_str_to_axis([f"r{i}" for i in range(len(tc.get_reduce_axes()))] + \ [f"u{i}" for i in range(len(tc.get_upcast_axes()))])])[::-1] tc_upcast_axes = tuple([base_upcast_axes[:int(math.log2(tc.elements_per_thread[i]))] for i in range(3)]) # permute the srcs srcs = list((ret.src[0] if ret.src[0].op is not Ops.CAST else ret.src[0].src[0]).src) for i, (src, permaxis) in enumerate(zip(srcs, tc.permutes_for_shape_str(self.shape_str()))): src_st = (src if src.op is Ops.LOAD else src.src[0]).st_arg srcs[i] = src.view(ShapeTracker.from_shape(src_st.shape).permute(tuple(permaxis))) # construct the op wmma_arg = (str(tc), tc.dims, tc.dtype_in, tc.dtype_out, self.opts.device, tc.threads, tc_upcast_axes, tc_reduce_axes) wmma = UOp(Ops.WMMA, dtype=tc.dtype_out.vec(tc.elements_per_thread[2]), src=( UOp(Ops.CONTRACT, dtype=srcs[0].dtype.vec(tc.elements_per_thread[0]), src=(srcs[0],), arg=tc_upcast_axes[0]), UOp(Ops.CONTRACT, dtype=srcs[1].dtype.vec(tc.elements_per_thread[1]), src=(srcs[1],), arg=tc_upcast_axes[1]), UOp.const(tc.dtype_out.vec(tc.elements_per_thread[2]), 0.0)), arg=wmma_arg) tc_uop = UOp(Ops.UNROLL, tc.dtype_out, (wmma,), arg=tc_upcast_axes[2]) # preserve any other reduce return ret.replace(src=(tc_uop,), arg=(Ops.ADD, new_axes)) if (new_axes := tuple(i for i in axes if i not in tc_reduce_axes)) else tc_uop ret = ret.replace(arg = (op.arg[0], axes)) if self.group_for_reduces and grouped_axes: local_shape = tuple([s if self.axis_types[i] not in (AxisType.GLOBAL, AxisType.REDUCE, AxisType.UNROLL) and \ (self.axis_types[i] is not AxisType.GROUP_REDUCE or i in grouped_axes) else 1 for i,s in enumerate(self.full_shape)]) st = ShapeTracker.from_shape(local_shape).expand(self.full_shape[:self.global_dims]+local_shape[self.global_dims:]) local_size = st.real_size() local_buffer = UOp(Ops.DEFINE_LOCAL, op.dtype.ptr(local_size, local=True), (), f"temp{self.reduceops.index(op)}") local_load = UOp(Ops.LOAD, op.dtype, (local_buffer.view(st), UOp.store(local_buffer.view(st), ret))) grouped_reduce = UOp(Ops.REDUCE_AXIS, op.dtype, (local_load,), arg=(op.arg[0], grouped_axes)) if op is self.reduceops[-1]: return grouped_reduce st = ShapeTracker.from_shape(tuple([1 if i in grouped_axes else a for i,a in enumerate(local_shape)])) return UOp(Ops.LOAD, op.dtype, (local_buffer.view(st), UOp.store(local_buffer.view(st), grouped_reduce))) return ret self.finalized = True fixed_ast = fixup_ast(self.ast) del fixup_ast return graph_rewrite(fixed_ast, view_left, name="fixup optimized AST") # TODO: update the tests and delete these methods @property def membufs(self) -> list[UOp]: return dedup([x.src[0].base for x in self.bufs if x.op in {Ops.LOAD, Ops.STORE}]) def DEPRECATED_linearize(self): self.to_program() return self def to_program(self, name_override:Optional[str]=None) -> ProgramSpec: from tinygrad.engine.realize import get_program ret = get_program(self.get_optimized_ast(name_override), self.opts) self.uops = ret.uops return ret