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520 lines
30 KiB
520 lines
30 KiB
from __future__ import annotations
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import itertools, functools, math
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from dataclasses import dataclass
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from collections import defaultdict
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from typing import Optional, cast, Final, Callable, Sequence
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from enum import Enum, auto
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from tinygrad.uop.ops import GroupOp, KernelInfo, UOp, Ops, can_pad, resolve, Variable, sint, graph_rewrite, smax
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from tinygrad.uop.spec import type_verify, ast_spec
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from tinygrad.device import Device
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from tinygrad.opt.tc import TensorCore
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from tinygrad.renderer import Renderer, ProgramSpec
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from tinygrad.dtype import ImageDType
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from tinygrad.helpers import all_same, colored, ansilen, dedup, prod, round_up, to_function_name, unwrap, DEBUG, TC_SELECT, TC_OPT, AMX
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from tinygrad.shape.shapetracker import ShapeTracker
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from tinygrad.shape.view import strides_for_shape, get_contraction
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from tinygrad.kernelize.kernelize import view_left
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class OptOps(Enum):
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TC = auto(); UPCAST = auto(); UNROLL = auto(); LOCAL = auto() # noqa: E702
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GROUP = auto(); GROUPTOP = auto(); NOLOCALS = auto(); PADTO = auto(); SWAP = auto() # noqa: E702
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def __lt__(self, x:OptOps): return self.value < x.value
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@dataclass(frozen=True, order=True)
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class Opt:
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op: OptOps
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axis: Optional[int] = None
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arg: Optional[int | tuple] = None
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def __repr__(self): return f"Opt(op={self.op}, axis={self.axis}, arg={self.arg})"
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class AxisType(Enum):
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GLOBAL = auto(); LOCAL = auto(); GROUP_REDUCE = auto(); REDUCE = auto(); UPCAST = auto(); UNROLL = auto() # noqa: E702
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axis_letters = {AxisType.GLOBAL: "g", AxisType.LOCAL: "l", AxisType.UPCAST: "u",
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AxisType.GROUP_REDUCE: "G", AxisType.REDUCE: "R", AxisType.UNROLL: "r"}
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axis_colors = {AxisType.GLOBAL: "blue", AxisType.LOCAL: "cyan", AxisType.UPCAST: "yellow",
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AxisType.GROUP_REDUCE: "green", AxisType.REDUCE: "red", AxisType.UNROLL: "magenta"}
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class KernelOptError(Exception): pass
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def check(cond:bool, msg:str=""):
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if not cond: raise KernelOptError(msg)
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@dataclass
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class TensorCoreOptions:
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axes: tuple[int, ...] # the location of the original N and M axes if still in the shape
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axes_exist: tuple[bool, ...] # true if the original N and M axes are still in the shape
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axis_pads: tuple[tuple[int, int], ...]
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def fix_axes(self, removed_axis:int): # adjust the TC axes if necessary when a dimension is removed
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axes, axes_exist = list(self.axes), list(self.axes_exist)
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for tc_dim in [i for i in range(2) if axes_exist[i]]:
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if removed_axis < axes[tc_dim]: axes[tc_dim] -= 1
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elif removed_axis == axes[tc_dim]: axes_exist[tc_dim] = False
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self.axes, self.axes_exist = tuple(axes), tuple(axes_exist)
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class Kernel:
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def __init__(self, ast:UOp, opts:Optional[Renderer]=None):
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assert ast.op is Ops.SINK, ast.op
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self.ast = ast
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self.opts = opts if opts is not None else Device[Device.DEFAULT].renderer
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# verify AST matches the spec
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if __debug__: type_verify(list(self.ast.toposort()), ast_spec)
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self.vars: list[Variable] = self.ast.variables()
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# NOTE: this requires a specific order with the [::-1], this is likely a bug
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self.bufs: list[UOp] = [x for x in self.ast.toposort() if x.op in GroupOp.Buffer][::-1]
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# create new shapetrackers inside this kernel, we will permute them
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self.sts: list[ShapeTracker] = [x.st_arg for x in self.bufs]
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# add the shapetrackers for each reduce
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# we use this to track which axes are reduced in each reduce
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self.reduceops = [x for x in self.ast.toposort() if x.op is Ops.REDUCE_AXIS]
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for x in self.reduceops:
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self.sts.append(unwrap(x.st))
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self.sts.append(unwrap(x.src[0].st))
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# add a shapetracker to the end to track the full shape, with 0 strides so it can merge
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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)))
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# parameters for optimization
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self.tensor_core: Optional[TensorCore] = None
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self.tensor_core_opts: Optional[TensorCoreOptions] = None
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self.use_tensor_cores: int = 0
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self.applied_opts: list[Opt] = []
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self.dont_use_locals = False
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self.finalized: bool = False
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# group simplifies
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self.simplify_ones()
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self.simplify_merge_adjacent()
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# axis types
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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)]
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# confirm all reduce axes are at the end
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final_reduces = [i for i,(s,n) in enumerate(zip(self.full_shape, self.output_shape)) if resolve(s != n)]
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if final_reduces != list(range(len(self.full_shape)-len(final_reduces), len(self.full_shape))):
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raise RuntimeError(f"reduces are not at the end of the shape {self.full_shape} -> {self.output_shape}")
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def copy(self):
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ret = type(self).__new__(type(self))
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# base linearizer params
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ret.opts, ret.ast = self.opts, self.ast
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# things downstream of the AST
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ret.reduceops, ret.vars, ret.bufs = self.reduceops, self.vars, self.bufs
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ret.sts = self.sts[:]
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ret.axis_types = self.axis_types[:]
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# parameters for optimizations
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ret.applied_opts, ret.dont_use_locals = self.applied_opts[:], self.dont_use_locals
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ret.tensor_core, ret.tensor_core_opts, ret.use_tensor_cores = self.tensor_core, self.tensor_core_opts, self.use_tensor_cores
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ret.finalized = self.finalized
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return ret
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@property
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def first_reduce(self) -> int:
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for i in range(self.first_upcast):
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if self.axis_types[i] in (AxisType.GROUP_REDUCE, AxisType.REDUCE): return i
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return self.first_upcast
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@property
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def first_upcast(self) -> int: return self.shape_len-self.upcasted
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@property
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def reduceop(self) -> UOp|None: return self.reduceops[0] if len(self.reduceops) > 0 else None
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@property
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def full_shape(self) -> tuple[sint, ...]: return self.sts[-1].shape
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@property
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def full_unupcasted_shape(self) -> tuple[sint, ...]: return self.full_shape[:self.first_upcast]
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@property
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def output_shape(self) -> tuple[sint, ...]: return self.sts[0].shape
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@property
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def shape_len(self) -> int: return len(self.sts[0].shape)
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@property
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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
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@property
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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
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@property
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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
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@property
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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
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# ******************** colors and names ********************
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def colors(self) -> list[str]:
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assert len(self.axis_types) == self.shape_len, "colors size mismatch"
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return [axis_colors[x] if not self.dont_use_locals or not x == AxisType.GLOBAL else "BLUE" for x in self.axis_types]
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def colored_shape(self, pad:Optional[int]=None, dense=False) -> str:
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shape_strs = [(s if dense else f"{s:4d}") if isinstance(s, int) else s.render() for s in self.full_shape]
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ret = ' '.join(colored(s, color) for s,color in zip(shape_strs, self.colors()))
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if pad: ret += ' '*(pad-ansilen(ret))
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return ret
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kernel_cnt: Final[defaultdict[str, int]] = defaultdict(int)
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@functools.cached_property
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def name(self) -> str:
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# kernel name (before late upcast)
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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")
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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())])
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name = kernel_type + (f"{len(self.ast.src)}" if len(self.ast.src) > 1 else "") + "_" + suffix
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# name the function something unique
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Kernel.kernel_cnt[(function_name := to_function_name(name))] += 1
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num = f"n{Kernel.kernel_cnt[function_name]-1}" if Kernel.kernel_cnt[function_name] > 1 else ""
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return name + colored(num, 'BLACK')
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# ******************** base simplifiers ********************
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# apply reshape and permute to all shapetrackers
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def reshape(self, new_shape_fxn:Callable[[tuple[sint, ...]], Sequence[sint]]):
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self.sts = [st.reshape(tuple(new_shape_fxn(st.shape))) for st in self.sts]
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def permute(self, new_axes:Sequence[int]): self.sts = [st.permute(tuple(new_axes)) for st in self.sts]
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# axis : the axis to pull from
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# amount : the amount to take
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# top : if you want to pull that amount from the top
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# insert_before : place to insert the new stuff
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def shift_to(self, axis:int, amount:int, new_type:AxisType, top:bool=False, insert_before:int|None=None):
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if insert_before is None: insert_before = self.shape_len
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self.axis_types.insert(insert_before, new_type)
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move_axis = axis if top else axis+1
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if move_axis < insert_before: insert_before += 1
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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:]
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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]
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self.reshape(new_shape_fxn)
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self.permute(new_axes)
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# ******************** complex simplifiers ********************
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def simplify_ones(self) -> bool:
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# remove places where the shape is all ones
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if any(all_ones:=[s==1 for s in self.full_shape]):
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if hasattr(self, 'axis_types'):
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self.axis_types = [x for i,x in enumerate(self.axis_types) if not all_ones[i]]
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self.reshape(lambda shape: [x for i,x in enumerate(shape) if not all_ones[i]])
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return True
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return False
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def simplify_merge_adjacent(self):
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if self.shape_len == 0: return
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shapes, strides = [x.shape for x in self.sts], [x.real_strides() for x in self.sts]
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# NOTE: we can't use self.first_reduce yet
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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)
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# if it's an image, insert fake strides such that this fusion doesn't happen across image axes
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if isinstance(self.membufs[0].dtype, ImageDType):
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base_shape = self.membufs[0].dtype.shape
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if shape_idx_groups := get_contraction(self.output_shape, base_shape):
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special_strides: tuple[sint, ...] = tuple()
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for i,g in enumerate(shape_idx_groups):
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shape_piece = tuple(self.output_shape[x] for x in g)
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assert prod(shape_piece) == base_shape[i], f"get_contraction was wrong? {shape_piece} != {base_shape[i]}"
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special_strides += strides_for_shape(shape_piece)
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# adding the fake image shape
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shapes.append(self.output_shape)
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strides.append(special_strides)
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# merge dimensions if we can, multi _merge_dims
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# NOTE: this does not always preserve the reduce dimension
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# TODO: move this into shapetracker, with tests!
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# TODO: how does this work with multi-reduce?
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rets = [[(s[0], st[0])] for s,st in zip(shapes, strides)]
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for i in range(1, len(shapes[0])):
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can_merge = []
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for s,st,ret in zip(shapes, strides, rets):
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# TODO: added the always mergeability of 1s, is this right? if so, add to shapetracker in the 1 case
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si, sti, last_st = s[i], st[i], ret[-1][1]
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can_merge.append((sti is not None) and ((sti != 0 and last_st == si*sti) or (sti == 0 and last_st == 0)))
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# more can merge than this
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mergeable = all(can_merge) and i != first_reduce
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for j,(s,st) in enumerate(zip(shapes, strides)):
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if mergeable: rets[j][-1] = (rets[j][-1][0] * s[i], st[i])
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else: rets[j].append((s[i], st[i]))
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# do the reshapes
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for i,x in enumerate(rets[:len(self.sts)]): self.sts[i] = self.sts[i].reshape(tuple([y[0] for y in x]))
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# ******************** apply optimizations ********************
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def real_axis(self, opt:Opt):
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if opt.axis is None: return -1
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if opt.op is OptOps.UNROLL: return self.first_reduce+opt.axis
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if opt.op in {OptOps.GROUP, OptOps.GROUPTOP}: return self.first_reduce+self.group_for_reduces+opt.axis
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return opt.axis
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def apply_opt(self, opt:Opt, append_opt:bool=True):
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if self.finalized: raise RuntimeError("can't optimize Kernel after it's finalized")
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if self.dont_use_locals: check(opt.op not in {OptOps.LOCAL, OptOps.GROUP, OptOps.GROUPTOP}, "not using locals")
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if opt.op is OptOps.TC:
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check(len(self.applied_opts) == 0, "tensor core opts must be first") # TODO: things like PADTO might be fine
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check(len(self.opts.tensor_cores) > 0, "must have tensor cores")
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check(opt.axis is not None, "tensor core opts must have an axis")
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check(opt.arg is not None and isinstance(opt.arg, tuple) and len(opt.arg) == 3, "tensor core opts must have valid arg")
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check(-1 <= (tc_select:=cast(tuple, opt.arg)[0]) < len(self.opts.tensor_cores), "tensor core opts must have valid tc_select")
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check(0 <= (tc_opt:=cast(tuple, opt.arg)[1]) <= 2, "tensor core opts must have valid tc_opt")
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check(0 < (use_tensor_cores:=cast(tuple, opt.arg)[2]) <= 2, "use_tensor_cores value is not valid")
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check(self._apply_tc_opt(use_tensor_cores, cast(int, opt.axis), tc_select, tc_opt), "no tensor core available")
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self.applied_opts.append(opt)
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return
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axis = self.real_axis(opt)
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check(axis < len(self.full_shape), "invalid axis")
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if opt.op is OptOps.SWAP: amt = cast(int, opt.arg) # arg is an axis in the SWAPs
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elif opt.arg is not None:
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check(isinstance(opt.arg, int), "arg should be int")
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amt = arg if (arg:=cast(int, opt.arg)) != 0 else self.full_shape[axis]
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check(isinstance(amt, int) and amt != 1, f"shift/padto of {amt=}, 1 or symbolic amount is meaningless")
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if opt.op is not OptOps.PADTO: check(self.full_shape[axis] % amt == 0, f"no longer valid shift {self.full_shape[axis]=}, {amt=}")
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else: amt = -1
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if self.reduceop is not None and (opt.op in {OptOps.GROUP, OptOps.GROUPTOP} or \
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(self.group_for_reduces and opt.op not in {OptOps.NOLOCALS, OptOps.PADTO})):
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acc_sz = self.reduceop.dtype.itemsize
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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])
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local_sz = prod(self.full_shape[self.first_reduce-self.local_dims:self.first_reduce+self.group_for_reduces])
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smem_sz = amt*acc_sz*upcast_sz*local_sz
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check(smem_sz <= self.opts.shared_max, f"exceeds maximum shared memory size: needs {smem_sz}, max {self.opts.shared_max}")
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if opt.op is OptOps.LOCAL: # cyan
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# NOTE: LLVM/CPU can use locals too, but they are treated the same as globals (still helpful for L1 cache)
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# it's disabled for now since it makes BEAM slow for little gain
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check(self.opts.has_local, "target does not support local")
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check(axis < self.global_dims, "local is for globals")
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self.shift_to(axis, amt, AxisType.LOCAL, insert_before=self.first_reduce)
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elif opt.op in {OptOps.GROUP, OptOps.GROUPTOP}: # green
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check(self.opts.has_local and self.opts.has_shared, "target does not support local or shared mem")
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check(self.first_reduce + self.group_for_reduces <= axis < self.first_upcast, "must be reduce axis to group")
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check(not self.tensor_core, "can't group with tensor cores")
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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")
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self.shift_to(axis, amt, AxisType.GROUP_REDUCE, top=(opt.op is OptOps.GROUPTOP), insert_before=self.first_reduce + self.group_for_reduces)
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elif opt.op is OptOps.UNROLL: # purple
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check(axis < self.first_upcast, "can't upcasted already upcasted")
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check(amt <= 32, "don't unroll more than 32")
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self.shift_to(axis, amt, AxisType.UNROLL, insert_before=None)
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elif opt.op is OptOps.UPCAST: # yellow
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check(axis < self.first_reduce, "upcast is for non-reduce")
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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")
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check((self.opts is not None and self.opts.device == "DSP") or amt <= 16, "don't upcast more than 16")
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self.shift_to(axis, amt, AxisType.UPCAST, insert_before=None)
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elif opt.op is OptOps.NOLOCALS:
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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")
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check(self.local_dims == 0 and self.group_for_reduces == 0, "can't have no locals with locals")
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self.dont_use_locals = True
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elif opt.op is OptOps.SWAP:
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check(axis < amt < self.global_dims, f"swap is only for globals with axis < amt, getting {amt=}, {axis=}, {self.global_dims=}")
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permute = list(range(self.shape_len))
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permute[axis], permute[amt] = permute[amt], permute[axis]
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self.permute(tuple(permute))
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elif opt.op is OptOps.PADTO:
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check(not self.vars, "does not work with symbolic shape")
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check(axis < self.first_upcast, "cannot pad upcasted")
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# ok to pad SUM if all parent ALU ops have f(0) = 0
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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}")
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padded = False
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for i,st in enumerate(self.sts):
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if (s:=st.shape[axis]) == 1: continue # reduced
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check(s > amt//4, f"pad adds more than quadruple the work {st.shape[axis]=} > {amt//4=}")
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if (ru := round_up(cast(int, s), amt) - s):
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# pad right seems to be faster
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self.sts[i] = st.pad(((0,0),) * axis + ((0,ru),) + ((0,0),) * (len(st.shape)-axis-1))
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padded = True
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check(padded, "nothing was padded")
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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
|
|
|