You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
227 lines
13 KiB
227 lines
13 KiB
# the job of the lowerer is to do indexing
|
|
import functools, itertools, operator, math
|
|
from dataclasses import dataclass
|
|
from typing import cast
|
|
from tinygrad.dtype import dtypes, PtrDType, least_upper_dtype
|
|
from tinygrad.ops import KernelInfo, UOp, Ops, graph_rewrite, PatternMatcher, UPat, sint, identity_element, sint_to_uop
|
|
from tinygrad.renderer import Renderer
|
|
from tinygrad.helpers import all_int, prod, partition, flatten, unwrap, QUANTIZE
|
|
from tinygrad.codegen.expander import expand_rewrite
|
|
from tinygrad.codegen.symbolic import symbolic
|
|
|
|
# returns the axes to create new_shape if new_shape can be created by combining axis from old_shape
|
|
def get_contraction(old_shape:tuple[sint, ...], new_shape:tuple[sint, ...]) -> list[list[int]]|None:
|
|
acc_old, acc_new = list(itertools.accumulate(old_shape, operator.mul)), list(itertools.accumulate(new_shape, operator.mul))
|
|
try: split = [acc_old.index(acc)+1 if acc != 1 else 0 for acc in acc_new]
|
|
except ValueError: return None
|
|
return [list(range(st,ed)) for st,ed in zip([0]+split[:-1], split[:-1]+[len(old_shape)])]
|
|
|
|
# ***** indexing *****
|
|
def _group_dims(dims:tuple[sint, ...], max_sizes:tuple[int, ...]):
|
|
# TODO: symbolic shape
|
|
if not all_int(dims): return dims
|
|
while len(dims) > len(max_sizes) or any(d > m for d,m in zip(dims, max_sizes)):
|
|
for i,m in enumerate(max_sizes):
|
|
if i < (len(dims)-1) and dims[i] * dims[i+1] <= m:
|
|
dims = dims[:i] + (dims[i]*dims[i+1],) + dims[i+2:]
|
|
break
|
|
else: return None
|
|
return dims
|
|
|
|
def _split_dims(dims, max_sizes):
|
|
if all(d <= m for d,m in zip(dims, max_sizes)): return dims
|
|
_dims = list(dims) + [1]*(3-len(dims))
|
|
for i in range(len(_dims)):
|
|
while _dims[i] > max_sizes[i]:
|
|
div = next((d for d in range(2, math.ceil(math.sqrt(_dims[i])) + 1) if (_dims[i] % d) == 0), 1)
|
|
if div == 1: raise RuntimeError(f"cannot limit dim {dims=}, {max_sizes=}")
|
|
_dims[i], _dims[(i+1)%len(_dims)] = _dims[i]//div, _dims[(i+1)%len(_dims)]*div
|
|
return tuple(_dims[:2] if _dims[2] == 1 else _dims[0] if _dims[1:3] == [1,1] else _dims)
|
|
|
|
def get_grouped_dims(prefix, dims:tuple[sint, ...], max_sizes:tuple[int, ...]|None, reverse=False) -> list[UOp]:
|
|
if reverse: dims = dims[::-1]
|
|
# try to group first: (a, b, c, d) -> (ab, c, d)
|
|
limited = (grouped if (grouped := _group_dims(dims, max_sizes)) else dims) if max_sizes is not None else dims
|
|
# check if grouping failed
|
|
if max_sizes is not None and len(limited) > len(max_sizes): raise RuntimeError(f"cannot limit dim {dims=}, {max_sizes=}")
|
|
# try to split up dims: (a,) -> (b, c)
|
|
if limited == dims: limited = _split_dims(dims, max_sizes) if max_sizes is not None else dims
|
|
ret = raw_idxs = [UOp(Ops.SPECIAL, dtypes.int, (), (f"{prefix}{i}", s)) for i,s in enumerate(limited)]
|
|
if len(limited) < len(dims):
|
|
ret = []
|
|
if (contraction:=get_contraction(dims, limited)) is None: raise AssertionError(f"get_contraction should not be None {dims=} {limited=}")
|
|
for idx, contraction_group in zip(raw_idxs, contraction):
|
|
for c in contraction_group[:-1]:
|
|
ret.append(idx % dims[c])
|
|
idx //= dims[c]
|
|
ret.append(idx)
|
|
elif len(limited) > len(dims):
|
|
a, b = len(limited), len(dims)
|
|
if a == 2 and b == 1: ret = [raw_idxs[0] * limited[1] + raw_idxs[1]]
|
|
if a == 3 and b == 1: ret = [raw_idxs[0] * (limited[1] * limited[2]) + raw_idxs[1] * limited[2] + raw_idxs[2]]
|
|
if a == 3 and b == 2: ret = [raw_idxs[0] * limited[1] + raw_idxs[1], raw_idxs[2]]
|
|
return ret[::-1] if reverse else ret
|
|
|
|
@dataclass
|
|
class IndexContext:
|
|
idxs: list[UOp]
|
|
ridxs: list[UOp]
|
|
acc_num: int = 0
|
|
|
|
def get_index(ast:UOp, opts:Renderer) -> IndexContext:
|
|
ki = ast.arg if isinstance(ast.arg, KernelInfo) else KernelInfo()
|
|
# NOTE: assumes the shape is <global dims> <local dims> <group_for_reduces> <reduces> <upcasts/unrolls>
|
|
full_shape = ast.full_shape
|
|
first_upcasted = len(full_shape)-ki.upcasted
|
|
# if there's no reduce, this is first_upcasted. assumes reduces are at the end
|
|
first_reduce = min([first_upcasted]+flatten(x.axis_arg for x in ast.toposort if x.op is Ops.REDUCE_AXIS))
|
|
local_loads = [x for x in ast.toposort if x.op is Ops.LOAD and x.src[0].op is Ops.DEFINE_LOCAL]
|
|
# NOTE: sum up the reduced axes looking across all local loads, yields the number of grouped reduces
|
|
group_for_reduces = sum([any(l.st_arg.shape[i]!=ast.src[0].st_arg.shape[i] for l in local_loads) for i in range(first_reduce,first_upcasted)])
|
|
global_dims = first_reduce-ki.local_dims
|
|
|
|
if opts.has_local:
|
|
if ki.dont_use_locals:
|
|
assert ki.local_dims == 0, "can't use locals if there's no local dims"
|
|
idxs = get_grouped_dims("idx", full_shape[:global_dims], opts.global_max, reverse=True)
|
|
else:
|
|
# define indexes for GPU-like execution
|
|
idxs = get_grouped_dims("gidx", full_shape[:global_dims], opts.global_max, reverse=True) + \
|
|
get_grouped_dims("lidx", full_shape[global_dims:first_reduce+group_for_reduces], opts.local_max)
|
|
else:
|
|
# all loops are RANGES
|
|
idxs = [UOp(Ops.RANGE, dtypes.int, (sint_to_uop(0), sint_to_uop(g)), i) for i,g in enumerate(full_shape[:first_reduce])]
|
|
|
|
# reduce loops
|
|
idxs += [UOp(Ops.RANGE, dtypes.int, (sint_to_uop(0), sint_to_uop(g)), i)
|
|
for i,g in enumerate(full_shape[first_reduce+group_for_reduces:first_upcasted], start=first_reduce+group_for_reduces)]
|
|
|
|
# upcast loops
|
|
for i,g in enumerate(full_shape[first_upcasted:], start=first_upcasted):
|
|
assert isinstance(g, int), "needs to be int to upcast/unroll"
|
|
idxs.append(UOp(Ops.UNROLL, dtypes.int, (UOp.const(dtypes.int.vec(g), tuple(range(g))),), ((i,g),)))
|
|
|
|
# late indexes (group for reduce)
|
|
ridxs = idxs[:]
|
|
for a in range(first_reduce, first_reduce+group_for_reduces):
|
|
ridxs[a] = UOp(Ops.RANGE, dtypes.int, (sint_to_uop(0), sint_to_uop(full_shape[a])), 1000+a)
|
|
|
|
return IndexContext(idxs, ridxs)
|
|
|
|
# ***** lowering (given index) *****
|
|
|
|
def lower_reduce_axis(ctx: IndexContext, x: UOp):
|
|
# NOTE: always using ridxs is fine here
|
|
reduce_range, reduce_expand = partition([ctx.ridxs[i] for i in x.axis_arg], lambda y: y.op is Ops.RANGE)
|
|
assert all(x.op is Ops.UNROLL for x in reduce_expand), f"not all UNROLLS in {reduce_expand} for {x.axis_arg}"
|
|
alu_op: Ops = x.arg[0]
|
|
ret = x.src[0]
|
|
# create acc
|
|
acc = UOp(Ops.DEFINE_ACC, x.dtype, (x.const_like(identity_element(alu_op, x.dtype.scalar())),) + tuple(reduce_range), (ctx.acc_num,))
|
|
ctx.acc_num += 1
|
|
if len(contract_axis:=flatten(x.arg for x in reduce_expand)):
|
|
ret = UOp(Ops.CONTRACT, x.dtype.vec(prod(x[1] for x in contract_axis)), (ret,), tuple(contract_axis))
|
|
ret = functools.reduce(lambda x,y: x.alu(alu_op, y), [acc]+[ret.gep(i) for i in range(ret.dtype.count)])
|
|
else:
|
|
ret = acc.alu(alu_op, ret)
|
|
if not len(reduce_range): return ret
|
|
# create ACC and assign
|
|
return acc.assign(ret)
|
|
|
|
def lower_load_store(ctx: IndexContext, x: UOp):
|
|
idx, valid = x.st_arg.to_indexed_uops(ctx.ridxs if x.op is Ops.LOAD and x.src[0].op is Ops.DEFINE_LOCAL else ctx.idxs)
|
|
buf = x.src[0]
|
|
if x.op is Ops.LOAD:
|
|
barrier = (UOp(Ops.BARRIER, dtypes.void, (x.src[2],)),) if x.src[0].op is Ops.DEFINE_LOCAL else ()
|
|
return UOp(Ops.LOAD, x.dtype, (buf.index(idx, valid),) + barrier)
|
|
# NOTE: only store the local reduceop in the threads that are actually doing the reduce
|
|
if cast(PtrDType, x.src[0].dtype).local and x.src[2].op is Ops.ASSIGN:
|
|
reduce_input = x.src[2].src[1].src[1] if x.src[2].src[1].src[1] is not x.src[2].src[0] else x.src[2].src[1].src[0]
|
|
store_back = reduce_input.op is Ops.LOAD and cast(PtrDType, reduce_input.src[0].dtype).local
|
|
else: store_back = False
|
|
# NOTE: If we're storing the reduced value back into each thread, need to zero-out the reduced axes
|
|
if store_back: idx, _ = x.st_arg.to_indexed_uops([u.const_like(0) if u in x.src[2].src else u for u in ctx.idxs])
|
|
if (not cast(PtrDType, x.src[0].dtype).local) or store_back:
|
|
for oidx, ridx in zip(ctx.idxs, ctx.ridxs):
|
|
if oidx is not ridx: valid = valid * oidx.eq(0)
|
|
return UOp(Ops.STORE, dtypes.void, (buf.index(idx, valid), x.src[2]))
|
|
|
|
def lower_const(x:UOp):
|
|
assert all(v.mask is None for v in unwrap(x.st).views), f"VIEW in CONST/DEFINE_VAR source must be unmasked, got {x.st}"
|
|
return x.replace(src=())
|
|
|
|
pm_lowerer = PatternMatcher([
|
|
(UPat(Ops.REDUCE_AXIS, name="x"), lower_reduce_axis),
|
|
(UPat((Ops.CONST, Ops.DEFINE_VAR), src=(UPat(Ops.VIEW),), name="x"), lower_const),
|
|
(UPat(Ops.VALID, src=(UPat(Ops.VIEW),), name="x"), lambda ctx,x: x.st_arg.to_indexed_uops(ctx.idxs)[1]),
|
|
# rewrite LOAD/STORE VIEW to LOAD/STORE with indexed
|
|
(UPat((Ops.LOAD, Ops.STORE), src=(UPat(), UPat(Ops.VIEW)), allow_any_len=True, name="x"), lower_load_store),
|
|
(UPat(Ops.INDEX, src=(UPat.var("b"), UPat.var("idx"), UPat.const(dtypes.bool, True))), lambda b, idx: b.index(idx)),
|
|
(UPat(Ops.IGNORE, name="x"), lambda x: x.src[0]),
|
|
])
|
|
|
|
# **** this is the "quantization preprocessor", it makes ONNX quantized models, and probably also others, actually use ints ****
|
|
|
|
FP = (1 << 16)
|
|
pm_quant = symbolic+PatternMatcher([
|
|
# cast after add/mul
|
|
(UPat.var("x").cast(dtypes.float32) + UPat.var("y").cast(dtypes.float32),
|
|
lambda x,y: (x.cast(least_upper_dtype(x.dtype, y.dtype))+y.cast(least_upper_dtype(x.dtype, y.dtype))).cast(dtypes.float32)),
|
|
(UPat.var("x").cast(dtypes.float32) * UPat.var("y").cast(dtypes.float32),
|
|
lambda x,y: (x.cast(least_upper_dtype(x.dtype, y.dtype))*y.cast(least_upper_dtype(x.dtype, y.dtype))).cast(dtypes.float32)),
|
|
|
|
# masked MUL after masked ADD
|
|
((UPat.var("x") + UPat.var("v").where(UPat.var('cadd'), UPat(Ops.CONST, arg=0))) * UPat.var("v").where(UPat.var('cmul'), UPat(Ops.CONST, arg=0)),
|
|
lambda x,v,cadd,cmul: x*v.where(cmul, 0)+v.where(cadd*cmul, 0)),
|
|
|
|
# MUL after reduce
|
|
(UPat(Ops.REDUCE_AXIS, src=(UPat.var("x") * UPat.cvar("c"),), name="r"), lambda x,c,r: r.replace(src=(x,))*c),
|
|
# CAST after reduce (doesn't work if it's a size change)
|
|
(UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.CAST, src=(UPat.var("x"),)),), name="r"),
|
|
lambda x,r: r.replace(dtype=x.dtype, src=(x,)).cast(r.dtype) if dtypes.is_float(r.dtype) else None),
|
|
|
|
# x*c1 + y*c2 -> (x+y)*c1 (if c1 and c2 are close floats)
|
|
(UPat.var("x")*UPat.cvar("c1", dtype=dtypes.floats) + UPat.var("y")*UPat.cvar("c2", dtype=dtypes.floats),
|
|
lambda x,y,c1,c2: (x+y)*c1 if abs(c1.arg-c2.arg) < 1e-9 else None),
|
|
# mul 0 * c1 is 0
|
|
(UPat(Ops.VALID, src=(UPat(Ops.VIEW, name="v"),)).where(UPat.cvar("c1"), UPat(Ops.CONST, arg=0)) *
|
|
UPat(Ops.LOAD, src=(UPat(), UPat(Ops.VIEW, name="v"))).cast(dtypes.int).cast(dtypes.float).named("ld"), lambda ld,v,c1: ld*c1),
|
|
# mul (with plus) 0 * c1 is 0
|
|
(UPat(Ops.VALID, src=(UPat(Ops.VIEW, name="v"),)).where(UPat.cvar("c1"), UPat(Ops.CONST, arg=0)) *
|
|
(UPat(Ops.LOAD, src=(UPat(), UPat(Ops.VIEW, name="v"))).cast(dtypes.int) + \
|
|
UPat(Ops.VALID, src=(UPat(Ops.VIEW, name="v"),)).where(UPat.cvar(), UPat(Ops.CONST, arg=0))).cast(dtypes.float).named("ld"),
|
|
lambda ld,v,c1: ld*c1),
|
|
|
|
# fixed point mult, replace (x.float()*c1+c2).int() with an int expression
|
|
((UPat.var("x").cast(dtypes.float)*UPat.var("c1")+UPat.var("c2")).cast(dtypes.int),
|
|
lambda x,c1,c2: (x * (c1 * FP).cast(dtypes.int) + (c2 * FP).cast(dtypes.int)) // FP),
|
|
# fixed point mult, replace (x.float()*c1 + y.float()*c2) with an int expression
|
|
((UPat.var("x").cast(dtypes.float)*UPat.var("c1")+UPat.var("y").cast(dtypes.float)*UPat.var("c2")),
|
|
lambda x,y,c1,c2: ((x * (c1 * FP).cast(dtypes.int) + y * (c2 * FP).cast(dtypes.int)) // FP).cast(dtypes.float)),
|
|
|
|
# where move
|
|
(UPat.var("valid").where(UPat.var("yes"), UPat(Ops.CONST, arg=0))*UPat.var("mul"), lambda valid, yes, mul:
|
|
(yes*mul*valid.where(UOp.const(mul.dtype, 1), UOp.const(mul.dtype, 0))) if yes.op is not Ops.CONST or yes.arg != 1 else None),
|
|
((UPat.var("x")*UPat.cvar("c"))*(UPat.var().where(UPat(Ops.CONST, arg=1), UPat(Ops.CONST, arg=0)).named("v")), lambda x,c,v: (x*v)*c),
|
|
(UPat.var("x").cast().named('c') * UPat.var('valid').where(UPat(Ops.CONST, arg=1), UPat(Ops.CONST, arg=0)), lambda x,c,valid:
|
|
(x*valid.where(UOp.const(x.dtype, 1), UOp.const(x.dtype, 0))).cast(c.dtype)),
|
|
((UPat.var('x') * UPat.var('v1').where(UPat(Ops.CONST, arg=1), UPat(Ops.CONST, arg=0)) *
|
|
UPat.var('v2').where(UPat(Ops.CONST, arg=1), UPat(Ops.CONST, arg=0))).named("mul"), lambda x, mul, v1, v2:
|
|
x * (v1&v2).where(UOp.const(mul.dtype, 1), UOp.const(mul.dtype, 0))),
|
|
|
|
# where on two adds
|
|
(UPat.var("x") + UPat.var("v").where(UPat.var("a0"), UPat.var("a1")) + UPat.var("v").where(UPat.var("b0"), UPat.var("b1")),
|
|
lambda x,v,a0,a1,b0,b1: x + v.where(a0+a1, b0+b1)),
|
|
|
|
# split REDUCE into multiple reduces
|
|
(UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.CAST, name="v1")+UPat.var("c1")) * UPat(Ops.CAST, name="v2",), name="r"),
|
|
lambda v1,v2,c1,r: r.replace(src=(v1*v2,)) + r.replace(src=(c1*v2,))),
|
|
(UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.CAST, name="v1")+UPat.var("c1")) * (UPat(Ops.CAST, name="v2",)+UPat.var("c2")), name="r"),
|
|
lambda v1,v2,c1,c2,r: r.replace(src=(v1*v2,)) + r.replace(src=(c2*v1,)) + r.replace(src=(c1*v2,))),
|
|
])
|
|
|
|
def rewrite_shapetracker_with_index(ast:UOp, opts:Renderer) -> UOp:
|
|
if QUANTIZE and opts.device in {"CPU", "DSP"}: ast = graph_rewrite(ast, pm_quant, name="quantize")
|
|
sink = graph_rewrite(ast, pm_lowerer, ctx=get_index(ast, opts))
|
|
# expand_rewrite turns this into a vectorized program
|
|
return expand_rewrite(sink)
|
|
|